diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md deleted file mode 100644 index f9ba8cf65..000000000 --- a/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,9 +0,0 @@ -# Microsoft Open Source Code of Conduct - -This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). - -Resources: - -- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) -- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) -- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 21071075c..000000000 --- a/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ - MIT License - - Copyright (c) Microsoft Corporation. All rights reserved. - - Permission is hereby granted, free of charge, to any person obtaining a copy - of this software and associated documentation files (the "Software"), to deal - in the Software without restriction, including without limitation the rights - to use, copy, modify, merge, publish, distribute, sublicense, and/or sell - copies of the Software, and to permit persons to whom the Software is - furnished to do so, subject to the following conditions: - - The above copyright notice and this permission notice shall be included in all - copies or substantial portions of the Software. - - THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - SOFTWARE diff --git a/Licenses/sdk-license/LICENSE b/Licenses/sdk-license/LICENSE deleted file mode 100644 index 183b10c2a..000000000 --- a/Licenses/sdk-license/LICENSE +++ /dev/null @@ -1,14 +0,0 @@ - -This software is made available to you on the condition that you agree to -[your agreement][1] governing your use of Azure. -If you do not have an existing agreement governing your use of Azure, you agree that -your agreement governing use of Azure is the [Microsoft Online Subscription Agreement][2] -(which incorporates the [Online Services Terms][3]). -By using the software you agree to these terms. This software may collect data -that is transmitted to Microsoft. Please see the [Microsoft Privacy Statement][4] -to learn more about how Microsoft processes personal data. - -[1]: https://azure.microsoft.com/en-us/support/legal/ -[2]: https://azure.microsoft.com/en-us/support/legal/subscription-agreement/ -[3]: http://www.microsoftvolumelicensing.com/DocumentSearch.aspx?Mode=3&DocumentTypeId=46 -[4]: http://go.microsoft.com/fwlink/?LinkId=248681 diff --git a/Licenses/sdk-preview-license/LICENSE b/Licenses/sdk-preview-license/LICENSE deleted file mode 100644 index 0dd657ca0..000000000 --- a/Licenses/sdk-preview-license/LICENSE +++ /dev/null @@ -1,15 +0,0 @@ -This Preview is made available to you on the condition that you agree to the -[Supplemental Terms of Use for Microsoft Azure Previews][1], which supplement -[your agreement][2] governing your use of Azure. -If you do not have an existing agreement governing your use of Azure, you agree that -your agreement governing use of Azure is the [Microsoft Online Subscription Agreement][3] -(which incorporates the [Online Services Terms][4]). -By using the Preview you agree to these terms. This Preview may collect data -that is transmitted to Microsoft. Please see the [Microsoft Privacy Statement][5] -to learn more about how Microsoft processes personal data. - -[1]: https://azure.microsoft.com/en-us/support/legal/preview-supplemental-terms/ -[2]: https://azure.microsoft.com/en-us/support/legal/ -[3]: https://azure.microsoft.com/en-us/support/legal/subscription-agreement/ -[4]: http://www.microsoftvolumelicensing.com/DocumentSearch.aspx?Mode=3&DocumentTypeId=46 -[5]: http://go.microsoft.com/fwlink/?LinkId=248681 diff --git a/NBSETUP.md b/NBSETUP.md deleted file mode 100644 index b3c683b30..000000000 --- a/NBSETUP.md +++ /dev/null @@ -1,95 +0,0 @@ -# Set up your notebook environment for Azure Machine Learning - -To run the notebooks in this repository use one of following options. - -## **Option 1: Use Azure Notebooks** -Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. Azure Machine Learning Python SDK is already pre-installed in the Azure Notebooks `Python 3.6` kernel. - -1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks) -[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks -1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace -1. Open one of the sample notebooks - - **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook by choosing Kernel > Change Kernel > Python 3.6 from the menus. - -## **Option 2: Use your own notebook server** - -### Quick installation -We recommend you create a Python virtual environment ([Miniconda](https://conda.io/miniconda.html) preferred but [virtualenv](https://virtualenv.pypa.io/en/latest/) works too) and install the SDK in it. -```sh -# install just the base SDK -pip install azureml-sdk - -# clone the sample repoistory -git clone https://github.com/Azure/MachineLearningNotebooks.git - -# below steps are optional -# install the base SDK, Jupyter notebook server and tensorboard -pip install azureml-sdk[notebooks,tensorboard] - -# install model explainability component -pip install azureml-sdk[interpret] - -# install automated ml components -pip install azureml-sdk[automl] - -# install experimental features (not ready for production use) -pip install azureml-sdk[contrib] -``` - -Note the _extras_ (the keywords inside the square brackets) can be combined. For example: -```sh -# install base SDK, Jupyter notebook and automated ml components -pip install azureml-sdk[notebooks,automl] -``` - -### Full instructions -[Install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python) - -Please make sure you start with the [Configuration](configuration.ipynb) notebook to create and connect to a workspace. - - -### Video walkthrough: - -[!VIDEO https://youtu.be/VIsXeTuW3FU] - -## **Option 3: Use Docker** - -You need to have Docker engine installed locally and running. Open a command line window and type the following command. - -__Note:__ We use version `1.0.10` below as an exmaple, but you can replace that with any available version number you like. - -```sh -# clone the sample repoistory -git clone https://github.com/Azure/MachineLearningNotebooks.git - -# change current directory to the folder -# where Dockerfile of the specific SDK version is located. -cd MachineLearningNotebooks/Dockerfiles/1.0.10 - -# build a Docker image with the a name (azuremlsdk for example) -# and a version number tag (1.0.10 for example). -# this can take several minutes depending on your computer speed and network bandwidth. -docker build . -t azuremlsdk:1.0.10 - -# launch the built Docker container which also automatically starts -# a Jupyter server instance listening on port 8887 of the host machine -docker run -it -p 8887:8887 azuremlsdk:1.0.10 -``` - -Now you can point your browser to http://localhost:8887. We recommend that you start from the `configuration.ipynb` notebook at the root directory. - -If you need additional Azure ML SDK components, you can either modify the Docker files before you build the Docker images to add additional steps, or install them through command line in the live container after you build the Docker image. For example: - -```sh -# install the core SDK and automated ml components -pip install azureml-sdk[automl] - -# install the core SDK and model explainability component -pip install azureml-sdk[interpret] - -# install the core SDK and experimental components -pip install azureml-sdk[contrib] -``` -Drag and Drop -The image will be downloaded by Fatkun \ No newline at end of file diff --git a/README.md b/README.md deleted file mode 100644 index d9ebd8206..000000000 --- a/README.md +++ /dev/null @@ -1,43 +0,0 @@ -# Azure Machine Learning Python SDK notebooks - -> a community-driven repository of examples using mlflow for tracking can be found at https://github.com/Azure/azureml-examples - -Welcome to the Azure Machine Learning Python SDK notebooks repository! - -## Getting started - -These notebooks are recommended for use in an Azure Machine Learning [Compute Instance](https://docs.microsoft.com/azure/machine-learning/concept-compute-instance), where you can run them without any additional set up. - -However, the notebooks can be run in any development environment with the correct `azureml` packages installed. - -Install the `azureml.core` Python package: - -```sh -pip install azureml-core -``` - -Install additional packages as needed: - -```sh -pip install azureml-mlflow -pip install azureml-dataset-runtime -pip install azureml-automl-runtime -pip install azureml-pipeline -pip install azureml-pipeline-steps -... -``` - -We recommend starting with one of the [quickstarts](tutorials/compute-instance-quickstarts). - -## Contributing - -This repository is a push-only mirror. Pull requests are ignored. - -## Code of Conduct - -This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). Please see the [code of conduct](CODE_OF_CONDUCT.md) for details. - -## Reference - -- [Documentation](https://docs.microsoft.com/azure/machine-learning) - diff --git a/configuration.ipynb b/configuration.ipynb index 8cfd5259a..a855f66f5 100644 --- a/configuration.ipynb +++ b/configuration.ipynb @@ -1,389 +1,389 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Configuration\n", - "\n", - "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n", - "\n", - "---\n", - "---\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - " 1. What is an Azure Machine Learning workspace\n", - "1. [Setup](#Setup)\n", - " 1. Azure subscription\n", - " 1. Azure ML SDK and other library installation\n", - " 1. Azure Container Instance registration\n", - "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n", - " 1. Workspace parameters\n", - " 1. Access your workspace\n", - " 1. Create a new workspace\n", - " 1. Create compute resources\n", - "1. [Next steps](#Next%20steps)\n", - "\n", - "---\n", - "\n", - "## Introduction\n", - "\n", - "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace. In this case, a library contains all of the notebooks in the current folder and any nested folders. You can configure this notebook library to use an existing workspace or create a new workspace.\n", - "\n", - "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here. If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n", - "\n", - "In this notebook you will\n", - "* Learn about getting an Azure subscription\n", - "* Specify your workspace parameters\n", - "* Access or create your workspace\n", - "* Add a default compute cluster for your workspace\n", - "\n", - "### What is an Azure Machine Learning workspace\n", - "\n", - "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows. In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "This section describes activities required before you can access any Azure ML services functionality." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Azure Subscription\n", - "\n", - "In order to create an Azure ML Workspace, first you need access to an Azure subscription. An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud. You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com). Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n", - "\n", - "### 2. Azure ML SDK and other library installation\n", - "\n", - "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment). If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n", - "\n", - "Also install following libraries to your environment. Many of the example notebooks depend on them\n", - "\n", - "```\n", - "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n", - "```\n", - "\n", - "Once installation is complete, the following cell checks the Azure ML SDK version:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "install" - ] - }, - "outputs": [], - "source": [ - "import azureml.core\n", - "\n", - "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n", - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n", - "\n", - "### 3. Azure Container Instance registration\n", - "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI. If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands. Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n", - "\n", - "```shell\n", - "# check to see if ACI is already registered\n", - "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n", - "\n", - "# if ACI is not registered, run this command.\n", - "# note you need to be the subscription owner in order to execute this command successfully.\n", - "(myenv) $ az provider register -n Microsoft.ContainerInstance\n", - "```\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure your Azure ML workspace\n", - "\n", - "### Workspace parameters\n", - "\n", - "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n", - "* Your subscription id\n", - "* A resource group name\n", - "* (optional) The region that will host your workspace\n", - "* A name for your workspace\n", - "\n", - "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n", - "\n", - "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group. You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com). If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n", - "\n", - "The region to host your workspace will be used if you are creating a new workspace. You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service). You should pick a region that is close to your location or that contains your data.\n", - "\n", - "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces. The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n", - "\n", - "The following cell allows you to specify your workspace parameters. This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation. If no environment variable exists, the parameters will be set to the specified default values. \n", - "\n", - "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace! You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n", - "\n", - "Replace the default values in the cell below with your workspace parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"\")\n", - "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"\")\n", - "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"\")\n", - "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Access your workspace\n", - "\n", - "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters. If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method. The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "try:\n", - " ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n", - " # write the details of the workspace to a configuration file to the notebook library\n", - " ws.write_config()\n", - " print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n", - "except:\n", - " print(\"Workspace not accessible. Change your parameters or create a new workspace below\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a new workspace\n", - "\n", - "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace. If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n", - "\n", - "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n", - "\n", - "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n", - "\n", - "This will fail if:\n", - "* You do not have permission to create a workspace in the resource group\n", - "* You do not have permission to create a resource group if it's non-existing.\n", - "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n", - "\n", - "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.\n", - "\n", - "**Note**: A Basic workspace is created by default. If you would like to create an Enterprise workspace, please specify sku = 'enterprise'.\n", - "Please visit our [pricing page](https://azure.microsoft.com/en-us/pricing/details/machine-learning/) for more details on our Enterprise edition.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "# Create the workspace using the specified parameters\n", - "ws = Workspace.create(name = workspace_name,\n", - " subscription_id = subscription_id,\n", - " resource_group = resource_group, \n", - " location = workspace_region,\n", - " create_resource_group = True,\n", - " sku = 'basic',\n", - " exist_ok = True)\n", - "ws.get_details()\n", - "\n", - "# write the details of the workspace to a configuration file to the notebook library\n", - "ws.write_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create compute resources for your training experiments\n", - "\n", - "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors. Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n", - "\n", - "The cluster parameters are:\n", - "* vm_size - this describes the virtual machine type and size used in the cluster. All machines in the cluster are the same type. You can get the list of vm sizes available in your region by using the CLI command\n", - "\n", - "```shell\n", - "az vm list-skus -o tsv\n", - "```\n", - "* min_nodes - this sets the minimum size of the cluster. If you set the minimum to 0 the cluster will shut down all nodes while not in use. Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n", - "* max_nodes - this sets the maximum size of the cluster. Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n", - "\n", - "\n", - "To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "cpu_cluster_name = \"cpu-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print(\"Found existing cpu-cluster\")\n", - "except ComputeTargetException:\n", - " print(\"Creating new cpu-cluster\")\n", - " \n", - " # Specify the configuration for the new cluster\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n", - " min_nodes=0,\n", - " max_nodes=4)\n", - "\n", - " # Create the cluster with the specified name and configuration\n", - " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - " \n", - " # Wait for the cluster to complete, show the output log\n", - " cpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your GPU cluster\n", - "gpu_cluster_name = \"gpu-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n", - " print(\"Found existing gpu cluster\")\n", - "except ComputeTargetException:\n", - " print(\"Creating new gpu-cluster\")\n", - " \n", - " # Specify the configuration for the new cluster\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n", - " min_nodes=0,\n", - " max_nodes=4)\n", - " # Create the cluster with the specified name and configuration\n", - " gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n", - "\n", - " # Wait for the cluster to complete, show the output log\n", - " gpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Next steps\n", - "\n", - "In this notebook you configured this notebook library to connect easily to an Azure ML workspace. You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n", - "\n", - "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models. A good place to start is the [train within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configuration\n", + "\n", + "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n", + "\n", + "---\n", + "---\n", + "\n", + "## Table of Contents\n", + "\n", + "1. [Introduction](#Introduction)\n", + " 1. What is an Azure Machine Learning workspace\n", + "1. [Setup](#Setup)\n", + " 1. Azure subscription\n", + " 1. Azure ML SDK and other library installation\n", + " 1. Azure Container Instance registration\n", + "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n", + " 1. Workspace parameters\n", + " 1. Access your workspace\n", + " 1. Create a new workspace\n", + " 1. Create compute resources\n", + "1. [Next steps](#Next%20steps)\n", + "\n", + "---\n", + "\n", + "## Introduction\n", + "\n", + "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace. In this case, a library contains all of the notebooks in the current folder and any nested folders. You can configure this notebook library to use an existing workspace or create a new workspace.\n", + "\n", + "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here. If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n", + "\n", + "In this notebook you will\n", + "* Learn about getting an Azure subscription\n", + "* Specify your workspace parameters\n", + "* Access or create your workspace\n", + "* Add a default compute cluster for your workspace\n", + "\n", + "### What is an Azure Machine Learning workspace\n", + "\n", + "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows. In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "This section describes activities required before you can access any Azure ML services functionality." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Azure Subscription\n", + "\n", + "In order to create an Azure ML Workspace, first you need access to an Azure subscription. An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud. You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com). Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n", + "\n", + "### 2. Azure ML SDK and other library installation\n", + "\n", + "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment). If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n", + "\n", + "Also install following libraries to your environment. Many of the example notebooks depend on them\n", + "\n", + "```\n", + "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n", + "```\n", + "\n", + "Once installation is complete, the following cell checks the Azure ML SDK version:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "install" + ] + }, + "outputs": [], + "source": [ + "import azureml.core\n", + "\n", + "print(\"This notebook was created using version AZUREML-SDK-VERSION of the Azure ML SDK\")\n", + "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n", + "\n", + "### 3. Azure Container Instance registration\n", + "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI. If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands. Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n", + "\n", + "```shell\n", + "# check to see if ACI is already registered\n", + "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n", + "\n", + "# if ACI is not registered, run this command.\n", + "# note you need to be the subscription owner in order to execute this command successfully.\n", + "(myenv) $ az provider register -n Microsoft.ContainerInstance\n", + "```\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configure your Azure ML workspace\n", + "\n", + "### Workspace parameters\n", + "\n", + "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n", + "* Your subscription id\n", + "* A resource group name\n", + "* (optional) The region that will host your workspace\n", + "* A name for your workspace\n", + "\n", + "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n", + "\n", + "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group. You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com). If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n", + "\n", + "The region to host your workspace will be used if you are creating a new workspace. You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service). You should pick a region that is close to your location or that contains your data.\n", + "\n", + "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces. The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n", + "\n", + "The following cell allows you to specify your workspace parameters. This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation. If no environment variable exists, the parameters will be set to the specified default values. \n", + "\n", + "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace! You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n", + "\n", + "Replace the default values in the cell below with your workspace parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"\")\n", + "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"\")\n", + "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"\")\n", + "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Access your workspace\n", + "\n", + "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters. If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method. The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "try:\n", + " ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n", + " # write the details of the workspace to a configuration file to the notebook library\n", + " ws.write_config()\n", + " print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n", + "except:\n", + " print(\"Workspace not accessible. Change your parameters or create a new workspace below\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a new workspace\n", + "\n", + "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace. If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n", + "\n", + "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n", + "\n", + "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n", + "\n", + "This will fail if:\n", + "* You do not have permission to create a workspace in the resource group\n", + "* You do not have permission to create a resource group if it's non-existing.\n", + "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n", + "\n", + "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.\n", + "\n", + "**Note**: A Basic workspace is created by default. If you would like to create an Enterprise workspace, please specify sku = 'enterprise'.\n", + "Please visit our [pricing page](https://azure.microsoft.com/en-us/pricing/details/machine-learning/) for more details on our Enterprise edition.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "# Create the workspace using the specified parameters\n", + "ws = Workspace.create(name = workspace_name,\n", + " subscription_id = subscription_id,\n", + " resource_group = resource_group, \n", + " location = workspace_region,\n", + " create_resource_group = True,\n", + " sku = 'basic',\n", + " exist_ok = True)\n", + "ws.get_details()\n", + "\n", + "# write the details of the workspace to a configuration file to the notebook library\n", + "ws.write_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create compute resources for your training experiments\n", + "\n", + "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\n", + "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors. Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n", + "\n", + "The cluster parameters are:\n", + "* vm_size - this describes the virtual machine type and size used in the cluster. All machines in the cluster are the same type. You can get the list of vm sizes available in your region by using the CLI command\n", + "\n", + "```shell\n", + "az vm list-skus -o tsv\n", + "```\n", + "* min_nodes - this sets the minimum size of the cluster. If you set the minimum to 0 the cluster will shut down all nodes while not in use. Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n", + "* max_nodes - this sets the maximum size of the cluster. Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n", + "\n", + "\n", + "To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your CPU cluster\n", + "cpu_cluster_name = \"cpu-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", + " print(\"Found existing cpu-cluster\")\n", + "except ComputeTargetException:\n", + " print(\"Creating new cpu-cluster\")\n", + " \n", + " # Specify the configuration for the new cluster\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n", + " min_nodes=0,\n", + " max_nodes=4)\n", + "\n", + " # Create the cluster with the specified name and configuration\n", + " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", + " \n", + " # Wait for the cluster to complete, show the output log\n", + " cpu_cluster.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your GPU cluster\n", + "gpu_cluster_name = \"gpu-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n", + " print(\"Found existing gpu cluster\")\n", + "except ComputeTargetException:\n", + " print(\"Creating new gpu-cluster\")\n", + " \n", + " # Specify the configuration for the new cluster\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n", + " min_nodes=0,\n", + " max_nodes=4)\n", + " # Create the cluster with the specified name and configuration\n", + " gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n", + "\n", + " # Wait for the cluster to complete, show the output log\n", + " gpu_cluster.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Next steps\n", + "\n", + "In this notebook you configured this notebook library to connect easily to an Azure ML workspace. You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n", + "\n", + "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models. A good place to start is the [train within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "authors": [ + { + "name": "ninhu" + } ], - "metadata": { - "authors": [ - { - "name": "ninhu" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/contrib/RAPIDS/README.md b/contrib/RAPIDS/README.md deleted file mode 100644 index f8ab2cbd7..000000000 --- a/contrib/RAPIDS/README.md +++ /dev/null @@ -1,305 +0,0 @@ -## How to use the RAPIDS on AzureML materials -### Setting up requirements -The material requires the use of the Azure ML SDK and of the Jupyter Notebook Server to run the interactive execution. Please refer to instructions to [setup the environment.](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local "Local Computer Set Up") Follow the instructions under **Local Computer**, make sure to run the last step: pip install \ with new package = progressbar2 (pip install progressbar2) - -After following the directions, the user should end up setting a conda environment (myenv)that can be activated in an Anaconda prompt - -The user would also require an Azure Subscription with a Machine Learning Services quota on the desired region for 24 nodes or more (to be able to select a vmSize with 4 GPUs as it is used on the Notebook) on the desired VM family ([NC\_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series), [NC\_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview)), the specific vmSize to be used within the chosen family would also need to be whitelisted for Machine Learning Services usage. - -  -### Getting and running the material -Clone the AzureML Notebooks repository in GitHub by running the following command on a local_directory: - -* C:\local_directory>git clone https://github.com/Azure/MachineLearningNotebooks.git - -On a conda prompt navigate to the local directory, activate the conda environment (myenv), where the Azure ML SDK was installed and launch Jupyter Notebook. - -* (myenv) C:\local_directory>jupyter notebook - -From the resulting browser at http://localhost:8888/tree, navigate to the master notebook: - -* http://localhost:8888/tree/MachineLearningNotebooks/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb - -  -The following notebook will appear: - -![](imgs/NotebookHome.png) - -  -### Master Jupyter Notebook -The notebook can be executed interactively step by step, by pressing the Run button (In a red circle in the above image.) - -The first couple of functional steps import the necessary AzureML libraries. If you experience any errors please refer back to the [setup the environment.](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local "Local Computer Set Up") instructions. - -  -#### Setting up a Workspace -The following step gathers the information necessary to set up a workspace to execute the RAPIDS script. This needs to be done only once, or not at all if you already have a workspace you can use set up on the Azure Portal: - -![](imgs/WorkSpaceSetUp.png) - - -It is important to be sure to set the correct values for the subscription\_id, resource\_group, workspace\_name, and region before executing the step. An example is: - - subscription_id = os.environ.get("SUBSCRIPTION_ID", "1358e503-xxxx-4043-xxxx-65b83xxxx32d") - resource_group = os.environ.get("RESOURCE_GROUP", "AML-Rapids-Testing") - workspace_name = os.environ.get("WORKSPACE_NAME", "AML_Rapids_Tester") - workspace_region = os.environ.get("WORKSPACE_REGION", "West US 2") - -  -The resource\_group and workspace_name could take any value, the region should match the region for which the subscription has the required Machine Learning Services node quota. - -The first time the code is executed it will redirect to the Azure Portal to validate subscription credentials. After the workspace is created, its related information is stored on a local file so that this step can be subsequently skipped. The immediate step will just load the saved workspace - -![](imgs/saved_workspace.png) - -Once a workspace has been created the user could skip its creation and just jump to this step. The configuration file resides in: - -* C:\local_directory\\MachineLearningNotebooks\contrib\RAPIDS\aml_config\config.json - -  -#### Creating an AML Compute Target -Following step, creates an AML Compute Target - -![](imgs/target_creation.png) - -Parameter vm\_size on function call AmlCompute.provisioning\_configuration() has to be a member of the VM families ([NC\_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series), [NC\_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview)) that are the ones provided with P40 or V100 GPUs, that are the ones supported by RAPIDS. In this particular case an Standard\_NC24s\_V2 was used. - -  -If the output of running the step has an error of the form: - -![](imgs/targeterror1.png) - -It is an indication that even though the subscription has a node quota for VMs for that family, it does not have a node quota for Machine Learning Services for that family. -You will need to request an increase node quota for that family in that region for **Machine Learning Services**. - -  -Another possible error is the following: - -![](imgs/targeterror2.png) - -Which indicates that specified vmSize has not been whitelisted for usage on Machine Learning Services and a request to do so should be filled. - -The successful creation of the compute target would have an output like the following: - -![](imgs/targetsuccess.png) -  -#### RAPIDS script uploading and viewing -The next step copies the RAPIDS script process_data.py, which is a slightly modified implementation of the [RAPIDS E2E example](https://github.com/rapidsai/notebooks/blob/master/mortgage/E2E.ipynb), into a script processing folder and it presents its contents to the user. (The script is discussed in the next section in detail). -If the user wants to use a different RAPIDS script, the references to the process_data.py script have to be changed - -![](imgs/scriptuploading.png) -  -#### Data Uploading -The RAPIDS script loads and extracts features from the Fannie Mae’s Mortgage Dataset to train an XGBoost prediction model. The script uses two years of data - -The next few steps download and decompress the data and is made available to the script as an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data). - -  -The following functions are used to download and decompress the input data - - -![](imgs/dcf1.png) -![](imgs/dcf2.png) -![](imgs/dcf3.png) -![](imgs/dcf4.png) - -  -The next step uses those functions to download locally file: -http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/mortgage_2000-2001.tgz' -And to decompress it, into local folder path = .\mortgage_2000-2001 -The step takes several minutes, the intermediate outputs provide progress indicators. - -![](imgs/downamddecom.png) - -  -The decompressed data should have the following structure: -* .\mortgage_2000-2001\acq\Acquisition_Q.txt -* .\mortgage_2000-2001\perf\Performance_Q.txt -* .\mortgage_2000-2001\names.csv - -The data is divided in partitions that roughly correspond to yearly quarters. RAPIDS includes support for multi-node, multi-GPU deployments, enabling scaling up and out on much larger dataset sizes. The user will be able to verify that the number of partitions that the script is able to process increases with the number of GPUs used. The RAPIDS script is implemented for single-machine scenarios. An example supporting multiple nodes will be published later. - -  -The next step upload the data into the [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) under reference fileroot = mortgage_2000-2001 - -The step takes several minutes to load the data, the output provides a progress indicator. - -![](imgs/datastore.png) - -Once the data has been loaded into the Azure Machine LEarning Data Store, in subsequent run, the user can comment out the ds.upload line and just make reference to the mortgage_2000-2001 data store reference - -  -#### Setting up required libraries and environment to run RAPIDS code -There are two options to setup the environment to run RAPIDS code. The following steps shows how to ues a prebuilt conda environment. A recommended alternative is to specify a base Docker image and package dependencies. You can find sample code for that in the notebook. - -![](imgs/install2.png) - -  -#### Wrapper function to submit the RAPIDS script as an Azure Machine Learning experiment - -The next step consists of the definition of a wrapper function to be used when the user attempts to run the RAPIDS script with different arguments. It takes as arguments: *cpu\_training*; a flag that indicates if the run is meant to be processed with CPU-only, *gpu\_count*; the number of GPUs to be used if they are meant to be used and part_count: the number of data partitions to be used - -![](imgs/wrapper.png) - -  -The core of the function resides in configuring the run by the instantiation of a ScriptRunConfig object, which defines the source_directory for the script to be executed, the name of the script and the arguments to be passed to the script. -In addition to the wrapper function arguments, two other arguments are passed: *data\_dir*, the directory where the data is stored and *end_year* is the largest year to use partition from. - - -As mentioned earlier the size of the data that can be processed increases with the number of gpus, in the function, dictionary *max\_gpu\_count\_data\_partition_mapping* maps the maximum number of partitions that we empirically found that the system can handle given the number of GPUs used. The function throws a warning when the number of partitions for a given number of gpus exceeds the maximum but the script is still executed, however the user should expect an error as an out of memory situation would be encountered -If the user wants to use a different RAPIDS script, the reference to the process_data.py script has to be changed - -  -#### Submitting Experiments -We are ready to submit experiments: launching the RAPIDS script with different sets of parameters. - -  -The following couple of steps submit experiments under different conditions. - -![](imgs/submission1.png) - -  -The user can change variable num\_gpu between one and the number of GPUs supported by the chosen vmSize. Variable part\_count can take any value between 1 and 11, but if it exceeds the maximum for num_gpu, the run would result in an error - -  -If the experiment is successfully submitted, it would be placed on a queue for processing, its status would appeared as Queued and an output like the following would appear - -![](imgs/queue.png) - -  -When the experiment starts running, its status would appeared as Running and the output would change to something like this: - -![](imgs/running.png) - -  -#### Reproducing the performance gains plot results on the Blog Post -When the run has finished successfully, its status would appeared as Completed and the output would change to something like this: - -  -![](imgs/completed.png) - -Which is the output for an experiment run with three partitions and one GPU, notice that the reported processing time is 49.16 seconds just as depicted on the performance gains plot on the blog post - -  - -![](imgs/2GPUs.png) - - -This output corresponds to a run with three partitions and two GPUs, notice that the reported processing time is 37.50 seconds just as depicted on the performance gains plot on the blog post - -  -![](imgs/3GPUs.png) - -This output corresponds to an experiment run with three partitions and three GPUs, notice that the reported processing time is 24.40 seconds just as depicted on the performance gains plot on the blog post - -  -![](imgs/4gpus.png) - -This output corresponds to an experiment run with three partitions and four GPUs, notice that the reported processing time is 23.33 seconds just as depicted on the performance gains plot on the blogpost - -  -![](imgs/CPUBase.png) - -This output corresponds to an experiment run with three partitions and using only CPU, notice that the reported processing time is 9 minutes and 1.21 seconds or 541.21 second just as depicted on the performance gains plot on the blog post - -  -![](imgs/OOM.png) - -This output corresponds to an experiment run with nine partitions and four GPUs, notice that the notebook throws a warning signaling that the number of partitions exceed the maximum that the system can handle with those many GPUs and the run ends up failing, hence having and status of Failed. - -  -##### Freeing Resources -In the last step the notebook deletes the compute target. (This step is optional especially if the min_nodes in the cluster is set to 0 with which the cluster will scale down to 0 nodes when there is no usage.) - -![](imgs/clusterdelete.png) - -  -### RAPIDS Script -The Master Notebook runs experiments by launching a RAPIDS script with different sets of parameters. In this section, the RAPIDS script, process_data.py in the material, is analyzed - -The script first imports all the necessary libraries and parses the arguments passed by the Master Notebook. - -The all internal functions to be used by the script are defined. - -  -#### Wrapper Auxiliary Functions: -The below functions are wrappers for a configuration module for librmm, the RAPIDS Memory Manager python interface: - -![](imgs/wap1.png)![](imgs/wap2.png) - -  -A couple of other functions are wrappers for the submission of jobs to the DASK client: - -![](imgs/wap3.png) -![](imgs/wap4.png) - -  -#### Data Loading Functions: -The data is loaded through the use of the following three functions - -![](imgs/DLF1.png)![](imgs/DLF2.png)![](imgs/DLF3.png) - -All three functions use library function cudf.read_csv(), cuDF version for the well known counterpart on Pandas. - -  -#### Data Transformation and Feature Extraction Functions: -The raw data is transformed and processed to extract features by joining, slicing, grouping, aggregating, factoring, etc, the original dataframes just as is done with Pandas. The following functions in the script are used for that purpose: -![](imgs/fef1.png)![](imgs/fef2.png)![](imgs/fef3.png)![](imgs/fef4.png)![](imgs/fef5.png) - -![](imgs/fef6.png)![](imgs/fef7.png)![](imgs/fef8.png)![](imgs/fef9.png) - -  -#### Main() Function -The previous functions are used in the Main function to accomplish several steps: Set up the Dask client, do all ETL operations, set up and train an XGBoost model, the function also assigns which data needs to be processed by each Dask client - -  -##### Setting Up DASK client: -The following lines: - -![](imgs/daskini.png) - -  -Initialize and set up a DASK client with a number of workers corresponding to the number of GPUs to be used on the run. A successful execution of the set up will result on the following output: - -![](imgs/daskoutput.png) - -##### All ETL functions are used on single calls to process\_quarter_gpu, one per data partition - -![](imgs/ETL.png) - -  -##### Concentrating the data assigned to each DASK worker -The partitions assigned to each worker are concatenated and set up for training. - -![](imgs/Dask2.png) - -  -##### Setting Training Parameters -The parameters used for the training of a gradient boosted decision tree model are set up in the following code block: -![](imgs/PArameters.png) - -Notice how the parameters are modified when using the CPU-only mode. - -  -##### Launching the training of a gradient boosted decision tree model using XGBoost. - -![](imgs/training.png) - -The outputs of the script can be observed in the master notebook as the script is executed - - - - - - - - - - - - - - - - - diff --git a/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb b/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb deleted file mode 100644 index 5d45bbf47..000000000 --- a/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb +++ /dev/null @@ -1,547 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/RAPIDS/azure-ml-with-nvidia-rapids/azure-ml-with-nvidia-rapids.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NVIDIA RAPIDS in Azure Machine Learning" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL\u00c2\u00a0and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model\u00c3\u201a\u00c2\u00a0in Azure.\n", - " \n", - "In this notebook, we will do the following:\n", - " \n", - "* Create an Azure Machine Learning Workspace\n", - "* Create an AMLCompute target\n", - "* Use a script to process our data and train a model\n", - "* Obtain the data required to run this sample\n", - "* Create an AML run configuration to launch a machine learning job\n", - "* Run the script to prepare data for training and train the model\n", - " \n", - "Prerequisites:\n", - "* An Azure subscription to create a Machine Learning Workspace\n", - "* Familiarity with the Azure ML SDK (refer to [notebook samples](https://github.com/Azure/MachineLearningNotebooks))\n", - "* A Jupyter notebook environment with Azure Machine Learning SDK installed. Refer to instructions to [setup the environment](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Verify if Azure ML SDK is installed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from azureml.core import Workspace, Experiment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "from azureml.data.data_reference import DataReference\n", - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core import ScriptRunConfig\n", - "from azureml.widgets import RunDetails" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Azure ML Workspace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following step is optional if you already have a workspace. If you want to use an existing workspace, then\n", - "skip this workspace creation step and move on to the next step to load the workspace.\n", - " \n", - "Important: in the code cell below, be sure to set the correct values for the subscription_id, \n", - "resource_group, workspace_name, region before executing this code cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"\")\n", - "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"\")\n", - "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"\")\n", - "workspace_region = os.environ.get(\"WORKSPACE_REGION\", \"\")\n", - "\n", - "ws = Workspace.create(workspace_name, subscription_id=subscription_id, resource_group=resource_group, location=workspace_region)\n", - "\n", - "# write config to a local directory for future use\n", - "ws.write_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load existing Workspace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# if a locally-saved configuration file for the workspace is not available, use the following to load workspace\n", - "# ws = Workspace(subscription_id=subscription_id, resource_group=resource_group, workspace_name=workspace_name)\n", - "\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')\n", - "\n", - "scripts_folder = \"scripts_folder\"\n", - "\n", - "if not os.path.isdir(scripts_folder):\n", - " os.mkdir(scripts_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create AML Compute Target" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because NVIDIA RAPIDS requires P40 or V100 GPUs, the user needs to specify compute targets from one of [NC_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series), [NC_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview) virtual machine types in Azure; these are the families of virtual machines in Azure that are provisioned with these GPUs.\n", - " \n", - "Pick one of the supported VM SKUs based on the number of GPUs you want to use for ETL and training in RAPIDS.\n", - " \n", - "The script in this notebook is implemented for single-machine scenarios. An example supporting multiple nodes will be published later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gpu_cluster_name = \"gpucluster\"\n", - "\n", - "if gpu_cluster_name in ws.compute_targets:\n", - " gpu_cluster = ws.compute_targets[gpu_cluster_name]\n", - " if gpu_cluster and type(gpu_cluster) is AmlCompute:\n", - " print('Found compute target. Will use {0} '.format(gpu_cluster_name))\n", - "else:\n", - " print(\"creating new cluster\")\n", - " # vm_size parameter below could be modified to one of the RAPIDS-supported VM types\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_NC6s_v2\", min_nodes=1, max_nodes = 1)\n", - "\n", - " # create the cluster\n", - " gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n", - " gpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Script to process data and train model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# copy process_data.py into the script folder\n", - "import shutil\n", - "shutil.copy('./process_data.py', os.path.join(scripts_folder, 'process_data.py'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data required to run this sample" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample uses [Fannie Mae's Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html). Once you obtain access to the data, you will need to make this data available in an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data), for use in this sample. The following code shows how to do that." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Downloading Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tarfile\n", - "import hashlib\n", - "from urllib.request import urlretrieve\n", - "\n", - "def validate_downloaded_data(path):\n", - " if(os.path.isdir(path) and os.path.exists(path + '//names.csv')) :\n", - " if(os.path.isdir(path + '//acq' ) and len(os.listdir(path + '//acq')) == 8):\n", - " if(os.path.isdir(path + '//perf' ) and len(os.listdir(path + '//perf')) == 11):\n", - " print(\"Data has been downloaded and decompressed at: {0}\".format(path))\n", - " return True\n", - " print(\"Data has not been downloaded and decompressed\")\n", - " return False\n", - "\n", - "def show_progress(count, block_size, total_size):\n", - " global pbar\n", - " global processed\n", - " \n", - " if count == 0:\n", - " pbar = ProgressBar(maxval=total_size)\n", - " processed = 0\n", - " \n", - " processed += block_size\n", - " processed = min(processed,total_size)\n", - " pbar.update(processed)\n", - "\n", - " \n", - "def download_file(fileroot):\n", - " filename = fileroot + '.tgz'\n", - " if(not os.path.exists(filename) or hashlib.md5(open(filename, 'rb').read()).hexdigest() != '82dd47135053303e9526c2d5c43befd5' ):\n", - " url_format = 'http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/{0}.tgz'\n", - " url = url_format.format(fileroot)\n", - " print(\"...Downloading file :{0}\".format(filename))\n", - " urlretrieve(url, filename)\n", - " pbar.finish()\n", - " print(\"...File :{0} finished downloading\".format(filename))\n", - " else:\n", - " print(\"...File :{0} has been downloaded already\".format(filename))\n", - " return filename\n", - "\n", - "def decompress_file(filename,path):\n", - " tar = tarfile.open(filename)\n", - " print(\"...Getting information from {0} about files to decompress\".format(filename))\n", - " members = tar.getmembers()\n", - " numFiles = len(members)\n", - " so_far = 0\n", - " for member_info in members:\n", - " tar.extract(member_info,path=path)\n", - " so_far += 1\n", - " print(\"...All {0} files have been decompressed\".format(numFiles))\n", - " tar.close()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fileroot = 'mortgage_2000-2001'\n", - "path = '.\\\\{0}'.format(fileroot)\n", - "pbar = None\n", - "processed = 0\n", - "\n", - "if(not validate_downloaded_data(path)):\n", - " print(\"Downloading and Decompressing Input Data\")\n", - " filename = download_file(fileroot)\n", - " decompress_file(filename,path)\n", - " print(\"Input Data has been Downloaded and Decompressed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Uploading Data to Workspace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds = ws.get_default_datastore()\n", - "\n", - "# download and uncompress data in a local directory before uploading to data store\n", - "# directory specified in src_dir parameter below should have the acq, perf directories with data and names.csv file\n", - "\n", - "# ---->>>> UNCOMMENT THE BELOW LINE TO UPLOAD YOUR DATA IF NOT DONE SO ALREADY <<<<----\n", - "# ds.upload(src_dir=path, target_path=fileroot, overwrite=True, show_progress=True)\n", - "\n", - "# data already uploaded to the datastore\n", - "data_ref = DataReference(data_reference_name='data', datastore=ds, path_on_datastore=fileroot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create AML run configuration to launch a machine learning job" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "RunConfiguration is used to submit jobs to Azure Machine Learning service. When creating RunConfiguration for a job, users can either \n", - "1. specify a Docker image with prebuilt conda environment and use it without any modifications to run the job, or \n", - "2. specify a Docker image as the base image and conda or pip packages as dependnecies to let AML build a new Docker image with a conda environment containing specified dependencies to use in the job\n", - "\n", - "The second option is the recommended option in AML. \n", - "The following steps have code for both options. You can pick the one that is more appropriate for your requirements. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Specify prebuilt conda environment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following code shows how to install RAPIDS using conda. The `rapids.yml` file contains the list of packages necessary to run this tutorial. **NOTE:** Initial build of the image might take up to 20 minutes as the service needs to build and cache the new image; once the image is built the subequent runs use the cached image and the overhead is minimal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cd = CondaDependencies(conda_dependencies_file_path='rapids.yml')\n", - "run_config = RunConfiguration(conda_dependencies=cd)\n", - "run_config.framework = 'python'\n", - "run_config.target = gpu_cluster_name\n", - "run_config.environment.docker.enabled = True\n", - "run_config.environment.docker.gpu_support = True\n", - "run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu20.04\"\n", - "run_config.environment.spark.precache_packages = False\n", - "run_config.data_references={'data':data_ref.to_config()}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Using Docker" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can specify RAPIDS Docker image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# run_config = RunConfiguration()\n", - "# run_config.framework = 'python'\n", - "# run_config.environment.python.user_managed_dependencies = True\n", - "# run_config.environment.python.interpreter_path = '/conda/envs/rapids/bin/python'\n", - "# run_config.target = gpu_cluster_name\n", - "# run_config.environment.docker.enabled = True\n", - "# run_config.environment.docker.gpu_support = True\n", - "# run_config.environment.docker.base_image = \"rapidsai/rapidsai:cuda9.2-runtime-ubuntu18.04\"\n", - "# # run_config.environment.docker.base_image_registry.address = '' # not required if the base_image is in Docker hub\n", - "# # run_config.environment.docker.base_image_registry.username = '' # needed only for private images\n", - "# # run_config.environment.docker.base_image_registry.password = '' # needed only for private images\n", - "# run_config.environment.spark.precache_packages = False\n", - "# run_config.data_references={'data':data_ref.to_config()}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wrapper function to submit Azure Machine Learning experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# parameter cpu_predictor indicates if training should be done on CPU. If set to true, GPUs are used *only* for ETL and *not* for training\n", - "# parameter num_gpu indicates number of GPUs to use among the GPUs available in the VM for ETL and if cpu_predictor is false, for training as well \n", - "def run_rapids_experiment(cpu_training, gpu_count, part_count):\n", - " # any value between 1-4 is allowed here depending the type of VMs available in gpu_cluster\n", - " if gpu_count not in [1, 2, 3, 4]:\n", - " raise Exception('Value specified for the number of GPUs to use {0} is invalid'.format(gpu_count))\n", - "\n", - " # following data partition mapping is empirical (specific to GPUs used and current data partitioning scheme) and may need to be tweaked\n", - " max_gpu_count_data_partition_mapping = {1: 3, 2: 4, 3: 6, 4: 8}\n", - " \n", - " if part_count > max_gpu_count_data_partition_mapping[gpu_count]:\n", - " print(\"Too many partitions for the number of GPUs, exceeding memory threshold\")\n", - " \n", - " if part_count > 11:\n", - " print(\"Warning: Maximum number of partitions available is 11\")\n", - " part_count = 11\n", - " \n", - " end_year = 2000\n", - " \n", - " if part_count > 4:\n", - " end_year = 2001 # use more data with more GPUs\n", - "\n", - " src = ScriptRunConfig(source_directory=scripts_folder, \n", - " script='process_data.py', \n", - " arguments = ['--num_gpu', gpu_count, '--data_dir', str(data_ref),\n", - " '--part_count', part_count, '--end_year', end_year,\n", - " '--cpu_predictor', cpu_training\n", - " ],\n", - " run_config=run_config\n", - " )\n", - "\n", - " exp = Experiment(ws, 'rapidstest')\n", - " run = exp.submit(config=src)\n", - " RunDetails(run).show()\n", - " return run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit experiment (ETL & training on GPU)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cpu_predictor = False\n", - "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n", - "num_gpu = 1\n", - "data_part_count = 1\n", - "# train using CPU, use GPU for both ETL and training\n", - "run = run_rapids_experiment(cpu_predictor, num_gpu, data_part_count)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit experiment (ETL on GPU, training on CPU)\n", - "\n", - "To observe performance difference between GPU-accelerated RAPIDS based training with CPU-only training, set 'cpu_predictor' predictor to 'True' and rerun the experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cpu_predictor = True\n", - "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n", - "num_gpu = 1\n", - "data_part_count = 1\n", - "# train using CPU, use GPU for ETL\n", - "run = run_rapids_experiment(cpu_predictor, num_gpu, data_part_count)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Delete cluster" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# delete the cluster\n", - "# gpu_cluster.delete()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "ksivas" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/contrib/RAPIDS/imgs/2GPUs.png b/contrib/RAPIDS/imgs/2GPUs.png deleted file mode 100644 index 07e383746..000000000 Binary files a/contrib/RAPIDS/imgs/2GPUs.png and /dev/null differ diff --git a/contrib/RAPIDS/imgs/3GPUs.png b/contrib/RAPIDS/imgs/3GPUs.png deleted file mode 100644 index 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as xgb -import cudf -from cudf.dataframe import DataFrame -from collections import OrderedDict -import gc -from glob import glob -import os -import argparse - -def run_dask_task(func, **kwargs): - task = func(**kwargs) - return task - -def process_quarter_gpu(client, col_names_path, acq_data_path, year=2000, quarter=1, perf_file=""): - dask_client = client - ml_arrays = run_dask_task(delayed(run_gpu_workflow), - col_path=col_names_path, - acq_path=acq_data_path, - quarter=quarter, - year=year, - perf_file=perf_file) - return dask_client.compute(ml_arrays, - optimize_graph=False, - fifo_timeout="0ms") - -def null_workaround(df, **kwargs): - for column, data_type in df.dtypes.items(): - if str(data_type) == "category": - df[column] = df[column].astype('int32').fillna(-1) - if str(data_type) in ['int8', 'int16', 'int32', 'int64', 'float32', 'float64']: - df[column] = df[column].fillna(-1) - return df - -def run_gpu_workflow(col_path, acq_path, quarter=1, year=2000, perf_file="", **kwargs): - names = gpu_load_names(col_path=col_path) - acq_gdf = gpu_load_acquisition_csv(acquisition_path= acq_path + "/Acquisition_" - + str(year) + "Q" + str(quarter) + ".txt") - acq_gdf = acq_gdf.merge(names, how='left', on=['seller_name']) - acq_gdf.drop_column('seller_name') - acq_gdf['seller_name'] = acq_gdf['new'] - acq_gdf.drop_column('new') - perf_df_tmp = gpu_load_performance_csv(perf_file) - gdf = perf_df_tmp - everdf = create_ever_features(gdf) - delinq_merge = create_delinq_features(gdf) - everdf = join_ever_delinq_features(everdf, delinq_merge) - del(delinq_merge) - joined_df = create_joined_df(gdf, everdf) - testdf = create_12_mon_features(joined_df) - joined_df = combine_joined_12_mon(joined_df, testdf) - del(testdf) - perf_df = final_performance_delinquency(gdf, joined_df) - del(gdf, joined_df) - final_gdf = join_perf_acq_gdfs(perf_df, acq_gdf) - del(perf_df) - del(acq_gdf) - final_gdf = last_mile_cleaning(final_gdf) - return final_gdf - -def gpu_load_performance_csv(performance_path, **kwargs): - """ Loads performance data - - Returns - ------- - GPU DataFrame - """ - - cols = [ - "loan_id", "monthly_reporting_period", "servicer", "interest_rate", "current_actual_upb", - "loan_age", "remaining_months_to_legal_maturity", "adj_remaining_months_to_maturity", - "maturity_date", "msa", "current_loan_delinquency_status", "mod_flag", "zero_balance_code", - "zero_balance_effective_date", "last_paid_installment_date", "foreclosed_after", - "disposition_date", "foreclosure_costs", "prop_preservation_and_repair_costs", - "asset_recovery_costs", "misc_holding_expenses", "holding_taxes", "net_sale_proceeds", - "credit_enhancement_proceeds", "repurchase_make_whole_proceeds", "other_foreclosure_proceeds", - "non_interest_bearing_upb", "principal_forgiveness_upb", "repurchase_make_whole_proceeds_flag", - "foreclosure_principal_write_off_amount", "servicing_activity_indicator" - ] - - dtypes = OrderedDict([ - ("loan_id", "int64"), - ("monthly_reporting_period", "date"), - ("servicer", "category"), - ("interest_rate", "float64"), - ("current_actual_upb", "float64"), - ("loan_age", "float64"), - ("remaining_months_to_legal_maturity", "float64"), - ("adj_remaining_months_to_maturity", "float64"), - ("maturity_date", "date"), - ("msa", "float64"), - ("current_loan_delinquency_status", "int32"), - ("mod_flag", "category"), - ("zero_balance_code", "category"), - ("zero_balance_effective_date", "date"), - ("last_paid_installment_date", "date"), - ("foreclosed_after", "date"), - ("disposition_date", "date"), - ("foreclosure_costs", "float64"), - ("prop_preservation_and_repair_costs", "float64"), - ("asset_recovery_costs", "float64"), - ("misc_holding_expenses", "float64"), - ("holding_taxes", "float64"), - ("net_sale_proceeds", "float64"), - ("credit_enhancement_proceeds", "float64"), - ("repurchase_make_whole_proceeds", "float64"), - ("other_foreclosure_proceeds", "float64"), - ("non_interest_bearing_upb", "float64"), - ("principal_forgiveness_upb", "float64"), - ("repurchase_make_whole_proceeds_flag", "category"), - ("foreclosure_principal_write_off_amount", "float64"), - ("servicing_activity_indicator", "category") - ]) - - print(performance_path) - - return cudf.read_csv(performance_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1) - -def gpu_load_acquisition_csv(acquisition_path, **kwargs): - """ Loads acquisition data - - Returns - ------- - GPU DataFrame - """ - - cols = [ - 'loan_id', 'orig_channel', 'seller_name', 'orig_interest_rate', 'orig_upb', 'orig_loan_term', - 'orig_date', 'first_pay_date', 'orig_ltv', 'orig_cltv', 'num_borrowers', 'dti', 'borrower_credit_score', - 'first_home_buyer', 'loan_purpose', 'property_type', 'num_units', 'occupancy_status', 'property_state', - 'zip', 'mortgage_insurance_percent', 'product_type', 'coborrow_credit_score', 'mortgage_insurance_type', - 'relocation_mortgage_indicator' - ] - - dtypes = OrderedDict([ - ("loan_id", "int64"), - ("orig_channel", "category"), - ("seller_name", "category"), - ("orig_interest_rate", "float64"), - ("orig_upb", "int64"), - ("orig_loan_term", "int64"), - ("orig_date", "date"), - ("first_pay_date", "date"), - ("orig_ltv", "float64"), - ("orig_cltv", "float64"), - ("num_borrowers", "float64"), - ("dti", "float64"), - ("borrower_credit_score", "float64"), - ("first_home_buyer", "category"), - ("loan_purpose", "category"), - ("property_type", "category"), - ("num_units", "int64"), - ("occupancy_status", "category"), - ("property_state", "category"), - ("zip", "int64"), - ("mortgage_insurance_percent", "float64"), - ("product_type", "category"), - ("coborrow_credit_score", "float64"), - ("mortgage_insurance_type", "float64"), - ("relocation_mortgage_indicator", "category") - ]) - - print(acquisition_path) - - return cudf.read_csv(acquisition_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1) - -def gpu_load_names(col_path): - """ Loads names used for renaming the banks - - Returns - ------- - GPU DataFrame - """ - - cols = [ - 'seller_name', 'new' - ] - - dtypes = OrderedDict([ - ("seller_name", "category"), - ("new", "category"), - ]) - - return cudf.read_csv(col_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1) - -def create_ever_features(gdf, **kwargs): - everdf = gdf[['loan_id', 'current_loan_delinquency_status']] - everdf = everdf.groupby('loan_id', method='hash').max().reset_index() - del(gdf) - everdf['ever_30'] = (everdf['current_loan_delinquency_status'] >= 1).astype('int8') - everdf['ever_90'] = (everdf['current_loan_delinquency_status'] >= 3).astype('int8') - everdf['ever_180'] = (everdf['current_loan_delinquency_status'] >= 6).astype('int8') - everdf.drop_column('current_loan_delinquency_status') - return everdf - -def create_delinq_features(gdf, **kwargs): - delinq_gdf = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status']] - del(gdf) - delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index() - delinq_30['delinquency_30'] = delinq_30['monthly_reporting_period'] - delinq_30.drop_column('monthly_reporting_period') - delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index() - delinq_90['delinquency_90'] = delinq_90['monthly_reporting_period'] - delinq_90.drop_column('monthly_reporting_period') - delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index() - delinq_180['delinquency_180'] = delinq_180['monthly_reporting_period'] - delinq_180.drop_column('monthly_reporting_period') - del(delinq_gdf) - delinq_merge = delinq_30.merge(delinq_90, how='left', on=['loan_id'], type='hash') - delinq_merge['delinquency_90'] = delinq_merge['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]')) - delinq_merge = delinq_merge.merge(delinq_180, how='left', on=['loan_id'], type='hash') - delinq_merge['delinquency_180'] = delinq_merge['delinquency_180'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]')) - del(delinq_30) - del(delinq_90) - del(delinq_180) - return delinq_merge - -def join_ever_delinq_features(everdf_tmp, delinq_merge, **kwargs): - everdf = everdf_tmp.merge(delinq_merge, on=['loan_id'], how='left', type='hash') - del(everdf_tmp) - del(delinq_merge) - everdf['delinquency_30'] = everdf['delinquency_30'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]')) - everdf['delinquency_90'] = everdf['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]')) - everdf['delinquency_180'] = everdf['delinquency_180'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]')) - return everdf - -def create_joined_df(gdf, everdf, **kwargs): - test = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status', 'current_actual_upb']] - del(gdf) - test['timestamp'] = test['monthly_reporting_period'] - test.drop_column('monthly_reporting_period') - test['timestamp_month'] = test['timestamp'].dt.month - test['timestamp_year'] = test['timestamp'].dt.year - test['delinquency_12'] = test['current_loan_delinquency_status'] - test.drop_column('current_loan_delinquency_status') - test['upb_12'] = test['current_actual_upb'] - test.drop_column('current_actual_upb') - test['upb_12'] = test['upb_12'].fillna(999999999) - test['delinquency_12'] = test['delinquency_12'].fillna(-1) - - joined_df = test.merge(everdf, how='left', on=['loan_id'], type='hash') - del(everdf) - del(test) - - joined_df['ever_30'] = joined_df['ever_30'].fillna(-1) - joined_df['ever_90'] = joined_df['ever_90'].fillna(-1) - joined_df['ever_180'] = joined_df['ever_180'].fillna(-1) - joined_df['delinquency_30'] = joined_df['delinquency_30'].fillna(-1) - joined_df['delinquency_90'] = joined_df['delinquency_90'].fillna(-1) - joined_df['delinquency_180'] = joined_df['delinquency_180'].fillna(-1) - - joined_df['timestamp_year'] = joined_df['timestamp_year'].astype('int32') - joined_df['timestamp_month'] = joined_df['timestamp_month'].astype('int32') - - return joined_df - -def create_12_mon_features(joined_df, **kwargs): - testdfs = [] - n_months = 12 - - for y in range(1, n_months + 1): - tmpdf = joined_df[['loan_id', 'timestamp_year', 'timestamp_month', 'delinquency_12', 'upb_12']] - tmpdf['josh_months'] = tmpdf['timestamp_year'] * 12 + tmpdf['timestamp_month'] - tmpdf['josh_mody_n'] = ((tmpdf['josh_months'].astype('float64') - 24000 - y) / 12).floor() - tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'}).reset_index() - tmpdf['delinquency_12'] = (tmpdf['delinquency_12']>3).astype('int32') - tmpdf['delinquency_12'] +=(tmpdf['upb_12']==0).astype('int32') - tmpdf['upb_12'] = tmpdf['upb_12'] - tmpdf['timestamp_year'] = (((tmpdf['josh_mody_n'] * n_months) + 24000 + (y - 1)) / 12).floor().astype('int16') - tmpdf['timestamp_month'] = np.int8(y) - tmpdf.drop_column('josh_mody_n') - testdfs.append(tmpdf) - del(tmpdf) - del(joined_df) - - return cudf.concat(testdfs) - -def combine_joined_12_mon(joined_df, testdf, **kwargs): - joined_df.drop_column('delinquency_12') - joined_df.drop_column('upb_12') - joined_df['timestamp_year'] = joined_df['timestamp_year'].astype('int16') - joined_df['timestamp_month'] = joined_df['timestamp_month'].astype('int8') - return joined_df.merge(testdf, how='left', on=['loan_id', 'timestamp_year', 'timestamp_month'], type='hash') - -def final_performance_delinquency(gdf, joined_df, **kwargs): - merged = null_workaround(gdf) - joined_df = null_workaround(joined_df) - merged['timestamp_month'] = merged['monthly_reporting_period'].dt.month - merged['timestamp_month'] = merged['timestamp_month'].astype('int8') - merged['timestamp_year'] = merged['monthly_reporting_period'].dt.year - merged['timestamp_year'] = merged['timestamp_year'].astype('int16') - merged = merged.merge(joined_df, how='left', on=['loan_id', 'timestamp_year', 'timestamp_month'], type='hash') - merged.drop_column('timestamp_year') - merged.drop_column('timestamp_month') - return merged - -def join_perf_acq_gdfs(perf, acq, **kwargs): - perf = null_workaround(perf) - acq = null_workaround(acq) - return perf.merge(acq, how='left', on=['loan_id'], type='hash') - -def last_mile_cleaning(df, **kwargs): - drop_list = [ - 'loan_id', 'orig_date', 'first_pay_date', 'seller_name', - 'monthly_reporting_period', 'last_paid_installment_date', 'maturity_date', 'ever_30', 'ever_90', 'ever_180', - 'delinquency_30', 'delinquency_90', 'delinquency_180', 'upb_12', - 'zero_balance_effective_date','foreclosed_after', 'disposition_date','timestamp' - ] - - for column in drop_list: - df.drop_column(column) - for col, dtype in df.dtypes.iteritems(): - if str(dtype)=='category': - df[col] = df[col].cat.codes - df[col] = df[col].astype('float32') - df['delinquency_12'] = df['delinquency_12'] > 0 - df['delinquency_12'] = df['delinquency_12'].fillna(False).astype('int32') - for column in df.columns: - df[column] = df[column].fillna(-1) - return df.to_arrow(preserve_index=False) - -def main(): - parser = argparse.ArgumentParser("rapidssample") - parser.add_argument("--data_dir", type=str, help="location of data") - parser.add_argument("--num_gpu", type=int, help="Number of GPUs to use", default=1) - parser.add_argument("--part_count", type=int, help="Number of data files to train against", default=2) - parser.add_argument("--end_year", type=int, help="Year to end the data load", default=2000) - parser.add_argument("--cpu_predictor", type=str, help="Flag to use CPU for prediction", default='False') - parser.add_argument('-f', type=str, default='') # added for notebook execution scenarios - args = parser.parse_args() - data_dir = args.data_dir - num_gpu = args.num_gpu - part_count = args.part_count - end_year = args.end_year - cpu_predictor = args.cpu_predictor.lower() in ('yes', 'true', 't', 'y', '1') - - if cpu_predictor: - print('Training with CPUs require num gpu = 1') - num_gpu = 1 - - print('data_dir = {0}'.format(data_dir)) - print('num_gpu = {0}'.format(num_gpu)) - print('part_count = {0}'.format(part_count)) - print('end_year = {0}'.format(end_year)) - print('cpu_predictor = {0}'.format(cpu_predictor)) - - import subprocess - - cmd = "hostname --all-ip-addresses" - process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE) - output, error = process.communicate() - IPADDR = str(output.decode()).split()[0] - - cluster = LocalCUDACluster(ip=IPADDR,n_workers=num_gpu) - client = Client(cluster) - client - print(client.ncores()) - - # to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly - acq_data_path = "{0}/acq".format(data_dir) #"/rapids/data/mortgage/acq" - perf_data_path = "{0}/perf".format(data_dir) #"/rapids/data/mortgage/perf" - col_names_path = "{0}/names.csv".format(data_dir) # "/rapids/data/mortgage/names.csv" - start_year = 2000 - - client - print('--->>> Workers used: {0}'.format(client.ncores())) - - # NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix. - # This can be optimized to avoid calculating the dropped features. - print("Reading ...") - t1 = datetime.datetime.now() - gpu_dfs = [] - gpu_time = 0 - quarter = 1 - year = start_year - count = 0 - while year <= end_year: - for file in glob(os.path.join(perf_data_path + "/Performance_" + str(year) + "Q" + str(quarter) + "*")): - if count < part_count: - gpu_dfs.append(process_quarter_gpu(client, col_names_path, acq_data_path, year=year, quarter=quarter, perf_file=file)) - count += 1 - print('file: {0}'.format(file)) - print('count: {0}'.format(count)) - quarter += 1 - if quarter == 5: - year += 1 - quarter = 1 - - wait(gpu_dfs) - t2 = datetime.datetime.now() - print("Reading time: {0}".format(str(t2-t1))) - print('--->>> Number of data parts: {0}'.format(len(gpu_dfs))) - - dxgb_gpu_params = { - 'nround': 100, - 'max_depth': 8, - 'max_leaves': 2**8, - 'alpha': 0.9, - 'eta': 0.1, - 'gamma': 0.1, - 'learning_rate': 0.1, - 'subsample': 1, - 'reg_lambda': 1, - 'scale_pos_weight': 2, - 'min_child_weight': 30, - 'tree_method': 'gpu_hist', - 'n_gpus': 1, - 'distributed_dask': True, - 'loss': 'ls', - 'objective': 'reg:squarederror', - 'max_features': 'auto', - 'criterion': 'friedman_mse', - 'grow_policy': 'lossguide', - 'verbose': True - } - - if cpu_predictor: - print('\n---->>>> Training using CPUs <<<<----\n') - dxgb_gpu_params['predictor'] = 'cpu_predictor' - dxgb_gpu_params['tree_method'] = 'hist' - dxgb_gpu_params['objective'] = 'reg:linear' - - else: - print('\n---->>>> Training using GPUs <<<<----\n') - - print('Training parameters are {0}'.format(dxgb_gpu_params)) - - gpu_dfs = [delayed(DataFrame.from_arrow)(gpu_df) for gpu_df in gpu_dfs[:part_count]] - gpu_dfs = [gpu_df for gpu_df in gpu_dfs] - wait(gpu_dfs) - - tmp_map = [(gpu_df, list(client.who_has(gpu_df).values())[0]) for gpu_df in gpu_dfs] - new_map = {} - for key, value in tmp_map: - if value not in new_map: - new_map[value] = [key] - else: - new_map[value].append(key) - - del(tmp_map) - gpu_dfs = [] - for list_delayed in new_map.values(): - gpu_dfs.append(delayed(cudf.concat)(list_delayed)) - - del(new_map) - gpu_dfs = [(gpu_df[['delinquency_12']], gpu_df[delayed(list)(gpu_df.columns.difference(['delinquency_12']))]) for gpu_df in gpu_dfs] - gpu_dfs = [(gpu_df[0].persist(), gpu_df[1].persist()) for gpu_df in gpu_dfs] - - gpu_dfs = [dask.delayed(xgb.DMatrix)(gpu_df[1], gpu_df[0]) for gpu_df in gpu_dfs] - gpu_dfs = [gpu_df.persist() for gpu_df in gpu_dfs] - gc.collect() - wait(gpu_dfs) - - # TRAIN THE MODEL - labels = None - t1 = datetime.datetime.now() - bst = dxgb_gpu.train(client, dxgb_gpu_params, gpu_dfs, labels, num_boost_round=dxgb_gpu_params['nround']) - t2 = datetime.datetime.now() - print('\n---->>>> Training time: {0} <<<<----\n'.format(str(t2-t1))) - print('Exiting script') - -if __name__ == '__main__': - main() diff --git a/contrib/fairness/fairlearn-azureml-mitigation.ipynb b/contrib/fairness/fairlearn-azureml-mitigation.ipynb deleted file mode 100644 index 68040ed55..000000000 --- a/contrib/fairness/fairlearn-azureml-mitigation.ipynb +++ /dev/null @@ -1,621 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/fairness/fairlearn-azureml-mitigation.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Unfairness Mitigation with Fairlearn and Azure Machine Learning\n", - "**This notebook shows how to upload results from Fairlearn's GridSearch mitigation algorithm into a dashboard in Azure Machine Learning Studio**\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "1. [Loading the Data](#LoadingData)\n", - "1. [Training an Unmitigated Model](#UnmitigatedModel)\n", - "1. [Mitigation with GridSearch](#Mitigation)\n", - "1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n", - " 1. Registering models\n", - " 1. Computing Fairness Metrics\n", - " 1. Uploading to Azure\n", - "1. [Conclusion](#Conclusion)\n", - "\n", - "\n", - "## Introduction\n", - "This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.org) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.org/).\n", - "\n", - "We will apply the [grid search algorithm](https://fairlearn.org/v0.4.6/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n", - "\n", - "### Setup\n", - "\n", - "To use this notebook, an Azure Machine Learning workspace is required.\n", - "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n", - "This notebook also requires the following packages:\n", - "* `azureml-contrib-fairness`\n", - "* `fairlearn>=0.6.2` (pre-v0.5.0 will work with minor modifications)\n", - "* `joblib`\n", - "* `liac-arff`\n", - "* `raiwidgets`\n", - "\n", - "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install --upgrade scikit-learn>=0.22.1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, please ensure that when you downloaded this notebook, you also downloaded the `fairness_nb_utils.py` file from the same location, and placed it in the same directory as this notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Loading the Data\n", - "We use the well-known `adult` census dataset, which we will fetch from the OpenML website. We start with a fairly unremarkable set of imports:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n", - "from raiwidgets import FairnessDashboard\n", - "\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", - "from sklearn.compose import make_column_selector as selector\n", - "from sklearn.pipeline import Pipeline\n", - "\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now load and inspect the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from fairness_nb_utils import fetch_census_dataset\n", - "\n", - "data = fetch_census_dataset()\n", - " \n", - "# Extract the items we want\n", - "X_raw = data.data\n", - "y = (data.target == '>50K') * 1\n", - "\n", - "X_raw[\"race\"].value_counts().to_dict()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are going to treat the sex and race of each individual as protected attributes, and in this particular case we are going to remove these attributes from the main data (this is not always the best option - see the [Fairlearn website](http://fairlearn.github.io/) for further discussion). Protected attributes are often denoted by 'A' in the literature, and we follow that convention here:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = X_raw[['sex','race']]\n", - "X_raw = X_raw.drop(labels=['sex', 'race'], axis = 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now preprocess our data. To avoid the problem of data leakage, we split our data into training and test sets before performing any other transformations. Subsequent transformations (such as scalings) will be fit to the training data set, and then applied to the test dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(X_train, X_test, y_train, y_test, A_train, A_test) = train_test_split(\n", - " X_raw, y, A, test_size=0.3, random_state=12345, stratify=y\n", - ")\n", - "\n", - "# Ensure indices are aligned between X, y and A,\n", - "# after all the slicing and splitting of DataFrames\n", - "# and Series\n", - "\n", - "X_train = X_train.reset_index(drop=True)\n", - "X_test = X_test.reset_index(drop=True)\n", - "y_train = y_train.reset_index(drop=True)\n", - "y_test = y_test.reset_index(drop=True)\n", - "A_train = A_train.reset_index(drop=True)\n", - "A_test = A_test.reset_index(drop=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have two types of column in the dataset - categorical columns which will need to be one-hot encoded, and numeric ones which will need to be rescaled. We also need to take care of missing values. We use a simple approach here, but please bear in mind that this is another way that bias could be introduced (especially if one subgroup tends to have more missing values).\n", - "\n", - "For this preprocessing, we make use of `Pipeline` objects from `sklearn`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "numeric_transformer = Pipeline(\n", - " steps=[\n", - " (\"impute\", SimpleImputer()),\n", - " (\"scaler\", StandardScaler()),\n", - " ]\n", - ")\n", - "\n", - "categorical_transformer = Pipeline(\n", - " [\n", - " (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n", - " (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\", sparse=False)),\n", - " ]\n", - ")\n", - "\n", - "preprocessor = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n", - " (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the preprocessing pipeline is defined, we can run it on our training data, and apply the generated transform to our test data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_train = preprocessor.fit_transform(X_train)\n", - "X_test = preprocessor.transform(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Training an Unmitigated Model\n", - "\n", - "So we have a point of comparison, we first train a model (specifically, logistic regression from scikit-learn) on the raw data, without applying any mitigation algorithm:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "unmitigated_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n", - "\n", - "unmitigated_predictor.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can view this model in the fairness dashboard, and see the disparities which appear:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FairnessDashboard(sensitive_features=A_test,\n", - " y_true=y_test,\n", - " y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the disparity in accuracy when we select 'Sex' as the sensitive feature, we see that males have an error rate about three times greater than the females. More interesting is the disparity in opportunitiy - males are offered loans at three times the rate of females.\n", - "\n", - "Despite the fact that we removed the feature from the training data, our predictor still discriminates based on sex. This demonstrates that simply ignoring a protected attribute when fitting a predictor rarely eliminates unfairness. There will generally be enough other features correlated with the removed attribute to lead to disparate impact." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Mitigation with GridSearch\n", - "\n", - "The `GridSearch` class in `Fairlearn` implements a simplified version of the exponentiated gradient reduction of [Agarwal et al. 2018](https://arxiv.org/abs/1803.02453). The user supplies a standard ML estimator, which is treated as a blackbox - for this simple example, we shall use the logistic regression estimator from scikit-learn. `GridSearch` works by generating a sequence of relabellings and reweightings, and trains a predictor for each.\n", - "\n", - "For this example, we specify demographic parity (on the protected attribute of sex) as the fairness metric. Demographic parity requires that individuals are offered the opportunity (a loan in this example) independent of membership in the protected class (i.e., females and males should be offered loans at the same rate). *We are using this metric for the sake of simplicity* in this example; the appropriate fairness metric can only be selected after *careful examination of the broader context* in which the model is to be used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sweep = GridSearch(LogisticRegression(solver='liblinear', fit_intercept=True),\n", - " constraints=DemographicParity(),\n", - " grid_size=71)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our estimator created, we can fit it to the data. After `fit()` completes, we extract the full set of predictors from the `GridSearch` object.\n", - "\n", - "The following cell trains a many copies of the underlying estimator, and may take a minute or two to run:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sweep.fit(X_train, y_train,\n", - " sensitive_features=A_train.sex)\n", - "\n", - "# For Fairlearn pre-v0.5.0, need sweep._predictors\n", - "predictors = sweep.predictors_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could load these predictors into the Fairness dashboard now. However, the plot would be somewhat confusing due to their number. In this case, we are going to remove the predictors which are dominated in the error-disparity space by others from the sweep (note that the disparity will only be calculated for the protected attribute; other potentially protected attributes will *not* be mitigated). In general, one might not want to do this, since there may be other considerations beyond the strict optimisation of error and disparity (of the given protected attribute)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "errors, disparities = [], []\n", - "for predictor in predictors:\n", - " error = ErrorRate()\n", - " error.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n", - " disparity = DemographicParity()\n", - " disparity.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n", - " \n", - " errors.append(error.gamma(predictor.predict)[0])\n", - " disparities.append(disparity.gamma(predictor.predict).max())\n", - " \n", - "all_results = pd.DataFrame( {\"predictor\": predictors, \"error\": errors, \"disparity\": disparities})\n", - "\n", - "dominant_models_dict = dict()\n", - "base_name_format = \"census_gs_model_{0}\"\n", - "row_id = 0\n", - "for row in all_results.itertuples():\n", - " model_name = base_name_format.format(row_id)\n", - " errors_for_lower_or_eq_disparity = all_results[\"error\"][all_results[\"disparity\"]<=row.disparity]\n", - " if row.error <= errors_for_lower_or_eq_disparity.min():\n", - " dominant_models_dict[model_name] = row.predictor\n", - " row_id = row_id + 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can construct predictions for the dominant models (we include the unmitigated predictor as well, for comparison):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictions_dominant = {\"census_unmitigated\": unmitigated_predictor.predict(X_test)}\n", - "models_dominant = {\"census_unmitigated\": unmitigated_predictor}\n", - "for name, predictor in dominant_models_dict.items():\n", - " value = predictor.predict(X_test)\n", - " predictions_dominant[name] = value\n", - " models_dominant[name] = predictor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These predictions may then be viewed in the fairness dashboard. We include the race column from the dataset, as an alternative basis for assessing the models. However, since we have not based our mitigation on it, the variation in the models with respect to race can be large." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FairnessDashboard(sensitive_features=A_test, \n", - " y_true=y_test.tolist(),\n", - " y_pred=predictions_dominant)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When using sex as the sensitive feature and accuracy as the metric, we see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy. Finally, we also see that the unmitigated model is towards the top right of the plot, with high accuracy, but worst disparity.\n", - "\n", - "By clicking on individual models on the plot, we can inspect their metrics for disparity and accuracy in greater detail. In a real example, we would then pick the model which represented the best trade-off between accuracy and disparity given the relevant business constraints." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Uploading a Fairness Dashboard to Azure\n", - "\n", - "Uploading a fairness dashboard to Azure is a two stage process. The `FairnessDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. By default, the dashboard in Azure Machine Learning Studio also requires the models to be registered. The required stages are therefore:\n", - "1. Register the dominant models\n", - "1. Precompute all the required metrics\n", - "1. Upload to Azure\n", - "\n", - "Before that, we need to connect to Azure Machine Learning Studio:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace, Experiment, Model\n", - "\n", - "ws = Workspace.from_config()\n", - "ws.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Registering Models\n", - "\n", - "The fairness dashboard is designed to integrate with registered models, so we need to do this for the models we want in the Studio portal. The assumption is that the names of the models specified in the dashboard dictionary correspond to the `id`s (i.e. `:` pairs) of registered models in the workspace. We register each of the models in the `models_dominant` dictionary into the workspace. For this, we have to save each model to a file, and then register that file:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import joblib\n", - "import os\n", - "\n", - "os.makedirs('models', exist_ok=True)\n", - "def register_model(name, model):\n", - " print(\"Registering \", name)\n", - " model_path = \"models/{0}.pkl\".format(name)\n", - " joblib.dump(value=model, filename=model_path)\n", - " registered_model = Model.register(model_path=model_path,\n", - " model_name=name,\n", - " workspace=ws)\n", - " print(\"Registered \", registered_model.id)\n", - " return registered_model.id\n", - "\n", - "model_name_id_mapping = dict()\n", - "for name, model in models_dominant.items():\n", - " m_id = register_model(name, model)\n", - " model_name_id_mapping[name] = m_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, produce new predictions dictionaries, with the updated names:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictions_dominant_ids = dict()\n", - "for name, y_pred in predictions_dominant.items():\n", - " predictions_dominant_ids[model_name_id_mapping[name]] = y_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Precomputing Metrics\n", - "\n", - "We create a _dashboard dictionary_ using Fairlearn's `metrics` package. The `_create_group_metric_set` method has arguments similar to the Dashboard constructor, except that the sensitive features are passed as a dictionary (to ensure that names are available), and we must specify the type of prediction. Note that we use the `predictions_dominant_ids` dictionary we just created:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sf = { 'sex': A_test.sex, 'race': A_test.race }\n", - "\n", - "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n", - "\n", - "\n", - "dash_dict = _create_group_metric_set(y_true=y_test,\n", - " predictions=predictions_dominant_ids,\n", - " sensitive_features=sf,\n", - " prediction_type='binary_classification')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Uploading the Dashboard\n", - "\n", - "Now, we import our `contrib` package which contains the routine to perform the upload:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can create an Experiment, then a Run, and upload our dashboard to it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exp = Experiment(ws, \"Test_Fairlearn_GridSearch_Census_Demo\")\n", - "print(exp)\n", - "\n", - "run = exp.start_logging()\n", - "try:\n", - " dashboard_title = \"Dominant Models from GridSearch\"\n", - " upload_id = upload_dashboard_dictionary(run,\n", - " dash_dict,\n", - " dashboard_name=dashboard_title)\n", - " print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n", - "\n", - " downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n", - "finally:\n", - " run.complete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dashboard can be viewed in the Run Details page.\n", - "\n", - "Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(dash_dict == downloaded_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Conclusion\n", - "\n", - "In this notebook we have demonstrated how to use the `GridSearch` algorithm from Fairlearn to generate a collection of models, and then present them in the fairness dashboard in Azure Machine Learning Studio. Please remember that this notebook has not attempted to discuss the many considerations which should be part of any approach to unfairness mitigation. The [Fairlearn website](http://fairlearn.org/) provides that discussion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "riedgar" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/contrib/fairness/fairlearn-azureml-mitigation.yml b/contrib/fairness/fairlearn-azureml-mitigation.yml deleted file mode 100644 index 0230d1a0b..000000000 --- a/contrib/fairness/fairlearn-azureml-mitigation.yml +++ /dev/null @@ -1,11 +0,0 @@ -name: fairlearn-azureml-mitigation -dependencies: -- pip: - - azureml-sdk - - azureml-contrib-fairness - - fairlearn>=0.6.2 - - joblib - - liac-arff - - raiwidgets~=0.17.0 - - itsdangerous==2.0.1 - - markupsafe<2.1.0 diff --git a/contrib/fairness/fairness_nb_utils.py b/contrib/fairness/fairness_nb_utils.py deleted file mode 100644 index a300ac097..000000000 --- a/contrib/fairness/fairness_nb_utils.py +++ /dev/null @@ -1,111 +0,0 @@ -# --------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# --------------------------------------------------------- - -"""Utilities for azureml-contrib-fairness notebooks.""" - -import arff -from collections import OrderedDict -from contextlib import closing -import gzip -import pandas as pd -from sklearn.datasets import fetch_openml -from sklearn.utils import Bunch -import time - - -def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60): - """Fetch a given dataset from OpenML with retries as specified.""" - for i in range(max_retries): - try: - print("Download attempt {0} of {1}".format(i + 1, max_retries)) - data = fetch_openml(data_id=data_id, as_frame=True) - break - except Exception as e: # noqa: B902 - print("Download attempt failed with exception:") - print(e) - if i + 1 != max_retries: - print("Will retry after {0} seconds".format(retry_delay)) - time.sleep(retry_delay) - retry_delay = retry_delay * 2 - else: - raise RuntimeError("Unable to download dataset from OpenML") - - return data - - -_categorical_columns = [ - 'workclass', - 'education', - 'marital-status', - 'occupation', - 'relationship', - 'race', - 'sex', - 'native-country' -] - - -def fetch_census_dataset(): - """Fetch the Adult Census Dataset. - - This uses a particular URL for the Adult Census dataset. The code - is a simplified version of fetch_openml() in sklearn. - - The data are copied from: - https://openml.org/data/v1/download/1595261.gz - (as of 2021-03-31) - """ - try: - from urllib import urlretrieve - except ImportError: - from urllib.request import urlretrieve - - filename = "1595261.gz" - data_url = "https://rainotebookscdn.blob.core.windows.net/datasets/" - - remaining_attempts = 5 - sleep_duration = 10 - while remaining_attempts > 0: - try: - urlretrieve(data_url + filename, filename) - - http_stream = gzip.GzipFile(filename=filename, mode='rb') - - with closing(http_stream): - def _stream_generator(response): - for line in response: - yield line.decode('utf-8') - - stream = _stream_generator(http_stream) - data = arff.load(stream) - except Exception as exc: # noqa: B902 - remaining_attempts -= 1 - print("Error downloading dataset from {} ({} attempt(s) remaining)" - .format(data_url, remaining_attempts)) - print(exc) - time.sleep(sleep_duration) - sleep_duration *= 2 - continue - else: - # dataset successfully downloaded - break - else: - raise Exception("Could not retrieve dataset from {}.".format(data_url)) - - attributes = OrderedDict(data['attributes']) - arff_columns = list(attributes) - - raw_df = pd.DataFrame(data=data['data'], columns=arff_columns) - - target_column_name = 'class' - target = raw_df.pop(target_column_name) - for col_name in _categorical_columns: - dtype = pd.api.types.CategoricalDtype(attributes[col_name]) - raw_df[col_name] = raw_df[col_name].astype(dtype, copy=False) - - result = Bunch() - result.data = raw_df - result.target = target - - return result diff --git a/contrib/fairness/upload-fairness-dashboard.ipynb b/contrib/fairness/upload-fairness-dashboard.ipynb deleted file mode 100644 index 35623f152..000000000 --- a/contrib/fairness/upload-fairness-dashboard.ipynb +++ /dev/null @@ -1,545 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/fairness/upload-fairness-dashboard.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Upload a Fairness Dashboard to Azure Machine Learning Studio\n", - "**This notebook shows how to generate and upload a fairness assessment dashboard from Fairlearn to AzureML Studio**\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "1. [Loading the Data](#LoadingData)\n", - "1. [Processing the Data](#ProcessingData)\n", - "1. [Training Models](#TrainingModels)\n", - "1. [Logging in to AzureML](#LoginAzureML)\n", - "1. [Registering the Models](#RegisterModels)\n", - "1. [Using the Fairness Dashboard](#LocalDashboard)\n", - "1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n", - " 1. Computing Fairness Metrics\n", - " 1. Uploading to Azure\n", - "1. [Conclusion](#Conclusion)\n", - " \n", - "\n", - "\n", - "## Introduction\n", - "\n", - "In this notebook, we walk through a simple example of using the `azureml-contrib-fairness` package to upload a collection of fairness statistics for a fairness dashboard. It is an example of integrating the [open source Fairlearn package](https://www.github.com/fairlearn/fairlearn) with Azure Machine Learning. This is not an example of fairness analysis or mitigation - this notebook simply shows how to get a fairness dashboard into the Azure Machine Learning portal. We will load the data and train a couple of simple models. We will then use Fairlearn to generate data for a Fairness dashboard, which we can upload to Azure Machine Learning portal and view there.\n", - "\n", - "### Setup\n", - "\n", - "To use this notebook, an Azure Machine Learning workspace is required.\n", - "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n", - "This notebook also requires the following packages:\n", - "* `azureml-contrib-fairness`\n", - "* `fairlearn>=0.6.2` (also works for pre-v0.5.0 with slight modifications)\n", - "* `joblib`\n", - "* `liac-arff`\n", - "* `raiwidgets`\n", - "\n", - "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install --upgrade scikit-learn>=0.22.1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, please ensure that when you downloaded this notebook, you also downloaded the `fairness_nb_utils.py` file from the same location, and placed it in the same directory as this notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Loading the Data\n", - "We use the well-known `adult` census dataset, which we fetch from the OpenML website. We start with a fairly unremarkable set of imports:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn import svm\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", - "from sklearn.compose import make_column_selector as selector\n", - "from sklearn.pipeline import Pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can load the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from fairness_nb_utils import fetch_census_dataset\n", - "\n", - "data = fetch_census_dataset()\n", - " \n", - "# Extract the items we want\n", - "X_raw = data.data\n", - "y = (data.target == '>50K') * 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can take a look at some of the data. For example, the next cells shows the counts of the different races identified in the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(X_raw[\"race\"].value_counts().to_dict())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Processing the Data\n", - "\n", - "With the data loaded, we process it for our needs. First, we extract the sensitive features of interest into `A` (conventionally used in the literature) and leave the rest of the feature data in `X_raw`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = X_raw[['sex','race']]\n", - "X_raw = X_raw.drop(labels=['sex', 'race'],axis = 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now preprocess our data. To avoid the problem of data leakage, we split our data into training and test sets before performing any other transformations. Subsequent transformations (such as scalings) will be fit to the training data set, and then applied to the test dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(X_train, X_test, y_train, y_test, A_train, A_test) = train_test_split(\n", - " X_raw, y, A, test_size=0.3, random_state=12345, stratify=y\n", - ")\n", - "\n", - "# Ensure indices are aligned between X, y and A,\n", - "# after all the slicing and splitting of DataFrames\n", - "# and Series\n", - "\n", - "X_train = X_train.reset_index(drop=True)\n", - "X_test = X_test.reset_index(drop=True)\n", - "y_train = y_train.reset_index(drop=True)\n", - "y_test = y_test.reset_index(drop=True)\n", - "A_train = A_train.reset_index(drop=True)\n", - "A_test = A_test.reset_index(drop=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have two types of column in the dataset - categorical columns which will need to be one-hot encoded, and numeric ones which will need to be rescaled. We also need to take care of missing values. We use a simple approach here, but please bear in mind that this is another way that bias could be introduced (especially if one subgroup tends to have more missing values).\n", - "\n", - "For this preprocessing, we make use of `Pipeline` objects from `sklearn`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "numeric_transformer = Pipeline(\n", - " steps=[\n", - " (\"impute\", SimpleImputer()),\n", - " (\"scaler\", StandardScaler()),\n", - " ]\n", - ")\n", - "\n", - "categorical_transformer = Pipeline(\n", - " [\n", - " (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n", - " (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\", sparse=False)),\n", - " ]\n", - ")\n", - "\n", - "preprocessor = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n", - " (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the preprocessing pipeline is defined, we can run it on our training data, and apply the generated transform to our test data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_train = preprocessor.fit_transform(X_train)\n", - "X_test = preprocessor.transform(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Training Models\n", - "\n", - "We now train a couple of different models on our data. The `adult` census dataset is a classification problem - the goal is to predict whether a particular individual exceeds an income threshold. For the purpose of generating a dashboard to upload, it is sufficient to train two basic classifiers. First, a logistic regression classifier:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lr_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n", - "\n", - "lr_predictor.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And for comparison, a support vector classifier:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "svm_predictor = svm.SVC()\n", - "\n", - "svm_predictor.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Logging in to AzureML\n", - "\n", - "With our two classifiers trained, we can log into our AzureML workspace:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace, Experiment, Model\n", - "\n", - "ws = Workspace.from_config()\n", - "ws.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Registering the Models\n", - "\n", - "Next, we register our models. By default, the subroutine which uploads the models checks that the names provided correspond to registered models in the workspace. We define a utility routine to do the registering:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import joblib\n", - "import os\n", - "\n", - "os.makedirs('models', exist_ok=True)\n", - "def register_model(name, model):\n", - " print(\"Registering \", name)\n", - " model_path = \"models/{0}.pkl\".format(name)\n", - " joblib.dump(value=model, filename=model_path)\n", - " registered_model = Model.register(model_path=model_path,\n", - " model_name=name,\n", - " workspace=ws)\n", - " print(\"Registered \", registered_model.id)\n", - " return registered_model.id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we register the models. For convenience in subsequent method calls, we store the results in a dictionary, which maps the `id` of the registered model (a string in `name:version` format) to the predictor itself:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_dict = {}\n", - "\n", - "lr_reg_id = register_model(\"fairness_linear_regression\", lr_predictor)\n", - "model_dict[lr_reg_id] = lr_predictor\n", - "svm_reg_id = register_model(\"fairness_svm\", svm_predictor)\n", - "model_dict[svm_reg_id] = svm_predictor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Using the Fairlearn Dashboard\n", - "\n", - "We can now examine the fairness of the two models we have training, both as a function of race and (binary) sex. Before uploading the dashboard to the AzureML portal, we will first instantiate a local instance of the Fairlearn dashboard.\n", - "\n", - "Regardless of the viewing location, the dashboard is based on three things - the true values, the model predictions and the sensitive feature values. The dashboard can use predictions from multiple models and multiple sensitive features if desired (as we are doing here).\n", - "\n", - "Our first step is to generate a dictionary mapping the `id` of the registered model to the corresponding array of predictions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ys_pred = {}\n", - "for n, p in model_dict.items():\n", - " ys_pred[n] = p.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can examine these predictions in a locally invoked Fairlearn dashboard. This can be compared to the dashboard uploaded to the portal (in the next section):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from raiwidgets import FairnessDashboard\n", - "\n", - "FairnessDashboard(sensitive_features=A_test, \n", - " y_true=y_test.tolist(),\n", - " y_pred=ys_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Uploading a Fairness Dashboard to Azure\n", - "\n", - "Uploading a fairness dashboard to Azure is a two stage process. The `FairnessDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. The required stages are therefore:\n", - "1. Precompute all the required metrics\n", - "1. Upload to Azure\n", - "\n", - "\n", - "### Computing Fairness Metrics\n", - "We use Fairlearn to create a dictionary which contains all the data required to display a dashboard. This includes both the raw data (true values, predicted values and sensitive features), and also the fairness metrics. The API is similar to that used to invoke the Dashboard locally. However, there are a few minor changes to the API, and the type of problem being examined (binary classification, regression etc.) needs to be specified explicitly:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sf = { 'Race': A_test.race, 'Sex': A_test.sex }\n", - "\n", - "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n", - "\n", - "dash_dict = _create_group_metric_set(y_true=y_test,\n", - " predictions=ys_pred,\n", - " sensitive_features=sf,\n", - " prediction_type='binary_classification')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `_create_group_metric_set()` method is currently underscored since its exact design is not yet final in Fairlearn." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Uploading to Azure\n", - "\n", - "We can now import the `azureml.contrib.fairness` package itself. We will round-trip the data, so there are two required subroutines:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can upload the generated dictionary to AzureML. The upload method requires a run, so we first create an experiment and a run. The uploaded dashboard can be seen on the corresponding Run Details page in AzureML Studio. For completeness, we also download the dashboard dictionary which we uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exp = Experiment(ws, \"notebook-01\")\n", - "print(exp)\n", - "\n", - "run = exp.start_logging()\n", - "try:\n", - " dashboard_title = \"Sample notebook upload\"\n", - " upload_id = upload_dashboard_dictionary(run,\n", - " dash_dict,\n", - " dashboard_name=dashboard_title)\n", - " print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n", - "\n", - " downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n", - "finally:\n", - " run.complete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(dash_dict == downloaded_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Conclusion\n", - "\n", - "In this notebook we have demonstrated how to generate and upload a fairness dashboard to AzureML Studio. We have not discussed how to analyse the results and apply mitigations. Those topics will be covered elsewhere." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "riedgar" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/contrib/fairness/upload-fairness-dashboard.yml b/contrib/fairness/upload-fairness-dashboard.yml deleted file mode 100644 index cab53a4da..000000000 --- a/contrib/fairness/upload-fairness-dashboard.yml +++ /dev/null @@ -1,11 +0,0 @@ -name: upload-fairness-dashboard -dependencies: -- pip: - - azureml-sdk - - azureml-contrib-fairness - - fairlearn>=0.6.2 - - joblib - - liac-arff - - raiwidgets~=0.17.0 - - itsdangerous==2.0.1 - - markupsafe<2.1.0 diff --git a/how-to-use-azureml/README.md b/how-to-use-azureml/README.md deleted file mode 100644 index 0accac070..000000000 --- a/how-to-use-azureml/README.md +++ /dev/null @@ -1,15 +0,0 @@ -## Examples to get started with Azure Machine Learning service - -Learn how to use Azure Machine Learning services for experimentation and model management. - -As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order. - -* [train-within-notebook](./training/train-within-notebook): Train a model while tracking run history, and learn how to deploy the model as web service to Azure Container Instance. -* [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration. -* [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure. -* [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs. -* [logging-api](./track-and-monitor-experiments/logging-api): Learn about the details of logging metrics to run history. -* [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service. -* [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service. - -Find quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/). diff --git a/how-to-use-azureml/automated-machine-learning/README.md b/how-to-use-azureml/automated-machine-learning/README.md deleted file mode 100644 index 63a11d198..000000000 --- a/how-to-use-azureml/automated-machine-learning/README.md +++ /dev/null @@ -1,299 +0,0 @@ -# Table of Contents -1. [Automated ML Introduction](#introduction) -1. [Setup using Compute Instances](#jupyter) -1. [Setup using a Local Conda environment](#localconda) -1. [Setup using Azure Databricks](#databricks) -1. [Automated ML SDK Sample Notebooks](#samples) -1. [Documentation](#documentation) -1. [Running using python command](#pythoncommand) -1. [Troubleshooting](#troubleshooting) - - -# Automated ML introduction -Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions. - - -If you are new to Data Science, automated ML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use. - -If you are an experienced data scientist, automated ML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. Automated ML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire. - -Below are the three execution environments supported by automated ML. - - - -## Setup using Compute Instances - Jupyter based notebooks from a Azure Virtual Machine - -1. Open the [ML Azure portal](https://ml.azure.com) -1. Select Compute -1. Select Compute Instances -1. Click New -1. Type a Compute Name, select a Virtual Machine type and select a Virtual Machine size -1. Click Create - - -## Setup using a Local Conda environment - -To run these notebook on your own notebook server, use these installation instructions. -The instructions below will install everything you need and then start a Jupyter notebook. - -### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.7 or higher. -- **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V). If you have a previous version installed, you can update it using the command: conda update conda. -There's no need to install mini-conda specifically. - -### 2. Downloading the sample notebooks -- Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory. The automated ML sample notebooks are in the "automated-machine-learning" folder. - -### 3. Setup a new conda environment -The **automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter. The default conda environment name is azure_automl. The exact command depends on the operating system. See the specific sections below for Windows, Mac and Linux. It can take about 10 minutes to execute. - -Packages installed by the **automl_setup** script: -
  • python
  • nb_conda
  • matplotlib
  • numpy
  • cython
  • urllib3
  • scipy
  • scikit-learn
  • pandas
  • tensorflow
  • py-xgboost
  • azureml-sdk
  • azureml-widgets
  • pandas-ml
- -For more details refer to the [automl_env.yml](./automl_env.yml) -## Windows -Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run: -``` -automl_setup -``` -## Mac -Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`). - -Start a Terminal windows, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run: - -``` -bash automl_setup_mac.sh -``` - -## Linux -cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run: - -``` -bash automl_setup_linux.sh -``` - -### 4. Running configuration.ipynb -- Before running any samples you next need to run the configuration notebook. Click on [configuration](../../configuration.ipynb) notebook -- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*) - -### 5. Running Samples -- Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks. -- Follow the instructions in the individual notebooks to explore various features in automated ML. - -### 6. Starting jupyter notebook manually -To start your Jupyter notebook manually, use: - -``` -conda activate azure_automl -jupyter notebook -``` - -or on Mac or Linux: - -``` -source activate azure_automl -jupyter notebook -``` - - -## Setup using Azure Databricks - -**NOTE**: Please create your Azure Databricks cluster as v7.1 (high concurrency preferred) with **Python 3** (dropdown). -**NOTE**: You should at least have contributor access to your Azure subcription to run the notebook. -- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/automl). -- Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/automl) and import into the Azure databricks workspace. -- Attach the notebook to the cluster. - - -# Automated ML SDK Sample Notebooks - -## Classification -- **Classify Credit Card Fraud** - - Dataset: [Kaggle's credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud) - - **[Jupyter Notebook (remote run)](classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb)** - - run the experiment remotely on AML Compute cluster - - test the performance of the best model in the local environment - - **[Jupyter Notebook (local run)](local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb)** - - run experiment in the local environment - - use Mimic Explainer for computing feature importance - - deploy the best model along with the explainer to an Azure Kubernetes (AKS) cluster, which will compute the raw and engineered feature importances at inference time -- **Predict Term Deposit Subscriptions in a Bank** - - Dataset: [UCI's bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset) - - **[Jupyter Notebook](classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)** - - run experiment remotely on AML Compute cluster to generate ONNX compatible models - - view the featurization steps that were applied during training - - view feature importance for the best model - - download the best model in ONNX format and use it for inferencing using ONNXRuntime - - deploy the best model in PKL format to Azure Container Instance (ACI) -- **Predict Newsgroup based on Text from News Article** - - Dataset: [20 newsgroups text dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html) - - **[Jupyter Notebook](classification-text-dnn/auto-ml-classification-text-dnn.ipynb)** - - AutoML highlights here include using deep neural networks (DNNs) to create embedded features from text data - - AutoML will use Bidirectional Encoder Representations from Transformers (BERT) when a GPU compute is used - - Bidirectional Long-Short Term neural network (BiLSTM) will be utilized when a CPU compute is used, thereby optimizing the choice of DNN - -## Regression -- **Predict Performance of Hardware Parts** - - Dataset: Hardware Performance Dataset - - **[Jupyter Notebook](regression/auto-ml-regression.ipynb)** - - run the experiment remotely on AML Compute cluster - - get best trained model for a different metric than the one the experiment was optimized for - - test the performance of the best model in the local environment - - **[Jupyter Notebook (advanced)](regression/auto-ml-regression.ipynb)** - - run the experiment remotely on AML Compute cluster - - customize featurization: override column purpose within the dataset, configure transformer parameters - - get best trained model for a different metric than the one the experiment was optimized for - - run a model explanation experiment on the remote cluster - - deploy the model along the explainer and run online inferencing - -## Time Series Forecasting -- **Forecast Energy Demand** - - Dataset: [NYC energy demand data](http://mis.nyiso.com/public/P-58Blist.htm) - - **[Jupyter Notebook](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)** - - run experiment remotely on AML Compute cluster - - use lags and rolling window features - - view the featurization steps that were applied during training - - get the best model, use it to forecast on test data and compare the accuracy of predictions against real data -- **Forecast Orange Juice Sales (Multi-Series)** - - Dataset: [Dominick's grocery sales of orange juice](forecasting-orange-juice-sales/dominicks_OJ.csv) - - **[Jupyter Notebook](forecasting-orange-juice-sales/dominicks_OJ.csv)** - - run experiment remotely on AML Compute cluster - - customize time-series featurization, change column purpose and override transformer hyper parameters - - evaluate locally the performance of the generated best model - - deploy the best model as a webservice on Azure Container Instance (ACI) - - get online predictions from the deployed model -- **Forecast Demand of a Bike-Sharing Service** - - Dataset: [Bike demand data](forecasting-bike-share/bike-no.csv) - - **[Jupyter Notebook](forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)** - - run experiment remotely on AML Compute cluster - - integrate holiday features - - run rolling forecast for test set that is longer than the forecast horizon - - compute metrics on the predictions from the remote forecast -- **The Forecast Function Interface** - - Dataset: Generated for sample purposes - - **[Jupyter Notebook](forecasting-forecast-function/auto-ml-forecasting-function.ipynb)** - - train a forecaster using a remote AML Compute cluster - - capabilities of forecast function (e.g. forecast farther into the horizon) - - generate confidence intervals -- **Forecast Beverage Production** - - Dataset: [Monthly beer production data](forecasting-beer-remote/Beer_no_valid_split_train.csv) - - **[Jupyter Notebook](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)** - - train using a remote AML Compute cluster - - enable the DNN learning model - - forecast on a remote compute cluster and compare different model performance -- **Continuous Retraining with NOAA Weather Data** - - Dataset: [NOAA weather data from Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/) - - **[Jupyter Notebook](continuous-retraining/auto-ml-continuous-retraining.ipynb)** - - continuously retrain a model using Pipelines and AutoML - - create a Pipeline to upload a time series dataset to an Azure blob - - create a Pipeline to run an AutoML experiment and register the best resulting model in the Workspace - - publish the training pipeline created and schedule it to run daily - - -See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments. - - -# Running using python command -Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file. -You can then run this file using the python command. -However, on Windows the file needs to be modified before it can be run. -The following condition must be added to the main code in the file: - - if __name__ == "__main__": - -The main code of the file must be indented so that it is under this condition. - - -# Troubleshooting -## automl_setup fails -1. On Windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window. You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt". If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed. In that case, you can install it [here](https://conda.io/miniconda.html) -2. Check that you have conda 64-bit installed rather than 32-bit. You can check this with the command `conda info`. The `platform` should be `win-64` for Windows or `osx-64` for Mac. -3. Check that you have conda 4.7.8 or later. You can check the version with the command `conda -V`. If you have a previous version installed, you can update it using the command: `conda update conda`. -4. On Linux, if the error is `gcc: error trying to exec 'cc1plus': execvp: No such file or directory`, install build essentials using the command `sudo apt-get install build-essential`. -5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n `. - -## automl_setup_linux.sh fails -If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execute 'gcc': No such file or directory` -1. Make sure that outbound ports 53 and 80 are enabled. On an Azure VM, you can do this from the Azure Portal by selecting the VM and clicking on Networking. -2. Run the command: `sudo apt-get update` -3. Run the command: `sudo apt-get install build-essential --fix-missing` -4. Run `automl_setup_linux.sh` again. - -## configuration.ipynb fails -1) For local conda, make sure that you have susccessfully run automl_setup first. -2) Check that the subscription_id is correct. You can find the subscription_id in the Azure Portal by selecting All Service and then Subscriptions. The characters "<" and ">" should not be included in the subscription_id value. For example, `subscription_id = "12345678-90ab-1234-5678-1234567890abcd"` has the valid format. -3) Check that you have Contributor or Owner access to the Subscription. -4) Check that the region is one of the supported regions: `eastus2`, `eastus`, `westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus` -5) Check that you have access to the region using the Azure Portal. - -## import AutoMLConfig fails after upgrade from before 1.0.76 to 1.0.76 or later -There were package changes in automated machine learning version 1.0.76, which require the previous version to be uninstalled before upgrading to the new version. -If you have manually upgraded from a version of automated machine learning before 1.0.76 to 1.0.76 or later, you may get the error: -`ImportError: cannot import name 'AutoMLConfig'` - -This can be resolved by running: -`pip uninstall azureml-train-automl` and then -`pip install azureml-train-automl` - -The automl_setup.cmd script does this automatically. - -## workspace.from_config fails -If the call `ws = Workspace.from_config()` fails: -1) Make sure that you have run the `configuration.ipynb` notebook successfully. -2) If you are running a notebook from a folder that is not under the folder where you ran `configuration.ipynb`, copy the folder aml_config and the file config.json that it contains to the new folder. Workspace.from_config reads the config.json for the notebook folder or it parent folder. -3) If you are switching to a new subscription, resource group, workspace or region, make sure that you run the `configuration.ipynb` notebook again. Changing config.json directly will only work if the workspace already exists in the specified resource group under the specified subscription. -4) If you want to change the region, please change the workspace, resource group or subscription. `Workspace.create` will not create or update a workspace if it already exists, even if the region specified is different. - -## Sample notebook fails -If a sample notebook fails with an error that property, method or library does not exist: -1) Check that you have selected correct kernel in jupyter notebook. The kernel is displayed in the top right of the notebook page. It can be changed using the `Kernel | Change Kernel` menu option. For Azure Notebooks, it should be `Python 3.6`. For local conda environments, it should be the conda envioronment name that you specified in automl_setup. The default is azure_automl. Note that the kernel is saved as part of the notebook. So, if you switch to a new conda environment, you will have to select the new kernel in the notebook. -2) Check that the notebook is for the SDK version that you are using. You can check the SDK version by executing `azureml.core.VERSION` in a jupyter notebook cell. You can download previous version of the sample notebooks from GitHub by clicking the `Branch` button, selecting the `Tags` tab and then selecting the version. - -## Numpy import fails on Windows -Some Windows environments see an error loading numpy with the latest Python version 3.6.8. If you see this issue, try with Python version 3.6.7. - -## Numpy import fails -Check the tensorflow version in the automated ml conda environment. Supported versions are < 1.13. Uninstall tensorflow from the environment if version is >= 1.13 -You may check the version of tensorflow and uninstall as follows -1) start a command shell, activate conda environment where automated ml packages are installed -2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13 -3) If the listed version is a not a supported version, `pip uninstall tensorflow` in the command shell and enter y for confirmation. - -## KeyError: 'brand' when running AutoML on local compute or Azure Databricks cluster** -If a new environment was created after 10 June 2020 using SDK 1.7.0 or lower, training may fail with the above error due to an update in the py-cpuinfo package. (Environments created on or before 10 June 2020 are unaffected, as well as experiments run on remote compute as cached training images are used.) To work around this issue, either of the two following steps can be taken: - -1) Update the SDK version to 1.8.0 or higher (this will also downgrade py-cpuinfo to 5.0.0): -`pip install --upgrade azureml-sdk[automl]` - -2) Downgrade the installed version of py-cpuinfo to 5.0.0: -`pip install py-cpuinfo==5.0.0` - -## Remote run: DsvmCompute.create fails -There are several reasons why the DsvmCompute.create can fail. The reason is usually in the error message but you have to look at the end of the error message for the detailed reason. Some common reasons are: -1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.` Note that underscore is not allowed in the name. -2) `The requested VM size xxxxx is not available in the current region.` You can select a different region or vm_size. - -## Remote run: Unable to establish SSH connection -Automated ML uses the SSH protocol to communicate with remote DSVMs. This defaults to port 22. Possible causes for this error are: -1) The DSVM is not ready for SSH connections. When DSVM creation completes, the DSVM might still not be ready to acceept SSH connections. The sample notebooks have a one minute delay to allow for this. -2) Your Azure Subscription may restrict the IP address ranges that can access the DSVM on port 22. You can check this in the Azure Portal by selecting the Virtual Machine and then clicking Networking. The Virtual Machine name is the name that you provided in the notebook plus 10 alpha numeric characters to make the name unique. The Inbound Port Rules define what can access the VM on specific ports. Note that there is a priority priority order. So, a Deny entry with a low priority number will override a Allow entry with a higher priority number. - -## Remote run: setup iteration fails -This is often an issue with the `get_data` method. -1) Check that the `get_data` method is valid by running it locally. -2) Make sure that `get_data` isn't referring to any local files. `get_data` is executed on the remote DSVM. So, it doesn't have direct access to local data files. Instead you can store the data files with DataStore. See [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb) -3) You can get to the error log for the setup iteration by clicking the `Click here to see the run in Azure portal` link, click `Back to Experiment`, click on the highest run number and then click on Logs. - -## Remote run: disk full -Automated ML creates files under /tmp/azureml_runs for each iteration that it runs. It creates a folder with the iteration id. For example: AutoML_9a038a18-77cc-48f1-80fb-65abdbc33abe_93. Under this, there is a azureml-logs folder, which contains logs. If you run too many iterations on the same DSVM, these files can fill the disk. -You can delete the files under /tmp/azureml_runs or just delete the VM and create a new one. -If your get_data downloads files, make sure the delete them or they can use disk space as well. -When using DataStore, it is good to specify an absolute path for the files so that they are downloaded just once. If you specify a relative path, it will download a file for each iteration. - -## Remote run: Iterations fail and the log contains "MemoryError" -This can be caused by insufficient memory on the DSVM. Automated ML loads all training data into memory. So, the available memory should be more than the training data size. -If you are using a remote DSVM, memory is needed for each concurrent iteration. The max_concurrent_iterations setting specifies the maximum concurrent iterations. For example, if the training data size is 8Gb and max_concurrent_iterations is set to 10, the minimum memory required is at least 80Gb. -To resolve this issue, allocate a DSVM with more memory or reduce the value specified for max_concurrent_iterations. - -## Remote run: Iterations show as "Not Responding" in the RunDetails widget. -This can be caused by too many concurrent iterations for a remote DSVM. Each concurrent iteration usually takes 100% of a core when it is running. Some iterations can use multiple cores. So, the max_concurrent_iterations setting should always be less than the number of cores of the DSVM. -To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting. diff --git a/how-to-use-azureml/automated-machine-learning/automl_env.yml b/how-to-use-azureml/automated-machine-learning/automl_env.yml deleted file mode 100644 index 6a90d4468..000000000 --- a/how-to-use-azureml/automated-machine-learning/automl_env.yml +++ /dev/null @@ -1,30 +0,0 @@ -name: azure_automl -channels: - - conda-forge - - pytorch - - main -dependencies: - # The python interpreter version. - # Currently Azure ML only supports 3.6.0 and later. -- pip==20.2.4 -- python>=3.6,<3.9 -- matplotlib==3.3.4 -- py-xgboost==1.3.3 -- pytorch::pytorch=1.4.0 -- conda-forge::fbprophet==0.7.1 -- cudatoolkit=10.1.243 -- tqdm==4.63.1 -- notebook -- pywin32==225 -- PySocks==1.7.1 -- conda-forge::pyqt==5.12.3 - -- pip: - # Required packages for AzureML execution, history, and data preparation. - - azureml-widgets~=1.40.0 - - pytorch-transformers==1.0.0 - - spacy==2.2.4 - - pystan==2.19.1.1 - - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz - - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.40.0/validated_win32_requirements.txt [--no-deps] - - arch==4.14 diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml b/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml deleted file mode 100644 index 93cc3aa49..000000000 --- a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml +++ /dev/null @@ -1,33 +0,0 @@ -name: azure_automl -channels: - - conda-forge - - pytorch - - main -dependencies: - # The python interpreter version. - # Currently Azure ML only supports 3.6.0 and later. -- pip==20.2.4 -- python>=3.6,<3.9 -- boto3==1.20.19 -- botocore<=1.23.19 -- matplotlib==3.3.4 -- numpy==1.19.5 -- cython==0.29.14 -- urllib3==1.26.7 -- scipy>=1.4.1,<=1.5.2 -- scikit-learn==0.22.1 -- py-xgboost<=1.3.3 -- holidays==0.10.3 -- conda-forge::fbprophet==0.7.1 -- pytorch::pytorch=1.4.0 -- cudatoolkit=10.1.243 - -- pip: - # Required packages for AzureML execution, history, and data preparation. - - azureml-widgets~=1.40.0 - - pytorch-transformers==1.0.0 - - spacy==2.2.4 - - pystan==2.19.1.1 - - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz - - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.40.0/validated_linux_requirements.txt [--no-deps] - - arch==4.14 diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml deleted file mode 100644 index 489913554..000000000 --- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml +++ /dev/null @@ -1,34 +0,0 @@ -name: azure_automl -channels: - - conda-forge - - pytorch - - main -dependencies: - # The python interpreter version. - # Currently Azure ML only supports 3.6.0 and later. -- pip==20.2.4 -- nomkl -- python>=3.6,<3.9 -- boto3==1.20.19 -- botocore<=1.23.19 -- matplotlib==3.3.4 -- numpy==1.19.5 -- cython==0.29.14 -- urllib3==1.26.7 -- scipy>=1.4.1,<=1.5.2 -- scikit-learn==0.22.1 -- py-xgboost<=1.3.3 -- holidays==0.10.3 -- conda-forge::fbprophet==0.7.1 -- pytorch::pytorch=1.4.0 -- cudatoolkit=9.0 - -- pip: - # Required packages for AzureML execution, history, and data preparation. - - azureml-widgets~=1.40.0 - - pytorch-transformers==1.0.0 - - spacy==2.2.4 - - pystan==2.19.1.1 - - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz - - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.40.0/validated_darwin_requirements.txt [--no-deps] - - arch==4.14 diff --git a/how-to-use-azureml/automated-machine-learning/automl_setup.cmd b/how-to-use-azureml/automated-machine-learning/automl_setup.cmd deleted file mode 100644 index 3f188e3fd..000000000 --- a/how-to-use-azureml/automated-machine-learning/automl_setup.cmd +++ /dev/null @@ -1,78 +0,0 @@ -@echo off -set conda_env_name=%1 -set automl_env_file=%2 -set options=%3 -set PIP_NO_WARN_SCRIPT_LOCATION=0 - -IF "%conda_env_name%"=="" SET conda_env_name="azure_automl" -IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml" -SET check_conda_version_script="check_conda_version.py" - -IF NOT EXIST %automl_env_file% GOTO YmlMissing - -IF "%CONDA_EXE%"=="" GOTO CondaMissing - -IF NOT EXIST %check_conda_version_script% GOTO VersionCheckMissing - -python "%check_conda_version_script%" -IF errorlevel 1 GOTO ErrorExit: - -SET replace_version_script="replace_latest_version.ps1" -IF EXIST %replace_version_script% ( - powershell -file %replace_version_script% %automl_env_file% -) - -call conda activate %conda_env_name% 2>nul: - -if not errorlevel 1 ( - echo Upgrading existing conda environment %conda_env_name% - call pip uninstall azureml-train-automl -y -q - call conda env update --name %conda_env_name% --file %automl_env_file% - if errorlevel 1 goto ErrorExit -) else ( - call conda env create -f %automl_env_file% -n %conda_env_name% -) - -call conda activate %conda_env_name% 2>nul: -if errorlevel 1 goto ErrorExit - -call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)" - -REM azureml.widgets is now installed as part of the pip install under the conda env. -REM Removing the old user install so that the notebooks will use the latest widget. -call jupyter nbextension uninstall --user --py azureml.widgets - -echo. -echo. -echo *************************************** -echo * AutoML setup completed successfully * -echo *************************************** -IF NOT "%options%"=="nolaunch" ( - echo. - echo Starting jupyter notebook - please run the configuration notebook - echo. - jupyter notebook --log-level=50 --notebook-dir='..\..' -) - -goto End - -:CondaMissing -echo Please run this script from an Anaconda Prompt window. -echo You can start an Anaconda Prompt window by -echo typing Anaconda Prompt on the Start menu. -echo If you don't see the Anaconda Prompt app, install Miniconda. -echo If you are running an older version of Miniconda or Anaconda, -echo you can upgrade using the command: conda update conda -goto End - -:VersionCheckMissing -echo File %check_conda_version_script% not found. -goto End - -:YmlMissing -echo File %automl_env_file% not found. - -:ErrorExit -echo Install failed - -:End \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh b/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh deleted file mode 100644 index 744f3e9f9..000000000 --- a/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh +++ /dev/null @@ -1,66 +0,0 @@ -#!/bin/bash - -CONDA_ENV_NAME=$1 -AUTOML_ENV_FILE=$2 -OPTIONS=$3 -PIP_NO_WARN_SCRIPT_LOCATION=0 -CHECK_CONDA_VERSION_SCRIPT="check_conda_version.py" - -if [ "$CONDA_ENV_NAME" == "" ] -then - CONDA_ENV_NAME="azure_automl" -fi - -if [ "$AUTOML_ENV_FILE" == "" ] -then - AUTOML_ENV_FILE="automl_env_linux.yml" -fi - -if [ ! -f $AUTOML_ENV_FILE ]; then - echo "File $AUTOML_ENV_FILE not found" - exit 1 -fi - -if [ ! -f $CHECK_CONDA_VERSION_SCRIPT ]; then - echo "File $CHECK_CONDA_VERSION_SCRIPT not found" - exit 1 -fi - -python "$CHECK_CONDA_VERSION_SCRIPT" -if [ $? -ne 0 ]; then - exit 1 -fi - -sed -i 's/AZUREML-SDK-VERSION/latest/' $AUTOML_ENV_FILE - -if source activate $CONDA_ENV_NAME 2> /dev/null -then - echo "Upgrading existing conda environment" $CONDA_ENV_NAME - pip uninstall azureml-train-automl -y -q - conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE && - jupyter nbextension uninstall --user --py azureml.widgets -else - conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME && - source activate $CONDA_ENV_NAME && - python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" && - jupyter nbextension uninstall --user --py azureml.widgets && - echo "" && - echo "" && - echo "***************************************" && - echo "* AutoML setup completed successfully *" && - echo "***************************************" && - if [ "$OPTIONS" != "nolaunch" ] - then - echo "" && - echo "Starting jupyter notebook - please run the configuration notebook" && - echo "" && - jupyter notebook --log-level=50 --notebook-dir '../..' - fi -fi - -if [ $? -gt 0 ] -then - echo "Installation failed" -fi - - diff --git a/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh b/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh deleted file mode 100644 index d2e97a989..000000000 --- a/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh +++ /dev/null @@ -1,69 +0,0 @@ -#!/bin/bash - -CONDA_ENV_NAME=$1 -AUTOML_ENV_FILE=$2 -OPTIONS=$3 -PIP_NO_WARN_SCRIPT_LOCATION=0 -CHECK_CONDA_VERSION_SCRIPT="check_conda_version.py" - -if [ "$CONDA_ENV_NAME" == "" ] -then - CONDA_ENV_NAME="azure_automl" -fi - -if [ "$AUTOML_ENV_FILE" == "" ] -then - AUTOML_ENV_FILE="automl_env_mac.yml" -fi - -if [ ! -f $AUTOML_ENV_FILE ]; then - echo "File $AUTOML_ENV_FILE not found" - exit 1 -fi - -if [ ! -f $CHECK_CONDA_VERSION_SCRIPT ]; then - echo "File $CHECK_CONDA_VERSION_SCRIPT not found" - exit 1 -fi - -python "$CHECK_CONDA_VERSION_SCRIPT" -if [ $? -ne 0 ]; then - exit 1 -fi - -sed -i '' 's/AZUREML-SDK-VERSION/latest/' $AUTOML_ENV_FILE -brew install libomp - -if source activate $CONDA_ENV_NAME 2> /dev/null -then - echo "Upgrading existing conda environment" $CONDA_ENV_NAME - pip uninstall azureml-train-automl -y -q - conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE && - jupyter nbextension uninstall --user --py azureml.widgets -else - conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME && - source activate $CONDA_ENV_NAME && - conda install lightgbm -c conda-forge -y && - python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" && - jupyter nbextension uninstall --user --py azureml.widgets && - echo "" && - echo "" && - echo "***************************************" && - echo "* AutoML setup completed successfully *" && - echo "***************************************" && - if [ "$OPTIONS" != "nolaunch" ] - then - echo "" && - echo "Starting jupyter notebook - please run the configuration notebook" && - echo "" && - jupyter notebook --log-level=50 --notebook-dir '../..' - fi -fi - -if [ $? -gt 0 ] -then - echo "Installation failed" -fi - - - diff --git a/how-to-use-azureml/automated-machine-learning/check_conda_version.py b/how-to-use-azureml/automated-machine-learning/check_conda_version.py deleted file mode 100644 index 691ea908a..000000000 --- a/how-to-use-azureml/automated-machine-learning/check_conda_version.py +++ /dev/null @@ -1,26 +0,0 @@ -from distutils.version import LooseVersion -import platform - -try: - import conda -except Exception: - print('Failed to import conda.') - print('This setup is usually run from the base conda environment.') - print('You can activate the base environment using the command "conda activate base"') - exit(1) - -architecture = platform.architecture()[0] - -if architecture != "64bit": - print('This setup requires 64bit Anaconda or Miniconda. Found: ' + architecture) - exit(1) - -minimumVersion = "4.7.8" - -versionInvalid = (LooseVersion(conda.__version__) < LooseVersion(minimumVersion)) - -if versionInvalid: - print('Setup requires conda version ' + minimumVersion + ' or higher.') - print('You can use the command "conda update conda" to upgrade conda.') - -exit(versionInvalid) diff --git a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb deleted file mode 100644 index 36e2d7367..000000000 --- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb +++ /dev/null @@ -1,1091 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Classification with Deployment using a Bank Marketing Dataset**_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Train](#Train)\n", - "1. [Results](#Results)\n", - "1. [Deploy](#Deploy)\n", - "1. [Test](#Test)\n", - "1. [Use auto-generated code for retraining](#Using-the-auto-generated-model-training-code-for-retraining-on-new-data)\n", - "1. [Acknowledgements](#Acknowledgements)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this example we use the UCI Bank Marketing dataset to showcase how you can use AutoML for a classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if the client will subscribe to a term deposit with the bank.\n", - "\n", - "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n", - "\n", - "Please find the ONNX related documentations [here](https://github.com/onnx/onnx).\n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create an experiment using an existing workspace.\n", - "2. Configure AutoML using `AutoMLConfig`.\n", - "3. Train the model using local compute with ONNX compatible config on.\n", - "4. Explore the results, featurization transparency options and save the ONNX model\n", - "5. Inference with the ONNX model.\n", - "6. Register the model.\n", - "7. Create a container image.\n", - "8. Create an Azure Container Instance (ACI) service.\n", - "9. Test the ACI service.\n", - "10. Leverage the auto generated training code and use it for retraining on an updated dataset\n", - "\n", - "In addition this notebook showcases the following features\n", - "- **Blocking** certain pipelines\n", - "- Specifying **target metrics** to indicate stopping criteria\n", - "- Handling **missing data** in the input" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "name": "automl-import" - }, - "outputs": [], - "source": [ - "import json\n", - "import logging\n", - "\n", - "from matplotlib import pyplot as plt\n", - "import pandas as pd\n", - "import os\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.train.automl import AutoMLConfig\n", - "from azureml.interpret import ExplanationClient" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Accessing the Azure ML workspace requires authentication with Azure.\n", - "\n", - "The default authentication is interactive authentication using the default tenant. Executing the `ws = Workspace.from_config()` line in the cell below will prompt for authentication the first time that it is run.\n", - "\n", - "If you have multiple Azure tenants, you can specify the tenant by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n", - "\n", - "```\n", - "from azureml.core.authentication import InteractiveLoginAuthentication\n", - "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n", - "ws = Workspace.from_config(auth = auth)\n", - "```\n", - "\n", - "If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n", - "\n", - "```\n", - "from azureml.core.authentication import ServicePrincipalAuthentication\n", - "auth = auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n", - "ws = Workspace.from_config(auth = auth)\n", - "```\n", - "For more details, see [aka.ms/aml-notebook-auth](http://aka.ms/aml-notebook-auth)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "name": "ws-setup" - }, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# choose a name for experiment\n", - "experiment_name = \"automl-classification-bmarketing-all\"\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Experiment Name\"] = experiment.name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "#### Creation of AmlCompute takes approximately 5 minutes. \n", - "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "cpu_cluster_name = \"cpu-cluster-4\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n", - " )\n", - " compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load Data\n", - "\n", - "Leverage azure compute to load the bank marketing dataset as a Tabular Dataset into the dataset variable. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv(\n", - " \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n", - ")\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add missing values in 75% of the lines.\n", - "import numpy as np\n", - "\n", - "missing_rate = 0.75\n", - "n_missing_samples = int(np.floor(data.shape[0] * missing_rate))\n", - "missing_samples = np.hstack(\n", - " (\n", - " np.zeros(data.shape[0] - n_missing_samples, dtype=np.bool),\n", - " np.ones(n_missing_samples, dtype=np.bool),\n", - " )\n", - ")\n", - "rng = np.random.RandomState(0)\n", - "rng.shuffle(missing_samples)\n", - "missing_features = rng.randint(0, data.shape[1], n_missing_samples)\n", - "data.values[np.where(missing_samples)[0], missing_features] = np.nan" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if not os.path.isdir(\"data\"):\n", - " os.mkdir(\"data\")\n", - "# Save the train data to a csv to be uploaded to the datastore\n", - "pd.DataFrame(data).to_csv(\"data/train_data.csv\", index=False)\n", - "\n", - "ds = ws.get_default_datastore()\n", - "ds.upload(\n", - " src_dir=\"./data\", target_path=\"bankmarketing\", overwrite=True, show_progress=True\n", - ")\n", - "\n", - "\n", - "# Upload the training data as a tabular dataset for access during training on remote compute\n", - "train_data = Dataset.Tabular.from_delimited_files(\n", - " path=ds.path(\"bankmarketing/train_data.csv\")\n", - ")\n", - "label = \"y\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Validation Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "validation_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n", - "validation_dataset = Dataset.Tabular.from_delimited_files(validation_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_test.csv\"\n", - "test_dataset = Dataset.Tabular.from_delimited_files(test_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "\n", - "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification or regression or forecasting|\n", - "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics:
accuracy
AUC_weighted
average_precision_score_weighted
norm_macro_recall
precision_score_weighted|\n", - "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n", - "|**blocked_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.

Allowed values for **Classification**
LogisticRegression
SGD
MultinomialNaiveBayes
BernoulliNaiveBayes
SVM
LinearSVM
KNN
DecisionTree
RandomForest
ExtremeRandomTrees
LightGBM
GradientBoosting
TensorFlowDNN
TensorFlowLinearClassifier

Allowed values for **Regression**
ElasticNet
GradientBoosting
DecisionTree
KNN
LassoLars
SGD
RandomForest
ExtremeRandomTrees
LightGBM
TensorFlowLinearRegressor
TensorFlowDNN

Allowed values for **Forecasting**
ElasticNet
GradientBoosting
DecisionTree
KNN
LassoLars
SGD
RandomForest
ExtremeRandomTrees
LightGBM
TensorFlowLinearRegressor
TensorFlowDNN
Arima
Prophet|\n", - "|**allowed_models** | *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blocked_models** allowed for **allowed_models**.|\n", - "|**experiment_exit_score**| Value indicating the target for *primary_metric*.
Once the target is surpassed the run terminates.|\n", - "|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n", - "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n", - "|**featurization**| 'auto' / 'off' Indicator for whether featurization step should be done automatically or not. Note: If the input data is sparse, featurization cannot be turned on.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|Input dataset, containing both features and label column.|\n", - "|**label_column_name**|The name of the label column.|\n", - "|**enable_code_generation**|Flag to enable generation of training code for each of the models that AutoML is creating.\n", - "\n", - "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"experiment_timeout_hours\": 0.3,\n", - " \"enable_early_stopping\": True,\n", - " \"iteration_timeout_minutes\": 5,\n", - " \"max_concurrent_iterations\": 4,\n", - " \"max_cores_per_iteration\": -1,\n", - " # \"n_cross_validations\": 2,\n", - " \"primary_metric\": \"AUC_weighted\",\n", - " \"featurization\": \"auto\",\n", - " \"verbosity\": logging.INFO,\n", - " \"enable_code_generation\": True,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"classification\",\n", - " debug_log=\"automl_errors.log\",\n", - " compute_target=compute_target,\n", - " experiment_exit_score=0.9984,\n", - " blocked_models=[\"KNN\", \"LinearSVM\"],\n", - " enable_onnx_compatible_models=True,\n", - " training_data=train_data,\n", - " label_column_name=label,\n", - " validation_data=validation_dataset,\n", - " **automl_settings,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "name": "experiment-submit" - }, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "Run the following cell to access previous runs. Uncomment the cell below and update the run_id." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from azureml.train.automl.run import AutoMLRun\n", - "# remote_run = AutoMLRun(experiment=experiment, run_id=' thresh else \"black\",\n", - " )\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Delete a Web Service\n", - "\n", - "Deletes the specified web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aci_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using the auto generated model training code for retraining on new data\n", - "\n", - "Because we enabled code generation when the original experiment was created, we now have access to the code that was used to generate any of the AutoML tried models. Below we'll be using the generated training script of the best model to retrain on a new dataset.\n", - "\n", - "For this demo, we'll begin by creating new retraining dataset by combining the Train & Validation datasets that were used in the original experiment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "original_train_data = pd.read_csv(\n", - " \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n", - ")\n", - "\n", - "valid_data = pd.read_csv(\n", - " \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n", - ")\n", - "\n", - "# we'll emulate an updated dataset for retraining by combining the Train & Validation datasets into a new one\n", - "retrain_pd = pd.concat([original_train_data, valid_data])\n", - "retrain_pd.to_csv(\"data/retrain_data.csv\", index=False)\n", - "ds.upload_files(\n", - " files=[\"data/retrain_data.csv\"],\n", - " target_path=\"bankmarketing/\",\n", - " overwrite=True,\n", - " show_progress=True,\n", - ")\n", - "retrain_dataset = Dataset.Tabular.from_delimited_files(\n", - " path=ds.path(\"bankmarketing/retrain_data.csv\")\n", - ")\n", - "\n", - "# after creating and uploading the retraining dataset, let's register it with the workspace for reuse\n", - "retrain_dataset = retrain_dataset.register(\n", - " workspace=ws,\n", - " name=\"Bankmarketing_retrain\",\n", - " description=\"Updated training dataset, includes validation data\",\n", - " create_new_version=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we'll download the generated script for the best run and use it for retraining. For more advanced scenarios, you can customize the training script as you need: change the featurization pipeline, change the learner algorithm or its hyperparameters, etc. \n", - "\n", - "For this exercise, we'll leave the script as it was generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# download the autogenerated training script into the generated_code folder\n", - "best_run.download_file(\n", - " \"outputs/generated_code/script.py\", \"generated_code/training_script.py\"\n", - ")\n", - "\n", - "# view the contents of the autogenerated training script\n", - "! cat generated_code/training_script.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "from azureml.core import ScriptRunConfig\n", - "from azureml._restclient.models import RunTypeV2\n", - "from azureml._restclient.models.create_run_dto import CreateRunDto\n", - "from azureml._restclient.run_client import RunClient\n", - "\n", - "codegen_runid = str(uuid.uuid4())\n", - "client = RunClient(\n", - " experiment.workspace.service_context,\n", - " experiment.name,\n", - " codegen_runid,\n", - " experiment_id=experiment.id,\n", - ")\n", - "\n", - "# override the training_dataset_id to point to our new retraining dataset we just registered above\n", - "dataset_arguments = [\"--training_dataset_id\", retrain_dataset.id]\n", - "\n", - "# create the retraining run as a child of the AutoML generated training run\n", - "create_run_dto = CreateRunDto(\n", - " run_id=codegen_runid,\n", - " parent_run_id=best_run.id,\n", - " description=\"AutoML Codegen Script Run using an updated training dataset\",\n", - " target=cpu_cluster_name,\n", - " run_type_v2=RunTypeV2(orchestrator=\"Execution\", traits=[\"automl-codegen\"]),\n", - ")\n", - "\n", - "# the script for retraining run is pointing to the AutoML generated script\n", - "src = ScriptRunConfig(\n", - " source_directory=\"generated_code\",\n", - " script=\"training_script.py\",\n", - " arguments=dataset_arguments,\n", - " compute_target=cpu_cluster_name,\n", - " environment=best_run.get_environment(),\n", - ")\n", - "run_dto = client.create_run(run_id=codegen_runid, create_run_dto=create_run_dto)\n", - "\n", - "# submit the experiment\n", - "retraining_run = experiment.submit(config=src, run_id=codegen_runid)\n", - "retraining_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the run completes, we can get download/test/deploy to the model it has built." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "retraining_run.wait_for_completion()\n", - "\n", - "retraining_run.download_file(\"outputs/model.pkl\", \"generated_code/model.pkl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Acknowledgements" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This Bank Marketing dataset is made available under the Creative Commons (CCO: Public Domain) License: https://creativecommons.org/publicdomain/zero/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: https://creativecommons.org/publicdomain/zero/1.0/ and is available at: https://www.kaggle.com/janiobachmann/bank-marketing-dataset .\n", - "\n", - "_**Acknowledgements**_\n", - "This data set is originally available within the UCI Machine Learning Database: https://archive.ics.uci.edu/ml/datasets/bank+marketing\n", - "\n", - "[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "ratanase" - } - ], - "category": "tutorial", - "compute": [ - "AML" - ], - "datasets": [ - "Bankmarketing" - ], - "deployment": [ - "ACI" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Automated ML run with basic edition features.", - "index_order": 5, - "kernel_info": { - "name": "python3-azureml" - }, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "nteract": { - "version": "nteract-front-end@1.0.0" - }, - "tags": [ - "featurization", - "explainability", - "remote_run", - "AutomatedML" - ], - "task": "Classification" - }, - "nbformat": 4, - "nbformat_minor": 1 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml deleted file mode 100644 index 0f30214b6..000000000 --- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-classification-bank-marketing-all-features -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb deleted file mode 100644 index 5bbd4a677..000000000 --- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb +++ /dev/null @@ -1,487 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Classification of credit card fraudulent transactions on remote compute **_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Train](#Train)\n", - "1. [Results](#Results)\n", - "1. [Test](#Test)\n", - "1. [Acknowledgements](#Acknowledgements)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this example we use the associated credit card dataset to showcase how you can use AutoML for a simple classification problem. The goal is to predict if a credit card transaction is considered a fraudulent charge.\n", - "\n", - "This notebook is using remote compute to train the model.\n", - "\n", - "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create an experiment using an existing workspace.\n", - "2. Configure AutoML using `AutoMLConfig`.\n", - "3. Train the model using remote compute.\n", - "4. Explore the results.\n", - "5. Test the fitted model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "from matplotlib import pyplot as plt\n", - "import pandas as pd\n", - "import os\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.train.automl import AutoMLConfig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# choose a name for experiment\n", - "experiment_name = \"automl-classification-ccard-remote\"\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Experiment Name\"] = experiment.name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "A compute target is required to execute the Automated ML run. In this tutorial, you create AmlCompute as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "#### Creation of AmlCompute takes approximately 5 minutes. \n", - "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "cpu_cluster_name = \"cpu-cluster-1\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n", - " )\n", - " compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load Data\n", - "\n", - "Load the credit card dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "name": "load-data" - }, - "outputs": [], - "source": [ - "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n", - "dataset = Dataset.Tabular.from_delimited_files(data)\n", - "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n", - "label_column_name = \"Class\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "\n", - "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification or regression|\n", - "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics:
accuracy
AUC_weighted
average_precision_score_weighted
norm_macro_recall
precision_score_weighted|\n", - "|**enable_early_stopping**|Stop the run if the metric score is not showing improvement.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|Input dataset, containing both features and label column.|\n", - "|**label_column_name**|The name of the label column.|\n", - "\n", - "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "name": "automl-config" - }, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"n_cross_validations\": 3,\n", - " \"primary_metric\": \"average_precision_score_weighted\",\n", - " \"enable_early_stopping\": True,\n", - " \"max_concurrent_iterations\": 2, # This is a limit for testing purpose, please increase it as per cluster size\n", - " \"experiment_timeout_hours\": 0.25, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n", - " \"verbosity\": logging.INFO,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"classification\",\n", - " debug_log=\"automl_errors.log\",\n", - " compute_target=compute_target,\n", - " training_data=training_data,\n", - " label_column_name=label_column_name,\n", - " **automl_settings,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If you need to retrieve a run that already started, use the following code\n", - "# from azureml.train.automl.run import AutoMLRun\n", - "# remote_run = AutoMLRun(experiment = experiment, run_id = '')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Widget for Monitoring Runs\n", - "\n", - "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n", - "\n", - "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "widget-rundetails-sample" - ] - }, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(remote_run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Explain model\n", - "\n", - "Automated ML models can be explained and visualized using the SDK Explainability library. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze results\n", - "\n", - "### Retrieve the Best Model\n", - "\n", - "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run, fitted_model = remote_run.get_output()\n", - "fitted_model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Print the properties of the model\n", - "The fitted_model is a python object and you can read the different properties of the object.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the fitted model\n", - "\n", - "Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# convert the test data to dataframe\n", - "X_test_df = validation_data.drop_columns(\n", - " columns=[label_column_name]\n", - ").to_pandas_dataframe()\n", - "y_test_df = validation_data.keep_columns(\n", - " columns=[label_column_name], validate=True\n", - ").to_pandas_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# call the predict functions on the model\n", - "y_pred = fitted_model.predict(X_test_df)\n", - "y_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate metrics for the prediction\n", - "\n", - "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n", - "from the trained model that was returned." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "import numpy as np\n", - "import itertools\n", - "\n", - "cf = confusion_matrix(y_test_df.values, y_pred)\n", - "plt.imshow(cf, cmap=plt.cm.Blues, interpolation=\"nearest\")\n", - "plt.colorbar()\n", - "plt.title(\"Confusion Matrix\")\n", - "plt.xlabel(\"Predicted\")\n", - "plt.ylabel(\"Actual\")\n", - "class_labels = [\"False\", \"True\"]\n", - "tick_marks = np.arange(len(class_labels))\n", - "plt.xticks(tick_marks, class_labels)\n", - "plt.yticks([-0.5, 0, 1, 1.5], [\"\", \"False\", \"True\", \"\"])\n", - "# plotting text value inside cells\n", - "thresh = cf.max() / 2.0\n", - "for i, j in itertools.product(range(cf.shape[0]), range(cf.shape[1])):\n", - " plt.text(\n", - " j,\n", - " i,\n", - " format(cf[i, j], \"d\"),\n", - " horizontalalignment=\"center\",\n", - " color=\"white\" if cf[i, j] > thresh else \"black\",\n", - " )\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Acknowledgements" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n", - "\n", - "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u00a9 Libre de Bruxelles) on big data mining and fraud detection.\n", - "More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n", - "\n", - "Please cite the following works:\n", - "\n", - "Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n", - "\n", - "Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n", - "\n", - "Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n", - "\n", - "Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n", - "\n", - "Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n", - "\n", - "Carcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing\n", - "\n", - "Bertrand Lebichot, Yann-A\u00c3\u00abl Le Borgne, Liyun He, Frederic Obl\u00c3\u00a9, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019\n", - "\n", - "Fabrizio Carcillo, Yann-A\u00c3\u00abl Le Borgne, Olivier Caelen, Frederic Obl\u00c3\u00a9, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "ratanase" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Creditcard" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "file_extension": ".py", - "framework": [ - "None" - ], - "friendly_name": "Classification of credit card fraudulent transactions using Automated ML", - "index_order": 5, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "tags": [ - "remote_run", - "AutomatedML" - ], - "task": "Classification", - "version": "3.6.7" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml deleted file mode 100644 index 148f33d50..000000000 --- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-classification-credit-card-fraud -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb deleted file mode 100644 index 36a26b5bb..000000000 --- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb +++ /dev/null @@ -1,592 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Text Classification Using Deep Learning**_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Data](#Data)\n", - "1. [Train](#Train)\n", - "1. [Evaluate](#Evaluate)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "This notebook demonstrates classification with text data using deep learning in AutoML.\n", - "\n", - "AutoML highlights here include using deep neural networks (DNNs) to create embedded features from text data. Depending on the compute cluster the user provides, AutoML tried out Bidirectional Encoder Representations from Transformers (BERT) when a GPU compute is used, and Bidirectional Long-Short Term neural network (BiLSTM) when a CPU compute is used, thereby optimizing the choice of DNN for the uesr's setup.\n", - "\n", - "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n", - "\n", - "Notebook synopsis:\n", - "\n", - "1. Creating an Experiment in an existing Workspace\n", - "2. Configuration and remote run of AutoML for a text dataset (20 Newsgroups dataset from scikit-learn) for classification\n", - "3. Registering the best model for future use\n", - "4. Evaluating the final model on a test set" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import logging\n", - "import os\n", - "import shutil\n", - "\n", - "import pandas as pd\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.core.compute import AmlCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "from azureml.core.run import Run\n", - "from azureml.widgets import RunDetails\n", - "from azureml.core.model import Model\n", - "from helper import run_inference, get_result_df\n", - "from azureml.train.automl import AutoMLConfig\n", - "from sklearn.datasets import fetch_20newsgroups" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As part of the setup you have already created a Workspace. To run AutoML, you also need to create an Experiment. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# Choose an experiment name.\n", - "experiment_name = \"automl-classification-text-dnn\"\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace Name\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Experiment Name\"] = experiment.name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up a compute cluster\n", - "This section uses a user-provided compute cluster (named \"dnntext-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "Whether you provide/select a CPU or GPU cluster, AutoML will choose the appropriate DNN for that setup - BiLSTM or BERT text featurizer will be included in the candidate featurizers on CPU and GPU respectively. If your goal is to obtain the most accurate model, we recommend you use GPU clusters since BERT featurizers usually outperform BiLSTM featurizers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "num_nodes = 2\n", - "\n", - "# Choose a name for your cluster.\n", - "amlcompute_cluster_name = \"dnntext-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_D2_V2\"\n", - " # To use BERT (this is recommended for best performance), select a GPU such as \"STANDARD_NC6\"\n", - " # or similar GPU option\n", - " # available in your workspace\n", - " idle_seconds_before_scaledown=60,\n", - " max_nodes=num_nodes,\n", - " )\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get data\n", - "For this notebook we will use 20 Newsgroups data from scikit-learn. We filter the data to contain four classes and take a sample as training data. Please note that for accuracy improvement, more data is needed. For this notebook we provide a small-data example so that you can use this template to use with your larger sized data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_dir = \"text-dnn-data\" # Local directory to store data\n", - "blobstore_datadir = data_dir # Blob store directory to store data in\n", - "target_column_name = \"y\"\n", - "feature_column_name = \"X\"\n", - "\n", - "\n", - "def get_20newsgroups_data():\n", - " \"\"\"Fetches 20 Newsgroups data from scikit-learn\n", - " Returns them in form of pandas dataframes\n", - " \"\"\"\n", - " remove = (\"headers\", \"footers\", \"quotes\")\n", - " categories = [\n", - " \"rec.sport.baseball\",\n", - " \"rec.sport.hockey\",\n", - " \"comp.graphics\",\n", - " \"sci.space\",\n", - " ]\n", - "\n", - " data = fetch_20newsgroups(\n", - " subset=\"train\",\n", - " categories=categories,\n", - " shuffle=True,\n", - " random_state=42,\n", - " remove=remove,\n", - " )\n", - " data = pd.DataFrame(\n", - " {feature_column_name: data.data, target_column_name: data.target}\n", - " )\n", - "\n", - " data_train = data[:200]\n", - " data_test = data[200:300]\n", - "\n", - " data_train = remove_blanks_20news(\n", - " data_train, feature_column_name, target_column_name\n", - " )\n", - " data_test = remove_blanks_20news(data_test, feature_column_name, target_column_name)\n", - "\n", - " return data_train, data_test\n", - "\n", - "\n", - "def remove_blanks_20news(data, feature_column_name, target_column_name):\n", - "\n", - " data[feature_column_name] = (\n", - " data[feature_column_name]\n", - " .replace(r\"\\n\", \" \", regex=True)\n", - " .apply(lambda x: x.strip())\n", - " )\n", - " data = data[data[feature_column_name] != \"\"]\n", - "\n", - " return data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Fetch data and upload to datastore for use in training" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_train, data_test = get_20newsgroups_data()\n", - "\n", - "if not os.path.isdir(data_dir):\n", - " os.mkdir(data_dir)\n", - "\n", - "train_data_fname = data_dir + \"/train_data.csv\"\n", - "test_data_fname = data_dir + \"/test_data.csv\"\n", - "\n", - "data_train.to_csv(train_data_fname, index=False)\n", - "data_test.to_csv(test_data_fname, index=False)\n", - "\n", - "datastore = ws.get_default_datastore()\n", - "datastore.upload(src_dir=data_dir, target_path=blobstore_datadir, overwrite=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = Dataset.Tabular.from_delimited_files(\n", - " path=[(datastore, blobstore_datadir + \"/train_data.csv\")]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare AutoML run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook uses the blocked_models parameter to exclude some models that can take a longer time to train on some text datasets. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"experiment_timeout_minutes\": 30,\n", - " \"primary_metric\": \"accuracy\",\n", - " \"max_concurrent_iterations\": num_nodes,\n", - " \"max_cores_per_iteration\": -1,\n", - " \"enable_dnn\": True,\n", - " \"enable_early_stopping\": True,\n", - " \"validation_size\": 0.3,\n", - " \"verbosity\": logging.INFO,\n", - " \"enable_voting_ensemble\": False,\n", - " \"enable_stack_ensemble\": False,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"classification\",\n", - " debug_log=\"automl_errors.log\",\n", - " compute_target=compute_target,\n", - " training_data=train_dataset,\n", - " label_column_name=target_column_name,\n", - " blocked_models=[\"LightGBM\", \"XGBoostClassifier\"],\n", - " **automl_settings,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Submit AutoML Run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_run = experiment.submit(automl_config, show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Displaying the run objects gives you links to the visual tools in the Azure Portal. Go try them!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve the Best Model\n", - "Below we select the best model pipeline from our iterations, use it to test on test data on the same compute cluster." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For local inferencing, you can load the model locally via. the method `remote_run.get_output()`. For more information on the arguments expected by this method, you can run `remote_run.get_output??`.\n", - "Note that when the model contains BERT, this step will require pytorch and pytorch-transformers installed in your local environment. The exact versions of these packages can be found in the **automl_env.yml** file located in the local copy of your azureml-examples folder here: \"azureml-examples/python-sdk/tutorials/automl-with-azureml\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve the best Run object\n", - "best_run = automl_run.get_best_child()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can now see what text transformations are used to convert text data to features for this dataset, including deep learning transformations based on BiLSTM or Transformer (BERT is one implementation of a Transformer) models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download the featurization summary JSON file locally\n", - "best_run.download_file(\n", - " \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n", - ")\n", - "\n", - "# Render the JSON as a pandas DataFrame\n", - "with open(\"featurization_summary.json\", \"r\") as f:\n", - " records = json.load(f)\n", - "\n", - "featurization_summary = pd.DataFrame.from_records(records)\n", - "featurization_summary[\"Transformations\"].tolist()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Registering the best model\n", - "We now register the best fitted model from the AutoML Run for use in future deployments. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get results stats, extract the best model from AutoML run, download and register the resultant best model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summary_df = get_result_df(automl_run)\n", - "best_dnn_run_id = summary_df[\"run_id\"].iloc[0]\n", - "best_dnn_run = Run(experiment, best_dnn_run_id)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_dir = \"Model\" # Local folder where the model will be stored temporarily\n", - "if not os.path.isdir(model_dir):\n", - " os.mkdir(model_dir)\n", - "\n", - "best_dnn_run.download_file(\"outputs/model.pkl\", model_dir + \"/model.pkl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Register the model in your Azure Machine Learning Workspace. If you previously registered a model, please make sure to delete it so as to replace it with this new model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Register the model\n", - "model_name = \"textDNN-20News\"\n", - "model = Model.register(\n", - " model_path=model_dir + \"/model.pkl\", model_name=model_name, tags=None, workspace=ws\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate on Test Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now use the best fitted model from the AutoML Run to make predictions on the test set. \n", - "\n", - "Test set schema should match that of the training set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_dataset = Dataset.Tabular.from_delimited_files(\n", - " path=[(datastore, blobstore_datadir + \"/test_data.csv\")]\n", - ")\n", - "\n", - "# preview the first 3 rows of the dataset\n", - "test_dataset.take(3).to_pandas_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_experiment = Experiment(ws, experiment_name + \"_test\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "script_folder = os.path.join(os.getcwd(), \"inference\")\n", - "os.makedirs(script_folder, exist_ok=True)\n", - "shutil.copy(\"infer.py\", script_folder)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_run = run_inference(\n", - " test_experiment,\n", - " compute_target,\n", - " script_folder,\n", - " best_dnn_run,\n", - " test_dataset,\n", - " target_column_name,\n", - " model_name,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display computed metrics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(test_run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_run.wait_for_completion()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.Series(test_run.get_metrics())" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "anshirga" - } - ], - "compute": [ - "AML Compute" - ], - "datasets": [ - "None" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "DNN Text Featurization", - "index_order": 2, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "tags": [ - "None" - ], - "task": "Text featurization using DNNs for classification" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml deleted file mode 100644 index 4c952264c..000000000 --- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-classification-text-dnn -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py deleted file mode 100644 index 90d67f83a..000000000 --- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py +++ /dev/null @@ -1,68 +0,0 @@ -import pandas as pd -from azureml.core import Environment -from azureml.train.estimator import Estimator -from azureml.core.run import Run - - -def run_inference( - test_experiment, - compute_target, - script_folder, - train_run, - test_dataset, - target_column_name, - model_name, -): - - inference_env = train_run.get_environment() - - est = Estimator( - source_directory=script_folder, - entry_script="infer.py", - script_params={ - "--target_column_name": target_column_name, - "--model_name": model_name, - }, - inputs=[test_dataset.as_named_input("test_data")], - compute_target=compute_target, - environment_definition=inference_env, - ) - - run = test_experiment.submit( - est, - tags={ - "training_run_id": train_run.id, - "run_algorithm": train_run.properties["run_algorithm"], - "valid_score": train_run.properties["score"], - "primary_metric": train_run.properties["primary_metric"], - }, - ) - - run.log("run_algorithm", run.tags["run_algorithm"]) - return run - - -def get_result_df(remote_run): - - children = list(remote_run.get_children(recursive=True)) - summary_df = pd.DataFrame( - index=["run_id", "run_algorithm", "primary_metric", "Score"] - ) - goal_minimize = False - for run in children: - if "run_algorithm" in run.properties and "score" in run.properties: - summary_df[run.id] = [ - run.id, - run.properties["run_algorithm"], - run.properties["primary_metric"], - float(run.properties["score"]), - ] - if "goal" in run.properties: - goal_minimize = run.properties["goal"].split("_")[-1] == "min" - - summary_df = summary_df.T.sort_values( - "Score", ascending=goal_minimize - ).drop_duplicates(["run_algorithm"]) - summary_df = summary_df.set_index("run_algorithm") - - return summary_df diff --git a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py deleted file mode 100644 index 28fd10b37..000000000 --- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py +++ /dev/null @@ -1,68 +0,0 @@ -import argparse - -import pandas as pd -import numpy as np - -from sklearn.externals import joblib - -from azureml.automl.runtime.shared.score import scoring, constants -from azureml.core import Run -from azureml.core.model import Model - - -parser = argparse.ArgumentParser() -parser.add_argument( - "--target_column_name", - type=str, - dest="target_column_name", - help="Target Column Name", -) -parser.add_argument( - "--model_name", type=str, dest="model_name", help="Name of registered model" -) - -args = parser.parse_args() -target_column_name = args.target_column_name -model_name = args.model_name - -print("args passed are: ") -print("Target column name: ", target_column_name) -print("Name of registered model: ", model_name) - -model_path = Model.get_model_path(model_name) -# deserialize the model file back into a sklearn model -model = joblib.load(model_path) - -run = Run.get_context() -# get input dataset by name -test_dataset = run.input_datasets["test_data"] - -X_test_df = test_dataset.drop_columns( - columns=[target_column_name] -).to_pandas_dataframe() -y_test_df = ( - test_dataset.with_timestamp_columns(None) - .keep_columns(columns=[target_column_name]) - .to_pandas_dataframe() -) - -predicted = model.predict_proba(X_test_df) - -if isinstance(predicted, pd.DataFrame): - predicted = predicted.values - -# Use the AutoML scoring module -train_labels = model.classes_ -class_labels = np.unique( - np.concatenate((y_test_df.values, np.reshape(train_labels, (-1, 1)))) -) -classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET) -scores = scoring.score_classification( - y_test_df.values, predicted, classification_metrics, class_labels, train_labels -) - -print("scores:") -print(scores) - -for key, value in scores.items(): - run.log(key, value) diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb deleted file mode 100644 index e572ecb71..000000000 --- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb +++ /dev/null @@ -1,585 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning \n", - "**Continuous retraining using Pipelines and Time-Series TabularDataset**\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "2. [Setup](#Setup)\n", - "3. [Compute](#Compute)\n", - "4. [Run Configuration](#Run-Configuration)\n", - "5. [Data Ingestion Pipeline](#Data-Ingestion-Pipeline)\n", - "6. [Training Pipeline](#Training-Pipeline)\n", - "7. [Publish Retraining Pipeline and Schedule](#Publish-Retraining-Pipeline-and-Schedule)\n", - "8. [Test Retraining](#Test-Retraining)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "In this example we use AutoML and Pipelines to enable contious retraining of a model based on updates to the training dataset. We will create two pipelines, the first one to demonstrate a training dataset that gets updated over time. We leverage time-series capabilities of `TabularDataset` to achieve this. The second pipeline utilizes pipeline `Schedule` to trigger continuous retraining. \n", - "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n", - "In this notebook you will learn how to:\n", - "* Create an Experiment in an existing Workspace.\n", - "* Configure AutoML using AutoMLConfig.\n", - "* Create data ingestion pipeline to update a time-series based TabularDataset\n", - "* Create training pipeline to prepare data, run AutoML, register the model and setup pipeline triggers.\n", - "\n", - "## Setup\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn import datasets\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.train.automl import AutoMLConfig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Accessing the Azure ML workspace requires authentication with Azure.\n", - "\n", - "The default authentication is interactive authentication using the default tenant. Executing the ws = Workspace.from_config() line in the cell below will prompt for authentication the first time that it is run.\n", - "\n", - "If you have multiple Azure tenants, you can specify the tenant by replacing the ws = Workspace.from_config() line in the cell below with the following:\n", - "```\n", - "from azureml.core.authentication import InteractiveLoginAuthentication\n", - "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n", - "ws = Workspace.from_config(auth = auth)\n", - "```\n", - "If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the ws = Workspace.from_config() line in the cell below with the following:\n", - "```\n", - "from azureml.core.authentication import ServicePrincipalAuthentication\n", - "auth = auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n", - "ws = Workspace.from_config(auth = auth)\n", - "```\n", - "For more details, see aka.ms/aml-notebook-auth" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "dstor = ws.get_default_datastore()\n", - "\n", - "# Choose a name for the run history container in the workspace.\n", - "experiment_name = \"retrain-noaaweather\"\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Run History Name\"] = experiment_name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute \n", - "\n", - "#### Create or Attach existing AmlCompute\n", - "\n", - "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "#### Creation of AmlCompute takes approximately 5 minutes. \n", - "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "amlcompute_cluster_name = \"cont-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n", - " )\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n", - "\n", - "# create a new RunConfig object\n", - "conda_run_config = RunConfiguration(framework=\"python\")\n", - "\n", - "# Set compute target to AmlCompute\n", - "conda_run_config.target = compute_target\n", - "\n", - "conda_run_config.environment.docker.enabled = True\n", - "\n", - "cd = CondaDependencies.create(\n", - " pip_packages=[\n", - " \"azureml-sdk[automl]\",\n", - " \"applicationinsights\",\n", - " \"azureml-opendatasets\",\n", - " \"azureml-defaults\",\n", - " ],\n", - " conda_packages=[\"numpy==1.16.2\"],\n", - " pin_sdk_version=False,\n", - ")\n", - "conda_run_config.environment.python.conda_dependencies = cd\n", - "\n", - "print(\"run config is ready\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Ingestion Pipeline \n", - "For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You can replace this with your own dataset, or you can skip this pipeline if you already have a time-series based `TabularDataset`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The name and target column of the Dataset to create\n", - "dataset = \"NOAA-Weather-DS4\"\n", - "target_column_name = \"temperature\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Upload Data Step\n", - "The data ingestion pipeline has a single step with a script to query the latest weather data and upload it to the blob store. During the first run, the script will create and register a time-series based `TabularDataset` with the past one week of weather data. For each subsequent run, the script will create a partition in the blob store by querying NOAA for new weather data since the last modified time of the dataset (`dataset.data_changed_time`) and creating a data.csv file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline, PipelineParameter\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "\n", - "ds_name = PipelineParameter(name=\"ds_name\", default_value=dataset)\n", - "upload_data_step = PythonScriptStep(\n", - " script_name=\"upload_weather_data.py\",\n", - " allow_reuse=False,\n", - " name=\"upload_weather_data\",\n", - " arguments=[\"--ds_name\", ds_name],\n", - " compute_target=compute_target,\n", - " runconfig=conda_run_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit Pipeline Run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_pipeline = Pipeline(\n", - " description=\"pipeline_with_uploaddata\", workspace=ws, steps=[upload_data_step]\n", - ")\n", - "data_pipeline_run = experiment.submit(\n", - " data_pipeline, pipeline_parameters={\"ds_name\": dataset}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_pipeline_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training Pipeline\n", - "### Prepare Training Data Step\n", - "\n", - "Script to check if new data is available since the model was last trained. If no new data is available, we cancel the remaining pipeline steps. We need to set allow_reuse flag to False to allow the pipeline to run even when inputs don't change. We also need the name of the model to check the time the model was last trained." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineData\n", - "\n", - "# The model name with which to register the trained model in the workspace.\n", - "model_name = PipelineParameter(\"model_name\", default_value=\"noaaweatherds\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_prep_step = PythonScriptStep(\n", - " script_name=\"check_data.py\",\n", - " allow_reuse=False,\n", - " name=\"check_data\",\n", - " arguments=[\"--ds_name\", ds_name, \"--model_name\", model_name],\n", - " compute_target=compute_target,\n", - " runconfig=conda_run_config,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "\n", - "train_ds = Dataset.get_by_name(ws, dataset)\n", - "train_ds = train_ds.drop_columns([\"partition_date\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### AutoMLStep\n", - "Create an AutoMLConfig and a training step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.automl import AutoMLConfig\n", - "from azureml.pipeline.steps import AutoMLStep\n", - "\n", - "automl_settings = {\n", - " \"iteration_timeout_minutes\": 10,\n", - " \"experiment_timeout_hours\": 0.25,\n", - " \"n_cross_validations\": 3,\n", - " \"primary_metric\": \"r2_score\",\n", - " \"max_concurrent_iterations\": 3,\n", - " \"max_cores_per_iteration\": -1,\n", - " \"verbosity\": logging.INFO,\n", - " \"enable_early_stopping\": True,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"regression\",\n", - " debug_log=\"automl_errors.log\",\n", - " path=\".\",\n", - " compute_target=compute_target,\n", - " training_data=train_ds,\n", - " label_column_name=target_column_name,\n", - " **automl_settings,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineData, TrainingOutput\n", - "\n", - "metrics_output_name = \"metrics_output\"\n", - "best_model_output_name = \"best_model_output\"\n", - "\n", - "metrics_data = PipelineData(\n", - " name=\"metrics_data\",\n", - " datastore=dstor,\n", - " pipeline_output_name=metrics_output_name,\n", - " training_output=TrainingOutput(type=\"Metrics\"),\n", - ")\n", - "model_data = PipelineData(\n", - " name=\"model_data\",\n", - " datastore=dstor,\n", - " pipeline_output_name=best_model_output_name,\n", - " training_output=TrainingOutput(type=\"Model\"),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_step = AutoMLStep(\n", - " name=\"automl_module\",\n", - " automl_config=automl_config,\n", - " outputs=[metrics_data, model_data],\n", - " allow_reuse=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Register Model Step\n", - "Script to register the model to the workspace. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "register_model_step = PythonScriptStep(\n", - " script_name=\"register_model.py\",\n", - " name=\"register_model\",\n", - " allow_reuse=False,\n", - " arguments=[\n", - " \"--model_name\",\n", - " model_name,\n", - " \"--model_path\",\n", - " model_data,\n", - " \"--ds_name\",\n", - " ds_name,\n", - " ],\n", - " inputs=[model_data],\n", - " compute_target=compute_target,\n", - " runconfig=conda_run_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit Pipeline Run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_pipeline = Pipeline(\n", - " description=\"training_pipeline\",\n", - " workspace=ws,\n", - " steps=[data_prep_step, automl_step, register_model_step],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_pipeline_run = experiment.submit(\n", - " training_pipeline,\n", - " pipeline_parameters={\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_pipeline_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Publish Retraining Pipeline and Schedule\n", - "Once we are happy with the pipeline, we can publish the training pipeline to the workspace and create a schedule to trigger on blob change. The schedule polls the blob store where the data is being uploaded and runs the retraining pipeline if there is a data change. A new version of the model will be registered to the workspace once the run is complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_name = \"Retraining-Pipeline-NOAAWeather\"\n", - "\n", - "published_pipeline = training_pipeline.publish(\n", - " name=pipeline_name, description=\"Pipeline that retrains AutoML model\"\n", - ")\n", - "\n", - "published_pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Schedule\n", - "\n", - "schedule = Schedule.create(\n", - " workspace=ws,\n", - " name=\"RetrainingSchedule\",\n", - " pipeline_parameters={\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n", - " pipeline_id=published_pipeline.id,\n", - " experiment_name=experiment_name,\n", - " datastore=dstor,\n", - " wait_for_provisioning=True,\n", - " polling_interval=1440,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test Retraining\n", - "Here we setup the data ingestion pipeline to run on a schedule, to verify that the retraining pipeline runs as expected. \n", - "\n", - "Note: \n", - "* Azure NOAA Weather data is updated daily and retraining will not trigger if there is no new data available. \n", - "* Depending on the polling interval set in the schedule, the retraining may take some time trigger after data ingestion pipeline completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_name = \"DataIngestion-Pipeline-NOAAWeather\"\n", - "\n", - "published_pipeline = training_pipeline.publish(\n", - " name=pipeline_name, description=\"Pipeline that updates NOAAWeather Dataset\"\n", - ")\n", - "\n", - "published_pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Schedule\n", - "\n", - "schedule = Schedule.create(\n", - " workspace=ws,\n", - " name=\"RetrainingSchedule-DataIngestion\",\n", - " pipeline_parameters={\"ds_name\": dataset},\n", - " pipeline_id=published_pipeline.id,\n", - " experiment_name=experiment_name,\n", - " datastore=dstor,\n", - " wait_for_provisioning=True,\n", - " polling_interval=1440,\n", - ")" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "vivijay" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml deleted file mode 100644 index 9b05ea1fc..000000000 --- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-continuous-retraining -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py b/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py deleted file mode 100644 index aec68d422..000000000 --- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py +++ /dev/null @@ -1,46 +0,0 @@ -import argparse -import os -import azureml.core -from datetime import datetime -import pandas as pd -import pytz -from azureml.core import Dataset, Model -from azureml.core.run import Run, _OfflineRun -from azureml.core import Workspace - -run = Run.get_context() -ws = None -if type(run) == _OfflineRun: - ws = Workspace.from_config() -else: - ws = run.experiment.workspace - -print("Check for new data.") - -parser = argparse.ArgumentParser("split") -parser.add_argument("--ds_name", help="input dataset name") -parser.add_argument("--model_name", help="name of the deployed model") - -args = parser.parse_args() - -print("Argument 1(ds_name): %s" % args.ds_name) -print("Argument 2(model_name): %s" % args.model_name) - -# Get the latest registered model -try: - model = Model(ws, args.model_name) - last_train_time = model.created_time - print("Model was last trained on {0}.".format(last_train_time)) -except Exception as e: - print("Could not get last model train time.") - last_train_time = datetime.min.replace(tzinfo=pytz.UTC) - -train_ds = Dataset.get_by_name(ws, args.ds_name) -dataset_changed_time = train_ds.data_changed_time - -if not dataset_changed_time > last_train_time: - print("Cancelling run since there is no new data.") - run.parent.cancel() -else: - # New data is available since the model was last trained - print("Dataset was last updated on {0}. Retraining...".format(dataset_changed_time)) diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/register_model.py b/how-to-use-azureml/automated-machine-learning/continuous-retraining/register_model.py deleted file mode 100644 index aa37ee86b..000000000 --- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/register_model.py +++ /dev/null @@ -1,35 +0,0 @@ -from azureml.core.model import Model, Dataset -from azureml.core.run import Run, _OfflineRun -from azureml.core import Workspace -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument("--model_name") -parser.add_argument("--model_path") -parser.add_argument("--ds_name") -args = parser.parse_args() - -print("Argument 1(model_name): %s" % args.model_name) -print("Argument 2(model_path): %s" % args.model_path) -print("Argument 3(ds_name): %s" % args.ds_name) - -run = Run.get_context() -ws = None -if type(run) == _OfflineRun: - ws = Workspace.from_config() -else: - ws = run.experiment.workspace - -train_ds = Dataset.get_by_name(ws, args.ds_name) -datasets = [(Dataset.Scenario.TRAINING, train_ds)] - -# Register model with training dataset - -model = Model.register( - workspace=ws, - model_path=args.model_path, - model_name=args.model_name, - datasets=datasets, -) - -print("Registered version {0} of model {1}".format(model.version, model.name)) diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py b/how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py deleted file mode 100644 index 28f30a65b..000000000 --- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py +++ /dev/null @@ -1,157 +0,0 @@ -import argparse -import os -from datetime import datetime -from dateutil.relativedelta import relativedelta -import pandas as pd -import traceback -from azureml.core import Dataset -from azureml.core.run import Run, _OfflineRun -from azureml.core import Workspace -from azureml.opendatasets import NoaaIsdWeather - -run = Run.get_context() -ws = None -if type(run) == _OfflineRun: - ws = Workspace.from_config() -else: - ws = run.experiment.workspace - -usaf_list = [ - "725724", - "722149", - "723090", - "722159", - "723910", - "720279", - "725513", - "725254", - "726430", - "720381", - "723074", - "726682", - "725486", - "727883", - "723177", - "722075", - "723086", - "724053", - "725070", - "722073", - "726060", - "725224", - "725260", - "724520", - "720305", - "724020", - "726510", - "725126", - "722523", - "703333", - "722249", - "722728", - "725483", - "722972", - "724975", - "742079", - "727468", - "722193", - "725624", - "722030", - "726380", - "720309", - "722071", - "720326", - "725415", - "724504", - "725665", - "725424", - "725066", -] - - -def get_noaa_data(start_time, end_time): - columns = [ - "usaf", - "wban", - "datetime", - "latitude", - "longitude", - "elevation", - "windAngle", - "windSpeed", - "temperature", - "stationName", - "p_k", - ] - isd = NoaaIsdWeather(start_time, end_time, cols=columns) - noaa_df = isd.to_pandas_dataframe() - df_filtered = noaa_df[noaa_df["usaf"].isin(usaf_list)] - df_filtered.reset_index(drop=True) - print( - "Received {0} rows of training data between {1} and {2}".format( - df_filtered.shape[0], start_time, end_time - ) - ) - return df_filtered - - -print("Check for new data and prepare the data") - -parser = argparse.ArgumentParser("split") -parser.add_argument("--ds_name", help="name of the Dataset to update") -args = parser.parse_args() - -print("Argument 1(ds_name): %s" % args.ds_name) - -dstor = ws.get_default_datastore() -register_dataset = False -end_time = datetime.utcnow() - -try: - ds = Dataset.get_by_name(ws, args.ds_name) - end_time_last_slice = ds.data_changed_time.replace(tzinfo=None) - print("Dataset {0} last updated on {1}".format(args.ds_name, end_time_last_slice)) -except Exception: - print(traceback.format_exc()) - print( - "Dataset with name {0} not found, registering new dataset.".format(args.ds_name) - ) - register_dataset = True - end_time = datetime(2021, 5, 1, 0, 0) - end_time_last_slice = end_time - relativedelta(weeks=2) - -train_df = get_noaa_data(end_time_last_slice, end_time) - -if train_df.size > 0: - print( - "Received {0} rows of new data after {1}.".format( - train_df.shape[0], end_time_last_slice - ) - ) - folder_name = "{}/{:04d}/{:02d}/{:02d}/{:02d}/{:02d}/{:02d}".format( - args.ds_name, - end_time.year, - end_time.month, - end_time.day, - end_time.hour, - end_time.minute, - end_time.second, - ) - file_path = "{0}/data.csv".format(folder_name) - - # Add a new partition to the registered dataset - os.makedirs(folder_name, exist_ok=True) - train_df.to_csv(file_path, index=False) - - dstor.upload_files( - files=[file_path], target_path=folder_name, overwrite=True, show_progress=True - ) -else: - print("No new data since {0}.".format(end_time_last_slice)) - -if register_dataset: - ds = Dataset.Tabular.from_delimited_files( - dstor.path("{}/**/*.csv".format(args.ds_name)), - partition_format="/{partition_date:yyyy/MM/dd/HH/mm/ss}/data.csv", - ) - ds.register(ws, name=args.ds_name) diff --git a/how-to-use-azureml/automated-machine-learning/experimental/README.md b/how-to-use-azureml/automated-machine-learning/experimental/README.md deleted file mode 100644 index 566f99998..000000000 --- a/how-to-use-azureml/automated-machine-learning/experimental/README.md +++ /dev/null @@ -1,92 +0,0 @@ -# Experimental Notebooks for Automated ML -Notebooks listed in this folder are leveraging experimental features. Namespaces or function signitures may change in future SDK releases. The notebooks published here will reflect the latest supported APIs. All of these notebooks can run on a client-only installation of the Automated ML SDK. -The client only installation doesn't contain any of the machine learning libraries, such as scikit-learn, xgboost, or tensorflow, making it much faster to install and is less likely to conflict with any packages in an existing environment. However, since the ML libraries are not available locally, models cannot be downloaded and loaded directly in the client. To replace the functionality of having models locally, these notebooks also demonstrate the ModelProxy feature which will allow you to submit a predict/forecast to the training environment. - - -## Setup using a Local Conda environment - -To run these notebook on your own notebook server, use these installation instructions. -The instructions below will install everything you need and then start a Jupyter notebook. -If you would like to use a lighter-weight version of the client that does not install all of the machine learning libraries locally, you can leverage the [experimental notebooks.](experimental/README.md) - -### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.7 or higher. -- **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V). If you have a previous version installed, you can update it using the command: conda update conda. -There's no need to install mini-conda specifically. - -### 2. Downloading the sample notebooks -- Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory. The automated ML sample notebooks are in the "automated-machine-learning" folder. - -### 3. Setup a new conda environment -The **automl_setup_thin_client** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter. The default conda environment name is azure_automl_experimental. The exact command depends on the operating system. See the specific sections below for Windows, Mac and Linux. It can take about 10 minutes to execute. - -Packages installed by the **automl_setup** script: -
  • python
  • nb_conda
  • matplotlib
  • numpy
  • cython
  • urllib3
  • pandas
  • azureml-sdk
  • azureml-widgets
  • pandas-ml
- -For more details refer to the [automl_env_thin_client.yml](./automl_env_thin_client.yml) -## Windows -Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run: -``` -automl_setup_thin_client -``` -## Mac -Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`). - -Start a Terminal windows, cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run: - -``` -bash automl_setup_thin_client_mac.sh -``` - -## Linux -cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run: - -``` -bash automl_setup_thin_client_linux.sh -``` - -### 4. Running configuration.ipynb -- Before running any samples you next need to run the configuration notebook. Click on [configuration](../../configuration.ipynb) notebook -- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*) - -### 5. Running Samples -- Please make sure you use the Python [conda env:azure_automl_experimental] kernel when trying the sample Notebooks. -- Follow the instructions in the individual notebooks to explore various features in automated ML. - -### 6. Starting jupyter notebook manually -To start your Jupyter notebook manually, use: - -``` -conda activate azure_automl -jupyter notebook -``` - -or on Mac or Linux: - -``` -source activate azure_automl -jupyter notebook -``` - - - -# Automated ML SDK Sample Notebooks - -- [auto-ml-regression-model-proxy.ipynb](regression-model-proxy/auto-ml-regression-model-proxy.ipynb) - - Dataset: Hardware Performance Dataset - - Simple example of using automated ML for regression - - Uses azure compute for training - - Uses ModelProxy for submitting prediction to training environment on azure compute - - -See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments. - - -# Running using python command -Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file. -You can then run this file using the python command. -However, on Windows the file needs to be modified before it can be run. -The following condition must be added to the main code in the file: - - if __name__ == "__main__": - -The main code of the file must be indented so that it is under this condition. diff --git a/how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client.cmd b/how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client.cmd deleted file mode 100644 index ac5123e61..000000000 --- a/how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client.cmd +++ /dev/null @@ -1,63 +0,0 @@ -@echo off -set conda_env_name=%1 -set automl_env_file=%2 -set options=%3 -set PIP_NO_WARN_SCRIPT_LOCATION=0 - -IF "%conda_env_name%"=="" SET conda_env_name="azure_automl_experimental" -IF "%automl_env_file%"=="" SET automl_env_file="automl_thin_client_env.yml" - -IF NOT EXIST %automl_env_file% GOTO YmlMissing - -IF "%CONDA_EXE%"=="" GOTO CondaMissing - -call conda activate %conda_env_name% 2>nul: - -if not errorlevel 1 ( - echo Upgrading existing conda environment %conda_env_name% - call pip uninstall azureml-train-automl -y -q - call conda env update --name %conda_env_name% --file %automl_env_file% - if errorlevel 1 goto ErrorExit -) else ( - call conda env create -f %automl_env_file% -n %conda_env_name% -) - -call conda activate %conda_env_name% 2>nul: -if errorlevel 1 goto ErrorExit - -call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)" - -REM azureml.widgets is now installed as part of the pip install under the conda env. -REM Removing the old user install so that the notebooks will use the latest widget. -call jupyter nbextension uninstall --user --py azureml.widgets - -echo. -echo. -echo *************************************** -echo * AutoML setup completed successfully * -echo *************************************** -IF NOT "%options%"=="nolaunch" ( - echo. - echo Starting jupyter notebook - please run the configuration notebook - echo. - jupyter notebook --log-level=50 --notebook-dir='..\..' -) - -goto End - -:CondaMissing -echo Please run this script from an Anaconda Prompt window. -echo You can start an Anaconda Prompt window by -echo typing Anaconda Prompt on the Start menu. -echo If you don't see the Anaconda Prompt app, install Miniconda. -echo If you are running an older version of Miniconda or Anaconda, -echo you can upgrade using the command: conda update conda -goto End - -:YmlMissing -echo File %automl_env_file% not found. - -:ErrorExit -echo Install failed - -:End \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_linux.sh b/how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_linux.sh deleted file mode 100644 index e73b59616..000000000 --- a/how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_linux.sh +++ /dev/null @@ -1,53 +0,0 @@ -#!/bin/bash - -CONDA_ENV_NAME=$1 -AUTOML_ENV_FILE=$2 -OPTIONS=$3 -PIP_NO_WARN_SCRIPT_LOCATION=0 - -if [ "$CONDA_ENV_NAME" == "" ] -then - CONDA_ENV_NAME="azure_automl_experimental" -fi - -if [ "$AUTOML_ENV_FILE" == "" ] -then - AUTOML_ENV_FILE="automl_thin_client_env.yml" -fi - -if [ ! -f $AUTOML_ENV_FILE ]; then - echo "File $AUTOML_ENV_FILE not found" - exit 1 -fi - -if source activate $CONDA_ENV_NAME 2> /dev/null -then - echo "Upgrading existing conda environment" $CONDA_ENV_NAME - pip uninstall azureml-train-automl -y -q - conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE && - jupyter nbextension uninstall --user --py azureml.widgets -else - conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME && - source activate $CONDA_ENV_NAME && - python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" && - jupyter nbextension uninstall --user --py azureml.widgets && - echo "" && - echo "" && - echo "***************************************" && - echo "* AutoML setup completed successfully *" && - echo "***************************************" && - if [ "$OPTIONS" != "nolaunch" ] - then - echo "" && - echo "Starting jupyter notebook - please run the configuration notebook" && - echo "" && - jupyter notebook --log-level=50 --notebook-dir '../..' - fi -fi - -if [ $? -gt 0 ] -then - echo "Installation failed" -fi - - diff --git a/how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_mac.sh b/how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_mac.sh deleted file mode 100644 index 506b6ecd4..000000000 --- a/how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_mac.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash - -CONDA_ENV_NAME=$1 -AUTOML_ENV_FILE=$2 -OPTIONS=$3 -PIP_NO_WARN_SCRIPT_LOCATION=0 - -if [ "$CONDA_ENV_NAME" == "" ] -then - CONDA_ENV_NAME="azure_automl_experimental" -fi - -if [ "$AUTOML_ENV_FILE" == "" ] -then - AUTOML_ENV_FILE="automl_thin_client_env_mac.yml" -fi - -if [ ! -f $AUTOML_ENV_FILE ]; then - echo "File $AUTOML_ENV_FILE not found" - exit 1 -fi - -if source activate $CONDA_ENV_NAME 2> /dev/null -then - echo "Upgrading existing conda environment" $CONDA_ENV_NAME - pip uninstall azureml-train-automl -y -q - conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE && - jupyter nbextension uninstall --user --py azureml.widgets -else - conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME && - source activate $CONDA_ENV_NAME && - conda install lightgbm -c conda-forge -y && - python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" && - jupyter nbextension uninstall --user --py azureml.widgets && - echo "" && - echo "" && - echo "***************************************" && - echo "* AutoML setup completed successfully *" && - echo "***************************************" && - if [ "$OPTIONS" != "nolaunch" ] - then - echo "" && - echo "Starting jupyter notebook - please run the configuration notebook" && - echo "" && - jupyter notebook --log-level=50 --notebook-dir '../..' - fi -fi - -if [ $? -gt 0 ] -then - echo "Installation failed" -fi - - - diff --git a/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml b/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml deleted file mode 100644 index bfdffe277..000000000 --- a/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml +++ /dev/null @@ -1,17 +0,0 @@ -name: azure_automl_experimental -dependencies: - # The python interpreter version. - # Currently Azure ML only supports 3.6.0 and later. -- pip<=20.2.4 -- python>=3.6.0,<3.9 -- cython==0.29.14 -- urllib3==1.26.7 -- PyJWT < 2.0.0 -- numpy==1.18.5 - -- pip: - # Required packages for AzureML execution, history, and data preparation. - - azureml-defaults - - azureml-sdk - - azureml-widgets - - pandas diff --git a/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml b/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml deleted file mode 100644 index 0ca016913..000000000 --- a/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml +++ /dev/null @@ -1,20 +0,0 @@ -name: azure_automl_experimental -channels: - - conda-forge - - main -dependencies: - # The python interpreter version. - # Currently Azure ML only supports 3.6.0 and later. -- pip<=20.2.4 -- nomkl -- python>=3.6.0,<3.9 -- urllib3==1.26.7 -- PyJWT < 2.0.0 -- numpy==1.19.5 - -- pip: - # Required packages for AzureML execution, history, and data preparation. - - azureml-defaults - - azureml-sdk - - azureml-widgets - - pandas diff --git a/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb b/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb deleted file mode 100644 index ee3db28e1..000000000 --- a/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb +++ /dev/null @@ -1,420 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Classification of credit card fraudulent transactions on local managed compute **_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Train](#Train)\n", - "1. [Results](#Results)\n", - "1. [Test](#Test)\n", - "1. [Acknowledgements](#Acknowledgements)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this example we use the associated credit card dataset to showcase how you can use AutoML for a simple classification problem. The goal is to predict if a credit card transaction is considered a fraudulent charge.\n", - "\n", - "This notebook is using local managed compute to train the model.\n", - "\n", - "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create an experiment using an existing workspace.\n", - "2. Configure AutoML using `AutoMLConfig`.\n", - "3. Train the model using local managed compute.\n", - "4. Explore the results.\n", - "5. Test the fitted model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "import pandas as pd\n", - "\n", - "import azureml.core\n", - "from azureml.core.compute_target import LocalTarget\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.train.automl import AutoMLConfig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n", - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# choose a name for experiment\n", - "experiment_name = 'automl-local-managed'\n", - "\n", - "experiment=Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output['Subscription ID'] = ws.subscription_id\n", - "output['Workspace'] = ws.name\n", - "output['Resource Group'] = ws.resource_group\n", - "output['Location'] = ws.location\n", - "output['Experiment Name'] = experiment.name\n", - "pd.set_option('display.max_colwidth', None)\n", - "outputDf = pd.DataFrame(data = output, index = [''])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Determine if local docker is configured for Linux images\n", - "\n", - "Local managed runs will leverage a Linux docker container to submit the run to. Due to this, the docker needs to be configured to use Linux containers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check if Docker is installed and Linux containers are enabled\n", - "import subprocess\n", - "from subprocess import CalledProcessError\n", - "try:\n", - " assert subprocess.run(\"docker -v\", shell=True).returncode == 0, 'Local Managed runs require docker to be installed.'\n", - " out = subprocess.check_output(\"docker system info\", shell=True).decode('ascii')\n", - " assert \"OSType: linux\" in out, 'Docker engine needs to be configured to use Linux containers.' \\\n", - " 'https://docs.docker.com/docker-for-windows/#switch-between-windows-and-linux-containers'\n", - "except CalledProcessError as ex:\n", - " raise Exception('Local Managed runs require docker to be installed.') from ex" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load Data\n", - "\n", - "Load the credit card dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n", - "dataset = Dataset.Tabular.from_delimited_files(data)\n", - "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n", - "label_column_name = 'Class'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "\n", - "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification or regression|\n", - "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics:
accuracy
AUC_weighted
average_precision_score_weighted
norm_macro_recall
precision_score_weighted|\n", - "|**enable_early_stopping**|Stop the run if the metric score is not showing improvement.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|Input dataset, containing both features and label column.|\n", - "|**label_column_name**|The name of the label column.|\n", - "|**enable_local_managed**|Enable the experimental local-managed scenario.|\n", - "\n", - "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"n_cross_validations\": 3,\n", - " \"primary_metric\": 'average_precision_score_weighted',\n", - " \"enable_early_stopping\": True,\n", - " \"experiment_timeout_hours\": 0.3, #for real scenarios we recommend a timeout of at least one hour \n", - " \"verbosity\": logging.INFO,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(task = 'classification',\n", - " debug_log = 'automl_errors.log',\n", - " compute_target = LocalTarget(),\n", - " enable_local_managed = True,\n", - " training_data = training_data,\n", - " label_column_name = label_column_name,\n", - " **automl_settings\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "parent_run = experiment.submit(automl_config, show_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If you need to retrieve a run that already started, use the following code\n", - "#from azureml.train.automl.run import AutoMLRun\n", - "#parent_run = AutoMLRun(experiment = experiment, run_id = '')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "parent_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Explain model\n", - "\n", - "Automated ML models can be explained and visualized using the SDK Explainability library. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze results\n", - "\n", - "### Retrieve the Best Child Run\n", - "\n", - "Below we select the best pipeline from our iterations. The `get_best_child` method returns the best run. Overloads on `get_best_child` allow you to retrieve the best run for *any* logged metric." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = parent_run.get_best_child()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the fitted model\n", - "\n", - "Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_test_df = validation_data.drop_columns(columns=[label_column_name])\n", - "y_test_df = validation_data.keep_columns(columns=[label_column_name], validate=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Creating ModelProxy for submitting prediction runs to the training environment.\n", - "We will create a ModelProxy for the best child run, which will allow us to submit a run that does the prediction in the training environment. Unlike the local client, which can have different versions of some libraries, the training environment will have all the compatible libraries for the model already." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.automl.model_proxy import ModelProxy\n", - "best_model_proxy = ModelProxy(best_run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# call the predict functions on the model proxy\n", - "y_pred = best_model_proxy.predict(X_test_df).to_pandas_dataframe()\n", - "y_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Acknowledgements" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n", - "\n", - "\n", - "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u0192\u00c2\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n", - "Please cite the following works: \n", - "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n", - "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n", - "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n", - "o\tDal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n", - "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n", - "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sekrupa" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Creditcard" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "file_extension": ".py", - "framework": [ - "None" - ], - "friendly_name": "Classification of credit card fraudulent transactions using Automated ML", - "index_order": 5, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "tags": [ - "AutomatedML" - ], - "task": "Classification", - "version": "3.6.7" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.yml b/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.yml deleted file mode 100644 index 1f2ef5b45..000000000 --- a/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-classification-credit-card-fraud-local-managed -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb deleted file mode 100644 index 518dc16d8..000000000 --- a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb +++ /dev/null @@ -1,459 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Regression with Aml Compute**_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Data](#Data)\n", - "1. [Train](#Train)\n", - "1. [Results](#Results)\n", - "1. [Test](#Test)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "In this example we use an experimental feature, Model Proxy, to do a predict on the best generated model without downloading the model locally. The prediction will happen on same compute and environment that was used to train the model. This feature is currently in the experimental state, which means that the API is prone to changing, please make sure to run on the latest version of this notebook if you face any issues.\n", - "This notebook will also leverage MLFlow for saving models, allowing for more portability of the resulting models. See https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow for more details around MLFlow is AzureML.\n", - "\n", - "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create an `Experiment` in an existing `Workspace`.\n", - "2. Configure AutoML using `AutoMLConfig`.\n", - "3. Train the model using remote compute.\n", - "4. Explore the results.\n", - "5. Test the best fitted model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "import json\n", - "\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.data.dataset_factory import TabularDatasetFactory\n", - "from azureml.train.automl import AutoMLConfig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n", - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# Choose a name for the experiment.\n", - "experiment_name = 'automl-regression-model-proxy'\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output['Subscription ID'] = ws.subscription_id\n", - "output['Workspace'] = ws.name\n", - "output['Resource Group'] = ws.resource_group\n", - "output['Location'] = ws.location\n", - "output['Run History Name'] = experiment_name\n", - "output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "# Try to ensure that the cluster name is unique across the notebooks\n", - "cpu_cluster_name = \"reg-model-proxy\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n", - " max_nodes=4)\n", - " compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load Data\n", - "Load the hardware dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n", - "dataset = Dataset.Tabular.from_delimited_files(data)\n", - "\n", - "# Split the dataset into train and test datasets\n", - "train_data, test_data = dataset.random_split(percentage=0.8, seed=223)\n", - "\n", - "label = \"ERP\"\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The split data will be used in the remote compute by ModelProxy and locally to compare results.\n", - "So, we need to persist the split data to avoid descrepencies from different package versions in the local and remote." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds = ws.get_default_datastore()\n", - "\n", - "train_data = TabularDatasetFactory.register_pandas_dataframe(\n", - " train_data.to_pandas_dataframe(), target=(ds, \"machineTrainData\"), name=\"train_data\")\n", - "\n", - "test_data = TabularDatasetFactory.register_pandas_dataframe(\n", - " test_data.to_pandas_dataframe(), target=(ds, \"machineTestData\"), name=\"test_data\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "\n", - "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification, regression or forecasting|\n", - "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics:
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n", - "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|\n", - "\n", - "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "automlconfig-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"n_cross_validations\": 3,\n", - " \"primary_metric\": 'r2_score',\n", - " \"enable_early_stopping\": True, \n", - " \"experiment_timeout_hours\": 0.3, #for real scenarios we recommend a timeout of at least one hour \n", - " \"max_concurrent_iterations\": 4,\n", - " \"max_cores_per_iteration\": -1,\n", - " \"verbosity\": logging.INFO,\n", - " \"save_mlflow\": True,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(task = 'regression',\n", - " compute_target = compute_target,\n", - " training_data = train_data,\n", - " label_column_name = label,\n", - " **automl_settings\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. Execution of remote runs is asynchronous. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output = False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If you need to retrieve a run that already started, use the following code\n", - "#from azureml.train.automl.run import AutoMLRun\n", - "#remote_run = AutoMLRun(experiment = experiment, run_id = '')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve the Best Child Run\n", - "\n", - "Below we select the best pipeline from our iterations. The `get_best_child` method returns the best run. Overloads on `get_best_child` allow you to retrieve the best run for *any* logged metric." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = remote_run.get_best_child()\n", - "print(best_run)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Show hyperparameters\n", - "Show the model pipeline used for the best run with its hyperparameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_properties = json.loads(best_run.get_details()['properties']['pipeline_script'])\n", - "print(json.dumps(run_properties, indent = 1)) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Best Child Run Based on Any Other Metric\n", - "Show the run and the model that has the smallest `root_mean_squared_error` value (which turned out to be the same as the one with largest `spearman_correlation` value):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lookup_metric = \"root_mean_squared_error\"\n", - "best_run = remote_run.get_best_child(metric = lookup_metric)\n", - "print(best_run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_test = test_data.keep_columns('ERP')\n", - "test_data = test_data.drop_columns('ERP')\n", - "\n", - "\n", - "y_train = train_data.keep_columns('ERP')\n", - "train_data = train_data.drop_columns('ERP')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Creating ModelProxy for submitting prediction runs to the training environment.\n", - "We will create a ModelProxy for the best child run, which will allow us to submit a run that does the prediction in the training environment. Unlike the local client, which can have different versions of some libraries, the training environment will have all the compatible libraries for the model already." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.automl.model_proxy import ModelProxy\n", - "best_model_proxy = ModelProxy(best_run)\n", - "y_pred_train = best_model_proxy.predict(train_data)\n", - "y_pred_test = best_model_proxy.predict(test_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Exploring results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred_train = y_pred_train.to_pandas_dataframe().values.flatten()\n", - "y_train = y_train.to_pandas_dataframe().values.flatten()\n", - "y_residual_train = y_train - y_pred_train\n", - "\n", - "y_pred_test = y_pred_test.to_pandas_dataframe().values.flatten()\n", - "y_test = y_test.to_pandas_dataframe().values.flatten()\n", - "y_residual_test = y_test - y_pred_test\n", - "print(y_residual_train)\n", - "print(y_residual_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "sekrupa" - } - ], - "categories": [ - "how-to-use-azureml", - "automated-machine-learning" - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.yml b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.yml deleted file mode 100644 index e5d127ea5..000000000 --- a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-regression-model-proxy -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/Backtesting.png b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/Backtesting.png deleted file mode 100644 index 4697e68fa..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/Backtesting.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/assets/score.py b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/assets/score.py deleted file mode 100644 index 43c9ac215..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/assets/score.py +++ /dev/null @@ -1,167 +0,0 @@ -from typing import Any, Dict, Optional, List - -import argparse -import json -import os -import re - -import pandas as pd - -from matplotlib import pyplot as plt -from matplotlib.backends.backend_pdf import PdfPages - -from azureml.automl.core.shared import constants -from azureml.automl.core.shared.types import GrainType -from azureml.automl.runtime.shared.score import scoring - -GRAIN = "time_series_id" -BACKTEST_ITER = "backtest_iteration" -ACTUALS = "actual_level" -PREDICTIONS = "predicted_level" -ALL_GRAINS = "all_sets" - -FORECASTS_FILE = "forecast.csv" -SCORES_FILE = "scores.csv" -PLOTS_FILE = "plots_fcst_vs_actual.pdf" -RE_INVALID_SYMBOLS = re.compile("[: ]") - - -def _compute_metrics(df: pd.DataFrame, metrics: List[str]): - """ - Compute metrics for one data frame. - - :param df: The data frame which contains actual_level and predicted_level columns. - :return: The data frame with two columns - metric_name and metric. - """ - scores = scoring.score_regression( - y_test=df[ACTUALS], y_pred=df[PREDICTIONS], metrics=metrics - ) - metrics_df = pd.DataFrame(list(scores.items()), columns=["metric_name", "metric"]) - metrics_df.sort_values(["metric_name"], inplace=True) - metrics_df.reset_index(drop=True, inplace=True) - return metrics_df - - -def _format_grain_name(grain: GrainType) -> str: - """ - Convert grain name to string. - - :param grain: the grain name. - :return: the string representation of the given grain. - """ - if not isinstance(grain, tuple) and not isinstance(grain, list): - return str(grain) - grain = list(map(str, grain)) - return "|".join(grain) - - -def compute_all_metrics( - fcst_df: pd.DataFrame, - ts_id_colnames: List[str], - metric_names: Optional[List[set]] = None, -): - """ - Calculate metrics per grain. - - :param fcst_df: forecast data frame. Must contain 2 columns: 'actual_level' and 'predicted_level' - :param metric_names: (optional) the list of metric names to return - :param ts_id_colnames: (optional) list of grain column names - :return: dictionary of summary table for all tests and final decision on stationary vs nonstaionary - """ - if not metric_names: - metric_names = list(constants.Metric.SCALAR_REGRESSION_SET) - - if ts_id_colnames is None: - ts_id_colnames = [] - - metrics_list = [] - if ts_id_colnames: - for grain, df in fcst_df.groupby(ts_id_colnames): - one_grain_metrics_df = _compute_metrics(df, metric_names) - one_grain_metrics_df[GRAIN] = _format_grain_name(grain) - metrics_list.append(one_grain_metrics_df) - - # overall metrics - one_grain_metrics_df = _compute_metrics(fcst_df, metric_names) - one_grain_metrics_df[GRAIN] = ALL_GRAINS - metrics_list.append(one_grain_metrics_df) - - # collect into a data frame - return pd.concat(metrics_list) - - -def _draw_one_plot( - df: pd.DataFrame, - time_column_name: str, - grain_column_names: List[str], - pdf: PdfPages, -) -> None: - """ - Draw the single plot. - - :param df: The data frame with the data to build plot. - :param time_column_name: The name of a time column. - :param grain_column_names: The name of grain columns. - :param pdf: The pdf backend used to render the plot. - """ - fig, _ = plt.subplots(figsize=(20, 10)) - df = df.set_index(time_column_name) - plt.plot(df[[ACTUALS, PREDICTIONS]]) - plt.xticks(rotation=45) - iteration = df[BACKTEST_ITER].iloc[0] - if grain_column_names: - grain_name = [df[grain].iloc[0] for grain in grain_column_names] - plt.title(f"Time series ID: {_format_grain_name(grain_name)} {iteration}") - plt.legend(["actual", "forecast"]) - plt.close(fig) - pdf.savefig(fig) - - -def calculate_scores_and_build_plots( - input_dir: str, output_dir: str, automl_settings: Dict[str, Any] -): - os.makedirs(output_dir, exist_ok=True) - grains = automl_settings.get(constants.TimeSeries.GRAIN_COLUMN_NAMES) - time_column_name = automl_settings.get(constants.TimeSeries.TIME_COLUMN_NAME) - if grains is None: - grains = [] - if isinstance(grains, str): - grains = [grains] - while BACKTEST_ITER in grains: - grains.remove(BACKTEST_ITER) - - dfs = [] - for fle in os.listdir(input_dir): - file_path = os.path.join(input_dir, fle) - if os.path.isfile(file_path) and file_path.endswith(".csv"): - df_iter = pd.read_csv(file_path, parse_dates=[time_column_name]) - for _, iteration in df_iter.groupby(BACKTEST_ITER): - dfs.append(iteration) - forecast_df = pd.concat(dfs, sort=False, ignore_index=True) - # To make sure plots are in order, sort the predictions by grain and iteration. - ts_index = grains + [BACKTEST_ITER] - forecast_df.sort_values(by=ts_index, inplace=True) - pdf = PdfPages(os.path.join(output_dir, PLOTS_FILE)) - for _, one_forecast in forecast_df.groupby(ts_index): - _draw_one_plot(one_forecast, time_column_name, grains, pdf) - pdf.close() - forecast_df.to_csv(os.path.join(output_dir, FORECASTS_FILE), index=False) - metrics = compute_all_metrics(forecast_df, grains + [BACKTEST_ITER]) - metrics.to_csv(os.path.join(output_dir, SCORES_FILE), index=False) - - -if __name__ == "__main__": - args = {"forecasts": "--forecasts", "scores_out": "--output-dir"} - parser = argparse.ArgumentParser("Parsing input arguments.") - for argname, arg in args.items(): - parser.add_argument(arg, dest=argname, required=True) - parsed_args, _ = parser.parse_known_args() - input_dir = parsed_args.forecasts - output_dir = parsed_args.scores_out - with open( - os.path.join( - os.path.dirname(os.path.realpath(__file__)), "automl_settings.json" - ) - ) as json_file: - automl_settings = json.load(json_file) - calculate_scores_and_build_plots(input_dir, output_dir, automl_settings) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb deleted file mode 100644 index 23f5a74be..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb +++ /dev/null @@ -1,725 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Many Models with Backtesting - Automated ML\n", - "**_Backtest many models time series forecasts with Automated Machine Learning_**\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this notebook we are using a synthetic dataset to demonstrate the back testing in many model scenario. This allows us to check historical performance of AutoML on a historical data. To do that we step back on the backtesting period by the data set several times and split the data to train and test sets. Then these data sets are used for training and evaluation of model.
\n", - "\n", - "Thus, it is a quick way of evaluating AutoML as if it was in production. Here, we do not test historical performance of a particular model, for this see the [notebook](../forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb). Instead, the best model for every backtest iteration can be different since AutoML chooses the best model for a given training set.\n", - "![Backtesting](Backtesting.png)\n", - "\n", - "**NOTE: There are limits on how many runs we can do in parallel per workspace, and we currently recommend to set the parallelism to maximum of 320 runs per experiment per workspace. If users want to have more parallelism and increase this limit they might encounter Too Many Requests errors (HTTP 429).**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prerequisites\n", - "You'll need to create a compute Instance by following the instructions in the [EnvironmentSetup.md](../Setup_Resources/EnvironmentSetup.md)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.0 Set up workspace, datastore, experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613003526897 - } - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "import azureml.core\n", - "from azureml.core import Workspace, Datastore\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from pandas.tseries.frequencies import to_offset\n", - "\n", - "# Set up your workspace\n", - "ws = Workspace.from_config()\n", - "ws.get_details()\n", - "\n", - "# Set up your datastores\n", - "dstore = ws.get_default_datastore()\n", - "\n", - "output = {}\n", - "output[\"SDK version\"] = azureml.core.VERSION\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Default datastore name\"] = dstore.name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is compatible with Azure ML SDK version 1.35.1 or later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Choose an experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613003540729 - } - }, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment = Experiment(ws, \"automl-many-models-backtest\")\n", - "\n", - "print(\"Experiment name: \" + experiment.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.0 Data\n", - "\n", - "#### 2.1 Data generation\n", - "For this notebook we will generate the artificial data set with two [time series IDs](https://docs.microsoft.com/en-us/python/api/azureml-automl-core/azureml.automl.core.forecasting_parameters.forecastingparameters?view=azure-ml-py). Then we will generate backtest folds and will upload it to the default BLOB storage and create a [TabularDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset?view=azure-ml-py)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# simulate data: 2 grains - 700\n", - "TIME_COLNAME = \"date\"\n", - "TARGET_COLNAME = \"value\"\n", - "TIME_SERIES_ID_COLNAME = \"ts_id\"\n", - "\n", - "sample_size = 700\n", - "# Set the random seed for reproducibility of results.\n", - "np.random.seed(20)\n", - "X1 = pd.DataFrame(\n", - " {\n", - " TIME_COLNAME: pd.date_range(start=\"2018-01-01\", periods=sample_size),\n", - " TARGET_COLNAME: np.random.normal(loc=100, scale=20, size=sample_size),\n", - " TIME_SERIES_ID_COLNAME: \"ts_A\",\n", - " }\n", - ")\n", - "X2 = pd.DataFrame(\n", - " {\n", - " TIME_COLNAME: pd.date_range(start=\"2018-01-01\", periods=sample_size),\n", - " TARGET_COLNAME: np.random.normal(loc=100, scale=20, size=sample_size),\n", - " TIME_SERIES_ID_COLNAME: \"ts_B\",\n", - " }\n", - ")\n", - "\n", - "X = pd.concat([X1, X2], ignore_index=True, sort=False)\n", - "print(\"Simulated dataset contains {} rows \\n\".format(X.shape[0]))\n", - "X.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will generate 8 backtesting folds with backtesting period of 7 days and with the same forecasting horizon. We will add the column \"backtest_iteration\", which will identify the backtesting period by the last training date." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "offset_type = \"7D\"\n", - "NUMBER_OF_BACKTESTS = 8 # number of train/test sets to generate\n", - "\n", - "dfs_train = []\n", - "dfs_test = []\n", - "for ts_id, df_one in X.groupby(TIME_SERIES_ID_COLNAME):\n", - "\n", - " data_end = df_one[TIME_COLNAME].max()\n", - "\n", - " for i in range(NUMBER_OF_BACKTESTS):\n", - " train_cutoff_date = data_end - to_offset(offset_type)\n", - " df_one = df_one.copy()\n", - " df_one[\"backtest_iteration\"] = \"iteration_\" + str(train_cutoff_date)\n", - " train = df_one[df_one[TIME_COLNAME] <= train_cutoff_date]\n", - " test = df_one[\n", - " (df_one[TIME_COLNAME] > train_cutoff_date)\n", - " & (df_one[TIME_COLNAME] <= data_end)\n", - " ]\n", - " data_end = train[TIME_COLNAME].max()\n", - " dfs_train.append(train)\n", - " dfs_test.append(test)\n", - "\n", - "X_train = pd.concat(dfs_train, sort=False, ignore_index=True)\n", - "X_test = pd.concat(dfs_test, sort=False, ignore_index=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.2 Create the Tabular Data Set.\n", - "\n", - "A Datastore is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target.\n", - "\n", - "Please refer to [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore(class)?view=azure-ml-py) documentation on how to access data from Datastore.\n", - "\n", - "In this next step, we will upload the data and create a TabularDataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.data.dataset_factory import TabularDatasetFactory\n", - "\n", - "ds = ws.get_default_datastore()\n", - "# Upload saved data to the default data store.\n", - "train_data = TabularDatasetFactory.register_pandas_dataframe(\n", - " X_train, target=(ds, \"data_mm\"), name=\"data_train\"\n", - ")\n", - "test_data = TabularDatasetFactory.register_pandas_dataframe(\n", - " X_test, target=(ds, \"data_mm\"), name=\"data_test\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.0 Build the training pipeline\n", - "Now that the dataset, WorkSpace, and datastore are set up, we can put together a pipeline for training.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Choose a compute target\n", - "\n", - "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n", - "\n", - "\\*\\*Creation of AmlCompute takes approximately 5 minutes.**\n", - "\n", - "If the AmlCompute with that name is already in your workspace this code will skip the creation process. As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this [article](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613007037308 - } - }, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "\n", - "# Name your cluster\n", - "compute_name = \"backtest-mm\"\n", - "\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " print(\"Found compute target: \" + compute_name)\n", - "else:\n", - " print(\"Creating a new compute target...\")\n", - " provisioning_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n", - " )\n", - " # Create the compute target\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - "\n", - " # Can poll for a minimum number of nodes and for a specific timeout.\n", - " # If no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(\n", - " show_output=True, min_node_count=None, timeout_in_minutes=20\n", - " )\n", - "\n", - " # For a more detailed view of current cluster status, use the 'status' property\n", - " print(compute_target.status.serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up training parameters\n", - "\n", - "This dictionary defines the AutoML and many models settings. For this forecasting task we need to define several settings including the name of the time column, the maximum forecast horizon, and the partition column name definition. Please note, that in this case we are setting grain_column_names to be the time series ID column plus iteration, because we want to train a separate model for each time series and iteration.\n", - "\n", - "| Property | Description|\n", - "| :--------------- | :------------------- |\n", - "| **task** | forecasting |\n", - "| **primary_metric** | This is the metric that you want to optimize.
Forecasting supports the following primary metrics
normalized_root_mean_squared_error
normalized_mean_absolute_error |\n", - "| **iteration_timeout_minutes** | Maximum amount of time in minutes that the model can train. This is optional but provides customers with greater control on exit criteria. |\n", - "| **iterations** | Number of models to train. This is optional but provides customers with greater control on exit criteria. |\n", - "| **experiment_timeout_hours** | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n", - "| **label_column_name** | The name of the label column. |\n", - "| **max_horizon** | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n", - "| **n_cross_validations** | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n", - "| **time_column_name** | The name of your time column. |\n", - "| **grain_column_names** | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n", - "| **track_child_runs** | Flag to disable tracking of child runs. Only best run is tracked if the flag is set to False (this includes the model and metrics of the run). |\n", - "| **partition_column_names** | The names of columns used to group your models. For timeseries, the groups must not split up individual time-series. That is, each group must contain one or more whole time-series. |" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613007061544 - } - }, - "outputs": [], - "source": [ - "from azureml.train.automl.runtime._many_models.many_models_parameters import (\n", - " ManyModelsTrainParameters,\n", - ")\n", - "\n", - "partition_column_names = [TIME_SERIES_ID_COLNAME, \"backtest_iteration\"]\n", - "automl_settings = {\n", - " \"task\": \"forecasting\",\n", - " \"primary_metric\": \"normalized_root_mean_squared_error\",\n", - " \"iteration_timeout_minutes\": 10, # This needs to be changed based on the dataset. We ask customer to explore how long training is taking before settings this value\n", - " \"iterations\": 15,\n", - " \"experiment_timeout_hours\": 0.25, # This also needs to be changed based on the dataset. For larger data set this number needs to be bigger.\n", - " \"label_column_name\": TARGET_COLNAME,\n", - " \"n_cross_validations\": 3,\n", - " \"time_column_name\": TIME_COLNAME,\n", - " \"max_horizon\": 6,\n", - " \"grain_column_names\": partition_column_names,\n", - " \"track_child_runs\": False,\n", - "}\n", - "\n", - "mm_paramters = ManyModelsTrainParameters(\n", - " automl_settings=automl_settings, partition_column_names=partition_column_names\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up many models pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parallel run step is leveraged to train multiple models at once. To configure the ParallelRunConfig you will need to determine the appropriate number of workers and nodes for your use case. The process_count_per_node is based off the number of cores of the compute VM. The node_count will determine the number of master nodes to use, increasing the node count will speed up the training process.\n", - "\n", - "| Property | Description|\n", - "| :--------------- | :------------------- |\n", - "| **experiment** | The experiment used for training. |\n", - "| **train_data** | The file dataset to be used as input to the training run. |\n", - "| **node_count** | The number of compute nodes to be used for running the user script. We recommend to start with 3 and increase the node_count if the training time is taking too long. |\n", - "| **process_count_per_node** | Process count per node, we recommend 2:1 ratio for number of cores: number of processes per node. eg. If node has 16 cores then configure 8 or less process count per node or optimal performance. |\n", - "| **train_pipeline_parameters** | The set of configuration parameters defined in the previous section. |\n", - "\n", - "Calling this method will create a new aggregated dataset which is generated dynamically on pipeline execution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n", - "\n", - "\n", - "training_pipeline_steps = AutoMLPipelineBuilder.get_many_models_train_steps(\n", - " experiment=experiment,\n", - " train_data=train_data,\n", - " compute_target=compute_target,\n", - " node_count=2,\n", - " process_count_per_node=2,\n", - " run_invocation_timeout=920,\n", - " train_pipeline_parameters=mm_paramters,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "\n", - "training_pipeline = Pipeline(ws, steps=training_pipeline_steps)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit the pipeline to run\n", - "Next we submit our pipeline to run. The whole training pipeline takes about 20 minutes using a STANDARD_DS12_V2 VM with our current ParallelRunConfig setting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_run = experiment.submit(training_pipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check the run status, if training_run is in completed state, continue to next section. Otherwise, check the portal for failures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.0 Backtesting\n", - "Now that we selected the best AutoML model for each backtest fold, we will use these models to generate the forecasts and compare with the actuals." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up output dataset for inference data\n", - "Output of inference can be represented as [OutputFileDatasetConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.output_dataset_config.outputdatasetconfig?view=azure-ml-py) object and OutputFileDatasetConfig can be registered as a dataset. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.data import OutputFileDatasetConfig\n", - "\n", - "output_inference_data_ds = OutputFileDatasetConfig(\n", - " name=\"many_models_inference_output\",\n", - " destination=(dstore, \"backtesting/inference_data/\"),\n", - ").register_on_complete(name=\"backtesting_data_ds\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For many models we need to provide the ManyModelsInferenceParameters object.\n", - "\n", - "#### ManyModelsInferenceParameters arguments\n", - "| Property | Description|\n", - "| :--------------- | :------------------- |\n", - "| **partition_column_names** | List of column names that identifies groups. |\n", - "| **target_column_name** | \\[Optional\\] Column name only if the inference dataset has the target. |\n", - "| **time_column_name** | Column name only if it is timeseries. |\n", - "| **many_models_run_id** | \\[Optional\\] Many models pipeline run id where models were trained. |\n", - "\n", - "#### get_many_models_batch_inference_steps arguments\n", - "| Property | Description|\n", - "| :--------------- | :------------------- |\n", - "| **experiment** | The experiment used for inference run. |\n", - "| **inference_data** | The data to use for inferencing. It should be the same schema as used for training.\n", - "| **compute_target** | The compute target that runs the inference pipeline.|\n", - "| **node_count** | The number of compute nodes to be used for running the user script. We recommend to start with the number of cores per node (varies by compute sku). |\n", - "| **process_count_per_node** | The number of processes per node.\n", - "| **train_run_id** | \\[Optional\\] The run id of the hierarchy training, by default it is the latest successful training many model run in the experiment. |\n", - "| **train_experiment_name** | \\[Optional\\] The train experiment that contains the train pipeline. This one is only needed when the train pipeline is not in the same experiement as the inference pipeline. |\n", - "| **process_count_per_node** | \\[Optional\\] The number of processes per node, by default it's 4. |" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n", - "from azureml.train.automl.runtime._many_models.many_models_parameters import (\n", - " ManyModelsInferenceParameters,\n", - ")\n", - "\n", - "mm_parameters = ManyModelsInferenceParameters(\n", - " partition_column_names=partition_column_names,\n", - " time_column_name=TIME_COLNAME,\n", - " target_column_name=TARGET_COLNAME,\n", - ")\n", - "\n", - "inference_steps = AutoMLPipelineBuilder.get_many_models_batch_inference_steps(\n", - " experiment=experiment,\n", - " inference_data=test_data,\n", - " node_count=2,\n", - " process_count_per_node=2,\n", - " compute_target=compute_target,\n", - " run_invocation_timeout=300,\n", - " output_datastore=output_inference_data_ds,\n", - " train_run_id=training_run.id,\n", - " train_experiment_name=training_run.experiment.name,\n", - " inference_pipeline_parameters=mm_parameters,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "\n", - "inference_pipeline = Pipeline(ws, steps=inference_steps)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inference_run = experiment.submit(inference_pipeline)\n", - "inference_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.0 Retrieve results and calculate metrics\n", - "\n", - "The pipeline returns one file with the predictions for each times series ID and outputs the result to the forecasting_output Blob container. The details of the blob container is listed in 'forecasting_output.txt' under Outputs+logs. \n", - "\n", - "The next code snippet does the following:\n", - "1. Downloads the contents of the output folder that is passed in the parallel run step \n", - "2. Reads the parallel_run_step.txt file that has the predictions as pandas dataframe \n", - "3. Saves the table in csv format and \n", - "4. Displays the top 10 rows of the predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.automl.pipeline.steps.utilities import get_output_from_mm_pipeline\n", - "\n", - "forecasting_results_name = \"forecasting_results\"\n", - "forecasting_output_name = \"many_models_inference_output\"\n", - "forecast_file = get_output_from_mm_pipeline(\n", - " inference_run, forecasting_results_name, forecasting_output_name\n", - ")\n", - "df = pd.read_csv(forecast_file, delimiter=\" \", header=None, parse_dates=[0])\n", - "df.columns = list(X_train.columns) + [\"predicted_level\"]\n", - "print(\n", - " \"Prediction has \", df.shape[0], \" rows. Here the first 10 rows are being displayed.\"\n", - ")\n", - "# Save the scv file with header to read it in the next step.\n", - "df.rename(columns={TARGET_COLNAME: \"actual_level\"}, inplace=True)\n", - "df.to_csv(os.path.join(forecasting_results_name, \"forecast.csv\"), index=False)\n", - "df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View metrics\n", - "We will read in the obtained results and run the helper script, which will generate metrics and create the plots of predicted versus actual values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from assets.score import calculate_scores_and_build_plots\n", - "\n", - "backtesting_results = \"backtesting_mm_results\"\n", - "os.makedirs(backtesting_results, exist_ok=True)\n", - "calculate_scores_and_build_plots(\n", - " forecasting_results_name, backtesting_results, automl_settings\n", - ")\n", - "pd.DataFrame({\"File\": os.listdir(backtesting_results)})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The directory contains a set of files with results:\n", - "- forecast.csv contains forecasts for all backtest iterations. The backtest_iteration column contains iteration identifier with the last training date as a suffix\n", - "- scores.csv contains all metrics. If data set contains several time series, the metrics are given for all combinations of time series id and iterations, as well as scores for all iterations and time series ids, which are marked as \"all_sets\"\n", - "- plots_fcst_vs_actual.pdf contains the predictions vs forecast plots for each iteration and, eash time series is saved as separate plot.\n", - "\n", - "For demonstration purposes we will display the table of metrics for one of the time series with ID \"ts0\". We will create the utility function, which will build the table with metrics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_metrics_for_ts(all_metrics, ts):\n", - " \"\"\"\n", - " Get the metrics for the time series with ID ts and return it as pandas data frame.\n", - "\n", - " :param all_metrics: The table with all the metrics.\n", - " :param ts: The ID of a time series of interest.\n", - " :return: The pandas DataFrame with metrics for one time series.\n", - " \"\"\"\n", - " results_df = None\n", - " for ts_id, one_series in all_metrics.groupby(\"time_series_id\"):\n", - " if not ts_id.startswith(ts):\n", - " continue\n", - " iteration = ts_id.split(\"|\")[-1]\n", - " df = one_series[[\"metric_name\", \"metric\"]]\n", - " df.rename({\"metric\": iteration}, axis=1, inplace=True)\n", - " df.set_index(\"metric_name\", inplace=True)\n", - " if results_df is None:\n", - " results_df = df\n", - " else:\n", - " results_df = results_df.merge(\n", - " df, how=\"inner\", left_index=True, right_index=True\n", - " )\n", - " results_df.sort_index(axis=1, inplace=True)\n", - " return results_df\n", - "\n", - "\n", - "metrics_df = pd.read_csv(os.path.join(backtesting_results, \"scores.csv\"))\n", - "ts = \"ts_A\"\n", - "get_metrics_for_ts(metrics_df, ts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Forecast vs actuals plots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import IFrame\n", - "\n", - "IFrame(\"./backtesting_mm_results/plots_fcst_vs_actual.pdf\", width=800, height=300)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jialiu" - } - ], - "categories": [ - "how-to-use-azureml", - "automated-machine-learning" - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.yml b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.yml deleted file mode 100644 index 55b2188d3..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-backtest-many-models -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/Backtesting.png b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/Backtesting.png deleted file mode 100644 index 4697e68fa..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/Backtesting.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/data_split.py b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/data_split.py deleted file mode 100644 index c9b6b8a89..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/data_split.py +++ /dev/null @@ -1,45 +0,0 @@ -import argparse -import os - -import pandas as pd - -import azureml.train.automl.runtime._hts.hts_runtime_utilities as hru - -from azureml.core import Run -from azureml.core.dataset import Dataset - -# Parse the arguments. -args = { - "step_size": "--step-size", - "step_number": "--step-number", - "time_column_name": "--time-column-name", - "time_series_id_column_names": "--time-series-id-column-names", - "out_dir": "--output-dir", -} -parser = argparse.ArgumentParser("Parsing input arguments.") -for argname, arg in args.items(): - parser.add_argument(arg, dest=argname, required=True) -parsed_args, _ = parser.parse_known_args() -step_number = int(parsed_args.step_number) -step_size = int(parsed_args.step_size) -# Create the working dirrectory to store the temporary csv files. -working_dir = parsed_args.out_dir -os.makedirs(working_dir, exist_ok=True) -# Set input and output -script_run = Run.get_context() -input_dataset = script_run.input_datasets["training_data"] -X_train = input_dataset.to_pandas_dataframe() -# Split the data. -for i in range(step_number): - file_name = os.path.join(working_dir, "backtest_{}.csv".format(i)) - if parsed_args.time_series_id_column_names: - dfs = [] - for _, one_series in X_train.groupby([parsed_args.time_series_id_column_names]): - one_series = one_series.sort_values( - by=[parsed_args.time_column_name], inplace=False - ) - dfs.append(one_series.iloc[: len(one_series) - step_size * i]) - pd.concat(dfs, sort=False, ignore_index=True).to_csv(file_name, index=False) - else: - X_train.sort_values(by=[parsed_args.time_column_name], inplace=True) - X_train.iloc[: len(X_train) - step_size * i].to_csv(file_name, index=False) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/retrain_models.py b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/retrain_models.py deleted file mode 100644 index a20c24e5c..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/retrain_models.py +++ /dev/null @@ -1,173 +0,0 @@ -# --------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# --------------------------------------------------------- -"""The batch script needed for back testing of models using PRS.""" -import argparse -import json -import logging -import os -import pickle -import re - -import pandas as pd - -from azureml.core.experiment import Experiment -from azureml.core.model import Model -from azureml.core.run import Run -from azureml.automl.core.shared import constants -from azureml.automl.runtime.shared.score import scoring -from azureml.train.automl import AutoMLConfig - -RE_INVALID_SYMBOLS = re.compile(r"[:\s]") - -model_name = None -target_column_name = None -current_step_run = None -output_dir = None - -logger = logging.getLogger(__name__) - - -def _get_automl_settings(): - with open( - os.path.join( - os.path.dirname(os.path.realpath(__file__)), "automl_settings.json" - ) - ) as json_file: - return json.load(json_file) - - -def init(): - global model_name - global target_column_name - global output_dir - global automl_settings - global model_uid - logger.info("Initialization of the run.") - parser = argparse.ArgumentParser("Parsing input arguments.") - parser.add_argument("--output-dir", dest="out", required=True) - parser.add_argument("--model-name", dest="model", default=None) - parser.add_argument("--model-uid", dest="model_uid", default=None) - - parsed_args, _ = parser.parse_known_args() - model_name = parsed_args.model - automl_settings = _get_automl_settings() - target_column_name = automl_settings.get("label_column_name") - output_dir = parsed_args.out - model_uid = parsed_args.model_uid - os.makedirs(output_dir, exist_ok=True) - os.environ["AUTOML_IGNORE_PACKAGE_VERSION_INCOMPATIBILITIES".lower()] = "True" - - -def get_run(): - global current_step_run - if current_step_run is None: - current_step_run = Run.get_context() - return current_step_run - - -def run_backtest(data_input_name: str, file_name: str, experiment: Experiment): - """Re-train the model and return metrics.""" - data_input = pd.read_csv( - data_input_name, - parse_dates=[automl_settings[constants.TimeSeries.TIME_COLUMN_NAME]], - ) - print(data_input.head()) - if not automl_settings.get(constants.TimeSeries.GRAIN_COLUMN_NAMES): - # There is no grains. - data_input.sort_values( - [automl_settings[constants.TimeSeries.TIME_COLUMN_NAME]], inplace=True - ) - X_train = data_input.iloc[: -automl_settings["max_horizon"]] - y_train = X_train.pop(target_column_name).values - X_test = data_input.iloc[-automl_settings["max_horizon"] :] - y_test = X_test.pop(target_column_name).values - else: - # The data contain grains. - dfs_train = [] - dfs_test = [] - for _, one_series in data_input.groupby( - automl_settings.get(constants.TimeSeries.GRAIN_COLUMN_NAMES) - ): - one_series.sort_values( - [automl_settings[constants.TimeSeries.TIME_COLUMN_NAME]], inplace=True - ) - dfs_train.append(one_series.iloc[: -automl_settings["max_horizon"]]) - dfs_test.append(one_series.iloc[-automl_settings["max_horizon"] :]) - X_train = pd.concat(dfs_train, sort=False, ignore_index=True) - y_train = X_train.pop(target_column_name).values - X_test = pd.concat(dfs_test, sort=False, ignore_index=True) - y_test = X_test.pop(target_column_name).values - - last_training_date = str( - X_train[automl_settings[constants.TimeSeries.TIME_COLUMN_NAME]].max() - ) - - if file_name: - # If file name is provided, we will load model and retrain it on backtest data. - with open(file_name, "rb") as fp: - fitted_model = pickle.load(fp) - fitted_model.fit(X_train, y_train) - else: - # We will run the experiment and select the best model. - X_train[target_column_name] = y_train - automl_config = AutoMLConfig(training_data=X_train, **automl_settings) - automl_run = current_step_run.submit_child(automl_config, show_output=True) - best_run, fitted_model = automl_run.get_output() - # As we have generated models, we need to register them for the future use. - description = "Backtest model example" - tags = {"last_training_date": last_training_date, "experiment": experiment.name} - if model_uid: - tags["model_uid"] = model_uid - automl_run.register_model( - model_name=best_run.properties["model_name"], - description=description, - tags=tags, - ) - print(f"The model {best_run.properties['model_name']} was registered.") - - _, x_pred = fitted_model.forecast(X_test) - x_pred.reset_index(inplace=True, drop=False) - columns = [automl_settings[constants.TimeSeries.TIME_COLUMN_NAME]] - if automl_settings.get(constants.TimeSeries.GRAIN_COLUMN_NAMES): - # We know that fitted_model.grain_column_names is a list. - columns.extend(fitted_model.grain_column_names) - columns.append(constants.TimeSeriesInternal.DUMMY_TARGET_COLUMN) - # Remove featurized columns. - x_pred = x_pred[columns] - x_pred.rename( - {constants.TimeSeriesInternal.DUMMY_TARGET_COLUMN: "predicted_level"}, - axis=1, - inplace=True, - ) - x_pred["actual_level"] = y_test - x_pred["backtest_iteration"] = f"iteration_{last_training_date}" - date_safe = RE_INVALID_SYMBOLS.sub("_", last_training_date) - x_pred.to_csv(os.path.join(output_dir, f"iteration_{date_safe}.csv"), index=False) - return x_pred - - -def run(input_files): - """Run the script""" - logger.info("Running mini batch.") - ws = get_run().experiment.workspace - file_name = None - if model_name: - models = Model.list(ws, name=model_name) - cloud_model = None - if models: - for one_mod in models: - if cloud_model is None or one_mod.version > cloud_model.version: - logger.info( - "Using existing model from the workspace. Model version: {}".format( - one_mod.version - ) - ) - cloud_model = one_mod - file_name = cloud_model.download(exist_ok=True) - - forecasts = [] - logger.info("Running backtest.") - for input_file in input_files: - forecasts.append(run_backtest(input_file, file_name, get_run().experiment)) - return pd.concat(forecasts) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/score.py b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/score.py deleted file mode 100644 index 43c9ac215..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/score.py +++ /dev/null @@ -1,167 +0,0 @@ -from typing import Any, Dict, Optional, List - -import argparse -import json -import os -import re - -import pandas as pd - -from matplotlib import pyplot as plt -from matplotlib.backends.backend_pdf import PdfPages - -from azureml.automl.core.shared import constants -from azureml.automl.core.shared.types import GrainType -from azureml.automl.runtime.shared.score import scoring - -GRAIN = "time_series_id" -BACKTEST_ITER = "backtest_iteration" -ACTUALS = "actual_level" -PREDICTIONS = "predicted_level" -ALL_GRAINS = "all_sets" - -FORECASTS_FILE = "forecast.csv" -SCORES_FILE = "scores.csv" -PLOTS_FILE = "plots_fcst_vs_actual.pdf" -RE_INVALID_SYMBOLS = re.compile("[: ]") - - -def _compute_metrics(df: pd.DataFrame, metrics: List[str]): - """ - Compute metrics for one data frame. - - :param df: The data frame which contains actual_level and predicted_level columns. - :return: The data frame with two columns - metric_name and metric. - """ - scores = scoring.score_regression( - y_test=df[ACTUALS], y_pred=df[PREDICTIONS], metrics=metrics - ) - metrics_df = pd.DataFrame(list(scores.items()), columns=["metric_name", "metric"]) - metrics_df.sort_values(["metric_name"], inplace=True) - metrics_df.reset_index(drop=True, inplace=True) - return metrics_df - - -def _format_grain_name(grain: GrainType) -> str: - """ - Convert grain name to string. - - :param grain: the grain name. - :return: the string representation of the given grain. - """ - if not isinstance(grain, tuple) and not isinstance(grain, list): - return str(grain) - grain = list(map(str, grain)) - return "|".join(grain) - - -def compute_all_metrics( - fcst_df: pd.DataFrame, - ts_id_colnames: List[str], - metric_names: Optional[List[set]] = None, -): - """ - Calculate metrics per grain. - - :param fcst_df: forecast data frame. Must contain 2 columns: 'actual_level' and 'predicted_level' - :param metric_names: (optional) the list of metric names to return - :param ts_id_colnames: (optional) list of grain column names - :return: dictionary of summary table for all tests and final decision on stationary vs nonstaionary - """ - if not metric_names: - metric_names = list(constants.Metric.SCALAR_REGRESSION_SET) - - if ts_id_colnames is None: - ts_id_colnames = [] - - metrics_list = [] - if ts_id_colnames: - for grain, df in fcst_df.groupby(ts_id_colnames): - one_grain_metrics_df = _compute_metrics(df, metric_names) - one_grain_metrics_df[GRAIN] = _format_grain_name(grain) - metrics_list.append(one_grain_metrics_df) - - # overall metrics - one_grain_metrics_df = _compute_metrics(fcst_df, metric_names) - one_grain_metrics_df[GRAIN] = ALL_GRAINS - metrics_list.append(one_grain_metrics_df) - - # collect into a data frame - return pd.concat(metrics_list) - - -def _draw_one_plot( - df: pd.DataFrame, - time_column_name: str, - grain_column_names: List[str], - pdf: PdfPages, -) -> None: - """ - Draw the single plot. - - :param df: The data frame with the data to build plot. - :param time_column_name: The name of a time column. - :param grain_column_names: The name of grain columns. - :param pdf: The pdf backend used to render the plot. - """ - fig, _ = plt.subplots(figsize=(20, 10)) - df = df.set_index(time_column_name) - plt.plot(df[[ACTUALS, PREDICTIONS]]) - plt.xticks(rotation=45) - iteration = df[BACKTEST_ITER].iloc[0] - if grain_column_names: - grain_name = [df[grain].iloc[0] for grain in grain_column_names] - plt.title(f"Time series ID: {_format_grain_name(grain_name)} {iteration}") - plt.legend(["actual", "forecast"]) - plt.close(fig) - pdf.savefig(fig) - - -def calculate_scores_and_build_plots( - input_dir: str, output_dir: str, automl_settings: Dict[str, Any] -): - os.makedirs(output_dir, exist_ok=True) - grains = automl_settings.get(constants.TimeSeries.GRAIN_COLUMN_NAMES) - time_column_name = automl_settings.get(constants.TimeSeries.TIME_COLUMN_NAME) - if grains is None: - grains = [] - if isinstance(grains, str): - grains = [grains] - while BACKTEST_ITER in grains: - grains.remove(BACKTEST_ITER) - - dfs = [] - for fle in os.listdir(input_dir): - file_path = os.path.join(input_dir, fle) - if os.path.isfile(file_path) and file_path.endswith(".csv"): - df_iter = pd.read_csv(file_path, parse_dates=[time_column_name]) - for _, iteration in df_iter.groupby(BACKTEST_ITER): - dfs.append(iteration) - forecast_df = pd.concat(dfs, sort=False, ignore_index=True) - # To make sure plots are in order, sort the predictions by grain and iteration. - ts_index = grains + [BACKTEST_ITER] - forecast_df.sort_values(by=ts_index, inplace=True) - pdf = PdfPages(os.path.join(output_dir, PLOTS_FILE)) - for _, one_forecast in forecast_df.groupby(ts_index): - _draw_one_plot(one_forecast, time_column_name, grains, pdf) - pdf.close() - forecast_df.to_csv(os.path.join(output_dir, FORECASTS_FILE), index=False) - metrics = compute_all_metrics(forecast_df, grains + [BACKTEST_ITER]) - metrics.to_csv(os.path.join(output_dir, SCORES_FILE), index=False) - - -if __name__ == "__main__": - args = {"forecasts": "--forecasts", "scores_out": "--output-dir"} - parser = argparse.ArgumentParser("Parsing input arguments.") - for argname, arg in args.items(): - parser.add_argument(arg, dest=argname, required=True) - parsed_args, _ = parser.parse_known_args() - input_dir = parsed_args.forecasts - output_dir = parsed_args.scores_out - with open( - os.path.join( - os.path.dirname(os.path.realpath(__file__)), "automl_settings.json" - ) - ) as json_file: - automl_settings = json.load(json_file) - calculate_scores_and_build_plots(input_dir, output_dir, automl_settings) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb deleted file mode 100644 index 5270d3c4a..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb +++ /dev/null @@ -1,719 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License.\n", - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/automl-forecasting-function.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated MachineLearning\n", - "_**The model backtesting**_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "2. [Setup](#Setup)\n", - "3. [Data](#Data)\n", - "4. [Prepare remote compute and data.](#prepare_remote)\n", - "5. [Create the configuration for AutoML backtesting](#train)\n", - "6. [Backtest AutoML](#backtest_automl)\n", - "7. [View metrics](#Metrics)\n", - "8. [Backtest the best model](#backtest_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "Model backtesting is used to evaluate its performance on historical data. To do that we step back on the backtesting period by the data set several times and split the data to train and test sets. Then these data sets are used for training and evaluation of model.
\n", - "This notebook is intended to demonstrate backtesting on a single model, this is the best solution for small data sets with a few or one time series in it. For scenarios where we would like to choose the best AutoML model for every backtest iteration, please see [AutoML Forecasting Backtest Many Models Example](../forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb) notebook.\n", - "![Backtesting](Backtesting.png)\n", - "This notebook demonstrates two ways of backtesting:\n", - "- AutoML backtesting: we will train separate AutoML models for historical data\n", - "- Model backtesting: from the first run we will select the best model trained on the most recent data, retrain it on the past data and evaluate." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import shutil\n", - "\n", - "import azureml.core\n", - "from azureml.core import Experiment, Model, Workspace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is compatible with Azure ML SDK version 1.35.1 or later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As part of the setup you have already created a Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"SKU\"] = ws.sku\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n", - "For the demonstration purposes we will simulate one year of daily data. To do this we need to specify the following parameters: time column name, time series ID column names and label column name. Our intention is to forecast for two weeks ahead." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TIME_COLUMN_NAME = \"date\"\n", - "TIME_SERIES_ID_COLUMN_NAMES = \"time_series_id\"\n", - "LABEL_COLUMN_NAME = \"y\"\n", - "FORECAST_HORIZON = 14\n", - "FREQUENCY = \"D\"\n", - "\n", - "\n", - "def simulate_timeseries_data(\n", - " train_len: int,\n", - " test_len: int,\n", - " time_column_name: str,\n", - " target_column_name: str,\n", - " time_series_id_column_name: str,\n", - " time_series_number: int = 1,\n", - " freq: str = \"H\",\n", - "):\n", - " \"\"\"\n", - " Return the time series of designed length.\n", - "\n", - " :param train_len: The length of training data (one series).\n", - " :type train_len: int\n", - " :param test_len: The length of testing data (one series).\n", - " :type test_len: int\n", - " :param time_column_name: The desired name of a time column.\n", - " :type time_column_name: str\n", - " :param time_series_number: The number of time series in the data set.\n", - " :type time_series_number: int\n", - " :param freq: The frequency string representing pandas offset.\n", - " see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n", - " :type freq: str\n", - " :returns: the tuple of train and test data sets.\n", - " :rtype: tuple\n", - "\n", - " \"\"\"\n", - " data_train = [] # type: List[pd.DataFrame]\n", - " data_test = [] # type: List[pd.DataFrame]\n", - " data_length = train_len + test_len\n", - " for i in range(time_series_number):\n", - " X = pd.DataFrame(\n", - " {\n", - " time_column_name: pd.date_range(\n", - " start=\"2000-01-01\", periods=data_length, freq=freq\n", - " ),\n", - " target_column_name: np.arange(data_length).astype(float)\n", - " + np.random.rand(data_length)\n", - " + i * 5,\n", - " \"ext_predictor\": np.asarray(range(42, 42 + data_length)),\n", - " time_series_id_column_name: np.repeat(\"ts{}\".format(i), data_length),\n", - " }\n", - " )\n", - " data_train.append(X[:train_len])\n", - " data_test.append(X[train_len:])\n", - " train = pd.concat(data_train)\n", - " label_train = train.pop(target_column_name).values\n", - " test = pd.concat(data_test)\n", - " label_test = test.pop(target_column_name).values\n", - " return train, label_train, test, label_test\n", - "\n", - "\n", - "n_test_periods = FORECAST_HORIZON\n", - "n_train_periods = 365\n", - "X_train, y_train, X_test, y_test = simulate_timeseries_data(\n", - " train_len=n_train_periods,\n", - " test_len=n_test_periods,\n", - " time_column_name=TIME_COLUMN_NAME,\n", - " target_column_name=LABEL_COLUMN_NAME,\n", - " time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAMES,\n", - " time_series_number=2,\n", - " freq=FREQUENCY,\n", - ")\n", - "X_train[LABEL_COLUMN_NAME] = y_train" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see what the training data looks like." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_train.tail()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare remote compute and data. \n", - "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace), is paired with the storage account, which contains the default data store. We will use it to upload the artificial data and create [tabular dataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.data.dataset_factory import TabularDatasetFactory\n", - "\n", - "ds = ws.get_default_datastore()\n", - "# Upload saved data to the default data store.\n", - "train_data = TabularDatasetFactory.register_pandas_dataframe(\n", - " X_train, target=(ds, \"data\"), name=\"data_backtest\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will need to create a compute target for backtesting. In this [tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute), you create AmlCompute as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "amlcompute_cluster_name = \"backtest-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n", - " )\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the configuration for AutoML backtesting \n", - "\n", - "This dictionary defines the AutoML and many models settings. For this forecasting task we need to define several settings including the name of the time column, the maximum forecast horizon, and the partition column name definition.\n", - "\n", - "| Property | Description|\n", - "| :--------------- | :------------------- |\n", - "| **task** | forecasting |\n", - "| **primary_metric** | This is the metric that you want to optimize.
Forecasting supports the following primary metrics
normalized_root_mean_squared_error
normalized_mean_absolute_error |\n", - "| **iteration_timeout_minutes** | Maximum amount of time in minutes that the model can train. This is optional but provides customers with greater control on exit criteria. |\n", - "| **iterations** | Number of models to train. This is optional but provides customers with greater control on exit criteria. |\n", - "| **experiment_timeout_hours** | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n", - "| **label_column_name** | The name of the label column. |\n", - "| **max_horizon** | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n", - "| **n_cross_validations** | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n", - "| **time_column_name** | The name of your time column. |\n", - "| **grain_column_names** | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"task\": \"forecasting\",\n", - " \"primary_metric\": \"normalized_root_mean_squared_error\",\n", - " \"iteration_timeout_minutes\": 10, # This needs to be changed based on the dataset. We ask customer to explore how long training is taking before settings this value\n", - " \"iterations\": 15,\n", - " \"experiment_timeout_hours\": 1, # This also needs to be changed based on the dataset. For larger data set this number needs to be bigger.\n", - " \"label_column_name\": LABEL_COLUMN_NAME,\n", - " \"n_cross_validations\": 3,\n", - " \"time_column_name\": TIME_COLUMN_NAME,\n", - " \"max_horizon\": FORECAST_HORIZON,\n", - " \"track_child_runs\": False,\n", - " \"grain_column_names\": TIME_SERIES_ID_COLUMN_NAMES,\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Backtest AutoML \n", - "First we set backtesting parameters: we will step back by 30 days and will make 5 such steps; for each step we will forecast for next two weeks." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The number of periods to step back on each backtest iteration.\n", - "BACKTESTING_PERIOD = 30\n", - "# The number of times we will back test the model.\n", - "NUMBER_OF_BACKTESTS = 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To train AutoML on backtesting folds we will use the [Azure Machine Learning pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines). It will generate backtest folds, then train model for each of them and calculate the accuracy metrics. To run pipeline, you also need to create an Experiment. An Experiment corresponds to a prediction problem you are trying to solve (here, it is a forecasting), while a Run corresponds to a specific approach to the problem." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from uuid import uuid1\n", - "\n", - "from pipeline_helper import get_backtest_pipeline\n", - "\n", - "pipeline_exp = Experiment(ws, \"automl-backtesting\")\n", - "\n", - "# We will create the unique identifier to mark our models.\n", - "model_uid = str(uuid1())\n", - "\n", - "pipeline = get_backtest_pipeline(\n", - " experiment=pipeline_exp,\n", - " dataset=train_data,\n", - " # The STANDARD_DS12_V2 has 4 vCPU per node, we will set 2 process per node to be safe.\n", - " process_per_node=2,\n", - " # The maximum number of nodes for our compute is 6.\n", - " node_count=6,\n", - " compute_target=compute_target,\n", - " automl_settings=automl_settings,\n", - " step_size=BACKTESTING_PERIOD,\n", - " step_number=NUMBER_OF_BACKTESTS,\n", - " model_uid=model_uid,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the pipeline and wait for results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run = pipeline_exp.submit(pipeline)\n", - "pipeline_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the run is complete, we can download the results. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metrics_output = pipeline_run.get_pipeline_output(\"results\")\n", - "metrics_output.download(\"backtest_metrics\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View metrics\n", - "To distinguish these metrics from the model backtest, which we will obtain in the next section, we will move the directory with metrics out of the backtest_metrics and will remove the parent folder. We will create the utility function for that." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def copy_scoring_directory(new_name):\n", - " scores_path = os.path.join(\"backtest_metrics\", \"azureml\")\n", - " directory_list = [os.path.join(scores_path, d) for d in os.listdir(scores_path)]\n", - " latest_file = max(directory_list, key=os.path.getctime)\n", - " print(\n", - " f\"The output directory {latest_file} was created on {pd.Timestamp(os.path.getctime(latest_file), unit='s')} GMT.\"\n", - " )\n", - " shutil.move(os.path.join(latest_file, \"results\"), new_name)\n", - " shutil.rmtree(\"backtest_metrics\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Move the directory and list its contents." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "copy_scoring_directory(\"automl_backtest\")\n", - "pd.DataFrame({\"File\": os.listdir(\"automl_backtest\")})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The directory contains a set of files with results:\n", - "- forecast.csv contains forecasts for all backtest iterations. The backtest_iteration column contains iteration identifier with the last training date as a suffix\n", - "- scores.csv contains all metrics. If data set contains several time series, the metrics are given for all combinations of time series id and iterations, as well as scores for all iterations and time series id are marked as \"all_sets\"\n", - "- plots_fcst_vs_actual.pdf contains the predictions vs forecast plots for each iteration and time series.\n", - "\n", - "For demonstration purposes we will display the table of metrics for one of the time series with ID \"ts0\". Again, we will create the utility function, which will be re used in model backtesting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_metrics_for_ts(all_metrics, ts):\n", - " \"\"\"\n", - " Get the metrics for the time series with ID ts and return it as pandas data frame.\n", - "\n", - " :param all_metrics: The table with all the metrics.\n", - " :param ts: The ID of a time series of interest.\n", - " :return: The pandas DataFrame with metrics for one time series.\n", - " \"\"\"\n", - " results_df = None\n", - " for ts_id, one_series in all_metrics.groupby(\"time_series_id\"):\n", - " if not ts_id.startswith(ts):\n", - " continue\n", - " iteration = ts_id.split(\"|\")[-1]\n", - " df = one_series[[\"metric_name\", \"metric\"]]\n", - " df.rename({\"metric\": iteration}, axis=1, inplace=True)\n", - " df.set_index(\"metric_name\", inplace=True)\n", - " if results_df is None:\n", - " results_df = df\n", - " else:\n", - " results_df = results_df.merge(\n", - " df, how=\"inner\", left_index=True, right_index=True\n", - " )\n", - " results_df.sort_index(axis=1, inplace=True)\n", - " return results_df\n", - "\n", - "\n", - "metrics_df = pd.read_csv(os.path.join(\"automl_backtest\", \"scores.csv\"))\n", - "ts_id = \"ts0\"\n", - "get_metrics_for_ts(metrics_df, ts_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Forecast vs actuals plots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import IFrame\n", - "\n", - "IFrame(\"./automl_backtest/plots_fcst_vs_actual.pdf\", width=800, height=300)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Backtest the best model \n", - "\n", - "For model backtesting we will use the same parameters we used to backtest AutoML. All the models, we have obtained in the previous run were registered in our workspace. To identify the model, each was assigned a tag with the last trainig date." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_list = Model.list(ws, tags={\"experiment\": \"automl-backtesting\"})\n", - "model_data = {\"name\": [], \"last_training_date\": []}\n", - "for model in model_list:\n", - " if (\n", - " \"last_training_date\" not in model.tags\n", - " or \"model_uid\" not in model.tags\n", - " or model.tags[\"model_uid\"] != model_uid\n", - " ):\n", - " continue\n", - " model_data[\"name\"].append(model.name)\n", - " model_data[\"last_training_date\"].append(\n", - " pd.Timestamp(model.tags[\"last_training_date\"])\n", - " )\n", - "df_models = pd.DataFrame(model_data)\n", - "df_models.sort_values([\"last_training_date\"], inplace=True)\n", - "df_models.reset_index(inplace=True, drop=True)\n", - "df_models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will backtest the model trained on the most recet data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_name = df_models[\"name\"].iloc[-1]\n", - "model_name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrain the models.\n", - "Assemble the pipeline, which will retrain the best model from AutoML run on historical data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_exp = Experiment(ws, \"model-backtesting\")\n", - "\n", - "pipeline = get_backtest_pipeline(\n", - " experiment=pipeline_exp,\n", - " dataset=train_data,\n", - " # The STANDARD_DS12_V2 has 4 vCPU per node, we will set 2 process per node to be safe.\n", - " process_per_node=2,\n", - " # The maximum number of nodes for our compute is 6.\n", - " node_count=6,\n", - " compute_target=compute_target,\n", - " automl_settings=automl_settings,\n", - " step_size=BACKTESTING_PERIOD,\n", - " step_number=NUMBER_OF_BACKTESTS,\n", - " model_name=model_name,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Launch the backtesting pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run = pipeline_exp.submit(pipeline)\n", - "pipeline_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The metrics are stored in the pipeline output named \"score\". The next code will download the table with metrics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metrics_output = pipeline_run.get_pipeline_output(\"results\")\n", - "metrics_output.download(\"backtest_metrics\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, we will copy the data files from the downloaded directory, but in this case we will call the folder \"model_backtest\"; it will contain the same files as the one for AutoML backtesting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "copy_scoring_directory(\"model_backtest\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we will display the metrics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_metrics_df = pd.read_csv(os.path.join(\"model_backtest\", \"scores.csv\"))\n", - "get_metrics_for_ts(model_metrics_df, ts_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Forecast vs actuals plots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import IFrame\n", - "\n", - "IFrame(\"./model_backtest/plots_fcst_vs_actual.pdf\", width=800, height=300)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jialiu" - } - ], - "category": "tutorial", - "compute": [ - "Remote" - ], - "datasets": [ - "None" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML AutoML" - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.yml b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.yml deleted file mode 100644 index 6871fafb4..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-backtest-single-model -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/pipeline_helper.py b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/pipeline_helper.py deleted file mode 100644 index c00793437..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/pipeline_helper.py +++ /dev/null @@ -1,166 +0,0 @@ -from typing import Any, Dict, Optional - -import os - -import azureml.train.automl.runtime._hts.hts_runtime_utilities as hru - -from azureml._restclient.jasmine_client import JasmineClient -from azureml.contrib.automl.pipeline.steps import utilities -from azureml.core import RunConfiguration -from azureml.core.compute import ComputeTarget -from azureml.core.experiment import Experiment -from azureml.data import LinkTabularOutputDatasetConfig, TabularDataset -from azureml.pipeline.core import Pipeline, PipelineData, PipelineParameter -from azureml.pipeline.steps import ParallelRunConfig, ParallelRunStep, PythonScriptStep -from azureml.train.automl.constants import Scenarios -from azureml.data.dataset_consumption_config import DatasetConsumptionConfig - - -PROJECT_FOLDER = "assets" -SETTINGS_FILE = "automl_settings.json" - - -def get_backtest_pipeline( - experiment: Experiment, - dataset: TabularDataset, - process_per_node: int, - node_count: int, - compute_target: ComputeTarget, - automl_settings: Dict[str, Any], - step_size: int, - step_number: int, - model_name: Optional[str] = None, - model_uid: Optional[str] = None, -) -> Pipeline: - """ - :param experiment: The experiment used to run the pipeline. - :param dataset: Tabular data set to be used for model training. - :param process_per_node: The number of processes per node. Generally it should be the number of cores - on the node divided by two. - :param node_count: The number of nodes to be used. - :param compute_target: The compute target to be used to run the pipeline. - :param model_name: The name of a model to be back tested. - :param automl_settings: The dictionary with automl settings. - :param step_size: The number of periods to step back in backtesting. - :param step_number: The number of backtesting iterations. - :param model_uid: The uid to mark models from this run of the experiment. - :return: The pipeline to be used for model retraining. - **Note:** The output will be uploaded in the pipeline output - called 'score'. - """ - jasmine_client = JasmineClient( - service_context=experiment.workspace.service_context, - experiment_name=experiment.name, - experiment_id=experiment.id, - ) - env = jasmine_client.get_curated_environment( - scenario=Scenarios.AUTOML, - enable_dnn=False, - enable_gpu=False, - compute=compute_target, - compute_sku=experiment.workspace.compute_targets.get( - compute_target.name - ).vm_size, - ) - data_results = PipelineData( - name="results", datastore=None, pipeline_output_name="results" - ) - ############################################################ - # Split the data set using python script. - ############################################################ - run_config = RunConfiguration() - run_config.docker.use_docker = True - run_config.environment = env - - split_data = PipelineData(name="split_data_output", datastore=None).as_dataset() - split_step = PythonScriptStep( - name="split_data_for_backtest", - script_name="data_split.py", - inputs=[dataset.as_named_input("training_data")], - outputs=[split_data], - source_directory=PROJECT_FOLDER, - arguments=[ - "--step-size", - step_size, - "--step-number", - step_number, - "--time-column-name", - automl_settings.get("time_column_name"), - "--time-series-id-column-names", - automl_settings.get("grain_column_names"), - "--output-dir", - split_data, - ], - runconfig=run_config, - compute_target=compute_target, - allow_reuse=False, - ) - ############################################################ - # We will do the backtest the parallel run step. - ############################################################ - settings_path = os.path.join(PROJECT_FOLDER, SETTINGS_FILE) - hru.dump_object_to_json(automl_settings, settings_path) - mini_batch_size = PipelineParameter(name="batch_size_param", default_value=str(1)) - back_test_config = ParallelRunConfig( - source_directory=PROJECT_FOLDER, - entry_script="retrain_models.py", - mini_batch_size=mini_batch_size, - error_threshold=-1, - output_action="append_row", - append_row_file_name="outputs.txt", - compute_target=compute_target, - environment=env, - process_count_per_node=process_per_node, - run_invocation_timeout=3600, - node_count=node_count, - ) - forecasts = PipelineData(name="forecasts", datastore=None) - if model_name: - parallel_step_name = "{}-backtest".format(model_name.replace("_", "-")) - else: - parallel_step_name = "AutoML-backtest" - - prs_args = [ - "--target_column_name", - automl_settings.get("label_column_name"), - "--output-dir", - forecasts, - ] - if model_name is not None: - prs_args.append("--model-name") - prs_args.append(model_name) - if model_uid is not None: - prs_args.append("--model-uid") - prs_args.append(model_uid) - backtest_prs = ParallelRunStep( - name=parallel_step_name, - parallel_run_config=back_test_config, - arguments=prs_args, - inputs=[split_data], - output=forecasts, - allow_reuse=False, - ) - ############################################################ - # Then we collect the output and return it as scores output. - ############################################################ - collection_step = PythonScriptStep( - name="score", - script_name="score.py", - inputs=[forecasts.as_mount()], - outputs=[data_results], - source_directory=PROJECT_FOLDER, - arguments=[ - "--forecasts", - forecasts, - "--output-dir", - data_results, - ], - runconfig=run_config, - compute_target=compute_target, - allow_reuse=False, - ) - # Build and return the pipeline. - return Pipeline( - workspace=experiment.workspace, - steps=[split_step, backtest_prs, collection_step], - ) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb deleted file mode 100644 index 37a62e07f..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb +++ /dev/null @@ -1,725 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "**BikeShare Demand Forecasting**\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Compute](#Compute)\n", - "1. [Data](#Data)\n", - "1. [Train](#Train)\n", - "1. [Featurization](#Featurization)\n", - "1. [Evaluate](#Evaluate)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "This notebook demonstrates demand forecasting for a bike-sharing service using AutoML.\n", - "\n", - "AutoML highlights here include built-in holiday featurization, accessing engineered feature names, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n", - "\n", - "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n", - "\n", - "Notebook synopsis:\n", - "1. Creating an Experiment in an existing Workspace\n", - "2. Configuration and local run of AutoML for a time-series model with lag and holiday features \n", - "3. Viewing the engineered names for featurized data and featurization summary for all raw features\n", - "4. Evaluating the fitted model using a rolling test " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import logging\n", - "from datetime import datetime\n", - "\n", - "import azureml.core\n", - "import numpy as np\n", - "import pandas as pd\n", - "from azureml.automl.core.featurization import FeaturizationConfig\n", - "from azureml.core import Dataset, Experiment, Workspace\n", - "from azureml.train.automl import AutoMLConfig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is compatible with Azure ML SDK version 1.35.0 or later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As part of the setup you have already created a Workspace. To run AutoML, you also need to create an Experiment. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# choose a name for the run history container in the workspace\n", - "experiment_name = \"automl-bikeshareforecasting\"\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"SKU\"] = ws.sku\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Run History Name\"] = experiment_name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute\n", - "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "#### Creation of AmlCompute takes approximately 5 minutes. \n", - "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your cluster.\n", - "amlcompute_cluster_name = \"bike-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n", - " )\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n", - "\n", - "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace) is paired with the storage account, which contains the default data store. We will use it to upload the bike share data and create [tabular dataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore = ws.get_default_datastore()\n", - "datastore.upload_files(\n", - " files=[\"./bike-no.csv\"], target_path=\"dataset/\", overwrite=True, show_progress=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's set up what we know about the dataset. \n", - "\n", - "**Target column** is what we want to forecast.\n", - "\n", - "**Time column** is the time axis along which to predict." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = \"cnt\"\n", - "time_column_name = \"date\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = Dataset.Tabular.from_delimited_files(\n", - " path=[(datastore, \"dataset/bike-no.csv\")]\n", - ").with_timestamp_columns(fine_grain_timestamp=time_column_name)\n", - "\n", - "# Drop the columns 'casual' and 'registered' as these columns are a breakdown of the total and therefore a leak.\n", - "dataset = dataset.drop_columns(columns=[\"casual\", \"registered\"])\n", - "\n", - "dataset.take(5).to_pandas_dataframe().reset_index(drop=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Split the data\n", - "\n", - "The first split we make is into train and test sets. Note we are splitting on time. Data before 9/1 will be used for training, and data after and including 9/1 will be used for testing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# select data that occurs before a specified date\n", - "train = dataset.time_before(datetime(2012, 8, 31), include_boundary=True)\n", - "train.to_pandas_dataframe().tail(5).reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test = dataset.time_after(datetime(2012, 9, 1), include_boundary=True)\n", - "test.to_pandas_dataframe().head(5).reset_index(drop=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Forecasting Parameters\n", - "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**time_column_name**|The name of your time column.|\n", - "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n", - "|**country_or_region_for_holidays**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n", - "|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n", - "|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "\n", - "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|forecasting|\n", - "|**primary_metric**|This is the metric that you want to optimize.
Forecasting supports the following primary metrics
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error\n", - "|**blocked_models**|Models in blocked_models won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n", - "|**experiment_timeout_hours**|Experimentation timeout in hours.|\n", - "|**training_data**|Input dataset, containing both features and label column.|\n", - "|**label_column_name**|The name of the label column.|\n", - "|**compute_target**|The remote compute for training.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**enable_early_stopping**|If early stopping is on, training will stop when the primary metric is no longer improving.|\n", - "|**forecasting_parameters**|A class that holds all the forecasting related parameters.|\n", - "\n", - "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting forecaster maximum horizon \n", - "\n", - "The forecast horizon is the number of periods into the future that the model should predict. Here, we set the horizon to 14 periods (i.e. 14 days). Notice that this is much shorter than the number of days in the test set; we will need to use a rolling test to evaluate the performance on the whole test set. For more discussion of forecast horizons and guiding principles for setting them, please see the [energy demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "forecast_horizon = 14" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert prediction type to integer\n", - "The featurization configuration can be used to change the default prediction type from decimal numbers to integer. This customization can be used in the scenario when the target column is expected to contain whole values as the number of rented bikes per day." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "featurization_config = FeaturizationConfig()\n", - "# Force the target column, to be integer type.\n", - "featurization_config.add_prediction_transform_type(\"Integer\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Config AutoML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n", - "\n", - "forecasting_parameters = ForecastingParameters(\n", - " time_column_name=time_column_name,\n", - " forecast_horizon=forecast_horizon,\n", - " country_or_region_for_holidays=\"US\", # set country_or_region will trigger holiday featurizer\n", - " target_lags=\"auto\", # use heuristic based lag setting\n", - " freq=\"D\", # Set the forecast frequency to be daily\n", - ")\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"forecasting\",\n", - " primary_metric=\"normalized_root_mean_squared_error\",\n", - " featurization=featurization_config,\n", - " blocked_models=[\"ExtremeRandomTrees\"],\n", - " experiment_timeout_hours=0.3,\n", - " training_data=train,\n", - " label_column_name=target_column_name,\n", - " compute_target=compute_target,\n", - " enable_early_stopping=True,\n", - " n_cross_validations=3,\n", - " max_concurrent_iterations=4,\n", - " max_cores_per_iteration=-1,\n", - " verbosity=logging.INFO,\n", - " forecasting_parameters=forecasting_parameters,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now run the experiment, you can go to Azure ML portal to view the run details. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve the Best Run details\n", - "Below we retrieve the best Run object from among all the runs in the experiment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = remote_run.get_best_child()\n", - "best_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Featurization\n", - "\n", - "We can look at the engineered feature names generated in time-series featurization via. the JSON file named 'engineered_feature_names.json' under the run outputs. Note that a number of named holiday periods are represented. We recommend that you have at least one year of data when using this feature to ensure that all yearly holidays are captured in the training featurization." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download the JSON file locally\n", - "best_run.download_file(\n", - " \"outputs/engineered_feature_names.json\", \"engineered_feature_names.json\"\n", - ")\n", - "with open(\"engineered_feature_names.json\", \"r\") as f:\n", - " records = json.load(f)\n", - "\n", - "records" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View the featurization summary\n", - "\n", - "You can also see what featurization steps were performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:\n", - "\n", - "- Raw feature name\n", - "- Number of engineered features formed out of this raw feature\n", - "- Type detected\n", - "- If feature was dropped\n", - "- List of feature transformations for the raw feature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download the featurization summary JSON file locally\n", - "best_run.download_file(\n", - " \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n", - ")\n", - "\n", - "# Render the JSON as a pandas DataFrame\n", - "with open(\"featurization_summary.json\", \"r\") as f:\n", - " records = json.load(f)\n", - "fs = pd.DataFrame.from_records(records)\n", - "\n", - "# View a summary of the featurization\n", - "fs[\n", - " [\n", - " \"RawFeatureName\",\n", - " \"TypeDetected\",\n", - " \"Dropped\",\n", - " \"EngineeredFeatureCount\",\n", - " \"Transformations\",\n", - " ]\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now use the best fitted model from the AutoML Run to make forecasts for the test set. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n", - "\n", - "The scoring will run on a remote compute. In this example, it will reuse the training compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_experiment = Experiment(ws, experiment_name + \"_test\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieving forecasts from the model\n", - "To run the forecast on the remote compute we will use a helper script: forecasting_script. This script contains the utility methods which will be used by the remote estimator. We copy the script to the project folder to upload it to remote compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import shutil\n", - "\n", - "script_folder = os.path.join(os.getcwd(), \"forecast\")\n", - "os.makedirs(script_folder, exist_ok=True)\n", - "shutil.copy(\"forecasting_script.py\", script_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For brevity, we have created a function called run_forecast that submits the test data to the best model determined during the training run and retrieves forecasts. The test set is longer than the forecast horizon specified at train time, so the forecasting script uses a so-called rolling evaluation to generate predictions over the whole test set. A rolling evaluation iterates the forecaster over the test set, using the actuals in the test set to make lag features as needed. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from run_forecast import run_rolling_forecast\n", - "\n", - "remote_run = run_rolling_forecast(\n", - " test_experiment, compute_target, best_run, test, target_column_name\n", - ")\n", - "remote_run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download the prediction result for metrics calculation\n", - "The test data with predictions are saved in artifact outputs/predictions.csv. You can download it and calculation some error metrics for the forecasts and vizualize the predictions vs. the actuals." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.download_file(\"outputs/predictions.csv\", \"predictions.csv\")\n", - "df_all = pd.read_csv(\"predictions.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.shared import constants\n", - "from azureml.automl.runtime.shared.score import scoring\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", - "from matplotlib import pyplot as plt\n", - "\n", - "# use automl metrics module\n", - "scores = scoring.score_regression(\n", - " y_test=df_all[target_column_name],\n", - " y_pred=df_all[\"predicted\"],\n", - " metrics=list(constants.Metric.SCALAR_REGRESSION_SET),\n", - ")\n", - "\n", - "print(\"[Test data scores]\\n\")\n", - "for key, value in scores.items():\n", - " print(\"{}: {:.3f}\".format(key, value))\n", - "\n", - "# Plot outputs\n", - "%matplotlib inline\n", - "test_pred = plt.scatter(df_all[target_column_name], df_all[\"predicted\"], color=\"b\")\n", - "test_test = plt.scatter(\n", - " df_all[target_column_name], df_all[target_column_name], color=\"g\"\n", - ")\n", - "plt.legend(\n", - " (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For more details on what metrics are included and how they are calculated, please refer to [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics). You could also calculate residuals, like described [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n", - "\n", - "\n", - "Since we did a rolling evaluation on the test set, we can analyze the predictions by their forecast horizon relative to the rolling origin. The model was initially trained at a forecast horizon of 14, so each prediction from the model is associated with a horizon value from 1 to 14. The horizon values are in a column named, \"horizon_origin,\" in the prediction set. For example, we can calculate some of the error metrics grouped by the horizon:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from metrics_helper import MAPE, APE\n", - "\n", - "df_all.groupby(\"horizon_origin\").apply(\n", - " lambda df: pd.Series(\n", - " {\n", - " \"MAPE\": MAPE(df[target_column_name], df[\"predicted\"]),\n", - " \"RMSE\": np.sqrt(\n", - " mean_squared_error(df[target_column_name], df[\"predicted\"])\n", - " ),\n", - " \"MAE\": mean_absolute_error(df[target_column_name], df[\"predicted\"]),\n", - " }\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To drill down more, we can look at the distributions of APE (absolute percentage error) by horizon. From the chart, it is clear that the overall MAPE is being skewed by one particular point where the actual value is of small absolute value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all[\"predicted\"]))\n", - "APEs = [\n", - " df_all_APE[df_all[\"horizon_origin\"] == h].APE.values\n", - " for h in range(1, forecast_horizon + 1)\n", - "]\n", - "\n", - "%matplotlib inline\n", - "plt.boxplot(APEs)\n", - "plt.yscale(\"log\")\n", - "plt.xlabel(\"horizon\")\n", - "plt.ylabel(\"APE (%)\")\n", - "plt.title(\"Absolute Percentage Errors by Forecast Horizon\")\n", - "\n", - "plt.show()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jialiu" - } - ], - "category": "tutorial", - "compute": [ - "Remote" - ], - "datasets": [ - "BikeShare" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "file_extension": ".py", - "framework": [ - "Azure ML AutoML" - ], - "friendly_name": "Forecasting BikeShare Demand", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "mimetype": "text/x-python", - "name": "python", - "npconvert_exporter": "python", - "pygments_lexer": "ipython3", - "tags": [ - "Forecasting" - ], - "task": "Forecasting", - "version": 3 - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml deleted file mode 100644 index 70a3271c4..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-bike-share -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv deleted file mode 100644 index ded279501..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv +++ /dev/null @@ -1,732 +0,0 @@ -instant,date,season,yr,mnth,weekday,weathersit,temp,atemp,hum,windspeed,casual,registered,cnt -1,1/1/2011,1,0,1,6,2,0.344167,0.363625,0.805833,0.160446,331,654,985 -2,1/2/2011,1,0,1,0,2,0.363478,0.353739,0.696087,0.248539,131,670,801 -3,1/3/2011,1,0,1,1,1,0.196364,0.189405,0.437273,0.248309,120,1229,1349 -4,1/4/2011,1,0,1,2,1,0.2,0.212122,0.590435,0.160296,108,1454,1562 -5,1/5/2011,1,0,1,3,1,0.226957,0.22927,0.436957,0.1869,82,1518,1600 -6,1/6/2011,1,0,1,4,1,0.204348,0.233209,0.518261,0.0895652,88,1518,1606 -7,1/7/2011,1,0,1,5,2,0.196522,0.208839,0.498696,0.168726,148,1362,1510 -8,1/8/2011,1,0,1,6,2,0.165,0.162254,0.535833,0.266804,68,891,959 -9,1/9/2011,1,0,1,0,1,0.138333,0.116175,0.434167,0.36195,54,768,822 -10,1/10/2011,1,0,1,1,1,0.150833,0.150888,0.482917,0.223267,41,1280,1321 -11,1/11/2011,1,0,1,2,2,0.169091,0.191464,0.686364,0.122132,43,1220,1263 -12,1/12/2011,1,0,1,3,1,0.172727,0.160473,0.599545,0.304627,25,1137,1162 -13,1/13/2011,1,0,1,4,1,0.165,0.150883,0.470417,0.301,38,1368,1406 -14,1/14/2011,1,0,1,5,1,0.16087,0.188413,0.537826,0.126548,54,1367,1421 -15,1/15/2011,1,0,1,6,2,0.233333,0.248112,0.49875,0.157963,222,1026,1248 -16,1/16/2011,1,0,1,0,1,0.231667,0.234217,0.48375,0.188433,251,953,1204 -17,1/17/2011,1,0,1,1,2,0.175833,0.176771,0.5375,0.194017,117,883,1000 -18,1/18/2011,1,0,1,2,2,0.216667,0.232333,0.861667,0.146775,9,674,683 -19,1/19/2011,1,0,1,3,2,0.292174,0.298422,0.741739,0.208317,78,1572,1650 -20,1/20/2011,1,0,1,4,2,0.261667,0.25505,0.538333,0.195904,83,1844,1927 -21,1/21/2011,1,0,1,5,1,0.1775,0.157833,0.457083,0.353242,75,1468,1543 -22,1/22/2011,1,0,1,6,1,0.0591304,0.0790696,0.4,0.17197,93,888,981 -23,1/23/2011,1,0,1,0,1,0.0965217,0.0988391,0.436522,0.2466,150,836,986 -24,1/24/2011,1,0,1,1,1,0.0973913,0.11793,0.491739,0.15833,86,1330,1416 -25,1/25/2011,1,0,1,2,2,0.223478,0.234526,0.616957,0.129796,186,1799,1985 -26,1/26/2011,1,0,1,3,3,0.2175,0.2036,0.8625,0.29385,34,472,506 -27,1/27/2011,1,0,1,4,1,0.195,0.2197,0.6875,0.113837,15,416,431 -28,1/28/2011,1,0,1,5,2,0.203478,0.223317,0.793043,0.1233,38,1129,1167 -29,1/29/2011,1,0,1,6,1,0.196522,0.212126,0.651739,0.145365,123,975,1098 -30,1/30/2011,1,0,1,0,1,0.216522,0.250322,0.722174,0.0739826,140,956,1096 -31,1/31/2011,1,0,1,1,2,0.180833,0.18625,0.60375,0.187192,42,1459,1501 -32,2/1/2011,1,0,2,2,2,0.192174,0.23453,0.829565,0.053213,47,1313,1360 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-parser = argparse.ArgumentParser() -parser.add_argument( - "--target_column_name", - type=str, - dest="target_column_name", - help="Target Column Name", -) -parser.add_argument( - "--test_dataset", type=str, dest="test_dataset", help="Test Dataset" -) - -args = parser.parse_args() -target_column_name = args.target_column_name -test_dataset_id = args.test_dataset - -run = Run.get_context() -ws = run.experiment.workspace - -# get the input dataset by id -test_dataset = Dataset.get_by_id(ws, id=test_dataset_id) - -X_test_df = ( - test_dataset.drop_columns(columns=[target_column_name]) - .to_pandas_dataframe() - .reset_index(drop=True) -) -y_test_df = ( - test_dataset.with_timestamp_columns(None) - .keep_columns(columns=[target_column_name]) - .to_pandas_dataframe() -) - -fitted_model = joblib.load("model.pkl") - -y_pred, X_trans = fitted_model.rolling_evaluation(X_test_df, y_test_df.values) - -# Add predictions, actuals, and horizon relative to rolling origin to the test feature data -assign_dict = { - "horizon_origin": X_trans["horizon_origin"].values, - "predicted": y_pred, - target_column_name: y_test_df[target_column_name].values, -} -df_all = X_test_df.assign(**assign_dict) - -file_name = "outputs/predictions.csv" -export_csv = df_all.to_csv(file_name, header=True) - -# Upload the predictions into artifacts -run.upload_file(name=file_name, path_or_stream=file_name) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/metrics_helper.py b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/metrics_helper.py deleted file mode 100644 index d6db2f420..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/metrics_helper.py +++ /dev/null @@ -1,22 +0,0 @@ -import pandas as pd -import numpy as np - - -def APE(actual, pred): - """ - Calculate absolute percentage error. - Returns a vector of APE values with same length as actual/pred. - """ - return 100 * np.abs((actual - pred) / actual) - - -def MAPE(actual, pred): - """ - Calculate mean absolute percentage error. - Remove NA and values where actual is close to zero - """ - not_na = ~(np.isnan(actual) | np.isnan(pred)) - not_zero = ~np.isclose(actual, 0.0) - actual_safe = actual[not_na & not_zero] - pred_safe = pred[not_na & not_zero] - return np.mean(APE(actual_safe, pred_safe)) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/run_forecast.py b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/run_forecast.py deleted file mode 100644 index 614d075a7..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/run_forecast.py +++ /dev/null @@ -1,40 +0,0 @@ -from azureml.core import ScriptRunConfig - - -def run_rolling_forecast( - test_experiment, - compute_target, - train_run, - test_dataset, - target_column_name, - inference_folder="./forecast", -): - train_run.download_file("outputs/model.pkl", inference_folder + "/model.pkl") - - inference_env = train_run.get_environment() - - config = ScriptRunConfig( - source_directory=inference_folder, - script="forecasting_script.py", - arguments=[ - "--target_column_name", - target_column_name, - "--test_dataset", - test_dataset.as_named_input(test_dataset.name), - ], - compute_target=compute_target, - environment=inference_env, - ) - - run = test_experiment.submit( - config, - tags={ - "training_run_id": train_run.id, - "run_algorithm": train_run.properties["run_algorithm"], - "valid_score": train_run.properties["score"], - "primary_metric": train_run.properties["primary_metric"], - }, - ) - - run.log("run_algorithm", run.tags["run_algorithm"]) - return run diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb deleted file mode 100644 index 9d632b646..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb +++ /dev/null @@ -1,785 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Forecasting using the Energy Demand Dataset**_\n", - "\n", - "## Contents\n", - "1. [Introduction](#introduction)\n", - "1. [Setup](#setup)\n", - "1. [Data and Forecasting Configurations](#data)\n", - "1. [Train](#train)\n", - "1. [Generate and Evaluate the Forecast](#forecast)\n", - "\n", - "Advanced Forecasting\n", - "1. [Advanced Training](#advanced_training)\n", - "1. [Advanced Results](#advanced_results)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction\n", - "\n", - "In this example we use the associated New York City energy demand dataset to showcase how you can use AutoML for a simple forecasting problem and explore the results. The goal is predict the energy demand for the next 48 hours based on historic time-series data.\n", - "\n", - "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first, if you haven't already, to establish your connection to the AzureML Workspace.\n", - "\n", - "In this notebook you will learn how to:\n", - "1. Creating an Experiment using an existing Workspace\n", - "1. Configure AutoML using 'AutoMLConfig'\n", - "1. Train the model using AmlCompute\n", - "1. Explore the engineered features and results\n", - "1. Generate the forecast and compute the out-of-sample accuracy metrics\n", - "1. Configuration and remote run of AutoML for a time-series model with lag and rolling window features\n", - "1. Run and explore the forecast with lagging features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import logging\n", - "\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from matplotlib import pyplot as plt\n", - "import pandas as pd\n", - "import numpy as np\n", - "import warnings\n", - "import os\n", - "\n", - "# Squash warning messages for cleaner output in the notebook\n", - "warnings.showwarning = lambda *args, **kwargs: None\n", - "\n", - "import azureml.core\n", - "from azureml.core import Experiment, Workspace, Dataset\n", - "from azureml.train.automl import AutoMLConfig\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is compatible with Azure ML SDK version 1.35.0 or later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# choose a name for the run history container in the workspace\n", - "experiment_name = \"automl-forecasting-energydemand\"\n", - "\n", - "# # project folder\n", - "# project_folder = './sample_projects/automl-forecasting-energy-demand'\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Run History Name\"] = experiment_name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "A compute target is required to execute a remote Automated ML run. \n", - "\n", - "[Azure Machine Learning Compute](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) is a managed-compute infrastructure that allows the user to easily create a single or multi-node compute. In this tutorial, you create AmlCompute as your training compute resource.\n", - "\n", - "#### Creation of AmlCompute takes approximately 5 minutes. \n", - "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your cluster.\n", - "amlcompute_cluster_name = \"energy-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n", - " )\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data\n", - "\n", - "We will use energy consumption [data from New York City](http://mis.nyiso.com/public/P-58Blist.htm) for model training. The data is stored in a tabular format and includes energy demand and basic weather data at an hourly frequency. \n", - "\n", - "With Azure Machine Learning datasets you can keep a single copy of data in your storage, easily access data during model training, share data and collaborate with other users. Below, we will upload the datatset and create a [tabular dataset](https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/service/how-to-create-register-datasets#dataset-types) to be used training and prediction." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's set up what we know about the dataset.\n", - "\n", - "Target column is what we want to forecast.

\n", - "Time column is the time axis along which to predict.\n", - "\n", - "The other columns, \"temp\" and \"precip\", are implicitly designated as features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = \"demand\"\n", - "time_column_name = \"timeStamp\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = Dataset.Tabular.from_delimited_files(\n", - " path=\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/nyc_energy.csv\"\n", - ").with_timestamp_columns(fine_grain_timestamp=time_column_name)\n", - "dataset.take(5).to_pandas_dataframe().reset_index(drop=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The NYC Energy dataset is missing energy demand values for all datetimes later than August 10th, 2017 5AM. Below, we trim the rows containing these missing values from the end of the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Cut off the end of the dataset due to large number of nan values\n", - "dataset = dataset.time_before(datetime(2017, 10, 10, 5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Split the data into train and test sets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first split we make is into train and test sets. Note that we are splitting on time. Data before and including August 8th, 2017 5AM will be used for training, and data after will be used for testing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# split into train based on time\n", - "train = dataset.time_before(datetime(2017, 8, 8, 5), include_boundary=True)\n", - "train.to_pandas_dataframe().reset_index(drop=True).sort_values(time_column_name).tail(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# split into test based on time\n", - "test = dataset.time_between(datetime(2017, 8, 8, 6), datetime(2017, 8, 10, 5))\n", - "test.to_pandas_dataframe().reset_index(drop=True).head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting the maximum forecast horizon\n", - "\n", - "The forecast horizon is the number of periods into the future that the model should predict. It is generally recommend that users set forecast horizons to less than 100 time periods (i.e. less than 100 hours in the NYC energy example). Furthermore, **AutoML's memory use and computation time increase in proportion to the length of the horizon**, so consider carefully how this value is set. If a long horizon forecast really is necessary, consider aggregating the series to a coarser time scale. \n", - "\n", - "Learn more about forecast horizons in our [Auto-train a time-series forecast model](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-auto-train-forecast#configure-and-run-experiment) guide.\n", - "\n", - "In this example, we set the horizon to 48 hours." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "forecast_horizon = 48" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Forecasting Parameters\n", - "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**time_column_name**|The name of your time column.|\n", - "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n", - "|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train\n", - "\n", - "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the forecasting parameters to hold all the additional forecasting parameters.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|forecasting|\n", - "|**primary_metric**|This is the metric that you want to optimize.
Forecasting supports the following primary metrics
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error|\n", - "|**blocked_models**|Models in blocked_models won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n", - "|**experiment_timeout_hours**|Maximum amount of time in hours that the experiment take before it terminates.|\n", - "|**training_data**|The training data to be used within the experiment.|\n", - "|**label_column_name**|The name of the label column.|\n", - "|**compute_target**|The remote compute for training.|\n", - "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n", - "|**enable_early_stopping**|Flag to enble early termination if the score is not improving in the short term.|\n", - "|**forecasting_parameters**|A class holds all the forecasting related parameters.|\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n", - "\n", - "forecasting_parameters = ForecastingParameters(\n", - " time_column_name=time_column_name,\n", - " forecast_horizon=forecast_horizon,\n", - " freq=\"H\", # Set the forecast frequency to be hourly\n", - ")\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"forecasting\",\n", - " primary_metric=\"normalized_root_mean_squared_error\",\n", - " blocked_models=[\"ExtremeRandomTrees\", \"AutoArima\", \"Prophet\"],\n", - " experiment_timeout_hours=0.3,\n", - " training_data=train,\n", - " label_column_name=target_column_name,\n", - " compute_target=compute_target,\n", - " enable_early_stopping=True,\n", - " n_cross_validations=3,\n", - " verbosity=logging.INFO,\n", - " forecasting_parameters=forecasting_parameters,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while.\n", - "One may specify `show_output = True` to print currently running iterations to the console." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrieve the Best Run details\n", - "Below we retrieve the best Run object from among all the runs in the experiment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = remote_run.get_best_child()\n", - "best_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Featurization\n", - "We can look at the engineered feature names generated in time-series featurization via. the JSON file named 'engineered_feature_names.json' under the run outputs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download the JSON file locally\n", - "best_run.download_file(\n", - " \"outputs/engineered_feature_names.json\", \"engineered_feature_names.json\"\n", - ")\n", - "with open(\"engineered_feature_names.json\", \"r\") as f:\n", - " records = json.load(f)\n", - "\n", - "records" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View featurization summary\n", - "You can also see what featurization steps were performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:\n", - "\n", - "+ Raw feature name\n", - "+ Number of engineered features formed out of this raw feature\n", - "+ Type detected\n", - "+ If feature was dropped\n", - "+ List of feature transformations for the raw feature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download the featurization summary JSON file locally\n", - "best_run.download_file(\n", - " \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n", - ")\n", - "\n", - "# Render the JSON as a pandas DataFrame\n", - "with open(\"featurization_summary.json\", \"r\") as f:\n", - " records = json.load(f)\n", - "fs = pd.DataFrame.from_records(records)\n", - "\n", - "# View a summary of the featurization\n", - "fs[\n", - " [\n", - " \"RawFeatureName\",\n", - " \"TypeDetected\",\n", - " \"Dropped\",\n", - " \"EngineeredFeatureCount\",\n", - " \"Transformations\",\n", - " ]\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Forecasting\n", - "\n", - "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n", - "\n", - "The inference will run on a remote compute. In this example, it will re-use the training compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_experiment = Experiment(ws, experiment_name + \"_inference\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieving forecasts from the model\n", - "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from run_forecast import run_remote_inference\n", - "\n", - "remote_run_infer = run_remote_inference(\n", - " test_experiment=test_experiment,\n", - " compute_target=compute_target,\n", - " train_run=best_run,\n", - " test_dataset=test,\n", - " target_column_name=target_column_name,\n", - ")\n", - "remote_run_infer.wait_for_completion(show_output=False)\n", - "\n", - "# download the inference output file to the local machine\n", - "remote_run_infer.download_file(\"outputs/predictions.csv\", \"predictions.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate\n", - "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# load forecast data frame\n", - "fcst_df = pd.read_csv(\"predictions.csv\", parse_dates=[time_column_name])\n", - "fcst_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.shared import constants\n", - "from azureml.automl.runtime.shared.score import scoring\n", - "from matplotlib import pyplot as plt\n", - "\n", - "# use automl metrics module\n", - "scores = scoring.score_regression(\n", - " y_test=fcst_df[target_column_name],\n", - " y_pred=fcst_df[\"predicted\"],\n", - " metrics=list(constants.Metric.SCALAR_REGRESSION_SET),\n", - ")\n", - "\n", - "print(\"[Test data scores]\\n\")\n", - "for key, value in scores.items():\n", - " print(\"{}: {:.3f}\".format(key, value))\n", - "\n", - "# Plot outputs\n", - "%matplotlib inline\n", - "test_pred = plt.scatter(fcst_df[target_column_name], fcst_df[\"predicted\"], color=\"b\")\n", - "test_test = plt.scatter(\n", - " fcst_df[target_column_name], fcst_df[target_column_name], color=\"g\"\n", - ")\n", - "plt.legend(\n", - " (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Advanced Training \n", - "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using lags and rolling window features\n", - "Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the `forecast_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n", - "\n", - "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the iteration_timeout_minutes parameter value to get results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "advanced_forecasting_parameters = ForecastingParameters(\n", - " time_column_name=time_column_name,\n", - " forecast_horizon=forecast_horizon,\n", - " target_lags=12,\n", - " target_rolling_window_size=4,\n", - ")\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"forecasting\",\n", - " primary_metric=\"normalized_root_mean_squared_error\",\n", - " blocked_models=[\n", - " \"ElasticNet\",\n", - " \"ExtremeRandomTrees\",\n", - " \"GradientBoosting\",\n", - " \"XGBoostRegressor\",\n", - " \"ExtremeRandomTrees\",\n", - " \"AutoArima\",\n", - " \"Prophet\",\n", - " ], # These models are blocked for tutorial purposes, remove this for real use cases.\n", - " experiment_timeout_hours=0.3,\n", - " training_data=train,\n", - " label_column_name=target_column_name,\n", - " compute_target=compute_target,\n", - " enable_early_stopping=True,\n", - " n_cross_validations=3,\n", - " verbosity=logging.INFO,\n", - " forecasting_parameters=advanced_forecasting_parameters,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now start a new remote run, this time with lag and rolling window featurization. AutoML applies featurizations in the setup stage, prior to iterating over ML models. The full training set is featurized first, followed by featurization of each of the CV splits. Lag and rolling window features introduce additional complexity, so the run will take longer than in the previous example that lacked these featurizations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "advanced_remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "advanced_remote_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve the Best Run details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run_lags = remote_run.get_best_child()\n", - "best_run_lags" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Advanced Results\n", - "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_experiment_advanced = Experiment(ws, experiment_name + \"_inference_advanced\")\n", - "advanced_remote_run_infer = run_remote_inference(\n", - " test_experiment=test_experiment_advanced,\n", - " compute_target=compute_target,\n", - " train_run=best_run_lags,\n", - " test_dataset=test,\n", - " target_column_name=target_column_name,\n", - " inference_folder=\"./forecast_advanced\",\n", - ")\n", - "advanced_remote_run_infer.wait_for_completion(show_output=False)\n", - "\n", - "# download the inference output file to the local machine\n", - "advanced_remote_run_infer.download_file(\n", - " \"outputs/predictions.csv\", \"predictions_advanced.csv\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fcst_adv_df = pd.read_csv(\"predictions_advanced.csv\", parse_dates=[time_column_name])\n", - "fcst_adv_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.shared import constants\n", - "from azureml.automl.runtime.shared.score import scoring\n", - "from matplotlib import pyplot as plt\n", - "\n", - "# use automl metrics module\n", - "scores = scoring.score_regression(\n", - " y_test=fcst_adv_df[target_column_name],\n", - " y_pred=fcst_adv_df[\"predicted\"],\n", - " metrics=list(constants.Metric.SCALAR_REGRESSION_SET),\n", - ")\n", - "\n", - "print(\"[Test data scores]\\n\")\n", - "for key, value in scores.items():\n", - " print(\"{}: {:.3f}\".format(key, value))\n", - "\n", - "# Plot outputs\n", - "%matplotlib inline\n", - "test_pred = plt.scatter(\n", - " fcst_adv_df[target_column_name], fcst_adv_df[\"predicted\"], color=\"b\"\n", - ")\n", - "test_test = plt.scatter(\n", - " fcst_adv_df[target_column_name], fcst_adv_df[target_column_name], color=\"g\"\n", - ")\n", - "plt.legend(\n", - " (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n", - ")\n", - "plt.show()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jialiu" - } - ], - "categories": [ - "how-to-use-azureml", - "automated-machine-learning" - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml deleted file mode 100644 index 13bd78f88..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-energy-demand -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/forecasting_script.py b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/forecasting_script.py deleted file mode 100644 index 40724de54..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/forecasting_script.py +++ /dev/null @@ -1,61 +0,0 @@ -""" -This is the script that is executed on the compute instance. It relies -on the model.pkl file which is uploaded along with this script to the -compute instance. -""" - -import argparse -from azureml.core import Dataset, Run -from sklearn.externals import joblib -from pandas.tseries.frequencies import to_offset - -parser = argparse.ArgumentParser() -parser.add_argument( - "--target_column_name", - type=str, - dest="target_column_name", - help="Target Column Name", -) -parser.add_argument( - "--test_dataset", type=str, dest="test_dataset", help="Test Dataset" -) - -args = parser.parse_args() -target_column_name = args.target_column_name -test_dataset_id = args.test_dataset - -run = Run.get_context() -ws = run.experiment.workspace - -# get the input dataset by id -test_dataset = Dataset.get_by_id(ws, id=test_dataset_id) - -X_test = test_dataset.to_pandas_dataframe().reset_index(drop=True) -y_test = X_test.pop(target_column_name).values - -# generate forecast -fitted_model = joblib.load("model.pkl") -# We have default quantiles values set as below(95th percentile) -quantiles = [0.025, 0.5, 0.975] -predicted_column_name = "predicted" -PI = "prediction_interval" -fitted_model.quantiles = quantiles -pred_quantiles = fitted_model.forecast_quantiles(X_test) -pred_quantiles[PI] = pred_quantiles[[min(quantiles), max(quantiles)]].apply( - lambda x: "[{}, {}]".format(x[0], x[1]), axis=1 -) -X_test[target_column_name] = y_test -X_test[PI] = pred_quantiles[PI] -X_test[predicted_column_name] = pred_quantiles[0.5] -# drop rows where prediction or actuals are nan -# happens because of missing actuals -# or at edges of time due to lags/rolling windows -clean = X_test[ - X_test[[target_column_name, predicted_column_name]].notnull().all(axis=1) -] - -file_name = "outputs/predictions.csv" -export_csv = clean.to_csv(file_name, header=True, index=False) # added Index - -# Upload the predictions into artifacts -run.upload_file(name=file_name, path_or_stream=file_name) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/run_forecast.py b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/run_forecast.py deleted file mode 100644 index cb1d9d886..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/run_forecast.py +++ /dev/null @@ -1,49 +0,0 @@ -import os -import shutil -from azureml.core import ScriptRunConfig - - -def run_remote_inference( - test_experiment, - compute_target, - train_run, - test_dataset, - target_column_name, - inference_folder="./forecast", -): - # Create local directory to copy the model.pkl and forecsting_script.py files into. - # These files will be uploaded to and executed on the compute instance. - os.makedirs(inference_folder, exist_ok=True) - shutil.copy("forecasting_script.py", inference_folder) - - train_run.download_file( - "outputs/model.pkl", os.path.join(inference_folder, "model.pkl") - ) - - inference_env = train_run.get_environment() - - config = ScriptRunConfig( - source_directory=inference_folder, - script="forecasting_script.py", - arguments=[ - "--target_column_name", - target_column_name, - "--test_dataset", - test_dataset.as_named_input(test_dataset.name), - ], - compute_target=compute_target, - environment=inference_env, - ) - - run = test_experiment.submit( - config, - tags={ - "training_run_id": train_run.id, - "run_algorithm": train_run.properties["run_algorithm"], - "valid_score": train_run.properties["score"], - "primary_metric": train_run.properties["primary_metric"], - }, - ) - - run.log("run_algorithm", run.tags["run_algorithm"]) - return run diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb deleted file mode 100644 index ee2d4b498..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb +++ /dev/null @@ -1,893 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "\n", - "#### Forecasting away from training data\n", - "\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "2. [Setup](#Setup)\n", - "3. [Data](#Data)\n", - "4. [Prepare remote compute and data.](#prepare_remote)\n", - "4. [Create the configuration and train a forecaster](#train)\n", - "5. [Forecasting from the trained model](#forecasting)\n", - "6. [Forecasting away from training data](#forecasting_away)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "This notebook demonstrates the full interface of the `forecast()` function. \n", - "\n", - "The best known and most frequent usage of `forecast` enables forecasting on test sets that immediately follows training data. \n", - "\n", - "However, in many use cases it is necessary to continue using the model for some time before retraining it. This happens especially in **high frequency forecasting** when forecasts need to be made more frequently than the model can be retrained. Examples are in Internet of Things and predictive cloud resource scaling.\n", - "\n", - "Here we show how to use the `forecast()` function when a time gap exists between training data and prediction period.\n", - "\n", - "Terminology:\n", - "* forecast origin: the last period when the target value is known\n", - "* forecast periods(s): the period(s) for which the value of the target is desired.\n", - "* lookback: how many past periods (before forecast origin) the model function depends on. The larger of number of lags and length of rolling window.\n", - "* prediction context: `lookback` periods immediately preceding the forecast origin\n", - "\n", - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/automl-forecasting-function.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Please make sure you have followed the `configuration.ipynb` notebook so that your ML workspace information is saved in the config file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import logging\n", - "import warnings\n", - "\n", - "import azureml.core\n", - "from azureml.core.dataset import Dataset\n", - "from pandas.tseries.frequencies import to_offset\n", - "from azureml.core.compute import AmlCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "# Squash warning messages for cleaner output in the notebook\n", - "warnings.showwarning = lambda *args, **kwargs: None\n", - "\n", - "np.set_printoptions(precision=4, suppress=True, linewidth=120)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is compatible with Azure ML SDK version 1.35.0 or later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.train.automl import AutoMLConfig\n", - "\n", - "ws = Workspace.from_config()\n", - "\n", - "# choose a name for the run history container in the workspace\n", - "experiment_name = \"automl-forecast-function-demo\"\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"SKU\"] = ws.sku\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Run History Name\"] = experiment_name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n", - "For the demonstration purposes we will generate the data artificially and use them for the forecasting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TIME_COLUMN_NAME = \"date\"\n", - "TIME_SERIES_ID_COLUMN_NAME = \"time_series_id\"\n", - "TARGET_COLUMN_NAME = \"y\"\n", - "\n", - "\n", - "def get_timeseries(\n", - " train_len: int,\n", - " test_len: int,\n", - " time_column_name: str,\n", - " target_column_name: str,\n", - " time_series_id_column_name: str,\n", - " time_series_number: int = 1,\n", - " freq: str = \"H\",\n", - "):\n", - " \"\"\"\n", - " Return the time series of designed length.\n", - "\n", - " :param train_len: The length of training data (one series).\n", - " :type train_len: int\n", - " :param test_len: The length of testing data (one series).\n", - " :type test_len: int\n", - " :param time_column_name: The desired name of a time column.\n", - " :type time_column_name: str\n", - " :param time_series_number: The number of time series in the data set.\n", - " :type time_series_number: int\n", - " :param freq: The frequency string representing pandas offset.\n", - " see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n", - " :type freq: str\n", - " :returns: the tuple of train and test data sets.\n", - " :rtype: tuple\n", - "\n", - " \"\"\"\n", - " data_train = [] # type: List[pd.DataFrame]\n", - " data_test = [] # type: List[pd.DataFrame]\n", - " data_length = train_len + test_len\n", - " for i in range(time_series_number):\n", - " X = pd.DataFrame(\n", - " {\n", - " time_column_name: pd.date_range(\n", - " start=\"2000-01-01\", periods=data_length, freq=freq\n", - " ),\n", - " target_column_name: np.arange(data_length).astype(float)\n", - " + np.random.rand(data_length)\n", - " + i * 5,\n", - " \"ext_predictor\": np.asarray(range(42, 42 + data_length)),\n", - " time_series_id_column_name: np.repeat(\"ts{}\".format(i), data_length),\n", - " }\n", - " )\n", - " data_train.append(X[:train_len])\n", - " data_test.append(X[train_len:])\n", - " X_train = pd.concat(data_train)\n", - " y_train = X_train.pop(target_column_name).values\n", - " X_test = pd.concat(data_test)\n", - " y_test = X_test.pop(target_column_name).values\n", - " return X_train, y_train, X_test, y_test\n", - "\n", - "\n", - "n_test_periods = 6\n", - "n_train_periods = 30\n", - "X_train, y_train, X_test, y_test = get_timeseries(\n", - " train_len=n_train_periods,\n", - " test_len=n_test_periods,\n", - " time_column_name=TIME_COLUMN_NAME,\n", - " target_column_name=TARGET_COLUMN_NAME,\n", - " time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n", - " time_series_number=2,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see what the training data looks like." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_train.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot the example time series\n", - "import matplotlib.pyplot as plt\n", - "\n", - "whole_data = X_train.copy()\n", - "target_label = \"y\"\n", - "whole_data[target_label] = y_train\n", - "for g in whole_data.groupby(\"time_series_id\"):\n", - " plt.plot(g[1][\"date\"].values, g[1][\"y\"].values, label=g[0])\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare remote compute and data. \n", - "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace), is paired with the storage account, which contains the default data store. We will use it to upload the artificial data and create [tabular dataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We need to save thw artificial data and then upload them to default workspace datastore.\n", - "DATA_PATH = \"fc_fn_data\"\n", - "DATA_PATH_X = \"{}/data_train.csv\".format(DATA_PATH)\n", - "if not os.path.isdir(\"data\"):\n", - " os.mkdir(\"data\")\n", - "pd.DataFrame(whole_data).to_csv(\"data/data_train.csv\", index=False)\n", - "# Upload saved data to the default data store.\n", - "ds = ws.get_default_datastore()\n", - "ds.upload(src_dir=\"./data\", target_path=DATA_PATH, overwrite=True, show_progress=True)\n", - "train_data = Dataset.Tabular.from_delimited_files(path=ds.path(DATA_PATH_X))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "amlcompute_cluster_name = \"fcfn-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n", - " )\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the configuration and train a forecaster \n", - "First generate the configuration, in which we:\n", - "* Set metadata columns: target, time column and time-series id column names.\n", - "* Validate our data using cross validation with rolling window method.\n", - "* Set normalized root mean squared error as a metric to select the best model.\n", - "* Set early termination to True, so the iterations through the models will stop when no improvements in accuracy score will be made.\n", - "* Set limitations on the length of experiment run to 15 minutes.\n", - "* Finally, we set the task to be forecasting.\n", - "* We apply the lag lead operator to the target value i.e. we use the previous values as a predictor for the future ones.\n", - "* [Optional] Forecast frequency parameter (freq) represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n", - "\n", - "lags = [1, 2, 3]\n", - "forecast_horizon = n_test_periods\n", - "forecasting_parameters = ForecastingParameters(\n", - " time_column_name=TIME_COLUMN_NAME,\n", - " forecast_horizon=forecast_horizon,\n", - " time_series_id_column_names=[TIME_SERIES_ID_COLUMN_NAME],\n", - " target_lags=lags,\n", - " freq=\"H\", # Set the forecast frequency to be hourly\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the model selection and training process. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.train.automl import AutoMLConfig\n", - "\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"forecasting\",\n", - " debug_log=\"automl_forecasting_function.log\",\n", - " primary_metric=\"normalized_root_mean_squared_error\",\n", - " experiment_timeout_hours=0.25,\n", - " enable_early_stopping=True,\n", - " training_data=train_data,\n", - " compute_target=compute_target,\n", - " n_cross_validations=3,\n", - " verbosity=logging.INFO,\n", - " max_concurrent_iterations=4,\n", - " max_cores_per_iteration=-1,\n", - " label_column_name=target_label,\n", - " forecasting_parameters=forecasting_parameters,\n", - ")\n", - "\n", - "remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.wait_for_completion()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve the best model to use it further.\n", - "_, fitted_model = remote_run.get_output()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Forecasting from the trained model " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section we will review the `forecast` interface for two main scenarios: forecasting right after the training data, and the more complex interface for forecasting when there is a gap (in the time sense) between training and testing data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### X_train is directly followed by the X_test\n", - "\n", - "Let's first consider the case when the prediction period immediately follows the training data. This is typical in scenarios where we have the time to retrain the model every time we wish to forecast. Forecasts that are made on daily and slower cadence typically fall into this category. Retraining the model every time benefits the accuracy because the most recent data is often the most informative.\n", - "\n", - "![Forecasting after training](forecast_function_at_train.png)\n", - "\n", - "We use `X_test` as a **forecast request** to generate the predictions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Typical path: X_test is known, forecast all upcoming periods" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The data set contains hourly data, the training set ends at 01/02/2000 at 05:00\n", - "\n", - "# These are predictions we are asking the model to make (does not contain thet target column y),\n", - "# for 6 periods beginning with 2000-01-02 06:00, which immediately follows the training data\n", - "X_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred_no_gap, xy_nogap = fitted_model.forecast(X_test)\n", - "\n", - "# xy_nogap contains the predictions in the _automl_target_col column.\n", - "# Those same numbers are output in y_pred_no_gap\n", - "xy_nogap" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Confidence intervals" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Forecasting model may be used for the prediction of forecasting intervals by running ```forecast_quantiles()```. \n", - "This method accepts the same parameters as forecast()." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "quantiles = fitted_model.forecast_quantiles(X_test)\n", - "quantiles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Distribution forecasts\n", - "\n", - "Often the figure of interest is not just the point prediction, but the prediction at some quantile of the distribution. \n", - "This arises when the forecast is used to control some kind of inventory, for example of grocery items or virtual machines for a cloud service. In such case, the control point is usually something like \"we want the item to be in stock and not run out 99% of the time\". This is called a \"service level\". Here is how you get quantile forecasts." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# specify which quantiles you would like\n", - "fitted_model.quantiles = [0.01, 0.5, 0.95]\n", - "# use forecast_quantiles function, not the forecast() one\n", - "y_pred_quantiles = fitted_model.forecast_quantiles(X_test)\n", - "\n", - "# quantile forecasts returned in a Dataframe along with the time and time series id columns\n", - "y_pred_quantiles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Destination-date forecast: \"just do something\"\n", - "\n", - "In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the forecast horizon from training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We will take the destination date as a last date in the test set.\n", - "dest = max(X_test[TIME_COLUMN_NAME])\n", - "y_pred_dest, xy_dest = fitted_model.forecast(forecast_destination=dest)\n", - "\n", - "# This form also shows how we imputed the predictors which were not given. (Not so well! Use with caution!)\n", - "xy_dest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Forecasting away from training data \n", - "\n", - "Suppose we trained a model, some time passed, and now we want to apply the model without re-training. If the model \"looks back\" -- uses previous values of the target -- then we somehow need to provide those values to the model.\n", - "\n", - "![Forecasting after training](forecast_function_away_from_train.png)\n", - "\n", - "The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per time-series, so each time-series can have a different forecast origin. \n", - "\n", - "The part of data before the forecast origin is the **prediction context**. To provide the context values the model needs when it looks back, we pass definite values in `y_test` (aligned with corresponding times in `X_test`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate the same kind of test data we trained on,\n", - "# but now make the train set much longer, so that the test set will be in the future\n", - "X_context, y_context, X_away, y_away = get_timeseries(\n", - " train_len=42, # train data was 30 steps long\n", - " test_len=4,\n", - " time_column_name=TIME_COLUMN_NAME,\n", - " target_column_name=TARGET_COLUMN_NAME,\n", - " time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n", - " time_series_number=2,\n", - ")\n", - "\n", - "# end of the data we trained on\n", - "print(X_train.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n", - "# start of the data we want to predict on\n", - "print(X_away.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].min())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is a gap of 12 hours between end of training and beginning of `X_away`. (It looks like 13 because all timestamps point to the start of the one hour periods.) Using only `X_away` will fail without adding context data for the model to consume." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " y_pred_away, xy_away = fitted_model.forecast(X_away)\n", - " xy_away\n", - "except Exception as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the forecast horizon. We need to provide a define `y` value to establish the forecast origin.\n", - "\n", - "We will use this helper function to take the required amount of context from the data preceding the testing data. It's definition is intentionally simplified to keep the idea in the clear." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def make_forecasting_query(\n", - " fulldata, time_column_name, target_column_name, forecast_origin, horizon, lookback\n", - "):\n", - "\n", - " \"\"\"\n", - " This function will take the full dataset, and create the query\n", - " to predict all values of the time series from the `forecast_origin`\n", - " forward for the next `horizon` horizons. Context from previous\n", - " `lookback` periods will be included.\n", - "\n", - "\n", - "\n", - " fulldata: pandas.DataFrame a time series dataset. Needs to contain X and y.\n", - " time_column_name: string which column (must be in fulldata) is the time axis\n", - " target_column_name: string which column (must be in fulldata) is to be forecast\n", - " forecast_origin: datetime type the last time we (pretend to) have target values\n", - " horizon: timedelta how far forward, in time units (not periods)\n", - " lookback: timedelta how far back does the model look\n", - "\n", - " Example:\n", - "\n", - "\n", - " ```\n", - "\n", - " forecast_origin = pd.to_datetime(\"2012-09-01\") + pd.DateOffset(days=5) # forecast 5 days after end of training\n", - " print(forecast_origin)\n", - "\n", - " X_query, y_query = make_forecasting_query(data,\n", - " forecast_origin = forecast_origin,\n", - " horizon = pd.DateOffset(days=7), # 7 days into the future\n", - " lookback = pd.DateOffset(days=1), # model has lag 1 period (day)\n", - " )\n", - "\n", - " ```\n", - " \"\"\"\n", - "\n", - " X_past = fulldata[\n", - " (fulldata[time_column_name] > forecast_origin - lookback)\n", - " & (fulldata[time_column_name] <= forecast_origin)\n", - " ]\n", - "\n", - " X_future = fulldata[\n", - " (fulldata[time_column_name] > forecast_origin)\n", - " & (fulldata[time_column_name] <= forecast_origin + horizon)\n", - " ]\n", - "\n", - " y_past = X_past.pop(target_column_name).values.astype(np.float)\n", - " y_future = X_future.pop(target_column_name).values.astype(np.float)\n", - "\n", - " # Now take y_future and turn it into question marks\n", - " y_query = y_future.copy().astype(\n", - " np.float\n", - " ) # because sometimes life hands you an int\n", - " y_query.fill(np.NaN)\n", - "\n", - " print(\"X_past is \" + str(X_past.shape) + \" - shaped\")\n", - " print(\"X_future is \" + str(X_future.shape) + \" - shaped\")\n", - " print(\"y_past is \" + str(y_past.shape) + \" - shaped\")\n", - " print(\"y_query is \" + str(y_query.shape) + \" - shaped\")\n", - "\n", - " X_pred = pd.concat([X_past, X_future])\n", - " y_pred = np.concatenate([y_past, y_query])\n", - " return X_pred, y_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see where the context data ends - it ends, by construction, just before the testing data starts." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\n", - " X_context.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(\n", - " [\"min\", \"max\", \"count\"]\n", - " )\n", - ")\n", - "print(\n", - " X_away.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(\n", - " [\"min\", \"max\", \"count\"]\n", - " )\n", - ")\n", - "X_context.tail(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Since the length of the lookback is 3,\n", - "# we need to add 3 periods from the context to the request\n", - "# so that the model has the data it needs\n", - "\n", - "# Put the X and y back together for a while.\n", - "# They like each other and it makes them happy.\n", - "X_context[TARGET_COLUMN_NAME] = y_context\n", - "X_away[TARGET_COLUMN_NAME] = y_away\n", - "fulldata = pd.concat([X_context, X_away])\n", - "\n", - "# forecast origin is the last point of data, which is one 1-hr period before test\n", - "forecast_origin = X_away[TIME_COLUMN_NAME].min() - pd.DateOffset(hours=1)\n", - "# it is indeed the last point of the context\n", - "assert forecast_origin == X_context[TIME_COLUMN_NAME].max()\n", - "print(\"Forecast origin: \" + str(forecast_origin))\n", - "\n", - "# the model uses lags and rolling windows to look back in time\n", - "n_lookback_periods = max(lags)\n", - "lookback = pd.DateOffset(hours=n_lookback_periods)\n", - "\n", - "horizon = pd.DateOffset(hours=forecast_horizon)\n", - "\n", - "# now make the forecast query from context (refer to figure)\n", - "X_pred, y_pred = make_forecasting_query(\n", - " fulldata, TIME_COLUMN_NAME, TARGET_COLUMN_NAME, forecast_origin, horizon, lookback\n", - ")\n", - "\n", - "# show the forecast request aligned\n", - "X_show = X_pred.copy()\n", - "X_show[TARGET_COLUMN_NAME] = y_pred\n", - "X_show" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the forecast origin is at 17:00 for both time-series, and periods from 18:00 are to be forecast." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Now everything works\n", - "y_pred_away, xy_away = fitted_model.forecast(X_pred, y_pred)\n", - "\n", - "# show the forecast aligned\n", - "X_show = xy_away.reset_index()\n", - "# without the generated features\n", - "X_show[[\"date\", \"time_series_id\", \"ext_predictor\", \"_automl_target_col\"]]\n", - "# prediction is in _automl_target_col" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Forecasting farther than the forecast horizon \n", - "When the forecast destination, or the latest date in the prediction data frame, is farther into the future than the specified forecast horizon, the `forecast()` function will still make point predictions out to the later date using a recursive operation mode. Internally, the method recursively applies the regular forecaster to generate context so that we can forecast further into the future. \n", - "\n", - "To illustrate the use-case and operation of recursive forecasting, we'll consider an example with a single time-series where the forecasting period directly follows the training period and is twice as long as the forecasting horizon given at training time.\n", - "\n", - "![Recursive_forecast_overview](recursive_forecast_overview_small.png)\n", - "\n", - "Internally, we apply the forecaster in an iterative manner and finish the forecast task in two interations. In the first iteration, we apply the forecaster and get the prediction for the first forecast-horizon periods (y_pred1). In the second iteraction, y_pred1 is used as the context to produce the prediction for the next forecast-horizon periods (y_pred2). The combination of (y_pred1 and y_pred2) gives the results for the total forecast periods. \n", - "\n", - "A caveat: forecast accuracy will likely be worse the farther we predict into the future since errors are compounded with recursive application of the forecaster.\n", - "\n", - "![Recursive_forecast_iter1](recursive_forecast_iter1.png)\n", - "![Recursive_forecast_iter2](recursive_forecast_iter2.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate the same kind of test data we trained on, but with a single time-series and test period twice as long\n", - "# as the forecast_horizon.\n", - "_, _, X_test_long, y_test_long = get_timeseries(\n", - " train_len=n_train_periods,\n", - " test_len=forecast_horizon * 2,\n", - " time_column_name=TIME_COLUMN_NAME,\n", - " target_column_name=TARGET_COLUMN_NAME,\n", - " time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n", - " time_series_number=1,\n", - ")\n", - "\n", - "print(X_test_long.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].min())\n", - "print(X_test_long.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# forecast() function will invoke the recursive forecast method internally.\n", - "y_pred_long, X_trans_long = fitted_model.forecast(X_test_long)\n", - "y_pred_long" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# What forecast() function does in this case is equivalent to iterating it twice over the test set as the following.\n", - "y_pred1, _ = fitted_model.forecast(X_test_long[:forecast_horizon])\n", - "y_pred_all, _ = fitted_model.forecast(\n", - " X_test_long, np.concatenate((y_pred1, np.full(forecast_horizon, np.nan)))\n", - ")\n", - "np.array_equal(y_pred_all, y_pred_long)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Confidence interval and distributional forecasts\n", - "AutoML cannot currently estimate forecast errors beyond the forecast horizon set during training, so the `forecast_quantiles()` function will return missing values for quantiles not equal to 0.5 beyond the forecast horizon. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fitted_model.forecast_quantiles(X_test_long)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly with the simple senarios illustrated above, forecasting farther than the forecast horizon in other senarios like 'multiple time-series', 'Destination-date forecast', and 'forecast away from the training data' are also automatically handled by the `forecast()` function. " - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jialiu" - } - ], - "category": "tutorial", - "compute": [ - "Remote" - ], - "datasets": [ - "None" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML AutoML" - ], - "friendly_name": "Forecasting away from training data", - "index_order": 3, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - }, - "tags": [ - "Forecasting", - "Confidence Intervals" - ], - "task": "Forecasting" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.yml b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.yml deleted file mode 100644 index 144797d64..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-function -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/forecast_function_at_train.png b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/forecast_function_at_train.png deleted file mode 100644 index 37d8ffddc..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/forecast_function_at_train.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/forecast_function_away_from_train.png b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/forecast_function_away_from_train.png deleted file mode 100644 index 7b7e20374..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/forecast_function_away_from_train.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/recursive_forecast_iter1.png b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/recursive_forecast_iter1.png deleted file mode 100644 index e2958b210..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/recursive_forecast_iter1.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/recursive_forecast_iter2.png b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/recursive_forecast_iter2.png deleted file mode 100644 index d2943fbfa..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/recursive_forecast_iter2.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/recursive_forecast_overview_small.png b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/recursive_forecast_overview_small.png deleted file mode 100644 index 538cdb3d6..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/recursive_forecast_overview_small.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb deleted file mode 100644 index c18587714..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb +++ /dev/null @@ -1,725 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "# Automated Machine Learning\n", - "**Github DAU Forecasting**\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Data](#Data)\n", - "1. [Train](#Train)\n", - "1. [Evaluate](#Evaluate)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "## Introduction\n", - "This notebook demonstrates demand forecasting for Github Daily Active Users Dataset using AutoML.\n", - "\n", - "AutoML highlights here include using Deep Learning forecasts, Arima, Prophet, Remote Execution and Remote Inferencing, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n", - "\n", - "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n", - "\n", - "Notebook synopsis:\n", - "\n", - "1. Creating an Experiment in an existing Workspace\n", - "2. Configuration and remote run of AutoML for a time-series model exploring Regression learners, Arima, Prophet and DNNs\n", - "4. Evaluating the fitted model using a rolling test " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "## Setup\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "import os\n", - "import azureml.core\n", - "import pandas as pd\n", - "import numpy as np\n", - "import logging\n", - "import warnings\n", - "\n", - "from pandas.tseries.frequencies import to_offset\n", - "\n", - "# Squash warning messages for cleaner output in the notebook\n", - "warnings.showwarning = lambda *args, **kwargs: None\n", - "\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.train.automl import AutoMLConfig\n", - "from matplotlib import pyplot as plt\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", - "from azureml.train.estimator import Estimator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is compatible with Azure ML SDK version 1.35.0 or later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "As part of the setup you have already created a Workspace. To run AutoML, you also need to create an Experiment. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# choose a name for the run history container in the workspace\n", - "experiment_name = \"github-remote-cpu\"\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Run History Name\"] = experiment_name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "### Using AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "cpu_cluster_name = \"github-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n", - " )\n", - " compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "## Data\n", - "Read Github DAU data from file, and preview data." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "Let's set up what we know about the dataset. \n", - "\n", - "**Target column** is what we want to forecast.\n", - "\n", - "**Time column** is the time axis along which to predict.\n", - "\n", - "**Time series identifier columns** are identified by values of the columns listed `time_series_id_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n", - "\n", - "**Forecast frequency (freq)** This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information.\n", - "\n", - "This dataset has only one time series. Please see the [orange juice notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales) for an example of a multi-time series dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from pandas import DataFrame\n", - "from pandas import Grouper\n", - "from pandas import concat\n", - "from pandas.plotting import register_matplotlib_converters\n", - "\n", - "register_matplotlib_converters()\n", - "plt.figure(figsize=(20, 10))\n", - "plt.tight_layout()\n", - "\n", - "plt.subplot(2, 1, 1)\n", - "plt.title(\"Github Daily Active User By Year\")\n", - "df = pd.read_csv(\"github_dau_2011-2018_train.csv\", parse_dates=True, index_col=\"date\")\n", - "test_df = pd.read_csv(\n", - " \"github_dau_2011-2018_test.csv\", parse_dates=True, index_col=\"date\"\n", - ")\n", - "plt.plot(df)\n", - "\n", - "plt.subplot(2, 1, 2)\n", - "plt.title(\"Github Daily Active User By Month\")\n", - "groups = df.groupby(df.index.month)\n", - "months = concat([DataFrame(x[1].values) for x in groups], axis=1)\n", - "months = DataFrame(months)\n", - "months.columns = range(1, 49)\n", - "months.boxplot()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "target_column_name = \"count\"\n", - "time_column_name = \"date\"\n", - "time_series_id_column_names = []\n", - "freq = \"D\" # Daily data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Split Training data into Train and Validation set and Upload to Datastores" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "from helper import split_fraction_by_grain\n", - "from helper import split_full_for_forecasting\n", - "\n", - "train, valid = split_full_for_forecasting(df, time_column_name)\n", - "train.to_csv(\"train.csv\")\n", - "valid.to_csv(\"valid.csv\")\n", - "test_df.to_csv(\"test.csv\")\n", - "\n", - "datastore = ws.get_default_datastore()\n", - "datastore.upload_files(\n", - " files=[\"./train.csv\"],\n", - " target_path=\"github-dataset/tabular/\",\n", - " overwrite=True,\n", - " show_progress=True,\n", - ")\n", - "datastore.upload_files(\n", - " files=[\"./valid.csv\"],\n", - " target_path=\"github-dataset/tabular/\",\n", - " overwrite=True,\n", - " show_progress=True,\n", - ")\n", - "datastore.upload_files(\n", - " files=[\"./test.csv\"],\n", - " target_path=\"github-dataset/tabular/\",\n", - " overwrite=True,\n", - " show_progress=True,\n", - ")\n", - "\n", - "from azureml.core import Dataset\n", - "\n", - "train_dataset = Dataset.Tabular.from_delimited_files(\n", - " path=[(datastore, \"github-dataset/tabular/train.csv\")]\n", - ")\n", - "valid_dataset = Dataset.Tabular.from_delimited_files(\n", - " path=[(datastore, \"github-dataset/tabular/valid.csv\")]\n", - ")\n", - "test_dataset = Dataset.Tabular.from_delimited_files(\n", - " path=[(datastore, \"github-dataset/tabular/test.csv\")]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "### Setting forecaster maximum horizon \n", - "\n", - "The forecast horizon is the number of periods into the future that the model should predict. Here, we set the horizon to 12 periods (i.e. 12 months). Notice that this is much shorter than the number of months in the test set; we will need to use a rolling test to evaluate the performance on the whole test set. For more discussion of forecast horizons and guiding principles for setting them, please see the [energy demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "forecast_horizon = 12" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "## Train\n", - "\n", - "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|forecasting|\n", - "|**primary_metric**|This is the metric that you want to optimize.
Forecasting supports the following primary metrics
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error\n", - "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n", - "|**training_data**|Input dataset, containing both features and label column.|\n", - "|**label_column_name**|The name of the label column.|\n", - "|**enable_dnn**|Enable Forecasting DNNs|\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n", - "\n", - "forecasting_parameters = ForecastingParameters(\n", - " time_column_name=time_column_name,\n", - " forecast_horizon=forecast_horizon,\n", - " freq=\"D\", # Set the forecast frequency to be daily\n", - ")\n", - "\n", - "# We will disable the enable_early_stopping flag to ensure the DNN model is recommended for demonstration purpose.\n", - "automl_config = AutoMLConfig(\n", - " task=\"forecasting\",\n", - " primary_metric=\"normalized_root_mean_squared_error\",\n", - " experiment_timeout_hours=1,\n", - " training_data=train_dataset,\n", - " label_column_name=target_column_name,\n", - " validation_data=valid_dataset,\n", - " verbosity=logging.INFO,\n", - " compute_target=compute_target,\n", - " max_concurrent_iterations=4,\n", - " max_cores_per_iteration=-1,\n", - " enable_dnn=True,\n", - " enable_early_stopping=False,\n", - " forecasting_parameters=forecasting_parameters,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "# If you need to retrieve a run that already started, use the following code\n", - "# from azureml.train.automl.run import AutoMLRun\n", - "# remote_run = AutoMLRun(experiment = experiment, run_id = '')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "Displaying the run objects gives you links to the visual tools in the Azure Portal. Go try them!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "### Retrieve the Best Model for Each Algorithm\n", - "Below we select the best pipeline from our iterations. The get_output method on automl_classifier returns the best run and the fitted model for the last fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "from helper import get_result_df\n", - "\n", - "summary_df = get_result_df(remote_run)\n", - "summary_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "from azureml.core.run import Run\n", - "from azureml.widgets import RunDetails\n", - "\n", - "forecast_model = \"TCNForecaster\"\n", - "if not forecast_model in summary_df[\"run_id\"]:\n", - " forecast_model = \"ForecastTCN\"\n", - "\n", - "best_dnn_run_id = summary_df[\"run_id\"][forecast_model]\n", - "best_dnn_run = Run(experiment, best_dnn_run_id)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "best_dnn_run.parent\n", - "RunDetails(best_dnn_run.parent).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "best_dnn_run\n", - "RunDetails(best_dnn_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "## Evaluate on Test Data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "source": [ - "We now use the best fitted model from the AutoML Run to make forecasts for the test set. \n", - "\n", - "We always score on the original dataset whose schema matches the training set schema." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "\n", - "test_dataset = Dataset.Tabular.from_delimited_files(\n", - " path=[(datastore, \"github-dataset/tabular/test.csv\")]\n", - ")\n", - "# preview the first 3 rows of the dataset\n", - "test_dataset.take(5).to_pandas_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "compute_target = ws.compute_targets[\"github-cluster\"]\n", - "test_experiment = Experiment(ws, experiment_name + \"_test\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "import os\n", - "import shutil\n", - "\n", - "script_folder = os.path.join(os.getcwd(), \"inference\")\n", - "os.makedirs(script_folder, exist_ok=True)\n", - "shutil.copy(\"infer.py\", script_folder)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from helper import run_inference\n", - "\n", - "test_run = run_inference(\n", - " test_experiment,\n", - " compute_target,\n", - " script_folder,\n", - " best_dnn_run,\n", - " test_dataset,\n", - " valid_dataset,\n", - " forecast_horizon,\n", - " target_column_name,\n", - " time_column_name,\n", - " freq,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(test_run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from helper import run_multiple_inferences\n", - "\n", - "summary_df = run_multiple_inferences(\n", - " summary_df,\n", - " experiment,\n", - " test_experiment,\n", - " compute_target,\n", - " script_folder,\n", - " test_dataset,\n", - " valid_dataset,\n", - " forecast_horizon,\n", - " target_column_name,\n", - " time_column_name,\n", - " freq,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "for run_name, run_summary in summary_df.iterrows():\n", - " print(run_name)\n", - " print(run_summary)\n", - " run_id = run_summary.run_id\n", - " test_run_id = run_summary.test_run_id\n", - " test_run = Run(test_experiment, test_run_id)\n", - " test_run.wait_for_completion()\n", - " test_score = test_run.get_metrics()[run_summary.primary_metric]\n", - " summary_df.loc[summary_df.run_id == run_id, \"Test Score\"] = test_score\n", - " print(\"Test Score: \", test_score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "summary_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "jialiu" - } - ], - "hide_code_all_hidden": false, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.yml b/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.yml deleted file mode 100644 index eee39bd4d..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-github-dau -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/github_dau_2011-2018_test.csv b/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/github_dau_2011-2018_test.csv deleted file mode 100644 index 6061b0d21..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/github_dau_2011-2018_test.csv +++ /dev/null @@ -1,455 +0,0 @@ -date,count,day_of_week,month_of_year,holiday -2017-06-04,104663,6.0,5.0,0.0 -2017-06-05,155824,0.0,5.0,0.0 -2017-06-06,164908,1.0,5.0,0.0 -2017-06-07,170309,2.0,5.0,0.0 -2017-06-08,164256,3.0,5.0,0.0 -2017-06-09,153406,4.0,5.0,0.0 -2017-06-10,97024,5.0,5.0,0.0 -2017-06-11,103442,6.0,5.0,0.0 -2017-06-12,160768,0.0,5.0,0.0 -2017-06-13,166288,1.0,5.0,0.0 -2017-06-14,163819,2.0,5.0,0.0 -2017-06-15,157593,3.0,5.0,0.0 -2017-06-16,149259,4.0,5.0,0.0 -2017-06-17,95579,5.0,5.0,0.0 -2017-06-18,98723,6.0,5.0,0.0 -2017-06-19,159076,0.0,5.0,0.0 -2017-06-20,163340,1.0,5.0,0.0 -2017-06-21,163344,2.0,5.0,0.0 -2017-06-22,159528,3.0,5.0,0.0 -2017-06-23,146563,4.0,5.0,0.0 -2017-06-24,92631,5.0,5.0,0.0 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-2011-07-13,10201,2.0,6.0,0.0 -2011-07-14,9771,3.0,6.0,0.0 -2011-07-15,9339,4.0,6.0,0.0 -2011-07-16,6690,5.0,6.0,0.0 -2011-07-17,7059,6.0,6.0,0.0 -2011-07-18,10367,0.0,6.0,0.0 -2011-07-19,10123,1.0,6.0,0.0 -2011-07-20,10370,2.0,6.0,0.0 -2011-07-21,10296,3.0,6.0,0.0 -2011-07-22,9479,4.0,6.0,0.0 -2011-07-23,6667,5.0,6.0,0.0 -2011-07-24,6929,6.0,6.0,0.0 -2011-07-25,9924,0.0,6.0,0.0 -2011-07-26,10840,1.0,6.0,0.0 -2011-07-27,10588,2.0,6.0,0.0 -2011-07-28,10195,3.0,6.0,0.0 -2011-07-29,9688,4.0,6.0,0.0 -2011-07-30,6070,5.0,6.0,0.0 -2011-07-31,6858,6.0,6.0,0.0 -2011-08-01,9822,0.0,7.0,0.0 -2011-08-02,10529,1.0,7.0,0.0 -2011-08-03,10392,2.0,7.0,0.0 -2011-08-04,10498,3.0,7.0,0.0 -2011-08-05,9775,4.0,7.0,0.0 -2011-08-06,6653,5.0,7.0,0.0 -2011-08-07,6361,6.0,7.0,0.0 -2011-08-08,10287,0.0,7.0,0.0 -2011-08-09,10742,1.0,7.0,0.0 -2011-08-10,10086,2.0,7.0,0.0 -2011-08-11,10391,3.0,7.0,0.0 -2011-08-12,9614,4.0,7.0,0.0 -2011-08-13,6835,5.0,7.0,0.0 -2011-08-14,6912,6.0,7.0,0.0 -2011-08-15,10075,0.0,7.0,0.0 -2011-08-16,10949,1.0,7.0,0.0 -2011-08-17,11041,2.0,7.0,0.0 -2011-08-18,10742,3.0,7.0,0.0 -2011-08-19,10146,4.0,7.0,0.0 -2011-08-20,6424,5.0,7.0,0.0 -2011-08-21,7248,6.0,7.0,0.0 -2011-08-22,10650,0.0,7.0,0.0 -2011-08-23,11171,1.0,7.0,0.0 -2011-08-24,11385,2.0,7.0,0.0 -2011-08-25,10968,3.0,7.0,0.0 -2011-08-26,10179,4.0,7.0,0.0 -2011-08-27,7129,5.0,7.0,0.0 -2011-08-28,7341,6.0,7.0,0.0 -2011-08-29,10953,0.0,7.0,0.0 -2011-08-30,11251,1.0,7.0,0.0 -2011-08-31,11103,2.0,7.0,0.0 -2011-09-01,11120,3.0,8.0,0.0 -2011-09-02,10610,4.0,8.0,0.0 -2011-09-03,7280,5.0,8.0,0.0 -2011-09-04,7798,6.0,8.0,0.0 -2011-09-05,10391,0.0,8.0,1.0 -2011-09-06,11625,1.0,8.0,0.0 -2011-09-07,11869,2.0,8.0,0.0 -2011-09-08,11653,3.0,8.0,0.0 -2011-09-09,10962,4.0,8.0,0.0 -2011-09-10,7616,5.0,8.0,0.0 -2011-09-11,8209,6.0,8.0,0.0 -2011-09-12,11410,0.0,8.0,0.0 -2011-09-13,12278,1.0,8.0,0.0 -2011-09-14,12162,2.0,8.0,0.0 -2011-09-15,11739,3.0,8.0,0.0 -2011-09-16,11476,4.0,8.0,0.0 -2011-09-17,7297,5.0,8.0,0.0 -2011-09-18,8467,6.0,8.0,0.0 -2011-09-19,11276,0.0,8.0,0.0 -2011-09-20,11934,1.0,8.0,0.0 -2011-09-21,12059,2.0,8.0,0.0 -2011-09-22,12279,3.0,8.0,0.0 -2011-09-23,11209,4.0,8.0,0.0 -2011-09-24,7928,5.0,8.0,0.0 -2011-09-25,8584,6.0,8.0,0.0 -2011-09-26,12586,0.0,8.0,0.0 -2011-09-27,13016,1.0,8.0,0.0 -2011-09-28,12805,2.0,8.0,0.0 -2011-09-29,12525,3.0,8.0,0.0 -2011-09-30,11612,4.0,8.0,0.0 -2011-10-01,7829,5.0,9.0,0.0 -2011-10-02,8493,6.0,9.0,0.0 -2011-10-03,11934,0.0,9.0,0.0 -2011-10-04,12469,1.0,9.0,0.0 -2011-10-05,12576,2.0,9.0,0.0 -2011-10-06,12347,3.0,9.0,0.0 -2011-10-07,11916,4.0,9.0,0.0 -2011-10-08,8281,5.0,9.0,0.0 -2011-10-09,8830,6.0,9.0,0.0 -2011-10-10,12618,0.0,9.0,1.0 -2011-10-11,13105,1.0,9.0,0.0 -2011-10-12,12897,2.0,9.0,0.0 -2011-10-13,12674,3.0,9.0,0.0 -2011-10-14,11783,4.0,9.0,0.0 -2011-10-15,8104,5.0,9.0,0.0 -2011-10-16,8805,6.0,9.0,0.0 -2011-10-17,12899,0.0,9.0,0.0 -2011-10-18,13196,1.0,9.0,0.0 -2011-10-19,13200,2.0,9.0,0.0 -2011-10-20,13142,3.0,9.0,0.0 -2011-10-21,12269,4.0,9.0,0.0 -2011-10-22,8506,5.0,9.0,0.0 -2011-10-23,9133,6.0,9.0,0.0 -2011-10-24,13230,0.0,9.0,0.0 -2011-10-25,13364,1.0,9.0,0.0 -2011-10-26,13443,2.0,9.0,0.0 -2011-10-27,11080,3.0,9.0,0.0 -2011-10-28,10718,4.0,9.0,0.0 -2011-10-29,7997,5.0,9.0,0.0 -2011-10-30,8613,6.0,9.0,0.0 -2011-10-31,12319,0.0,9.0,0.0 -2011-11-01,12598,1.0,10.0,0.0 -2011-11-02,13218,2.0,10.0,0.0 -2011-11-03,12805,3.0,10.0,0.0 -2011-11-04,12883,4.0,10.0,0.0 -2011-11-05,8569,5.0,10.0,0.0 -2011-11-06,9090,6.0,10.0,0.0 -2011-11-07,11174,0.0,10.0,0.0 -2011-11-08,14122,1.0,10.0,0.0 -2011-11-09,12036,2.0,10.0,0.0 -2011-11-10,12966,3.0,10.0,0.0 -2011-11-11,12005,4.0,10.0,1.0 -2011-11-12,8419,5.0,10.0,0.0 -2011-11-13,9036,6.0,10.0,0.0 -2011-11-14,12804,0.0,10.0,0.0 -2011-11-15,13378,1.0,10.0,0.0 -2011-11-16,12693,2.0,10.0,0.0 -2011-11-17,13360,3.0,10.0,0.0 -2011-11-18,11744,4.0,10.0,0.0 -2011-11-19,8190,5.0,10.0,0.0 -2011-11-20,9690,6.0,10.0,0.0 -2011-11-21,12145,0.0,10.0,0.0 -2011-11-22,13212,1.0,10.0,0.0 -2011-11-23,13477,2.0,10.0,0.0 -2011-11-24,12085,3.0,10.0,1.0 -2011-11-25,10505,4.0,10.0,0.0 -2011-11-26,8705,5.0,10.0,0.0 -2011-11-27,9648,6.0,10.0,0.0 -2011-11-28,13613,0.0,10.0,0.0 -2011-11-29,14272,1.0,10.0,0.0 -2011-11-30,13957,2.0,10.0,0.0 -2011-12-01,14827,3.0,11.0,0.0 -2011-12-02,13591,4.0,11.0,0.0 -2011-12-03,9827,5.0,11.0,0.0 -2011-12-04,10540,6.0,11.0,0.0 -2011-12-05,14286,0.0,11.0,0.0 -2011-12-06,14420,1.0,11.0,0.0 -2011-12-07,13800,2.0,11.0,0.0 -2011-12-08,13077,3.0,11.0,0.0 -2011-12-09,13409,4.0,11.0,0.0 -2011-12-10,9537,5.0,11.0,0.0 -2011-12-11,9686,6.0,11.0,0.0 -2011-12-12,14003,0.0,11.0,0.0 -2011-12-13,13616,1.0,11.0,0.0 -2011-12-14,13695,2.0,11.0,0.0 -2011-12-15,13702,3.0,11.0,0.0 -2011-12-16,13328,4.0,11.0,0.0 -2011-12-17,8779,5.0,11.0,0.0 -2011-12-18,9541,6.0,11.0,0.0 -2011-12-19,13250,0.0,11.0,0.0 -2011-12-20,12924,1.0,11.0,0.0 -2011-12-21,12238,2.0,11.0,0.0 -2011-12-22,11812,3.0,11.0,0.0 -2011-12-23,10407,4.0,11.0,0.0 -2011-12-24,6600,5.0,11.0,0.0 -2011-12-25,5670,6.0,11.0,0.0 -2011-12-26,7446,0.0,11.0,1.0 -2011-12-27,9742,1.0,11.0,0.0 -2011-12-28,10019,2.0,11.0,0.0 -2011-12-29,10927,3.0,11.0,0.0 -2011-12-30,10146,4.0,11.0,0.0 -2012-01-01,6587,6.0,0.0,0.0 -2012-01-02,10254,0.0,0.0,1.0 -2012-01-03,12412,1.0,0.0,0.0 -2012-01-04,11806,2.0,0.0,0.0 -2012-01-05,13030,3.0,0.0,0.0 -2012-01-06,13081,4.0,0.0,0.0 -2012-01-07,9688,5.0,0.0,0.0 -2012-01-08,9682,6.0,0.0,0.0 -2012-01-09,12389,0.0,0.0,0.0 -2012-01-10,12888,1.0,0.0,0.0 -2012-01-11,14916,2.0,0.0,0.0 -2012-01-12,13966,3.0,0.0,0.0 -2012-01-13,13629,4.0,0.0,0.0 -2012-01-14,9862,5.0,0.0,0.0 -2012-01-15,10764,6.0,0.0,0.0 -2012-01-16,14066,0.0,0.0,1.0 -2012-01-17,14636,1.0,0.0,0.0 -2012-01-18,14308,2.0,0.0,0.0 -2012-01-19,14301,3.0,0.0,0.0 -2012-01-20,13525,4.0,0.0,0.0 -2012-01-21,10410,5.0,0.0,0.0 -2012-01-22,10384,6.0,0.0,0.0 -2012-01-23,14114,0.0,0.0,0.0 -2012-01-24,14996,1.0,0.0,0.0 -2012-01-25,14904,2.0,0.0,0.0 -2012-01-26,14957,3.0,0.0,0.0 -2012-01-27,15145,4.0,0.0,0.0 -2012-01-28,11182,5.0,0.0,0.0 -2012-01-29,11845,6.0,0.0,0.0 -2012-01-30,15747,0.0,0.0,0.0 -2012-01-31,16974,1.0,0.0,0.0 -2012-02-01,16410,2.0,1.0,0.0 -2012-02-02,15344,3.0,1.0,0.0 -2012-02-03,15275,4.0,1.0,0.0 -2012-02-04,10634,5.0,1.0,0.0 -2012-02-05,11996,6.0,1.0,0.0 -2012-02-06,13976,0.0,1.0,0.0 -2012-02-07,14838,1.0,1.0,0.0 -2012-02-08,15306,2.0,1.0,0.0 -2012-02-09,15598,3.0,1.0,0.0 -2012-02-10,14349,4.0,1.0,0.0 -2012-02-11,11061,5.0,1.0,0.0 -2012-02-12,12209,6.0,1.0,0.0 -2012-02-13,13869,0.0,1.0,0.0 -2012-02-14,15581,1.0,1.0,0.0 -2012-02-15,13850,2.0,1.0,0.0 -2012-02-16,15864,3.0,1.0,0.0 -2012-02-17,15855,4.0,1.0,0.0 -2012-02-18,11506,5.0,1.0,0.0 -2012-02-19,12713,6.0,1.0,0.0 -2012-02-20,15871,0.0,1.0,1.0 -2012-02-21,18141,1.0,1.0,0.0 -2012-02-22,18658,2.0,1.0,0.0 -2012-02-23,18336,3.0,1.0,0.0 -2012-02-24,17493,4.0,1.0,0.0 -2012-02-25,13047,5.0,1.0,0.0 -2012-02-26,13470,6.0,1.0,0.0 -2012-02-27,18588,0.0,1.0,0.0 -2012-02-28,19337,1.0,1.0,0.0 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-2015-07-30,86852,3.0,6.0,0.0 -2015-07-31,80715,4.0,6.0,0.0 -2015-08-01,49693,5.0,7.0,0.0 -2015-08-02,51980,6.0,7.0,0.0 -2015-08-03,83065,0.0,7.0,0.0 -2015-08-04,87753,1.0,7.0,0.0 -2015-08-05,87047,2.0,7.0,0.0 -2015-08-06,85675,3.0,7.0,0.0 -2015-08-07,79329,4.0,7.0,0.0 -2015-08-08,50372,5.0,7.0,0.0 -2015-08-09,53900,6.0,7.0,0.0 -2015-08-10,84498,0.0,7.0,0.0 -2015-08-11,88065,1.0,7.0,0.0 -2015-08-12,88003,2.0,7.0,0.0 -2015-08-13,86159,3.0,7.0,0.0 -2015-08-14,80407,4.0,7.0,0.0 -2015-08-15,52148,5.0,7.0,0.0 -2015-08-16,55563,6.0,7.0,0.0 -2015-08-17,85716,0.0,7.0,0.0 -2015-08-18,90098,1.0,7.0,0.0 -2015-08-19,90311,2.0,7.0,0.0 -2015-08-20,89112,3.0,7.0,0.0 -2015-08-21,83607,4.0,7.0,0.0 -2015-08-22,54685,5.0,7.0,0.0 -2015-08-23,59679,6.0,7.0,0.0 -2015-08-24,87916,0.0,7.0,0.0 -2015-08-25,89785,1.0,7.0,0.0 -2015-08-26,90842,2.0,7.0,0.0 -2015-08-27,89589,3.0,7.0,0.0 -2015-08-28,84012,4.0,7.0,0.0 -2015-08-29,52998,5.0,7.0,0.0 -2015-08-30,55886,6.0,7.0,0.0 -2015-08-31,86983,0.0,7.0,0.0 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-2015-10-04,62592,6.0,9.0,0.0 -2015-10-05,95603,0.0,9.0,0.0 -2015-10-06,99837,1.0,9.0,0.0 -2015-10-07,100860,2.0,9.0,0.0 -2015-10-08,102409,3.0,9.0,0.0 -2015-10-09,95631,4.0,9.0,0.0 -2015-10-10,66043,5.0,9.0,0.0 -2015-10-11,66601,6.0,9.0,0.0 -2015-10-12,98066,0.0,9.0,1.0 -2015-10-13,106570,1.0,9.0,0.0 -2015-10-14,105415,2.0,9.0,0.0 -2015-10-15,104366,3.0,9.0,0.0 -2015-10-16,97556,4.0,9.0,0.0 -2015-10-17,64064,5.0,9.0,0.0 -2015-10-18,69221,6.0,9.0,0.0 -2015-10-19,105710,0.0,9.0,0.0 -2015-10-20,108226,1.0,9.0,0.0 -2015-10-21,107216,2.0,9.0,0.0 -2015-10-22,106180,3.0,9.0,0.0 -2015-10-23,99348,4.0,9.0,0.0 -2015-10-24,67090,5.0,9.0,0.0 -2015-10-25,73283,6.0,9.0,0.0 -2015-10-26,104805,0.0,9.0,0.0 -2015-10-27,111076,1.0,9.0,0.0 -2015-10-28,110991,2.0,9.0,0.0 -2015-10-29,109068,3.0,9.0,0.0 -2015-10-30,100655,4.0,9.0,0.0 -2015-10-31,63910,5.0,9.0,0.0 -2015-11-01,67454,6.0,10.0,0.0 -2015-11-02,106405,0.0,10.0,0.0 -2015-11-03,113189,1.0,10.0,0.0 -2015-11-04,112399,2.0,10.0,0.0 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-2017-06-01,165586,3.0,5.0,0.0 -2017-06-02,154671,4.0,5.0,0.0 -2017-06-03,99082,5.0,5.0,0.0 diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/helper.py b/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/helper.py deleted file mode 100644 index f67458c95..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/helper.py +++ /dev/null @@ -1,178 +0,0 @@ -import pandas as pd -from azureml.core import Environment -from azureml.core.conda_dependencies import CondaDependencies -from azureml.train.estimator import Estimator -from azureml.core.run import Run -from azureml.automl.core.shared import constants - - -def split_fraction_by_grain(df, fraction, time_column_name, grain_column_names=None): - if not grain_column_names: - df["tmp_grain_column"] = "grain" - grain_column_names = ["tmp_grain_column"] - - """Group df by grain and split on last n rows for each group.""" - df_grouped = df.sort_values(time_column_name).groupby( - grain_column_names, group_keys=False - ) - - df_head = df_grouped.apply( - lambda dfg: dfg.iloc[: -int(len(dfg) * fraction)] if fraction > 0 else dfg - ) - - df_tail = df_grouped.apply( - lambda dfg: dfg.iloc[-int(len(dfg) * fraction) :] if fraction > 0 else dfg[:0] - ) - - if "tmp_grain_column" in grain_column_names: - for df2 in (df, df_head, df_tail): - df2.drop("tmp_grain_column", axis=1, inplace=True) - - grain_column_names.remove("tmp_grain_column") - - return df_head, df_tail - - -def split_full_for_forecasting( - df, time_column_name, grain_column_names=None, test_split=0.2 -): - index_name = df.index.name - - # Assumes that there isn't already a column called tmpindex - - df["tmpindex"] = df.index - - train_df, test_df = split_fraction_by_grain( - df, test_split, time_column_name, grain_column_names - ) - - train_df = train_df.set_index("tmpindex") - train_df.index.name = index_name - - test_df = test_df.set_index("tmpindex") - test_df.index.name = index_name - - df.drop("tmpindex", axis=1, inplace=True) - - return train_df, test_df - - -def get_result_df(remote_run): - children = list(remote_run.get_children(recursive=True)) - summary_df = pd.DataFrame( - index=["run_id", "run_algorithm", "primary_metric", "Score"] - ) - goal_minimize = False - for run in children: - if ( - run.get_status().lower() == constants.RunState.COMPLETE_RUN - and "run_algorithm" in run.properties - and "score" in run.properties - ): - # We only count in the completed child runs. - summary_df[run.id] = [ - run.id, - run.properties["run_algorithm"], - run.properties["primary_metric"], - float(run.properties["score"]), - ] - if "goal" in run.properties: - goal_minimize = run.properties["goal"].split("_")[-1] == "min" - - summary_df = summary_df.T.sort_values( - "Score", ascending=goal_minimize - ).drop_duplicates(["run_algorithm"]) - summary_df = summary_df.set_index("run_algorithm") - return summary_df - - -def run_inference( - test_experiment, - compute_target, - script_folder, - train_run, - test_dataset, - lookback_dataset, - max_horizon, - target_column_name, - time_column_name, - freq, -): - model_base_name = "model.pkl" - if "model_data_location" in train_run.properties: - model_location = train_run.properties["model_data_location"] - _, model_base_name = model_location.rsplit("/", 1) - train_run.download_file( - "outputs/{}".format(model_base_name), "inference/{}".format(model_base_name) - ) - - inference_env = train_run.get_environment() - - est = Estimator( - source_directory=script_folder, - entry_script="infer.py", - script_params={ - "--max_horizon": max_horizon, - "--target_column_name": target_column_name, - "--time_column_name": time_column_name, - "--frequency": freq, - "--model_path": model_base_name, - }, - inputs=[ - test_dataset.as_named_input("test_data"), - lookback_dataset.as_named_input("lookback_data"), - ], - compute_target=compute_target, - environment_definition=inference_env, - ) - - run = test_experiment.submit( - est, - tags={ - "training_run_id": train_run.id, - "run_algorithm": train_run.properties["run_algorithm"], - "valid_score": train_run.properties["score"], - "primary_metric": train_run.properties["primary_metric"], - }, - ) - - run.log("run_algorithm", run.tags["run_algorithm"]) - return run - - -def run_multiple_inferences( - summary_df, - train_experiment, - test_experiment, - compute_target, - script_folder, - test_dataset, - lookback_dataset, - max_horizon, - target_column_name, - time_column_name, - freq, -): - for run_name, run_summary in summary_df.iterrows(): - print(run_name) - print(run_summary) - run_id = run_summary.run_id - train_run = Run(train_experiment, run_id) - - test_run = run_inference( - test_experiment, - compute_target, - script_folder, - train_run, - test_dataset, - lookback_dataset, - max_horizon, - target_column_name, - time_column_name, - freq, - ) - - print(test_run) - summary_df.loc[summary_df.run_id == run_id, "test_run_id"] = test_run.id - - return summary_df diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/infer.py b/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/infer.py deleted file mode 100644 index 7b2f1eee4..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/infer.py +++ /dev/null @@ -1,386 +0,0 @@ -import argparse -import os - -import numpy as np -import pandas as pd - -from pandas.tseries.frequencies import to_offset -from sklearn.externals import joblib -from sklearn.metrics import mean_absolute_error, mean_squared_error - -from azureml.automl.runtime.shared.score import scoring, constants -from azureml.core import Run - -try: - import torch - - _torch_present = True -except ImportError: - _torch_present = False - - -def align_outputs( - y_predicted, - X_trans, - X_test, - y_test, - predicted_column_name="predicted", - horizon_colname="horizon_origin", -): - """ - Demonstrates how to get the output aligned to the inputs - using pandas indexes. Helps understand what happened if - the output's shape differs from the input shape, or if - the data got re-sorted by time and grain during forecasting. - - Typical causes of misalignment are: - * we predicted some periods that were missing in actuals -> drop from eval - * model was asked to predict past max_horizon -> increase max horizon - * data at start of X_test was needed for lags -> provide previous periods - """ - if horizon_colname in X_trans: - df_fcst = pd.DataFrame( - { - predicted_column_name: y_predicted, - horizon_colname: X_trans[horizon_colname], - } - ) - else: - df_fcst = pd.DataFrame({predicted_column_name: y_predicted}) - - # y and X outputs are aligned by forecast() function contract - df_fcst.index = X_trans.index - - # align original X_test to y_test - X_test_full = X_test.copy() - X_test_full[target_column_name] = y_test - - # X_test_full's index does not include origin, so reset for merge - df_fcst.reset_index(inplace=True) - X_test_full = X_test_full.reset_index().drop(columns="index") - together = df_fcst.merge(X_test_full, how="right") - - # drop rows where prediction or actuals are nan - # happens because of missing actuals - # or at edges of time due to lags/rolling windows - clean = together[ - together[[target_column_name, predicted_column_name]].notnull().all(axis=1) - ] - return clean - - -def do_rolling_forecast_with_lookback( - fitted_model, X_test, y_test, max_horizon, X_lookback, y_lookback, freq="D" -): - """ - Produce forecasts on a rolling origin over the given test set. - - Each iteration makes a forecast for the next 'max_horizon' periods - with respect to the current origin, then advances the origin by the - horizon time duration. The prediction context for each forecast is set so - that the forecaster uses the actual target values prior to the current - origin time for constructing lag features. - - This function returns a concatenated DataFrame of rolling forecasts. - """ - print("Using lookback of size: ", y_lookback.size) - df_list = [] - origin_time = X_test[time_column_name].min() - X = X_lookback.append(X_test) - y = np.concatenate((y_lookback, y_test), axis=0) - while origin_time <= X_test[time_column_name].max(): - # Set the horizon time - end date of the forecast - horizon_time = origin_time + max_horizon * to_offset(freq) - - # Extract test data from an expanding window up-to the horizon - expand_wind = X[time_column_name] < horizon_time - X_test_expand = X[expand_wind] - y_query_expand = np.zeros(len(X_test_expand)).astype(np.float) - y_query_expand.fill(np.NaN) - - if origin_time != X[time_column_name].min(): - # Set the context by including actuals up-to the origin time - test_context_expand_wind = X[time_column_name] < origin_time - context_expand_wind = X_test_expand[time_column_name] < origin_time - y_query_expand[context_expand_wind] = y[test_context_expand_wind] - - # Print some debug info - print( - "Horizon_time:", - horizon_time, - " origin_time: ", - origin_time, - " max_horizon: ", - max_horizon, - " freq: ", - freq, - ) - print("expand_wind: ", expand_wind) - print("y_query_expand") - print(y_query_expand) - print("X_test") - print(X) - print("X_test_expand") - print(X_test_expand) - print("Type of X_test_expand: ", type(X_test_expand)) - print("Type of y_query_expand: ", type(y_query_expand)) - - print("y_query_expand") - print(y_query_expand) - - # Make a forecast out to the maximum horizon - # y_fcst, X_trans = y_query_expand, X_test_expand - y_fcst, X_trans = fitted_model.forecast(X_test_expand, y_query_expand) - - print("y_fcst") - print(y_fcst) - - # Align forecast with test set for dates within - # the current rolling window - trans_tindex = X_trans.index.get_level_values(time_column_name) - trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_time) - test_roll_wind = expand_wind & (X[time_column_name] >= origin_time) - df_list.append( - align_outputs( - y_fcst[trans_roll_wind], - X_trans[trans_roll_wind], - X[test_roll_wind], - y[test_roll_wind], - ) - ) - - # Advance the origin time - origin_time = horizon_time - - return pd.concat(df_list, ignore_index=True) - - -def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq="D"): - """ - Produce forecasts on a rolling origin over the given test set. - - Each iteration makes a forecast for the next 'max_horizon' periods - with respect to the current origin, then advances the origin by the - horizon time duration. The prediction context for each forecast is set so - that the forecaster uses the actual target values prior to the current - origin time for constructing lag features. - - This function returns a concatenated DataFrame of rolling forecasts. - """ - df_list = [] - origin_time = X_test[time_column_name].min() - while origin_time <= X_test[time_column_name].max(): - # Set the horizon time - end date of the forecast - horizon_time = origin_time + max_horizon * to_offset(freq) - - # Extract test data from an expanding window up-to the horizon - expand_wind = X_test[time_column_name] < horizon_time - X_test_expand = X_test[expand_wind] - y_query_expand = np.zeros(len(X_test_expand)).astype(np.float) - y_query_expand.fill(np.NaN) - - if origin_time != X_test[time_column_name].min(): - # Set the context by including actuals up-to the origin time - test_context_expand_wind = X_test[time_column_name] < origin_time - context_expand_wind = X_test_expand[time_column_name] < origin_time - y_query_expand[context_expand_wind] = y_test[test_context_expand_wind] - - # Print some debug info - print( - "Horizon_time:", - horizon_time, - " origin_time: ", - origin_time, - " max_horizon: ", - max_horizon, - " freq: ", - freq, - ) - print("expand_wind: ", expand_wind) - print("y_query_expand") - print(y_query_expand) - print("X_test") - print(X_test) - print("X_test_expand") - print(X_test_expand) - print("Type of X_test_expand: ", type(X_test_expand)) - print("Type of y_query_expand: ", type(y_query_expand)) - print("y_query_expand") - print(y_query_expand) - - # Make a forecast out to the maximum horizon - y_fcst, X_trans = fitted_model.forecast(X_test_expand, y_query_expand) - - print("y_fcst") - print(y_fcst) - - # Align forecast with test set for dates within the - # current rolling window - trans_tindex = X_trans.index.get_level_values(time_column_name) - trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_time) - test_roll_wind = expand_wind & (X_test[time_column_name] >= origin_time) - df_list.append( - align_outputs( - y_fcst[trans_roll_wind], - X_trans[trans_roll_wind], - X_test[test_roll_wind], - y_test[test_roll_wind], - ) - ) - - # Advance the origin time - origin_time = horizon_time - - return pd.concat(df_list, ignore_index=True) - - -def APE(actual, pred): - """ - Calculate absolute percentage error. - Returns a vector of APE values with same length as actual/pred. - """ - return 100 * np.abs((actual - pred) / actual) - - -def MAPE(actual, pred): - """ - Calculate mean absolute percentage error. - Remove NA and values where actual is close to zero - """ - not_na = ~(np.isnan(actual) | np.isnan(pred)) - not_zero = ~np.isclose(actual, 0.0) - actual_safe = actual[not_na & not_zero] - pred_safe = pred[not_na & not_zero] - return np.mean(APE(actual_safe, pred_safe)) - - -def map_location_cuda(storage, loc): - return storage.cuda() - - -parser = argparse.ArgumentParser() -parser.add_argument( - "--max_horizon", - type=int, - dest="max_horizon", - default=10, - help="Max Horizon for forecasting", -) -parser.add_argument( - "--target_column_name", - type=str, - dest="target_column_name", - help="Target Column Name", -) -parser.add_argument( - "--time_column_name", type=str, dest="time_column_name", help="Time Column Name" -) -parser.add_argument( - "--frequency", type=str, dest="freq", help="Frequency of prediction" -) -parser.add_argument( - "--model_path", - type=str, - dest="model_path", - default="model.pkl", - help="Filename of model to be loaded", -) - -args = parser.parse_args() -max_horizon = args.max_horizon -target_column_name = args.target_column_name -time_column_name = args.time_column_name -freq = args.freq -model_path = args.model_path - -print("args passed are: ") -print(max_horizon) -print(target_column_name) -print(time_column_name) -print(freq) -print(model_path) - -run = Run.get_context() -# get input dataset by name -test_dataset = run.input_datasets["test_data"] -lookback_dataset = run.input_datasets["lookback_data"] - -grain_column_names = [] - -df = test_dataset.to_pandas_dataframe() - -print("Read df") -print(df) - -X_test_df = test_dataset.drop_columns(columns=[target_column_name]) -y_test_df = test_dataset.with_timestamp_columns(None).keep_columns( - columns=[target_column_name] -) - -X_lookback_df = lookback_dataset.drop_columns(columns=[target_column_name]) -y_lookback_df = lookback_dataset.with_timestamp_columns(None).keep_columns( - columns=[target_column_name] -) - -_, ext = os.path.splitext(model_path) -if ext == ".pt": - # Load the fc-tcn torch model. - assert _torch_present - if torch.cuda.is_available(): - map_location = map_location_cuda - else: - map_location = "cpu" - with open(model_path, "rb") as fh: - fitted_model = torch.load(fh, map_location=map_location) -else: - # Load the sklearn pipeline. - fitted_model = joblib.load(model_path) - -if hasattr(fitted_model, "get_lookback"): - lookback = fitted_model.get_lookback() - df_all = do_rolling_forecast_with_lookback( - fitted_model, - X_test_df.to_pandas_dataframe(), - y_test_df.to_pandas_dataframe().values.T[0], - max_horizon, - X_lookback_df.to_pandas_dataframe()[-lookback:], - y_lookback_df.to_pandas_dataframe().values.T[0][-lookback:], - freq, - ) -else: - df_all = do_rolling_forecast( - fitted_model, - X_test_df.to_pandas_dataframe(), - y_test_df.to_pandas_dataframe().values.T[0], - max_horizon, - freq, - ) - -print(df_all) - -print("target values:::") -print(df_all[target_column_name]) -print("predicted values:::") -print(df_all["predicted"]) - -# Use the AutoML scoring module -regression_metrics = list(constants.REGRESSION_SCALAR_SET) -y_test = np.array(df_all[target_column_name]) -y_pred = np.array(df_all["predicted"]) -scores = scoring.score_regression(y_test, y_pred, regression_metrics) - -print("scores:") -print(scores) - -for key, value in scores.items(): - run.log(key, value) - -print("Simple forecasting model") -rmse = np.sqrt(mean_squared_error(df_all[target_column_name], df_all["predicted"])) -print("[Test Data] \nRoot Mean squared error: %.2f" % rmse) -mae = mean_absolute_error(df_all[target_column_name], df_all["predicted"]) -print("mean_absolute_error score: %.2f" % mae) -print("MAPE: %.2f" % MAPE(df_all[target_column_name], df_all["predicted"])) - -run.log("rmse", rmse) -run.log("mae", mae) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/Data/hts-sample-test.csv b/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/Data/hts-sample-test.csv deleted file mode 100644 index 646c62726..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/Data/hts-sample-test.csv +++ /dev/null @@ -1,1351 +0,0 @@ -date,state,store_id,product_category,SKU,price -2016-07-28,CA,1,A,A1,7.92 -2016-07-29,CA,1,A,A1,7.88 -2016-07-30,CA,1,A,A1,8.0 -2016-07-31,CA,1,A,A1,7.94 -2016-08-01,CA,1,A,A1,8.0 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-2016-08-08,FL,2,A,A3,3.46 -2016-08-09,FL,2,A,A3,3.52 -2016-08-10,FL,2,A,A3,3.45 -2016-08-11,FL,2,A,A3,3.49 -2016-08-12,FL,2,A,A3,3.53 -2016-08-13,FL,2,A,A3,3.49 -2016-08-14,FL,2,A,A3,3.46 -2016-08-15,FL,2,A,A3,3.44 -2016-08-16,FL,2,A,A3,3.47 -2016-08-17,FL,2,A,A3,3.5 -2016-08-18,FL,2,A,A3,3.52 -2016-08-19,FL,2,A,A3,3.48 -2016-08-20,FL,2,A,A3,3.5 -2016-08-21,FL,2,A,A3,3.45 -2016-08-22,FL,2,A,A3,3.46 -2016-08-23,FL,2,A,A3,3.49 -2016-08-24,FL,2,A,A3,3.52 -2016-08-25,FL,2,A,A3,3.47 -2016-08-26,FL,2,A,A3,3.43 -2016-07-28,FL,2,B,B1,2.5 -2016-07-29,FL,2,B,B1,2.5 -2016-07-30,FL,2,B,B1,2.48 -2016-07-31,FL,2,B,B1,2.5 -2016-08-01,FL,2,B,B1,2.48 -2016-08-02,FL,2,B,B1,2.47 -2016-08-03,FL,2,B,B1,2.52 -2016-08-04,FL,2,B,B1,2.54 -2016-08-05,FL,2,B,B1,2.53 -2016-08-06,FL,2,B,B1,2.51 -2016-08-07,FL,2,B,B1,2.5 -2016-08-08,FL,2,B,B1,2.51 -2016-08-09,FL,2,B,B1,2.51 -2016-08-10,FL,2,B,B1,2.44 -2016-08-11,FL,2,B,B1,2.51 -2016-08-12,FL,2,B,B1,2.5 -2016-08-13,FL,2,B,B1,2.49 -2016-08-14,FL,2,B,B1,2.47 -2016-08-15,FL,2,B,B1,2.51 -2016-08-16,FL,2,B,B1,2.48 -2016-08-17,FL,2,B,B1,2.51 -2016-08-18,FL,2,B,B1,2.54 -2016-08-19,FL,2,B,B1,2.49 -2016-08-20,FL,2,B,B1,2.5 -2016-08-21,FL,2,B,B1,2.48 -2016-08-22,FL,2,B,B1,2.49 -2016-08-23,FL,2,B,B1,2.47 -2016-08-24,FL,2,B,B1,2.49 -2016-08-25,FL,2,B,B1,2.52 -2016-08-26,FL,2,B,B1,2.5 -2016-07-28,FL,2,B,B2,7.98 -2016-07-29,FL,2,B,B2,7.91 -2016-07-30,FL,2,B,B2,8.01 -2016-07-31,FL,2,B,B2,7.95 -2016-08-01,FL,2,B,B2,7.93 -2016-08-02,FL,2,B,B2,8.01 -2016-08-03,FL,2,B,B2,7.93 -2016-08-04,FL,2,B,B2,8.1 -2016-08-05,FL,2,B,B2,8.01 -2016-08-06,FL,2,B,B2,7.94 -2016-08-07,FL,2,B,B2,7.82 -2016-08-08,FL,2,B,B2,8.0 -2016-08-09,FL,2,B,B2,7.86 -2016-08-10,FL,2,B,B2,7.91 -2016-08-11,FL,2,B,B2,7.99 -2016-08-12,FL,2,B,B2,7.91 -2016-08-13,FL,2,B,B2,7.97 -2016-08-14,FL,2,B,B2,8.0 -2016-08-15,FL,2,B,B2,7.82 -2016-08-16,FL,2,B,B2,7.97 -2016-08-17,FL,2,B,B2,7.84 -2016-08-18,FL,2,B,B2,8.06 -2016-08-19,FL,2,B,B2,7.99 -2016-08-20,FL,2,B,B2,8.0 -2016-08-21,FL,2,B,B2,7.94 -2016-08-22,FL,2,B,B2,7.94 -2016-08-23,FL,2,B,B2,8.04 -2016-08-24,FL,2,B,B2,8.01 -2016-08-25,FL,2,B,B2,7.89 -2016-08-26,FL,2,B,B2,7.86 -2016-07-28,FL,3,A,A1,4.05 -2016-07-29,FL,3,A,A1,4.18 -2016-07-30,FL,3,A,A1,4.13 -2016-07-31,FL,3,A,A1,4.12 -2016-08-01,FL,3,A,A1,4.09 -2016-08-02,FL,3,A,A1,4.14 -2016-08-03,FL,3,A,A1,4.14 -2016-08-04,FL,3,A,A1,4.16 -2016-08-05,FL,3,A,A1,4.13 -2016-08-06,FL,3,A,A1,4.07 -2016-08-07,FL,3,A,A1,4.13 -2016-08-08,FL,3,A,A1,4.12 -2016-08-09,FL,3,A,A1,4.14 -2016-08-10,FL,3,A,A1,4.08 -2016-08-11,FL,3,A,A1,4.11 -2016-08-12,FL,3,A,A1,4.16 -2016-08-13,FL,3,A,A1,4.1 -2016-08-14,FL,3,A,A1,4.18 -2016-08-15,FL,3,A,A1,4.15 -2016-08-16,FL,3,A,A1,4.11 -2016-08-17,FL,3,A,A1,4.1 -2016-08-18,FL,3,A,A1,4.12 -2016-08-19,FL,3,A,A1,4.19 -2016-08-20,FL,3,A,A1,4.15 -2016-08-21,FL,3,A,A1,4.05 -2016-08-22,FL,3,A,A1,4.08 -2016-08-23,FL,3,A,A1,4.13 -2016-08-24,FL,3,A,A1,4.05 -2016-08-25,FL,3,A,A1,4.17 -2016-08-26,FL,3,A,A1,4.11 -2016-07-28,FL,3,A,A2,7.68 -2016-07-29,FL,3,A,A2,7.78 -2016-07-30,FL,3,A,A2,7.68 -2016-07-31,FL,3,A,A2,7.71 -2016-08-01,FL,3,A,A2,7.71 -2016-08-02,FL,3,A,A2,7.74 -2016-08-03,FL,3,A,A2,7.84 -2016-08-04,FL,3,A,A2,7.63 -2016-08-05,FL,3,A,A2,7.77 -2016-08-06,FL,3,A,A2,7.6 -2016-08-07,FL,3,A,A2,7.74 -2016-08-08,FL,3,A,A2,7.67 -2016-08-09,FL,3,A,A2,7.76 -2016-08-10,FL,3,A,A2,7.68 -2016-08-11,FL,3,A,A2,7.81 -2016-08-12,FL,3,A,A2,7.64 -2016-08-13,FL,3,A,A2,7.61 -2016-08-14,FL,3,A,A2,7.72 -2016-08-15,FL,3,A,A2,7.71 -2016-08-16,FL,3,A,A2,7.68 -2016-08-17,FL,3,A,A2,7.82 -2016-08-18,FL,3,A,A2,7.78 -2016-08-19,FL,3,A,A2,7.69 -2016-08-20,FL,3,A,A2,7.75 -2016-08-21,FL,3,A,A2,7.67 -2016-08-22,FL,3,A,A2,7.71 -2016-08-23,FL,3,A,A2,7.67 -2016-08-24,FL,3,A,A2,7.81 -2016-08-25,FL,3,A,A2,7.69 -2016-08-26,FL,3,A,A2,7.71 -2016-07-28,FL,3,A,A3,5.46 -2016-07-29,FL,3,A,A3,5.33 -2016-07-30,FL,3,A,A3,5.45 -2016-07-31,FL,3,A,A3,5.42 -2016-08-01,FL,3,A,A3,5.47 -2016-08-02,FL,3,A,A3,5.43 -2016-08-03,FL,3,A,A3,5.4 -2016-08-04,FL,3,A,A3,5.51 -2016-08-05,FL,3,A,A3,5.46 -2016-08-06,FL,3,A,A3,5.41 -2016-08-07,FL,3,A,A3,5.38 -2016-08-08,FL,3,A,A3,5.37 -2016-08-09,FL,3,A,A3,5.33 -2016-08-10,FL,3,A,A3,5.4 -2016-08-11,FL,3,A,A3,5.45 -2016-08-12,FL,3,A,A3,5.34 -2016-08-13,FL,3,A,A3,5.4 -2016-08-14,FL,3,A,A3,5.39 -2016-08-15,FL,3,A,A3,5.38 -2016-08-16,FL,3,A,A3,5.34 -2016-08-17,FL,3,A,A3,5.38 -2016-08-18,FL,3,A,A3,5.41 -2016-08-19,FL,3,A,A3,5.33 -2016-08-20,FL,3,A,A3,5.38 -2016-08-21,FL,3,A,A3,5.32 -2016-08-22,FL,3,A,A3,5.46 -2016-08-23,FL,3,A,A3,5.45 -2016-08-24,FL,3,A,A3,5.38 -2016-08-25,FL,3,A,A3,5.44 -2016-08-26,FL,3,A,A3,5.38 -2016-07-28,FL,3,B,B1,2.49 -2016-07-29,FL,3,B,B1,2.49 -2016-07-30,FL,3,B,B1,2.48 -2016-07-31,FL,3,B,B1,2.47 -2016-08-01,FL,3,B,B1,2.48 -2016-08-02,FL,3,B,B1,2.48 -2016-08-03,FL,3,B,B1,2.47 -2016-08-04,FL,3,B,B1,2.48 -2016-08-05,FL,3,B,B1,2.44 -2016-08-06,FL,3,B,B1,2.45 -2016-08-07,FL,3,B,B1,2.49 -2016-08-08,FL,3,B,B1,2.48 -2016-08-09,FL,3,B,B1,2.48 -2016-08-10,FL,3,B,B1,2.42 -2016-08-11,FL,3,B,B1,2.45 -2016-08-12,FL,3,B,B1,2.45 -2016-08-13,FL,3,B,B1,2.45 -2016-08-14,FL,3,B,B1,2.44 -2016-08-15,FL,3,B,B1,2.49 -2016-08-16,FL,3,B,B1,2.43 -2016-08-17,FL,3,B,B1,2.48 -2016-08-18,FL,3,B,B1,2.44 -2016-08-19,FL,3,B,B1,2.48 -2016-08-20,FL,3,B,B1,2.46 -2016-08-21,FL,3,B,B1,2.42 -2016-08-22,FL,3,B,B1,2.47 -2016-08-23,FL,3,B,B1,2.43 -2016-08-24,FL,3,B,B1,2.43 -2016-08-25,FL,3,B,B1,2.44 -2016-08-26,FL,3,B,B1,2.43 -2016-07-28,FL,3,B,B2,6.82 -2016-07-29,FL,3,B,B2,6.83 -2016-07-30,FL,3,B,B2,6.8 -2016-07-31,FL,3,B,B2,6.96 -2016-08-01,FL,3,B,B2,6.78 -2016-08-02,FL,3,B,B2,6.69 -2016-08-03,FL,3,B,B2,6.78 -2016-08-04,FL,3,B,B2,6.93 -2016-08-05,FL,3,B,B2,6.86 -2016-08-06,FL,3,B,B2,6.83 -2016-08-07,FL,3,B,B2,6.76 -2016-08-08,FL,3,B,B2,6.86 -2016-08-09,FL,3,B,B2,6.84 -2016-08-10,FL,3,B,B2,6.86 -2016-08-11,FL,3,B,B2,6.87 -2016-08-12,FL,3,B,B2,6.86 -2016-08-13,FL,3,B,B2,6.74 -2016-08-14,FL,3,B,B2,6.77 -2016-08-15,FL,3,B,B2,6.9 -2016-08-16,FL,3,B,B2,6.81 -2016-08-17,FL,3,B,B2,6.86 -2016-08-18,FL,3,B,B2,6.92 -2016-08-19,FL,3,B,B2,6.84 -2016-08-20,FL,3,B,B2,6.87 -2016-08-21,FL,3,B,B2,6.98 -2016-08-22,FL,3,B,B2,6.84 -2016-08-23,FL,3,B,B2,6.81 -2016-08-24,FL,3,B,B2,6.77 -2016-08-25,FL,3,B,B2,6.78 -2016-08-26,FL,3,B,B2,6.89 -2016-07-28,WA,1,A,A1,3.47 -2016-07-29,WA,1,A,A1,3.54 -2016-07-30,WA,1,A,A1,3.49 -2016-07-31,WA,1,A,A1,3.48 -2016-08-01,WA,1,A,A1,3.52 -2016-08-02,WA,1,A,A1,3.48 -2016-08-03,WA,1,A,A1,3.47 -2016-08-04,WA,1,A,A1,3.53 -2016-08-05,WA,1,A,A1,3.53 -2016-08-06,WA,1,A,A1,3.42 -2016-08-07,WA,1,A,A1,3.52 -2016-08-08,WA,1,A,A1,3.51 -2016-08-09,WA,1,A,A1,3.48 -2016-08-10,WA,1,A,A1,3.46 -2016-08-11,WA,1,A,A1,3.46 -2016-08-12,WA,1,A,A1,3.49 -2016-08-13,WA,1,A,A1,3.51 -2016-08-14,WA,1,A,A1,3.47 -2016-08-15,WA,1,A,A1,3.52 -2016-08-16,WA,1,A,A1,3.49 -2016-08-17,WA,1,A,A1,3.54 -2016-08-18,WA,1,A,A1,3.5 -2016-08-19,WA,1,A,A1,3.5 -2016-08-20,WA,1,A,A1,3.45 -2016-08-21,WA,1,A,A1,3.44 -2016-08-22,WA,1,A,A1,3.52 -2016-08-23,WA,1,A,A1,3.48 -2016-08-24,WA,1,A,A1,3.48 -2016-08-25,WA,1,A,A1,3.46 -2016-08-26,WA,1,A,A1,3.52 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-2015-09-23,CA,1,A,A1,7.89,7.0 -2015-09-24,CA,1,A,A1,7.93,7.0 -2015-09-25,CA,1,A,A1,7.83,8.0 -2015-09-26,CA,1,A,A1,7.97,7.0 -2015-09-27,CA,1,A,A1,7.81,8.0 -2015-09-28,CA,1,A,A1,7.96,7.0 -2015-09-29,CA,1,A,A1,7.92,8.0 -2015-09-30,CA,1,A,A1,8.0,7.0 -2015-10-01,CA,1,A,A1,7.93,7.0 -2015-10-02,CA,1,A,A1,7.98,7.0 -2015-10-03,CA,1,A,A1,8.02,8.0 -2015-10-04,CA,1,A,A1,7.89,8.0 -2015-10-05,CA,1,A,A1,7.99,7.0 -2015-10-06,CA,1,A,A1,7.94,7.0 -2015-10-07,CA,1,A,A1,7.99,7.0 -2015-10-08,CA,1,A,A1,8.02,7.0 -2015-10-09,CA,1,A,A1,7.97,7.0 -2015-10-10,CA,1,A,A1,7.91,8.0 -2015-10-11,CA,1,A,A1,7.84,7.0 -2015-10-12,CA,1,A,A1,7.98,7.0 -2015-10-13,CA,1,A,A1,7.84,7.0 -2015-10-14,CA,1,A,A1,7.95,7.0 -2015-10-15,CA,1,A,A1,7.95,7.0 -2015-10-16,CA,1,A,A1,8.0,7.0 -2015-10-17,CA,1,A,A1,7.83,8.0 -2015-10-18,CA,1,A,A1,7.92,8.0 -2015-10-19,CA,1,A,A1,7.92,7.0 -2015-10-20,CA,1,A,A1,7.84,8.0 -2015-10-21,CA,1,A,A1,7.94,8.0 -2015-10-22,CA,1,A,A1,7.82,8.0 -2015-10-23,CA,1,A,A1,7.96,8.0 -2015-10-24,CA,1,A,A1,8.0,8.0 -2015-10-25,CA,1,A,A1,7.88,8.0 -2015-10-26,CA,1,A,A1,7.77,8.0 -2015-10-27,CA,1,A,A1,8.04,7.0 -2015-10-28,CA,1,A,A1,8.0,7.0 -2015-10-29,CA,1,A,A1,8.0,7.0 -2015-10-30,CA,1,A,A1,8.03,8.0 -2015-10-31,CA,1,A,A1,7.93,8.0 -2015-11-01,CA,1,A,A1,7.94,7.0 -2015-11-02,CA,1,A,A1,7.84,8.0 -2015-11-03,CA,1,A,A1,7.98,7.0 -2015-11-04,CA,1,A,A1,7.87,7.0 -2015-11-05,CA,1,A,A1,7.92,8.0 -2015-11-06,CA,1,A,A1,7.74,7.0 -2015-11-07,CA,1,A,A1,8.01,7.0 -2015-11-08,CA,1,A,A1,7.87,8.0 -2015-11-09,CA,1,A,A1,7.74,8.0 -2015-11-10,CA,1,A,A1,7.94,7.0 -2015-11-11,CA,1,A,A1,7.87,8.0 -2015-11-12,CA,1,A,A1,7.96,7.0 -2015-11-13,CA,1,A,A1,7.9,7.0 -2015-11-14,CA,1,A,A1,7.84,8.0 -2015-11-15,CA,1,A,A1,7.85,8.0 -2015-11-16,CA,1,A,A1,7.84,7.0 -2015-11-17,CA,1,A,A1,7.99,7.0 -2015-11-18,CA,1,A,A1,8.11,8.0 -2015-11-19,CA,1,A,A1,7.94,7.0 -2015-11-20,CA,1,A,A1,7.99,7.0 -2015-11-21,CA,1,A,A1,7.95,8.0 -2015-11-22,CA,1,A,A1,7.91,7.0 -2015-11-23,CA,1,A,A1,7.77,7.0 -2015-11-24,CA,1,A,A1,7.95,8.0 -2015-11-25,CA,1,A,A1,8.07,7.0 -2015-11-26,CA,1,A,A1,7.91,7.0 -2015-11-27,CA,1,A,A1,7.78,8.0 -2015-11-28,CA,1,A,A1,8.01,8.0 -2015-11-29,CA,1,A,A1,7.88,7.0 -2015-11-30,CA,1,A,A1,7.77,7.0 -2015-12-01,CA,1,A,A1,7.95,8.0 -2015-12-02,CA,1,A,A1,7.78,8.0 -2015-12-03,CA,1,A,A1,8.0,7.0 -2015-12-04,CA,1,A,A1,7.81,7.0 -2015-12-05,CA,1,A,A1,7.72,8.0 -2015-12-06,CA,1,A,A1,7.97,7.0 -2015-12-07,CA,1,A,A1,7.96,7.0 -2015-12-08,CA,1,A,A1,7.76,8.0 -2015-12-09,CA,1,A,A1,7.79,7.0 -2015-12-10,CA,1,A,A1,7.85,7.0 -2015-12-11,CA,1,A,A1,7.83,8.0 -2015-12-12,CA,1,A,A1,7.99,8.0 -2015-12-13,CA,1,A,A1,7.89,8.0 -2015-12-14,CA,1,A,A1,7.87,8.0 -2015-12-15,CA,1,A,A1,7.96,7.0 -2015-12-16,CA,1,A,A1,8.03,7.0 -2015-12-17,CA,1,A,A1,7.92,8.0 -2015-12-18,CA,1,A,A1,7.98,8.0 -2015-12-19,CA,1,A,A1,7.9,8.0 -2015-12-20,CA,1,A,A1,8.0,8.0 -2015-12-21,CA,1,A,A1,8.12,7.0 -2015-12-22,CA,1,A,A1,8.0,7.0 -2015-12-23,CA,1,A,A1,7.86,7.0 -2015-12-24,CA,1,A,A1,7.92,7.0 -2015-12-25,CA,1,A,A1,7.95,7.0 -2015-12-26,CA,1,A,A1,7.87,8.0 -2015-12-27,CA,1,A,A1,7.88,7.0 -2015-12-28,CA,1,A,A1,7.87,8.0 -2015-12-29,CA,1,A,A1,7.95,7.0 -2015-12-30,CA,1,A,A1,7.88,7.0 -2015-12-31,CA,1,A,A1,7.84,8.0 -2016-01-01,CA,1,A,A1,7.88,8.0 -2016-01-02,CA,1,A,A1,7.83,8.0 -2016-01-03,CA,1,A,A1,7.9,8.0 -2016-01-04,CA,1,A,A1,7.88,8.0 -2016-01-05,CA,1,A,A1,7.91,8.0 -2016-01-06,CA,1,A,A1,7.95,8.0 -2016-01-07,CA,1,A,A1,7.97,7.0 -2016-01-08,CA,1,A,A1,8.0,8.0 -2016-01-09,CA,1,A,A1,8.07,8.0 -2016-01-10,CA,1,A,A1,7.81,8.0 -2016-01-11,CA,1,A,A1,7.93,7.0 -2016-01-12,CA,1,A,A1,8.03,7.0 -2016-01-13,CA,1,A,A1,8.02,7.0 -2016-01-14,CA,1,A,A1,8.03,7.0 -2016-01-15,CA,1,A,A1,7.93,7.0 -2016-01-16,CA,1,A,A1,7.96,8.0 -2016-01-17,CA,1,A,A1,7.91,7.0 -2016-01-18,CA,1,A,A1,7.98,7.0 -2016-01-19,CA,1,A,A1,8.03,7.0 -2016-01-20,CA,1,A,A1,7.85,7.0 -2016-01-21,CA,1,A,A1,7.93,7.0 -2016-01-22,CA,1,A,A1,7.95,7.0 -2016-01-23,CA,1,A,A1,7.87,8.0 -2016-01-24,CA,1,A,A1,7.91,8.0 -2016-01-25,CA,1,A,A1,7.97,7.0 -2016-01-26,CA,1,A,A1,7.93,7.0 -2016-01-27,CA,1,A,A1,7.97,7.0 -2016-01-28,CA,1,A,A1,7.87,7.0 -2016-01-29,CA,1,A,A1,8.03,7.0 -2016-01-30,CA,1,A,A1,7.89,8.0 -2016-01-31,CA,1,A,A1,7.94,8.0 -2016-02-01,CA,1,A,A1,7.89,8.0 -2016-02-02,CA,1,A,A1,7.99,8.0 -2016-02-03,CA,1,A,A1,7.89,8.0 -2016-02-04,CA,1,A,A1,7.88,7.0 -2016-02-05,CA,1,A,A1,7.81,7.0 -2016-02-06,CA,1,A,A1,7.9,8.0 -2016-02-07,CA,1,A,A1,7.85,8.0 -2016-02-08,CA,1,A,A1,7.9,7.0 -2016-02-09,CA,1,A,A1,7.9,8.0 -2016-02-10,CA,1,A,A1,7.87,7.0 -2016-02-11,CA,1,A,A1,8.0,7.0 -2016-02-12,CA,1,A,A1,7.97,7.0 -2016-02-13,CA,1,A,A1,8.0,8.0 -2016-02-14,CA,1,A,A1,7.89,7.0 -2016-02-15,CA,1,A,A1,7.95,7.0 -2016-02-16,CA,1,A,A1,7.9,8.0 -2016-02-17,CA,1,A,A1,7.9,7.0 -2016-02-18,CA,1,A,A1,7.92,7.0 -2016-02-19,CA,1,A,A1,7.92,8.0 -2016-02-20,CA,1,A,A1,8.02,8.0 -2016-02-21,CA,1,A,A1,7.86,7.0 -2016-02-22,CA,1,A,A1,7.92,7.0 -2016-02-23,CA,1,A,A1,7.92,8.0 -2016-02-24,CA,1,A,A1,7.77,8.0 -2016-02-25,CA,1,A,A1,7.96,8.0 -2016-02-26,CA,1,A,A1,7.85,7.0 -2016-02-27,CA,1,A,A1,7.85,8.0 -2016-02-28,CA,1,A,A1,7.96,8.0 -2016-02-29,CA,1,A,A1,7.94,7.0 -2016-03-01,CA,1,A,A1,7.87,7.0 -2016-03-02,CA,1,A,A1,7.91,8.0 -2016-03-03,CA,1,A,A1,8.06,7.0 -2016-03-04,CA,1,A,A1,7.94,7.0 -2016-03-05,CA,1,A,A1,7.91,8.0 -2016-03-06,CA,1,A,A1,7.92,8.0 -2016-03-07,CA,1,A,A1,7.97,7.0 -2016-03-08,CA,1,A,A1,7.97,8.0 -2016-03-09,CA,1,A,A1,7.91,7.0 -2016-03-10,CA,1,A,A1,7.98,7.0 -2016-03-11,CA,1,A,A1,7.88,8.0 -2016-03-12,CA,1,A,A1,7.98,8.0 -2016-03-13,CA,1,A,A1,7.78,8.0 -2016-03-14,CA,1,A,A1,7.89,8.0 -2016-03-15,CA,1,A,A1,7.9,8.0 -2016-03-16,CA,1,A,A1,7.93,8.0 -2016-03-17,CA,1,A,A1,7.9,7.0 -2016-03-18,CA,1,A,A1,7.89,8.0 -2016-03-19,CA,1,A,A1,8.01,8.0 -2016-03-20,CA,1,A,A1,7.9,8.0 -2016-03-21,CA,1,A,A1,7.92,7.0 -2016-03-22,CA,1,A,A1,7.89,8.0 -2016-03-23,CA,1,A,A1,8.01,7.0 -2016-03-24,CA,1,A,A1,7.89,7.0 -2016-03-25,CA,1,A,A1,7.94,8.0 -2016-03-26,CA,1,A,A1,7.88,8.0 -2016-03-27,CA,1,A,A1,7.9,8.0 -2016-03-28,CA,1,A,A1,7.91,7.0 -2016-03-29,CA,1,A,A1,7.97,8.0 -2016-03-30,CA,1,A,A1,7.84,7.0 -2016-03-31,CA,1,A,A1,7.84,8.0 -2016-04-01,CA,1,A,A1,7.89,8.0 -2016-04-02,CA,1,A,A1,7.91,8.0 -2016-04-03,CA,1,A,A1,7.83,8.0 -2016-04-04,CA,1,A,A1,7.94,7.0 -2016-04-05,CA,1,A,A1,7.94,8.0 -2016-04-06,CA,1,A,A1,8.07,8.0 -2016-04-07,CA,1,A,A1,7.99,7.0 -2016-04-08,CA,1,A,A1,7.9,8.0 -2016-04-09,CA,1,A,A1,7.93,8.0 -2016-04-10,CA,1,A,A1,7.97,7.0 -2016-04-11,CA,1,A,A1,8.0,8.0 -2016-04-12,CA,1,A,A1,7.85,8.0 -2016-04-13,CA,1,A,A1,8.07,7.0 -2016-04-14,CA,1,A,A1,7.91,7.0 -2016-04-15,CA,1,A,A1,7.94,8.0 -2016-04-16,CA,1,A,A1,8.02,8.0 -2016-04-17,CA,1,A,A1,7.89,8.0 -2016-04-18,CA,1,A,A1,7.91,8.0 -2016-04-19,CA,1,A,A1,7.91,7.0 -2016-04-20,CA,1,A,A1,7.94,7.0 -2016-04-21,CA,1,A,A1,7.93,7.0 -2016-04-22,CA,1,A,A1,7.94,7.0 -2016-04-23,CA,1,A,A1,7.91,8.0 -2016-04-24,CA,1,A,A1,7.99,7.0 -2016-04-25,CA,1,A,A1,7.96,7.0 -2016-04-26,CA,1,A,A1,7.9,7.0 -2016-04-27,CA,1,A,A1,7.85,8.0 -2016-04-28,CA,1,A,A1,7.83,8.0 -2016-04-29,CA,1,A,A1,8.03,8.0 -2016-04-30,CA,1,A,A1,7.81,8.0 -2016-05-01,CA,1,A,A1,8.03,8.0 -2016-05-02,CA,1,A,A1,7.86,8.0 -2016-05-03,CA,1,A,A1,7.91,7.0 -2016-05-04,CA,1,A,A1,7.84,7.0 -2016-05-05,CA,1,A,A1,7.95,8.0 -2016-05-06,CA,1,A,A1,8.0,7.0 -2016-05-07,CA,1,A,A1,7.99,8.0 -2016-05-08,CA,1,A,A1,8.03,8.0 -2016-05-09,CA,1,A,A1,7.88,7.0 -2016-05-10,CA,1,A,A1,7.99,7.0 -2016-05-11,CA,1,A,A1,8.0,7.0 -2016-05-12,CA,1,A,A1,7.92,7.0 -2016-05-13,CA,1,A,A1,7.87,8.0 -2016-05-14,CA,1,A,A1,7.93,8.0 -2016-05-15,CA,1,A,A1,7.84,7.0 -2016-05-16,CA,1,A,A1,7.88,7.0 -2016-05-17,CA,1,A,A1,7.99,8.0 -2016-05-18,CA,1,A,A1,7.87,7.0 -2016-05-19,CA,1,A,A1,7.95,8.0 -2016-05-20,CA,1,A,A1,7.89,7.0 -2016-05-21,CA,1,A,A1,8.05,8.0 -2016-05-22,CA,1,A,A1,7.9,7.0 -2016-05-23,CA,1,A,A1,7.93,7.0 -2016-05-24,CA,1,A,A1,7.96,8.0 -2016-05-25,CA,1,A,A1,7.98,8.0 -2016-05-26,CA,1,A,A1,7.87,8.0 -2016-05-27,CA,1,A,A1,7.98,8.0 -2016-05-28,CA,1,A,A1,8.06,7.0 -2016-05-29,CA,1,A,A1,7.87,7.0 -2016-05-30,CA,1,A,A1,8.01,7.0 -2016-05-31,CA,1,A,A1,7.83,8.0 -2016-06-01,CA,1,A,A1,7.8,8.0 -2016-06-02,CA,1,A,A1,7.89,7.0 -2016-06-03,CA,1,A,A1,7.86,8.0 -2016-06-04,CA,1,A,A1,8.0,8.0 -2016-06-05,CA,1,A,A1,7.95,7.0 -2016-06-06,CA,1,A,A1,7.94,7.0 -2016-06-07,CA,1,A,A1,7.96,8.0 -2016-06-08,CA,1,A,A1,7.92,7.0 -2016-06-09,CA,1,A,A1,7.9,8.0 -2016-06-10,CA,1,A,A1,7.94,7.0 -2016-06-11,CA,1,A,A1,7.91,8.0 -2016-06-12,CA,1,A,A1,7.98,8.0 -2016-06-13,CA,1,A,A1,7.79,8.0 -2016-06-14,CA,1,A,A1,7.7,8.0 -2016-06-15,CA,1,A,A1,7.98,7.0 -2016-06-16,CA,1,A,A1,8.08,7.0 -2016-06-17,CA,1,A,A1,7.92,7.0 -2016-06-18,CA,1,A,A1,8.03,7.0 -2016-06-19,CA,1,A,A1,7.91,7.0 -2016-06-20,CA,1,A,A1,7.92,7.0 -2016-06-21,CA,1,A,A1,8.06,8.0 -2016-06-22,CA,1,A,A1,8.01,7.0 -2016-06-23,CA,1,A,A1,7.96,8.0 -2016-06-24,CA,1,A,A1,7.94,8.0 -2016-06-25,CA,1,A,A1,7.85,8.0 -2016-06-26,CA,1,A,A1,7.89,8.0 -2016-06-27,CA,1,A,A1,7.92,7.0 -2016-06-28,CA,1,A,A1,7.92,8.0 -2016-06-29,CA,1,A,A1,7.84,8.0 -2016-06-30,CA,1,A,A1,7.93,7.0 -2016-07-01,CA,1,A,A1,7.96,7.0 -2016-07-02,CA,1,A,A1,7.92,7.0 -2016-07-03,CA,1,A,A1,7.88,7.0 -2016-07-04,CA,1,A,A1,7.91,8.0 -2016-07-05,CA,1,A,A1,7.94,7.0 -2016-07-06,CA,1,A,A1,7.75,7.0 -2016-07-07,CA,1,A,A1,7.77,8.0 -2016-07-08,CA,1,A,A1,7.86,7.0 -2016-07-09,CA,1,A,A1,7.89,8.0 -2016-07-10,CA,1,A,A1,8.01,8.0 -2016-07-11,CA,1,A,A1,7.89,8.0 -2016-07-12,CA,1,A,A1,8.03,8.0 -2016-07-13,CA,1,A,A1,7.85,8.0 -2016-07-14,CA,1,A,A1,8.03,8.0 -2016-07-15,CA,1,A,A1,7.95,7.0 -2016-07-16,CA,1,A,A1,7.81,8.0 -2016-07-17,CA,1,A,A1,7.86,8.0 -2016-07-18,CA,1,A,A1,7.94,7.0 -2016-07-19,CA,1,A,A1,7.82,8.0 -2016-07-20,CA,1,A,A1,7.89,7.0 -2016-07-21,CA,1,A,A1,7.93,8.0 -2016-07-22,CA,1,A,A1,8.04,7.0 -2016-07-23,CA,1,A,A1,7.75,8.0 -2016-07-24,CA,1,A,A1,7.94,8.0 -2016-07-25,CA,1,A,A1,7.92,7.0 -2016-07-26,CA,1,A,A1,7.81,7.0 -2016-07-27,CA,1,A,A1,7.92,8.0 -2015-01-05,CA,1,A,A2,4.3,9.0 -2015-01-06,CA,1,A,A2,4.34,10.0 -2015-01-07,CA,1,A,A2,4.37,9.0 -2015-01-08,CA,1,A,A2,4.37,9.0 -2015-01-09,CA,1,A,A2,4.28,10.0 -2015-01-10,CA,1,A,A2,4.27,10.0 -2015-01-11,CA,1,A,A2,4.32,9.0 -2015-01-12,CA,1,A,A2,4.34,10.0 -2015-01-13,CA,1,A,A2,4.35,9.0 -2015-01-14,CA,1,A,A2,4.31,9.0 -2015-01-15,CA,1,A,A2,4.34,10.0 -2015-01-16,CA,1,A,A2,4.32,10.0 -2015-01-17,CA,1,A,A2,4.23,11.0 -2015-01-18,CA,1,A,A2,4.28,10.0 -2015-01-19,CA,1,A,A2,4.25,10.0 -2015-01-20,CA,1,A,A2,4.35,10.0 -2015-01-21,CA,1,A,A2,4.27,10.0 -2015-01-22,CA,1,A,A2,4.4,10.0 -2015-01-23,CA,1,A,A2,4.37,10.0 -2015-01-24,CA,1,A,A2,4.32,11.0 -2015-01-25,CA,1,A,A2,4.3,10.0 -2015-01-26,CA,1,A,A2,4.32,10.0 -2015-01-27,CA,1,A,A2,4.36,10.0 -2015-01-28,CA,1,A,A2,4.32,10.0 -2015-01-29,CA,1,A,A2,4.27,10.0 -2015-01-30,CA,1,A,A2,4.32,10.0 -2015-01-31,CA,1,A,A2,4.24,11.0 -2015-02-01,CA,1,A,A2,4.35,9.0 -2015-02-02,CA,1,A,A2,4.25,10.0 -2015-02-03,CA,1,A,A2,4.33,10.0 -2015-02-04,CA,1,A,A2,4.35,11.0 -2015-02-05,CA,1,A,A2,4.32,10.0 -2015-02-06,CA,1,A,A2,4.33,10.0 -2015-02-07,CA,1,A,A2,4.36,10.0 -2015-02-08,CA,1,A,A2,4.29,10.0 -2015-02-09,CA,1,A,A2,4.26,10.0 -2015-02-10,CA,1,A,A2,4.37,10.0 -2015-02-11,CA,1,A,A2,4.29,10.0 -2015-02-12,CA,1,A,A2,4.34,10.0 -2015-02-13,CA,1,A,A2,4.35,9.0 -2015-02-14,CA,1,A,A2,4.2,11.0 -2015-02-15,CA,1,A,A2,4.31,10.0 -2015-02-16,CA,1,A,A2,4.3,9.0 -2015-02-17,CA,1,A,A2,4.32,10.0 -2015-02-18,CA,1,A,A2,4.25,11.0 -2015-02-19,CA,1,A,A2,4.32,10.0 -2015-02-20,CA,1,A,A2,4.29,11.0 -2015-02-21,CA,1,A,A2,4.25,10.0 -2015-02-22,CA,1,A,A2,4.35,10.0 -2015-02-23,CA,1,A,A2,4.38,9.0 -2015-02-24,CA,1,A,A2,4.31,10.0 -2015-02-25,CA,1,A,A2,4.31,10.0 -2015-02-26,CA,1,A,A2,4.34,10.0 -2015-02-27,CA,1,A,A2,4.41,11.0 -2015-02-28,CA,1,A,A2,4.32,10.0 -2015-03-01,CA,1,A,A2,4.33,11.0 -2015-03-02,CA,1,A,A2,4.27,11.0 -2015-03-03,CA,1,A,A2,4.32,11.0 -2015-03-04,CA,1,A,A2,4.24,10.0 -2015-03-05,CA,1,A,A2,4.31,10.0 -2015-03-06,CA,1,A,A2,4.28,11.0 -2015-03-07,CA,1,A,A2,4.37,11.0 -2015-03-08,CA,1,A,A2,4.35,10.0 -2015-03-09,CA,1,A,A2,4.42,9.0 -2015-03-10,CA,1,A,A2,4.29,11.0 -2015-03-11,CA,1,A,A2,4.32,10.0 -2015-03-12,CA,1,A,A2,4.38,10.0 -2015-03-13,CA,1,A,A2,4.35,10.0 -2015-03-14,CA,1,A,A2,4.32,9.0 -2015-03-15,CA,1,A,A2,4.34,11.0 -2015-03-16,CA,1,A,A2,4.35,10.0 -2015-03-17,CA,1,A,A2,4.29,10.0 -2015-03-18,CA,1,A,A2,4.34,9.0 -2015-03-19,CA,1,A,A2,4.36,10.0 -2015-03-20,CA,1,A,A2,4.31,10.0 -2015-03-21,CA,1,A,A2,4.39,11.0 -2015-03-22,CA,1,A,A2,4.31,10.0 -2015-03-23,CA,1,A,A2,4.18,10.0 -2015-03-24,CA,1,A,A2,4.3,10.0 -2015-03-25,CA,1,A,A2,4.27,11.0 -2015-03-26,CA,1,A,A2,4.3,11.0 -2015-03-27,CA,1,A,A2,4.37,10.0 -2015-03-28,CA,1,A,A2,4.3,12.0 -2015-03-29,CA,1,A,A2,4.29,11.0 -2015-03-30,CA,1,A,A2,4.39,10.0 -2015-03-31,CA,1,A,A2,4.3,11.0 -2015-04-01,CA,1,A,A2,4.34,11.0 -2015-04-02,CA,1,A,A2,4.32,9.0 -2015-04-03,CA,1,A,A2,4.29,10.0 -2015-04-04,CA,1,A,A2,4.33,11.0 -2015-04-05,CA,1,A,A2,4.33,11.0 -2015-04-06,CA,1,A,A2,4.42,9.0 -2015-04-07,CA,1,A,A2,4.35,11.0 -2015-04-08,CA,1,A,A2,4.33,10.0 -2015-04-09,CA,1,A,A2,4.34,10.0 -2015-04-10,CA,1,A,A2,4.3,10.0 -2015-04-11,CA,1,A,A2,4.35,11.0 -2015-04-12,CA,1,A,A2,4.27,11.0 -2015-04-13,CA,1,A,A2,4.31,10.0 -2015-04-14,CA,1,A,A2,4.2,10.0 -2015-04-15,CA,1,A,A2,4.3,10.0 -2015-04-16,CA,1,A,A2,4.28,10.0 -2015-04-17,CA,1,A,A2,4.3,11.0 -2015-04-18,CA,1,A,A2,4.37,11.0 -2015-04-19,CA,1,A,A2,4.38,10.0 -2015-04-20,CA,1,A,A2,4.34,11.0 -2015-04-21,CA,1,A,A2,4.36,10.0 -2015-04-22,CA,1,A,A2,4.34,10.0 -2015-04-23,CA,1,A,A2,4.36,10.0 -2015-04-24,CA,1,A,A2,4.38,10.0 -2015-04-25,CA,1,A,A2,4.29,10.0 -2015-04-26,CA,1,A,A2,4.32,10.0 -2015-04-27,CA,1,A,A2,4.33,9.0 -2015-04-28,CA,1,A,A2,4.31,10.0 -2015-04-29,CA,1,A,A2,4.37,9.0 -2015-04-30,CA,1,A,A2,4.32,10.0 -2015-05-01,CA,1,A,A2,4.35,10.0 -2015-05-02,CA,1,A,A2,4.31,10.0 -2015-05-03,CA,1,A,A2,4.28,10.0 -2015-05-04,CA,1,A,A2,4.3,11.0 -2015-05-05,CA,1,A,A2,4.27,11.0 -2015-05-06,CA,1,A,A2,4.23,10.0 -2015-05-07,CA,1,A,A2,4.32,10.0 -2015-05-08,CA,1,A,A2,4.32,10.0 -2015-05-09,CA,1,A,A2,4.33,11.0 -2015-05-10,CA,1,A,A2,4.26,10.0 -2015-05-11,CA,1,A,A2,4.32,10.0 -2015-05-12,CA,1,A,A2,4.3,10.0 -2015-05-13,CA,1,A,A2,4.3,10.0 -2015-05-14,CA,1,A,A2,4.28,10.0 -2015-05-15,CA,1,A,A2,4.33,10.0 -2015-05-16,CA,1,A,A2,4.25,11.0 -2015-05-17,CA,1,A,A2,4.29,11.0 -2015-05-18,CA,1,A,A2,4.35,11.0 -2015-05-19,CA,1,A,A2,4.32,10.0 -2015-05-20,CA,1,A,A2,4.33,10.0 -2015-05-21,CA,1,A,A2,4.31,10.0 -2015-05-22,CA,1,A,A2,4.4,10.0 -2015-05-23,CA,1,A,A2,4.32,11.0 -2015-05-24,CA,1,A,A2,4.28,10.0 -2015-05-25,CA,1,A,A2,4.28,9.0 -2015-05-26,CA,1,A,A2,4.34,10.0 -2015-05-27,CA,1,A,A2,4.4,10.0 -2015-05-28,CA,1,A,A2,4.32,10.0 -2015-05-29,CA,1,A,A2,4.24,10.0 -2015-05-30,CA,1,A,A2,4.28,10.0 -2015-05-31,CA,1,A,A2,4.32,10.0 -2015-06-01,CA,1,A,A2,4.26,10.0 -2015-06-02,CA,1,A,A2,4.31,10.0 -2015-06-03,CA,1,A,A2,4.29,10.0 -2015-06-04,CA,1,A,A2,4.35,10.0 -2015-06-05,CA,1,A,A2,4.33,11.0 -2015-06-06,CA,1,A,A2,4.3,11.0 -2015-06-07,CA,1,A,A2,4.28,11.0 -2015-06-08,CA,1,A,A2,4.27,10.0 -2015-06-09,CA,1,A,A2,4.33,10.0 -2015-06-10,CA,1,A,A2,4.28,10.0 -2015-06-11,CA,1,A,A2,4.36,10.0 -2015-06-12,CA,1,A,A2,4.37,11.0 -2015-06-13,CA,1,A,A2,4.32,10.0 -2015-06-14,CA,1,A,A2,4.33,11.0 -2015-06-15,CA,1,A,A2,4.26,10.0 -2015-06-16,CA,1,A,A2,4.31,10.0 -2015-06-17,CA,1,A,A2,4.35,10.0 -2015-06-18,CA,1,A,A2,4.27,10.0 -2015-06-19,CA,1,A,A2,4.33,10.0 -2015-06-20,CA,1,A,A2,4.26,11.0 -2015-06-21,CA,1,A,A2,4.23,10.0 -2015-06-22,CA,1,A,A2,4.34,9.0 -2015-06-23,CA,1,A,A2,4.34,11.0 -2015-06-24,CA,1,A,A2,4.33,11.0 -2015-06-25,CA,1,A,A2,4.26,10.0 -2015-06-26,CA,1,A,A2,4.37,11.0 -2015-06-27,CA,1,A,A2,4.31,11.0 -2015-06-28,CA,1,A,A2,4.32,11.0 -2015-06-29,CA,1,A,A2,4.34,10.0 -2015-06-30,CA,1,A,A2,4.37,10.0 -2015-07-01,CA,1,A,A2,4.26,10.0 -2015-07-02,CA,1,A,A2,4.36,11.0 -2015-07-03,CA,1,A,A2,4.36,10.0 -2015-07-04,CA,1,A,A2,4.37,11.0 -2015-07-05,CA,1,A,A2,4.32,10.0 -2015-07-06,CA,1,A,A2,4.26,10.0 -2015-07-07,CA,1,A,A2,4.28,10.0 -2015-07-08,CA,1,A,A2,4.32,10.0 -2015-07-09,CA,1,A,A2,4.27,10.0 -2015-07-10,CA,1,A,A2,4.3,10.0 -2015-07-11,CA,1,A,A2,4.35,10.0 -2015-07-12,CA,1,A,A2,4.28,10.0 -2015-07-13,CA,1,A,A2,4.31,10.0 -2015-07-14,CA,1,A,A2,4.33,10.0 -2015-07-15,CA,1,A,A2,4.3,10.0 -2015-07-16,CA,1,A,A2,4.38,10.0 -2015-07-17,CA,1,A,A2,4.31,10.0 -2015-07-18,CA,1,A,A2,4.37,11.0 -2015-07-19,CA,1,A,A2,4.34,11.0 -2015-07-20,CA,1,A,A2,4.34,10.0 -2015-07-21,CA,1,A,A2,4.31,10.0 -2015-07-22,CA,1,A,A2,4.3,11.0 -2015-07-23,CA,1,A,A2,4.3,10.0 -2015-07-24,CA,1,A,A2,4.39,10.0 -2015-07-25,CA,1,A,A2,4.36,11.0 -2015-07-26,CA,1,A,A2,4.25,10.0 -2015-07-27,CA,1,A,A2,4.33,10.0 -2015-07-28,CA,1,A,A2,4.42,10.0 -2015-07-29,CA,1,A,A2,4.37,10.0 -2015-07-30,CA,1,A,A2,4.28,11.0 -2015-07-31,CA,1,A,A2,4.39,10.0 -2015-08-01,CA,1,A,A2,4.28,11.0 -2015-08-02,CA,1,A,A2,4.33,11.0 -2015-08-03,CA,1,A,A2,4.36,10.0 -2015-08-04,CA,1,A,A2,4.29,10.0 -2015-08-05,CA,1,A,A2,4.37,10.0 -2015-08-06,CA,1,A,A2,4.35,10.0 -2015-08-07,CA,1,A,A2,4.34,10.0 -2015-08-08,CA,1,A,A2,4.29,11.0 -2015-08-09,CA,1,A,A2,4.33,11.0 -2015-08-10,CA,1,A,A2,4.37,10.0 -2015-08-11,CA,1,A,A2,4.33,11.0 -2015-08-12,CA,1,A,A2,4.23,11.0 -2015-08-13,CA,1,A,A2,4.36,11.0 -2015-08-14,CA,1,A,A2,4.31,10.0 -2015-08-15,CA,1,A,A2,4.35,10.0 -2015-08-16,CA,1,A,A2,4.32,10.0 -2015-08-17,CA,1,A,A2,4.36,11.0 -2015-08-18,CA,1,A,A2,4.32,10.0 -2015-08-19,CA,1,A,A2,4.31,11.0 -2015-08-20,CA,1,A,A2,4.3,10.0 -2015-08-21,CA,1,A,A2,4.32,10.0 -2015-08-22,CA,1,A,A2,4.26,11.0 -2015-08-23,CA,1,A,A2,4.32,10.0 -2015-08-24,CA,1,A,A2,4.32,10.0 -2015-08-25,CA,1,A,A2,4.32,11.0 -2015-08-26,CA,1,A,A2,4.28,11.0 -2015-08-27,CA,1,A,A2,4.32,11.0 -2015-08-28,CA,1,A,A2,4.32,10.0 -2015-08-29,CA,1,A,A2,4.39,11.0 -2015-08-30,CA,1,A,A2,4.3,10.0 -2015-08-31,CA,1,A,A2,4.29,10.0 -2015-09-01,CA,1,A,A2,4.36,10.0 -2015-09-02,CA,1,A,A2,4.3,10.0 -2015-09-03,CA,1,A,A2,4.35,10.0 -2015-09-04,CA,1,A,A2,4.26,11.0 -2015-09-05,CA,1,A,A2,4.35,11.0 -2015-09-06,CA,1,A,A2,4.37,11.0 -2015-09-07,CA,1,A,A2,4.32,10.0 -2015-09-08,CA,1,A,A2,4.34,10.0 -2015-09-09,CA,1,A,A2,4.31,11.0 -2015-09-10,CA,1,A,A2,4.33,10.0 -2015-09-11,CA,1,A,A2,4.3,10.0 -2015-09-12,CA,1,A,A2,4.36,10.0 -2015-09-13,CA,1,A,A2,4.38,11.0 -2015-09-14,CA,1,A,A2,4.27,10.0 -2015-09-15,CA,1,A,A2,4.26,10.0 -2015-09-16,CA,1,A,A2,4.33,10.0 -2015-09-17,CA,1,A,A2,4.32,10.0 -2015-09-18,CA,1,A,A2,4.36,10.0 -2015-09-19,CA,1,A,A2,4.33,11.0 -2015-09-20,CA,1,A,A2,4.37,10.0 -2015-09-21,CA,1,A,A2,4.21,9.0 -2015-09-22,CA,1,A,A2,4.3,10.0 -2015-09-23,CA,1,A,A2,4.24,11.0 -2015-09-24,CA,1,A,A2,4.36,10.0 -2015-09-25,CA,1,A,A2,4.26,11.0 -2015-09-26,CA,1,A,A2,4.29,11.0 -2015-09-27,CA,1,A,A2,4.38,10.0 -2015-09-28,CA,1,A,A2,4.24,10.0 -2015-09-29,CA,1,A,A2,4.4,10.0 -2015-09-30,CA,1,A,A2,4.25,10.0 -2015-10-01,CA,1,A,A2,4.28,10.0 -2015-10-02,CA,1,A,A2,4.39,10.0 -2015-10-03,CA,1,A,A2,4.42,11.0 -2015-10-04,CA,1,A,A2,4.29,10.0 -2015-10-05,CA,1,A,A2,4.27,10.0 -2015-10-06,CA,1,A,A2,4.33,10.0 -2015-10-07,CA,1,A,A2,4.36,10.0 -2015-10-08,CA,1,A,A2,4.33,9.0 -2015-10-09,CA,1,A,A2,4.26,9.0 -2015-10-10,CA,1,A,A2,4.32,11.0 -2015-10-11,CA,1,A,A2,4.33,10.0 -2015-10-12,CA,1,A,A2,4.37,10.0 -2015-10-13,CA,1,A,A2,4.38,11.0 -2015-10-14,CA,1,A,A2,4.4,10.0 -2015-10-15,CA,1,A,A2,4.44,10.0 -2015-10-16,CA,1,A,A2,4.34,11.0 -2015-10-17,CA,1,A,A2,4.25,11.0 -2015-10-18,CA,1,A,A2,4.31,10.0 -2015-10-19,CA,1,A,A2,4.31,10.0 -2015-10-20,CA,1,A,A2,4.34,9.0 -2015-10-21,CA,1,A,A2,4.33,11.0 -2015-10-22,CA,1,A,A2,4.36,10.0 -2015-10-23,CA,1,A,A2,4.29,10.0 -2015-10-24,CA,1,A,A2,4.38,10.0 -2015-10-25,CA,1,A,A2,4.3,10.0 -2015-10-26,CA,1,A,A2,4.31,10.0 -2015-10-27,CA,1,A,A2,4.35,11.0 -2015-10-28,CA,1,A,A2,4.4,10.0 -2015-10-29,CA,1,A,A2,4.29,11.0 -2015-10-30,CA,1,A,A2,4.32,10.0 -2015-10-31,CA,1,A,A2,4.33,11.0 -2015-11-01,CA,1,A,A2,4.32,10.0 -2015-11-02,CA,1,A,A2,4.37,11.0 -2015-11-03,CA,1,A,A2,4.25,10.0 -2015-11-04,CA,1,A,A2,4.33,10.0 -2015-11-05,CA,1,A,A2,4.35,10.0 -2015-11-06,CA,1,A,A2,4.38,10.0 -2015-11-07,CA,1,A,A2,4.41,11.0 -2015-11-08,CA,1,A,A2,4.36,10.0 -2015-11-09,CA,1,A,A2,4.31,10.0 -2015-11-10,CA,1,A,A2,4.34,10.0 -2015-11-11,CA,1,A,A2,4.35,10.0 -2015-11-12,CA,1,A,A2,4.29,10.0 -2015-11-13,CA,1,A,A2,4.39,10.0 -2015-11-14,CA,1,A,A2,4.28,11.0 -2015-11-15,CA,1,A,A2,4.39,10.0 -2015-11-16,CA,1,A,A2,4.34,10.0 -2015-11-17,CA,1,A,A2,4.39,10.0 -2015-11-18,CA,1,A,A2,4.28,10.0 -2015-11-19,CA,1,A,A2,4.3,10.0 -2015-11-20,CA,1,A,A2,4.32,11.0 -2015-11-21,CA,1,A,A2,4.26,11.0 -2015-11-22,CA,1,A,A2,4.35,11.0 -2015-11-23,CA,1,A,A2,4.31,10.0 -2015-11-24,CA,1,A,A2,4.3,10.0 -2015-11-25,CA,1,A,A2,4.23,10.0 -2015-11-26,CA,1,A,A2,4.38,11.0 -2015-11-27,CA,1,A,A2,4.39,10.0 -2015-11-28,CA,1,A,A2,4.34,10.0 -2015-11-29,CA,1,A,A2,4.32,10.0 -2015-11-30,CA,1,A,A2,4.32,10.0 -2015-12-01,CA,1,A,A2,4.32,11.0 -2015-12-02,CA,1,A,A2,4.38,9.0 -2015-12-03,CA,1,A,A2,4.34,11.0 -2015-12-04,CA,1,A,A2,4.27,11.0 -2015-12-05,CA,1,A,A2,4.25,11.0 -2015-12-06,CA,1,A,A2,4.34,11.0 -2015-12-07,CA,1,A,A2,4.36,10.0 -2015-12-08,CA,1,A,A2,4.37,10.0 -2015-12-09,CA,1,A,A2,4.33,10.0 -2015-12-10,CA,1,A,A2,4.28,10.0 -2015-12-11,CA,1,A,A2,4.39,10.0 -2015-12-12,CA,1,A,A2,4.3,11.0 -2015-12-13,CA,1,A,A2,4.34,10.0 -2015-12-14,CA,1,A,A2,4.36,10.0 -2015-12-15,CA,1,A,A2,4.26,10.0 -2015-12-16,CA,1,A,A2,4.35,10.0 -2015-12-17,CA,1,A,A2,4.39,10.0 -2015-12-18,CA,1,A,A2,4.36,10.0 -2015-12-19,CA,1,A,A2,4.37,10.0 -2015-12-20,CA,1,A,A2,4.36,11.0 -2015-12-21,CA,1,A,A2,4.25,11.0 -2015-12-22,CA,1,A,A2,4.32,11.0 -2015-12-23,CA,1,A,A2,4.35,10.0 -2015-12-24,CA,1,A,A2,4.31,11.0 -2015-12-25,CA,1,A,A2,4.35,10.0 -2015-12-26,CA,1,A,A2,4.35,11.0 -2015-12-27,CA,1,A,A2,4.25,11.0 -2015-12-28,CA,1,A,A2,4.25,10.0 -2015-12-29,CA,1,A,A2,4.36,11.0 -2015-12-30,CA,1,A,A2,4.31,10.0 -2015-12-31,CA,1,A,A2,4.3,11.0 -2016-01-01,CA,1,A,A2,4.24,11.0 -2016-01-02,CA,1,A,A2,4.41,10.0 -2016-01-03,CA,1,A,A2,4.33,11.0 -2016-01-04,CA,1,A,A2,4.33,10.0 -2016-01-05,CA,1,A,A2,4.34,10.0 -2016-01-06,CA,1,A,A2,4.34,10.0 -2016-01-07,CA,1,A,A2,4.34,10.0 -2016-01-08,CA,1,A,A2,4.33,10.0 -2016-01-09,CA,1,A,A2,4.32,11.0 -2016-01-10,CA,1,A,A2,4.36,10.0 -2016-01-11,CA,1,A,A2,4.32,10.0 -2016-01-12,CA,1,A,A2,4.26,10.0 -2016-01-13,CA,1,A,A2,4.33,11.0 -2016-01-14,CA,1,A,A2,4.4,10.0 -2016-01-15,CA,1,A,A2,4.41,11.0 -2016-01-16,CA,1,A,A2,4.43,10.0 -2016-01-17,CA,1,A,A2,4.4,10.0 -2016-01-18,CA,1,A,A2,4.29,10.0 -2016-01-19,CA,1,A,A2,4.24,10.0 -2016-01-20,CA,1,A,A2,4.3,10.0 -2016-01-21,CA,1,A,A2,4.33,10.0 -2016-01-22,CA,1,A,A2,4.37,10.0 -2016-01-23,CA,1,A,A2,4.29,10.0 -2016-01-24,CA,1,A,A2,4.33,10.0 -2016-01-25,CA,1,A,A2,4.28,10.0 -2016-01-26,CA,1,A,A2,4.31,10.0 -2016-01-27,CA,1,A,A2,4.36,10.0 -2016-01-28,CA,1,A,A2,4.33,11.0 -2016-01-29,CA,1,A,A2,4.36,10.0 -2016-01-30,CA,1,A,A2,4.29,10.0 -2016-01-31,CA,1,A,A2,4.38,10.0 -2016-02-01,CA,1,A,A2,4.3,10.0 -2016-02-02,CA,1,A,A2,4.29,11.0 -2016-02-03,CA,1,A,A2,4.23,11.0 -2016-02-04,CA,1,A,A2,4.39,10.0 -2016-02-05,CA,1,A,A2,4.34,10.0 -2016-02-06,CA,1,A,A2,4.29,10.0 -2016-02-07,CA,1,A,A2,4.26,10.0 -2016-02-08,CA,1,A,A2,4.33,10.0 -2016-02-09,CA,1,A,A2,4.28,10.0 -2016-02-10,CA,1,A,A2,4.31,10.0 -2016-02-11,CA,1,A,A2,4.34,10.0 -2016-02-12,CA,1,A,A2,4.33,10.0 -2016-02-13,CA,1,A,A2,4.37,10.0 -2016-02-14,CA,1,A,A2,4.32,10.0 -2016-02-15,CA,1,A,A2,4.34,11.0 -2016-02-16,CA,1,A,A2,4.31,10.0 -2016-02-17,CA,1,A,A2,4.37,9.0 -2016-02-18,CA,1,A,A2,4.35,10.0 -2016-02-19,CA,1,A,A2,4.33,11.0 -2016-02-20,CA,1,A,A2,4.31,10.0 -2016-02-21,CA,1,A,A2,4.34,10.0 -2016-02-22,CA,1,A,A2,4.34,9.0 -2016-02-23,CA,1,A,A2,4.4,10.0 -2016-02-24,CA,1,A,A2,4.29,11.0 -2016-02-25,CA,1,A,A2,4.35,10.0 -2016-02-26,CA,1,A,A2,4.34,9.0 -2016-02-27,CA,1,A,A2,4.33,11.0 -2016-02-28,CA,1,A,A2,4.34,10.0 -2016-02-29,CA,1,A,A2,4.41,10.0 -2016-03-01,CA,1,A,A2,4.29,10.0 -2016-03-02,CA,1,A,A2,4.34,11.0 -2016-03-03,CA,1,A,A2,4.28,10.0 -2016-03-04,CA,1,A,A2,4.37,10.0 -2016-03-05,CA,1,A,A2,4.29,10.0 -2016-03-06,CA,1,A,A2,4.26,10.0 -2016-03-07,CA,1,A,A2,4.3,10.0 -2016-03-08,CA,1,A,A2,4.42,11.0 -2016-03-09,CA,1,A,A2,4.25,10.0 -2016-03-10,CA,1,A,A2,4.32,10.0 -2016-03-11,CA,1,A,A2,4.31,11.0 -2016-03-12,CA,1,A,A2,4.26,11.0 -2016-03-13,CA,1,A,A2,4.35,11.0 -2016-03-14,CA,1,A,A2,4.31,10.0 -2016-03-15,CA,1,A,A2,4.39,10.0 -2016-03-16,CA,1,A,A2,4.35,10.0 -2016-03-17,CA,1,A,A2,4.28,10.0 -2016-03-18,CA,1,A,A2,4.38,11.0 -2016-03-19,CA,1,A,A2,4.35,10.0 -2016-03-20,CA,1,A,A2,4.31,10.0 -2016-03-21,CA,1,A,A2,4.37,10.0 -2016-03-22,CA,1,A,A2,4.25,11.0 -2016-03-23,CA,1,A,A2,4.38,11.0 -2016-03-24,CA,1,A,A2,4.4,10.0 -2016-03-25,CA,1,A,A2,4.42,10.0 -2016-03-26,CA,1,A,A2,4.32,11.0 -2016-03-27,CA,1,A,A2,4.3,10.0 -2016-03-28,CA,1,A,A2,4.4,10.0 -2016-03-29,CA,1,A,A2,4.35,11.0 -2016-03-30,CA,1,A,A2,4.32,10.0 -2016-03-31,CA,1,A,A2,4.42,10.0 -2016-04-01,CA,1,A,A2,4.28,10.0 -2016-04-02,CA,1,A,A2,4.32,10.0 -2016-04-03,CA,1,A,A2,4.37,10.0 -2016-04-04,CA,1,A,A2,4.29,11.0 -2016-04-05,CA,1,A,A2,4.3,10.0 -2016-04-06,CA,1,A,A2,4.37,10.0 -2016-04-07,CA,1,A,A2,4.29,10.0 -2016-04-08,CA,1,A,A2,4.38,10.0 -2016-04-09,CA,1,A,A2,4.3,10.0 -2016-04-10,CA,1,A,A2,4.25,11.0 -2016-04-11,CA,1,A,A2,4.4,10.0 -2016-04-12,CA,1,A,A2,4.39,10.0 -2016-04-13,CA,1,A,A2,4.24,10.0 -2016-04-14,CA,1,A,A2,4.28,10.0 -2016-04-15,CA,1,A,A2,4.41,11.0 -2016-04-16,CA,1,A,A2,4.34,10.0 -2016-04-17,CA,1,A,A2,4.36,11.0 -2016-04-18,CA,1,A,A2,4.34,11.0 -2016-04-19,CA,1,A,A2,4.29,10.0 -2016-04-20,CA,1,A,A2,4.31,10.0 -2016-04-21,CA,1,A,A2,4.35,10.0 -2016-04-22,CA,1,A,A2,4.25,11.0 -2016-04-23,CA,1,A,A2,4.31,11.0 -2016-04-24,CA,1,A,A2,4.37,10.0 -2016-04-25,CA,1,A,A2,4.34,10.0 -2016-04-26,CA,1,A,A2,4.28,10.0 -2016-04-27,CA,1,A,A2,4.28,10.0 -2016-04-28,CA,1,A,A2,4.3,11.0 -2016-04-29,CA,1,A,A2,4.38,10.0 -2016-04-30,CA,1,A,A2,4.3,11.0 -2016-05-01,CA,1,A,A2,4.41,10.0 -2016-05-02,CA,1,A,A2,4.32,10.0 -2016-05-03,CA,1,A,A2,4.39,11.0 -2016-05-04,CA,1,A,A2,4.37,10.0 -2016-05-05,CA,1,A,A2,4.3,10.0 -2016-05-06,CA,1,A,A2,4.21,10.0 -2016-05-07,CA,1,A,A2,4.29,10.0 -2016-05-08,CA,1,A,A2,4.39,10.0 -2016-05-09,CA,1,A,A2,4.31,10.0 -2016-05-10,CA,1,A,A2,4.32,10.0 -2016-05-11,CA,1,A,A2,4.38,10.0 -2016-05-12,CA,1,A,A2,4.4,9.0 -2016-05-13,CA,1,A,A2,4.31,11.0 -2016-05-14,CA,1,A,A2,4.25,11.0 -2016-05-15,CA,1,A,A2,4.35,11.0 -2016-05-16,CA,1,A,A2,4.37,10.0 -2016-05-17,CA,1,A,A2,4.24,10.0 -2016-05-18,CA,1,A,A2,4.36,10.0 -2016-05-19,CA,1,A,A2,4.31,10.0 -2016-05-20,CA,1,A,A2,4.39,11.0 -2016-05-21,CA,1,A,A2,4.36,11.0 -2016-05-22,CA,1,A,A2,4.35,10.0 -2016-05-23,CA,1,A,A2,4.36,10.0 -2016-05-24,CA,1,A,A2,4.36,10.0 -2016-05-25,CA,1,A,A2,4.32,10.0 -2016-05-26,CA,1,A,A2,4.28,11.0 -2016-05-27,CA,1,A,A2,4.34,10.0 -2016-05-28,CA,1,A,A2,4.25,10.0 -2016-05-29,CA,1,A,A2,4.33,10.0 -2016-05-30,CA,1,A,A2,4.29,10.0 -2016-05-31,CA,1,A,A2,4.31,10.0 -2016-06-01,CA,1,A,A2,4.29,10.0 -2016-06-02,CA,1,A,A2,4.36,10.0 -2016-06-03,CA,1,A,A2,4.27,11.0 -2016-06-04,CA,1,A,A2,4.33,11.0 -2016-06-05,CA,1,A,A2,4.4,11.0 -2016-06-06,CA,1,A,A2,4.34,11.0 -2016-06-07,CA,1,A,A2,4.31,10.0 -2016-06-08,CA,1,A,A2,4.31,10.0 -2016-06-09,CA,1,A,A2,4.34,10.0 -2016-06-10,CA,1,A,A2,4.29,10.0 -2016-06-11,CA,1,A,A2,4.24,11.0 -2016-06-12,CA,1,A,A2,4.36,11.0 -2016-06-13,CA,1,A,A2,4.29,10.0 -2016-06-14,CA,1,A,A2,4.34,10.0 -2016-06-15,CA,1,A,A2,4.35,10.0 -2016-06-16,CA,1,A,A2,4.32,10.0 -2016-06-17,CA,1,A,A2,4.34,10.0 -2016-06-18,CA,1,A,A2,4.31,11.0 -2016-06-19,CA,1,A,A2,4.3,11.0 -2016-06-20,CA,1,A,A2,4.31,11.0 -2016-06-21,CA,1,A,A2,4.33,11.0 -2016-06-22,CA,1,A,A2,4.31,9.0 -2016-06-23,CA,1,A,A2,4.32,10.0 -2016-06-24,CA,1,A,A2,4.33,11.0 -2016-06-25,CA,1,A,A2,4.35,10.0 -2016-06-26,CA,1,A,A2,4.3,11.0 -2016-06-27,CA,1,A,A2,4.42,9.0 -2016-06-28,CA,1,A,A2,4.34,10.0 -2016-06-29,CA,1,A,A2,4.37,10.0 -2016-06-30,CA,1,A,A2,4.3,9.0 -2016-07-01,CA,1,A,A2,4.3,10.0 -2016-07-02,CA,1,A,A2,4.33,11.0 -2016-07-03,CA,1,A,A2,4.31,10.0 -2016-07-04,CA,1,A,A2,4.29,11.0 -2016-07-05,CA,1,A,A2,4.31,10.0 -2016-07-06,CA,1,A,A2,4.37,10.0 -2016-07-07,CA,1,A,A2,4.31,11.0 -2016-07-08,CA,1,A,A2,4.33,10.0 -2016-07-09,CA,1,A,A2,4.33,10.0 -2016-07-10,CA,1,A,A2,4.3,11.0 -2016-07-11,CA,1,A,A2,4.32,10.0 -2016-07-12,CA,1,A,A2,4.3,10.0 -2016-07-13,CA,1,A,A2,4.33,10.0 -2016-07-14,CA,1,A,A2,4.34,10.0 -2016-07-15,CA,1,A,A2,4.36,10.0 -2016-07-16,CA,1,A,A2,4.23,11.0 -2016-07-17,CA,1,A,A2,4.32,10.0 -2016-07-18,CA,1,A,A2,4.24,10.0 -2016-07-19,CA,1,A,A2,4.37,10.0 -2016-07-20,CA,1,A,A2,4.34,11.0 -2016-07-21,CA,1,A,A2,4.31,10.0 -2016-07-22,CA,1,A,A2,4.27,11.0 -2016-07-23,CA,1,A,A2,4.28,12.0 -2016-07-24,CA,1,A,A2,4.4,10.0 -2016-07-25,CA,1,A,A2,4.32,10.0 -2016-07-26,CA,1,A,A2,4.26,10.0 -2016-07-27,CA,1,A,A2,4.31,10.0 -2015-01-05,CA,1,A,A3,2.58,198.0 -2015-01-06,CA,1,A,A3,2.61,190.0 -2015-01-07,CA,1,A,A3,2.61,177.0 -2015-01-08,CA,1,A,A3,2.6,193.0 -2015-01-09,CA,1,A,A3,2.62,195.0 -2015-01-10,CA,1,A,A3,2.58,204.0 -2015-01-11,CA,1,A,A3,2.61,186.0 -2015-01-12,CA,1,A,A3,2.59,175.0 -2015-01-13,CA,1,A,A3,2.6,173.0 -2015-01-14,CA,1,A,A3,2.54,203.0 -2015-01-15,CA,1,A,A3,2.62,197.0 -2015-01-16,CA,1,A,A3,2.59,191.0 -2015-01-17,CA,1,A,A3,2.6,177.0 -2015-01-18,CA,1,A,A3,2.55,205.0 -2015-01-19,CA,1,A,A3,2.63,181.0 -2015-01-20,CA,1,A,A3,2.59,183.0 -2015-01-21,CA,1,A,A3,2.61,177.0 -2015-01-22,CA,1,A,A3,2.58,195.0 -2015-01-23,CA,1,A,A3,2.57,200.0 -2015-01-24,CA,1,A,A3,2.65,185.0 -2015-01-25,CA,1,A,A3,2.6,201.0 -2015-01-26,CA,1,A,A3,2.61,173.0 -2015-01-27,CA,1,A,A3,2.62,181.0 -2015-01-28,CA,1,A,A3,2.59,174.0 -2015-01-29,CA,1,A,A3,2.58,192.0 -2015-01-30,CA,1,A,A3,2.58,193.0 -2015-01-31,CA,1,A,A3,2.62,205.0 -2015-02-01,CA,1,A,A3,2.6,188.0 -2015-02-02,CA,1,A,A3,2.6,187.0 -2015-02-03,CA,1,A,A3,2.59,205.0 -2015-02-04,CA,1,A,A3,2.59,191.0 -2015-02-05,CA,1,A,A3,2.64,191.0 -2015-02-06,CA,1,A,A3,2.61,178.0 -2015-02-07,CA,1,A,A3,2.59,189.0 -2015-02-08,CA,1,A,A3,2.55,204.0 -2015-02-09,CA,1,A,A3,2.55,209.0 -2015-02-10,CA,1,A,A3,2.56,199.0 -2015-02-11,CA,1,A,A3,2.58,191.0 -2015-02-12,CA,1,A,A3,2.61,199.0 -2015-02-13,CA,1,A,A3,2.62,192.0 -2015-02-14,CA,1,A,A3,2.57,199.0 -2015-02-15,CA,1,A,A3,2.62,183.0 -2015-02-16,CA,1,A,A3,2.62,174.0 -2015-02-17,CA,1,A,A3,2.62,200.0 -2015-02-18,CA,1,A,A3,2.62,188.0 -2015-02-19,CA,1,A,A3,2.59,200.0 -2015-02-20,CA,1,A,A3,2.6,204.0 -2015-02-21,CA,1,A,A3,2.6,190.0 -2015-02-22,CA,1,A,A3,2.61,174.0 -2015-02-23,CA,1,A,A3,2.62,171.0 -2015-02-24,CA,1,A,A3,2.58,200.0 -2015-02-25,CA,1,A,A3,2.58,165.0 -2015-02-26,CA,1,A,A3,2.62,186.0 -2015-02-27,CA,1,A,A3,2.57,197.0 -2015-02-28,CA,1,A,A3,2.62,190.0 -2015-03-01,CA,1,A,A3,2.59,195.0 -2015-03-02,CA,1,A,A3,2.56,196.0 -2015-03-03,CA,1,A,A3,2.58,191.0 -2015-03-04,CA,1,A,A3,2.61,208.0 -2015-03-05,CA,1,A,A3,2.61,205.0 -2015-03-06,CA,1,A,A3,2.58,204.0 -2015-03-07,CA,1,A,A3,2.57,218.0 -2015-03-08,CA,1,A,A3,2.59,212.0 -2015-03-09,CA,1,A,A3,2.58,184.0 -2015-03-10,CA,1,A,A3,2.58,206.0 -2015-03-11,CA,1,A,A3,2.56,210.0 -2015-03-12,CA,1,A,A3,2.65,189.0 -2015-03-13,CA,1,A,A3,2.6,203.0 -2015-03-14,CA,1,A,A3,2.59,204.0 -2015-03-15,CA,1,A,A3,2.6,205.0 -2015-03-16,CA,1,A,A3,2.64,171.0 -2015-03-17,CA,1,A,A3,2.57,179.0 -2015-03-18,CA,1,A,A3,2.62,192.0 -2015-03-19,CA,1,A,A3,2.53,203.0 -2015-03-20,CA,1,A,A3,2.63,194.0 -2015-03-21,CA,1,A,A3,2.59,194.0 -2015-03-22,CA,1,A,A3,2.55,207.0 -2015-03-23,CA,1,A,A3,2.56,195.0 -2015-03-24,CA,1,A,A3,2.6,196.0 -2015-03-25,CA,1,A,A3,2.59,186.0 -2015-03-26,CA,1,A,A3,2.58,188.0 -2015-03-27,CA,1,A,A3,2.6,190.0 -2015-03-28,CA,1,A,A3,2.61,213.0 -2015-03-29,CA,1,A,A3,2.6,181.0 -2015-03-30,CA,1,A,A3,2.6,205.0 -2015-03-31,CA,1,A,A3,2.58,189.0 -2015-04-01,CA,1,A,A3,2.61,179.0 -2015-04-02,CA,1,A,A3,2.53,208.0 -2015-04-03,CA,1,A,A3,2.58,194.0 -2015-04-04,CA,1,A,A3,2.61,192.0 -2015-04-05,CA,1,A,A3,2.56,200.0 -2015-04-06,CA,1,A,A3,2.64,188.0 -2015-04-07,CA,1,A,A3,2.64,196.0 -2015-04-08,CA,1,A,A3,2.63,193.0 -2015-04-09,CA,1,A,A3,2.6,187.0 -2015-04-10,CA,1,A,A3,2.6,191.0 -2015-04-11,CA,1,A,A3,2.57,217.0 -2015-04-12,CA,1,A,A3,2.6,190.0 -2015-04-13,CA,1,A,A3,2.58,188.0 -2015-04-14,CA,1,A,A3,2.59,203.0 -2015-04-15,CA,1,A,A3,2.56,197.0 -2015-04-16,CA,1,A,A3,2.63,189.0 -2015-04-17,CA,1,A,A3,2.58,190.0 -2015-04-18,CA,1,A,A3,2.62,186.0 -2015-04-19,CA,1,A,A3,2.56,194.0 -2015-04-20,CA,1,A,A3,2.64,190.0 -2015-04-21,CA,1,A,A3,2.57,193.0 -2015-04-22,CA,1,A,A3,2.6,190.0 -2015-04-23,CA,1,A,A3,2.57,200.0 -2015-04-24,CA,1,A,A3,2.55,197.0 -2015-04-25,CA,1,A,A3,2.65,196.0 -2015-04-26,CA,1,A,A3,2.62,194.0 -2015-04-27,CA,1,A,A3,2.6,203.0 -2015-04-28,CA,1,A,A3,2.58,208.0 -2015-04-29,CA,1,A,A3,2.61,195.0 -2015-04-30,CA,1,A,A3,2.6,183.0 -2015-05-01,CA,1,A,A3,2.62,187.0 -2015-05-02,CA,1,A,A3,2.63,200.0 -2015-05-03,CA,1,A,A3,2.61,188.0 -2015-05-04,CA,1,A,A3,2.62,201.0 -2015-05-05,CA,1,A,A3,2.62,197.0 -2015-05-06,CA,1,A,A3,2.58,207.0 -2015-05-07,CA,1,A,A3,2.56,211.0 -2015-05-08,CA,1,A,A3,2.57,194.0 -2015-05-09,CA,1,A,A3,2.55,214.0 -2015-05-10,CA,1,A,A3,2.55,217.0 -2015-05-11,CA,1,A,A3,2.63,184.0 -2015-05-12,CA,1,A,A3,2.61,206.0 -2015-05-13,CA,1,A,A3,2.55,219.0 -2015-05-14,CA,1,A,A3,2.54,211.0 -2015-05-15,CA,1,A,A3,2.55,196.0 -2015-05-16,CA,1,A,A3,2.64,200.0 -2015-05-17,CA,1,A,A3,2.57,209.0 -2015-05-18,CA,1,A,A3,2.59,195.0 -2015-05-19,CA,1,A,A3,2.55,202.0 -2015-05-20,CA,1,A,A3,2.6,205.0 -2015-05-21,CA,1,A,A3,2.58,206.0 -2015-05-22,CA,1,A,A3,2.58,205.0 -2015-05-23,CA,1,A,A3,2.64,190.0 -2015-05-24,CA,1,A,A3,2.61,190.0 -2015-05-25,CA,1,A,A3,2.56,197.0 -2015-05-26,CA,1,A,A3,2.6,213.0 -2015-05-27,CA,1,A,A3,2.54,202.0 -2015-05-28,CA,1,A,A3,2.6,206.0 -2015-05-29,CA,1,A,A3,2.6,200.0 -2015-05-30,CA,1,A,A3,2.62,211.0 -2015-05-31,CA,1,A,A3,2.64,202.0 -2015-06-01,CA,1,A,A3,2.6,199.0 -2015-06-02,CA,1,A,A3,2.57,183.0 -2015-06-03,CA,1,A,A3,2.6,202.0 -2015-06-04,CA,1,A,A3,2.61,184.0 -2015-06-05,CA,1,A,A3,2.6,209.0 -2015-06-06,CA,1,A,A3,2.61,195.0 -2015-06-07,CA,1,A,A3,2.56,219.0 -2015-06-08,CA,1,A,A3,2.54,201.0 -2015-06-09,CA,1,A,A3,2.61,196.0 -2015-06-10,CA,1,A,A3,2.59,208.0 -2015-06-11,CA,1,A,A3,2.58,201.0 -2015-06-12,CA,1,A,A3,2.62,190.0 -2015-06-13,CA,1,A,A3,2.6,194.0 -2015-06-14,CA,1,A,A3,2.57,225.0 -2015-06-15,CA,1,A,A3,2.63,193.0 -2015-06-16,CA,1,A,A3,2.61,195.0 -2015-06-17,CA,1,A,A3,2.59,194.0 -2015-06-18,CA,1,A,A3,2.63,187.0 -2015-06-19,CA,1,A,A3,2.59,206.0 -2015-06-20,CA,1,A,A3,2.63,196.0 -2015-06-21,CA,1,A,A3,2.57,204.0 -2015-06-22,CA,1,A,A3,2.59,205.0 -2015-06-23,CA,1,A,A3,2.59,203.0 -2015-06-24,CA,1,A,A3,2.61,203.0 -2015-06-25,CA,1,A,A3,2.61,200.0 -2015-06-26,CA,1,A,A3,2.59,207.0 -2015-06-27,CA,1,A,A3,2.55,225.0 -2015-06-28,CA,1,A,A3,2.57,201.0 -2015-06-29,CA,1,A,A3,2.64,185.0 -2015-06-30,CA,1,A,A3,2.62,195.0 -2015-07-01,CA,1,A,A3,2.59,211.0 -2015-07-02,CA,1,A,A3,2.56,199.0 -2015-07-03,CA,1,A,A3,2.6,215.0 -2015-07-04,CA,1,A,A3,2.61,201.0 -2015-07-05,CA,1,A,A3,2.61,200.0 -2015-07-06,CA,1,A,A3,2.58,208.0 -2015-07-07,CA,1,A,A3,2.61,189.0 -2015-07-08,CA,1,A,A3,2.6,204.0 -2015-07-09,CA,1,A,A3,2.57,209.0 -2015-07-10,CA,1,A,A3,2.57,210.0 -2015-07-11,CA,1,A,A3,2.58,212.0 -2015-07-12,CA,1,A,A3,2.57,198.0 -2015-07-13,CA,1,A,A3,2.62,189.0 -2015-07-14,CA,1,A,A3,2.61,206.0 -2015-07-15,CA,1,A,A3,2.63,205.0 -2015-07-16,CA,1,A,A3,2.61,192.0 -2015-07-17,CA,1,A,A3,2.6,213.0 -2015-07-18,CA,1,A,A3,2.61,197.0 -2015-07-19,CA,1,A,A3,2.6,201.0 -2015-07-20,CA,1,A,A3,2.59,195.0 -2015-07-21,CA,1,A,A3,2.55,215.0 -2015-07-22,CA,1,A,A3,2.62,191.0 -2015-07-23,CA,1,A,A3,2.58,202.0 -2015-07-24,CA,1,A,A3,2.58,210.0 -2015-07-25,CA,1,A,A3,2.54,212.0 -2015-07-26,CA,1,A,A3,2.63,200.0 -2015-07-27,CA,1,A,A3,2.62,185.0 -2015-07-28,CA,1,A,A3,2.59,204.0 -2015-07-29,CA,1,A,A3,2.59,213.0 -2015-07-30,CA,1,A,A3,2.6,200.0 -2015-07-31,CA,1,A,A3,2.62,186.0 -2015-08-01,CA,1,A,A3,2.58,229.0 -2015-08-02,CA,1,A,A3,2.61,202.0 -2015-08-03,CA,1,A,A3,2.58,198.0 -2015-08-04,CA,1,A,A3,2.61,193.0 -2015-08-05,CA,1,A,A3,2.6,198.0 -2015-08-06,CA,1,A,A3,2.59,187.0 -2015-08-07,CA,1,A,A3,2.62,188.0 -2015-08-08,CA,1,A,A3,2.56,217.0 -2015-08-09,CA,1,A,A3,2.62,198.0 -2015-08-10,CA,1,A,A3,2.6,191.0 -2015-08-11,CA,1,A,A3,2.64,189.0 -2015-08-12,CA,1,A,A3,2.62,196.0 -2015-08-13,CA,1,A,A3,2.58,196.0 -2015-08-14,CA,1,A,A3,2.57,197.0 -2015-08-15,CA,1,A,A3,2.58,211.0 -2015-08-16,CA,1,A,A3,2.61,198.0 -2015-08-17,CA,1,A,A3,2.57,222.0 -2015-08-18,CA,1,A,A3,2.58,198.0 -2015-08-19,CA,1,A,A3,2.59,209.0 -2015-08-20,CA,1,A,A3,2.59,194.0 -2015-08-21,CA,1,A,A3,2.57,192.0 -2015-08-22,CA,1,A,A3,2.62,214.0 -2015-08-23,CA,1,A,A3,2.59,216.0 -2015-08-24,CA,1,A,A3,2.62,195.0 -2015-08-25,CA,1,A,A3,2.59,203.0 -2015-08-26,CA,1,A,A3,2.6,200.0 -2015-08-27,CA,1,A,A3,2.58,224.0 -2015-08-28,CA,1,A,A3,2.61,206.0 -2015-08-29,CA,1,A,A3,2.62,210.0 -2015-08-30,CA,1,A,A3,2.58,219.0 -2015-08-31,CA,1,A,A3,2.56,201.0 -2015-09-01,CA,1,A,A3,2.6,206.0 -2015-09-02,CA,1,A,A3,2.53,214.0 -2015-09-03,CA,1,A,A3,2.6,209.0 -2015-09-04,CA,1,A,A3,2.58,205.0 -2015-09-05,CA,1,A,A3,2.61,203.0 -2015-09-06,CA,1,A,A3,2.57,227.0 -2015-09-07,CA,1,A,A3,2.62,191.0 -2015-09-08,CA,1,A,A3,2.62,192.0 -2015-09-09,CA,1,A,A3,2.54,216.0 -2015-09-10,CA,1,A,A3,2.59,212.0 -2015-09-11,CA,1,A,A3,2.62,209.0 -2015-09-12,CA,1,A,A3,2.62,214.0 -2015-09-13,CA,1,A,A3,2.59,207.0 -2015-09-14,CA,1,A,A3,2.58,194.0 -2015-09-15,CA,1,A,A3,2.59,189.0 -2015-09-16,CA,1,A,A3,2.63,196.0 -2015-09-17,CA,1,A,A3,2.66,172.0 -2015-09-18,CA,1,A,A3,2.61,212.0 -2015-09-19,CA,1,A,A3,2.53,232.0 -2015-09-20,CA,1,A,A3,2.59,215.0 -2015-09-21,CA,1,A,A3,2.57,176.0 -2015-09-22,CA,1,A,A3,2.56,191.0 -2015-09-23,CA,1,A,A3,2.6,205.0 -2015-09-24,CA,1,A,A3,2.61,204.0 -2015-09-25,CA,1,A,A3,2.57,196.0 -2015-09-26,CA,1,A,A3,2.64,203.0 -2015-09-27,CA,1,A,A3,2.59,206.0 -2015-09-28,CA,1,A,A3,2.6,198.0 -2015-09-29,CA,1,A,A3,2.6,202.0 -2015-09-30,CA,1,A,A3,2.62,184.0 -2015-10-01,CA,1,A,A3,2.58,213.0 -2015-10-02,CA,1,A,A3,2.58,198.0 -2015-10-03,CA,1,A,A3,2.56,226.0 -2015-10-04,CA,1,A,A3,2.59,214.0 -2015-10-05,CA,1,A,A3,2.54,212.0 -2015-10-06,CA,1,A,A3,2.65,206.0 -2015-10-07,CA,1,A,A3,2.55,210.0 -2015-10-08,CA,1,A,A3,2.58,204.0 -2015-10-09,CA,1,A,A3,2.62,195.0 -2015-10-10,CA,1,A,A3,2.59,223.0 -2015-10-11,CA,1,A,A3,2.59,199.0 -2015-10-12,CA,1,A,A3,2.6,182.0 -2015-10-13,CA,1,A,A3,2.55,201.0 -2015-10-14,CA,1,A,A3,2.63,187.0 -2015-10-15,CA,1,A,A3,2.63,182.0 -2015-10-16,CA,1,A,A3,2.64,194.0 -2015-10-17,CA,1,A,A3,2.66,200.0 -2015-10-18,CA,1,A,A3,2.52,200.0 -2015-10-19,CA,1,A,A3,2.62,187.0 -2015-10-20,CA,1,A,A3,2.59,202.0 -2015-10-21,CA,1,A,A3,2.55,198.0 -2015-10-22,CA,1,A,A3,2.59,217.0 -2015-10-23,CA,1,A,A3,2.55,219.0 -2015-10-24,CA,1,A,A3,2.61,204.0 -2015-10-25,CA,1,A,A3,2.58,215.0 -2015-10-26,CA,1,A,A3,2.61,199.0 -2015-10-27,CA,1,A,A3,2.64,194.0 -2015-10-28,CA,1,A,A3,2.61,214.0 -2015-10-29,CA,1,A,A3,2.57,204.0 -2015-10-30,CA,1,A,A3,2.62,189.0 -2015-10-31,CA,1,A,A3,2.64,194.0 -2015-11-01,CA,1,A,A3,2.6,192.0 -2015-11-02,CA,1,A,A3,2.61,187.0 -2015-11-03,CA,1,A,A3,2.58,199.0 -2015-11-04,CA,1,A,A3,2.54,223.0 -2015-11-05,CA,1,A,A3,2.6,186.0 -2015-11-06,CA,1,A,A3,2.61,203.0 -2015-11-07,CA,1,A,A3,2.63,209.0 -2015-11-08,CA,1,A,A3,2.62,194.0 -2015-11-09,CA,1,A,A3,2.52,211.0 -2015-11-10,CA,1,A,A3,2.6,190.0 -2015-11-11,CA,1,A,A3,2.61,203.0 -2015-11-12,CA,1,A,A3,2.64,203.0 -2015-11-13,CA,1,A,A3,2.6,208.0 -2015-11-14,CA,1,A,A3,2.59,207.0 -2015-11-15,CA,1,A,A3,2.6,194.0 -2015-11-16,CA,1,A,A3,2.56,217.0 -2015-11-17,CA,1,A,A3,2.55,203.0 -2015-11-18,CA,1,A,A3,2.59,205.0 -2015-11-19,CA,1,A,A3,2.59,191.0 -2015-11-20,CA,1,A,A3,2.61,188.0 -2015-11-21,CA,1,A,A3,2.58,213.0 -2015-11-22,CA,1,A,A3,2.59,190.0 -2015-11-23,CA,1,A,A3,2.6,215.0 -2015-11-24,CA,1,A,A3,2.63,197.0 -2015-11-25,CA,1,A,A3,2.63,188.0 -2015-11-26,CA,1,A,A3,2.55,215.0 -2015-11-27,CA,1,A,A3,2.6,196.0 -2015-11-28,CA,1,A,A3,2.55,218.0 -2015-11-29,CA,1,A,A3,2.58,207.0 -2015-11-30,CA,1,A,A3,2.58,197.0 -2015-12-01,CA,1,A,A3,2.54,208.0 -2015-12-02,CA,1,A,A3,2.57,220.0 -2015-12-03,CA,1,A,A3,2.58,194.0 -2015-12-04,CA,1,A,A3,2.58,196.0 -2015-12-05,CA,1,A,A3,2.57,220.0 -2015-12-06,CA,1,A,A3,2.56,212.0 -2015-12-07,CA,1,A,A3,2.59,181.0 -2015-12-08,CA,1,A,A3,2.63,199.0 -2015-12-09,CA,1,A,A3,2.58,205.0 -2015-12-10,CA,1,A,A3,2.61,190.0 -2015-12-11,CA,1,A,A3,2.55,197.0 -2015-12-12,CA,1,A,A3,2.58,216.0 -2015-12-13,CA,1,A,A3,2.62,194.0 -2015-12-14,CA,1,A,A3,2.58,198.0 -2015-12-15,CA,1,A,A3,2.57,194.0 -2015-12-16,CA,1,A,A3,2.63,186.0 -2015-12-17,CA,1,A,A3,2.61,197.0 -2015-12-18,CA,1,A,A3,2.63,194.0 -2015-12-19,CA,1,A,A3,2.62,208.0 -2015-12-20,CA,1,A,A3,2.59,217.0 -2015-12-21,CA,1,A,A3,2.57,192.0 -2015-12-22,CA,1,A,A3,2.6,203.0 -2015-12-23,CA,1,A,A3,2.61,177.0 -2015-12-24,CA,1,A,A3,2.57,200.0 -2015-12-25,CA,1,A,A3,2.57,193.0 -2015-12-26,CA,1,A,A3,2.59,229.0 -2015-12-27,CA,1,A,A3,2.57,215.0 -2015-12-28,CA,1,A,A3,2.57,214.0 -2015-12-29,CA,1,A,A3,2.63,208.0 -2015-12-30,CA,1,A,A3,2.55,214.0 -2015-12-31,CA,1,A,A3,2.58,206.0 -2016-01-01,CA,1,A,A3,2.58,200.0 -2016-01-02,CA,1,A,A3,2.59,222.0 -2016-01-03,CA,1,A,A3,2.61,187.0 -2016-01-04,CA,1,A,A3,2.6,183.0 -2016-01-05,CA,1,A,A3,2.6,194.0 -2016-01-06,CA,1,A,A3,2.6,206.0 -2016-01-07,CA,1,A,A3,2.59,198.0 -2016-01-08,CA,1,A,A3,2.6,199.0 -2016-01-09,CA,1,A,A3,2.56,217.0 -2016-01-10,CA,1,A,A3,2.56,208.0 -2016-01-11,CA,1,A,A3,2.61,190.0 -2016-01-12,CA,1,A,A3,2.58,208.0 -2016-01-13,CA,1,A,A3,2.57,208.0 -2016-01-14,CA,1,A,A3,2.6,195.0 -2016-01-15,CA,1,A,A3,2.61,207.0 -2016-01-16,CA,1,A,A3,2.58,203.0 -2016-01-17,CA,1,A,A3,2.57,193.0 -2016-01-18,CA,1,A,A3,2.63,196.0 -2016-01-19,CA,1,A,A3,2.6,211.0 -2016-01-20,CA,1,A,A3,2.6,198.0 -2016-01-21,CA,1,A,A3,2.6,191.0 -2016-01-22,CA,1,A,A3,2.56,203.0 -2016-01-23,CA,1,A,A3,2.59,219.0 -2016-01-24,CA,1,A,A3,2.61,219.0 -2016-01-25,CA,1,A,A3,2.58,195.0 -2016-01-26,CA,1,A,A3,2.59,207.0 -2016-01-27,CA,1,A,A3,2.62,189.0 -2016-01-28,CA,1,A,A3,2.61,214.0 -2016-01-29,CA,1,A,A3,2.58,194.0 -2016-01-30,CA,1,A,A3,2.64,201.0 -2016-01-31,CA,1,A,A3,2.55,205.0 -2016-02-01,CA,1,A,A3,2.6,209.0 -2016-02-02,CA,1,A,A3,2.54,209.0 -2016-02-03,CA,1,A,A3,2.62,196.0 -2016-02-04,CA,1,A,A3,2.58,197.0 -2016-02-05,CA,1,A,A3,2.58,193.0 -2016-02-06,CA,1,A,A3,2.55,210.0 -2016-02-07,CA,1,A,A3,2.59,207.0 -2016-02-08,CA,1,A,A3,2.51,204.0 -2016-02-09,CA,1,A,A3,2.62,199.0 -2016-02-10,CA,1,A,A3,2.66,188.0 -2016-02-11,CA,1,A,A3,2.62,191.0 -2016-02-12,CA,1,A,A3,2.63,193.0 -2016-02-13,CA,1,A,A3,2.6,211.0 -2016-02-14,CA,1,A,A3,2.59,218.0 -2016-02-15,CA,1,A,A3,2.65,184.0 -2016-02-16,CA,1,A,A3,2.6,189.0 -2016-02-17,CA,1,A,A3,2.59,216.0 -2016-02-18,CA,1,A,A3,2.66,171.0 -2016-02-19,CA,1,A,A3,2.57,212.0 -2016-02-20,CA,1,A,A3,2.58,212.0 -2016-02-21,CA,1,A,A3,2.6,196.0 -2016-02-22,CA,1,A,A3,2.61,194.0 -2016-02-23,CA,1,A,A3,2.6,211.0 -2016-02-24,CA,1,A,A3,2.56,194.0 -2016-02-25,CA,1,A,A3,2.56,203.0 -2016-02-26,CA,1,A,A3,2.61,196.0 -2016-02-27,CA,1,A,A3,2.62,196.0 -2016-02-28,CA,1,A,A3,2.61,187.0 -2016-02-29,CA,1,A,A3,2.59,195.0 -2016-03-01,CA,1,A,A3,2.58,211.0 -2016-03-02,CA,1,A,A3,2.6,212.0 -2016-03-03,CA,1,A,A3,2.6,188.0 -2016-03-04,CA,1,A,A3,2.59,203.0 -2016-03-05,CA,1,A,A3,2.56,211.0 -2016-03-06,CA,1,A,A3,2.59,217.0 -2016-03-07,CA,1,A,A3,2.61,202.0 -2016-03-08,CA,1,A,A3,2.61,206.0 -2016-03-09,CA,1,A,A3,2.57,203.0 -2016-03-10,CA,1,A,A3,2.59,200.0 -2016-03-11,CA,1,A,A3,2.58,210.0 -2016-03-12,CA,1,A,A3,2.63,194.0 -2016-03-13,CA,1,A,A3,2.62,213.0 -2016-03-14,CA,1,A,A3,2.56,217.0 -2016-03-15,CA,1,A,A3,2.58,205.0 -2016-03-16,CA,1,A,A3,2.58,202.0 -2016-03-17,CA,1,A,A3,2.62,203.0 -2016-03-18,CA,1,A,A3,2.61,205.0 -2016-03-19,CA,1,A,A3,2.54,218.0 -2016-03-20,CA,1,A,A3,2.58,204.0 -2016-03-21,CA,1,A,A3,2.57,196.0 -2016-03-22,CA,1,A,A3,2.62,182.0 -2016-03-23,CA,1,A,A3,2.58,208.0 -2016-03-24,CA,1,A,A3,2.58,202.0 -2016-03-25,CA,1,A,A3,2.61,202.0 -2016-03-26,CA,1,A,A3,2.58,207.0 -2016-03-27,CA,1,A,A3,2.59,204.0 -2016-03-28,CA,1,A,A3,2.6,188.0 -2016-03-29,CA,1,A,A3,2.63,198.0 -2016-03-30,CA,1,A,A3,2.62,187.0 -2016-03-31,CA,1,A,A3,2.64,191.0 -2016-04-01,CA,1,A,A3,2.56,218.0 -2016-04-02,CA,1,A,A3,2.58,216.0 -2016-04-03,CA,1,A,A3,2.61,199.0 -2016-04-04,CA,1,A,A3,2.56,193.0 -2016-04-05,CA,1,A,A3,2.58,225.0 -2016-04-06,CA,1,A,A3,2.54,208.0 -2016-04-07,CA,1,A,A3,2.56,218.0 -2016-04-08,CA,1,A,A3,2.58,194.0 -2016-04-09,CA,1,A,A3,2.63,190.0 -2016-04-10,CA,1,A,A3,2.58,203.0 -2016-04-11,CA,1,A,A3,2.62,176.0 -2016-04-12,CA,1,A,A3,2.58,195.0 -2016-04-13,CA,1,A,A3,2.59,208.0 -2016-04-14,CA,1,A,A3,2.6,222.0 -2016-04-15,CA,1,A,A3,2.56,212.0 -2016-04-16,CA,1,A,A3,2.58,205.0 -2016-04-17,CA,1,A,A3,2.62,178.0 -2016-04-18,CA,1,A,A3,2.59,204.0 -2016-04-19,CA,1,A,A3,2.59,208.0 -2016-04-20,CA,1,A,A3,2.55,202.0 -2016-04-21,CA,1,A,A3,2.61,198.0 -2016-04-22,CA,1,A,A3,2.6,198.0 -2016-04-23,CA,1,A,A3,2.57,204.0 -2016-04-24,CA,1,A,A3,2.63,207.0 -2016-04-25,CA,1,A,A3,2.57,204.0 -2016-04-26,CA,1,A,A3,2.63,190.0 -2016-04-27,CA,1,A,A3,2.6,194.0 -2016-04-28,CA,1,A,A3,2.59,197.0 -2016-04-29,CA,1,A,A3,2.57,207.0 -2016-04-30,CA,1,A,A3,2.6,191.0 -2016-05-01,CA,1,A,A3,2.65,198.0 -2016-05-02,CA,1,A,A3,2.61,199.0 -2016-05-03,CA,1,A,A3,2.59,196.0 -2016-05-04,CA,1,A,A3,2.57,215.0 -2016-05-05,CA,1,A,A3,2.62,194.0 -2016-05-06,CA,1,A,A3,2.58,210.0 -2016-05-07,CA,1,A,A3,2.6,210.0 -2016-05-08,CA,1,A,A3,2.6,213.0 -2016-05-09,CA,1,A,A3,2.6,191.0 -2016-05-10,CA,1,A,A3,2.56,216.0 -2016-05-11,CA,1,A,A3,2.63,187.0 -2016-05-12,CA,1,A,A3,2.58,206.0 -2016-05-13,CA,1,A,A3,2.62,197.0 -2016-05-14,CA,1,A,A3,2.61,210.0 -2016-05-15,CA,1,A,A3,2.59,208.0 -2016-05-16,CA,1,A,A3,2.6,194.0 -2016-05-17,CA,1,A,A3,2.57,222.0 -2016-05-18,CA,1,A,A3,2.56,197.0 -2016-05-19,CA,1,A,A3,2.63,201.0 -2016-05-20,CA,1,A,A3,2.59,215.0 -2016-05-21,CA,1,A,A3,2.6,224.0 -2016-05-22,CA,1,A,A3,2.59,198.0 -2016-05-23,CA,1,A,A3,2.58,181.0 -2016-05-24,CA,1,A,A3,2.58,205.0 -2016-05-25,CA,1,A,A3,2.64,201.0 -2016-05-26,CA,1,A,A3,2.59,195.0 -2016-05-27,CA,1,A,A3,2.63,209.0 -2016-05-28,CA,1,A,A3,2.61,204.0 -2016-05-29,CA,1,A,A3,2.57,205.0 -2016-05-30,CA,1,A,A3,2.64,189.0 -2016-05-31,CA,1,A,A3,2.55,208.0 -2016-06-01,CA,1,A,A3,2.56,203.0 -2016-06-02,CA,1,A,A3,2.54,222.0 -2016-06-03,CA,1,A,A3,2.63,201.0 -2016-06-04,CA,1,A,A3,2.63,202.0 -2016-06-05,CA,1,A,A3,2.62,187.0 -2016-06-06,CA,1,A,A3,2.61,191.0 -2016-06-07,CA,1,A,A3,2.57,200.0 -2016-06-08,CA,1,A,A3,2.6,199.0 -2016-06-09,CA,1,A,A3,2.64,184.0 -2016-06-10,CA,1,A,A3,2.59,200.0 -2016-06-11,CA,1,A,A3,2.59,219.0 -2016-06-12,CA,1,A,A3,2.61,189.0 -2016-06-13,CA,1,A,A3,2.62,181.0 -2016-06-14,CA,1,A,A3,2.58,210.0 -2016-06-15,CA,1,A,A3,2.61,213.0 -2016-06-16,CA,1,A,A3,2.63,184.0 -2016-06-17,CA,1,A,A3,2.55,220.0 -2016-06-18,CA,1,A,A3,2.58,196.0 -2016-06-19,CA,1,A,A3,2.6,206.0 -2016-06-20,CA,1,A,A3,2.63,190.0 -2016-06-21,CA,1,A,A3,2.59,201.0 -2016-06-22,CA,1,A,A3,2.64,195.0 -2016-06-23,CA,1,A,A3,2.63,193.0 -2016-06-24,CA,1,A,A3,2.6,207.0 -2016-06-25,CA,1,A,A3,2.59,225.0 -2016-06-26,CA,1,A,A3,2.59,195.0 -2016-06-27,CA,1,A,A3,2.6,193.0 -2016-06-28,CA,1,A,A3,2.62,208.0 -2016-06-29,CA,1,A,A3,2.6,204.0 -2016-06-30,CA,1,A,A3,2.63,194.0 -2016-07-01,CA,1,A,A3,2.63,181.0 -2016-07-02,CA,1,A,A3,2.58,200.0 -2016-07-03,CA,1,A,A3,2.63,196.0 -2016-07-04,CA,1,A,A3,2.59,186.0 -2016-07-05,CA,1,A,A3,2.6,213.0 -2016-07-06,CA,1,A,A3,2.6,192.0 -2016-07-07,CA,1,A,A3,2.59,195.0 -2016-07-08,CA,1,A,A3,2.6,207.0 -2016-07-09,CA,1,A,A3,2.62,206.0 -2016-07-10,CA,1,A,A3,2.6,192.0 -2016-07-11,CA,1,A,A3,2.57,193.0 -2016-07-12,CA,1,A,A3,2.62,194.0 -2016-07-13,CA,1,A,A3,2.58,192.0 -2016-07-14,CA,1,A,A3,2.62,201.0 -2016-07-15,CA,1,A,A3,2.61,201.0 -2016-07-16,CA,1,A,A3,2.57,206.0 -2016-07-17,CA,1,A,A3,2.58,223.0 -2016-07-18,CA,1,A,A3,2.62,192.0 -2016-07-19,CA,1,A,A3,2.62,187.0 -2016-07-20,CA,1,A,A3,2.59,194.0 -2016-07-21,CA,1,A,A3,2.57,219.0 -2016-07-22,CA,1,A,A3,2.65,197.0 -2016-07-23,CA,1,A,A3,2.59,221.0 -2016-07-24,CA,1,A,A3,2.54,206.0 -2016-07-25,CA,1,A,A3,2.62,186.0 -2016-07-26,CA,1,A,A3,2.55,199.0 -2016-07-27,CA,1,A,A3,2.61,203.0 -2015-01-05,CA,1,B,B1,2.25,120.0 -2015-01-06,CA,1,B,B1,2.24,120.0 -2015-01-07,CA,1,B,B1,2.24,118.0 -2015-01-08,CA,1,B,B1,2.25,118.0 -2015-01-09,CA,1,B,B1,2.23,111.0 -2015-01-10,CA,1,B,B1,2.27,127.0 -2015-01-11,CA,1,B,B1,2.24,117.0 -2015-01-12,CA,1,B,B1,2.29,114.0 -2015-01-13,CA,1,B,B1,2.27,112.0 -2015-01-14,CA,1,B,B1,2.28,105.0 -2015-01-15,CA,1,B,B1,2.28,102.0 -2015-01-16,CA,1,B,B1,2.27,128.0 -2015-01-17,CA,1,B,B1,2.24,120.0 -2015-01-18,CA,1,B,B1,2.25,115.0 -2015-01-19,CA,1,B,B1,2.27,112.0 -2015-01-20,CA,1,B,B1,2.26,116.0 -2015-01-21,CA,1,B,B1,2.24,117.0 -2015-01-22,CA,1,B,B1,2.26,109.0 -2015-01-23,CA,1,B,B1,2.25,112.0 -2015-01-24,CA,1,B,B1,2.21,130.0 -2015-01-25,CA,1,B,B1,2.25,118.0 -2015-01-26,CA,1,B,B1,2.23,111.0 -2015-01-27,CA,1,B,B1,2.26,110.0 -2015-01-28,CA,1,B,B1,2.27,112.0 -2015-01-29,CA,1,B,B1,2.25,105.0 -2015-01-30,CA,1,B,B1,2.26,110.0 -2015-01-31,CA,1,B,B1,2.31,126.0 -2015-02-01,CA,1,B,B1,2.27,111.0 -2015-02-02,CA,1,B,B1,2.25,118.0 -2015-02-03,CA,1,B,B1,2.28,110.0 -2015-02-04,CA,1,B,B1,2.29,105.0 -2015-02-05,CA,1,B,B1,2.29,117.0 -2015-02-06,CA,1,B,B1,2.27,106.0 -2015-02-07,CA,1,B,B1,2.27,120.0 -2015-02-08,CA,1,B,B1,2.25,120.0 -2015-02-09,CA,1,B,B1,2.27,118.0 -2015-02-10,CA,1,B,B1,2.29,114.0 -2015-02-11,CA,1,B,B1,2.27,109.0 -2015-02-12,CA,1,B,B1,2.25,108.0 -2015-02-13,CA,1,B,B1,2.23,116.0 -2015-02-14,CA,1,B,B1,2.28,124.0 -2015-02-15,CA,1,B,B1,2.25,124.0 -2015-02-16,CA,1,B,B1,2.24,102.0 -2015-02-17,CA,1,B,B1,2.23,120.0 -2015-02-18,CA,1,B,B1,2.25,118.0 -2015-02-19,CA,1,B,B1,2.25,114.0 -2015-02-20,CA,1,B,B1,2.27,103.0 -2015-02-21,CA,1,B,B1,2.29,114.0 -2015-02-22,CA,1,B,B1,2.29,117.0 -2015-02-23,CA,1,B,B1,2.24,115.0 -2015-02-24,CA,1,B,B1,2.26,107.0 -2015-02-25,CA,1,B,B1,2.26,106.0 -2015-02-26,CA,1,B,B1,2.26,115.0 -2015-02-27,CA,1,B,B1,2.25,112.0 -2015-02-28,CA,1,B,B1,2.28,119.0 -2015-03-01,CA,1,B,B1,2.26,116.0 -2015-03-02,CA,1,B,B1,2.33,102.0 -2015-03-03,CA,1,B,B1,2.24,119.0 -2015-03-04,CA,1,B,B1,2.24,114.0 -2015-03-05,CA,1,B,B1,2.27,127.0 -2015-03-06,CA,1,B,B1,2.22,111.0 -2015-03-07,CA,1,B,B1,2.23,116.0 -2015-03-08,CA,1,B,B1,2.27,121.0 -2015-03-09,CA,1,B,B1,2.28,114.0 -2015-03-10,CA,1,B,B1,2.25,114.0 -2015-03-11,CA,1,B,B1,2.27,109.0 -2015-03-12,CA,1,B,B1,2.25,117.0 -2015-03-13,CA,1,B,B1,2.28,115.0 -2015-03-14,CA,1,B,B1,2.3,123.0 -2015-03-15,CA,1,B,B1,2.26,117.0 -2015-03-16,CA,1,B,B1,2.28,104.0 -2015-03-17,CA,1,B,B1,2.24,113.0 -2015-03-18,CA,1,B,B1,2.27,104.0 -2015-03-19,CA,1,B,B1,2.25,107.0 -2015-03-20,CA,1,B,B1,2.26,113.0 -2015-03-21,CA,1,B,B1,2.3,113.0 -2015-03-22,CA,1,B,B1,2.29,120.0 -2015-03-23,CA,1,B,B1,2.27,113.0 -2015-03-24,CA,1,B,B1,2.26,109.0 -2015-03-25,CA,1,B,B1,2.3,113.0 -2015-03-26,CA,1,B,B1,2.26,110.0 -2015-03-27,CA,1,B,B1,2.3,124.0 -2015-03-28,CA,1,B,B1,2.26,118.0 -2015-03-29,CA,1,B,B1,2.26,121.0 -2015-03-30,CA,1,B,B1,2.26,115.0 -2015-03-31,CA,1,B,B1,2.26,109.0 -2015-04-01,CA,1,B,B1,2.21,121.0 -2015-04-02,CA,1,B,B1,2.29,107.0 -2015-04-03,CA,1,B,B1,2.27,107.0 -2015-04-04,CA,1,B,B1,2.26,117.0 -2015-04-05,CA,1,B,B1,2.24,114.0 -2015-04-06,CA,1,B,B1,2.28,121.0 -2015-04-07,CA,1,B,B1,2.26,103.0 -2015-04-08,CA,1,B,B1,2.27,112.0 -2015-04-09,CA,1,B,B1,2.28,117.0 -2015-04-10,CA,1,B,B1,2.26,113.0 -2015-04-11,CA,1,B,B1,2.29,114.0 -2015-04-12,CA,1,B,B1,2.28,117.0 -2015-04-13,CA,1,B,B1,2.31,105.0 -2015-04-14,CA,1,B,B1,2.31,106.0 -2015-04-15,CA,1,B,B1,2.24,104.0 -2015-04-16,CA,1,B,B1,2.26,116.0 -2015-04-17,CA,1,B,B1,2.25,131.0 -2015-04-18,CA,1,B,B1,2.22,119.0 -2015-04-19,CA,1,B,B1,2.26,120.0 -2015-04-20,CA,1,B,B1,2.3,103.0 -2015-04-21,CA,1,B,B1,2.28,117.0 -2015-04-22,CA,1,B,B1,2.24,123.0 -2015-04-23,CA,1,B,B1,2.31,115.0 -2015-04-24,CA,1,B,B1,2.24,115.0 -2015-04-25,CA,1,B,B1,2.25,123.0 -2015-04-26,CA,1,B,B1,2.24,113.0 -2015-04-27,CA,1,B,B1,2.27,115.0 -2015-04-28,CA,1,B,B1,2.26,115.0 -2015-04-29,CA,1,B,B1,2.3,107.0 -2015-04-30,CA,1,B,B1,2.28,109.0 -2015-05-01,CA,1,B,B1,2.22,124.0 -2015-05-02,CA,1,B,B1,2.27,117.0 -2015-05-03,CA,1,B,B1,2.24,121.0 -2015-05-04,CA,1,B,B1,2.29,108.0 -2015-05-05,CA,1,B,B1,2.23,120.0 -2015-05-06,CA,1,B,B1,2.23,120.0 -2015-05-07,CA,1,B,B1,2.26,110.0 -2015-05-08,CA,1,B,B1,2.26,116.0 -2015-05-09,CA,1,B,B1,2.22,117.0 -2015-05-10,CA,1,B,B1,2.25,123.0 -2015-05-11,CA,1,B,B1,2.28,109.0 -2015-05-12,CA,1,B,B1,2.25,110.0 -2015-05-13,CA,1,B,B1,2.2,121.0 -2015-05-14,CA,1,B,B1,2.23,117.0 -2015-05-15,CA,1,B,B1,2.27,107.0 -2015-05-16,CA,1,B,B1,2.27,122.0 -2015-05-17,CA,1,B,B1,2.25,111.0 -2015-05-18,CA,1,B,B1,2.27,101.0 -2015-05-19,CA,1,B,B1,2.28,119.0 -2015-05-20,CA,1,B,B1,2.27,117.0 -2015-05-21,CA,1,B,B1,2.25,110.0 -2015-05-22,CA,1,B,B1,2.28,111.0 -2015-05-23,CA,1,B,B1,2.23,116.0 -2015-05-24,CA,1,B,B1,2.26,121.0 -2015-05-25,CA,1,B,B1,2.26,104.0 -2015-05-26,CA,1,B,B1,2.25,123.0 -2015-05-27,CA,1,B,B1,2.24,112.0 -2015-05-28,CA,1,B,B1,2.25,119.0 -2015-05-29,CA,1,B,B1,2.3,112.0 -2015-05-30,CA,1,B,B1,2.32,119.0 -2015-05-31,CA,1,B,B1,2.28,117.0 -2015-06-01,CA,1,B,B1,2.26,107.0 -2015-06-02,CA,1,B,B1,2.28,102.0 -2015-06-03,CA,1,B,B1,2.28,117.0 -2015-06-04,CA,1,B,B1,2.23,121.0 -2015-06-05,CA,1,B,B1,2.22,119.0 -2015-06-06,CA,1,B,B1,2.23,117.0 -2015-06-07,CA,1,B,B1,2.22,115.0 -2015-06-08,CA,1,B,B1,2.25,103.0 -2015-06-09,CA,1,B,B1,2.29,114.0 -2015-06-10,CA,1,B,B1,2.24,104.0 -2015-06-11,CA,1,B,B1,2.27,115.0 -2015-06-12,CA,1,B,B1,2.24,119.0 -2015-06-13,CA,1,B,B1,2.24,116.0 -2015-06-14,CA,1,B,B1,2.3,119.0 -2015-06-15,CA,1,B,B1,2.25,109.0 -2015-06-16,CA,1,B,B1,2.27,109.0 -2015-06-17,CA,1,B,B1,2.28,118.0 -2015-06-18,CA,1,B,B1,2.23,118.0 -2015-06-19,CA,1,B,B1,2.28,114.0 -2015-06-20,CA,1,B,B1,2.26,115.0 -2015-06-21,CA,1,B,B1,2.24,120.0 -2015-06-22,CA,1,B,B1,2.26,112.0 -2015-06-23,CA,1,B,B1,2.31,117.0 -2015-06-24,CA,1,B,B1,2.31,111.0 -2015-06-25,CA,1,B,B1,2.27,105.0 -2015-06-26,CA,1,B,B1,2.26,117.0 -2015-06-27,CA,1,B,B1,2.28,121.0 -2015-06-28,CA,1,B,B1,2.23,118.0 -2015-06-29,CA,1,B,B1,2.32,105.0 -2015-06-30,CA,1,B,B1,2.26,112.0 -2015-07-01,CA,1,B,B1,2.28,111.0 -2015-07-02,CA,1,B,B1,2.25,119.0 -2015-07-03,CA,1,B,B1,2.24,117.0 -2015-07-04,CA,1,B,B1,2.27,116.0 -2015-07-05,CA,1,B,B1,2.25,119.0 -2015-07-06,CA,1,B,B1,2.26,107.0 -2015-07-07,CA,1,B,B1,2.28,121.0 -2015-07-08,CA,1,B,B1,2.27,118.0 -2015-07-09,CA,1,B,B1,2.28,114.0 -2015-07-10,CA,1,B,B1,2.26,108.0 -2015-07-11,CA,1,B,B1,2.25,124.0 -2015-07-12,CA,1,B,B1,2.27,115.0 -2015-07-13,CA,1,B,B1,2.26,116.0 -2015-07-14,CA,1,B,B1,2.3,102.0 -2015-07-15,CA,1,B,B1,2.26,108.0 -2015-07-16,CA,1,B,B1,2.24,117.0 -2015-07-17,CA,1,B,B1,2.23,112.0 -2015-07-18,CA,1,B,B1,2.23,122.0 -2015-07-19,CA,1,B,B1,2.27,117.0 -2015-07-20,CA,1,B,B1,2.24,118.0 -2015-07-21,CA,1,B,B1,2.28,105.0 -2015-07-22,CA,1,B,B1,2.27,105.0 -2015-07-23,CA,1,B,B1,2.27,113.0 -2015-07-24,CA,1,B,B1,2.24,113.0 -2015-07-25,CA,1,B,B1,2.29,116.0 -2015-07-26,CA,1,B,B1,2.27,119.0 -2015-07-27,CA,1,B,B1,2.25,111.0 -2015-07-28,CA,1,B,B1,2.29,111.0 -2015-07-29,CA,1,B,B1,2.25,115.0 -2015-07-30,CA,1,B,B1,2.22,121.0 -2015-07-31,CA,1,B,B1,2.25,119.0 -2015-08-01,CA,1,B,B1,2.25,128.0 -2015-08-02,CA,1,B,B1,2.31,115.0 -2015-08-03,CA,1,B,B1,2.27,106.0 -2015-08-04,CA,1,B,B1,2.25,120.0 -2015-08-05,CA,1,B,B1,2.26,114.0 -2015-08-06,CA,1,B,B1,2.25,116.0 -2015-08-07,CA,1,B,B1,2.28,121.0 -2015-08-08,CA,1,B,B1,2.26,122.0 -2015-08-09,CA,1,B,B1,2.29,121.0 -2015-08-10,CA,1,B,B1,2.23,117.0 -2015-08-11,CA,1,B,B1,2.24,113.0 -2015-08-12,CA,1,B,B1,2.28,119.0 -2015-08-13,CA,1,B,B1,2.24,113.0 -2015-08-14,CA,1,B,B1,2.26,122.0 -2015-08-15,CA,1,B,B1,2.26,121.0 -2015-08-16,CA,1,B,B1,2.29,124.0 -2015-08-17,CA,1,B,B1,2.23,112.0 -2015-08-18,CA,1,B,B1,2.26,111.0 -2015-08-19,CA,1,B,B1,2.27,124.0 -2015-08-20,CA,1,B,B1,2.27,122.0 -2015-08-21,CA,1,B,B1,2.21,115.0 -2015-08-22,CA,1,B,B1,2.27,116.0 -2015-08-23,CA,1,B,B1,2.25,113.0 -2015-08-24,CA,1,B,B1,2.29,110.0 -2015-08-25,CA,1,B,B1,2.24,114.0 -2015-08-26,CA,1,B,B1,2.32,119.0 -2015-08-27,CA,1,B,B1,2.26,123.0 -2015-08-28,CA,1,B,B1,2.26,111.0 -2015-08-29,CA,1,B,B1,2.24,123.0 -2015-08-30,CA,1,B,B1,2.27,123.0 -2015-08-31,CA,1,B,B1,2.27,111.0 -2015-09-01,CA,1,B,B1,2.28,118.0 -2015-09-02,CA,1,B,B1,2.27,107.0 -2015-09-03,CA,1,B,B1,2.26,115.0 -2015-09-04,CA,1,B,B1,2.27,117.0 -2015-09-05,CA,1,B,B1,2.26,135.0 -2015-09-06,CA,1,B,B1,2.25,114.0 -2015-09-07,CA,1,B,B1,2.24,108.0 -2015-09-08,CA,1,B,B1,2.26,114.0 -2015-09-09,CA,1,B,B1,2.23,117.0 -2015-09-10,CA,1,B,B1,2.26,111.0 -2015-09-11,CA,1,B,B1,2.22,125.0 -2015-09-12,CA,1,B,B1,2.26,119.0 -2015-09-13,CA,1,B,B1,2.26,122.0 -2015-09-14,CA,1,B,B1,2.25,117.0 -2015-09-15,CA,1,B,B1,2.23,114.0 -2015-09-16,CA,1,B,B1,2.28,119.0 -2015-09-17,CA,1,B,B1,2.26,117.0 -2015-09-18,CA,1,B,B1,2.21,114.0 -2015-09-19,CA,1,B,B1,2.31,107.0 -2015-09-20,CA,1,B,B1,2.26,119.0 -2015-09-21,CA,1,B,B1,2.29,112.0 -2015-09-22,CA,1,B,B1,2.23,112.0 -2015-09-23,CA,1,B,B1,2.22,119.0 -2015-09-24,CA,1,B,B1,2.26,110.0 -2015-09-25,CA,1,B,B1,2.24,115.0 -2015-09-26,CA,1,B,B1,2.26,115.0 -2015-09-27,CA,1,B,B1,2.25,116.0 -2015-09-28,CA,1,B,B1,2.26,103.0 -2015-09-29,CA,1,B,B1,2.3,110.0 -2015-09-30,CA,1,B,B1,2.29,119.0 -2015-10-01,CA,1,B,B1,2.24,115.0 -2015-10-02,CA,1,B,B1,2.27,112.0 -2015-10-03,CA,1,B,B1,2.26,125.0 -2015-10-04,CA,1,B,B1,2.27,124.0 -2015-10-05,CA,1,B,B1,2.27,118.0 -2015-10-06,CA,1,B,B1,2.25,118.0 -2015-10-07,CA,1,B,B1,2.25,119.0 -2015-10-08,CA,1,B,B1,2.24,115.0 -2015-10-09,CA,1,B,B1,2.27,110.0 -2015-10-10,CA,1,B,B1,2.26,125.0 -2015-10-11,CA,1,B,B1,2.24,119.0 -2015-10-12,CA,1,B,B1,2.29,117.0 -2015-10-13,CA,1,B,B1,2.24,118.0 -2015-10-14,CA,1,B,B1,2.27,120.0 -2015-10-15,CA,1,B,B1,2.26,115.0 -2015-10-16,CA,1,B,B1,2.28,106.0 -2015-10-17,CA,1,B,B1,2.29,117.0 -2015-10-18,CA,1,B,B1,2.24,119.0 -2015-10-19,CA,1,B,B1,2.28,109.0 -2015-10-20,CA,1,B,B1,2.28,114.0 -2015-10-21,CA,1,B,B1,2.2,116.0 -2015-10-22,CA,1,B,B1,2.27,107.0 -2015-10-23,CA,1,B,B1,2.25,117.0 -2015-10-24,CA,1,B,B1,2.31,112.0 -2015-10-25,CA,1,B,B1,2.31,122.0 -2015-10-26,CA,1,B,B1,2.3,113.0 -2015-10-27,CA,1,B,B1,2.28,108.0 -2015-10-28,CA,1,B,B1,2.28,109.0 -2015-10-29,CA,1,B,B1,2.26,102.0 -2015-10-30,CA,1,B,B1,2.25,118.0 -2015-10-31,CA,1,B,B1,2.26,122.0 -2015-11-01,CA,1,B,B1,2.28,108.0 -2015-11-02,CA,1,B,B1,2.28,111.0 -2015-11-03,CA,1,B,B1,2.26,110.0 -2015-11-04,CA,1,B,B1,2.24,108.0 -2015-11-05,CA,1,B,B1,2.26,117.0 -2015-11-06,CA,1,B,B1,2.25,120.0 -2015-11-07,CA,1,B,B1,2.25,123.0 -2015-11-08,CA,1,B,B1,2.29,108.0 -2015-11-09,CA,1,B,B1,2.22,111.0 -2015-11-10,CA,1,B,B1,2.27,121.0 -2015-11-11,CA,1,B,B1,2.28,113.0 -2015-11-12,CA,1,B,B1,2.25,111.0 -2015-11-13,CA,1,B,B1,2.27,101.0 -2015-11-14,CA,1,B,B1,2.27,126.0 -2015-11-15,CA,1,B,B1,2.27,120.0 -2015-11-16,CA,1,B,B1,2.31,119.0 -2015-11-17,CA,1,B,B1,2.25,115.0 -2015-11-18,CA,1,B,B1,2.21,118.0 -2015-11-19,CA,1,B,B1,2.26,119.0 -2015-11-20,CA,1,B,B1,2.27,110.0 -2015-11-21,CA,1,B,B1,2.27,116.0 -2015-11-22,CA,1,B,B1,2.26,103.0 -2015-11-23,CA,1,B,B1,2.25,109.0 -2015-11-24,CA,1,B,B1,2.22,107.0 -2015-11-25,CA,1,B,B1,2.29,111.0 -2015-11-26,CA,1,B,B1,2.23,113.0 -2015-11-27,CA,1,B,B1,2.24,124.0 -2015-11-28,CA,1,B,B1,2.27,116.0 -2015-11-29,CA,1,B,B1,2.27,114.0 -2015-11-30,CA,1,B,B1,2.27,110.0 -2015-12-01,CA,1,B,B1,2.28,117.0 -2015-12-02,CA,1,B,B1,2.24,110.0 -2015-12-03,CA,1,B,B1,2.3,116.0 -2015-12-04,CA,1,B,B1,2.31,117.0 -2015-12-05,CA,1,B,B1,2.25,120.0 -2015-12-06,CA,1,B,B1,2.26,121.0 -2015-12-07,CA,1,B,B1,2.28,113.0 -2015-12-08,CA,1,B,B1,2.3,110.0 -2015-12-09,CA,1,B,B1,2.26,111.0 -2015-12-10,CA,1,B,B1,2.26,117.0 -2015-12-11,CA,1,B,B1,2.28,111.0 -2015-12-12,CA,1,B,B1,2.27,122.0 -2015-12-13,CA,1,B,B1,2.25,115.0 -2015-12-14,CA,1,B,B1,2.28,113.0 -2015-12-15,CA,1,B,B1,2.27,107.0 -2015-12-16,CA,1,B,B1,2.26,111.0 -2015-12-17,CA,1,B,B1,2.24,111.0 -2015-12-18,CA,1,B,B1,2.28,119.0 -2015-12-19,CA,1,B,B1,2.27,109.0 -2015-12-20,CA,1,B,B1,2.32,115.0 -2015-12-21,CA,1,B,B1,2.23,109.0 -2015-12-22,CA,1,B,B1,2.25,117.0 -2015-12-23,CA,1,B,B1,2.28,118.0 -2015-12-24,CA,1,B,B1,2.27,117.0 -2015-12-25,CA,1,B,B1,2.24,115.0 -2015-12-26,CA,1,B,B1,2.25,125.0 -2015-12-27,CA,1,B,B1,2.26,114.0 -2015-12-28,CA,1,B,B1,2.25,126.0 -2015-12-29,CA,1,B,B1,2.26,104.0 -2015-12-30,CA,1,B,B1,2.23,107.0 -2015-12-31,CA,1,B,B1,2.25,103.0 -2016-01-01,CA,1,B,B1,2.26,117.0 -2016-01-02,CA,1,B,B1,2.25,123.0 -2016-01-03,CA,1,B,B1,2.26,116.0 -2016-01-04,CA,1,B,B1,2.28,110.0 -2016-01-05,CA,1,B,B1,2.25,108.0 -2016-01-06,CA,1,B,B1,2.24,112.0 -2016-01-07,CA,1,B,B1,2.26,103.0 -2016-01-08,CA,1,B,B1,2.27,109.0 -2016-01-09,CA,1,B,B1,2.25,120.0 -2016-01-10,CA,1,B,B1,2.24,112.0 -2016-01-11,CA,1,B,B1,2.24,108.0 -2016-01-12,CA,1,B,B1,2.27,120.0 -2016-01-13,CA,1,B,B1,2.22,118.0 -2016-01-14,CA,1,B,B1,2.26,113.0 -2016-01-15,CA,1,B,B1,2.28,111.0 -2016-01-16,CA,1,B,B1,2.3,119.0 -2016-01-17,CA,1,B,B1,2.23,114.0 -2016-01-18,CA,1,B,B1,2.26,111.0 -2016-01-19,CA,1,B,B1,2.26,104.0 -2016-01-20,CA,1,B,B1,2.22,114.0 -2016-01-21,CA,1,B,B1,2.22,109.0 -2016-01-22,CA,1,B,B1,2.27,118.0 -2016-01-23,CA,1,B,B1,2.24,116.0 -2016-01-24,CA,1,B,B1,2.31,122.0 -2016-01-25,CA,1,B,B1,2.23,119.0 -2016-01-26,CA,1,B,B1,2.24,121.0 -2016-01-27,CA,1,B,B1,2.28,108.0 -2016-01-28,CA,1,B,B1,2.25,115.0 -2016-01-29,CA,1,B,B1,2.28,113.0 -2016-01-30,CA,1,B,B1,2.24,122.0 -2016-01-31,CA,1,B,B1,2.29,120.0 -2016-02-01,CA,1,B,B1,2.22,108.0 -2016-02-02,CA,1,B,B1,2.26,115.0 -2016-02-03,CA,1,B,B1,2.27,109.0 -2016-02-04,CA,1,B,B1,2.27,124.0 -2016-02-05,CA,1,B,B1,2.23,117.0 -2016-02-06,CA,1,B,B1,2.26,123.0 -2016-02-07,CA,1,B,B1,2.26,114.0 -2016-02-08,CA,1,B,B1,2.28,119.0 -2016-02-09,CA,1,B,B1,2.27,115.0 -2016-02-10,CA,1,B,B1,2.26,107.0 -2016-02-11,CA,1,B,B1,2.24,110.0 -2016-02-12,CA,1,B,B1,2.26,122.0 -2016-02-13,CA,1,B,B1,2.25,121.0 -2016-02-14,CA,1,B,B1,2.24,120.0 -2016-02-15,CA,1,B,B1,2.26,118.0 -2016-02-16,CA,1,B,B1,2.27,118.0 -2016-02-17,CA,1,B,B1,2.29,115.0 -2016-02-18,CA,1,B,B1,2.3,102.0 -2016-02-19,CA,1,B,B1,2.24,113.0 -2016-02-20,CA,1,B,B1,2.23,116.0 -2016-02-21,CA,1,B,B1,2.26,114.0 -2016-02-22,CA,1,B,B1,2.26,108.0 -2016-02-23,CA,1,B,B1,2.25,118.0 -2016-02-24,CA,1,B,B1,2.23,113.0 -2016-02-25,CA,1,B,B1,2.25,126.0 -2016-02-26,CA,1,B,B1,2.22,124.0 -2016-02-27,CA,1,B,B1,2.24,117.0 -2016-02-28,CA,1,B,B1,2.26,118.0 -2016-02-29,CA,1,B,B1,2.27,111.0 -2016-03-01,CA,1,B,B1,2.26,110.0 -2016-03-02,CA,1,B,B1,2.25,118.0 -2016-03-03,CA,1,B,B1,2.27,107.0 -2016-03-04,CA,1,B,B1,2.29,116.0 -2016-03-05,CA,1,B,B1,2.27,123.0 -2016-03-06,CA,1,B,B1,2.25,108.0 -2016-03-07,CA,1,B,B1,2.26,120.0 -2016-03-08,CA,1,B,B1,2.31,110.0 -2016-03-09,CA,1,B,B1,2.29,110.0 -2016-03-10,CA,1,B,B1,2.24,117.0 -2016-03-11,CA,1,B,B1,2.23,110.0 -2016-03-12,CA,1,B,B1,2.24,123.0 -2016-03-13,CA,1,B,B1,2.28,111.0 -2016-03-14,CA,1,B,B1,2.26,109.0 -2016-03-15,CA,1,B,B1,2.26,110.0 -2016-03-16,CA,1,B,B1,2.28,112.0 -2016-03-17,CA,1,B,B1,2.27,117.0 -2016-03-18,CA,1,B,B1,2.23,115.0 -2016-03-19,CA,1,B,B1,2.27,117.0 -2016-03-20,CA,1,B,B1,2.31,119.0 -2016-03-21,CA,1,B,B1,2.31,116.0 -2016-03-22,CA,1,B,B1,2.26,116.0 -2016-03-23,CA,1,B,B1,2.24,122.0 -2016-03-24,CA,1,B,B1,2.27,116.0 -2016-03-25,CA,1,B,B1,2.26,124.0 -2016-03-26,CA,1,B,B1,2.3,126.0 -2016-03-27,CA,1,B,B1,2.28,115.0 -2016-03-28,CA,1,B,B1,2.26,106.0 -2016-03-29,CA,1,B,B1,2.28,121.0 -2016-03-30,CA,1,B,B1,2.27,106.0 -2016-03-31,CA,1,B,B1,2.31,120.0 -2016-04-01,CA,1,B,B1,2.24,105.0 -2016-04-02,CA,1,B,B1,2.26,123.0 -2016-04-03,CA,1,B,B1,2.26,114.0 -2016-04-04,CA,1,B,B1,2.22,114.0 -2016-04-05,CA,1,B,B1,2.26,117.0 -2016-04-06,CA,1,B,B1,2.27,121.0 -2016-04-07,CA,1,B,B1,2.27,108.0 -2016-04-08,CA,1,B,B1,2.31,117.0 -2016-04-09,CA,1,B,B1,2.3,116.0 -2016-04-10,CA,1,B,B1,2.29,110.0 -2016-04-11,CA,1,B,B1,2.24,114.0 -2016-04-12,CA,1,B,B1,2.25,106.0 -2016-04-13,CA,1,B,B1,2.26,109.0 -2016-04-14,CA,1,B,B1,2.24,115.0 -2016-04-15,CA,1,B,B1,2.26,104.0 -2016-04-16,CA,1,B,B1,2.24,112.0 -2016-04-17,CA,1,B,B1,2.28,114.0 -2016-04-18,CA,1,B,B1,2.31,113.0 -2016-04-19,CA,1,B,B1,2.28,109.0 -2016-04-20,CA,1,B,B1,2.28,119.0 -2016-04-21,CA,1,B,B1,2.28,107.0 -2016-04-22,CA,1,B,B1,2.27,118.0 -2016-04-23,CA,1,B,B1,2.25,122.0 -2016-04-24,CA,1,B,B1,2.2,114.0 -2016-04-25,CA,1,B,B1,2.26,111.0 -2016-04-26,CA,1,B,B1,2.27,114.0 -2016-04-27,CA,1,B,B1,2.29,105.0 -2016-04-28,CA,1,B,B1,2.27,112.0 -2016-04-29,CA,1,B,B1,2.28,110.0 -2016-04-30,CA,1,B,B1,2.29,120.0 -2016-05-01,CA,1,B,B1,2.27,122.0 -2016-05-02,CA,1,B,B1,2.22,111.0 -2016-05-03,CA,1,B,B1,2.31,101.0 -2016-05-04,CA,1,B,B1,2.27,107.0 -2016-05-05,CA,1,B,B1,2.3,116.0 -2016-05-06,CA,1,B,B1,2.27,119.0 -2016-05-07,CA,1,B,B1,2.25,134.0 -2016-05-08,CA,1,B,B1,2.24,115.0 -2016-05-09,CA,1,B,B1,2.26,110.0 -2016-05-10,CA,1,B,B1,2.24,111.0 -2016-05-11,CA,1,B,B1,2.27,104.0 -2016-05-12,CA,1,B,B1,2.28,121.0 -2016-05-13,CA,1,B,B1,2.24,118.0 -2016-05-14,CA,1,B,B1,2.24,122.0 -2016-05-15,CA,1,B,B1,2.26,109.0 -2016-05-16,CA,1,B,B1,2.25,115.0 -2016-05-17,CA,1,B,B1,2.29,110.0 -2016-05-18,CA,1,B,B1,2.25,124.0 -2016-05-19,CA,1,B,B1,2.24,113.0 -2016-05-20,CA,1,B,B1,2.29,106.0 -2016-05-21,CA,1,B,B1,2.28,123.0 -2016-05-22,CA,1,B,B1,2.27,110.0 -2016-05-23,CA,1,B,B1,2.26,118.0 -2016-05-24,CA,1,B,B1,2.25,118.0 -2016-05-25,CA,1,B,B1,2.25,109.0 -2016-05-26,CA,1,B,B1,2.3,116.0 -2016-05-27,CA,1,B,B1,2.27,117.0 -2016-05-28,CA,1,B,B1,2.29,118.0 -2016-05-29,CA,1,B,B1,2.27,117.0 -2016-05-30,CA,1,B,B1,2.27,106.0 -2016-05-31,CA,1,B,B1,2.27,112.0 -2016-06-01,CA,1,B,B1,2.27,118.0 -2016-06-02,CA,1,B,B1,2.25,118.0 -2016-06-03,CA,1,B,B1,2.3,115.0 -2016-06-04,CA,1,B,B1,2.29,114.0 -2016-06-05,CA,1,B,B1,2.26,114.0 -2016-06-06,CA,1,B,B1,2.27,113.0 -2016-06-07,CA,1,B,B1,2.23,121.0 -2016-06-08,CA,1,B,B1,2.29,110.0 -2016-06-09,CA,1,B,B1,2.27,118.0 -2016-06-10,CA,1,B,B1,2.23,111.0 -2016-06-11,CA,1,B,B1,2.31,123.0 -2016-06-12,CA,1,B,B1,2.25,112.0 -2016-06-13,CA,1,B,B1,2.29,105.0 -2016-06-14,CA,1,B,B1,2.29,110.0 -2016-06-15,CA,1,B,B1,2.3,111.0 -2016-06-16,CA,1,B,B1,2.27,118.0 -2016-06-17,CA,1,B,B1,2.25,118.0 -2016-06-18,CA,1,B,B1,2.3,121.0 -2016-06-19,CA,1,B,B1,2.23,126.0 -2016-06-20,CA,1,B,B1,2.26,94.0 -2016-06-21,CA,1,B,B1,2.29,112.0 -2016-06-22,CA,1,B,B1,2.26,110.0 -2016-06-23,CA,1,B,B1,2.29,115.0 -2016-06-24,CA,1,B,B1,2.27,109.0 -2016-06-25,CA,1,B,B1,2.23,120.0 -2016-06-26,CA,1,B,B1,2.23,126.0 -2016-06-27,CA,1,B,B1,2.27,113.0 -2016-06-28,CA,1,B,B1,2.24,115.0 -2016-06-29,CA,1,B,B1,2.26,113.0 -2016-06-30,CA,1,B,B1,2.24,108.0 -2016-07-01,CA,1,B,B1,2.28,110.0 -2016-07-02,CA,1,B,B1,2.26,114.0 -2016-07-03,CA,1,B,B1,2.26,109.0 -2016-07-04,CA,1,B,B1,2.23,120.0 -2016-07-05,CA,1,B,B1,2.26,113.0 -2016-07-06,CA,1,B,B1,2.27,115.0 -2016-07-07,CA,1,B,B1,2.24,112.0 -2016-07-08,CA,1,B,B1,2.2,119.0 -2016-07-09,CA,1,B,B1,2.21,124.0 -2016-07-10,CA,1,B,B1,2.27,122.0 -2016-07-11,CA,1,B,B1,2.27,124.0 -2016-07-12,CA,1,B,B1,2.28,106.0 -2016-07-13,CA,1,B,B1,2.26,109.0 -2016-07-14,CA,1,B,B1,2.22,112.0 -2016-07-15,CA,1,B,B1,2.24,111.0 -2016-07-16,CA,1,B,B1,2.27,123.0 -2016-07-17,CA,1,B,B1,2.27,119.0 -2016-07-18,CA,1,B,B1,2.26,113.0 -2016-07-19,CA,1,B,B1,2.28,107.0 -2016-07-20,CA,1,B,B1,2.29,113.0 -2016-07-21,CA,1,B,B1,2.22,117.0 -2016-07-22,CA,1,B,B1,2.26,114.0 -2016-07-23,CA,1,B,B1,2.29,118.0 -2016-07-24,CA,1,B,B1,2.27,113.0 -2016-07-25,CA,1,B,B1,2.26,108.0 -2016-07-26,CA,1,B,B1,2.25,110.0 -2016-07-27,CA,1,B,B1,2.25,117.0 -2015-01-05,CA,1,B,B2,6.29,45.0 -2015-01-06,CA,1,B,B2,6.28,54.0 -2015-01-07,CA,1,B,B2,6.25,52.0 -2015-01-08,CA,1,B,B2,6.32,51.0 -2015-01-09,CA,1,B,B2,6.34,51.0 -2015-01-10,CA,1,B,B2,6.27,54.0 -2015-01-11,CA,1,B,B2,6.18,50.0 -2015-01-12,CA,1,B,B2,6.13,51.0 -2015-01-13,CA,1,B,B2,6.28,50.0 -2015-01-14,CA,1,B,B2,6.23,49.0 -2015-01-15,CA,1,B,B2,6.31,46.0 -2015-01-16,CA,1,B,B2,6.31,46.0 -2015-01-17,CA,1,B,B2,6.42,49.0 -2015-01-18,CA,1,B,B2,6.35,50.0 -2015-01-19,CA,1,B,B2,6.27,46.0 -2015-01-20,CA,1,B,B2,6.44,45.0 -2015-01-21,CA,1,B,B2,6.26,51.0 -2015-01-22,CA,1,B,B2,6.36,49.0 -2015-01-23,CA,1,B,B2,6.28,56.0 -2015-01-24,CA,1,B,B2,6.48,50.0 -2015-01-25,CA,1,B,B2,6.32,50.0 -2015-01-26,CA,1,B,B2,6.33,46.0 -2015-01-27,CA,1,B,B2,6.34,47.0 -2015-01-28,CA,1,B,B2,6.3,51.0 -2015-01-29,CA,1,B,B2,6.32,53.0 -2015-01-30,CA,1,B,B2,6.33,46.0 -2015-01-31,CA,1,B,B2,6.28,49.0 -2015-02-01,CA,1,B,B2,6.21,56.0 -2015-02-02,CA,1,B,B2,6.24,48.0 -2015-02-03,CA,1,B,B2,6.23,53.0 -2015-02-04,CA,1,B,B2,6.32,50.0 -2015-02-05,CA,1,B,B2,6.28,49.0 -2015-02-06,CA,1,B,B2,6.35,50.0 -2015-02-07,CA,1,B,B2,6.26,54.0 -2015-02-08,CA,1,B,B2,6.2,54.0 -2015-02-09,CA,1,B,B2,6.29,41.0 -2015-02-10,CA,1,B,B2,6.17,57.0 -2015-02-11,CA,1,B,B2,6.4,48.0 -2015-02-12,CA,1,B,B2,6.28,53.0 -2015-02-13,CA,1,B,B2,6.21,52.0 -2015-02-14,CA,1,B,B2,6.28,52.0 -2015-02-15,CA,1,B,B2,6.34,51.0 -2015-02-16,CA,1,B,B2,6.29,50.0 -2015-02-17,CA,1,B,B2,6.31,51.0 -2015-02-18,CA,1,B,B2,6.29,49.0 -2015-02-19,CA,1,B,B2,6.34,49.0 -2015-02-20,CA,1,B,B2,6.32,52.0 -2015-02-21,CA,1,B,B2,6.34,53.0 -2015-02-22,CA,1,B,B2,6.35,52.0 -2015-02-23,CA,1,B,B2,6.23,52.0 -2015-02-24,CA,1,B,B2,6.36,46.0 -2015-02-25,CA,1,B,B2,6.23,55.0 -2015-02-26,CA,1,B,B2,6.38,56.0 -2015-02-27,CA,1,B,B2,6.29,49.0 -2015-02-28,CA,1,B,B2,6.4,56.0 -2015-03-01,CA,1,B,B2,6.32,55.0 -2015-03-02,CA,1,B,B2,6.16,52.0 -2015-03-03,CA,1,B,B2,6.24,49.0 -2015-03-04,CA,1,B,B2,6.33,50.0 -2015-03-05,CA,1,B,B2,6.23,52.0 -2015-03-06,CA,1,B,B2,6.39,48.0 -2015-03-07,CA,1,B,B2,6.42,53.0 -2015-03-08,CA,1,B,B2,6.3,55.0 -2015-03-09,CA,1,B,B2,6.4,53.0 -2015-03-10,CA,1,B,B2,6.39,49.0 -2015-03-11,CA,1,B,B2,6.34,53.0 -2015-03-12,CA,1,B,B2,6.31,46.0 -2015-03-13,CA,1,B,B2,6.39,54.0 -2015-03-14,CA,1,B,B2,6.24,53.0 -2015-03-15,CA,1,B,B2,6.31,51.0 -2015-03-16,CA,1,B,B2,6.39,45.0 -2015-03-17,CA,1,B,B2,6.23,53.0 -2015-03-18,CA,1,B,B2,6.41,49.0 -2015-03-19,CA,1,B,B2,6.34,54.0 -2015-03-20,CA,1,B,B2,6.26,55.0 -2015-03-21,CA,1,B,B2,6.39,56.0 -2015-03-22,CA,1,B,B2,6.37,47.0 -2015-03-23,CA,1,B,B2,6.24,50.0 -2015-03-24,CA,1,B,B2,6.29,53.0 -2015-03-25,CA,1,B,B2,6.32,48.0 -2015-03-26,CA,1,B,B2,6.32,49.0 -2015-03-27,CA,1,B,B2,6.43,45.0 -2015-03-28,CA,1,B,B2,6.2,57.0 -2015-03-29,CA,1,B,B2,6.47,43.0 -2015-03-30,CA,1,B,B2,6.41,52.0 -2015-03-31,CA,1,B,B2,6.31,54.0 -2015-04-01,CA,1,B,B2,6.37,49.0 -2015-04-02,CA,1,B,B2,6.33,50.0 -2015-04-03,CA,1,B,B2,6.31,54.0 -2015-04-04,CA,1,B,B2,6.39,48.0 -2015-04-05,CA,1,B,B2,6.31,55.0 -2015-04-06,CA,1,B,B2,6.32,47.0 -2015-04-07,CA,1,B,B2,6.27,53.0 -2015-04-08,CA,1,B,B2,6.26,54.0 -2015-04-09,CA,1,B,B2,6.37,49.0 -2015-04-10,CA,1,B,B2,6.28,47.0 -2015-04-11,CA,1,B,B2,6.39,53.0 -2015-04-12,CA,1,B,B2,6.31,52.0 -2015-04-13,CA,1,B,B2,6.41,48.0 -2015-04-14,CA,1,B,B2,6.28,50.0 -2015-04-15,CA,1,B,B2,6.34,50.0 -2015-04-16,CA,1,B,B2,6.43,49.0 -2015-04-17,CA,1,B,B2,6.2,53.0 -2015-04-18,CA,1,B,B2,6.42,52.0 -2015-04-19,CA,1,B,B2,6.34,54.0 -2015-04-20,CA,1,B,B2,6.37,48.0 -2015-04-21,CA,1,B,B2,6.26,53.0 -2015-04-22,CA,1,B,B2,6.37,48.0 -2015-04-23,CA,1,B,B2,6.38,48.0 -2015-04-24,CA,1,B,B2,6.34,47.0 -2015-04-25,CA,1,B,B2,6.21,57.0 -2015-04-26,CA,1,B,B2,6.25,55.0 -2015-04-27,CA,1,B,B2,6.27,51.0 -2015-04-28,CA,1,B,B2,6.26,49.0 -2015-04-29,CA,1,B,B2,6.35,49.0 -2015-04-30,CA,1,B,B2,6.36,54.0 -2015-05-01,CA,1,B,B2,6.27,53.0 -2015-05-02,CA,1,B,B2,6.36,53.0 -2015-05-03,CA,1,B,B2,6.29,55.0 -2015-05-04,CA,1,B,B2,6.29,47.0 -2015-05-05,CA,1,B,B2,6.2,53.0 -2015-05-06,CA,1,B,B2,6.32,51.0 -2015-05-07,CA,1,B,B2,6.3,48.0 -2015-05-08,CA,1,B,B2,6.37,49.0 -2015-05-09,CA,1,B,B2,6.25,51.0 -2015-05-10,CA,1,B,B2,6.33,50.0 -2015-05-11,CA,1,B,B2,6.38,51.0 -2015-05-12,CA,1,B,B2,6.34,52.0 -2015-05-13,CA,1,B,B2,6.32,51.0 -2015-05-14,CA,1,B,B2,6.36,48.0 -2015-05-15,CA,1,B,B2,6.43,47.0 -2015-05-16,CA,1,B,B2,6.35,51.0 -2015-05-17,CA,1,B,B2,6.25,56.0 -2015-05-18,CA,1,B,B2,6.34,45.0 -2015-05-19,CA,1,B,B2,6.42,46.0 -2015-05-20,CA,1,B,B2,6.28,50.0 -2015-05-21,CA,1,B,B2,6.31,48.0 -2015-05-22,CA,1,B,B2,6.29,50.0 -2015-05-23,CA,1,B,B2,6.34,57.0 -2015-05-24,CA,1,B,B2,6.33,48.0 -2015-05-25,CA,1,B,B2,6.25,46.0 -2015-05-26,CA,1,B,B2,6.29,53.0 -2015-05-27,CA,1,B,B2,6.3,50.0 -2015-05-28,CA,1,B,B2,6.39,52.0 -2015-05-29,CA,1,B,B2,6.35,53.0 -2015-05-30,CA,1,B,B2,6.39,54.0 -2015-05-31,CA,1,B,B2,6.24,49.0 -2015-06-01,CA,1,B,B2,6.28,52.0 -2015-06-02,CA,1,B,B2,6.36,52.0 -2015-06-03,CA,1,B,B2,6.21,54.0 -2015-06-04,CA,1,B,B2,6.4,47.0 -2015-06-05,CA,1,B,B2,6.29,48.0 -2015-06-06,CA,1,B,B2,6.28,51.0 -2015-06-07,CA,1,B,B2,6.37,49.0 -2015-06-08,CA,1,B,B2,6.41,51.0 -2015-06-09,CA,1,B,B2,6.2,52.0 -2015-06-10,CA,1,B,B2,6.27,49.0 -2015-06-11,CA,1,B,B2,6.33,53.0 -2015-06-12,CA,1,B,B2,6.29,57.0 -2015-06-13,CA,1,B,B2,6.29,54.0 -2015-06-14,CA,1,B,B2,6.39,50.0 -2015-06-15,CA,1,B,B2,6.23,50.0 -2015-06-16,CA,1,B,B2,6.39,51.0 -2015-06-17,CA,1,B,B2,6.37,51.0 -2015-06-18,CA,1,B,B2,6.39,48.0 -2015-06-19,CA,1,B,B2,6.29,55.0 -2015-06-20,CA,1,B,B2,6.32,56.0 -2015-06-21,CA,1,B,B2,6.32,50.0 -2015-06-22,CA,1,B,B2,6.26,53.0 -2015-06-23,CA,1,B,B2,6.22,49.0 -2015-06-24,CA,1,B,B2,6.39,48.0 -2015-06-25,CA,1,B,B2,6.31,52.0 -2015-06-26,CA,1,B,B2,6.3,51.0 -2015-06-27,CA,1,B,B2,6.38,49.0 -2015-06-28,CA,1,B,B2,6.31,52.0 -2015-06-29,CA,1,B,B2,6.37,51.0 -2015-06-30,CA,1,B,B2,6.26,48.0 -2015-07-01,CA,1,B,B2,6.26,50.0 -2015-07-02,CA,1,B,B2,6.33,52.0 -2015-07-03,CA,1,B,B2,6.31,53.0 -2015-07-04,CA,1,B,B2,6.23,55.0 -2015-07-05,CA,1,B,B2,6.27,48.0 -2015-07-06,CA,1,B,B2,6.27,53.0 -2015-07-07,CA,1,B,B2,6.4,50.0 -2015-07-08,CA,1,B,B2,6.31,45.0 -2015-07-09,CA,1,B,B2,6.37,56.0 -2015-07-10,CA,1,B,B2,6.31,49.0 -2015-07-11,CA,1,B,B2,6.31,52.0 -2015-07-12,CA,1,B,B2,6.39,46.0 -2015-07-13,CA,1,B,B2,6.33,49.0 -2015-07-14,CA,1,B,B2,6.32,49.0 -2015-07-15,CA,1,B,B2,6.2,50.0 -2015-07-16,CA,1,B,B2,6.28,49.0 -2015-07-17,CA,1,B,B2,6.32,48.0 -2015-07-18,CA,1,B,B2,6.27,55.0 -2015-07-19,CA,1,B,B2,6.32,51.0 -2015-07-20,CA,1,B,B2,6.26,46.0 -2015-07-21,CA,1,B,B2,6.34,48.0 -2015-07-22,CA,1,B,B2,6.34,51.0 -2015-07-23,CA,1,B,B2,6.29,47.0 -2015-07-24,CA,1,B,B2,6.38,54.0 -2015-07-25,CA,1,B,B2,6.23,52.0 -2015-07-26,CA,1,B,B2,6.27,51.0 -2015-07-27,CA,1,B,B2,6.34,50.0 -2015-07-28,CA,1,B,B2,6.41,51.0 -2015-07-29,CA,1,B,B2,6.41,51.0 -2015-07-30,CA,1,B,B2,6.43,51.0 -2015-07-31,CA,1,B,B2,6.24,56.0 -2015-08-01,CA,1,B,B2,6.21,55.0 -2015-08-02,CA,1,B,B2,6.43,51.0 -2015-08-03,CA,1,B,B2,6.31,52.0 -2015-08-04,CA,1,B,B2,6.32,46.0 -2015-08-05,CA,1,B,B2,6.35,45.0 -2015-08-06,CA,1,B,B2,6.35,49.0 -2015-08-07,CA,1,B,B2,6.43,48.0 -2015-08-08,CA,1,B,B2,6.35,55.0 -2015-08-09,CA,1,B,B2,6.33,47.0 -2015-08-10,CA,1,B,B2,6.39,49.0 -2015-08-11,CA,1,B,B2,6.36,53.0 -2015-08-12,CA,1,B,B2,6.32,51.0 -2015-08-13,CA,1,B,B2,6.25,46.0 -2015-08-14,CA,1,B,B2,6.47,46.0 -2015-08-15,CA,1,B,B2,6.27,55.0 -2015-08-16,CA,1,B,B2,6.3,51.0 -2015-08-17,CA,1,B,B2,6.22,47.0 -2015-08-18,CA,1,B,B2,6.32,54.0 -2015-08-19,CA,1,B,B2,6.36,50.0 -2015-08-20,CA,1,B,B2,6.24,51.0 -2015-08-21,CA,1,B,B2,6.32,46.0 -2015-08-22,CA,1,B,B2,6.19,55.0 -2015-08-23,CA,1,B,B2,6.34,49.0 -2015-08-24,CA,1,B,B2,6.4,46.0 -2015-08-25,CA,1,B,B2,6.39,46.0 -2015-08-26,CA,1,B,B2,6.38,50.0 -2015-08-27,CA,1,B,B2,6.34,47.0 -2015-08-28,CA,1,B,B2,6.31,52.0 -2015-08-29,CA,1,B,B2,6.31,53.0 -2015-08-30,CA,1,B,B2,6.36,51.0 -2015-08-31,CA,1,B,B2,6.35,51.0 -2015-09-01,CA,1,B,B2,6.31,47.0 -2015-09-02,CA,1,B,B2,6.32,48.0 -2015-09-03,CA,1,B,B2,6.21,49.0 -2015-09-04,CA,1,B,B2,6.38,48.0 -2015-09-05,CA,1,B,B2,6.33,58.0 -2015-09-06,CA,1,B,B2,6.36,52.0 -2015-09-07,CA,1,B,B2,6.38,51.0 -2015-09-08,CA,1,B,B2,6.3,50.0 -2015-09-09,CA,1,B,B2,6.33,51.0 -2015-09-10,CA,1,B,B2,6.27,53.0 -2015-09-11,CA,1,B,B2,6.3,57.0 -2015-09-12,CA,1,B,B2,6.45,49.0 -2015-09-13,CA,1,B,B2,6.29,53.0 -2015-09-14,CA,1,B,B2,6.29,50.0 -2015-09-15,CA,1,B,B2,6.28,54.0 -2015-09-16,CA,1,B,B2,6.36,51.0 -2015-09-17,CA,1,B,B2,6.32,50.0 -2015-09-18,CA,1,B,B2,6.29,52.0 -2015-09-19,CA,1,B,B2,6.34,53.0 -2015-09-20,CA,1,B,B2,6.29,52.0 -2015-09-21,CA,1,B,B2,6.3,48.0 -2015-09-22,CA,1,B,B2,6.37,49.0 -2015-09-23,CA,1,B,B2,6.27,48.0 -2015-09-24,CA,1,B,B2,6.41,49.0 -2015-09-25,CA,1,B,B2,6.27,52.0 -2015-09-26,CA,1,B,B2,6.24,49.0 -2015-09-27,CA,1,B,B2,6.43,53.0 -2015-09-28,CA,1,B,B2,6.33,49.0 -2015-09-29,CA,1,B,B2,6.29,50.0 -2015-09-30,CA,1,B,B2,6.18,51.0 -2015-10-01,CA,1,B,B2,6.35,49.0 -2015-10-02,CA,1,B,B2,6.28,53.0 -2015-10-03,CA,1,B,B2,6.36,52.0 -2015-10-04,CA,1,B,B2,6.3,54.0 -2015-10-05,CA,1,B,B2,6.12,46.0 -2015-10-06,CA,1,B,B2,6.25,51.0 -2015-10-07,CA,1,B,B2,6.41,54.0 -2015-10-08,CA,1,B,B2,6.33,53.0 -2015-10-09,CA,1,B,B2,6.41,48.0 -2015-10-10,CA,1,B,B2,6.27,51.0 -2015-10-11,CA,1,B,B2,6.31,52.0 -2015-10-12,CA,1,B,B2,6.4,49.0 -2015-10-13,CA,1,B,B2,6.32,48.0 -2015-10-14,CA,1,B,B2,6.38,54.0 -2015-10-15,CA,1,B,B2,6.34,47.0 -2015-10-16,CA,1,B,B2,6.34,50.0 -2015-10-17,CA,1,B,B2,6.24,51.0 -2015-10-18,CA,1,B,B2,6.28,46.0 -2015-10-19,CA,1,B,B2,6.22,47.0 -2015-10-20,CA,1,B,B2,6.39,52.0 -2015-10-21,CA,1,B,B2,6.28,49.0 -2015-10-22,CA,1,B,B2,6.28,53.0 -2015-10-23,CA,1,B,B2,6.34,49.0 -2015-10-24,CA,1,B,B2,6.25,53.0 -2015-10-25,CA,1,B,B2,6.23,56.0 -2015-10-26,CA,1,B,B2,6.23,53.0 -2015-10-27,CA,1,B,B2,6.29,52.0 -2015-10-28,CA,1,B,B2,6.38,50.0 -2015-10-29,CA,1,B,B2,6.27,52.0 -2015-10-30,CA,1,B,B2,6.32,52.0 -2015-10-31,CA,1,B,B2,6.34,54.0 -2015-11-01,CA,1,B,B2,6.24,52.0 -2015-11-02,CA,1,B,B2,6.38,54.0 -2015-11-03,CA,1,B,B2,6.34,51.0 -2015-11-04,CA,1,B,B2,6.47,52.0 -2015-11-05,CA,1,B,B2,6.35,52.0 -2015-11-06,CA,1,B,B2,6.35,51.0 -2015-11-07,CA,1,B,B2,6.19,51.0 -2015-11-08,CA,1,B,B2,6.37,55.0 -2015-11-09,CA,1,B,B2,6.38,53.0 -2015-11-10,CA,1,B,B2,6.38,52.0 -2015-11-11,CA,1,B,B2,6.28,48.0 -2015-11-12,CA,1,B,B2,6.35,47.0 -2015-11-13,CA,1,B,B2,6.33,49.0 -2015-11-14,CA,1,B,B2,6.2,55.0 -2015-11-15,CA,1,B,B2,6.29,51.0 -2015-11-16,CA,1,B,B2,6.4,48.0 -2015-11-17,CA,1,B,B2,6.23,51.0 -2015-11-18,CA,1,B,B2,6.26,53.0 -2015-11-19,CA,1,B,B2,6.3,51.0 -2015-11-20,CA,1,B,B2,6.35,49.0 -2015-11-21,CA,1,B,B2,6.32,52.0 -2015-11-22,CA,1,B,B2,6.36,52.0 -2015-11-23,CA,1,B,B2,6.33,51.0 -2015-11-24,CA,1,B,B2,6.24,54.0 -2015-11-25,CA,1,B,B2,6.27,52.0 -2015-11-26,CA,1,B,B2,6.31,49.0 -2015-11-27,CA,1,B,B2,6.38,52.0 -2015-11-28,CA,1,B,B2,6.35,49.0 -2015-11-29,CA,1,B,B2,6.32,54.0 -2015-11-30,CA,1,B,B2,6.27,53.0 -2015-12-01,CA,1,B,B2,6.37,49.0 -2015-12-02,CA,1,B,B2,6.35,46.0 -2015-12-03,CA,1,B,B2,6.28,50.0 -2015-12-04,CA,1,B,B2,6.35,51.0 -2015-12-05,CA,1,B,B2,6.28,54.0 -2015-12-06,CA,1,B,B2,6.26,48.0 -2015-12-07,CA,1,B,B2,6.3,50.0 -2015-12-08,CA,1,B,B2,6.33,54.0 -2015-12-09,CA,1,B,B2,6.2,56.0 -2015-12-10,CA,1,B,B2,6.24,54.0 -2015-12-11,CA,1,B,B2,6.3,48.0 -2015-12-12,CA,1,B,B2,6.43,55.0 -2015-12-13,CA,1,B,B2,6.37,50.0 -2015-12-14,CA,1,B,B2,6.39,51.0 -2015-12-15,CA,1,B,B2,6.32,51.0 -2015-12-16,CA,1,B,B2,6.33,50.0 -2015-12-17,CA,1,B,B2,6.36,51.0 -2015-12-18,CA,1,B,B2,6.3,54.0 -2015-12-19,CA,1,B,B2,6.32,51.0 -2015-12-20,CA,1,B,B2,6.35,56.0 -2015-12-21,CA,1,B,B2,6.31,52.0 -2015-12-22,CA,1,B,B2,6.28,54.0 -2015-12-23,CA,1,B,B2,6.35,51.0 -2015-12-24,CA,1,B,B2,6.36,46.0 -2015-12-25,CA,1,B,B2,6.23,50.0 -2015-12-26,CA,1,B,B2,6.35,52.0 -2015-12-27,CA,1,B,B2,6.28,53.0 -2015-12-28,CA,1,B,B2,6.32,52.0 -2015-12-29,CA,1,B,B2,6.27,52.0 -2015-12-30,CA,1,B,B2,6.34,55.0 -2015-12-31,CA,1,B,B2,6.25,51.0 -2016-01-01,CA,1,B,B2,6.32,48.0 -2016-01-02,CA,1,B,B2,6.32,53.0 -2016-01-03,CA,1,B,B2,6.26,49.0 -2016-01-04,CA,1,B,B2,6.3,47.0 -2016-01-05,CA,1,B,B2,6.33,47.0 -2016-01-06,CA,1,B,B2,6.34,48.0 -2016-01-07,CA,1,B,B2,6.32,45.0 -2016-01-08,CA,1,B,B2,6.26,52.0 -2016-01-09,CA,1,B,B2,6.27,52.0 -2016-01-10,CA,1,B,B2,6.39,49.0 -2016-01-11,CA,1,B,B2,6.35,50.0 -2016-01-12,CA,1,B,B2,6.36,48.0 -2016-01-13,CA,1,B,B2,6.36,46.0 -2016-01-14,CA,1,B,B2,6.42,48.0 -2016-01-15,CA,1,B,B2,6.41,48.0 -2016-01-16,CA,1,B,B2,6.31,54.0 -2016-01-17,CA,1,B,B2,6.46,50.0 -2016-01-18,CA,1,B,B2,6.28,48.0 -2016-01-19,CA,1,B,B2,6.43,48.0 -2016-01-20,CA,1,B,B2,6.22,50.0 -2016-01-21,CA,1,B,B2,6.24,50.0 -2016-01-22,CA,1,B,B2,6.29,56.0 -2016-01-23,CA,1,B,B2,6.27,53.0 -2016-01-24,CA,1,B,B2,6.4,49.0 -2016-01-25,CA,1,B,B2,6.33,50.0 -2016-01-26,CA,1,B,B2,6.27,51.0 -2016-01-27,CA,1,B,B2,6.41,53.0 -2016-01-28,CA,1,B,B2,6.33,47.0 -2016-01-29,CA,1,B,B2,6.4,50.0 -2016-01-30,CA,1,B,B2,6.44,49.0 -2016-01-31,CA,1,B,B2,6.4,55.0 -2016-02-01,CA,1,B,B2,6.32,55.0 -2016-02-02,CA,1,B,B2,6.4,48.0 -2016-02-03,CA,1,B,B2,6.34,52.0 -2016-02-04,CA,1,B,B2,6.51,47.0 -2016-02-05,CA,1,B,B2,6.36,51.0 -2016-02-06,CA,1,B,B2,6.22,55.0 -2016-02-07,CA,1,B,B2,6.28,52.0 -2016-02-08,CA,1,B,B2,6.31,50.0 -2016-02-09,CA,1,B,B2,6.41,51.0 -2016-02-10,CA,1,B,B2,6.26,52.0 -2016-02-11,CA,1,B,B2,6.35,46.0 -2016-02-12,CA,1,B,B2,6.26,48.0 -2016-02-13,CA,1,B,B2,6.31,55.0 -2016-02-14,CA,1,B,B2,6.3,51.0 -2016-02-15,CA,1,B,B2,6.39,48.0 -2016-02-16,CA,1,B,B2,6.31,50.0 -2016-02-17,CA,1,B,B2,6.3,47.0 -2016-02-18,CA,1,B,B2,6.29,52.0 -2016-02-19,CA,1,B,B2,6.34,51.0 -2016-02-20,CA,1,B,B2,6.26,56.0 -2016-02-21,CA,1,B,B2,6.3,50.0 -2016-02-22,CA,1,B,B2,6.29,51.0 -2016-02-23,CA,1,B,B2,6.3,49.0 -2016-02-24,CA,1,B,B2,6.19,50.0 -2016-02-25,CA,1,B,B2,6.27,50.0 -2016-02-26,CA,1,B,B2,6.29,53.0 -2016-02-27,CA,1,B,B2,6.31,52.0 -2016-02-28,CA,1,B,B2,6.32,52.0 -2016-02-29,CA,1,B,B2,6.44,46.0 -2016-03-01,CA,1,B,B2,6.32,46.0 -2016-03-02,CA,1,B,B2,6.31,50.0 -2016-03-03,CA,1,B,B2,6.28,51.0 -2016-03-04,CA,1,B,B2,6.36,48.0 -2016-03-05,CA,1,B,B2,6.4,52.0 -2016-03-06,CA,1,B,B2,6.32,54.0 -2016-03-07,CA,1,B,B2,6.29,50.0 -2016-03-08,CA,1,B,B2,6.43,52.0 -2016-03-09,CA,1,B,B2,6.35,50.0 -2016-03-10,CA,1,B,B2,6.26,48.0 -2016-03-11,CA,1,B,B2,6.4,49.0 -2016-03-12,CA,1,B,B2,6.23,49.0 -2016-03-13,CA,1,B,B2,6.31,51.0 -2016-03-14,CA,1,B,B2,6.46,50.0 -2016-03-15,CA,1,B,B2,6.38,51.0 -2016-03-16,CA,1,B,B2,6.4,49.0 -2016-03-17,CA,1,B,B2,6.33,44.0 -2016-03-18,CA,1,B,B2,6.29,52.0 -2016-03-19,CA,1,B,B2,6.46,53.0 -2016-03-20,CA,1,B,B2,6.38,51.0 -2016-03-21,CA,1,B,B2,6.32,49.0 -2016-03-22,CA,1,B,B2,6.39,53.0 -2016-03-23,CA,1,B,B2,6.19,51.0 -2016-03-24,CA,1,B,B2,6.42,48.0 -2016-03-25,CA,1,B,B2,6.34,51.0 -2016-03-26,CA,1,B,B2,6.32,54.0 -2016-03-27,CA,1,B,B2,6.29,52.0 -2016-03-28,CA,1,B,B2,6.23,48.0 -2016-03-29,CA,1,B,B2,6.35,53.0 -2016-03-30,CA,1,B,B2,6.37,50.0 -2016-03-31,CA,1,B,B2,6.37,52.0 -2016-04-01,CA,1,B,B2,6.38,52.0 -2016-04-02,CA,1,B,B2,6.29,53.0 -2016-04-03,CA,1,B,B2,6.31,53.0 -2016-04-04,CA,1,B,B2,6.37,49.0 -2016-04-05,CA,1,B,B2,6.33,50.0 -2016-04-06,CA,1,B,B2,6.21,48.0 -2016-04-07,CA,1,B,B2,6.31,49.0 -2016-04-08,CA,1,B,B2,6.42,50.0 -2016-04-09,CA,1,B,B2,6.28,53.0 -2016-04-10,CA,1,B,B2,6.45,55.0 -2016-04-11,CA,1,B,B2,6.4,46.0 -2016-04-12,CA,1,B,B2,6.37,54.0 -2016-04-13,CA,1,B,B2,6.35,50.0 -2016-04-14,CA,1,B,B2,6.25,51.0 -2016-04-15,CA,1,B,B2,6.36,51.0 -2016-04-16,CA,1,B,B2,6.27,54.0 -2016-04-17,CA,1,B,B2,6.28,48.0 -2016-04-18,CA,1,B,B2,6.35,48.0 -2016-04-19,CA,1,B,B2,6.21,54.0 -2016-04-20,CA,1,B,B2,6.41,50.0 -2016-04-21,CA,1,B,B2,6.34,52.0 -2016-04-22,CA,1,B,B2,6.41,50.0 -2016-04-23,CA,1,B,B2,6.29,53.0 -2016-04-24,CA,1,B,B2,6.28,53.0 -2016-04-25,CA,1,B,B2,6.28,45.0 -2016-04-26,CA,1,B,B2,6.28,49.0 -2016-04-27,CA,1,B,B2,6.42,44.0 -2016-04-28,CA,1,B,B2,6.24,54.0 -2016-04-29,CA,1,B,B2,6.37,51.0 -2016-04-30,CA,1,B,B2,6.33,52.0 -2016-05-01,CA,1,B,B2,6.31,53.0 -2016-05-02,CA,1,B,B2,6.3,46.0 -2016-05-03,CA,1,B,B2,6.3,50.0 -2016-05-04,CA,1,B,B2,6.32,48.0 -2016-05-05,CA,1,B,B2,6.31,52.0 -2016-05-06,CA,1,B,B2,6.4,51.0 -2016-05-07,CA,1,B,B2,6.32,54.0 -2016-05-08,CA,1,B,B2,6.33,50.0 -2016-05-09,CA,1,B,B2,6.31,52.0 -2016-05-10,CA,1,B,B2,6.29,53.0 -2016-05-11,CA,1,B,B2,6.4,51.0 -2016-05-12,CA,1,B,B2,6.37,54.0 -2016-05-13,CA,1,B,B2,6.31,53.0 -2016-05-14,CA,1,B,B2,6.27,54.0 -2016-05-15,CA,1,B,B2,6.21,49.0 -2016-05-16,CA,1,B,B2,6.32,45.0 -2016-05-17,CA,1,B,B2,6.34,49.0 -2016-05-18,CA,1,B,B2,6.38,47.0 -2016-05-19,CA,1,B,B2,6.28,51.0 -2016-05-20,CA,1,B,B2,6.28,53.0 -2016-05-21,CA,1,B,B2,6.27,54.0 -2016-05-22,CA,1,B,B2,6.29,49.0 -2016-05-23,CA,1,B,B2,6.34,49.0 -2016-05-24,CA,1,B,B2,6.33,45.0 -2016-05-25,CA,1,B,B2,6.39,53.0 -2016-05-26,CA,1,B,B2,6.33,52.0 -2016-05-27,CA,1,B,B2,6.25,54.0 -2016-05-28,CA,1,B,B2,6.36,50.0 -2016-05-29,CA,1,B,B2,6.33,54.0 -2016-05-30,CA,1,B,B2,6.24,47.0 -2016-05-31,CA,1,B,B2,6.18,51.0 -2016-06-01,CA,1,B,B2,6.35,54.0 -2016-06-02,CA,1,B,B2,6.31,49.0 -2016-06-03,CA,1,B,B2,6.38,53.0 -2016-06-04,CA,1,B,B2,6.36,53.0 -2016-06-05,CA,1,B,B2,6.35,53.0 -2016-06-06,CA,1,B,B2,6.28,50.0 -2016-06-07,CA,1,B,B2,6.35,49.0 -2016-06-08,CA,1,B,B2,6.29,51.0 -2016-06-09,CA,1,B,B2,6.35,46.0 -2016-06-10,CA,1,B,B2,6.31,56.0 -2016-06-11,CA,1,B,B2,6.36,55.0 -2016-06-12,CA,1,B,B2,6.39,49.0 -2016-06-13,CA,1,B,B2,6.23,48.0 -2016-06-14,CA,1,B,B2,6.32,52.0 -2016-06-15,CA,1,B,B2,6.3,48.0 -2016-06-16,CA,1,B,B2,6.29,50.0 -2016-06-17,CA,1,B,B2,6.33,52.0 -2016-06-18,CA,1,B,B2,6.29,55.0 -2016-06-19,CA,1,B,B2,6.23,49.0 -2016-06-20,CA,1,B,B2,6.27,50.0 -2016-06-21,CA,1,B,B2,6.29,45.0 -2016-06-22,CA,1,B,B2,6.42,48.0 -2016-06-23,CA,1,B,B2,6.34,50.0 -2016-06-24,CA,1,B,B2,6.3,51.0 -2016-06-25,CA,1,B,B2,6.48,49.0 -2016-06-26,CA,1,B,B2,6.29,55.0 -2016-06-27,CA,1,B,B2,6.35,49.0 -2016-06-28,CA,1,B,B2,6.34,50.0 -2016-06-29,CA,1,B,B2,6.31,49.0 -2016-06-30,CA,1,B,B2,6.43,48.0 -2016-07-01,CA,1,B,B2,6.32,51.0 -2016-07-02,CA,1,B,B2,6.27,53.0 -2016-07-03,CA,1,B,B2,6.35,45.0 -2016-07-04,CA,1,B,B2,6.36,47.0 -2016-07-05,CA,1,B,B2,6.39,48.0 -2016-07-06,CA,1,B,B2,6.36,44.0 -2016-07-07,CA,1,B,B2,6.24,52.0 -2016-07-08,CA,1,B,B2,6.33,50.0 -2016-07-09,CA,1,B,B2,6.16,53.0 -2016-07-10,CA,1,B,B2,6.3,55.0 -2016-07-11,CA,1,B,B2,6.42,45.0 -2016-07-12,CA,1,B,B2,6.4,51.0 -2016-07-13,CA,1,B,B2,6.35,48.0 -2016-07-14,CA,1,B,B2,6.25,51.0 -2016-07-15,CA,1,B,B2,6.37,48.0 -2016-07-16,CA,1,B,B2,6.33,51.0 -2016-07-17,CA,1,B,B2,6.26,50.0 -2016-07-18,CA,1,B,B2,6.25,50.0 -2016-07-19,CA,1,B,B2,6.35,55.0 -2016-07-20,CA,1,B,B2,6.32,51.0 -2016-07-21,CA,1,B,B2,6.29,49.0 -2016-07-22,CA,1,B,B2,6.41,50.0 -2016-07-23,CA,1,B,B2,6.41,50.0 -2016-07-24,CA,1,B,B2,6.3,52.0 -2016-07-25,CA,1,B,B2,6.27,52.0 -2016-07-26,CA,1,B,B2,6.33,52.0 -2016-07-27,CA,1,B,B2,6.31,47.0 -2015-01-05,CA,2,A,A1,4.0,284.0 -2015-01-06,CA,2,A,A1,4.08,283.0 -2015-01-07,CA,2,A,A1,3.97,332.0 -2015-01-08,CA,2,A,A1,3.9,274.0 -2015-01-09,CA,2,A,A1,4.03,291.0 -2015-01-10,CA,2,A,A1,4.03,337.0 -2015-01-11,CA,2,A,A1,3.94,314.0 -2015-01-12,CA,2,A,A1,4.02,291.0 -2015-01-13,CA,2,A,A1,3.91,336.0 -2015-01-14,CA,2,A,A1,3.99,325.0 -2015-01-15,CA,2,A,A1,4.04,315.0 -2015-01-16,CA,2,A,A1,4.0,297.0 -2015-01-17,CA,2,A,A1,3.93,309.0 -2015-01-18,CA,2,A,A1,3.98,345.0 -2015-01-19,CA,2,A,A1,3.95,291.0 -2015-01-20,CA,2,A,A1,3.97,337.0 -2015-01-21,CA,2,A,A1,3.97,287.0 -2015-01-22,CA,2,A,A1,4.0,285.0 -2015-01-23,CA,2,A,A1,4.0,301.0 -2015-01-24,CA,2,A,A1,3.97,317.0 -2015-01-25,CA,2,A,A1,3.96,303.0 -2015-01-26,CA,2,A,A1,3.97,313.0 -2015-01-27,CA,2,A,A1,4.02,315.0 -2015-01-28,CA,2,A,A1,3.99,306.0 -2015-01-29,CA,2,A,A1,4.0,301.0 -2015-01-30,CA,2,A,A1,3.94,308.0 -2015-01-31,CA,2,A,A1,3.96,323.0 -2015-02-01,CA,2,A,A1,3.94,318.0 -2015-02-02,CA,2,A,A1,4.02,295.0 -2015-02-03,CA,2,A,A1,3.97,312.0 -2015-02-04,CA,2,A,A1,3.94,340.0 -2015-02-05,CA,2,A,A1,3.94,335.0 -2015-02-06,CA,2,A,A1,3.9,325.0 -2015-02-07,CA,2,A,A1,3.92,329.0 -2015-02-08,CA,2,A,A1,4.01,317.0 -2015-02-09,CA,2,A,A1,3.98,319.0 -2015-02-10,CA,2,A,A1,3.96,324.0 -2015-02-11,CA,2,A,A1,4.01,310.0 -2015-02-12,CA,2,A,A1,4.05,314.0 -2015-02-13,CA,2,A,A1,3.94,344.0 -2015-02-14,CA,2,A,A1,3.95,328.0 -2015-02-15,CA,2,A,A1,3.91,351.0 -2015-02-16,CA,2,A,A1,3.94,308.0 -2015-02-17,CA,2,A,A1,4.03,299.0 -2015-02-18,CA,2,A,A1,3.94,316.0 -2015-02-19,CA,2,A,A1,3.98,292.0 -2015-02-20,CA,2,A,A1,3.99,306.0 -2015-02-21,CA,2,A,A1,3.95,338.0 -2015-02-22,CA,2,A,A1,3.98,313.0 -2015-02-23,CA,2,A,A1,3.91,317.0 -2015-02-24,CA,2,A,A1,3.96,340.0 -2015-02-25,CA,2,A,A1,4.01,301.0 -2015-02-26,CA,2,A,A1,3.99,335.0 -2015-02-27,CA,2,A,A1,3.97,340.0 -2015-02-28,CA,2,A,A1,3.88,327.0 -2015-03-01,CA,2,A,A1,3.97,317.0 -2015-03-02,CA,2,A,A1,3.94,322.0 -2015-03-03,CA,2,A,A1,3.97,327.0 -2015-03-04,CA,2,A,A1,3.99,320.0 -2015-03-05,CA,2,A,A1,4.03,316.0 -2015-03-06,CA,2,A,A1,3.92,296.0 -2015-03-07,CA,2,A,A1,3.92,335.0 -2015-03-08,CA,2,A,A1,3.87,320.0 -2015-03-09,CA,2,A,A1,4.0,311.0 -2015-03-10,CA,2,A,A1,3.98,322.0 -2015-03-11,CA,2,A,A1,3.97,334.0 -2015-03-12,CA,2,A,A1,4.02,321.0 -2015-03-13,CA,2,A,A1,4.01,310.0 -2015-03-14,CA,2,A,A1,3.97,329.0 -2015-03-15,CA,2,A,A1,3.94,326.0 -2015-03-16,CA,2,A,A1,3.93,315.0 -2015-03-17,CA,2,A,A1,4.06,304.0 -2015-03-18,CA,2,A,A1,3.88,323.0 -2015-03-19,CA,2,A,A1,4.02,329.0 -2015-03-20,CA,2,A,A1,4.01,319.0 -2015-03-21,CA,2,A,A1,3.93,341.0 -2015-03-22,CA,2,A,A1,3.93,329.0 -2015-03-23,CA,2,A,A1,4.0,341.0 -2015-03-24,CA,2,A,A1,4.01,333.0 -2015-03-25,CA,2,A,A1,4.01,331.0 -2015-03-26,CA,2,A,A1,3.97,311.0 -2015-03-27,CA,2,A,A1,3.97,323.0 -2015-03-28,CA,2,A,A1,3.93,336.0 -2015-03-29,CA,2,A,A1,3.94,337.0 -2015-03-30,CA,2,A,A1,3.95,319.0 -2015-03-31,CA,2,A,A1,4.03,334.0 -2015-04-01,CA,2,A,A1,3.94,317.0 -2015-04-02,CA,2,A,A1,4.04,310.0 -2015-04-03,CA,2,A,A1,3.97,336.0 -2015-04-04,CA,2,A,A1,3.96,349.0 -2015-04-05,CA,2,A,A1,3.95,336.0 -2015-04-06,CA,2,A,A1,4.01,309.0 -2015-04-07,CA,2,A,A1,3.96,344.0 -2015-04-08,CA,2,A,A1,4.01,296.0 -2015-04-09,CA,2,A,A1,4.02,302.0 -2015-04-10,CA,2,A,A1,3.94,314.0 -2015-04-11,CA,2,A,A1,3.94,334.0 -2015-04-12,CA,2,A,A1,3.97,342.0 -2015-04-13,CA,2,A,A1,3.91,333.0 -2015-04-14,CA,2,A,A1,4.01,342.0 -2015-04-15,CA,2,A,A1,3.92,336.0 -2015-04-16,CA,2,A,A1,3.92,333.0 -2015-04-17,CA,2,A,A1,3.99,350.0 -2015-04-18,CA,2,A,A1,3.97,347.0 -2015-04-19,CA,2,A,A1,3.93,307.0 -2015-04-20,CA,2,A,A1,4.04,312.0 -2015-04-21,CA,2,A,A1,3.97,364.0 -2015-04-22,CA,2,A,A1,4.0,332.0 -2015-04-23,CA,2,A,A1,3.94,333.0 -2015-04-24,CA,2,A,A1,3.93,349.0 -2015-04-25,CA,2,A,A1,3.99,334.0 -2015-04-26,CA,2,A,A1,3.95,329.0 -2015-04-27,CA,2,A,A1,3.97,333.0 -2015-04-28,CA,2,A,A1,3.96,319.0 -2015-04-29,CA,2,A,A1,4.09,316.0 -2015-04-30,CA,2,A,A1,3.93,328.0 -2015-05-01,CA,2,A,A1,4.04,346.0 -2015-05-02,CA,2,A,A1,4.01,329.0 -2015-05-03,CA,2,A,A1,3.98,317.0 -2015-05-04,CA,2,A,A1,3.99,309.0 -2015-05-05,CA,2,A,A1,3.97,346.0 -2015-05-06,CA,2,A,A1,4.02,339.0 -2015-05-07,CA,2,A,A1,3.92,329.0 -2015-05-08,CA,2,A,A1,3.93,324.0 -2015-05-09,CA,2,A,A1,3.98,326.0 -2015-05-10,CA,2,A,A1,3.89,340.0 -2015-05-11,CA,2,A,A1,3.96,301.0 -2015-05-12,CA,2,A,A1,3.92,358.0 -2015-05-13,CA,2,A,A1,3.98,320.0 -2015-05-14,CA,2,A,A1,3.91,318.0 -2015-05-15,CA,2,A,A1,3.93,336.0 -2015-05-16,CA,2,A,A1,3.98,343.0 -2015-05-17,CA,2,A,A1,3.99,334.0 -2015-05-18,CA,2,A,A1,3.92,343.0 -2015-05-19,CA,2,A,A1,3.96,333.0 -2015-05-20,CA,2,A,A1,4.02,326.0 -2015-05-21,CA,2,A,A1,3.91,341.0 -2015-05-22,CA,2,A,A1,4.0,327.0 -2015-05-23,CA,2,A,A1,3.94,336.0 -2015-05-24,CA,2,A,A1,3.97,318.0 -2015-05-25,CA,2,A,A1,3.97,313.0 -2015-05-26,CA,2,A,A1,4.02,317.0 -2015-05-27,CA,2,A,A1,3.95,338.0 -2015-05-28,CA,2,A,A1,4.01,310.0 -2015-05-29,CA,2,A,A1,4.02,332.0 -2015-05-30,CA,2,A,A1,3.95,345.0 -2015-05-31,CA,2,A,A1,3.96,340.0 -2015-06-01,CA,2,A,A1,3.91,335.0 -2015-06-02,CA,2,A,A1,4.04,339.0 -2015-06-03,CA,2,A,A1,3.99,308.0 -2015-06-04,CA,2,A,A1,4.04,337.0 -2015-06-05,CA,2,A,A1,3.99,347.0 -2015-06-06,CA,2,A,A1,3.96,356.0 -2015-06-07,CA,2,A,A1,3.95,342.0 -2015-06-08,CA,2,A,A1,3.9,325.0 -2015-06-09,CA,2,A,A1,3.96,330.0 -2015-06-10,CA,2,A,A1,3.99,339.0 -2015-06-11,CA,2,A,A1,3.95,327.0 -2015-06-12,CA,2,A,A1,3.96,329.0 -2015-06-13,CA,2,A,A1,3.94,354.0 -2015-06-14,CA,2,A,A1,3.95,348.0 -2015-06-15,CA,2,A,A1,3.96,326.0 -2015-06-16,CA,2,A,A1,3.92,329.0 -2015-06-17,CA,2,A,A1,3.96,311.0 -2015-06-18,CA,2,A,A1,3.99,339.0 -2015-06-19,CA,2,A,A1,4.03,347.0 -2015-06-20,CA,2,A,A1,3.98,332.0 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-2015-07-21,CA,2,A,A1,3.89,342.0 -2015-07-22,CA,2,A,A1,4.01,337.0 -2015-07-23,CA,2,A,A1,4.03,316.0 -2015-07-24,CA,2,A,A1,3.97,326.0 -2015-07-25,CA,2,A,A1,3.94,351.0 -2015-07-26,CA,2,A,A1,4.02,348.0 -2015-07-27,CA,2,A,A1,3.93,341.0 -2015-07-28,CA,2,A,A1,4.01,342.0 -2015-07-29,CA,2,A,A1,3.95,339.0 -2015-07-30,CA,2,A,A1,3.99,327.0 -2015-07-31,CA,2,A,A1,3.9,346.0 -2015-08-01,CA,2,A,A1,4.0,336.0 -2015-08-02,CA,2,A,A1,3.96,310.0 -2015-08-03,CA,2,A,A1,3.91,310.0 -2015-08-04,CA,2,A,A1,4.03,318.0 -2015-08-05,CA,2,A,A1,4.07,319.0 -2015-08-06,CA,2,A,A1,3.97,326.0 -2015-08-07,CA,2,A,A1,4.11,333.0 -2015-08-08,CA,2,A,A1,3.97,328.0 -2015-08-09,CA,2,A,A1,4.02,326.0 -2015-08-10,CA,2,A,A1,3.93,343.0 -2015-08-11,CA,2,A,A1,3.94,329.0 -2015-08-12,CA,2,A,A1,3.98,336.0 -2015-08-13,CA,2,A,A1,4.02,331.0 -2015-08-14,CA,2,A,A1,3.95,308.0 -2015-08-15,CA,2,A,A1,3.95,329.0 -2015-08-16,CA,2,A,A1,4.03,318.0 -2015-08-17,CA,2,A,A1,3.97,332.0 -2015-08-18,CA,2,A,A1,4.0,296.0 -2015-08-19,CA,2,A,A1,3.98,337.0 -2015-08-20,CA,2,A,A1,4.02,341.0 -2015-08-21,CA,2,A,A1,3.96,338.0 -2015-08-22,CA,2,A,A1,3.97,339.0 -2015-08-23,CA,2,A,A1,3.93,303.0 -2015-08-24,CA,2,A,A1,4.0,320.0 -2015-08-25,CA,2,A,A1,3.99,343.0 -2015-08-26,CA,2,A,A1,3.99,330.0 -2015-08-27,CA,2,A,A1,4.0,329.0 -2015-08-28,CA,2,A,A1,4.0,322.0 -2015-08-29,CA,2,A,A1,3.96,342.0 -2015-08-30,CA,2,A,A1,3.93,369.0 -2015-08-31,CA,2,A,A1,4.0,309.0 -2015-09-01,CA,2,A,A1,3.94,329.0 -2015-09-02,CA,2,A,A1,4.01,326.0 -2015-09-03,CA,2,A,A1,3.98,320.0 -2015-09-04,CA,2,A,A1,3.94,312.0 -2015-09-05,CA,2,A,A1,3.98,360.0 -2015-09-06,CA,2,A,A1,4.08,351.0 -2015-09-07,CA,2,A,A1,3.95,316.0 -2015-09-08,CA,2,A,A1,4.02,301.0 -2015-09-09,CA,2,A,A1,3.95,362.0 -2015-09-10,CA,2,A,A1,3.94,328.0 -2015-09-11,CA,2,A,A1,3.99,333.0 -2015-09-12,CA,2,A,A1,3.97,333.0 -2015-09-13,CA,2,A,A1,3.93,342.0 -2015-09-14,CA,2,A,A1,3.95,333.0 -2015-09-15,CA,2,A,A1,4.03,324.0 -2015-09-16,CA,2,A,A1,3.97,321.0 -2015-09-17,CA,2,A,A1,3.93,343.0 -2015-09-18,CA,2,A,A1,4.03,334.0 -2015-09-19,CA,2,A,A1,4.0,351.0 -2015-09-20,CA,2,A,A1,3.91,319.0 -2015-09-21,CA,2,A,A1,4.03,306.0 -2015-09-22,CA,2,A,A1,4.03,328.0 -2015-09-23,CA,2,A,A1,4.05,330.0 -2015-09-24,CA,2,A,A1,3.91,337.0 -2015-09-25,CA,2,A,A1,4.0,308.0 -2015-09-26,CA,2,A,A1,3.96,345.0 -2015-09-27,CA,2,A,A1,3.99,336.0 -2015-09-28,CA,2,A,A1,3.96,308.0 -2015-09-29,CA,2,A,A1,4.03,319.0 -2015-09-30,CA,2,A,A1,4.0,319.0 -2015-10-01,CA,2,A,A1,3.91,339.0 -2015-10-02,CA,2,A,A1,4.02,344.0 -2015-10-03,CA,2,A,A1,3.97,337.0 -2015-10-04,CA,2,A,A1,3.95,327.0 -2015-10-05,CA,2,A,A1,3.94,321.0 -2015-10-06,CA,2,A,A1,3.93,334.0 -2015-10-07,CA,2,A,A1,3.86,340.0 -2015-10-08,CA,2,A,A1,4.0,355.0 -2015-10-09,CA,2,A,A1,3.96,331.0 -2015-10-10,CA,2,A,A1,3.96,363.0 -2015-10-11,CA,2,A,A1,3.95,344.0 -2015-10-12,CA,2,A,A1,3.93,338.0 -2015-10-13,CA,2,A,A1,3.97,336.0 -2015-10-14,CA,2,A,A1,4.01,343.0 -2015-10-15,CA,2,A,A1,3.93,342.0 -2015-10-16,CA,2,A,A1,4.0,326.0 -2015-10-17,CA,2,A,A1,3.97,349.0 -2015-10-18,CA,2,A,A1,3.98,325.0 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-2015-12-18,CA,2,A,A1,3.95,339.0 -2015-12-19,CA,2,A,A1,4.01,324.0 -2015-12-20,CA,2,A,A1,4.02,340.0 -2015-12-21,CA,2,A,A1,3.99,332.0 -2015-12-22,CA,2,A,A1,4.05,311.0 -2015-12-23,CA,2,A,A1,3.92,338.0 -2015-12-24,CA,2,A,A1,3.95,312.0 -2015-12-25,CA,2,A,A1,4.02,346.0 -2015-12-26,CA,2,A,A1,4.01,320.0 -2015-12-27,CA,2,A,A1,3.98,318.0 -2015-12-28,CA,2,A,A1,3.96,323.0 -2015-12-29,CA,2,A,A1,4.01,324.0 -2015-12-30,CA,2,A,A1,4.0,338.0 -2015-12-31,CA,2,A,A1,3.97,326.0 -2016-01-01,CA,2,A,A1,3.96,302.0 -2016-01-02,CA,2,A,A1,3.96,349.0 -2016-01-03,CA,2,A,A1,3.94,344.0 -2016-01-04,CA,2,A,A1,3.9,322.0 -2016-01-05,CA,2,A,A1,4.0,322.0 -2016-01-06,CA,2,A,A1,4.01,313.0 -2016-01-07,CA,2,A,A1,3.91,332.0 -2016-01-08,CA,2,A,A1,3.97,340.0 -2016-01-09,CA,2,A,A1,3.94,348.0 -2016-01-10,CA,2,A,A1,3.99,345.0 -2016-01-11,CA,2,A,A1,3.99,335.0 -2016-01-12,CA,2,A,A1,3.92,344.0 -2016-01-13,CA,2,A,A1,3.98,309.0 -2016-01-14,CA,2,A,A1,3.98,333.0 -2016-01-15,CA,2,A,A1,3.97,315.0 -2016-01-16,CA,2,A,A1,4.04,371.0 -2016-01-17,CA,2,A,A1,3.99,327.0 -2016-01-18,CA,2,A,A1,3.99,300.0 -2016-01-19,CA,2,A,A1,3.93,324.0 -2016-01-20,CA,2,A,A1,3.97,353.0 -2016-01-21,CA,2,A,A1,4.02,306.0 -2016-01-22,CA,2,A,A1,3.98,345.0 -2016-01-23,CA,2,A,A1,3.96,331.0 -2016-01-24,CA,2,A,A1,3.97,350.0 -2016-01-25,CA,2,A,A1,3.97,323.0 -2016-01-26,CA,2,A,A1,3.93,342.0 -2016-01-27,CA,2,A,A1,3.97,336.0 -2016-01-28,CA,2,A,A1,4.02,319.0 -2016-01-29,CA,2,A,A1,3.95,360.0 -2016-01-30,CA,2,A,A1,4.02,362.0 -2016-01-31,CA,2,A,A1,3.98,311.0 -2016-02-01,CA,2,A,A1,3.92,327.0 -2016-02-02,CA,2,A,A1,3.98,334.0 -2016-02-03,CA,2,A,A1,3.94,330.0 -2016-02-04,CA,2,A,A1,4.01,336.0 -2016-02-05,CA,2,A,A1,3.97,342.0 -2016-02-06,CA,2,A,A1,3.99,365.0 -2016-02-07,CA,2,A,A1,3.99,337.0 -2016-02-08,CA,2,A,A1,4.02,321.0 -2016-02-09,CA,2,A,A1,4.0,336.0 -2016-02-10,CA,2,A,A1,4.02,311.0 -2016-02-11,CA,2,A,A1,3.92,333.0 -2016-02-12,CA,2,A,A1,3.99,306.0 -2016-02-13,CA,2,A,A1,4.0,334.0 -2016-02-14,CA,2,A,A1,3.94,336.0 -2016-02-15,CA,2,A,A1,3.97,329.0 -2016-02-16,CA,2,A,A1,4.0,310.0 -2016-02-17,CA,2,A,A1,3.96,343.0 -2016-02-18,CA,2,A,A1,3.99,330.0 -2016-02-19,CA,2,A,A1,3.95,339.0 -2016-02-20,CA,2,A,A1,4.0,343.0 -2016-02-21,CA,2,A,A1,3.95,347.0 -2016-02-22,CA,2,A,A1,4.03,348.0 -2016-02-23,CA,2,A,A1,3.97,320.0 -2016-02-24,CA,2,A,A1,3.98,329.0 -2016-02-25,CA,2,A,A1,3.97,329.0 -2016-02-26,CA,2,A,A1,4.07,323.0 -2016-02-27,CA,2,A,A1,3.95,339.0 -2016-02-28,CA,2,A,A1,3.95,321.0 -2016-02-29,CA,2,A,A1,3.98,336.0 -2016-03-01,CA,2,A,A1,3.95,321.0 -2016-03-02,CA,2,A,A1,3.97,327.0 -2016-03-03,CA,2,A,A1,3.88,351.0 -2016-03-04,CA,2,A,A1,3.96,338.0 -2016-03-05,CA,2,A,A1,3.95,372.0 -2016-03-06,CA,2,A,A1,4.02,333.0 -2016-03-07,CA,2,A,A1,4.05,320.0 -2016-03-08,CA,2,A,A1,4.03,310.0 -2016-03-09,CA,2,A,A1,4.05,334.0 -2016-03-10,CA,2,A,A1,3.97,323.0 -2016-03-11,CA,2,A,A1,3.98,351.0 -2016-03-12,CA,2,A,A1,3.89,354.0 -2016-03-13,CA,2,A,A1,3.94,348.0 -2016-03-14,CA,2,A,A1,3.95,320.0 -2016-03-15,CA,2,A,A1,3.98,314.0 -2016-03-16,CA,2,A,A1,4.1,325.0 -2016-03-17,CA,2,A,A1,3.99,333.0 -2016-03-18,CA,2,A,A1,4.04,308.0 -2016-03-19,CA,2,A,A1,3.99,349.0 -2016-03-20,CA,2,A,A1,4.02,340.0 -2016-03-21,CA,2,A,A1,3.98,332.0 -2016-03-22,CA,2,A,A1,3.95,340.0 -2016-03-23,CA,2,A,A1,4.03,332.0 -2016-03-24,CA,2,A,A1,4.01,326.0 -2016-03-25,CA,2,A,A1,4.0,316.0 -2016-03-26,CA,2,A,A1,3.92,331.0 -2016-03-27,CA,2,A,A1,4.03,304.0 -2016-03-28,CA,2,A,A1,3.93,336.0 -2016-03-29,CA,2,A,A1,3.94,325.0 -2016-03-30,CA,2,A,A1,4.03,317.0 -2016-03-31,CA,2,A,A1,3.95,321.0 -2016-04-01,CA,2,A,A1,3.96,333.0 -2016-04-02,CA,2,A,A1,3.94,344.0 -2016-04-03,CA,2,A,A1,4.02,349.0 -2016-04-04,CA,2,A,A1,3.98,299.0 -2016-04-05,CA,2,A,A1,3.94,321.0 -2016-04-06,CA,2,A,A1,3.95,338.0 -2016-04-07,CA,2,A,A1,3.97,320.0 -2016-04-08,CA,2,A,A1,3.91,336.0 -2016-04-09,CA,2,A,A1,3.94,356.0 -2016-04-10,CA,2,A,A1,3.91,355.0 -2016-04-11,CA,2,A,A1,3.99,321.0 -2016-04-12,CA,2,A,A1,3.94,331.0 -2016-04-13,CA,2,A,A1,3.94,329.0 -2016-04-14,CA,2,A,A1,3.99,323.0 -2016-04-15,CA,2,A,A1,3.99,312.0 -2016-04-16,CA,2,A,A1,4.03,326.0 -2016-04-17,CA,2,A,A1,3.96,330.0 -2016-04-18,CA,2,A,A1,3.99,353.0 -2016-04-19,CA,2,A,A1,4.0,338.0 -2016-04-20,CA,2,A,A1,4.02,295.0 -2016-04-21,CA,2,A,A1,3.95,328.0 -2016-04-22,CA,2,A,A1,4.01,335.0 -2016-04-23,CA,2,A,A1,4.01,320.0 -2016-04-24,CA,2,A,A1,4.03,313.0 -2016-04-25,CA,2,A,A1,3.94,327.0 -2016-04-26,CA,2,A,A1,3.96,334.0 -2016-04-27,CA,2,A,A1,3.97,337.0 -2016-04-28,CA,2,A,A1,3.97,343.0 -2016-04-29,CA,2,A,A1,3.97,326.0 -2016-04-30,CA,2,A,A1,4.0,340.0 -2016-05-01,CA,2,A,A1,3.96,327.0 -2016-05-02,CA,2,A,A1,3.99,303.0 -2016-05-03,CA,2,A,A1,3.99,328.0 -2016-05-04,CA,2,A,A1,3.95,359.0 -2016-05-05,CA,2,A,A1,3.96,322.0 -2016-05-06,CA,2,A,A1,3.95,311.0 -2016-05-07,CA,2,A,A1,3.97,327.0 -2016-05-08,CA,2,A,A1,4.07,324.0 -2016-05-09,CA,2,A,A1,3.98,321.0 -2016-05-10,CA,2,A,A1,4.01,338.0 -2016-05-11,CA,2,A,A1,4.02,328.0 -2016-05-12,CA,2,A,A1,4.0,298.0 -2016-05-13,CA,2,A,A1,3.98,315.0 -2016-05-14,CA,2,A,A1,3.96,332.0 -2016-05-15,CA,2,A,A1,3.97,315.0 -2016-05-16,CA,2,A,A1,4.02,300.0 -2016-05-17,CA,2,A,A1,3.92,340.0 -2016-05-18,CA,2,A,A1,3.99,316.0 -2016-05-19,CA,2,A,A1,3.93,322.0 -2016-05-20,CA,2,A,A1,3.97,332.0 -2016-05-21,CA,2,A,A1,3.94,353.0 -2016-05-22,CA,2,A,A1,4.03,329.0 -2016-05-23,CA,2,A,A1,3.99,304.0 -2016-05-24,CA,2,A,A1,4.02,348.0 -2016-05-25,CA,2,A,A1,3.92,326.0 -2016-05-26,CA,2,A,A1,3.99,329.0 -2016-05-27,CA,2,A,A1,3.98,303.0 -2016-05-28,CA,2,A,A1,3.95,350.0 -2016-05-29,CA,2,A,A1,4.0,336.0 -2016-05-30,CA,2,A,A1,3.92,318.0 -2016-05-31,CA,2,A,A1,3.98,323.0 -2016-06-01,CA,2,A,A1,4.03,312.0 -2016-06-02,CA,2,A,A1,4.05,321.0 -2016-06-03,CA,2,A,A1,4.02,345.0 -2016-06-04,CA,2,A,A1,4.01,337.0 -2016-06-05,CA,2,A,A1,4.02,334.0 -2016-06-06,CA,2,A,A1,3.96,343.0 -2016-06-07,CA,2,A,A1,3.94,319.0 -2016-06-08,CA,2,A,A1,3.84,316.0 -2016-06-09,CA,2,A,A1,3.99,337.0 -2016-06-10,CA,2,A,A1,3.91,350.0 -2016-06-11,CA,2,A,A1,4.0,352.0 -2016-06-12,CA,2,A,A1,3.98,329.0 -2016-06-13,CA,2,A,A1,3.93,330.0 -2016-06-14,CA,2,A,A1,4.0,327.0 -2016-06-15,CA,2,A,A1,3.91,328.0 -2016-06-16,CA,2,A,A1,3.95,301.0 -2016-06-17,CA,2,A,A1,3.92,311.0 -2016-06-18,CA,2,A,A1,3.99,319.0 -2016-06-19,CA,2,A,A1,4.09,312.0 -2016-06-20,CA,2,A,A1,3.95,338.0 -2016-06-21,CA,2,A,A1,3.99,340.0 -2016-06-22,CA,2,A,A1,4.03,336.0 -2016-06-23,CA,2,A,A1,3.96,347.0 -2016-06-24,CA,2,A,A1,4.0,313.0 -2016-06-25,CA,2,A,A1,4.03,328.0 -2016-06-26,CA,2,A,A1,3.93,334.0 -2016-06-27,CA,2,A,A1,4.0,325.0 -2016-06-28,CA,2,A,A1,3.96,312.0 -2016-06-29,CA,2,A,A1,3.95,323.0 -2016-06-30,CA,2,A,A1,3.99,331.0 -2016-07-01,CA,2,A,A1,3.98,336.0 -2016-07-02,CA,2,A,A1,3.96,355.0 -2016-07-03,CA,2,A,A1,3.92,355.0 -2016-07-04,CA,2,A,A1,3.97,317.0 -2016-07-05,CA,2,A,A1,4.01,331.0 -2016-07-06,CA,2,A,A1,3.96,350.0 -2016-07-07,CA,2,A,A1,3.96,338.0 -2016-07-08,CA,2,A,A1,3.92,351.0 -2016-07-09,CA,2,A,A1,3.96,348.0 -2016-07-10,CA,2,A,A1,4.0,348.0 -2016-07-11,CA,2,A,A1,3.96,330.0 -2016-07-12,CA,2,A,A1,4.03,349.0 -2016-07-13,CA,2,A,A1,3.93,333.0 -2016-07-14,CA,2,A,A1,3.98,331.0 -2016-07-15,CA,2,A,A1,3.87,333.0 -2016-07-16,CA,2,A,A1,3.97,355.0 -2016-07-17,CA,2,A,A1,4.03,315.0 -2016-07-18,CA,2,A,A1,3.95,364.0 -2016-07-19,CA,2,A,A1,3.95,319.0 -2016-07-20,CA,2,A,A1,3.97,313.0 -2016-07-21,CA,2,A,A1,4.0,328.0 -2016-07-22,CA,2,A,A1,3.92,329.0 -2016-07-23,CA,2,A,A1,4.0,335.0 -2016-07-24,CA,2,A,A1,3.94,354.0 -2016-07-25,CA,2,A,A1,4.05,321.0 -2016-07-26,CA,2,A,A1,4.02,346.0 -2016-07-27,CA,2,A,A1,3.99,335.0 -2015-01-05,CA,2,A,A2,2.34,36.0 -2015-01-06,CA,2,A,A2,2.33,36.0 -2015-01-07,CA,2,A,A2,2.37,32.0 -2015-01-08,CA,2,A,A2,2.3,37.0 -2015-01-09,CA,2,A,A2,2.31,37.0 -2015-01-10,CA,2,A,A2,2.34,33.0 -2015-01-11,CA,2,A,A2,2.33,34.0 -2015-01-12,CA,2,A,A2,2.32,35.0 -2015-01-13,CA,2,A,A2,2.3,39.0 -2015-01-14,CA,2,A,A2,2.31,35.0 -2015-01-15,CA,2,A,A2,2.32,37.0 -2015-01-16,CA,2,A,A2,2.31,36.0 -2015-01-17,CA,2,A,A2,2.33,36.0 -2015-01-18,CA,2,A,A2,2.32,38.0 -2015-01-19,CA,2,A,A2,2.37,34.0 -2015-01-20,CA,2,A,A2,2.36,36.0 -2015-01-21,CA,2,A,A2,2.33,36.0 -2015-01-22,CA,2,A,A2,2.29,35.0 -2015-01-23,CA,2,A,A2,2.33,37.0 -2015-01-24,CA,2,A,A2,2.36,38.0 -2015-01-25,CA,2,A,A2,2.33,38.0 -2015-01-26,CA,2,A,A2,2.35,35.0 -2015-01-27,CA,2,A,A2,2.32,32.0 -2015-01-28,CA,2,A,A2,2.31,36.0 -2015-01-29,CA,2,A,A2,2.34,32.0 -2015-01-30,CA,2,A,A2,2.38,35.0 -2015-01-31,CA,2,A,A2,2.37,37.0 -2015-02-01,CA,2,A,A2,2.35,36.0 -2015-02-02,CA,2,A,A2,2.33,35.0 -2015-02-03,CA,2,A,A2,2.3,36.0 -2015-02-04,CA,2,A,A2,2.35,36.0 -2015-02-05,CA,2,A,A2,2.37,31.0 -2015-02-06,CA,2,A,A2,2.36,37.0 -2015-02-07,CA,2,A,A2,2.33,37.0 -2015-02-08,CA,2,A,A2,2.3,39.0 -2015-02-09,CA,2,A,A2,2.32,35.0 -2015-02-10,CA,2,A,A2,2.38,35.0 -2015-02-11,CA,2,A,A2,2.35,36.0 -2015-02-12,CA,2,A,A2,2.33,34.0 -2015-02-13,CA,2,A,A2,2.31,35.0 -2015-02-14,CA,2,A,A2,2.35,38.0 -2015-02-15,CA,2,A,A2,2.32,35.0 -2015-02-16,CA,2,A,A2,2.33,34.0 -2015-02-17,CA,2,A,A2,2.36,34.0 -2015-02-18,CA,2,A,A2,2.27,36.0 -2015-02-19,CA,2,A,A2,2.36,38.0 -2015-02-20,CA,2,A,A2,2.34,33.0 -2015-02-21,CA,2,A,A2,2.34,37.0 -2015-02-22,CA,2,A,A2,2.35,36.0 -2015-02-23,CA,2,A,A2,2.36,35.0 -2015-02-24,CA,2,A,A2,2.35,35.0 -2015-02-25,CA,2,A,A2,2.36,37.0 -2015-02-26,CA,2,A,A2,2.37,33.0 -2015-02-27,CA,2,A,A2,2.35,40.0 -2015-02-28,CA,2,A,A2,2.33,38.0 -2015-03-01,CA,2,A,A2,2.31,41.0 -2015-03-02,CA,2,A,A2,2.3,37.0 -2015-03-03,CA,2,A,A2,2.35,36.0 -2015-03-04,CA,2,A,A2,2.4,34.0 -2015-03-05,CA,2,A,A2,2.33,36.0 -2015-03-06,CA,2,A,A2,2.33,37.0 -2015-03-07,CA,2,A,A2,2.32,36.0 -2015-03-08,CA,2,A,A2,2.32,35.0 -2015-03-09,CA,2,A,A2,2.31,35.0 -2015-03-10,CA,2,A,A2,2.36,32.0 -2015-03-11,CA,2,A,A2,2.33,38.0 -2015-03-12,CA,2,A,A2,2.31,36.0 -2015-03-13,CA,2,A,A2,2.38,33.0 -2015-03-14,CA,2,A,A2,2.37,37.0 -2015-03-15,CA,2,A,A2,2.32,37.0 -2015-03-16,CA,2,A,A2,2.33,36.0 -2015-03-17,CA,2,A,A2,2.34,36.0 -2015-03-18,CA,2,A,A2,2.29,37.0 -2015-03-19,CA,2,A,A2,2.36,38.0 -2015-03-20,CA,2,A,A2,2.34,36.0 -2015-03-21,CA,2,A,A2,2.34,40.0 -2015-03-22,CA,2,A,A2,2.32,36.0 -2015-03-23,CA,2,A,A2,2.35,36.0 -2015-03-24,CA,2,A,A2,2.33,34.0 -2015-03-25,CA,2,A,A2,2.35,37.0 -2015-03-26,CA,2,A,A2,2.32,35.0 -2015-03-27,CA,2,A,A2,2.32,35.0 -2015-03-28,CA,2,A,A2,2.33,38.0 -2015-03-29,CA,2,A,A2,2.36,35.0 -2015-03-30,CA,2,A,A2,2.35,37.0 -2015-03-31,CA,2,A,A2,2.35,37.0 -2015-04-01,CA,2,A,A2,2.32,36.0 -2015-04-02,CA,2,A,A2,2.35,34.0 -2015-04-03,CA,2,A,A2,2.33,37.0 -2015-04-04,CA,2,A,A2,2.35,39.0 -2015-04-05,CA,2,A,A2,2.32,36.0 -2015-04-06,CA,2,A,A2,2.35,37.0 -2015-04-07,CA,2,A,A2,2.31,35.0 -2015-04-08,CA,2,A,A2,2.32,36.0 -2015-04-09,CA,2,A,A2,2.36,37.0 -2015-04-10,CA,2,A,A2,2.38,37.0 -2015-04-11,CA,2,A,A2,2.28,37.0 -2015-04-12,CA,2,A,A2,2.34,35.0 -2015-04-13,CA,2,A,A2,2.35,33.0 -2015-04-14,CA,2,A,A2,2.33,31.0 -2015-04-15,CA,2,A,A2,2.34,34.0 -2015-04-16,CA,2,A,A2,2.3,37.0 -2015-04-17,CA,2,A,A2,2.32,35.0 -2015-04-18,CA,2,A,A2,2.35,38.0 -2015-04-19,CA,2,A,A2,2.34,35.0 -2015-04-20,CA,2,A,A2,2.33,37.0 -2015-04-21,CA,2,A,A2,2.36,36.0 -2015-04-22,CA,2,A,A2,2.3,37.0 -2015-04-23,CA,2,A,A2,2.34,35.0 -2015-04-24,CA,2,A,A2,2.33,36.0 -2015-04-25,CA,2,A,A2,2.37,37.0 -2015-04-26,CA,2,A,A2,2.37,36.0 -2015-04-27,CA,2,A,A2,2.32,40.0 -2015-04-28,CA,2,A,A2,2.32,35.0 -2015-04-29,CA,2,A,A2,2.32,36.0 -2015-04-30,CA,2,A,A2,2.35,35.0 -2015-05-01,CA,2,A,A2,2.32,37.0 -2015-05-02,CA,2,A,A2,2.36,39.0 -2015-05-03,CA,2,A,A2,2.34,36.0 -2015-05-04,CA,2,A,A2,2.31,37.0 -2015-05-05,CA,2,A,A2,2.31,36.0 -2015-05-06,CA,2,A,A2,2.34,35.0 -2015-05-07,CA,2,A,A2,2.36,35.0 -2015-05-08,CA,2,A,A2,2.34,37.0 -2015-05-09,CA,2,A,A2,2.33,38.0 -2015-05-10,CA,2,A,A2,2.29,40.0 -2015-05-11,CA,2,A,A2,2.38,35.0 -2015-05-12,CA,2,A,A2,2.35,37.0 -2015-05-13,CA,2,A,A2,2.37,35.0 -2015-05-14,CA,2,A,A2,2.32,36.0 -2015-05-15,CA,2,A,A2,2.34,35.0 -2015-05-16,CA,2,A,A2,2.34,38.0 -2015-05-17,CA,2,A,A2,2.33,38.0 -2015-05-18,CA,2,A,A2,2.36,31.0 -2015-05-19,CA,2,A,A2,2.32,36.0 -2015-05-20,CA,2,A,A2,2.35,36.0 -2015-05-21,CA,2,A,A2,2.35,38.0 -2015-05-22,CA,2,A,A2,2.35,34.0 -2015-05-23,CA,2,A,A2,2.31,37.0 -2015-05-24,CA,2,A,A2,2.37,38.0 -2015-05-25,CA,2,A,A2,2.36,33.0 -2015-05-26,CA,2,A,A2,2.37,34.0 -2015-05-27,CA,2,A,A2,2.35,35.0 -2015-05-28,CA,2,A,A2,2.33,37.0 -2015-05-29,CA,2,A,A2,2.36,36.0 -2015-05-30,CA,2,A,A2,2.33,38.0 -2015-05-31,CA,2,A,A2,2.31,38.0 -2015-06-01,CA,2,A,A2,2.36,35.0 -2015-06-02,CA,2,A,A2,2.31,33.0 -2015-06-03,CA,2,A,A2,2.31,36.0 -2015-06-04,CA,2,A,A2,2.34,35.0 -2015-06-05,CA,2,A,A2,2.35,36.0 -2015-06-06,CA,2,A,A2,2.34,39.0 -2015-06-07,CA,2,A,A2,2.34,34.0 -2015-06-08,CA,2,A,A2,2.34,37.0 -2015-06-09,CA,2,A,A2,2.37,36.0 -2015-06-10,CA,2,A,A2,2.32,37.0 -2015-06-11,CA,2,A,A2,2.37,35.0 -2015-06-12,CA,2,A,A2,2.33,37.0 -2015-06-13,CA,2,A,A2,2.35,33.0 -2015-06-14,CA,2,A,A2,2.32,39.0 -2015-06-15,CA,2,A,A2,2.31,34.0 -2015-06-16,CA,2,A,A2,2.36,38.0 -2015-06-17,CA,2,A,A2,2.31,37.0 -2015-06-18,CA,2,A,A2,2.36,35.0 -2015-06-19,CA,2,A,A2,2.37,37.0 -2015-06-20,CA,2,A,A2,2.34,35.0 -2015-06-21,CA,2,A,A2,2.31,36.0 -2015-06-22,CA,2,A,A2,2.35,36.0 -2015-06-23,CA,2,A,A2,2.31,34.0 -2015-06-24,CA,2,A,A2,2.31,36.0 -2015-06-25,CA,2,A,A2,2.3,39.0 -2015-06-26,CA,2,A,A2,2.39,34.0 -2015-06-27,CA,2,A,A2,2.34,36.0 -2015-06-28,CA,2,A,A2,2.36,34.0 -2015-06-29,CA,2,A,A2,2.31,37.0 -2015-06-30,CA,2,A,A2,2.35,35.0 -2015-07-01,CA,2,A,A2,2.35,36.0 -2015-07-02,CA,2,A,A2,2.34,35.0 -2015-07-03,CA,2,A,A2,2.34,37.0 -2015-07-04,CA,2,A,A2,2.32,39.0 -2015-07-05,CA,2,A,A2,2.33,34.0 -2015-07-06,CA,2,A,A2,2.37,35.0 -2015-07-07,CA,2,A,A2,2.37,35.0 -2015-07-08,CA,2,A,A2,2.33,35.0 -2015-07-09,CA,2,A,A2,2.36,37.0 -2015-07-10,CA,2,A,A2,2.34,38.0 -2015-07-11,CA,2,A,A2,2.31,39.0 -2015-07-12,CA,2,A,A2,2.35,39.0 -2015-07-13,CA,2,A,A2,2.34,34.0 -2015-07-14,CA,2,A,A2,2.35,36.0 -2015-07-15,CA,2,A,A2,2.33,37.0 -2015-07-16,CA,2,A,A2,2.33,38.0 -2015-07-17,CA,2,A,A2,2.37,35.0 -2015-07-18,CA,2,A,A2,2.34,38.0 -2015-07-19,CA,2,A,A2,2.34,40.0 -2015-07-20,CA,2,A,A2,2.34,38.0 -2015-07-21,CA,2,A,A2,2.36,37.0 -2015-07-22,CA,2,A,A2,2.34,33.0 -2015-07-23,CA,2,A,A2,2.39,33.0 -2015-07-24,CA,2,A,A2,2.36,36.0 -2015-07-25,CA,2,A,A2,2.31,38.0 -2015-07-26,CA,2,A,A2,2.35,37.0 -2015-07-27,CA,2,A,A2,2.34,36.0 -2015-07-28,CA,2,A,A2,2.33,38.0 -2015-07-29,CA,2,A,A2,2.34,37.0 -2015-07-30,CA,2,A,A2,2.37,39.0 -2015-07-31,CA,2,A,A2,2.32,32.0 -2015-08-01,CA,2,A,A2,2.35,35.0 -2015-08-02,CA,2,A,A2,2.36,36.0 -2015-08-03,CA,2,A,A2,2.32,37.0 -2015-08-04,CA,2,A,A2,2.33,37.0 -2015-08-05,CA,2,A,A2,2.34,38.0 -2015-08-06,CA,2,A,A2,2.33,37.0 -2015-08-07,CA,2,A,A2,2.31,38.0 -2015-08-08,CA,2,A,A2,2.35,38.0 -2015-08-09,CA,2,A,A2,2.35,37.0 -2015-08-10,CA,2,A,A2,2.36,35.0 -2015-08-11,CA,2,A,A2,2.34,39.0 -2015-08-12,CA,2,A,A2,2.35,36.0 -2015-08-13,CA,2,A,A2,2.32,36.0 -2015-08-14,CA,2,A,A2,2.34,36.0 -2015-08-15,CA,2,A,A2,2.36,42.0 -2015-08-16,CA,2,A,A2,2.37,35.0 -2015-08-17,CA,2,A,A2,2.34,35.0 -2015-08-18,CA,2,A,A2,2.36,37.0 -2015-08-19,CA,2,A,A2,2.31,39.0 -2015-08-20,CA,2,A,A2,2.34,35.0 -2015-08-21,CA,2,A,A2,2.36,35.0 -2015-08-22,CA,2,A,A2,2.32,38.0 -2015-08-23,CA,2,A,A2,2.39,31.0 -2015-08-24,CA,2,A,A2,2.35,37.0 -2015-08-25,CA,2,A,A2,2.36,36.0 -2015-08-26,CA,2,A,A2,2.35,34.0 -2015-08-27,CA,2,A,A2,2.34,34.0 -2015-08-28,CA,2,A,A2,2.3,36.0 -2015-08-29,CA,2,A,A2,2.32,39.0 -2015-08-30,CA,2,A,A2,2.34,38.0 -2015-08-31,CA,2,A,A2,2.38,33.0 -2015-09-01,CA,2,A,A2,2.39,36.0 -2015-09-02,CA,2,A,A2,2.35,34.0 -2015-09-03,CA,2,A,A2,2.33,37.0 -2015-09-04,CA,2,A,A2,2.34,38.0 -2015-09-05,CA,2,A,A2,2.33,35.0 -2015-09-06,CA,2,A,A2,2.36,37.0 -2015-09-07,CA,2,A,A2,2.35,36.0 -2015-09-08,CA,2,A,A2,2.34,37.0 -2015-09-09,CA,2,A,A2,2.35,35.0 -2015-09-10,CA,2,A,A2,2.35,33.0 -2015-09-11,CA,2,A,A2,2.3,39.0 -2015-09-12,CA,2,A,A2,2.36,38.0 -2015-09-13,CA,2,A,A2,2.33,37.0 -2015-09-14,CA,2,A,A2,2.36,32.0 -2015-09-15,CA,2,A,A2,2.33,36.0 -2015-09-16,CA,2,A,A2,2.31,38.0 -2015-09-17,CA,2,A,A2,2.36,36.0 -2015-09-18,CA,2,A,A2,2.33,36.0 -2015-09-19,CA,2,A,A2,2.33,35.0 -2015-09-20,CA,2,A,A2,2.35,34.0 -2015-09-21,CA,2,A,A2,2.36,38.0 -2015-09-22,CA,2,A,A2,2.32,34.0 -2015-09-23,CA,2,A,A2,2.35,36.0 -2015-09-24,CA,2,A,A2,2.37,36.0 -2015-09-25,CA,2,A,A2,2.31,36.0 -2015-09-26,CA,2,A,A2,2.29,37.0 -2015-09-27,CA,2,A,A2,2.34,36.0 -2015-09-28,CA,2,A,A2,2.38,36.0 -2015-09-29,CA,2,A,A2,2.38,37.0 -2015-09-30,CA,2,A,A2,2.33,36.0 -2015-10-01,CA,2,A,A2,2.3,35.0 -2015-10-02,CA,2,A,A2,2.33,38.0 -2015-10-03,CA,2,A,A2,2.33,38.0 -2015-10-04,CA,2,A,A2,2.35,36.0 -2015-10-05,CA,2,A,A2,2.34,33.0 -2015-10-06,CA,2,A,A2,2.32,34.0 -2015-10-07,CA,2,A,A2,2.32,36.0 -2015-10-08,CA,2,A,A2,2.33,37.0 -2015-10-09,CA,2,A,A2,2.36,35.0 -2015-10-10,CA,2,A,A2,2.34,37.0 -2015-10-11,CA,2,A,A2,2.35,38.0 -2015-10-12,CA,2,A,A2,2.35,35.0 -2015-10-13,CA,2,A,A2,2.34,36.0 -2015-10-14,CA,2,A,A2,2.35,36.0 -2015-10-15,CA,2,A,A2,2.36,34.0 -2015-10-16,CA,2,A,A2,2.32,36.0 -2015-10-17,CA,2,A,A2,2.35,39.0 -2015-10-18,CA,2,A,A2,2.38,37.0 -2015-10-19,CA,2,A,A2,2.34,34.0 -2015-10-20,CA,2,A,A2,2.31,36.0 -2015-10-21,CA,2,A,A2,2.32,38.0 -2015-10-22,CA,2,A,A2,2.34,37.0 -2015-10-23,CA,2,A,A2,2.33,35.0 -2015-10-24,CA,2,A,A2,2.33,34.0 -2015-10-25,CA,2,A,A2,2.36,35.0 -2015-10-26,CA,2,A,A2,2.35,35.0 -2015-10-27,CA,2,A,A2,2.34,36.0 -2015-10-28,CA,2,A,A2,2.35,35.0 -2015-10-29,CA,2,A,A2,2.33,37.0 -2015-10-30,CA,2,A,A2,2.32,39.0 -2015-10-31,CA,2,A,A2,2.33,39.0 -2015-11-01,CA,2,A,A2,2.38,39.0 -2015-11-02,CA,2,A,A2,2.31,34.0 -2015-11-03,CA,2,A,A2,2.35,38.0 -2015-11-04,CA,2,A,A2,2.33,34.0 -2015-11-05,CA,2,A,A2,2.32,39.0 -2015-11-06,CA,2,A,A2,2.33,37.0 -2015-11-07,CA,2,A,A2,2.37,38.0 -2015-11-08,CA,2,A,A2,2.34,38.0 -2015-11-09,CA,2,A,A2,2.32,34.0 -2015-11-10,CA,2,A,A2,2.34,36.0 -2015-11-11,CA,2,A,A2,2.35,35.0 -2015-11-12,CA,2,A,A2,2.34,37.0 -2015-11-13,CA,2,A,A2,2.37,35.0 -2015-11-14,CA,2,A,A2,2.34,36.0 -2015-11-15,CA,2,A,A2,2.33,34.0 -2015-11-16,CA,2,A,A2,2.33,35.0 -2015-11-17,CA,2,A,A2,2.36,38.0 -2015-11-18,CA,2,A,A2,2.36,36.0 -2015-11-19,CA,2,A,A2,2.35,33.0 -2015-11-20,CA,2,A,A2,2.32,37.0 -2015-11-21,CA,2,A,A2,2.37,38.0 -2015-11-22,CA,2,A,A2,2.34,36.0 -2015-11-23,CA,2,A,A2,2.36,36.0 -2015-11-24,CA,2,A,A2,2.36,37.0 -2015-11-25,CA,2,A,A2,2.3,38.0 -2015-11-26,CA,2,A,A2,2.36,35.0 -2015-11-27,CA,2,A,A2,2.35,36.0 -2015-11-28,CA,2,A,A2,2.33,36.0 -2015-11-29,CA,2,A,A2,2.4,34.0 -2015-11-30,CA,2,A,A2,2.36,35.0 -2015-12-01,CA,2,A,A2,2.32,37.0 -2015-12-02,CA,2,A,A2,2.34,36.0 -2015-12-03,CA,2,A,A2,2.35,36.0 -2015-12-04,CA,2,A,A2,2.35,38.0 -2015-12-05,CA,2,A,A2,2.36,36.0 -2015-12-06,CA,2,A,A2,2.3,35.0 -2015-12-07,CA,2,A,A2,2.37,36.0 -2015-12-08,CA,2,A,A2,2.36,36.0 -2015-12-09,CA,2,A,A2,2.32,35.0 -2015-12-10,CA,2,A,A2,2.34,36.0 -2015-12-11,CA,2,A,A2,2.33,38.0 -2015-12-12,CA,2,A,A2,2.34,38.0 -2015-12-13,CA,2,A,A2,2.34,37.0 -2015-12-14,CA,2,A,A2,2.31,38.0 -2015-12-15,CA,2,A,A2,2.36,35.0 -2015-12-16,CA,2,A,A2,2.34,34.0 -2015-12-17,CA,2,A,A2,2.32,35.0 -2015-12-18,CA,2,A,A2,2.38,34.0 -2015-12-19,CA,2,A,A2,2.35,36.0 -2015-12-20,CA,2,A,A2,2.33,37.0 -2015-12-21,CA,2,A,A2,2.33,35.0 -2015-12-22,CA,2,A,A2,2.34,36.0 -2015-12-23,CA,2,A,A2,2.36,36.0 -2015-12-24,CA,2,A,A2,2.36,35.0 -2015-12-25,CA,2,A,A2,2.31,35.0 -2015-12-26,CA,2,A,A2,2.35,38.0 -2015-12-27,CA,2,A,A2,2.32,37.0 -2015-12-28,CA,2,A,A2,2.36,35.0 -2015-12-29,CA,2,A,A2,2.33,38.0 -2015-12-30,CA,2,A,A2,2.31,40.0 -2015-12-31,CA,2,A,A2,2.33,34.0 -2016-01-01,CA,2,A,A2,2.31,37.0 -2016-01-02,CA,2,A,A2,2.34,36.0 -2016-01-03,CA,2,A,A2,2.36,36.0 -2016-01-04,CA,2,A,A2,2.37,33.0 -2016-01-05,CA,2,A,A2,2.34,38.0 -2016-01-06,CA,2,A,A2,2.33,37.0 -2016-01-07,CA,2,A,A2,2.38,39.0 -2016-01-08,CA,2,A,A2,2.28,38.0 -2016-01-09,CA,2,A,A2,2.36,35.0 -2016-01-10,CA,2,A,A2,2.38,35.0 -2016-01-11,CA,2,A,A2,2.31,36.0 -2016-01-12,CA,2,A,A2,2.34,35.0 -2016-01-13,CA,2,A,A2,2.32,35.0 -2016-01-14,CA,2,A,A2,2.38,35.0 -2016-01-15,CA,2,A,A2,2.36,35.0 -2016-01-16,CA,2,A,A2,2.38,39.0 -2016-01-17,CA,2,A,A2,2.35,36.0 -2016-01-18,CA,2,A,A2,2.35,34.0 -2016-01-19,CA,2,A,A2,2.34,36.0 -2016-01-20,CA,2,A,A2,2.35,35.0 -2016-01-21,CA,2,A,A2,2.32,35.0 -2016-01-22,CA,2,A,A2,2.34,37.0 -2016-01-23,CA,2,A,A2,2.32,38.0 -2016-01-24,CA,2,A,A2,2.32,35.0 -2016-01-25,CA,2,A,A2,2.29,38.0 -2016-01-26,CA,2,A,A2,2.31,35.0 -2016-01-27,CA,2,A,A2,2.3,35.0 -2016-01-28,CA,2,A,A2,2.34,37.0 -2016-01-29,CA,2,A,A2,2.38,36.0 -2016-01-30,CA,2,A,A2,2.35,40.0 -2016-01-31,CA,2,A,A2,2.33,36.0 -2016-02-01,CA,2,A,A2,2.37,34.0 -2016-02-02,CA,2,A,A2,2.34,36.0 -2016-02-03,CA,2,A,A2,2.31,37.0 -2016-02-04,CA,2,A,A2,2.36,35.0 -2016-02-05,CA,2,A,A2,2.36,35.0 -2016-02-06,CA,2,A,A2,2.36,37.0 -2016-02-07,CA,2,A,A2,2.31,38.0 -2016-02-08,CA,2,A,A2,2.36,36.0 -2016-02-09,CA,2,A,A2,2.34,34.0 -2016-02-10,CA,2,A,A2,2.3,37.0 -2016-02-11,CA,2,A,A2,2.33,38.0 -2016-02-12,CA,2,A,A2,2.36,37.0 -2016-02-13,CA,2,A,A2,2.35,40.0 -2016-02-14,CA,2,A,A2,2.33,36.0 -2016-02-15,CA,2,A,A2,2.38,34.0 -2016-02-16,CA,2,A,A2,2.36,32.0 -2016-02-17,CA,2,A,A2,2.32,39.0 -2016-02-18,CA,2,A,A2,2.36,38.0 -2016-02-19,CA,2,A,A2,2.37,37.0 -2016-02-20,CA,2,A,A2,2.29,38.0 -2016-02-21,CA,2,A,A2,2.31,34.0 -2016-02-22,CA,2,A,A2,2.37,35.0 -2016-02-23,CA,2,A,A2,2.33,37.0 -2016-02-24,CA,2,A,A2,2.39,35.0 -2016-02-25,CA,2,A,A2,2.39,37.0 -2016-02-26,CA,2,A,A2,2.29,35.0 -2016-02-27,CA,2,A,A2,2.32,37.0 -2016-02-28,CA,2,A,A2,2.35,39.0 -2016-02-29,CA,2,A,A2,2.33,35.0 -2016-03-01,CA,2,A,A2,2.33,36.0 -2016-03-02,CA,2,A,A2,2.31,36.0 -2016-03-03,CA,2,A,A2,2.35,37.0 -2016-03-04,CA,2,A,A2,2.39,35.0 -2016-03-05,CA,2,A,A2,2.34,38.0 -2016-03-06,CA,2,A,A2,2.3,36.0 -2016-03-07,CA,2,A,A2,2.34,34.0 -2016-03-08,CA,2,A,A2,2.35,36.0 -2016-03-09,CA,2,A,A2,2.32,39.0 -2016-03-10,CA,2,A,A2,2.33,36.0 -2016-03-11,CA,2,A,A2,2.31,39.0 -2016-03-12,CA,2,A,A2,2.34,37.0 -2016-03-13,CA,2,A,A2,2.34,36.0 -2016-03-14,CA,2,A,A2,2.27,37.0 -2016-03-15,CA,2,A,A2,2.3,38.0 -2016-03-16,CA,2,A,A2,2.36,36.0 -2016-03-17,CA,2,A,A2,2.37,37.0 -2016-03-18,CA,2,A,A2,2.36,37.0 -2016-03-19,CA,2,A,A2,2.34,38.0 -2016-03-20,CA,2,A,A2,2.36,37.0 -2016-03-21,CA,2,A,A2,2.34,34.0 -2016-03-22,CA,2,A,A2,2.34,35.0 -2016-03-23,CA,2,A,A2,2.3,37.0 -2016-03-24,CA,2,A,A2,2.37,32.0 -2016-03-25,CA,2,A,A2,2.35,37.0 -2016-03-26,CA,2,A,A2,2.36,37.0 -2016-03-27,CA,2,A,A2,2.34,37.0 -2016-03-28,CA,2,A,A2,2.33,37.0 -2016-03-29,CA,2,A,A2,2.4,35.0 -2016-03-30,CA,2,A,A2,2.32,35.0 -2016-03-31,CA,2,A,A2,2.35,37.0 -2016-04-01,CA,2,A,A2,2.36,32.0 -2016-04-02,CA,2,A,A2,2.33,37.0 -2016-04-03,CA,2,A,A2,2.35,34.0 -2016-04-04,CA,2,A,A2,2.35,33.0 -2016-04-05,CA,2,A,A2,2.33,39.0 -2016-04-06,CA,2,A,A2,2.32,35.0 -2016-04-07,CA,2,A,A2,2.33,38.0 -2016-04-08,CA,2,A,A2,2.4,37.0 -2016-04-09,CA,2,A,A2,2.36,35.0 -2016-04-10,CA,2,A,A2,2.33,38.0 -2016-04-11,CA,2,A,A2,2.33,35.0 -2016-04-12,CA,2,A,A2,2.33,37.0 -2016-04-13,CA,2,A,A2,2.33,33.0 -2016-04-14,CA,2,A,A2,2.34,36.0 -2016-04-15,CA,2,A,A2,2.39,37.0 -2016-04-16,CA,2,A,A2,2.34,35.0 -2016-04-17,CA,2,A,A2,2.35,35.0 -2016-04-18,CA,2,A,A2,2.34,36.0 -2016-04-19,CA,2,A,A2,2.35,35.0 -2016-04-20,CA,2,A,A2,2.32,35.0 -2016-04-21,CA,2,A,A2,2.34,35.0 -2016-04-22,CA,2,A,A2,2.28,40.0 -2016-04-23,CA,2,A,A2,2.37,39.0 -2016-04-24,CA,2,A,A2,2.38,34.0 -2016-04-25,CA,2,A,A2,2.33,36.0 -2016-04-26,CA,2,A,A2,2.34,34.0 -2016-04-27,CA,2,A,A2,2.32,38.0 -2016-04-28,CA,2,A,A2,2.32,37.0 -2016-04-29,CA,2,A,A2,2.31,40.0 -2016-04-30,CA,2,A,A2,2.34,34.0 -2016-05-01,CA,2,A,A2,2.36,36.0 -2016-05-02,CA,2,A,A2,2.33,38.0 -2016-05-03,CA,2,A,A2,2.35,35.0 -2016-05-04,CA,2,A,A2,2.34,39.0 -2016-05-05,CA,2,A,A2,2.34,37.0 -2016-05-06,CA,2,A,A2,2.32,35.0 -2016-05-07,CA,2,A,A2,2.35,37.0 -2016-05-08,CA,2,A,A2,2.35,37.0 -2016-05-09,CA,2,A,A2,2.31,36.0 -2016-05-10,CA,2,A,A2,2.33,36.0 -2016-05-11,CA,2,A,A2,2.31,37.0 -2016-05-12,CA,2,A,A2,2.34,34.0 -2016-05-13,CA,2,A,A2,2.35,37.0 -2016-05-14,CA,2,A,A2,2.31,37.0 -2016-05-15,CA,2,A,A2,2.33,36.0 -2016-05-16,CA,2,A,A2,2.33,37.0 -2016-05-17,CA,2,A,A2,2.33,35.0 -2016-05-18,CA,2,A,A2,2.32,36.0 -2016-05-19,CA,2,A,A2,2.33,37.0 -2016-05-20,CA,2,A,A2,2.34,37.0 -2016-05-21,CA,2,A,A2,2.34,40.0 -2016-05-22,CA,2,A,A2,2.36,34.0 -2016-05-23,CA,2,A,A2,2.33,35.0 -2016-05-24,CA,2,A,A2,2.35,38.0 -2016-05-25,CA,2,A,A2,2.37,37.0 -2016-05-26,CA,2,A,A2,2.34,35.0 -2016-05-27,CA,2,A,A2,2.35,35.0 -2016-05-28,CA,2,A,A2,2.34,38.0 -2016-05-29,CA,2,A,A2,2.34,33.0 -2016-05-30,CA,2,A,A2,2.33,35.0 -2016-05-31,CA,2,A,A2,2.34,34.0 -2016-06-01,CA,2,A,A2,2.36,36.0 -2016-06-02,CA,2,A,A2,2.36,36.0 -2016-06-03,CA,2,A,A2,2.35,34.0 -2016-06-04,CA,2,A,A2,2.35,38.0 -2016-06-05,CA,2,A,A2,2.29,38.0 -2016-06-06,CA,2,A,A2,2.35,36.0 -2016-06-07,CA,2,A,A2,2.36,34.0 -2016-06-08,CA,2,A,A2,2.34,34.0 -2016-06-09,CA,2,A,A2,2.35,34.0 -2016-06-10,CA,2,A,A2,2.27,37.0 -2016-06-11,CA,2,A,A2,2.34,36.0 -2016-06-12,CA,2,A,A2,2.33,37.0 -2016-06-13,CA,2,A,A2,2.28,33.0 -2016-06-14,CA,2,A,A2,2.31,34.0 -2016-06-15,CA,2,A,A2,2.34,36.0 -2016-06-16,CA,2,A,A2,2.34,36.0 -2016-06-17,CA,2,A,A2,2.37,37.0 -2016-06-18,CA,2,A,A2,2.34,37.0 -2016-06-19,CA,2,A,A2,2.34,38.0 -2016-06-20,CA,2,A,A2,2.39,36.0 -2016-06-21,CA,2,A,A2,2.28,37.0 -2016-06-22,CA,2,A,A2,2.33,40.0 -2016-06-23,CA,2,A,A2,2.36,34.0 -2016-06-24,CA,2,A,A2,2.34,36.0 -2016-06-25,CA,2,A,A2,2.33,36.0 -2016-06-26,CA,2,A,A2,2.35,38.0 -2016-06-27,CA,2,A,A2,2.32,33.0 -2016-06-28,CA,2,A,A2,2.35,35.0 -2016-06-29,CA,2,A,A2,2.34,38.0 -2016-06-30,CA,2,A,A2,2.32,38.0 -2016-07-01,CA,2,A,A2,2.38,38.0 -2016-07-02,CA,2,A,A2,2.32,39.0 -2016-07-03,CA,2,A,A2,2.39,37.0 -2016-07-04,CA,2,A,A2,2.36,39.0 -2016-07-05,CA,2,A,A2,2.33,37.0 -2016-07-06,CA,2,A,A2,2.3,36.0 -2016-07-07,CA,2,A,A2,2.31,35.0 -2016-07-08,CA,2,A,A2,2.31,35.0 -2016-07-09,CA,2,A,A2,2.27,42.0 -2016-07-10,CA,2,A,A2,2.28,39.0 -2016-07-11,CA,2,A,A2,2.35,34.0 -2016-07-12,CA,2,A,A2,2.36,36.0 -2016-07-13,CA,2,A,A2,2.33,34.0 -2016-07-14,CA,2,A,A2,2.3,38.0 -2016-07-15,CA,2,A,A2,2.33,35.0 -2016-07-16,CA,2,A,A2,2.38,37.0 -2016-07-17,CA,2,A,A2,2.38,34.0 -2016-07-18,CA,2,A,A2,2.39,33.0 -2016-07-19,CA,2,A,A2,2.34,39.0 -2016-07-20,CA,2,A,A2,2.33,37.0 -2016-07-21,CA,2,A,A2,2.29,36.0 -2016-07-22,CA,2,A,A2,2.37,37.0 -2016-07-23,CA,2,A,A2,2.3,39.0 -2016-07-24,CA,2,A,A2,2.32,37.0 -2016-07-25,CA,2,A,A2,2.39,37.0 -2016-07-26,CA,2,A,A2,2.33,37.0 -2016-07-27,CA,2,A,A2,2.3,37.0 -2015-01-05,CA,2,A,A3,5.96,119.0 -2015-01-06,CA,2,A,A3,6.07,119.0 -2015-01-07,CA,2,A,A3,6.0,119.0 -2015-01-08,CA,2,A,A3,5.9,125.0 -2015-01-09,CA,2,A,A3,5.83,140.0 -2015-01-10,CA,2,A,A3,5.92,129.0 -2015-01-11,CA,2,A,A3,6.03,124.0 -2015-01-12,CA,2,A,A3,6.0,126.0 -2015-01-13,CA,2,A,A3,5.91,134.0 -2015-01-14,CA,2,A,A3,5.96,124.0 -2015-01-15,CA,2,A,A3,6.06,120.0 -2015-01-16,CA,2,A,A3,5.97,130.0 -2015-01-17,CA,2,A,A3,5.96,134.0 -2015-01-18,CA,2,A,A3,5.99,139.0 -2015-01-19,CA,2,A,A3,5.96,137.0 -2015-01-20,CA,2,A,A3,5.99,132.0 -2015-01-21,CA,2,A,A3,6.0,130.0 -2015-01-22,CA,2,A,A3,5.97,129.0 -2015-01-23,CA,2,A,A3,5.97,135.0 -2015-01-24,CA,2,A,A3,6.02,135.0 -2015-01-25,CA,2,A,A3,6.01,143.0 -2015-01-26,CA,2,A,A3,5.97,118.0 -2015-01-27,CA,2,A,A3,5.89,133.0 -2015-01-28,CA,2,A,A3,5.93,125.0 -2015-01-29,CA,2,A,A3,5.96,135.0 -2015-01-30,CA,2,A,A3,6.17,131.0 -2015-01-31,CA,2,A,A3,6.01,136.0 -2015-02-01,CA,2,A,A3,5.93,133.0 -2015-02-02,CA,2,A,A3,5.99,129.0 -2015-02-03,CA,2,A,A3,5.97,119.0 -2015-02-04,CA,2,A,A3,6.01,131.0 -2015-02-05,CA,2,A,A3,6.01,140.0 -2015-02-06,CA,2,A,A3,6.09,128.0 -2015-02-07,CA,2,A,A3,6.09,134.0 -2015-02-08,CA,2,A,A3,5.99,137.0 -2015-02-09,CA,2,A,A3,5.91,128.0 -2015-02-10,CA,2,A,A3,6.02,128.0 -2015-02-11,CA,2,A,A3,5.86,140.0 -2015-02-12,CA,2,A,A3,5.94,133.0 -2015-02-13,CA,2,A,A3,5.88,139.0 -2015-02-14,CA,2,A,A3,5.94,139.0 -2015-02-15,CA,2,A,A3,6.1,129.0 -2015-02-16,CA,2,A,A3,6.0,129.0 -2015-02-17,CA,2,A,A3,5.9,138.0 -2015-02-18,CA,2,A,A3,5.96,138.0 -2015-02-19,CA,2,A,A3,5.94,144.0 -2015-02-20,CA,2,A,A3,5.89,138.0 -2015-02-21,CA,2,A,A3,5.92,145.0 -2015-02-22,CA,2,A,A3,5.89,133.0 -2015-02-23,CA,2,A,A3,6.0,130.0 -2015-02-24,CA,2,A,A3,5.98,141.0 -2015-02-25,CA,2,A,A3,5.94,139.0 -2015-02-26,CA,2,A,A3,6.05,128.0 -2015-02-27,CA,2,A,A3,6.0,131.0 -2015-02-28,CA,2,A,A3,6.0,137.0 -2015-03-01,CA,2,A,A3,6.01,133.0 -2015-03-02,CA,2,A,A3,5.92,143.0 -2015-03-03,CA,2,A,A3,5.94,133.0 -2015-03-04,CA,2,A,A3,6.0,134.0 -2015-03-05,CA,2,A,A3,5.92,137.0 -2015-03-06,CA,2,A,A3,5.97,132.0 -2015-03-07,CA,2,A,A3,5.91,149.0 -2015-03-08,CA,2,A,A3,5.93,141.0 -2015-03-09,CA,2,A,A3,6.03,131.0 -2015-03-10,CA,2,A,A3,5.95,130.0 -2015-03-11,CA,2,A,A3,5.88,139.0 -2015-03-12,CA,2,A,A3,5.98,128.0 -2015-03-13,CA,2,A,A3,5.89,131.0 -2015-03-14,CA,2,A,A3,5.93,152.0 -2015-03-15,CA,2,A,A3,6.02,128.0 -2015-03-16,CA,2,A,A3,5.87,135.0 -2015-03-17,CA,2,A,A3,6.02,136.0 -2015-03-18,CA,2,A,A3,5.94,143.0 -2015-03-19,CA,2,A,A3,5.97,133.0 -2015-03-20,CA,2,A,A3,5.99,136.0 -2015-03-21,CA,2,A,A3,5.9,150.0 -2015-03-22,CA,2,A,A3,5.99,136.0 -2015-03-23,CA,2,A,A3,5.95,127.0 -2015-03-24,CA,2,A,A3,5.95,144.0 -2015-03-25,CA,2,A,A3,5.9,144.0 -2015-03-26,CA,2,A,A3,5.91,135.0 -2015-03-27,CA,2,A,A3,6.05,137.0 -2015-03-28,CA,2,A,A3,5.9,155.0 -2015-03-29,CA,2,A,A3,5.82,135.0 -2015-03-30,CA,2,A,A3,6.0,122.0 -2015-03-31,CA,2,A,A3,5.96,139.0 -2015-04-01,CA,2,A,A3,5.99,138.0 -2015-04-02,CA,2,A,A3,6.06,127.0 -2015-04-03,CA,2,A,A3,6.01,135.0 -2015-04-04,CA,2,A,A3,6.02,145.0 -2015-04-05,CA,2,A,A3,6.02,135.0 -2015-04-06,CA,2,A,A3,5.97,143.0 -2015-04-07,CA,2,A,A3,5.91,144.0 -2015-04-08,CA,2,A,A3,6.03,136.0 -2015-04-09,CA,2,A,A3,6.03,138.0 -2015-04-10,CA,2,A,A3,5.94,131.0 -2015-04-11,CA,2,A,A3,5.94,146.0 -2015-04-12,CA,2,A,A3,6.02,146.0 -2015-04-13,CA,2,A,A3,5.94,139.0 -2015-04-14,CA,2,A,A3,5.84,133.0 -2015-04-15,CA,2,A,A3,5.91,151.0 -2015-04-16,CA,2,A,A3,5.98,130.0 -2015-04-17,CA,2,A,A3,6.08,131.0 -2015-04-18,CA,2,A,A3,5.95,145.0 -2015-04-19,CA,2,A,A3,6.0,146.0 -2015-04-20,CA,2,A,A3,5.95,128.0 -2015-04-21,CA,2,A,A3,5.98,141.0 -2015-04-22,CA,2,A,A3,5.96,147.0 -2015-04-23,CA,2,A,A3,5.96,133.0 -2015-04-24,CA,2,A,A3,5.87,139.0 -2015-04-25,CA,2,A,A3,5.94,146.0 -2015-04-26,CA,2,A,A3,5.94,143.0 -2015-04-27,CA,2,A,A3,5.88,146.0 -2015-04-28,CA,2,A,A3,6.05,134.0 -2015-04-29,CA,2,A,A3,6.02,132.0 -2015-04-30,CA,2,A,A3,5.96,144.0 -2015-05-01,CA,2,A,A3,5.92,137.0 -2015-05-02,CA,2,A,A3,5.96,142.0 -2015-05-03,CA,2,A,A3,5.87,145.0 -2015-05-04,CA,2,A,A3,5.93,136.0 -2015-05-05,CA,2,A,A3,5.95,138.0 -2015-05-06,CA,2,A,A3,5.98,130.0 -2015-05-07,CA,2,A,A3,6.03,135.0 -2015-05-08,CA,2,A,A3,5.98,134.0 -2015-05-09,CA,2,A,A3,5.97,137.0 -2015-05-10,CA,2,A,A3,5.89,138.0 -2015-05-11,CA,2,A,A3,5.93,146.0 -2015-05-12,CA,2,A,A3,6.04,129.0 -2015-05-13,CA,2,A,A3,5.89,137.0 -2015-05-14,CA,2,A,A3,6.04,138.0 -2015-05-15,CA,2,A,A3,5.88,135.0 -2015-05-16,CA,2,A,A3,6.01,133.0 -2015-05-17,CA,2,A,A3,5.95,140.0 -2015-05-18,CA,2,A,A3,5.94,128.0 -2015-05-19,CA,2,A,A3,5.94,142.0 -2015-05-20,CA,2,A,A3,5.93,144.0 -2015-05-21,CA,2,A,A3,5.96,143.0 -2015-05-22,CA,2,A,A3,5.9,140.0 -2015-05-23,CA,2,A,A3,5.97,146.0 -2015-05-24,CA,2,A,A3,5.89,149.0 -2015-05-25,CA,2,A,A3,6.04,128.0 -2015-05-26,CA,2,A,A3,6.0,129.0 -2015-05-27,CA,2,A,A3,5.9,140.0 -2015-05-28,CA,2,A,A3,5.95,145.0 -2015-05-29,CA,2,A,A3,5.94,148.0 -2015-05-30,CA,2,A,A3,5.96,142.0 -2015-05-31,CA,2,A,A3,5.97,130.0 -2015-06-01,CA,2,A,A3,6.05,132.0 -2015-06-02,CA,2,A,A3,6.01,130.0 -2015-06-03,CA,2,A,A3,6.02,136.0 -2015-06-04,CA,2,A,A3,5.97,136.0 -2015-06-05,CA,2,A,A3,5.96,135.0 -2015-06-06,CA,2,A,A3,5.99,142.0 -2015-06-07,CA,2,A,A3,5.85,138.0 -2015-06-08,CA,2,A,A3,5.81,142.0 -2015-06-09,CA,2,A,A3,6.02,143.0 -2015-06-10,CA,2,A,A3,5.95,143.0 -2015-06-11,CA,2,A,A3,5.97,135.0 -2015-06-12,CA,2,A,A3,5.95,149.0 -2015-06-13,CA,2,A,A3,6.14,132.0 -2015-06-14,CA,2,A,A3,5.94,146.0 -2015-06-15,CA,2,A,A3,5.94,146.0 -2015-06-16,CA,2,A,A3,6.01,141.0 -2015-06-17,CA,2,A,A3,5.89,140.0 -2015-06-18,CA,2,A,A3,6.02,141.0 -2015-06-19,CA,2,A,A3,6.03,142.0 -2015-06-20,CA,2,A,A3,6.01,131.0 -2015-06-21,CA,2,A,A3,5.86,149.0 -2015-06-22,CA,2,A,A3,5.96,132.0 -2015-06-23,CA,2,A,A3,6.0,144.0 -2015-06-24,CA,2,A,A3,6.0,131.0 -2015-06-25,CA,2,A,A3,5.93,141.0 -2015-06-26,CA,2,A,A3,6.0,135.0 -2015-06-27,CA,2,A,A3,5.88,142.0 -2015-06-28,CA,2,A,A3,5.96,137.0 -2015-06-29,CA,2,A,A3,5.91,138.0 -2015-06-30,CA,2,A,A3,6.04,131.0 -2015-07-01,CA,2,A,A3,5.95,137.0 -2015-07-02,CA,2,A,A3,5.97,131.0 -2015-07-03,CA,2,A,A3,5.89,139.0 -2015-07-04,CA,2,A,A3,5.87,136.0 -2015-07-05,CA,2,A,A3,5.98,134.0 -2015-07-06,CA,2,A,A3,5.95,137.0 -2015-07-07,CA,2,A,A3,5.95,131.0 -2015-07-08,CA,2,A,A3,5.99,136.0 -2015-07-09,CA,2,A,A3,5.97,133.0 -2015-07-10,CA,2,A,A3,5.94,153.0 -2015-07-11,CA,2,A,A3,5.96,150.0 -2015-07-12,CA,2,A,A3,6.08,135.0 -2015-07-13,CA,2,A,A3,5.9,141.0 -2015-07-14,CA,2,A,A3,5.93,123.0 -2015-07-15,CA,2,A,A3,5.97,139.0 -2015-07-16,CA,2,A,A3,5.99,133.0 -2015-07-17,CA,2,A,A3,5.96,133.0 -2015-07-18,CA,2,A,A3,6.07,140.0 -2015-07-19,CA,2,A,A3,6.01,137.0 -2015-07-20,CA,2,A,A3,5.8,150.0 -2015-07-21,CA,2,A,A3,5.86,137.0 -2015-07-22,CA,2,A,A3,5.78,146.0 -2015-07-23,CA,2,A,A3,6.01,136.0 -2015-07-24,CA,2,A,A3,5.99,139.0 -2015-07-25,CA,2,A,A3,5.94,145.0 -2015-07-26,CA,2,A,A3,6.04,140.0 -2015-07-27,CA,2,A,A3,6.03,131.0 -2015-07-28,CA,2,A,A3,6.0,131.0 -2015-07-29,CA,2,A,A3,5.9,145.0 -2015-07-30,CA,2,A,A3,5.94,142.0 -2015-07-31,CA,2,A,A3,5.96,145.0 -2015-08-01,CA,2,A,A3,5.99,150.0 -2015-08-02,CA,2,A,A3,5.98,143.0 -2015-08-03,CA,2,A,A3,5.96,122.0 -2015-08-04,CA,2,A,A3,5.98,128.0 -2015-08-05,CA,2,A,A3,5.99,127.0 -2015-08-06,CA,2,A,A3,6.03,137.0 -2015-08-07,CA,2,A,A3,5.94,136.0 -2015-08-08,CA,2,A,A3,5.94,138.0 -2015-08-09,CA,2,A,A3,5.95,140.0 -2015-08-10,CA,2,A,A3,5.99,126.0 -2015-08-11,CA,2,A,A3,5.98,128.0 -2015-08-12,CA,2,A,A3,6.0,137.0 -2015-08-13,CA,2,A,A3,5.91,139.0 -2015-08-14,CA,2,A,A3,5.96,147.0 -2015-08-15,CA,2,A,A3,6.01,143.0 -2015-08-16,CA,2,A,A3,6.02,132.0 -2015-08-17,CA,2,A,A3,5.95,136.0 -2015-08-18,CA,2,A,A3,5.92,145.0 -2015-08-19,CA,2,A,A3,5.9,139.0 -2015-08-20,CA,2,A,A3,6.07,126.0 -2015-08-21,CA,2,A,A3,5.83,148.0 -2015-08-22,CA,2,A,A3,5.98,146.0 -2015-08-23,CA,2,A,A3,6.03,139.0 -2015-08-24,CA,2,A,A3,5.91,137.0 -2015-08-25,CA,2,A,A3,6.06,135.0 -2015-08-26,CA,2,A,A3,5.92,143.0 -2015-08-27,CA,2,A,A3,5.86,136.0 -2015-08-28,CA,2,A,A3,5.91,130.0 -2015-08-29,CA,2,A,A3,5.98,135.0 -2015-08-30,CA,2,A,A3,5.98,145.0 -2015-08-31,CA,2,A,A3,5.93,144.0 -2015-09-01,CA,2,A,A3,6.0,129.0 -2015-09-02,CA,2,A,A3,6.07,135.0 -2015-09-03,CA,2,A,A3,5.94,144.0 -2015-09-04,CA,2,A,A3,5.99,141.0 -2015-09-05,CA,2,A,A3,6.02,146.0 -2015-09-06,CA,2,A,A3,6.08,139.0 -2015-09-07,CA,2,A,A3,5.9,143.0 -2015-09-08,CA,2,A,A3,5.98,140.0 -2015-09-09,CA,2,A,A3,5.94,135.0 -2015-09-10,CA,2,A,A3,6.03,116.0 -2015-09-11,CA,2,A,A3,5.81,139.0 -2015-09-12,CA,2,A,A3,5.96,150.0 -2015-09-13,CA,2,A,A3,5.99,144.0 -2015-09-14,CA,2,A,A3,5.96,126.0 -2015-09-15,CA,2,A,A3,6.03,136.0 -2015-09-16,CA,2,A,A3,5.93,136.0 -2015-09-17,CA,2,A,A3,5.9,140.0 -2015-09-18,CA,2,A,A3,6.03,126.0 -2015-09-19,CA,2,A,A3,6.0,147.0 -2015-09-20,CA,2,A,A3,5.88,144.0 -2015-09-21,CA,2,A,A3,6.04,134.0 -2015-09-22,CA,2,A,A3,5.88,138.0 -2015-09-23,CA,2,A,A3,5.92,139.0 -2015-09-24,CA,2,A,A3,6.01,137.0 -2015-09-25,CA,2,A,A3,5.84,148.0 -2015-09-26,CA,2,A,A3,5.99,141.0 -2015-09-27,CA,2,A,A3,5.87,135.0 -2015-09-28,CA,2,A,A3,5.97,130.0 -2015-09-29,CA,2,A,A3,5.95,147.0 -2015-09-30,CA,2,A,A3,5.98,143.0 -2015-10-01,CA,2,A,A3,5.89,150.0 -2015-10-02,CA,2,A,A3,5.93,138.0 -2015-10-03,CA,2,A,A3,5.87,149.0 -2015-10-04,CA,2,A,A3,5.95,138.0 -2015-10-05,CA,2,A,A3,5.83,149.0 -2015-10-06,CA,2,A,A3,6.01,133.0 -2015-10-07,CA,2,A,A3,6.0,131.0 -2015-10-08,CA,2,A,A3,5.94,132.0 -2015-10-09,CA,2,A,A3,5.95,135.0 -2015-10-10,CA,2,A,A3,5.97,142.0 -2015-10-11,CA,2,A,A3,5.88,148.0 -2015-10-12,CA,2,A,A3,5.94,145.0 -2015-10-13,CA,2,A,A3,5.94,135.0 -2015-10-14,CA,2,A,A3,5.9,141.0 -2015-10-15,CA,2,A,A3,5.92,128.0 -2015-10-16,CA,2,A,A3,5.93,143.0 -2015-10-17,CA,2,A,A3,6.04,150.0 -2015-10-18,CA,2,A,A3,5.99,142.0 -2015-10-19,CA,2,A,A3,5.98,147.0 -2015-10-20,CA,2,A,A3,5.94,137.0 -2015-10-21,CA,2,A,A3,5.96,141.0 -2015-10-22,CA,2,A,A3,6.05,126.0 -2015-10-23,CA,2,A,A3,6.0,130.0 -2015-10-24,CA,2,A,A3,5.97,139.0 -2015-10-25,CA,2,A,A3,5.99,143.0 -2015-10-26,CA,2,A,A3,6.0,129.0 -2015-10-27,CA,2,A,A3,5.84,140.0 -2015-10-28,CA,2,A,A3,5.95,130.0 -2015-10-29,CA,2,A,A3,5.84,133.0 -2015-10-30,CA,2,A,A3,5.87,143.0 -2015-10-31,CA,2,A,A3,6.05,149.0 -2015-11-01,CA,2,A,A3,5.94,147.0 -2015-11-02,CA,2,A,A3,5.99,136.0 -2015-11-03,CA,2,A,A3,5.96,143.0 -2015-11-04,CA,2,A,A3,5.98,134.0 -2015-11-05,CA,2,A,A3,5.94,140.0 -2015-11-06,CA,2,A,A3,5.93,145.0 -2015-11-07,CA,2,A,A3,5.92,147.0 -2015-11-08,CA,2,A,A3,5.96,142.0 -2015-11-09,CA,2,A,A3,6.05,132.0 -2015-11-10,CA,2,A,A3,5.9,141.0 -2015-11-11,CA,2,A,A3,5.91,147.0 -2015-11-12,CA,2,A,A3,6.04,134.0 -2015-11-13,CA,2,A,A3,6.0,133.0 -2015-11-14,CA,2,A,A3,6.07,129.0 -2015-11-15,CA,2,A,A3,6.05,134.0 -2015-11-16,CA,2,A,A3,6.05,126.0 -2015-11-17,CA,2,A,A3,5.89,134.0 -2015-11-18,CA,2,A,A3,5.9,143.0 -2015-11-19,CA,2,A,A3,6.08,133.0 -2015-11-20,CA,2,A,A3,5.89,134.0 -2015-11-21,CA,2,A,A3,5.98,143.0 -2015-11-22,CA,2,A,A3,6.01,138.0 -2015-11-23,CA,2,A,A3,5.98,139.0 -2015-11-24,CA,2,A,A3,5.95,133.0 -2015-11-25,CA,2,A,A3,5.98,137.0 -2015-11-26,CA,2,A,A3,5.99,136.0 -2015-11-27,CA,2,A,A3,5.96,139.0 -2015-11-28,CA,2,A,A3,6.01,146.0 -2015-11-29,CA,2,A,A3,5.9,148.0 -2015-11-30,CA,2,A,A3,5.95,122.0 -2015-12-01,CA,2,A,A3,6.04,125.0 -2015-12-02,CA,2,A,A3,6.08,138.0 -2015-12-03,CA,2,A,A3,5.97,144.0 -2015-12-04,CA,2,A,A3,5.93,135.0 -2015-12-05,CA,2,A,A3,5.97,141.0 -2015-12-06,CA,2,A,A3,5.97,138.0 -2015-12-07,CA,2,A,A3,5.94,138.0 -2015-12-08,CA,2,A,A3,5.98,136.0 -2015-12-09,CA,2,A,A3,6.02,132.0 -2015-12-10,CA,2,A,A3,5.98,145.0 -2015-12-11,CA,2,A,A3,5.91,138.0 -2015-12-12,CA,2,A,A3,6.06,137.0 -2015-12-13,CA,2,A,A3,6.0,129.0 -2015-12-14,CA,2,A,A3,5.94,142.0 -2015-12-15,CA,2,A,A3,6.02,136.0 -2015-12-16,CA,2,A,A3,5.94,133.0 -2015-12-17,CA,2,A,A3,5.93,136.0 -2015-12-18,CA,2,A,A3,6.03,142.0 -2015-12-19,CA,2,A,A3,5.93,150.0 -2015-12-20,CA,2,A,A3,5.94,138.0 -2015-12-21,CA,2,A,A3,5.97,143.0 -2015-12-22,CA,2,A,A3,5.98,135.0 -2015-12-23,CA,2,A,A3,5.9,143.0 -2015-12-24,CA,2,A,A3,5.9,137.0 -2015-12-25,CA,2,A,A3,6.02,133.0 -2015-12-26,CA,2,A,A3,5.94,139.0 -2015-12-27,CA,2,A,A3,5.98,141.0 -2015-12-28,CA,2,A,A3,6.0,117.0 -2015-12-29,CA,2,A,A3,5.98,138.0 -2015-12-30,CA,2,A,A3,5.98,127.0 -2015-12-31,CA,2,A,A3,5.94,147.0 -2016-01-01,CA,2,A,A3,5.93,151.0 -2016-01-02,CA,2,A,A3,5.97,142.0 -2016-01-03,CA,2,A,A3,6.02,139.0 -2016-01-04,CA,2,A,A3,6.0,132.0 -2016-01-05,CA,2,A,A3,6.03,129.0 -2016-01-06,CA,2,A,A3,5.98,146.0 -2016-01-07,CA,2,A,A3,5.85,145.0 -2016-01-08,CA,2,A,A3,5.99,134.0 -2016-01-09,CA,2,A,A3,5.87,151.0 -2016-01-10,CA,2,A,A3,6.1,129.0 -2016-01-11,CA,2,A,A3,6.05,132.0 -2016-01-12,CA,2,A,A3,5.98,126.0 -2016-01-13,CA,2,A,A3,5.97,133.0 -2016-01-14,CA,2,A,A3,5.93,147.0 -2016-01-15,CA,2,A,A3,5.93,135.0 -2016-01-16,CA,2,A,A3,5.91,147.0 -2016-01-17,CA,2,A,A3,5.93,145.0 -2016-01-18,CA,2,A,A3,5.98,126.0 -2016-01-19,CA,2,A,A3,6.04,142.0 -2016-01-20,CA,2,A,A3,5.94,145.0 -2016-01-21,CA,2,A,A3,6.01,125.0 -2016-01-22,CA,2,A,A3,5.98,132.0 -2016-01-23,CA,2,A,A3,6.01,139.0 -2016-01-24,CA,2,A,A3,6.01,136.0 -2016-01-25,CA,2,A,A3,5.95,133.0 -2016-01-26,CA,2,A,A3,5.98,136.0 -2016-01-27,CA,2,A,A3,5.97,130.0 -2016-01-28,CA,2,A,A3,5.84,146.0 -2016-01-29,CA,2,A,A3,6.0,135.0 -2016-01-30,CA,2,A,A3,6.0,141.0 -2016-01-31,CA,2,A,A3,5.88,147.0 -2016-02-01,CA,2,A,A3,5.97,127.0 -2016-02-02,CA,2,A,A3,6.05,133.0 -2016-02-03,CA,2,A,A3,5.98,129.0 -2016-02-04,CA,2,A,A3,5.93,136.0 -2016-02-05,CA,2,A,A3,5.96,130.0 -2016-02-06,CA,2,A,A3,5.98,150.0 -2016-02-07,CA,2,A,A3,5.94,146.0 -2016-02-08,CA,2,A,A3,5.99,129.0 -2016-02-09,CA,2,A,A3,6.03,136.0 -2016-02-10,CA,2,A,A3,6.03,143.0 -2016-02-11,CA,2,A,A3,5.9,142.0 -2016-02-12,CA,2,A,A3,6.04,129.0 -2016-02-13,CA,2,A,A3,5.98,144.0 -2016-02-14,CA,2,A,A3,5.89,147.0 -2016-02-15,CA,2,A,A3,5.93,136.0 -2016-02-16,CA,2,A,A3,5.97,139.0 -2016-02-17,CA,2,A,A3,6.03,131.0 -2016-02-18,CA,2,A,A3,5.93,138.0 -2016-02-19,CA,2,A,A3,5.96,133.0 -2016-02-20,CA,2,A,A3,6.08,141.0 -2016-02-21,CA,2,A,A3,5.97,138.0 -2016-02-22,CA,2,A,A3,5.87,137.0 -2016-02-23,CA,2,A,A3,5.94,141.0 -2016-02-24,CA,2,A,A3,5.92,132.0 -2016-02-25,CA,2,A,A3,6.04,124.0 -2016-02-26,CA,2,A,A3,5.99,143.0 -2016-02-27,CA,2,A,A3,5.9,149.0 -2016-02-28,CA,2,A,A3,5.88,159.0 -2016-02-29,CA,2,A,A3,5.93,138.0 -2016-03-01,CA,2,A,A3,6.0,147.0 -2016-03-02,CA,2,A,A3,6.11,133.0 -2016-03-03,CA,2,A,A3,5.88,141.0 -2016-03-04,CA,2,A,A3,5.97,129.0 -2016-03-05,CA,2,A,A3,6.11,139.0 -2016-03-06,CA,2,A,A3,6.02,136.0 -2016-03-07,CA,2,A,A3,5.98,141.0 -2016-03-08,CA,2,A,A3,6.02,131.0 -2016-03-09,CA,2,A,A3,5.95,133.0 -2016-03-10,CA,2,A,A3,5.89,145.0 -2016-03-11,CA,2,A,A3,5.92,134.0 -2016-03-12,CA,2,A,A3,6.02,139.0 -2016-03-13,CA,2,A,A3,5.91,137.0 -2016-03-14,CA,2,A,A3,5.98,132.0 -2016-03-15,CA,2,A,A3,5.9,140.0 -2016-03-16,CA,2,A,A3,5.96,139.0 -2016-03-17,CA,2,A,A3,6.04,143.0 -2016-03-18,CA,2,A,A3,5.98,142.0 -2016-03-19,CA,2,A,A3,6.04,142.0 -2016-03-20,CA,2,A,A3,5.89,146.0 -2016-03-21,CA,2,A,A3,5.96,138.0 -2016-03-22,CA,2,A,A3,5.99,138.0 -2016-03-23,CA,2,A,A3,5.96,138.0 -2016-03-24,CA,2,A,A3,6.05,135.0 -2016-03-25,CA,2,A,A3,6.06,127.0 -2016-03-26,CA,2,A,A3,5.93,143.0 -2016-03-27,CA,2,A,A3,5.88,146.0 -2016-03-28,CA,2,A,A3,5.96,137.0 -2016-03-29,CA,2,A,A3,5.95,145.0 -2016-03-30,CA,2,A,A3,6.1,126.0 -2016-03-31,CA,2,A,A3,5.99,131.0 -2016-04-01,CA,2,A,A3,6.06,127.0 -2016-04-02,CA,2,A,A3,6.03,141.0 -2016-04-03,CA,2,A,A3,5.95,136.0 -2016-04-04,CA,2,A,A3,5.96,124.0 -2016-04-05,CA,2,A,A3,5.94,134.0 -2016-04-06,CA,2,A,A3,6.03,120.0 -2016-04-07,CA,2,A,A3,5.79,142.0 -2016-04-08,CA,2,A,A3,6.01,128.0 -2016-04-09,CA,2,A,A3,5.88,142.0 -2016-04-10,CA,2,A,A3,5.99,138.0 -2016-04-11,CA,2,A,A3,5.98,128.0 -2016-04-12,CA,2,A,A3,5.92,139.0 -2016-04-13,CA,2,A,A3,6.06,137.0 -2016-04-14,CA,2,A,A3,6.05,124.0 -2016-04-15,CA,2,A,A3,5.96,137.0 -2016-04-16,CA,2,A,A3,5.99,146.0 -2016-04-17,CA,2,A,A3,5.99,136.0 -2016-04-18,CA,2,A,A3,5.95,143.0 -2016-04-19,CA,2,A,A3,5.94,144.0 -2016-04-20,CA,2,A,A3,6.04,130.0 -2016-04-21,CA,2,A,A3,5.92,134.0 -2016-04-22,CA,2,A,A3,5.88,143.0 -2016-04-23,CA,2,A,A3,5.99,142.0 -2016-04-24,CA,2,A,A3,5.97,135.0 -2016-04-25,CA,2,A,A3,5.96,134.0 -2016-04-26,CA,2,A,A3,5.98,137.0 -2016-04-27,CA,2,A,A3,6.0,146.0 -2016-04-28,CA,2,A,A3,6.02,135.0 -2016-04-29,CA,2,A,A3,6.02,148.0 -2016-04-30,CA,2,A,A3,5.95,141.0 -2016-05-01,CA,2,A,A3,5.89,146.0 -2016-05-02,CA,2,A,A3,5.96,133.0 -2016-05-03,CA,2,A,A3,6.01,131.0 -2016-05-04,CA,2,A,A3,5.97,150.0 -2016-05-05,CA,2,A,A3,5.94,150.0 -2016-05-06,CA,2,A,A3,5.98,138.0 -2016-05-07,CA,2,A,A3,5.97,143.0 -2016-05-08,CA,2,A,A3,5.94,140.0 -2016-05-09,CA,2,A,A3,5.93,132.0 -2016-05-10,CA,2,A,A3,6.01,133.0 -2016-05-11,CA,2,A,A3,5.93,141.0 -2016-05-12,CA,2,A,A3,5.93,133.0 -2016-05-13,CA,2,A,A3,5.93,148.0 -2016-05-14,CA,2,A,A3,6.03,143.0 -2016-05-15,CA,2,A,A3,5.92,136.0 -2016-05-16,CA,2,A,A3,6.05,129.0 -2016-05-17,CA,2,A,A3,5.86,138.0 -2016-05-18,CA,2,A,A3,5.86,136.0 -2016-05-19,CA,2,A,A3,6.05,135.0 -2016-05-20,CA,2,A,A3,6.01,144.0 -2016-05-21,CA,2,A,A3,6.04,135.0 -2016-05-22,CA,2,A,A3,6.1,144.0 -2016-05-23,CA,2,A,A3,6.02,130.0 -2016-05-24,CA,2,A,A3,6.1,119.0 -2016-05-25,CA,2,A,A3,6.0,140.0 -2016-05-26,CA,2,A,A3,6.04,137.0 -2016-05-27,CA,2,A,A3,5.94,140.0 -2016-05-28,CA,2,A,A3,5.94,138.0 -2016-05-29,CA,2,A,A3,5.92,147.0 -2016-05-30,CA,2,A,A3,5.98,132.0 -2016-05-31,CA,2,A,A3,5.96,136.0 -2016-06-01,CA,2,A,A3,5.89,144.0 -2016-06-02,CA,2,A,A3,6.02,131.0 -2016-06-03,CA,2,A,A3,5.93,136.0 -2016-06-04,CA,2,A,A3,5.96,142.0 -2016-06-05,CA,2,A,A3,5.95,147.0 -2016-06-06,CA,2,A,A3,5.94,139.0 -2016-06-07,CA,2,A,A3,5.94,124.0 -2016-06-08,CA,2,A,A3,6.13,125.0 -2016-06-09,CA,2,A,A3,5.93,133.0 -2016-06-10,CA,2,A,A3,6.01,135.0 -2016-06-11,CA,2,A,A3,5.83,154.0 -2016-06-12,CA,2,A,A3,6.02,131.0 -2016-06-13,CA,2,A,A3,5.94,133.0 -2016-06-14,CA,2,A,A3,6.08,132.0 -2016-06-15,CA,2,A,A3,5.96,130.0 -2016-06-16,CA,2,A,A3,5.95,146.0 -2016-06-17,CA,2,A,A3,5.99,130.0 -2016-06-18,CA,2,A,A3,6.05,153.0 -2016-06-19,CA,2,A,A3,5.91,146.0 -2016-06-20,CA,2,A,A3,5.81,138.0 -2016-06-21,CA,2,A,A3,5.92,137.0 -2016-06-22,CA,2,A,A3,5.9,143.0 -2016-06-23,CA,2,A,A3,5.94,144.0 -2016-06-24,CA,2,A,A3,5.99,129.0 -2016-06-25,CA,2,A,A3,5.96,148.0 -2016-06-26,CA,2,A,A3,5.95,142.0 -2016-06-27,CA,2,A,A3,6.02,133.0 -2016-06-28,CA,2,A,A3,5.98,130.0 -2016-06-29,CA,2,A,A3,5.93,142.0 -2016-06-30,CA,2,A,A3,5.87,144.0 -2016-07-01,CA,2,A,A3,5.9,137.0 -2016-07-02,CA,2,A,A3,5.92,141.0 -2016-07-03,CA,2,A,A3,6.01,130.0 -2016-07-04,CA,2,A,A3,6.06,127.0 -2016-07-05,CA,2,A,A3,5.93,139.0 -2016-07-06,CA,2,A,A3,5.91,143.0 -2016-07-07,CA,2,A,A3,5.98,134.0 -2016-07-08,CA,2,A,A3,5.99,139.0 -2016-07-09,CA,2,A,A3,6.05,153.0 -2016-07-10,CA,2,A,A3,6.04,137.0 -2016-07-11,CA,2,A,A3,6.01,133.0 -2016-07-12,CA,2,A,A3,5.98,132.0 -2016-07-13,CA,2,A,A3,5.98,131.0 -2016-07-14,CA,2,A,A3,6.0,136.0 -2016-07-15,CA,2,A,A3,6.06,131.0 -2016-07-16,CA,2,A,A3,6.05,140.0 -2016-07-17,CA,2,A,A3,5.99,144.0 -2016-07-18,CA,2,A,A3,6.04,134.0 -2016-07-19,CA,2,A,A3,5.91,141.0 -2016-07-20,CA,2,A,A3,6.03,138.0 -2016-07-21,CA,2,A,A3,5.92,146.0 -2016-07-22,CA,2,A,A3,5.98,138.0 -2016-07-23,CA,2,A,A3,5.93,148.0 -2016-07-24,CA,2,A,A3,5.97,138.0 -2016-07-25,CA,2,A,A3,6.03,125.0 -2016-07-26,CA,2,A,A3,5.99,142.0 -2016-07-27,CA,2,A,A3,5.93,135.0 -2015-01-05,CA,2,B,B1,7.72,98.0 -2015-01-06,CA,2,B,B1,7.65,97.0 -2015-01-07,CA,2,B,B1,7.72,103.0 -2015-01-08,CA,2,B,B1,7.69,106.0 -2015-01-09,CA,2,B,B1,7.74,109.0 -2015-01-10,CA,2,B,B1,7.69,111.0 -2015-01-11,CA,2,B,B1,7.74,102.0 -2015-01-12,CA,2,B,B1,7.57,110.0 -2015-01-13,CA,2,B,B1,7.83,101.0 -2015-01-14,CA,2,B,B1,7.68,104.0 -2015-01-15,CA,2,B,B1,7.72,105.0 -2015-01-16,CA,2,B,B1,7.78,109.0 -2015-01-17,CA,2,B,B1,7.78,111.0 -2015-01-18,CA,2,B,B1,7.77,99.0 -2015-01-19,CA,2,B,B1,7.52,103.0 -2015-01-20,CA,2,B,B1,7.65,109.0 -2015-01-21,CA,2,B,B1,7.75,100.0 -2015-01-22,CA,2,B,B1,7.62,105.0 -2015-01-23,CA,2,B,B1,7.67,103.0 -2015-01-24,CA,2,B,B1,7.63,115.0 -2015-01-25,CA,2,B,B1,7.72,111.0 -2015-01-26,CA,2,B,B1,7.74,108.0 -2015-01-27,CA,2,B,B1,7.72,105.0 -2015-01-28,CA,2,B,B1,7.91,100.0 -2015-01-29,CA,2,B,B1,7.69,105.0 -2015-01-30,CA,2,B,B1,7.59,111.0 -2015-01-31,CA,2,B,B1,7.74,116.0 -2015-02-01,CA,2,B,B1,7.78,110.0 -2015-02-02,CA,2,B,B1,7.49,111.0 -2015-02-03,CA,2,B,B1,7.68,106.0 -2015-02-04,CA,2,B,B1,7.8,102.0 -2015-02-05,CA,2,B,B1,7.77,108.0 -2015-02-06,CA,2,B,B1,7.59,116.0 -2015-02-07,CA,2,B,B1,7.71,106.0 -2015-02-08,CA,2,B,B1,7.68,112.0 -2015-02-09,CA,2,B,B1,7.5,115.0 -2015-02-10,CA,2,B,B1,7.67,103.0 -2015-02-11,CA,2,B,B1,7.67,105.0 -2015-02-12,CA,2,B,B1,7.76,106.0 -2015-02-13,CA,2,B,B1,7.71,106.0 -2015-02-14,CA,2,B,B1,7.69,108.0 -2015-02-15,CA,2,B,B1,7.63,112.0 -2015-02-16,CA,2,B,B1,7.8,103.0 -2015-02-17,CA,2,B,B1,7.78,106.0 -2015-02-18,CA,2,B,B1,7.65,111.0 -2015-02-19,CA,2,B,B1,7.77,110.0 -2015-02-20,CA,2,B,B1,7.73,108.0 -2015-02-21,CA,2,B,B1,7.59,117.0 -2015-02-22,CA,2,B,B1,7.7,108.0 -2015-02-23,CA,2,B,B1,7.85,101.0 -2015-02-24,CA,2,B,B1,7.81,106.0 -2015-02-25,CA,2,B,B1,7.7,106.0 -2015-02-26,CA,2,B,B1,7.82,112.0 -2015-02-27,CA,2,B,B1,7.82,111.0 -2015-02-28,CA,2,B,B1,7.7,110.0 -2015-03-01,CA,2,B,B1,7.85,111.0 -2015-03-02,CA,2,B,B1,7.72,107.0 -2015-03-03,CA,2,B,B1,7.69,118.0 -2015-03-04,CA,2,B,B1,7.84,104.0 -2015-03-05,CA,2,B,B1,7.74,116.0 -2015-03-06,CA,2,B,B1,7.8,114.0 -2015-03-07,CA,2,B,B1,7.61,109.0 -2015-03-08,CA,2,B,B1,7.7,108.0 -2015-03-09,CA,2,B,B1,7.68,97.0 -2015-03-10,CA,2,B,B1,7.77,109.0 -2015-03-11,CA,2,B,B1,7.78,103.0 -2015-03-12,CA,2,B,B1,7.83,102.0 -2015-03-13,CA,2,B,B1,7.63,114.0 -2015-03-14,CA,2,B,B1,7.74,105.0 -2015-03-15,CA,2,B,B1,7.75,115.0 -2015-03-16,CA,2,B,B1,7.86,116.0 -2015-03-17,CA,2,B,B1,7.78,114.0 -2015-03-18,CA,2,B,B1,7.69,112.0 -2015-03-19,CA,2,B,B1,7.69,109.0 -2015-03-20,CA,2,B,B1,7.7,112.0 -2015-03-21,CA,2,B,B1,7.83,109.0 -2015-03-22,CA,2,B,B1,7.75,107.0 -2015-03-23,CA,2,B,B1,7.56,118.0 -2015-03-24,CA,2,B,B1,7.73,108.0 -2015-03-25,CA,2,B,B1,7.66,120.0 -2015-03-26,CA,2,B,B1,7.69,115.0 -2015-03-27,CA,2,B,B1,7.79,105.0 -2015-03-28,CA,2,B,B1,7.8,112.0 -2015-03-29,CA,2,B,B1,7.8,109.0 -2015-03-30,CA,2,B,B1,7.72,108.0 -2015-03-31,CA,2,B,B1,7.75,111.0 -2015-04-01,CA,2,B,B1,7.64,114.0 -2015-04-02,CA,2,B,B1,7.69,119.0 -2015-04-03,CA,2,B,B1,7.84,112.0 -2015-04-04,CA,2,B,B1,7.74,121.0 -2015-04-05,CA,2,B,B1,7.69,114.0 -2015-04-06,CA,2,B,B1,7.69,103.0 -2015-04-07,CA,2,B,B1,7.69,114.0 -2015-04-08,CA,2,B,B1,7.75,109.0 -2015-04-09,CA,2,B,B1,7.66,121.0 -2015-04-10,CA,2,B,B1,7.66,116.0 -2015-04-11,CA,2,B,B1,7.67,117.0 -2015-04-12,CA,2,B,B1,7.61,117.0 -2015-04-13,CA,2,B,B1,7.74,111.0 -2015-04-14,CA,2,B,B1,7.72,122.0 -2015-04-15,CA,2,B,B1,7.75,118.0 -2015-04-16,CA,2,B,B1,7.49,126.0 -2015-04-17,CA,2,B,B1,7.81,108.0 -2015-04-18,CA,2,B,B1,7.69,121.0 -2015-04-19,CA,2,B,B1,7.68,127.0 -2015-04-20,CA,2,B,B1,7.69,112.0 -2015-04-21,CA,2,B,B1,7.73,108.0 -2015-04-22,CA,2,B,B1,7.75,121.0 -2015-04-23,CA,2,B,B1,7.72,122.0 -2015-04-24,CA,2,B,B1,7.74,112.0 -2015-04-25,CA,2,B,B1,7.61,126.0 -2015-04-26,CA,2,B,B1,7.59,120.0 -2015-04-27,CA,2,B,B1,7.76,108.0 -2015-04-28,CA,2,B,B1,7.57,118.0 -2015-04-29,CA,2,B,B1,7.58,119.0 -2015-04-30,CA,2,B,B1,7.62,118.0 -2015-05-01,CA,2,B,B1,7.67,119.0 -2015-05-02,CA,2,B,B1,7.64,122.0 -2015-05-03,CA,2,B,B1,7.73,111.0 -2015-05-04,CA,2,B,B1,7.85,113.0 -2015-05-05,CA,2,B,B1,7.48,124.0 -2015-05-06,CA,2,B,B1,7.8,116.0 -2015-05-07,CA,2,B,B1,7.7,119.0 -2015-05-08,CA,2,B,B1,7.63,120.0 -2015-05-09,CA,2,B,B1,7.74,117.0 -2015-05-10,CA,2,B,B1,7.78,117.0 -2015-05-11,CA,2,B,B1,7.8,116.0 -2015-05-12,CA,2,B,B1,7.74,115.0 -2015-05-13,CA,2,B,B1,7.74,116.0 -2015-05-14,CA,2,B,B1,7.76,114.0 -2015-05-15,CA,2,B,B1,7.72,112.0 -2015-05-16,CA,2,B,B1,7.68,117.0 -2015-05-17,CA,2,B,B1,7.71,118.0 -2015-05-18,CA,2,B,B1,7.72,109.0 -2015-05-19,CA,2,B,B1,7.77,110.0 -2015-05-20,CA,2,B,B1,7.61,116.0 -2015-05-21,CA,2,B,B1,7.66,114.0 -2015-05-22,CA,2,B,B1,7.74,121.0 -2015-05-23,CA,2,B,B1,7.73,116.0 -2015-05-24,CA,2,B,B1,7.67,122.0 -2015-05-25,CA,2,B,B1,7.62,119.0 -2015-05-26,CA,2,B,B1,7.58,121.0 -2015-05-27,CA,2,B,B1,7.72,124.0 -2015-05-28,CA,2,B,B1,7.69,118.0 -2015-05-29,CA,2,B,B1,7.54,133.0 -2015-05-30,CA,2,B,B1,7.69,120.0 -2015-05-31,CA,2,B,B1,7.66,125.0 -2015-06-01,CA,2,B,B1,7.68,112.0 -2015-06-02,CA,2,B,B1,7.75,120.0 -2015-06-03,CA,2,B,B1,7.72,118.0 -2015-06-04,CA,2,B,B1,7.76,115.0 -2015-06-05,CA,2,B,B1,7.64,123.0 -2015-06-06,CA,2,B,B1,7.78,125.0 -2015-06-07,CA,2,B,B1,7.48,131.0 -2015-06-08,CA,2,B,B1,7.57,119.0 -2015-06-09,CA,2,B,B1,7.68,121.0 -2015-06-10,CA,2,B,B1,7.72,115.0 -2015-06-11,CA,2,B,B1,7.62,124.0 -2015-06-12,CA,2,B,B1,7.72,120.0 -2015-06-13,CA,2,B,B1,7.84,123.0 -2015-06-14,CA,2,B,B1,7.59,116.0 -2015-06-15,CA,2,B,B1,7.77,120.0 -2015-06-16,CA,2,B,B1,7.75,113.0 -2015-06-17,CA,2,B,B1,7.71,118.0 -2015-06-18,CA,2,B,B1,7.76,120.0 -2015-06-19,CA,2,B,B1,7.67,114.0 -2015-06-20,CA,2,B,B1,7.66,126.0 -2015-06-21,CA,2,B,B1,7.79,121.0 -2015-06-22,CA,2,B,B1,7.81,120.0 -2015-06-23,CA,2,B,B1,7.66,123.0 -2015-06-24,CA,2,B,B1,7.71,113.0 -2015-06-25,CA,2,B,B1,7.68,118.0 -2015-06-26,CA,2,B,B1,7.65,123.0 -2015-06-27,CA,2,B,B1,7.75,118.0 -2015-06-28,CA,2,B,B1,7.7,114.0 -2015-06-29,CA,2,B,B1,7.84,115.0 -2015-06-30,CA,2,B,B1,7.78,117.0 -2015-07-01,CA,2,B,B1,7.74,115.0 -2015-07-02,CA,2,B,B1,7.75,120.0 -2015-07-03,CA,2,B,B1,7.71,121.0 -2015-07-04,CA,2,B,B1,7.68,116.0 -2015-07-05,CA,2,B,B1,7.76,119.0 -2015-07-06,CA,2,B,B1,7.62,121.0 -2015-07-07,CA,2,B,B1,7.65,124.0 -2015-07-08,CA,2,B,B1,7.6,124.0 -2015-07-09,CA,2,B,B1,7.44,129.0 -2015-07-10,CA,2,B,B1,7.69,122.0 -2015-07-11,CA,2,B,B1,7.66,119.0 -2015-07-12,CA,2,B,B1,7.57,127.0 -2015-07-13,CA,2,B,B1,7.66,120.0 -2015-07-14,CA,2,B,B1,7.69,117.0 -2015-07-15,CA,2,B,B1,7.87,113.0 -2015-07-16,CA,2,B,B1,7.62,113.0 -2015-07-17,CA,2,B,B1,7.77,113.0 -2015-07-18,CA,2,B,B1,7.74,119.0 -2015-07-19,CA,2,B,B1,7.82,121.0 -2015-07-20,CA,2,B,B1,7.85,118.0 -2015-07-21,CA,2,B,B1,7.84,116.0 -2015-07-22,CA,2,B,B1,7.74,119.0 -2015-07-23,CA,2,B,B1,7.65,113.0 -2015-07-24,CA,2,B,B1,7.87,117.0 -2015-07-25,CA,2,B,B1,7.8,124.0 -2015-07-26,CA,2,B,B1,7.71,125.0 -2015-07-27,CA,2,B,B1,7.69,113.0 -2015-07-28,CA,2,B,B1,7.46,125.0 -2015-07-29,CA,2,B,B1,7.73,122.0 -2015-07-30,CA,2,B,B1,7.66,129.0 -2015-07-31,CA,2,B,B1,7.71,128.0 -2015-08-01,CA,2,B,B1,7.64,127.0 -2015-08-02,CA,2,B,B1,7.72,121.0 -2015-08-03,CA,2,B,B1,7.61,115.0 -2015-08-04,CA,2,B,B1,7.85,112.0 -2015-08-05,CA,2,B,B1,7.78,117.0 -2015-08-06,CA,2,B,B1,7.76,119.0 -2015-08-07,CA,2,B,B1,7.8,117.0 -2015-08-08,CA,2,B,B1,7.67,132.0 -2015-08-09,CA,2,B,B1,7.69,120.0 -2015-08-10,CA,2,B,B1,7.55,124.0 -2015-08-11,CA,2,B,B1,7.54,126.0 -2015-08-12,CA,2,B,B1,7.7,114.0 -2015-08-13,CA,2,B,B1,7.68,122.0 -2015-08-14,CA,2,B,B1,7.78,126.0 -2015-08-15,CA,2,B,B1,7.79,122.0 -2015-08-16,CA,2,B,B1,7.75,127.0 -2015-08-17,CA,2,B,B1,7.63,114.0 -2015-08-18,CA,2,B,B1,7.67,128.0 -2015-08-19,CA,2,B,B1,7.68,123.0 -2015-08-20,CA,2,B,B1,7.76,115.0 -2015-08-21,CA,2,B,B1,7.57,128.0 -2015-08-22,CA,2,B,B1,7.76,116.0 -2015-08-23,CA,2,B,B1,7.73,120.0 -2015-08-24,CA,2,B,B1,7.58,118.0 -2015-08-25,CA,2,B,B1,7.6,128.0 -2015-08-26,CA,2,B,B1,7.74,121.0 -2015-08-27,CA,2,B,B1,7.79,116.0 -2015-08-28,CA,2,B,B1,7.75,120.0 -2015-08-29,CA,2,B,B1,7.6,131.0 -2015-08-30,CA,2,B,B1,7.69,115.0 -2015-08-31,CA,2,B,B1,7.83,111.0 -2015-09-01,CA,2,B,B1,7.6,120.0 -2015-09-02,CA,2,B,B1,7.75,127.0 -2015-09-03,CA,2,B,B1,7.8,115.0 -2015-09-04,CA,2,B,B1,7.7,123.0 -2015-09-05,CA,2,B,B1,7.67,132.0 -2015-09-06,CA,2,B,B1,7.65,121.0 -2015-09-07,CA,2,B,B1,7.75,117.0 -2015-09-08,CA,2,B,B1,7.72,120.0 -2015-09-09,CA,2,B,B1,7.7,119.0 -2015-09-10,CA,2,B,B1,7.71,126.0 -2015-09-11,CA,2,B,B1,7.68,126.0 -2015-09-12,CA,2,B,B1,7.92,117.0 -2015-09-13,CA,2,B,B1,7.73,121.0 -2015-09-14,CA,2,B,B1,7.7,121.0 -2015-09-15,CA,2,B,B1,7.62,126.0 -2015-09-16,CA,2,B,B1,7.65,127.0 -2015-09-17,CA,2,B,B1,7.68,119.0 -2015-09-18,CA,2,B,B1,7.71,113.0 -2015-09-19,CA,2,B,B1,7.67,136.0 -2015-09-20,CA,2,B,B1,7.89,116.0 -2015-09-21,CA,2,B,B1,7.77,116.0 -2015-09-22,CA,2,B,B1,7.76,121.0 -2015-09-23,CA,2,B,B1,7.65,120.0 -2015-09-24,CA,2,B,B1,7.66,118.0 -2015-09-25,CA,2,B,B1,7.71,123.0 -2015-09-26,CA,2,B,B1,7.67,122.0 -2015-09-27,CA,2,B,B1,7.84,117.0 -2015-09-28,CA,2,B,B1,7.73,127.0 -2015-09-29,CA,2,B,B1,7.64,126.0 -2015-09-30,CA,2,B,B1,7.76,117.0 -2015-10-01,CA,2,B,B1,7.81,113.0 -2015-10-02,CA,2,B,B1,7.58,126.0 -2015-10-03,CA,2,B,B1,7.68,130.0 -2015-10-04,CA,2,B,B1,7.79,120.0 -2015-10-05,CA,2,B,B1,7.84,111.0 -2015-10-06,CA,2,B,B1,7.78,114.0 -2015-10-07,CA,2,B,B1,7.82,122.0 -2015-10-08,CA,2,B,B1,7.59,126.0 -2015-10-09,CA,2,B,B1,7.72,120.0 -2015-10-10,CA,2,B,B1,7.71,129.0 -2015-10-11,CA,2,B,B1,7.74,125.0 -2015-10-12,CA,2,B,B1,7.78,111.0 -2015-10-13,CA,2,B,B1,7.75,119.0 -2015-10-14,CA,2,B,B1,7.66,128.0 -2015-10-15,CA,2,B,B1,7.56,125.0 -2015-10-16,CA,2,B,B1,7.76,118.0 -2015-10-17,CA,2,B,B1,7.75,123.0 -2015-10-18,CA,2,B,B1,7.61,131.0 -2015-10-19,CA,2,B,B1,7.66,125.0 -2015-10-20,CA,2,B,B1,7.71,114.0 -2015-10-21,CA,2,B,B1,7.63,121.0 -2015-10-22,CA,2,B,B1,7.63,124.0 -2015-10-23,CA,2,B,B1,7.8,117.0 -2015-10-24,CA,2,B,B1,7.77,126.0 -2015-10-25,CA,2,B,B1,7.58,131.0 -2015-10-26,CA,2,B,B1,7.61,127.0 -2015-10-27,CA,2,B,B1,7.72,115.0 -2015-10-28,CA,2,B,B1,7.68,128.0 -2015-10-29,CA,2,B,B1,7.76,121.0 -2015-10-30,CA,2,B,B1,7.67,117.0 -2015-10-31,CA,2,B,B1,7.78,122.0 -2015-11-01,CA,2,B,B1,7.77,111.0 -2015-11-02,CA,2,B,B1,7.8,122.0 -2015-11-03,CA,2,B,B1,7.65,127.0 -2015-11-04,CA,2,B,B1,7.63,125.0 -2015-11-05,CA,2,B,B1,7.67,117.0 -2015-11-06,CA,2,B,B1,7.62,128.0 -2015-11-07,CA,2,B,B1,7.61,138.0 -2015-11-08,CA,2,B,B1,7.81,119.0 -2015-11-09,CA,2,B,B1,7.72,116.0 -2015-11-10,CA,2,B,B1,7.77,118.0 -2015-11-11,CA,2,B,B1,7.76,123.0 -2015-11-12,CA,2,B,B1,7.72,119.0 -2015-11-13,CA,2,B,B1,7.75,122.0 -2015-11-14,CA,2,B,B1,7.76,127.0 -2015-11-15,CA,2,B,B1,7.63,120.0 -2015-11-16,CA,2,B,B1,7.66,119.0 -2015-11-17,CA,2,B,B1,7.84,123.0 -2015-11-18,CA,2,B,B1,7.77,121.0 -2015-11-19,CA,2,B,B1,7.7,121.0 -2015-11-20,CA,2,B,B1,7.62,132.0 -2015-11-21,CA,2,B,B1,7.69,124.0 -2015-11-22,CA,2,B,B1,7.8,121.0 -2015-11-23,CA,2,B,B1,7.62,124.0 -2015-11-24,CA,2,B,B1,7.68,126.0 -2015-11-25,CA,2,B,B1,7.76,123.0 -2015-11-26,CA,2,B,B1,7.66,123.0 -2015-11-27,CA,2,B,B1,7.68,125.0 -2015-11-28,CA,2,B,B1,7.76,128.0 -2015-11-29,CA,2,B,B1,7.68,125.0 -2015-11-30,CA,2,B,B1,7.65,117.0 -2015-12-01,CA,2,B,B1,7.6,124.0 -2015-12-02,CA,2,B,B1,7.7,117.0 -2015-12-03,CA,2,B,B1,7.69,124.0 -2015-12-04,CA,2,B,B1,7.7,123.0 -2015-12-05,CA,2,B,B1,7.72,124.0 -2015-12-06,CA,2,B,B1,7.66,124.0 -2015-12-07,CA,2,B,B1,7.75,117.0 -2015-12-08,CA,2,B,B1,7.75,115.0 -2015-12-09,CA,2,B,B1,7.58,123.0 -2015-12-10,CA,2,B,B1,7.72,123.0 -2015-12-11,CA,2,B,B1,7.71,114.0 -2015-12-12,CA,2,B,B1,7.62,123.0 -2015-12-13,CA,2,B,B1,7.61,118.0 -2015-12-14,CA,2,B,B1,7.7,121.0 -2015-12-15,CA,2,B,B1,7.71,121.0 -2015-12-16,CA,2,B,B1,7.74,123.0 -2015-12-17,CA,2,B,B1,7.7,120.0 -2015-12-18,CA,2,B,B1,7.55,124.0 -2015-12-19,CA,2,B,B1,7.75,123.0 -2015-12-20,CA,2,B,B1,7.74,122.0 -2015-12-21,CA,2,B,B1,7.76,111.0 -2015-12-22,CA,2,B,B1,7.65,127.0 -2015-12-23,CA,2,B,B1,7.64,126.0 -2015-12-24,CA,2,B,B1,7.75,124.0 -2015-12-25,CA,2,B,B1,7.62,120.0 -2015-12-26,CA,2,B,B1,7.54,128.0 -2015-12-27,CA,2,B,B1,7.58,126.0 -2015-12-28,CA,2,B,B1,7.68,122.0 -2015-12-29,CA,2,B,B1,7.58,124.0 -2015-12-30,CA,2,B,B1,7.67,128.0 -2015-12-31,CA,2,B,B1,7.74,119.0 -2016-01-01,CA,2,B,B1,7.68,122.0 -2016-01-02,CA,2,B,B1,7.75,131.0 -2016-01-03,CA,2,B,B1,7.7,120.0 -2016-01-04,CA,2,B,B1,7.83,117.0 -2016-01-05,CA,2,B,B1,7.7,118.0 -2016-01-06,CA,2,B,B1,7.69,115.0 -2016-01-07,CA,2,B,B1,7.67,122.0 -2016-01-08,CA,2,B,B1,7.76,119.0 -2016-01-09,CA,2,B,B1,7.57,132.0 -2016-01-10,CA,2,B,B1,7.65,128.0 -2016-01-11,CA,2,B,B1,7.75,112.0 -2016-01-12,CA,2,B,B1,7.73,120.0 -2016-01-13,CA,2,B,B1,7.6,121.0 -2016-01-14,CA,2,B,B1,7.7,124.0 -2016-01-15,CA,2,B,B1,7.55,137.0 -2016-01-16,CA,2,B,B1,7.64,135.0 -2016-01-17,CA,2,B,B1,7.69,125.0 -2016-01-18,CA,2,B,B1,7.69,119.0 -2016-01-19,CA,2,B,B1,7.76,118.0 -2016-01-20,CA,2,B,B1,7.8,118.0 -2016-01-21,CA,2,B,B1,7.77,122.0 -2016-01-22,CA,2,B,B1,7.76,123.0 -2016-01-23,CA,2,B,B1,7.65,127.0 -2016-01-24,CA,2,B,B1,7.61,128.0 -2016-01-25,CA,2,B,B1,7.74,118.0 -2016-01-26,CA,2,B,B1,7.76,119.0 -2016-01-27,CA,2,B,B1,7.78,113.0 -2016-01-28,CA,2,B,B1,7.7,127.0 -2016-01-29,CA,2,B,B1,7.89,124.0 -2016-01-30,CA,2,B,B1,7.78,125.0 -2016-01-31,CA,2,B,B1,7.67,126.0 -2016-02-01,CA,2,B,B1,7.63,128.0 -2016-02-02,CA,2,B,B1,7.76,121.0 -2016-02-03,CA,2,B,B1,7.69,120.0 -2016-02-04,CA,2,B,B1,7.8,119.0 -2016-02-05,CA,2,B,B1,7.66,121.0 -2016-02-06,CA,2,B,B1,7.56,137.0 -2016-02-07,CA,2,B,B1,7.62,123.0 -2016-02-08,CA,2,B,B1,7.84,117.0 -2016-02-09,CA,2,B,B1,7.61,133.0 -2016-02-10,CA,2,B,B1,7.74,119.0 -2016-02-11,CA,2,B,B1,7.74,131.0 -2016-02-12,CA,2,B,B1,7.73,126.0 -2016-02-13,CA,2,B,B1,7.66,132.0 -2016-02-14,CA,2,B,B1,7.73,124.0 -2016-02-15,CA,2,B,B1,7.68,120.0 -2016-02-16,CA,2,B,B1,7.63,132.0 -2016-02-17,CA,2,B,B1,7.75,127.0 -2016-02-18,CA,2,B,B1,7.74,126.0 -2016-02-19,CA,2,B,B1,7.65,129.0 -2016-02-20,CA,2,B,B1,7.58,132.0 -2016-02-21,CA,2,B,B1,7.78,125.0 -2016-02-22,CA,2,B,B1,7.76,120.0 -2016-02-23,CA,2,B,B1,7.63,116.0 -2016-02-24,CA,2,B,B1,7.77,121.0 -2016-02-25,CA,2,B,B1,7.61,132.0 -2016-02-26,CA,2,B,B1,7.63,124.0 -2016-02-27,CA,2,B,B1,7.8,124.0 -2016-02-28,CA,2,B,B1,7.67,125.0 -2016-02-29,CA,2,B,B1,7.74,122.0 -2016-03-01,CA,2,B,B1,7.59,127.0 -2016-03-02,CA,2,B,B1,7.75,118.0 -2016-03-03,CA,2,B,B1,7.68,116.0 -2016-03-04,CA,2,B,B1,7.72,118.0 -2016-03-05,CA,2,B,B1,7.65,128.0 -2016-03-06,CA,2,B,B1,7.9,126.0 -2016-03-07,CA,2,B,B1,7.63,133.0 -2016-03-08,CA,2,B,B1,7.64,119.0 -2016-03-09,CA,2,B,B1,7.62,125.0 -2016-03-10,CA,2,B,B1,7.89,116.0 -2016-03-11,CA,2,B,B1,7.72,117.0 -2016-03-12,CA,2,B,B1,7.75,126.0 -2016-03-13,CA,2,B,B1,7.79,123.0 -2016-03-14,CA,2,B,B1,7.81,120.0 -2016-03-15,CA,2,B,B1,7.65,123.0 -2016-03-16,CA,2,B,B1,7.68,121.0 -2016-03-17,CA,2,B,B1,7.73,117.0 -2016-03-18,CA,2,B,B1,7.66,126.0 -2016-03-19,CA,2,B,B1,7.79,123.0 -2016-03-20,CA,2,B,B1,7.68,126.0 -2016-03-21,CA,2,B,B1,7.69,122.0 -2016-03-22,CA,2,B,B1,7.63,123.0 -2016-03-23,CA,2,B,B1,7.59,129.0 -2016-03-24,CA,2,B,B1,7.66,122.0 -2016-03-25,CA,2,B,B1,7.75,121.0 -2016-03-26,CA,2,B,B1,7.68,131.0 -2016-03-27,CA,2,B,B1,7.65,119.0 -2016-03-28,CA,2,B,B1,7.65,129.0 -2016-03-29,CA,2,B,B1,7.72,118.0 -2016-03-30,CA,2,B,B1,7.72,124.0 -2016-03-31,CA,2,B,B1,7.69,125.0 -2016-04-01,CA,2,B,B1,7.67,124.0 -2016-04-02,CA,2,B,B1,7.68,127.0 -2016-04-03,CA,2,B,B1,7.62,129.0 -2016-04-04,CA,2,B,B1,7.72,116.0 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-2016-05-05,CA,2,B,B1,7.76,131.0 -2016-05-06,CA,2,B,B1,7.66,125.0 -2016-05-07,CA,2,B,B1,7.67,123.0 -2016-05-08,CA,2,B,B1,7.67,123.0 -2016-05-09,CA,2,B,B1,7.79,118.0 -2016-05-10,CA,2,B,B1,7.75,119.0 -2016-05-11,CA,2,B,B1,7.74,123.0 -2016-05-12,CA,2,B,B1,7.64,124.0 -2016-05-13,CA,2,B,B1,7.7,121.0 -2016-05-14,CA,2,B,B1,7.64,134.0 -2016-05-15,CA,2,B,B1,7.58,128.0 -2016-05-16,CA,2,B,B1,7.66,123.0 -2016-05-17,CA,2,B,B1,7.8,122.0 -2016-05-18,CA,2,B,B1,7.74,123.0 -2016-05-19,CA,2,B,B1,7.66,131.0 -2016-05-20,CA,2,B,B1,7.76,117.0 -2016-05-21,CA,2,B,B1,7.7,125.0 -2016-05-22,CA,2,B,B1,7.62,126.0 -2016-05-23,CA,2,B,B1,7.76,120.0 -2016-05-24,CA,2,B,B1,7.61,116.0 -2016-05-25,CA,2,B,B1,7.8,119.0 -2016-05-26,CA,2,B,B1,7.68,122.0 -2016-05-27,CA,2,B,B1,7.61,121.0 -2016-05-28,CA,2,B,B1,7.7,127.0 -2016-05-29,CA,2,B,B1,7.81,119.0 -2016-05-30,CA,2,B,B1,7.6,120.0 -2016-05-31,CA,2,B,B1,7.77,124.0 -2016-06-01,CA,2,B,B1,7.7,120.0 -2016-06-02,CA,2,B,B1,7.74,125.0 -2016-06-03,CA,2,B,B1,7.76,117.0 -2016-06-04,CA,2,B,B1,7.81,128.0 -2016-06-05,CA,2,B,B1,7.77,117.0 -2016-06-06,CA,2,B,B1,7.67,121.0 -2016-06-07,CA,2,B,B1,7.65,132.0 -2016-06-08,CA,2,B,B1,7.63,124.0 -2016-06-09,CA,2,B,B1,7.61,127.0 -2016-06-10,CA,2,B,B1,7.75,120.0 -2016-06-11,CA,2,B,B1,7.73,134.0 -2016-06-12,CA,2,B,B1,7.8,115.0 -2016-06-13,CA,2,B,B1,7.75,123.0 -2016-06-14,CA,2,B,B1,7.65,122.0 -2016-06-15,CA,2,B,B1,7.7,122.0 -2016-06-16,CA,2,B,B1,7.65,130.0 -2016-06-17,CA,2,B,B1,7.69,128.0 -2016-06-18,CA,2,B,B1,7.76,131.0 -2016-06-19,CA,2,B,B1,7.75,121.0 -2016-06-20,CA,2,B,B1,7.65,120.0 -2016-06-21,CA,2,B,B1,7.74,118.0 -2016-06-22,CA,2,B,B1,7.79,118.0 -2016-06-23,CA,2,B,B1,7.74,121.0 -2016-06-24,CA,2,B,B1,7.77,118.0 -2016-06-25,CA,2,B,B1,7.53,137.0 -2016-06-26,CA,2,B,B1,7.61,128.0 -2016-06-27,CA,2,B,B1,7.9,115.0 -2016-06-28,CA,2,B,B1,7.74,129.0 -2016-06-29,CA,2,B,B1,7.81,118.0 -2016-06-30,CA,2,B,B1,7.66,129.0 -2016-07-01,CA,2,B,B1,7.64,122.0 -2016-07-02,CA,2,B,B1,7.68,131.0 -2016-07-03,CA,2,B,B1,7.85,120.0 -2016-07-04,CA,2,B,B1,7.62,122.0 -2016-07-05,CA,2,B,B1,7.54,131.0 -2016-07-06,CA,2,B,B1,7.79,115.0 -2016-07-07,CA,2,B,B1,7.65,122.0 -2016-07-08,CA,2,B,B1,7.73,120.0 -2016-07-09,CA,2,B,B1,7.8,124.0 -2016-07-10,CA,2,B,B1,7.71,126.0 -2016-07-11,CA,2,B,B1,7.75,116.0 -2016-07-12,CA,2,B,B1,7.75,110.0 -2016-07-13,CA,2,B,B1,7.62,126.0 -2016-07-14,CA,2,B,B1,7.71,120.0 -2016-07-15,CA,2,B,B1,7.59,122.0 -2016-07-16,CA,2,B,B1,7.65,130.0 -2016-07-17,CA,2,B,B1,7.54,126.0 -2016-07-18,CA,2,B,B1,7.82,119.0 -2016-07-19,CA,2,B,B1,7.7,130.0 -2016-07-20,CA,2,B,B1,7.64,122.0 -2016-07-21,CA,2,B,B1,7.55,124.0 -2016-07-22,CA,2,B,B1,7.73,122.0 -2016-07-23,CA,2,B,B1,7.62,126.0 -2016-07-24,CA,2,B,B1,7.89,118.0 -2016-07-25,CA,2,B,B1,7.61,124.0 -2016-07-26,CA,2,B,B1,7.67,127.0 -2016-07-27,CA,2,B,B1,7.7,123.0 -2015-01-05,CA,2,B,B2,4.58,15.0 -2015-01-06,CA,2,B,B2,4.54,14.0 -2015-01-07,CA,2,B,B2,4.5,16.0 -2015-01-08,CA,2,B,B2,4.47,16.0 -2015-01-09,CA,2,B,B2,4.46,17.0 -2015-01-10,CA,2,B,B2,4.47,18.0 -2015-01-11,CA,2,B,B2,4.49,16.0 -2015-01-12,CA,2,B,B2,4.5,16.0 -2015-01-13,CA,2,B,B2,4.53,17.0 -2015-01-14,CA,2,B,B2,4.55,16.0 -2015-01-15,CA,2,B,B2,4.51,17.0 -2015-01-16,CA,2,B,B2,4.45,17.0 -2015-01-17,CA,2,B,B2,4.5,18.0 -2015-01-18,CA,2,B,B2,4.55,16.0 -2015-01-19,CA,2,B,B2,4.54,16.0 -2015-01-20,CA,2,B,B2,4.53,17.0 -2015-01-21,CA,2,B,B2,4.49,17.0 -2015-01-22,CA,2,B,B2,4.52,17.0 -2015-01-23,CA,2,B,B2,4.61,17.0 -2015-01-24,CA,2,B,B2,4.54,18.0 -2015-01-25,CA,2,B,B2,4.56,16.0 -2015-01-26,CA,2,B,B2,4.45,18.0 -2015-01-27,CA,2,B,B2,4.47,19.0 -2015-01-28,CA,2,B,B2,4.53,18.0 -2015-01-29,CA,2,B,B2,4.52,17.0 -2015-01-30,CA,2,B,B2,4.52,15.0 -2015-01-31,CA,2,B,B2,4.48,18.0 -2015-02-01,CA,2,B,B2,4.49,18.0 -2015-02-02,CA,2,B,B2,4.53,16.0 -2015-02-03,CA,2,B,B2,4.57,16.0 -2015-02-04,CA,2,B,B2,4.58,17.0 -2015-02-05,CA,2,B,B2,4.56,16.0 -2015-02-06,CA,2,B,B2,4.6,17.0 -2015-02-07,CA,2,B,B2,4.57,18.0 -2015-02-08,CA,2,B,B2,4.5,18.0 -2015-02-09,CA,2,B,B2,4.47,18.0 -2015-02-10,CA,2,B,B2,4.53,18.0 -2015-02-11,CA,2,B,B2,4.38,20.0 -2015-02-12,CA,2,B,B2,4.52,18.0 -2015-02-13,CA,2,B,B2,4.46,18.0 -2015-02-14,CA,2,B,B2,4.53,17.0 -2015-02-15,CA,2,B,B2,4.56,19.0 -2015-02-16,CA,2,B,B2,4.5,17.0 -2015-02-17,CA,2,B,B2,4.46,20.0 -2015-02-18,CA,2,B,B2,4.48,18.0 -2015-02-19,CA,2,B,B2,4.43,20.0 -2015-02-20,CA,2,B,B2,4.51,18.0 -2015-02-21,CA,2,B,B2,4.56,17.0 -2015-02-22,CA,2,B,B2,4.51,19.0 -2015-02-23,CA,2,B,B2,4.56,18.0 -2015-02-24,CA,2,B,B2,4.53,18.0 -2015-02-25,CA,2,B,B2,4.49,18.0 -2015-02-26,CA,2,B,B2,4.56,18.0 -2015-02-27,CA,2,B,B2,4.45,19.0 -2015-02-28,CA,2,B,B2,4.53,19.0 -2015-03-01,CA,2,B,B2,4.48,20.0 -2015-03-02,CA,2,B,B2,4.44,19.0 -2015-03-03,CA,2,B,B2,4.46,19.0 -2015-03-04,CA,2,B,B2,4.53,19.0 -2015-03-05,CA,2,B,B2,4.48,18.0 -2015-03-06,CA,2,B,B2,4.47,18.0 -2015-03-07,CA,2,B,B2,4.5,19.0 -2015-03-08,CA,2,B,B2,4.47,19.0 -2015-03-09,CA,2,B,B2,4.58,17.0 -2015-03-10,CA,2,B,B2,4.55,18.0 -2015-03-11,CA,2,B,B2,4.46,19.0 -2015-03-12,CA,2,B,B2,4.48,19.0 -2015-03-13,CA,2,B,B2,4.55,17.0 -2015-03-14,CA,2,B,B2,4.47,21.0 -2015-03-15,CA,2,B,B2,4.45,20.0 -2015-03-16,CA,2,B,B2,4.48,19.0 -2015-03-17,CA,2,B,B2,4.47,19.0 -2015-03-18,CA,2,B,B2,4.51,18.0 -2015-03-19,CA,2,B,B2,4.46,19.0 -2015-03-20,CA,2,B,B2,4.49,19.0 -2015-03-21,CA,2,B,B2,4.54,19.0 -2015-03-22,CA,2,B,B2,4.46,20.0 -2015-03-23,CA,2,B,B2,4.45,18.0 -2015-03-24,CA,2,B,B2,4.42,20.0 -2015-03-25,CA,2,B,B2,4.56,18.0 -2015-03-26,CA,2,B,B2,4.5,18.0 -2015-03-27,CA,2,B,B2,4.52,19.0 -2015-03-28,CA,2,B,B2,4.49,19.0 -2015-03-29,CA,2,B,B2,4.52,19.0 -2015-03-30,CA,2,B,B2,4.46,20.0 -2015-03-31,CA,2,B,B2,4.43,20.0 -2015-04-01,CA,2,B,B2,4.5,20.0 -2015-04-02,CA,2,B,B2,4.53,18.0 -2015-04-03,CA,2,B,B2,4.48,19.0 -2015-04-04,CA,2,B,B2,4.55,19.0 -2015-04-05,CA,2,B,B2,4.54,19.0 -2015-04-06,CA,2,B,B2,4.5,19.0 -2015-04-07,CA,2,B,B2,4.51,18.0 -2015-04-08,CA,2,B,B2,4.52,19.0 -2015-04-09,CA,2,B,B2,4.47,19.0 -2015-04-10,CA,2,B,B2,4.49,18.0 -2015-04-11,CA,2,B,B2,4.57,18.0 -2015-04-12,CA,2,B,B2,4.54,19.0 -2015-04-13,CA,2,B,B2,4.5,19.0 -2015-04-14,CA,2,B,B2,4.52,19.0 -2015-04-15,CA,2,B,B2,4.61,16.0 -2015-04-16,CA,2,B,B2,4.54,19.0 -2015-04-17,CA,2,B,B2,4.43,19.0 -2015-04-18,CA,2,B,B2,4.43,19.0 -2015-04-19,CA,2,B,B2,4.56,17.0 -2015-04-20,CA,2,B,B2,4.47,20.0 -2015-04-21,CA,2,B,B2,4.58,19.0 -2015-04-22,CA,2,B,B2,4.42,19.0 -2015-04-23,CA,2,B,B2,4.44,19.0 -2015-04-24,CA,2,B,B2,4.45,20.0 -2015-04-25,CA,2,B,B2,4.55,20.0 -2015-04-26,CA,2,B,B2,4.48,20.0 -2015-04-27,CA,2,B,B2,4.51,17.0 -2015-04-28,CA,2,B,B2,4.44,20.0 -2015-04-29,CA,2,B,B2,4.54,18.0 -2015-04-30,CA,2,B,B2,4.52,18.0 -2015-05-01,CA,2,B,B2,4.51,18.0 -2015-05-02,CA,2,B,B2,4.45,20.0 -2015-05-03,CA,2,B,B2,4.48,19.0 -2015-05-04,CA,2,B,B2,4.47,20.0 -2015-05-05,CA,2,B,B2,4.4,20.0 -2015-05-06,CA,2,B,B2,4.61,18.0 -2015-05-07,CA,2,B,B2,4.57,17.0 -2015-05-08,CA,2,B,B2,4.44,19.0 -2015-05-09,CA,2,B,B2,4.44,20.0 -2015-05-10,CA,2,B,B2,4.47,19.0 -2015-05-11,CA,2,B,B2,4.51,18.0 -2015-05-12,CA,2,B,B2,4.55,18.0 -2015-05-13,CA,2,B,B2,4.53,18.0 -2015-05-14,CA,2,B,B2,4.47,20.0 -2015-05-15,CA,2,B,B2,4.56,18.0 -2015-05-16,CA,2,B,B2,4.49,19.0 -2015-05-17,CA,2,B,B2,4.44,19.0 -2015-05-18,CA,2,B,B2,4.5,19.0 -2015-05-19,CA,2,B,B2,4.45,20.0 -2015-05-20,CA,2,B,B2,4.44,19.0 -2015-05-21,CA,2,B,B2,4.5,20.0 -2015-05-22,CA,2,B,B2,4.48,19.0 -2015-05-23,CA,2,B,B2,4.58,18.0 -2015-05-24,CA,2,B,B2,4.5,19.0 -2015-05-25,CA,2,B,B2,4.56,16.0 -2015-05-26,CA,2,B,B2,4.56,19.0 -2015-05-27,CA,2,B,B2,4.44,19.0 -2015-05-28,CA,2,B,B2,4.54,18.0 -2015-05-29,CA,2,B,B2,4.46,19.0 -2015-05-30,CA,2,B,B2,4.57,18.0 -2015-05-31,CA,2,B,B2,4.5,20.0 -2015-06-01,CA,2,B,B2,4.42,19.0 -2015-06-02,CA,2,B,B2,4.53,18.0 -2015-06-03,CA,2,B,B2,4.46,19.0 -2015-06-04,CA,2,B,B2,4.44,20.0 -2015-06-05,CA,2,B,B2,4.47,19.0 -2015-06-06,CA,2,B,B2,4.5,20.0 -2015-06-07,CA,2,B,B2,4.61,18.0 -2015-06-08,CA,2,B,B2,4.54,18.0 -2015-06-09,CA,2,B,B2,4.46,20.0 -2015-06-10,CA,2,B,B2,4.5,18.0 -2015-06-11,CA,2,B,B2,4.56,17.0 -2015-06-12,CA,2,B,B2,4.45,19.0 -2015-06-13,CA,2,B,B2,4.51,19.0 -2015-06-14,CA,2,B,B2,4.47,20.0 -2015-06-15,CA,2,B,B2,4.49,19.0 -2015-06-16,CA,2,B,B2,4.57,18.0 -2015-06-17,CA,2,B,B2,4.47,19.0 -2015-06-18,CA,2,B,B2,4.6,17.0 -2015-06-19,CA,2,B,B2,4.55,19.0 -2015-06-20,CA,2,B,B2,4.58,18.0 -2015-06-21,CA,2,B,B2,4.46,20.0 -2015-06-22,CA,2,B,B2,4.47,19.0 -2015-06-23,CA,2,B,B2,4.43,21.0 -2015-06-24,CA,2,B,B2,4.58,18.0 -2015-06-25,CA,2,B,B2,4.46,21.0 -2015-06-26,CA,2,B,B2,4.45,19.0 -2015-06-27,CA,2,B,B2,4.57,18.0 -2015-06-28,CA,2,B,B2,4.53,19.0 -2015-06-29,CA,2,B,B2,4.45,20.0 -2015-06-30,CA,2,B,B2,4.57,18.0 -2015-07-01,CA,2,B,B2,4.42,21.0 -2015-07-02,CA,2,B,B2,4.55,17.0 -2015-07-03,CA,2,B,B2,4.52,19.0 -2015-07-04,CA,2,B,B2,4.52,19.0 -2015-07-05,CA,2,B,B2,4.5,19.0 -2015-07-06,CA,2,B,B2,4.53,18.0 -2015-07-07,CA,2,B,B2,4.48,19.0 -2015-07-08,CA,2,B,B2,4.52,18.0 -2015-07-09,CA,2,B,B2,4.56,18.0 -2015-07-10,CA,2,B,B2,4.6,16.0 -2015-07-11,CA,2,B,B2,4.65,17.0 -2015-07-12,CA,2,B,B2,4.58,19.0 -2015-07-13,CA,2,B,B2,4.56,19.0 -2015-07-14,CA,2,B,B2,4.44,20.0 -2015-07-15,CA,2,B,B2,4.54,19.0 -2015-07-16,CA,2,B,B2,4.58,19.0 -2015-07-17,CA,2,B,B2,4.54,19.0 -2015-07-18,CA,2,B,B2,4.51,19.0 -2015-07-19,CA,2,B,B2,4.53,19.0 -2015-07-20,CA,2,B,B2,4.56,18.0 -2015-07-21,CA,2,B,B2,4.51,19.0 -2015-07-22,CA,2,B,B2,4.54,18.0 -2015-07-23,CA,2,B,B2,4.57,18.0 -2015-07-24,CA,2,B,B2,4.47,19.0 -2015-07-25,CA,2,B,B2,4.5,19.0 -2015-07-26,CA,2,B,B2,4.44,20.0 -2015-07-27,CA,2,B,B2,4.46,20.0 -2015-07-28,CA,2,B,B2,4.58,17.0 -2015-07-29,CA,2,B,B2,4.57,17.0 -2015-07-30,CA,2,B,B2,4.53,19.0 -2015-07-31,CA,2,B,B2,4.46,18.0 -2015-08-01,CA,2,B,B2,4.51,20.0 -2015-08-02,CA,2,B,B2,4.48,19.0 -2015-08-03,CA,2,B,B2,4.55,17.0 -2015-08-04,CA,2,B,B2,4.49,19.0 -2015-08-05,CA,2,B,B2,4.52,18.0 -2015-08-06,CA,2,B,B2,4.48,20.0 -2015-08-07,CA,2,B,B2,4.53,19.0 -2015-08-08,CA,2,B,B2,4.5,19.0 -2015-08-09,CA,2,B,B2,4.47,19.0 -2015-08-10,CA,2,B,B2,4.61,17.0 -2015-08-11,CA,2,B,B2,4.49,19.0 -2015-08-12,CA,2,B,B2,4.54,18.0 -2015-08-13,CA,2,B,B2,4.51,18.0 -2015-08-14,CA,2,B,B2,4.53,17.0 -2015-08-15,CA,2,B,B2,4.47,21.0 -2015-08-16,CA,2,B,B2,4.46,19.0 -2015-08-17,CA,2,B,B2,4.48,19.0 -2015-08-18,CA,2,B,B2,4.52,18.0 -2015-08-19,CA,2,B,B2,4.49,19.0 -2015-08-20,CA,2,B,B2,4.57,16.0 -2015-08-21,CA,2,B,B2,4.49,18.0 -2015-08-22,CA,2,B,B2,4.53,18.0 -2015-08-23,CA,2,B,B2,4.42,20.0 -2015-08-24,CA,2,B,B2,4.56,18.0 -2015-08-25,CA,2,B,B2,4.36,21.0 -2015-08-26,CA,2,B,B2,4.47,19.0 -2015-08-27,CA,2,B,B2,4.53,19.0 -2015-08-28,CA,2,B,B2,4.47,18.0 -2015-08-29,CA,2,B,B2,4.52,19.0 -2015-08-30,CA,2,B,B2,4.48,20.0 -2015-08-31,CA,2,B,B2,4.47,19.0 -2015-09-01,CA,2,B,B2,4.52,18.0 -2015-09-02,CA,2,B,B2,4.55,19.0 -2015-09-03,CA,2,B,B2,4.48,19.0 -2015-09-04,CA,2,B,B2,4.47,18.0 -2015-09-05,CA,2,B,B2,4.48,19.0 -2015-09-06,CA,2,B,B2,4.48,19.0 -2015-09-07,CA,2,B,B2,4.52,19.0 -2015-09-08,CA,2,B,B2,4.42,20.0 -2015-09-09,CA,2,B,B2,4.5,19.0 -2015-09-10,CA,2,B,B2,4.48,19.0 -2015-09-11,CA,2,B,B2,4.52,19.0 -2015-09-12,CA,2,B,B2,4.51,19.0 -2015-09-13,CA,2,B,B2,4.46,19.0 -2015-09-14,CA,2,B,B2,4.54,17.0 -2015-09-15,CA,2,B,B2,4.56,18.0 -2015-09-16,CA,2,B,B2,4.51,19.0 -2015-09-17,CA,2,B,B2,4.54,18.0 -2015-09-18,CA,2,B,B2,4.4,21.0 -2015-09-19,CA,2,B,B2,4.63,18.0 -2015-09-20,CA,2,B,B2,4.54,19.0 -2015-09-21,CA,2,B,B2,4.49,18.0 -2015-09-22,CA,2,B,B2,4.54,18.0 -2015-09-23,CA,2,B,B2,4.46,20.0 -2015-09-24,CA,2,B,B2,4.43,20.0 -2015-09-25,CA,2,B,B2,4.48,19.0 -2015-09-26,CA,2,B,B2,4.51,18.0 -2015-09-27,CA,2,B,B2,4.57,17.0 -2015-09-28,CA,2,B,B2,4.48,18.0 -2015-09-29,CA,2,B,B2,4.47,19.0 -2015-09-30,CA,2,B,B2,4.5,19.0 -2015-10-01,CA,2,B,B2,4.53,18.0 -2015-10-02,CA,2,B,B2,4.47,19.0 -2015-10-03,CA,2,B,B2,4.46,20.0 -2015-10-04,CA,2,B,B2,4.59,18.0 -2015-10-05,CA,2,B,B2,4.46,19.0 -2015-10-06,CA,2,B,B2,4.47,18.0 -2015-10-07,CA,2,B,B2,4.49,19.0 -2015-10-08,CA,2,B,B2,4.53,19.0 -2015-10-09,CA,2,B,B2,4.53,18.0 -2015-10-10,CA,2,B,B2,4.41,20.0 -2015-10-11,CA,2,B,B2,4.5,18.0 -2015-10-12,CA,2,B,B2,4.54,17.0 -2015-10-13,CA,2,B,B2,4.43,20.0 -2015-10-14,CA,2,B,B2,4.53,18.0 -2015-10-15,CA,2,B,B2,4.46,19.0 -2015-10-16,CA,2,B,B2,4.4,20.0 -2015-10-17,CA,2,B,B2,4.48,20.0 -2015-10-18,CA,2,B,B2,4.43,19.0 -2015-10-19,CA,2,B,B2,4.49,18.0 -2015-10-20,CA,2,B,B2,4.55,18.0 -2015-10-21,CA,2,B,B2,4.52,18.0 -2015-10-22,CA,2,B,B2,4.46,19.0 -2015-10-23,CA,2,B,B2,4.59,16.0 -2015-10-24,CA,2,B,B2,4.44,21.0 -2015-10-25,CA,2,B,B2,4.46,21.0 -2015-10-26,CA,2,B,B2,4.38,21.0 -2015-10-27,CA,2,B,B2,4.56,17.0 -2015-10-28,CA,2,B,B2,4.54,19.0 -2015-10-29,CA,2,B,B2,4.49,20.0 -2015-10-30,CA,2,B,B2,4.5,18.0 -2015-10-31,CA,2,B,B2,4.46,20.0 -2015-11-01,CA,2,B,B2,4.57,18.0 -2015-11-02,CA,2,B,B2,4.43,20.0 -2015-11-03,CA,2,B,B2,4.53,18.0 -2015-11-04,CA,2,B,B2,4.51,18.0 -2015-11-05,CA,2,B,B2,4.56,16.0 -2015-11-06,CA,2,B,B2,4.48,19.0 -2015-11-07,CA,2,B,B2,4.57,19.0 -2015-11-08,CA,2,B,B2,4.54,19.0 -2015-11-09,CA,2,B,B2,4.53,19.0 -2015-11-10,CA,2,B,B2,4.5,18.0 -2015-11-11,CA,2,B,B2,4.46,19.0 -2015-11-12,CA,2,B,B2,4.57,18.0 -2015-11-13,CA,2,B,B2,4.55,17.0 -2015-11-14,CA,2,B,B2,4.46,19.0 -2015-11-15,CA,2,B,B2,4.53,18.0 -2015-11-16,CA,2,B,B2,4.57,18.0 -2015-11-17,CA,2,B,B2,4.42,20.0 -2015-11-18,CA,2,B,B2,4.47,18.0 -2015-11-19,CA,2,B,B2,4.5,19.0 -2015-11-20,CA,2,B,B2,4.43,20.0 -2015-11-21,CA,2,B,B2,4.45,19.0 -2015-11-22,CA,2,B,B2,4.54,18.0 -2015-11-23,CA,2,B,B2,4.45,18.0 -2015-11-24,CA,2,B,B2,4.53,18.0 -2015-11-25,CA,2,B,B2,4.49,19.0 -2015-11-26,CA,2,B,B2,4.6,18.0 -2015-11-27,CA,2,B,B2,4.52,20.0 -2015-11-28,CA,2,B,B2,4.49,19.0 -2015-11-29,CA,2,B,B2,4.5,18.0 -2015-11-30,CA,2,B,B2,4.46,20.0 -2015-12-01,CA,2,B,B2,4.57,18.0 -2015-12-02,CA,2,B,B2,4.45,19.0 -2015-12-03,CA,2,B,B2,4.48,19.0 -2015-12-04,CA,2,B,B2,4.55,18.0 -2015-12-05,CA,2,B,B2,4.53,19.0 -2015-12-06,CA,2,B,B2,4.48,20.0 -2015-12-07,CA,2,B,B2,4.49,18.0 -2015-12-08,CA,2,B,B2,4.46,20.0 -2015-12-09,CA,2,B,B2,4.51,20.0 -2015-12-10,CA,2,B,B2,4.46,20.0 -2015-12-11,CA,2,B,B2,4.46,21.0 -2015-12-12,CA,2,B,B2,4.5,20.0 -2015-12-13,CA,2,B,B2,4.54,18.0 -2015-12-14,CA,2,B,B2,4.52,18.0 -2015-12-15,CA,2,B,B2,4.45,20.0 -2015-12-16,CA,2,B,B2,4.47,20.0 -2015-12-17,CA,2,B,B2,4.42,19.0 -2015-12-18,CA,2,B,B2,4.54,19.0 -2015-12-19,CA,2,B,B2,4.57,18.0 -2015-12-20,CA,2,B,B2,4.47,19.0 -2015-12-21,CA,2,B,B2,4.43,19.0 -2015-12-22,CA,2,B,B2,4.54,19.0 -2015-12-23,CA,2,B,B2,4.47,20.0 -2015-12-24,CA,2,B,B2,4.53,19.0 -2015-12-25,CA,2,B,B2,4.52,19.0 -2015-12-26,CA,2,B,B2,4.51,19.0 -2015-12-27,CA,2,B,B2,4.39,20.0 -2015-12-28,CA,2,B,B2,4.45,20.0 -2015-12-29,CA,2,B,B2,4.48,18.0 -2015-12-30,CA,2,B,B2,4.56,18.0 -2015-12-31,CA,2,B,B2,4.55,19.0 -2016-01-01,CA,2,B,B2,4.56,18.0 -2016-01-02,CA,2,B,B2,4.52,19.0 -2016-01-03,CA,2,B,B2,4.54,18.0 -2016-01-04,CA,2,B,B2,4.57,19.0 -2016-01-05,CA,2,B,B2,4.55,18.0 -2016-01-06,CA,2,B,B2,4.55,17.0 -2016-01-07,CA,2,B,B2,4.5,18.0 -2016-01-08,CA,2,B,B2,4.51,19.0 -2016-01-09,CA,2,B,B2,4.51,20.0 -2016-01-10,CA,2,B,B2,4.48,19.0 -2016-01-11,CA,2,B,B2,4.5,18.0 -2016-01-12,CA,2,B,B2,4.45,19.0 -2016-01-13,CA,2,B,B2,4.49,18.0 -2016-01-14,CA,2,B,B2,4.51,20.0 -2016-01-15,CA,2,B,B2,4.43,20.0 -2016-01-16,CA,2,B,B2,4.58,18.0 -2016-01-17,CA,2,B,B2,4.45,20.0 -2016-01-18,CA,2,B,B2,4.53,18.0 -2016-01-19,CA,2,B,B2,4.48,19.0 -2016-01-20,CA,2,B,B2,4.58,17.0 -2016-01-21,CA,2,B,B2,4.54,17.0 -2016-01-22,CA,2,B,B2,4.51,19.0 -2016-01-23,CA,2,B,B2,4.5,19.0 -2016-01-24,CA,2,B,B2,4.47,19.0 -2016-01-25,CA,2,B,B2,4.5,18.0 -2016-01-26,CA,2,B,B2,4.53,18.0 -2016-01-27,CA,2,B,B2,4.44,19.0 -2016-01-28,CA,2,B,B2,4.51,18.0 -2016-01-29,CA,2,B,B2,4.55,18.0 -2016-01-30,CA,2,B,B2,4.53,20.0 -2016-01-31,CA,2,B,B2,4.51,19.0 -2016-02-01,CA,2,B,B2,4.57,18.0 -2016-02-02,CA,2,B,B2,4.51,18.0 -2016-02-03,CA,2,B,B2,4.46,20.0 -2016-02-04,CA,2,B,B2,4.52,18.0 -2016-02-05,CA,2,B,B2,4.53,18.0 -2016-02-06,CA,2,B,B2,4.5,19.0 -2016-02-07,CA,2,B,B2,4.46,19.0 -2016-02-08,CA,2,B,B2,4.54,18.0 -2016-02-09,CA,2,B,B2,4.5,19.0 -2016-02-10,CA,2,B,B2,4.58,18.0 -2016-02-11,CA,2,B,B2,4.46,20.0 -2016-02-12,CA,2,B,B2,4.57,17.0 -2016-02-13,CA,2,B,B2,4.45,19.0 -2016-02-14,CA,2,B,B2,4.44,21.0 -2016-02-15,CA,2,B,B2,4.5,19.0 -2016-02-16,CA,2,B,B2,4.56,19.0 -2016-02-17,CA,2,B,B2,4.48,19.0 -2016-02-18,CA,2,B,B2,4.45,19.0 -2016-02-19,CA,2,B,B2,4.55,19.0 -2016-02-20,CA,2,B,B2,4.54,18.0 -2016-02-21,CA,2,B,B2,4.54,19.0 -2016-02-22,CA,2,B,B2,4.49,19.0 -2016-02-23,CA,2,B,B2,4.47,19.0 -2016-02-24,CA,2,B,B2,4.54,18.0 -2016-02-25,CA,2,B,B2,4.56,18.0 -2016-02-26,CA,2,B,B2,4.54,19.0 -2016-02-27,CA,2,B,B2,4.56,19.0 -2016-02-28,CA,2,B,B2,4.52,18.0 -2016-02-29,CA,2,B,B2,4.51,19.0 -2016-03-01,CA,2,B,B2,4.48,19.0 -2016-03-02,CA,2,B,B2,4.53,18.0 -2016-03-03,CA,2,B,B2,4.46,19.0 -2016-03-04,CA,2,B,B2,4.51,19.0 -2016-03-05,CA,2,B,B2,4.52,19.0 -2016-03-06,CA,2,B,B2,4.48,20.0 -2016-03-07,CA,2,B,B2,4.5,18.0 -2016-03-08,CA,2,B,B2,4.54,18.0 -2016-03-09,CA,2,B,B2,4.5,19.0 -2016-03-10,CA,2,B,B2,4.51,19.0 -2016-03-11,CA,2,B,B2,4.57,19.0 -2016-03-12,CA,2,B,B2,4.43,21.0 -2016-03-13,CA,2,B,B2,4.45,20.0 -2016-03-14,CA,2,B,B2,4.5,19.0 -2016-03-15,CA,2,B,B2,4.53,19.0 -2016-03-16,CA,2,B,B2,4.56,18.0 -2016-03-17,CA,2,B,B2,4.36,22.0 -2016-03-18,CA,2,B,B2,4.53,19.0 -2016-03-19,CA,2,B,B2,4.46,19.0 -2016-03-20,CA,2,B,B2,4.54,19.0 -2016-03-21,CA,2,B,B2,4.47,19.0 -2016-03-22,CA,2,B,B2,4.53,18.0 -2016-03-23,CA,2,B,B2,4.56,18.0 -2016-03-24,CA,2,B,B2,4.56,17.0 -2016-03-25,CA,2,B,B2,4.53,19.0 -2016-03-26,CA,2,B,B2,4.53,18.0 -2016-03-27,CA,2,B,B2,4.43,20.0 -2016-03-28,CA,2,B,B2,4.56,18.0 -2016-03-29,CA,2,B,B2,4.52,18.0 -2016-03-30,CA,2,B,B2,4.53,19.0 -2016-03-31,CA,2,B,B2,4.51,18.0 -2016-04-01,CA,2,B,B2,4.56,19.0 -2016-04-02,CA,2,B,B2,4.53,20.0 -2016-04-03,CA,2,B,B2,4.55,19.0 -2016-04-04,CA,2,B,B2,4.55,19.0 -2016-04-05,CA,2,B,B2,4.54,18.0 -2016-04-06,CA,2,B,B2,4.54,17.0 -2016-04-07,CA,2,B,B2,4.54,18.0 -2016-04-08,CA,2,B,B2,4.44,19.0 -2016-04-09,CA,2,B,B2,4.61,18.0 -2016-04-10,CA,2,B,B2,4.44,20.0 -2016-04-11,CA,2,B,B2,4.48,19.0 -2016-04-12,CA,2,B,B2,4.43,20.0 -2016-04-13,CA,2,B,B2,4.53,18.0 -2016-04-14,CA,2,B,B2,4.45,20.0 -2016-04-15,CA,2,B,B2,4.54,17.0 -2016-04-16,CA,2,B,B2,4.48,19.0 -2016-04-17,CA,2,B,B2,4.47,20.0 -2016-04-18,CA,2,B,B2,4.59,17.0 -2016-04-19,CA,2,B,B2,4.45,19.0 -2016-04-20,CA,2,B,B2,4.5,18.0 -2016-04-21,CA,2,B,B2,4.5,18.0 -2016-04-22,CA,2,B,B2,4.44,19.0 -2016-04-23,CA,2,B,B2,4.53,19.0 -2016-04-24,CA,2,B,B2,4.56,19.0 -2016-04-25,CA,2,B,B2,4.53,18.0 -2016-04-26,CA,2,B,B2,4.55,19.0 -2016-04-27,CA,2,B,B2,4.56,18.0 -2016-04-28,CA,2,B,B2,4.49,20.0 -2016-04-29,CA,2,B,B2,4.51,20.0 -2016-04-30,CA,2,B,B2,4.49,19.0 -2016-05-01,CA,2,B,B2,4.46,19.0 -2016-05-02,CA,2,B,B2,4.43,19.0 -2016-05-03,CA,2,B,B2,4.46,20.0 -2016-05-04,CA,2,B,B2,4.46,20.0 -2016-05-05,CA,2,B,B2,4.51,18.0 -2016-05-06,CA,2,B,B2,4.56,19.0 -2016-05-07,CA,2,B,B2,4.55,20.0 -2016-05-08,CA,2,B,B2,4.49,20.0 -2016-05-09,CA,2,B,B2,4.5,17.0 -2016-05-10,CA,2,B,B2,4.55,19.0 -2016-05-11,CA,2,B,B2,4.57,17.0 -2016-05-12,CA,2,B,B2,4.51,19.0 -2016-05-13,CA,2,B,B2,4.55,18.0 -2016-05-14,CA,2,B,B2,4.53,19.0 -2016-05-15,CA,2,B,B2,4.55,18.0 -2016-05-16,CA,2,B,B2,4.51,18.0 -2016-05-17,CA,2,B,B2,4.57,18.0 -2016-05-18,CA,2,B,B2,4.51,20.0 -2016-05-19,CA,2,B,B2,4.44,20.0 -2016-05-20,CA,2,B,B2,4.48,20.0 -2016-05-21,CA,2,B,B2,4.54,19.0 -2016-05-22,CA,2,B,B2,4.53,20.0 -2016-05-23,CA,2,B,B2,4.5,18.0 -2016-05-24,CA,2,B,B2,4.51,18.0 -2016-05-25,CA,2,B,B2,4.4,20.0 -2016-05-26,CA,2,B,B2,4.54,18.0 -2016-05-27,CA,2,B,B2,4.53,18.0 -2016-05-28,CA,2,B,B2,4.5,19.0 -2016-05-29,CA,2,B,B2,4.56,18.0 -2016-05-30,CA,2,B,B2,4.43,20.0 -2016-05-31,CA,2,B,B2,4.56,18.0 -2016-06-01,CA,2,B,B2,4.53,19.0 -2016-06-02,CA,2,B,B2,4.45,19.0 -2016-06-03,CA,2,B,B2,4.5,18.0 -2016-06-04,CA,2,B,B2,4.49,19.0 -2016-06-05,CA,2,B,B2,4.47,19.0 -2016-06-06,CA,2,B,B2,4.48,19.0 -2016-06-07,CA,2,B,B2,4.55,18.0 -2016-06-08,CA,2,B,B2,4.53,19.0 -2016-06-09,CA,2,B,B2,4.47,20.0 -2016-06-10,CA,2,B,B2,4.46,19.0 -2016-06-11,CA,2,B,B2,4.59,18.0 -2016-06-12,CA,2,B,B2,4.53,19.0 -2016-06-13,CA,2,B,B2,4.51,18.0 -2016-06-14,CA,2,B,B2,4.54,18.0 -2016-06-15,CA,2,B,B2,4.49,18.0 -2016-06-16,CA,2,B,B2,4.53,19.0 -2016-06-17,CA,2,B,B2,4.51,18.0 -2016-06-18,CA,2,B,B2,4.52,18.0 -2016-06-19,CA,2,B,B2,4.49,20.0 -2016-06-20,CA,2,B,B2,4.52,19.0 -2016-06-21,CA,2,B,B2,4.53,18.0 -2016-06-22,CA,2,B,B2,4.51,19.0 -2016-06-23,CA,2,B,B2,4.54,18.0 -2016-06-24,CA,2,B,B2,4.6,17.0 -2016-06-25,CA,2,B,B2,4.5,18.0 -2016-06-26,CA,2,B,B2,4.48,18.0 -2016-06-27,CA,2,B,B2,4.51,18.0 -2016-06-28,CA,2,B,B2,4.5,19.0 -2016-06-29,CA,2,B,B2,4.49,19.0 -2016-06-30,CA,2,B,B2,4.52,19.0 -2016-07-01,CA,2,B,B2,4.49,18.0 -2016-07-02,CA,2,B,B2,4.49,19.0 -2016-07-03,CA,2,B,B2,4.53,18.0 -2016-07-04,CA,2,B,B2,4.48,18.0 -2016-07-05,CA,2,B,B2,4.49,20.0 -2016-07-06,CA,2,B,B2,4.51,17.0 -2016-07-07,CA,2,B,B2,4.55,18.0 -2016-07-08,CA,2,B,B2,4.51,19.0 -2016-07-09,CA,2,B,B2,4.47,19.0 -2016-07-10,CA,2,B,B2,4.55,18.0 -2016-07-11,CA,2,B,B2,4.44,19.0 -2016-07-12,CA,2,B,B2,4.58,18.0 -2016-07-13,CA,2,B,B2,4.46,20.0 -2016-07-14,CA,2,B,B2,4.52,19.0 -2016-07-15,CA,2,B,B2,4.58,18.0 -2016-07-16,CA,2,B,B2,4.48,20.0 -2016-07-17,CA,2,B,B2,4.46,18.0 -2016-07-18,CA,2,B,B2,4.46,19.0 -2016-07-19,CA,2,B,B2,4.49,20.0 -2016-07-20,CA,2,B,B2,4.5,19.0 -2016-07-21,CA,2,B,B2,4.54,19.0 -2016-07-22,CA,2,B,B2,4.46,18.0 -2016-07-23,CA,2,B,B2,4.52,20.0 -2016-07-24,CA,2,B,B2,4.45,21.0 -2016-07-25,CA,2,B,B2,4.43,20.0 -2016-07-26,CA,2,B,B2,4.55,18.0 -2016-07-27,CA,2,B,B2,4.48,19.0 -2015-01-05,CA,3,A,A1,6.83,18.0 -2015-01-06,CA,3,A,A1,6.71,18.0 -2015-01-07,CA,3,A,A1,6.85,17.0 -2015-01-08,CA,3,A,A1,6.89,17.0 -2015-01-09,CA,3,A,A1,6.78,17.0 -2015-01-10,CA,3,A,A1,6.84,17.0 -2015-01-11,CA,3,A,A1,6.84,17.0 -2015-01-12,CA,3,A,A1,6.88,18.0 -2015-01-13,CA,3,A,A1,6.77,17.0 -2015-01-14,CA,3,A,A1,6.72,17.0 -2015-01-15,CA,3,A,A1,6.82,16.0 -2015-01-16,CA,3,A,A1,6.74,17.0 -2015-01-17,CA,3,A,A1,6.81,17.0 -2015-01-18,CA,3,A,A1,6.75,17.0 -2015-01-19,CA,3,A,A1,6.77,16.0 -2015-01-20,CA,3,A,A1,6.72,18.0 -2015-01-21,CA,3,A,A1,6.75,17.0 -2015-01-22,CA,3,A,A1,6.74,18.0 -2015-01-23,CA,3,A,A1,6.8,17.0 -2015-01-24,CA,3,A,A1,6.77,16.0 -2015-01-25,CA,3,A,A1,6.69,18.0 -2015-01-26,CA,3,A,A1,6.75,18.0 -2015-01-27,CA,3,A,A1,6.68,17.0 -2015-01-28,CA,3,A,A1,6.87,16.0 -2015-01-29,CA,3,A,A1,6.77,17.0 -2015-01-30,CA,3,A,A1,6.67,17.0 -2015-01-31,CA,3,A,A1,6.73,18.0 -2015-02-01,CA,3,A,A1,6.78,19.0 -2015-02-02,CA,3,A,A1,6.74,16.0 -2015-02-03,CA,3,A,A1,6.83,17.0 -2015-02-04,CA,3,A,A1,6.83,18.0 -2015-02-05,CA,3,A,A1,6.73,17.0 -2015-02-06,CA,3,A,A1,6.78,18.0 -2015-02-07,CA,3,A,A1,6.85,19.0 -2015-02-08,CA,3,A,A1,6.82,17.0 -2015-02-09,CA,3,A,A1,6.84,17.0 -2015-02-10,CA,3,A,A1,6.67,17.0 -2015-02-11,CA,3,A,A1,6.78,18.0 -2015-02-12,CA,3,A,A1,6.82,18.0 -2015-02-13,CA,3,A,A1,6.84,17.0 -2015-02-14,CA,3,A,A1,6.81,17.0 -2015-02-15,CA,3,A,A1,6.73,18.0 -2015-02-16,CA,3,A,A1,6.73,17.0 -2015-02-17,CA,3,A,A1,6.71,17.0 -2015-02-18,CA,3,A,A1,6.67,16.0 -2015-02-19,CA,3,A,A1,6.75,19.0 -2015-02-20,CA,3,A,A1,6.79,18.0 -2015-02-21,CA,3,A,A1,6.81,18.0 -2015-02-22,CA,3,A,A1,6.8,16.0 -2015-02-23,CA,3,A,A1,6.69,16.0 -2015-02-24,CA,3,A,A1,6.78,19.0 -2015-02-25,CA,3,A,A1,6.7,18.0 -2015-02-26,CA,3,A,A1,6.74,16.0 -2015-02-27,CA,3,A,A1,6.53,18.0 -2015-02-28,CA,3,A,A1,6.78,17.0 -2015-03-01,CA,3,A,A1,6.71,18.0 -2015-03-02,CA,3,A,A1,6.8,17.0 -2015-03-03,CA,3,A,A1,6.87,18.0 -2015-03-04,CA,3,A,A1,6.77,18.0 -2015-03-05,CA,3,A,A1,6.72,19.0 -2015-03-06,CA,3,A,A1,6.74,18.0 -2015-03-07,CA,3,A,A1,6.87,17.0 -2015-03-08,CA,3,A,A1,6.78,18.0 -2015-03-09,CA,3,A,A1,6.78,18.0 -2015-03-10,CA,3,A,A1,6.77,17.0 -2015-03-11,CA,3,A,A1,6.82,18.0 -2015-03-12,CA,3,A,A1,6.78,19.0 -2015-03-13,CA,3,A,A1,6.65,18.0 -2015-03-14,CA,3,A,A1,6.85,18.0 -2015-03-15,CA,3,A,A1,6.79,18.0 -2015-03-16,CA,3,A,A1,6.76,15.0 -2015-03-17,CA,3,A,A1,6.71,18.0 -2015-03-18,CA,3,A,A1,6.73,18.0 -2015-03-19,CA,3,A,A1,6.74,16.0 -2015-03-20,CA,3,A,A1,6.7,18.0 -2015-03-21,CA,3,A,A1,6.75,18.0 -2015-03-22,CA,3,A,A1,6.75,17.0 -2015-03-23,CA,3,A,A1,6.69,18.0 -2015-03-24,CA,3,A,A1,6.79,18.0 -2015-03-25,CA,3,A,A1,6.63,18.0 -2015-03-26,CA,3,A,A1,6.74,17.0 -2015-03-27,CA,3,A,A1,6.66,18.0 -2015-03-28,CA,3,A,A1,6.83,18.0 -2015-03-29,CA,3,A,A1,6.67,18.0 -2015-03-30,CA,3,A,A1,6.71,18.0 -2015-03-31,CA,3,A,A1,6.7,18.0 -2015-04-01,CA,3,A,A1,6.71,16.0 -2015-04-02,CA,3,A,A1,6.89,16.0 -2015-04-03,CA,3,A,A1,6.71,18.0 -2015-04-04,CA,3,A,A1,6.73,19.0 -2015-04-05,CA,3,A,A1,6.77,18.0 -2015-04-06,CA,3,A,A1,6.72,17.0 -2015-04-07,CA,3,A,A1,6.87,15.0 -2015-04-08,CA,3,A,A1,6.83,17.0 -2015-04-09,CA,3,A,A1,6.74,17.0 -2015-04-10,CA,3,A,A1,6.73,17.0 -2015-04-11,CA,3,A,A1,6.86,15.0 -2015-04-12,CA,3,A,A1,6.78,17.0 -2015-04-13,CA,3,A,A1,6.67,18.0 -2015-04-14,CA,3,A,A1,6.86,16.0 -2015-04-15,CA,3,A,A1,6.71,17.0 -2015-04-16,CA,3,A,A1,6.73,17.0 -2015-04-17,CA,3,A,A1,6.76,18.0 -2015-04-18,CA,3,A,A1,6.94,17.0 -2015-04-19,CA,3,A,A1,6.85,18.0 -2015-04-20,CA,3,A,A1,6.82,17.0 -2015-04-21,CA,3,A,A1,6.72,16.0 -2015-04-22,CA,3,A,A1,6.59,18.0 -2015-04-23,CA,3,A,A1,6.81,18.0 -2015-04-24,CA,3,A,A1,6.72,17.0 -2015-04-25,CA,3,A,A1,6.69,19.0 -2015-04-26,CA,3,A,A1,6.72,18.0 -2015-04-27,CA,3,A,A1,6.72,17.0 -2015-04-28,CA,3,A,A1,6.7,17.0 -2015-04-29,CA,3,A,A1,6.73,17.0 -2015-04-30,CA,3,A,A1,6.78,16.0 -2015-05-01,CA,3,A,A1,6.69,17.0 -2015-05-02,CA,3,A,A1,6.75,18.0 -2015-05-03,CA,3,A,A1,6.69,19.0 -2015-05-04,CA,3,A,A1,6.78,18.0 -2015-05-05,CA,3,A,A1,6.69,18.0 -2015-05-06,CA,3,A,A1,6.79,16.0 -2015-05-07,CA,3,A,A1,6.74,17.0 -2015-05-08,CA,3,A,A1,6.78,17.0 -2015-05-09,CA,3,A,A1,6.82,17.0 -2015-05-10,CA,3,A,A1,6.77,17.0 -2015-05-11,CA,3,A,A1,6.78,18.0 -2015-05-12,CA,3,A,A1,6.75,18.0 -2015-05-13,CA,3,A,A1,6.88,17.0 -2015-05-14,CA,3,A,A1,6.75,19.0 -2015-05-15,CA,3,A,A1,6.73,18.0 -2015-05-16,CA,3,A,A1,6.7,18.0 -2015-05-17,CA,3,A,A1,6.76,18.0 -2015-05-18,CA,3,A,A1,6.84,17.0 -2015-05-19,CA,3,A,A1,6.84,18.0 -2015-05-20,CA,3,A,A1,6.78,17.0 -2015-05-21,CA,3,A,A1,6.71,17.0 -2015-05-22,CA,3,A,A1,6.76,16.0 -2015-05-23,CA,3,A,A1,6.72,19.0 -2015-05-24,CA,3,A,A1,6.66,17.0 -2015-05-25,CA,3,A,A1,6.8,17.0 -2015-05-26,CA,3,A,A1,6.75,20.0 -2015-05-27,CA,3,A,A1,6.76,18.0 -2015-05-28,CA,3,A,A1,6.67,15.0 -2015-05-29,CA,3,A,A1,6.75,18.0 -2015-05-30,CA,3,A,A1,6.87,18.0 -2015-05-31,CA,3,A,A1,6.77,17.0 -2015-06-01,CA,3,A,A1,6.79,17.0 -2015-06-02,CA,3,A,A1,6.77,16.0 -2015-06-03,CA,3,A,A1,6.79,17.0 -2015-06-04,CA,3,A,A1,6.75,20.0 -2015-06-05,CA,3,A,A1,6.85,16.0 -2015-06-06,CA,3,A,A1,6.79,17.0 -2015-06-07,CA,3,A,A1,6.74,17.0 -2015-06-08,CA,3,A,A1,6.74,16.0 -2015-06-09,CA,3,A,A1,6.84,17.0 -2015-06-10,CA,3,A,A1,6.83,17.0 -2015-06-11,CA,3,A,A1,6.75,18.0 -2015-06-12,CA,3,A,A1,6.81,17.0 -2015-06-13,CA,3,A,A1,6.69,17.0 -2015-06-14,CA,3,A,A1,6.9,18.0 -2015-06-15,CA,3,A,A1,6.79,16.0 -2015-06-16,CA,3,A,A1,6.77,16.0 -2015-06-17,CA,3,A,A1,6.76,17.0 -2015-06-18,CA,3,A,A1,6.65,19.0 -2015-06-19,CA,3,A,A1,6.84,17.0 -2015-06-20,CA,3,A,A1,6.72,19.0 -2015-06-21,CA,3,A,A1,6.67,19.0 -2015-06-22,CA,3,A,A1,6.8,18.0 -2015-06-23,CA,3,A,A1,6.76,18.0 -2015-06-24,CA,3,A,A1,6.75,18.0 -2015-06-25,CA,3,A,A1,6.79,17.0 -2015-06-26,CA,3,A,A1,6.81,15.0 -2015-06-27,CA,3,A,A1,6.8,17.0 -2015-06-28,CA,3,A,A1,6.72,16.0 -2015-06-29,CA,3,A,A1,6.82,17.0 -2015-06-30,CA,3,A,A1,6.8,18.0 -2015-07-01,CA,3,A,A1,6.79,16.0 -2015-07-02,CA,3,A,A1,6.89,15.0 -2015-07-03,CA,3,A,A1,6.72,19.0 -2015-07-04,CA,3,A,A1,6.62,19.0 -2015-07-05,CA,3,A,A1,6.9,17.0 -2015-07-06,CA,3,A,A1,6.81,17.0 -2015-07-07,CA,3,A,A1,6.86,16.0 -2015-07-08,CA,3,A,A1,6.73,18.0 -2015-07-09,CA,3,A,A1,6.82,17.0 -2015-07-10,CA,3,A,A1,6.74,17.0 -2015-07-11,CA,3,A,A1,6.7,17.0 -2015-07-12,CA,3,A,A1,6.75,17.0 -2015-07-13,CA,3,A,A1,6.78,18.0 -2015-07-14,CA,3,A,A1,6.86,17.0 -2015-07-15,CA,3,A,A1,6.89,18.0 -2015-07-16,CA,3,A,A1,6.79,17.0 -2015-07-17,CA,3,A,A1,6.69,18.0 -2015-07-18,CA,3,A,A1,6.82,19.0 -2015-07-19,CA,3,A,A1,6.72,18.0 -2015-07-20,CA,3,A,A1,6.69,18.0 -2015-07-21,CA,3,A,A1,6.73,17.0 -2015-07-22,CA,3,A,A1,6.89,18.0 -2015-07-23,CA,3,A,A1,6.78,18.0 -2015-07-24,CA,3,A,A1,6.87,16.0 -2015-07-25,CA,3,A,A1,6.79,18.0 -2015-07-26,CA,3,A,A1,6.85,16.0 -2015-07-27,CA,3,A,A1,6.78,17.0 -2015-07-28,CA,3,A,A1,6.65,18.0 -2015-07-29,CA,3,A,A1,6.74,17.0 -2015-07-30,CA,3,A,A1,6.73,17.0 -2015-07-31,CA,3,A,A1,6.84,18.0 -2015-08-01,CA,3,A,A1,6.72,18.0 -2015-08-02,CA,3,A,A1,6.75,18.0 -2015-08-03,CA,3,A,A1,6.76,18.0 -2015-08-04,CA,3,A,A1,6.85,15.0 -2015-08-05,CA,3,A,A1,6.83,15.0 -2015-08-06,CA,3,A,A1,6.77,16.0 -2015-08-07,CA,3,A,A1,6.66,18.0 -2015-08-08,CA,3,A,A1,6.85,18.0 -2015-08-09,CA,3,A,A1,6.74,18.0 -2015-08-10,CA,3,A,A1,6.75,17.0 -2015-08-11,CA,3,A,A1,6.72,17.0 -2015-08-12,CA,3,A,A1,6.73,18.0 -2015-08-13,CA,3,A,A1,6.82,19.0 -2015-08-14,CA,3,A,A1,6.82,18.0 -2015-08-15,CA,3,A,A1,6.7,19.0 -2015-08-16,CA,3,A,A1,6.69,18.0 -2015-08-17,CA,3,A,A1,6.75,16.0 -2015-08-18,CA,3,A,A1,6.69,18.0 -2015-08-19,CA,3,A,A1,6.73,17.0 -2015-08-20,CA,3,A,A1,6.78,17.0 -2015-08-21,CA,3,A,A1,6.66,19.0 -2015-08-22,CA,3,A,A1,6.78,18.0 -2015-08-23,CA,3,A,A1,6.76,19.0 -2015-08-24,CA,3,A,A1,6.75,17.0 -2015-08-25,CA,3,A,A1,6.77,16.0 -2015-08-26,CA,3,A,A1,6.86,17.0 -2015-08-27,CA,3,A,A1,6.8,16.0 -2015-08-28,CA,3,A,A1,6.73,19.0 -2015-08-29,CA,3,A,A1,6.86,18.0 -2015-08-30,CA,3,A,A1,6.77,17.0 -2015-08-31,CA,3,A,A1,6.77,16.0 -2015-09-01,CA,3,A,A1,6.76,18.0 -2015-09-02,CA,3,A,A1,6.74,18.0 -2015-09-03,CA,3,A,A1,6.8,17.0 -2015-09-04,CA,3,A,A1,6.79,19.0 -2015-09-05,CA,3,A,A1,6.78,18.0 -2015-09-06,CA,3,A,A1,6.84,18.0 -2015-09-07,CA,3,A,A1,6.77,17.0 -2015-09-08,CA,3,A,A1,6.81,17.0 -2015-09-09,CA,3,A,A1,6.83,17.0 -2015-09-10,CA,3,A,A1,6.69,18.0 -2015-09-11,CA,3,A,A1,6.76,15.0 -2015-09-12,CA,3,A,A1,6.66,19.0 -2015-09-13,CA,3,A,A1,6.77,18.0 -2015-09-14,CA,3,A,A1,6.65,16.0 -2015-09-15,CA,3,A,A1,6.91,17.0 -2015-09-16,CA,3,A,A1,6.65,17.0 -2015-09-17,CA,3,A,A1,6.78,17.0 -2015-09-18,CA,3,A,A1,6.68,20.0 -2015-09-19,CA,3,A,A1,6.8,16.0 -2015-09-20,CA,3,A,A1,6.71,18.0 -2015-09-21,CA,3,A,A1,6.76,16.0 -2015-09-22,CA,3,A,A1,6.77,18.0 -2015-09-23,CA,3,A,A1,6.83,17.0 -2015-09-24,CA,3,A,A1,6.82,16.0 -2015-09-25,CA,3,A,A1,6.8,19.0 -2015-09-26,CA,3,A,A1,6.69,19.0 -2015-09-27,CA,3,A,A1,6.78,18.0 -2015-09-28,CA,3,A,A1,6.81,17.0 -2015-09-29,CA,3,A,A1,6.71,18.0 -2015-09-30,CA,3,A,A1,6.84,19.0 -2015-10-01,CA,3,A,A1,6.69,18.0 -2015-10-02,CA,3,A,A1,6.76,19.0 -2015-10-03,CA,3,A,A1,6.69,18.0 -2015-10-04,CA,3,A,A1,6.75,18.0 -2015-10-05,CA,3,A,A1,6.67,20.0 -2015-10-06,CA,3,A,A1,6.8,18.0 -2015-10-07,CA,3,A,A1,6.7,16.0 -2015-10-08,CA,3,A,A1,6.75,16.0 -2015-10-09,CA,3,A,A1,6.74,17.0 -2015-10-10,CA,3,A,A1,6.84,19.0 -2015-10-11,CA,3,A,A1,6.75,18.0 -2015-10-12,CA,3,A,A1,6.72,18.0 -2015-10-13,CA,3,A,A1,6.84,17.0 -2015-10-14,CA,3,A,A1,6.84,17.0 -2015-10-15,CA,3,A,A1,6.76,17.0 -2015-10-16,CA,3,A,A1,6.83,16.0 -2015-10-17,CA,3,A,A1,6.71,19.0 -2015-10-18,CA,3,A,A1,6.73,18.0 -2015-10-19,CA,3,A,A1,6.76,16.0 -2015-10-20,CA,3,A,A1,6.78,17.0 -2015-10-21,CA,3,A,A1,6.72,18.0 -2015-10-22,CA,3,A,A1,6.73,19.0 -2015-10-23,CA,3,A,A1,6.75,18.0 -2015-10-24,CA,3,A,A1,6.67,21.0 -2015-10-25,CA,3,A,A1,6.78,17.0 -2015-10-26,CA,3,A,A1,6.61,18.0 -2015-10-27,CA,3,A,A1,6.82,18.0 -2015-10-28,CA,3,A,A1,6.87,16.0 -2015-10-29,CA,3,A,A1,6.74,18.0 -2015-10-30,CA,3,A,A1,6.74,16.0 -2015-10-31,CA,3,A,A1,6.69,17.0 -2015-11-01,CA,3,A,A1,6.55,20.0 -2015-11-02,CA,3,A,A1,6.81,17.0 -2015-11-03,CA,3,A,A1,6.69,19.0 -2015-11-04,CA,3,A,A1,6.8,18.0 -2015-11-05,CA,3,A,A1,6.7,17.0 -2015-11-06,CA,3,A,A1,6.79,16.0 -2015-11-07,CA,3,A,A1,6.64,18.0 -2015-11-08,CA,3,A,A1,6.81,17.0 -2015-11-09,CA,3,A,A1,6.66,19.0 -2015-11-10,CA,3,A,A1,6.75,18.0 -2015-11-11,CA,3,A,A1,6.65,18.0 -2015-11-12,CA,3,A,A1,6.78,17.0 -2015-11-13,CA,3,A,A1,6.77,19.0 -2015-11-14,CA,3,A,A1,6.69,19.0 -2015-11-15,CA,3,A,A1,6.86,17.0 -2015-11-16,CA,3,A,A1,6.59,18.0 -2015-11-17,CA,3,A,A1,6.85,18.0 -2015-11-18,CA,3,A,A1,6.7,17.0 -2015-11-19,CA,3,A,A1,6.81,17.0 -2015-11-20,CA,3,A,A1,6.75,18.0 -2015-11-21,CA,3,A,A1,6.74,17.0 -2015-11-22,CA,3,A,A1,6.8,17.0 -2015-11-23,CA,3,A,A1,6.8,17.0 -2015-11-24,CA,3,A,A1,6.85,16.0 -2015-11-25,CA,3,A,A1,6.75,16.0 -2015-11-26,CA,3,A,A1,6.76,16.0 -2015-11-27,CA,3,A,A1,6.79,17.0 -2015-11-28,CA,3,A,A1,6.8,16.0 -2015-11-29,CA,3,A,A1,6.67,19.0 -2015-11-30,CA,3,A,A1,6.83,17.0 -2015-12-01,CA,3,A,A1,6.65,17.0 -2015-12-02,CA,3,A,A1,6.78,18.0 -2015-12-03,CA,3,A,A1,6.69,18.0 -2015-12-04,CA,3,A,A1,6.75,17.0 -2015-12-05,CA,3,A,A1,6.72,18.0 -2015-12-06,CA,3,A,A1,6.66,17.0 -2015-12-07,CA,3,A,A1,6.76,16.0 -2015-12-08,CA,3,A,A1,6.77,17.0 -2015-12-09,CA,3,A,A1,6.84,17.0 -2015-12-10,CA,3,A,A1,6.88,15.0 -2015-12-11,CA,3,A,A1,6.76,19.0 -2015-12-12,CA,3,A,A1,6.8,18.0 -2015-12-13,CA,3,A,A1,6.9,17.0 -2015-12-14,CA,3,A,A1,6.79,16.0 -2015-12-15,CA,3,A,A1,6.76,16.0 -2015-12-16,CA,3,A,A1,6.84,18.0 -2015-12-17,CA,3,A,A1,6.71,18.0 -2015-12-18,CA,3,A,A1,6.63,18.0 -2015-12-19,CA,3,A,A1,6.91,18.0 -2015-12-20,CA,3,A,A1,6.77,17.0 -2015-12-21,CA,3,A,A1,6.7,17.0 -2015-12-22,CA,3,A,A1,6.79,16.0 -2015-12-23,CA,3,A,A1,6.77,19.0 -2015-12-24,CA,3,A,A1,6.75,17.0 -2015-12-25,CA,3,A,A1,6.74,18.0 -2015-12-26,CA,3,A,A1,6.78,17.0 -2015-12-27,CA,3,A,A1,6.72,17.0 -2015-12-28,CA,3,A,A1,6.8,17.0 -2015-12-29,CA,3,A,A1,6.73,17.0 -2015-12-30,CA,3,A,A1,6.78,16.0 -2015-12-31,CA,3,A,A1,6.66,17.0 -2016-01-01,CA,3,A,A1,6.67,17.0 -2016-01-02,CA,3,A,A1,6.74,18.0 -2016-01-03,CA,3,A,A1,6.69,18.0 -2016-01-04,CA,3,A,A1,6.88,17.0 -2016-01-05,CA,3,A,A1,6.73,18.0 -2016-01-06,CA,3,A,A1,6.83,17.0 -2016-01-07,CA,3,A,A1,6.86,17.0 -2016-01-08,CA,3,A,A1,6.81,16.0 -2016-01-09,CA,3,A,A1,6.6,19.0 -2016-01-10,CA,3,A,A1,6.67,18.0 -2016-01-11,CA,3,A,A1,6.68,18.0 -2016-01-12,CA,3,A,A1,6.66,18.0 -2016-01-13,CA,3,A,A1,6.8,17.0 -2016-01-14,CA,3,A,A1,6.7,18.0 -2016-01-15,CA,3,A,A1,6.95,15.0 -2016-01-16,CA,3,A,A1,6.77,18.0 -2016-01-17,CA,3,A,A1,6.75,18.0 -2016-01-18,CA,3,A,A1,6.77,18.0 -2016-01-19,CA,3,A,A1,6.81,16.0 -2016-01-20,CA,3,A,A1,6.78,17.0 -2016-01-21,CA,3,A,A1,6.86,16.0 -2016-01-22,CA,3,A,A1,6.79,17.0 -2016-01-23,CA,3,A,A1,6.75,16.0 -2016-01-24,CA,3,A,A1,6.72,19.0 -2016-01-25,CA,3,A,A1,6.88,16.0 -2016-01-26,CA,3,A,A1,6.75,18.0 -2016-01-27,CA,3,A,A1,6.74,17.0 -2016-01-28,CA,3,A,A1,6.85,18.0 -2016-01-29,CA,3,A,A1,6.81,17.0 -2016-01-30,CA,3,A,A1,6.68,18.0 -2016-01-31,CA,3,A,A1,6.69,19.0 -2016-02-01,CA,3,A,A1,6.91,17.0 -2016-02-02,CA,3,A,A1,6.79,17.0 -2016-02-03,CA,3,A,A1,6.75,18.0 -2016-02-04,CA,3,A,A1,6.69,21.0 -2016-02-05,CA,3,A,A1,6.73,16.0 -2016-02-06,CA,3,A,A1,6.74,18.0 -2016-02-07,CA,3,A,A1,6.82,18.0 -2016-02-08,CA,3,A,A1,6.74,17.0 -2016-02-09,CA,3,A,A1,6.76,16.0 -2016-02-10,CA,3,A,A1,6.84,17.0 -2016-02-11,CA,3,A,A1,6.72,17.0 -2016-02-12,CA,3,A,A1,6.89,18.0 -2016-02-13,CA,3,A,A1,6.68,19.0 -2016-02-14,CA,3,A,A1,6.67,17.0 -2016-02-15,CA,3,A,A1,6.7,18.0 -2016-02-16,CA,3,A,A1,6.75,17.0 -2016-02-17,CA,3,A,A1,6.84,17.0 -2016-02-18,CA,3,A,A1,6.78,18.0 -2016-02-19,CA,3,A,A1,6.75,18.0 -2016-02-20,CA,3,A,A1,6.75,19.0 -2016-02-21,CA,3,A,A1,6.85,15.0 -2016-02-22,CA,3,A,A1,6.82,16.0 -2016-02-23,CA,3,A,A1,6.83,17.0 -2016-02-24,CA,3,A,A1,6.83,18.0 -2016-02-25,CA,3,A,A1,6.85,18.0 -2016-02-26,CA,3,A,A1,6.94,16.0 -2016-02-27,CA,3,A,A1,6.88,17.0 -2016-02-28,CA,3,A,A1,6.79,18.0 -2016-02-29,CA,3,A,A1,6.83,18.0 -2016-03-01,CA,3,A,A1,6.78,16.0 -2016-03-02,CA,3,A,A1,6.74,17.0 -2016-03-03,CA,3,A,A1,6.83,17.0 -2016-03-04,CA,3,A,A1,6.7,17.0 -2016-03-05,CA,3,A,A1,6.71,19.0 -2016-03-06,CA,3,A,A1,6.81,17.0 -2016-03-07,CA,3,A,A1,6.75,18.0 -2016-03-08,CA,3,A,A1,6.88,16.0 -2016-03-09,CA,3,A,A1,6.72,18.0 -2016-03-10,CA,3,A,A1,6.74,17.0 -2016-03-11,CA,3,A,A1,6.99,18.0 -2016-03-12,CA,3,A,A1,6.71,18.0 -2016-03-13,CA,3,A,A1,6.84,17.0 -2016-03-14,CA,3,A,A1,6.84,16.0 -2016-03-15,CA,3,A,A1,6.75,19.0 -2016-03-16,CA,3,A,A1,6.84,17.0 -2016-03-17,CA,3,A,A1,6.83,17.0 -2016-03-18,CA,3,A,A1,6.79,19.0 -2016-03-19,CA,3,A,A1,6.73,18.0 -2016-03-20,CA,3,A,A1,6.81,16.0 -2016-03-21,CA,3,A,A1,6.75,18.0 -2016-03-22,CA,3,A,A1,6.74,18.0 -2016-03-23,CA,3,A,A1,6.77,18.0 -2016-03-24,CA,3,A,A1,6.71,18.0 -2016-03-25,CA,3,A,A1,6.72,20.0 -2016-03-26,CA,3,A,A1,6.71,17.0 -2016-03-27,CA,3,A,A1,6.79,17.0 -2016-03-28,CA,3,A,A1,6.67,16.0 -2016-03-29,CA,3,A,A1,6.67,16.0 -2016-03-30,CA,3,A,A1,6.81,17.0 -2016-03-31,CA,3,A,A1,6.82,17.0 -2016-04-01,CA,3,A,A1,6.69,16.0 -2016-04-02,CA,3,A,A1,6.65,20.0 -2016-04-03,CA,3,A,A1,6.88,18.0 -2016-04-04,CA,3,A,A1,6.79,17.0 -2016-04-05,CA,3,A,A1,6.74,17.0 -2016-04-06,CA,3,A,A1,6.77,19.0 -2016-04-07,CA,3,A,A1,6.76,18.0 -2016-04-08,CA,3,A,A1,6.74,18.0 -2016-04-09,CA,3,A,A1,6.74,17.0 -2016-04-10,CA,3,A,A1,6.69,16.0 -2016-04-11,CA,3,A,A1,6.67,17.0 -2016-04-12,CA,3,A,A1,6.84,17.0 -2016-04-13,CA,3,A,A1,6.82,16.0 -2016-04-14,CA,3,A,A1,6.84,18.0 -2016-04-15,CA,3,A,A1,6.77,16.0 -2016-04-16,CA,3,A,A1,6.75,19.0 -2016-04-17,CA,3,A,A1,6.74,17.0 -2016-04-18,CA,3,A,A1,6.73,17.0 -2016-04-19,CA,3,A,A1,6.74,18.0 -2016-04-20,CA,3,A,A1,6.83,17.0 -2016-04-21,CA,3,A,A1,6.69,18.0 -2016-04-22,CA,3,A,A1,6.76,18.0 -2016-04-23,CA,3,A,A1,6.68,19.0 -2016-04-24,CA,3,A,A1,6.78,17.0 -2016-04-25,CA,3,A,A1,6.69,18.0 -2016-04-26,CA,3,A,A1,6.88,17.0 -2016-04-27,CA,3,A,A1,6.68,17.0 -2016-04-28,CA,3,A,A1,6.89,17.0 -2016-04-29,CA,3,A,A1,6.83,18.0 -2016-04-30,CA,3,A,A1,6.73,19.0 -2016-05-01,CA,3,A,A1,6.81,16.0 -2016-05-02,CA,3,A,A1,6.83,18.0 -2016-05-03,CA,3,A,A1,6.67,18.0 -2016-05-04,CA,3,A,A1,6.77,17.0 -2016-05-05,CA,3,A,A1,6.73,18.0 -2016-05-06,CA,3,A,A1,6.6,19.0 -2016-05-07,CA,3,A,A1,6.72,18.0 -2016-05-08,CA,3,A,A1,6.84,17.0 -2016-05-09,CA,3,A,A1,6.78,17.0 -2016-05-10,CA,3,A,A1,6.79,18.0 -2016-05-11,CA,3,A,A1,6.77,18.0 -2016-05-12,CA,3,A,A1,6.82,16.0 -2016-05-13,CA,3,A,A1,6.86,16.0 -2016-05-14,CA,3,A,A1,6.83,19.0 -2016-05-15,CA,3,A,A1,6.65,18.0 -2016-05-16,CA,3,A,A1,6.77,18.0 -2016-05-17,CA,3,A,A1,6.73,19.0 -2016-05-18,CA,3,A,A1,6.81,17.0 -2016-05-19,CA,3,A,A1,6.77,17.0 -2016-05-20,CA,3,A,A1,6.84,18.0 -2016-05-21,CA,3,A,A1,6.67,18.0 -2016-05-22,CA,3,A,A1,6.74,17.0 -2016-05-23,CA,3,A,A1,6.79,16.0 -2016-05-24,CA,3,A,A1,6.78,18.0 -2016-05-25,CA,3,A,A1,6.72,19.0 -2016-05-26,CA,3,A,A1,6.83,17.0 -2016-05-27,CA,3,A,A1,6.86,17.0 -2016-05-28,CA,3,A,A1,6.67,18.0 -2016-05-29,CA,3,A,A1,6.93,17.0 -2016-05-30,CA,3,A,A1,6.62,17.0 -2016-05-31,CA,3,A,A1,6.71,20.0 -2016-06-01,CA,3,A,A1,6.71,17.0 -2016-06-02,CA,3,A,A1,6.8,17.0 -2016-06-03,CA,3,A,A1,6.64,18.0 -2016-06-04,CA,3,A,A1,6.84,16.0 -2016-06-05,CA,3,A,A1,6.71,17.0 -2016-06-06,CA,3,A,A1,6.82,17.0 -2016-06-07,CA,3,A,A1,6.75,18.0 -2016-06-08,CA,3,A,A1,6.76,18.0 -2016-06-09,CA,3,A,A1,6.77,16.0 -2016-06-10,CA,3,A,A1,6.75,18.0 -2016-06-11,CA,3,A,A1,6.8,20.0 -2016-06-12,CA,3,A,A1,6.83,18.0 -2016-06-13,CA,3,A,A1,6.8,16.0 -2016-06-14,CA,3,A,A1,6.74,17.0 -2016-06-15,CA,3,A,A1,6.75,16.0 -2016-06-16,CA,3,A,A1,6.8,18.0 -2016-06-17,CA,3,A,A1,6.74,19.0 -2016-06-18,CA,3,A,A1,6.76,17.0 -2016-06-19,CA,3,A,A1,6.86,18.0 -2016-06-20,CA,3,A,A1,6.77,17.0 -2016-06-21,CA,3,A,A1,6.75,18.0 -2016-06-22,CA,3,A,A1,6.69,16.0 -2016-06-23,CA,3,A,A1,6.86,17.0 -2016-06-24,CA,3,A,A1,6.71,17.0 -2016-06-25,CA,3,A,A1,6.75,18.0 -2016-06-26,CA,3,A,A1,6.84,17.0 -2016-06-27,CA,3,A,A1,6.74,18.0 -2016-06-28,CA,3,A,A1,6.75,16.0 -2016-06-29,CA,3,A,A1,6.76,19.0 -2016-06-30,CA,3,A,A1,6.69,17.0 -2016-07-01,CA,3,A,A1,6.71,18.0 -2016-07-02,CA,3,A,A1,6.79,17.0 -2016-07-03,CA,3,A,A1,6.69,19.0 -2016-07-04,CA,3,A,A1,6.77,16.0 -2016-07-05,CA,3,A,A1,6.76,17.0 -2016-07-06,CA,3,A,A1,6.71,18.0 -2016-07-07,CA,3,A,A1,6.67,18.0 -2016-07-08,CA,3,A,A1,6.95,16.0 -2016-07-09,CA,3,A,A1,6.71,18.0 -2016-07-10,CA,3,A,A1,6.71,18.0 -2016-07-11,CA,3,A,A1,6.81,17.0 -2016-07-12,CA,3,A,A1,6.77,15.0 -2016-07-13,CA,3,A,A1,6.69,18.0 -2016-07-14,CA,3,A,A1,6.82,17.0 -2016-07-15,CA,3,A,A1,6.81,17.0 -2016-07-16,CA,3,A,A1,6.8,19.0 -2016-07-17,CA,3,A,A1,6.81,18.0 -2016-07-18,CA,3,A,A1,6.73,16.0 -2016-07-19,CA,3,A,A1,6.73,17.0 -2016-07-20,CA,3,A,A1,6.82,17.0 -2016-07-21,CA,3,A,A1,6.73,16.0 -2016-07-22,CA,3,A,A1,6.86,18.0 -2016-07-23,CA,3,A,A1,6.8,17.0 -2016-07-24,CA,3,A,A1,6.87,16.0 -2016-07-25,CA,3,A,A1,6.73,17.0 -2016-07-26,CA,3,A,A1,6.76,16.0 -2016-07-27,CA,3,A,A1,6.69,17.0 -2015-01-05,CA,3,A,A2,6.54,616.0 -2015-01-06,CA,3,A,A2,6.56,681.0 -2015-01-07,CA,3,A,A2,6.54,618.0 -2015-01-08,CA,3,A,A2,6.6,665.0 -2015-01-09,CA,3,A,A2,6.55,635.0 -2015-01-10,CA,3,A,A2,6.61,733.0 -2015-01-11,CA,3,A,A2,6.55,743.0 -2015-01-12,CA,3,A,A2,6.67,679.0 -2015-01-13,CA,3,A,A2,6.49,688.0 -2015-01-14,CA,3,A,A2,6.57,703.0 -2015-01-15,CA,3,A,A2,6.46,724.0 -2015-01-16,CA,3,A,A2,6.55,698.0 -2015-01-17,CA,3,A,A2,6.58,724.0 -2015-01-18,CA,3,A,A2,6.64,718.0 -2015-01-19,CA,3,A,A2,6.54,741.0 -2015-01-20,CA,3,A,A2,6.56,741.0 -2015-01-21,CA,3,A,A2,6.61,706.0 -2015-01-22,CA,3,A,A2,6.57,653.0 -2015-01-23,CA,3,A,A2,6.62,699.0 -2015-01-24,CA,3,A,A2,6.49,775.0 -2015-01-25,CA,3,A,A2,6.61,704.0 -2015-01-26,CA,3,A,A2,6.63,626.0 -2015-01-27,CA,3,A,A2,6.55,750.0 -2015-01-28,CA,3,A,A2,6.57,675.0 -2015-01-29,CA,3,A,A2,6.65,729.0 -2015-01-30,CA,3,A,A2,6.57,749.0 -2015-01-31,CA,3,A,A2,6.51,730.0 -2015-02-01,CA,3,A,A2,6.6,683.0 -2015-02-02,CA,3,A,A2,6.58,716.0 -2015-02-03,CA,3,A,A2,6.59,715.0 -2015-02-04,CA,3,A,A2,6.75,719.0 -2015-02-05,CA,3,A,A2,6.6,786.0 -2015-02-06,CA,3,A,A2,6.6,663.0 -2015-02-07,CA,3,A,A2,6.48,688.0 -2015-02-08,CA,3,A,A2,6.45,749.0 -2015-02-09,CA,3,A,A2,6.45,744.0 -2015-02-10,CA,3,A,A2,6.53,764.0 -2015-02-11,CA,3,A,A2,6.59,706.0 -2015-02-12,CA,3,A,A2,6.68,656.0 -2015-02-13,CA,3,A,A2,6.53,660.0 -2015-02-14,CA,3,A,A2,6.47,800.0 -2015-02-15,CA,3,A,A2,6.48,760.0 -2015-02-16,CA,3,A,A2,6.61,716.0 -2015-02-17,CA,3,A,A2,6.49,760.0 -2015-02-18,CA,3,A,A2,6.56,712.0 -2015-02-19,CA,3,A,A2,6.45,691.0 -2015-02-20,CA,3,A,A2,6.61,690.0 -2015-02-21,CA,3,A,A2,6.53,798.0 -2015-02-22,CA,3,A,A2,6.51,730.0 -2015-02-23,CA,3,A,A2,6.58,660.0 -2015-02-24,CA,3,A,A2,6.49,786.0 -2015-02-25,CA,3,A,A2,6.54,719.0 -2015-02-26,CA,3,A,A2,6.62,722.0 -2015-02-27,CA,3,A,A2,6.64,760.0 -2015-02-28,CA,3,A,A2,6.49,796.0 -2015-03-01,CA,3,A,A2,6.56,740.0 -2015-03-02,CA,3,A,A2,6.55,743.0 -2015-03-03,CA,3,A,A2,6.51,743.0 -2015-03-04,CA,3,A,A2,6.5,737.0 -2015-03-05,CA,3,A,A2,6.6,737.0 -2015-03-06,CA,3,A,A2,6.66,716.0 -2015-03-07,CA,3,A,A2,6.46,801.0 -2015-03-08,CA,3,A,A2,6.54,714.0 -2015-03-09,CA,3,A,A2,6.55,675.0 -2015-03-10,CA,3,A,A2,6.61,692.0 -2015-03-11,CA,3,A,A2,6.41,726.0 -2015-03-12,CA,3,A,A2,6.54,762.0 -2015-03-13,CA,3,A,A2,6.61,740.0 -2015-03-14,CA,3,A,A2,6.53,761.0 -2015-03-15,CA,3,A,A2,6.61,721.0 -2015-03-16,CA,3,A,A2,6.42,762.0 -2015-03-17,CA,3,A,A2,6.54,690.0 -2015-03-18,CA,3,A,A2,6.58,717.0 -2015-03-19,CA,3,A,A2,6.54,730.0 -2015-03-20,CA,3,A,A2,6.38,781.0 -2015-03-21,CA,3,A,A2,6.48,773.0 -2015-03-22,CA,3,A,A2,6.48,769.0 -2015-03-23,CA,3,A,A2,6.6,740.0 -2015-03-24,CA,3,A,A2,6.5,730.0 -2015-03-25,CA,3,A,A2,6.58,750.0 -2015-03-26,CA,3,A,A2,6.56,725.0 -2015-03-27,CA,3,A,A2,6.38,708.0 -2015-03-28,CA,3,A,A2,6.64,819.0 -2015-03-29,CA,3,A,A2,6.47,739.0 -2015-03-30,CA,3,A,A2,6.53,699.0 -2015-03-31,CA,3,A,A2,6.57,672.0 -2015-04-01,CA,3,A,A2,6.55,739.0 -2015-04-02,CA,3,A,A2,6.73,714.0 -2015-04-03,CA,3,A,A2,6.65,714.0 -2015-04-04,CA,3,A,A2,6.68,760.0 -2015-04-05,CA,3,A,A2,6.61,759.0 -2015-04-06,CA,3,A,A2,6.64,752.0 -2015-04-07,CA,3,A,A2,6.57,703.0 -2015-04-08,CA,3,A,A2,6.62,762.0 -2015-04-09,CA,3,A,A2,6.67,753.0 -2015-04-10,CA,3,A,A2,6.47,704.0 -2015-04-11,CA,3,A,A2,6.67,799.0 -2015-04-12,CA,3,A,A2,6.51,709.0 -2015-04-13,CA,3,A,A2,6.59,746.0 -2015-04-14,CA,3,A,A2,6.53,700.0 -2015-04-15,CA,3,A,A2,6.49,748.0 -2015-04-16,CA,3,A,A2,6.55,730.0 -2015-04-17,CA,3,A,A2,6.53,748.0 -2015-04-18,CA,3,A,A2,6.49,793.0 -2015-04-19,CA,3,A,A2,6.51,710.0 -2015-04-20,CA,3,A,A2,6.45,735.0 -2015-04-21,CA,3,A,A2,6.53,778.0 -2015-04-22,CA,3,A,A2,6.57,739.0 -2015-04-23,CA,3,A,A2,6.62,720.0 -2015-04-24,CA,3,A,A2,6.63,737.0 -2015-04-25,CA,3,A,A2,6.53,761.0 -2015-04-26,CA,3,A,A2,6.6,760.0 -2015-04-27,CA,3,A,A2,6.45,752.0 -2015-04-28,CA,3,A,A2,6.53,725.0 -2015-04-29,CA,3,A,A2,6.55,750.0 -2015-04-30,CA,3,A,A2,6.61,713.0 -2015-05-01,CA,3,A,A2,6.59,733.0 -2015-05-02,CA,3,A,A2,6.47,859.0 -2015-05-03,CA,3,A,A2,6.64,724.0 -2015-05-04,CA,3,A,A2,6.54,789.0 -2015-05-05,CA,3,A,A2,6.6,689.0 -2015-05-06,CA,3,A,A2,6.47,758.0 -2015-05-07,CA,3,A,A2,6.59,707.0 -2015-05-08,CA,3,A,A2,6.53,734.0 -2015-05-09,CA,3,A,A2,6.45,735.0 -2015-05-10,CA,3,A,A2,6.64,758.0 -2015-05-11,CA,3,A,A2,6.53,778.0 -2015-05-12,CA,3,A,A2,6.5,713.0 -2015-05-13,CA,3,A,A2,6.58,757.0 -2015-05-14,CA,3,A,A2,6.56,710.0 -2015-05-15,CA,3,A,A2,6.57,743.0 -2015-05-16,CA,3,A,A2,6.57,875.0 -2015-05-17,CA,3,A,A2,6.62,738.0 -2015-05-18,CA,3,A,A2,6.67,724.0 -2015-05-19,CA,3,A,A2,6.54,706.0 -2015-05-20,CA,3,A,A2,6.45,703.0 -2015-05-21,CA,3,A,A2,6.54,706.0 -2015-05-22,CA,3,A,A2,6.58,763.0 -2015-05-23,CA,3,A,A2,6.64,770.0 -2015-05-24,CA,3,A,A2,6.59,732.0 -2015-05-25,CA,3,A,A2,6.53,751.0 -2015-05-26,CA,3,A,A2,6.67,681.0 -2015-05-27,CA,3,A,A2,6.48,786.0 -2015-05-28,CA,3,A,A2,6.58,742.0 -2015-05-29,CA,3,A,A2,6.42,721.0 -2015-05-30,CA,3,A,A2,6.56,771.0 -2015-05-31,CA,3,A,A2,6.59,728.0 -2015-06-01,CA,3,A,A2,6.6,763.0 -2015-06-02,CA,3,A,A2,6.52,760.0 -2015-06-03,CA,3,A,A2,6.53,721.0 -2015-06-04,CA,3,A,A2,6.45,736.0 -2015-06-05,CA,3,A,A2,6.54,737.0 -2015-06-06,CA,3,A,A2,6.58,764.0 -2015-06-07,CA,3,A,A2,6.61,715.0 -2015-06-08,CA,3,A,A2,6.56,685.0 -2015-06-09,CA,3,A,A2,6.53,684.0 -2015-06-10,CA,3,A,A2,6.61,689.0 -2015-06-11,CA,3,A,A2,6.57,711.0 -2015-06-12,CA,3,A,A2,6.5,756.0 -2015-06-13,CA,3,A,A2,6.52,762.0 -2015-06-14,CA,3,A,A2,6.62,702.0 -2015-06-15,CA,3,A,A2,6.58,717.0 -2015-06-16,CA,3,A,A2,6.54,741.0 -2015-06-17,CA,3,A,A2,6.7,692.0 -2015-06-18,CA,3,A,A2,6.56,747.0 -2015-06-19,CA,3,A,A2,6.53,746.0 -2015-06-20,CA,3,A,A2,6.45,733.0 -2015-06-21,CA,3,A,A2,6.54,778.0 -2015-06-22,CA,3,A,A2,6.4,756.0 -2015-06-23,CA,3,A,A2,6.53,779.0 -2015-06-24,CA,3,A,A2,6.5,720.0 -2015-06-25,CA,3,A,A2,6.55,770.0 -2015-06-26,CA,3,A,A2,6.52,755.0 -2015-06-27,CA,3,A,A2,6.53,793.0 -2015-06-28,CA,3,A,A2,6.49,718.0 -2015-06-29,CA,3,A,A2,6.64,753.0 -2015-06-30,CA,3,A,A2,6.62,742.0 -2015-07-01,CA,3,A,A2,6.59,712.0 -2015-07-02,CA,3,A,A2,6.58,683.0 -2015-07-03,CA,3,A,A2,6.61,724.0 -2015-07-04,CA,3,A,A2,6.56,717.0 -2015-07-05,CA,3,A,A2,6.53,689.0 -2015-07-06,CA,3,A,A2,6.67,728.0 -2015-07-07,CA,3,A,A2,6.55,691.0 -2015-07-08,CA,3,A,A2,6.49,700.0 -2015-07-09,CA,3,A,A2,6.51,709.0 -2015-07-10,CA,3,A,A2,6.58,704.0 -2015-07-11,CA,3,A,A2,6.48,765.0 -2015-07-12,CA,3,A,A2,6.54,751.0 -2015-07-13,CA,3,A,A2,6.43,691.0 -2015-07-14,CA,3,A,A2,6.53,739.0 -2015-07-15,CA,3,A,A2,6.62,736.0 -2015-07-16,CA,3,A,A2,6.58,736.0 -2015-07-17,CA,3,A,A2,6.53,719.0 -2015-07-18,CA,3,A,A2,6.6,739.0 -2015-07-19,CA,3,A,A2,6.49,781.0 -2015-07-20,CA,3,A,A2,6.59,689.0 -2015-07-21,CA,3,A,A2,6.46,713.0 -2015-07-22,CA,3,A,A2,6.51,714.0 -2015-07-23,CA,3,A,A2,6.55,695.0 -2015-07-24,CA,3,A,A2,6.6,681.0 -2015-07-25,CA,3,A,A2,6.5,781.0 -2015-07-26,CA,3,A,A2,6.58,799.0 -2015-07-27,CA,3,A,A2,6.54,687.0 -2015-07-28,CA,3,A,A2,6.5,685.0 -2015-07-29,CA,3,A,A2,6.61,708.0 -2015-07-30,CA,3,A,A2,6.51,742.0 -2015-07-31,CA,3,A,A2,6.54,726.0 -2015-08-01,CA,3,A,A2,6.54,725.0 -2015-08-02,CA,3,A,A2,6.46,734.0 -2015-08-03,CA,3,A,A2,6.53,724.0 -2015-08-04,CA,3,A,A2,6.42,677.0 -2015-08-05,CA,3,A,A2,6.57,777.0 -2015-08-06,CA,3,A,A2,6.61,764.0 -2015-08-07,CA,3,A,A2,6.57,768.0 -2015-08-08,CA,3,A,A2,6.47,766.0 -2015-08-09,CA,3,A,A2,6.6,696.0 -2015-08-10,CA,3,A,A2,6.58,745.0 -2015-08-11,CA,3,A,A2,6.67,733.0 -2015-08-12,CA,3,A,A2,6.45,709.0 -2015-08-13,CA,3,A,A2,6.56,701.0 -2015-08-14,CA,3,A,A2,6.35,744.0 -2015-08-15,CA,3,A,A2,6.55,763.0 -2015-08-16,CA,3,A,A2,6.43,727.0 -2015-08-17,CA,3,A,A2,6.5,736.0 -2015-08-18,CA,3,A,A2,6.57,743.0 -2015-08-19,CA,3,A,A2,6.43,733.0 -2015-08-20,CA,3,A,A2,6.64,709.0 -2015-08-21,CA,3,A,A2,6.59,718.0 -2015-08-22,CA,3,A,A2,6.55,735.0 -2015-08-23,CA,3,A,A2,6.55,750.0 -2015-08-24,CA,3,A,A2,6.54,701.0 -2015-08-25,CA,3,A,A2,6.53,730.0 -2015-08-26,CA,3,A,A2,6.53,754.0 -2015-08-27,CA,3,A,A2,6.61,746.0 -2015-08-28,CA,3,A,A2,6.65,724.0 -2015-08-29,CA,3,A,A2,6.53,728.0 -2015-08-30,CA,3,A,A2,6.63,729.0 -2015-08-31,CA,3,A,A2,6.6,659.0 -2015-09-01,CA,3,A,A2,6.56,708.0 -2015-09-02,CA,3,A,A2,6.69,790.0 -2015-09-03,CA,3,A,A2,6.61,759.0 -2015-09-04,CA,3,A,A2,6.55,756.0 -2015-09-05,CA,3,A,A2,6.56,724.0 -2015-09-06,CA,3,A,A2,6.62,736.0 -2015-09-07,CA,3,A,A2,6.56,711.0 -2015-09-08,CA,3,A,A2,6.54,745.0 -2015-09-09,CA,3,A,A2,6.65,745.0 -2015-09-10,CA,3,A,A2,6.57,729.0 -2015-09-11,CA,3,A,A2,6.6,733.0 -2015-09-12,CA,3,A,A2,6.53,709.0 -2015-09-13,CA,3,A,A2,6.46,767.0 -2015-09-14,CA,3,A,A2,6.52,714.0 -2015-09-15,CA,3,A,A2,6.58,695.0 -2015-09-16,CA,3,A,A2,6.5,728.0 -2015-09-17,CA,3,A,A2,6.51,674.0 -2015-09-18,CA,3,A,A2,6.56,768.0 -2015-09-19,CA,3,A,A2,6.55,772.0 -2015-09-20,CA,3,A,A2,6.5,792.0 -2015-09-21,CA,3,A,A2,6.58,717.0 -2015-09-22,CA,3,A,A2,6.53,805.0 -2015-09-23,CA,3,A,A2,6.62,751.0 -2015-09-24,CA,3,A,A2,6.51,742.0 -2015-09-25,CA,3,A,A2,6.53,727.0 -2015-09-26,CA,3,A,A2,6.6,754.0 -2015-09-27,CA,3,A,A2,6.64,722.0 -2015-09-28,CA,3,A,A2,6.37,752.0 -2015-09-29,CA,3,A,A2,6.57,751.0 -2015-09-30,CA,3,A,A2,6.63,759.0 -2015-10-01,CA,3,A,A2,6.5,747.0 -2015-10-02,CA,3,A,A2,6.57,748.0 -2015-10-03,CA,3,A,A2,6.5,744.0 -2015-10-04,CA,3,A,A2,6.56,698.0 -2015-10-05,CA,3,A,A2,6.61,756.0 -2015-10-06,CA,3,A,A2,6.55,723.0 -2015-10-07,CA,3,A,A2,6.62,671.0 -2015-10-08,CA,3,A,A2,6.57,723.0 -2015-10-09,CA,3,A,A2,6.47,704.0 -2015-10-10,CA,3,A,A2,6.46,806.0 -2015-10-11,CA,3,A,A2,6.48,743.0 -2015-10-12,CA,3,A,A2,6.48,737.0 -2015-10-13,CA,3,A,A2,6.65,784.0 -2015-10-14,CA,3,A,A2,6.59,710.0 -2015-10-15,CA,3,A,A2,6.45,683.0 -2015-10-16,CA,3,A,A2,6.53,725.0 -2015-10-17,CA,3,A,A2,6.53,755.0 -2015-10-18,CA,3,A,A2,6.59,752.0 -2015-10-19,CA,3,A,A2,6.63,706.0 -2015-10-20,CA,3,A,A2,6.54,754.0 -2015-10-21,CA,3,A,A2,6.56,767.0 -2015-10-22,CA,3,A,A2,6.53,659.0 -2015-10-23,CA,3,A,A2,6.62,761.0 -2015-10-24,CA,3,A,A2,6.58,749.0 -2015-10-25,CA,3,A,A2,6.66,723.0 -2015-10-26,CA,3,A,A2,6.49,752.0 -2015-10-27,CA,3,A,A2,6.67,765.0 -2015-10-28,CA,3,A,A2,6.51,710.0 -2015-10-29,CA,3,A,A2,6.64,810.0 -2015-10-30,CA,3,A,A2,6.56,692.0 -2015-10-31,CA,3,A,A2,6.53,765.0 -2015-11-01,CA,3,A,A2,6.46,719.0 -2015-11-02,CA,3,A,A2,6.48,749.0 -2015-11-03,CA,3,A,A2,6.47,759.0 -2015-11-04,CA,3,A,A2,6.49,739.0 -2015-11-05,CA,3,A,A2,6.51,709.0 -2015-11-06,CA,3,A,A2,6.62,699.0 -2015-11-07,CA,3,A,A2,6.47,745.0 -2015-11-08,CA,3,A,A2,6.74,725.0 -2015-11-09,CA,3,A,A2,6.43,739.0 -2015-11-10,CA,3,A,A2,6.6,713.0 -2015-11-11,CA,3,A,A2,6.61,795.0 -2015-11-12,CA,3,A,A2,6.59,661.0 -2015-11-13,CA,3,A,A2,6.57,736.0 -2015-11-14,CA,3,A,A2,6.58,764.0 -2015-11-15,CA,3,A,A2,6.53,787.0 -2015-11-16,CA,3,A,A2,6.45,749.0 -2015-11-17,CA,3,A,A2,6.56,742.0 -2015-11-18,CA,3,A,A2,6.59,730.0 -2015-11-19,CA,3,A,A2,6.54,761.0 -2015-11-20,CA,3,A,A2,6.59,742.0 -2015-11-21,CA,3,A,A2,6.57,710.0 -2015-11-22,CA,3,A,A2,6.51,751.0 -2015-11-23,CA,3,A,A2,6.55,780.0 -2015-11-24,CA,3,A,A2,6.41,732.0 -2015-11-25,CA,3,A,A2,6.48,737.0 -2015-11-26,CA,3,A,A2,6.65,759.0 -2015-11-27,CA,3,A,A2,6.62,698.0 -2015-11-28,CA,3,A,A2,6.58,708.0 -2015-11-29,CA,3,A,A2,6.55,772.0 -2015-11-30,CA,3,A,A2,6.53,654.0 -2015-12-01,CA,3,A,A2,6.58,724.0 -2015-12-02,CA,3,A,A2,6.47,725.0 -2015-12-03,CA,3,A,A2,6.52,704.0 -2015-12-04,CA,3,A,A2,6.55,790.0 -2015-12-05,CA,3,A,A2,6.6,794.0 -2015-12-06,CA,3,A,A2,6.57,699.0 -2015-12-07,CA,3,A,A2,6.62,675.0 -2015-12-08,CA,3,A,A2,6.57,747.0 -2015-12-09,CA,3,A,A2,6.51,751.0 -2015-12-10,CA,3,A,A2,6.51,754.0 -2015-12-11,CA,3,A,A2,6.54,696.0 -2015-12-12,CA,3,A,A2,6.52,767.0 -2015-12-13,CA,3,A,A2,6.64,740.0 -2015-12-14,CA,3,A,A2,6.49,709.0 -2015-12-15,CA,3,A,A2,6.48,775.0 -2015-12-16,CA,3,A,A2,6.45,708.0 -2015-12-17,CA,3,A,A2,6.62,726.0 -2015-12-18,CA,3,A,A2,6.38,736.0 -2015-12-19,CA,3,A,A2,6.47,701.0 -2015-12-20,CA,3,A,A2,6.51,771.0 -2015-12-21,CA,3,A,A2,6.51,700.0 -2015-12-22,CA,3,A,A2,6.56,689.0 -2015-12-23,CA,3,A,A2,6.43,736.0 -2015-12-24,CA,3,A,A2,6.56,698.0 -2015-12-25,CA,3,A,A2,6.55,812.0 -2015-12-26,CA,3,A,A2,6.37,781.0 -2015-12-27,CA,3,A,A2,6.48,768.0 -2015-12-28,CA,3,A,A2,6.61,735.0 -2015-12-29,CA,3,A,A2,6.5,753.0 -2015-12-30,CA,3,A,A2,6.59,716.0 -2015-12-31,CA,3,A,A2,6.62,755.0 -2016-01-01,CA,3,A,A2,6.56,729.0 -2016-01-02,CA,3,A,A2,6.62,690.0 -2016-01-03,CA,3,A,A2,6.56,753.0 -2016-01-04,CA,3,A,A2,6.64,743.0 -2016-01-05,CA,3,A,A2,6.44,701.0 -2016-01-06,CA,3,A,A2,6.51,723.0 -2016-01-07,CA,3,A,A2,6.55,747.0 -2016-01-08,CA,3,A,A2,6.6,700.0 -2016-01-09,CA,3,A,A2,6.63,802.0 -2016-01-10,CA,3,A,A2,6.63,735.0 -2016-01-11,CA,3,A,A2,6.52,696.0 -2016-01-12,CA,3,A,A2,6.49,658.0 -2016-01-13,CA,3,A,A2,6.65,762.0 -2016-01-14,CA,3,A,A2,6.54,712.0 -2016-01-15,CA,3,A,A2,6.66,706.0 -2016-01-16,CA,3,A,A2,6.57,750.0 -2016-01-17,CA,3,A,A2,6.49,747.0 -2016-01-18,CA,3,A,A2,6.46,740.0 -2016-01-19,CA,3,A,A2,6.55,763.0 -2016-01-20,CA,3,A,A2,6.48,691.0 -2016-01-21,CA,3,A,A2,6.48,739.0 -2016-01-22,CA,3,A,A2,6.69,685.0 -2016-01-23,CA,3,A,A2,6.64,808.0 -2016-01-24,CA,3,A,A2,6.62,688.0 -2016-01-25,CA,3,A,A2,6.5,709.0 -2016-01-26,CA,3,A,A2,6.63,723.0 -2016-01-27,CA,3,A,A2,6.47,779.0 -2016-01-28,CA,3,A,A2,6.47,739.0 -2016-01-29,CA,3,A,A2,6.6,725.0 -2016-01-30,CA,3,A,A2,6.51,760.0 -2016-01-31,CA,3,A,A2,6.49,684.0 -2016-02-01,CA,3,A,A2,6.4,680.0 -2016-02-02,CA,3,A,A2,6.57,719.0 -2016-02-03,CA,3,A,A2,6.62,791.0 -2016-02-04,CA,3,A,A2,6.48,732.0 -2016-02-05,CA,3,A,A2,6.51,782.0 -2016-02-06,CA,3,A,A2,6.48,719.0 -2016-02-07,CA,3,A,A2,6.53,759.0 -2016-02-08,CA,3,A,A2,6.54,782.0 -2016-02-09,CA,3,A,A2,6.6,709.0 -2016-02-10,CA,3,A,A2,6.56,765.0 -2016-02-11,CA,3,A,A2,6.62,699.0 -2016-02-12,CA,3,A,A2,6.58,671.0 -2016-02-13,CA,3,A,A2,6.53,718.0 -2016-02-14,CA,3,A,A2,6.59,751.0 -2016-02-15,CA,3,A,A2,6.54,664.0 -2016-02-16,CA,3,A,A2,6.59,738.0 -2016-02-17,CA,3,A,A2,6.44,783.0 -2016-02-18,CA,3,A,A2,6.45,739.0 -2016-02-19,CA,3,A,A2,6.46,708.0 -2016-02-20,CA,3,A,A2,6.47,814.0 -2016-02-21,CA,3,A,A2,6.57,746.0 -2016-02-22,CA,3,A,A2,6.63,734.0 -2016-02-23,CA,3,A,A2,6.6,747.0 -2016-02-24,CA,3,A,A2,6.61,781.0 -2016-02-25,CA,3,A,A2,6.59,805.0 -2016-02-26,CA,3,A,A2,6.4,685.0 -2016-02-27,CA,3,A,A2,6.53,815.0 -2016-02-28,CA,3,A,A2,6.63,736.0 -2016-02-29,CA,3,A,A2,6.45,771.0 -2016-03-01,CA,3,A,A2,6.54,737.0 -2016-03-02,CA,3,A,A2,6.61,753.0 -2016-03-03,CA,3,A,A2,6.49,767.0 -2016-03-04,CA,3,A,A2,6.5,726.0 -2016-03-05,CA,3,A,A2,6.58,780.0 -2016-03-06,CA,3,A,A2,6.6,713.0 -2016-03-07,CA,3,A,A2,6.5,726.0 -2016-03-08,CA,3,A,A2,6.51,709.0 -2016-03-09,CA,3,A,A2,6.5,729.0 -2016-03-10,CA,3,A,A2,6.49,708.0 -2016-03-11,CA,3,A,A2,6.58,735.0 -2016-03-12,CA,3,A,A2,6.61,773.0 -2016-03-13,CA,3,A,A2,6.55,745.0 -2016-03-14,CA,3,A,A2,6.62,693.0 -2016-03-15,CA,3,A,A2,6.46,661.0 -2016-03-16,CA,3,A,A2,6.55,736.0 -2016-03-17,CA,3,A,A2,6.5,726.0 -2016-03-18,CA,3,A,A2,6.57,694.0 -2016-03-19,CA,3,A,A2,6.68,749.0 -2016-03-20,CA,3,A,A2,6.54,756.0 -2016-03-21,CA,3,A,A2,6.45,748.0 -2016-03-22,CA,3,A,A2,6.62,738.0 -2016-03-23,CA,3,A,A2,6.65,735.0 -2016-03-24,CA,3,A,A2,6.46,738.0 -2016-03-25,CA,3,A,A2,6.58,735.0 -2016-03-26,CA,3,A,A2,6.63,771.0 -2016-03-27,CA,3,A,A2,6.48,713.0 -2016-03-28,CA,3,A,A2,6.67,704.0 -2016-03-29,CA,3,A,A2,6.54,777.0 -2016-03-30,CA,3,A,A2,6.64,722.0 -2016-03-31,CA,3,A,A2,6.61,749.0 -2016-04-01,CA,3,A,A2,6.57,776.0 -2016-04-02,CA,3,A,A2,6.58,726.0 -2016-04-03,CA,3,A,A2,6.73,726.0 -2016-04-04,CA,3,A,A2,6.53,734.0 -2016-04-05,CA,3,A,A2,6.43,768.0 -2016-04-06,CA,3,A,A2,6.52,724.0 -2016-04-07,CA,3,A,A2,6.49,690.0 -2016-04-08,CA,3,A,A2,6.64,701.0 -2016-04-09,CA,3,A,A2,6.61,790.0 -2016-04-10,CA,3,A,A2,6.53,784.0 -2016-04-11,CA,3,A,A2,6.54,741.0 -2016-04-12,CA,3,A,A2,6.54,677.0 -2016-04-13,CA,3,A,A2,6.56,734.0 -2016-04-14,CA,3,A,A2,6.51,700.0 -2016-04-15,CA,3,A,A2,6.54,736.0 -2016-04-16,CA,3,A,A2,6.49,762.0 -2016-04-17,CA,3,A,A2,6.61,769.0 -2016-04-18,CA,3,A,A2,6.45,727.0 -2016-04-19,CA,3,A,A2,6.51,727.0 -2016-04-20,CA,3,A,A2,6.49,702.0 -2016-04-21,CA,3,A,A2,6.53,698.0 -2016-04-22,CA,3,A,A2,6.6,770.0 -2016-04-23,CA,3,A,A2,6.63,726.0 -2016-04-24,CA,3,A,A2,6.52,764.0 -2016-04-25,CA,3,A,A2,6.52,705.0 -2016-04-26,CA,3,A,A2,6.64,751.0 -2016-04-27,CA,3,A,A2,6.64,756.0 -2016-04-28,CA,3,A,A2,6.52,740.0 -2016-04-29,CA,3,A,A2,6.56,708.0 -2016-04-30,CA,3,A,A2,6.58,742.0 -2016-05-01,CA,3,A,A2,6.39,736.0 -2016-05-02,CA,3,A,A2,6.59,682.0 -2016-05-03,CA,3,A,A2,6.42,748.0 -2016-05-04,CA,3,A,A2,6.57,784.0 -2016-05-05,CA,3,A,A2,6.64,722.0 -2016-05-06,CA,3,A,A2,6.52,757.0 -2016-05-07,CA,3,A,A2,6.48,772.0 -2016-05-08,CA,3,A,A2,6.49,780.0 -2016-05-09,CA,3,A,A2,6.49,655.0 -2016-05-10,CA,3,A,A2,6.52,712.0 -2016-05-11,CA,3,A,A2,6.56,755.0 -2016-05-12,CA,3,A,A2,6.55,718.0 -2016-05-13,CA,3,A,A2,6.61,683.0 -2016-05-14,CA,3,A,A2,6.64,759.0 -2016-05-15,CA,3,A,A2,6.56,705.0 -2016-05-16,CA,3,A,A2,6.6,667.0 -2016-05-17,CA,3,A,A2,6.62,798.0 -2016-05-18,CA,3,A,A2,6.62,715.0 -2016-05-19,CA,3,A,A2,6.44,755.0 -2016-05-20,CA,3,A,A2,6.56,746.0 -2016-05-21,CA,3,A,A2,6.5,765.0 -2016-05-22,CA,3,A,A2,6.47,774.0 -2016-05-23,CA,3,A,A2,6.59,752.0 -2016-05-24,CA,3,A,A2,6.65,730.0 -2016-05-25,CA,3,A,A2,6.57,741.0 -2016-05-26,CA,3,A,A2,6.56,679.0 -2016-05-27,CA,3,A,A2,6.53,752.0 -2016-05-28,CA,3,A,A2,6.66,783.0 -2016-05-29,CA,3,A,A2,6.54,776.0 -2016-05-30,CA,3,A,A2,6.65,686.0 -2016-05-31,CA,3,A,A2,6.51,740.0 -2016-06-01,CA,3,A,A2,6.54,752.0 -2016-06-02,CA,3,A,A2,6.52,713.0 -2016-06-03,CA,3,A,A2,6.68,705.0 -2016-06-04,CA,3,A,A2,6.49,780.0 -2016-06-05,CA,3,A,A2,6.54,729.0 -2016-06-06,CA,3,A,A2,6.56,698.0 -2016-06-07,CA,3,A,A2,6.46,693.0 -2016-06-08,CA,3,A,A2,6.5,755.0 -2016-06-09,CA,3,A,A2,6.48,761.0 -2016-06-10,CA,3,A,A2,6.53,767.0 -2016-06-11,CA,3,A,A2,6.55,744.0 -2016-06-12,CA,3,A,A2,6.58,725.0 -2016-06-13,CA,3,A,A2,6.49,664.0 -2016-06-14,CA,3,A,A2,6.52,773.0 -2016-06-15,CA,3,A,A2,6.47,837.0 -2016-06-16,CA,3,A,A2,6.55,723.0 -2016-06-17,CA,3,A,A2,6.64,708.0 -2016-06-18,CA,3,A,A2,6.54,746.0 -2016-06-19,CA,3,A,A2,6.55,709.0 -2016-06-20,CA,3,A,A2,6.47,740.0 -2016-06-21,CA,3,A,A2,6.54,746.0 -2016-06-22,CA,3,A,A2,6.47,708.0 -2016-06-23,CA,3,A,A2,6.59,753.0 -2016-06-24,CA,3,A,A2,6.52,708.0 -2016-06-25,CA,3,A,A2,6.49,734.0 -2016-06-26,CA,3,A,A2,6.64,765.0 -2016-06-27,CA,3,A,A2,6.57,722.0 -2016-06-28,CA,3,A,A2,6.61,739.0 -2016-06-29,CA,3,A,A2,6.65,706.0 -2016-06-30,CA,3,A,A2,6.54,760.0 -2016-07-01,CA,3,A,A2,6.51,719.0 -2016-07-02,CA,3,A,A2,6.49,730.0 -2016-07-03,CA,3,A,A2,6.46,783.0 -2016-07-04,CA,3,A,A2,6.55,681.0 -2016-07-05,CA,3,A,A2,6.51,728.0 -2016-07-06,CA,3,A,A2,6.41,764.0 -2016-07-07,CA,3,A,A2,6.48,769.0 -2016-07-08,CA,3,A,A2,6.62,675.0 -2016-07-09,CA,3,A,A2,6.44,768.0 -2016-07-10,CA,3,A,A2,6.57,792.0 -2016-07-11,CA,3,A,A2,6.56,728.0 -2016-07-12,CA,3,A,A2,6.63,733.0 -2016-07-13,CA,3,A,A2,6.54,745.0 -2016-07-14,CA,3,A,A2,6.49,755.0 -2016-07-15,CA,3,A,A2,6.52,822.0 -2016-07-16,CA,3,A,A2,6.55,819.0 -2016-07-17,CA,3,A,A2,6.48,705.0 -2016-07-18,CA,3,A,A2,6.67,689.0 -2016-07-19,CA,3,A,A2,6.5,682.0 -2016-07-20,CA,3,A,A2,6.42,722.0 -2016-07-21,CA,3,A,A2,6.67,745.0 -2016-07-22,CA,3,A,A2,6.42,732.0 -2016-07-23,CA,3,A,A2,6.52,846.0 -2016-07-24,CA,3,A,A2,6.55,759.0 -2016-07-25,CA,3,A,A2,6.56,702.0 -2016-07-26,CA,3,A,A2,6.57,748.0 -2016-07-27,CA,3,A,A2,6.66,752.0 -2015-01-05,CA,3,A,A3,7.74,35.0 -2015-01-06,CA,3,A,A3,7.84,36.0 -2015-01-07,CA,3,A,A3,7.94,35.0 -2015-01-08,CA,3,A,A3,7.74,35.0 -2015-01-09,CA,3,A,A3,7.72,41.0 -2015-01-10,CA,3,A,A3,7.75,39.0 -2015-01-11,CA,3,A,A3,7.74,39.0 -2015-01-12,CA,3,A,A3,7.89,35.0 -2015-01-13,CA,3,A,A3,7.88,36.0 -2015-01-14,CA,3,A,A3,7.79,35.0 -2015-01-15,CA,3,A,A3,7.89,36.0 -2015-01-16,CA,3,A,A3,7.84,38.0 -2015-01-17,CA,3,A,A3,7.77,40.0 -2015-01-18,CA,3,A,A3,7.84,37.0 -2015-01-19,CA,3,A,A3,7.78,38.0 -2015-01-20,CA,3,A,A3,7.7,39.0 -2015-01-21,CA,3,A,A3,7.76,39.0 -2015-01-22,CA,3,A,A3,7.71,40.0 -2015-01-23,CA,3,A,A3,7.74,38.0 -2015-01-24,CA,3,A,A3,7.61,42.0 -2015-01-25,CA,3,A,A3,8.02,33.0 -2015-01-26,CA,3,A,A3,7.85,34.0 -2015-01-27,CA,3,A,A3,7.75,37.0 -2015-01-28,CA,3,A,A3,7.83,38.0 -2015-01-29,CA,3,A,A3,7.82,38.0 -2015-01-30,CA,3,A,A3,7.78,41.0 -2015-01-31,CA,3,A,A3,7.8,42.0 -2015-02-01,CA,3,A,A3,7.74,36.0 -2015-02-02,CA,3,A,A3,7.62,40.0 -2015-02-03,CA,3,A,A3,7.84,37.0 -2015-02-04,CA,3,A,A3,7.77,38.0 -2015-02-05,CA,3,A,A3,7.84,37.0 -2015-02-06,CA,3,A,A3,7.84,37.0 -2015-02-07,CA,3,A,A3,7.73,44.0 -2015-02-08,CA,3,A,A3,7.77,41.0 -2015-02-09,CA,3,A,A3,7.67,37.0 -2015-02-10,CA,3,A,A3,7.74,38.0 -2015-02-11,CA,3,A,A3,7.89,37.0 -2015-02-12,CA,3,A,A3,7.76,41.0 -2015-02-13,CA,3,A,A3,7.81,36.0 -2015-02-14,CA,3,A,A3,7.67,40.0 -2015-02-15,CA,3,A,A3,7.75,37.0 -2015-02-16,CA,3,A,A3,7.69,40.0 -2015-02-17,CA,3,A,A3,7.87,36.0 -2015-02-18,CA,3,A,A3,7.74,39.0 -2015-02-19,CA,3,A,A3,7.74,38.0 -2015-02-20,CA,3,A,A3,7.69,38.0 -2015-02-21,CA,3,A,A3,7.84,40.0 -2015-02-22,CA,3,A,A3,7.73,40.0 -2015-02-23,CA,3,A,A3,7.76,39.0 -2015-02-24,CA,3,A,A3,7.72,37.0 -2015-02-25,CA,3,A,A3,7.71,36.0 -2015-02-26,CA,3,A,A3,7.71,37.0 -2015-02-27,CA,3,A,A3,7.81,41.0 -2015-02-28,CA,3,A,A3,7.74,40.0 -2015-03-01,CA,3,A,A3,7.88,38.0 -2015-03-02,CA,3,A,A3,7.81,38.0 -2015-03-03,CA,3,A,A3,7.84,39.0 -2015-03-04,CA,3,A,A3,7.89,40.0 -2015-03-05,CA,3,A,A3,7.75,33.0 -2015-03-06,CA,3,A,A3,7.74,38.0 -2015-03-07,CA,3,A,A3,7.83,37.0 -2015-03-08,CA,3,A,A3,7.86,40.0 -2015-03-09,CA,3,A,A3,7.72,38.0 -2015-03-10,CA,3,A,A3,7.8,39.0 -2015-03-11,CA,3,A,A3,7.69,40.0 -2015-03-12,CA,3,A,A3,7.73,40.0 -2015-03-13,CA,3,A,A3,7.76,40.0 -2015-03-14,CA,3,A,A3,7.78,43.0 -2015-03-15,CA,3,A,A3,7.89,40.0 -2015-03-16,CA,3,A,A3,7.93,38.0 -2015-03-17,CA,3,A,A3,7.75,38.0 -2015-03-18,CA,3,A,A3,7.78,39.0 -2015-03-19,CA,3,A,A3,7.86,37.0 -2015-03-20,CA,3,A,A3,7.78,34.0 -2015-03-21,CA,3,A,A3,7.81,44.0 -2015-03-22,CA,3,A,A3,7.92,37.0 -2015-03-23,CA,3,A,A3,7.71,40.0 -2015-03-24,CA,3,A,A3,7.88,37.0 -2015-03-25,CA,3,A,A3,7.82,37.0 -2015-03-26,CA,3,A,A3,7.76,39.0 -2015-03-27,CA,3,A,A3,7.75,33.0 -2015-03-28,CA,3,A,A3,7.84,38.0 -2015-03-29,CA,3,A,A3,7.78,38.0 -2015-03-30,CA,3,A,A3,7.88,36.0 -2015-03-31,CA,3,A,A3,7.73,38.0 -2015-04-01,CA,3,A,A3,7.76,38.0 -2015-04-02,CA,3,A,A3,7.85,40.0 -2015-04-03,CA,3,A,A3,7.8,39.0 -2015-04-04,CA,3,A,A3,7.76,42.0 -2015-04-05,CA,3,A,A3,7.6,41.0 -2015-04-06,CA,3,A,A3,7.74,40.0 -2015-04-07,CA,3,A,A3,7.93,37.0 -2015-04-08,CA,3,A,A3,7.79,40.0 -2015-04-09,CA,3,A,A3,7.73,39.0 -2015-04-10,CA,3,A,A3,7.89,40.0 -2015-04-11,CA,3,A,A3,7.87,40.0 -2015-04-12,CA,3,A,A3,7.7,40.0 -2015-04-13,CA,3,A,A3,7.6,40.0 -2015-04-14,CA,3,A,A3,7.8,37.0 -2015-04-15,CA,3,A,A3,7.82,39.0 -2015-04-16,CA,3,A,A3,7.8,38.0 -2015-04-17,CA,3,A,A3,7.87,37.0 -2015-04-18,CA,3,A,A3,7.84,35.0 -2015-04-19,CA,3,A,A3,7.82,36.0 -2015-04-20,CA,3,A,A3,7.82,37.0 -2015-04-21,CA,3,A,A3,7.82,42.0 -2015-04-22,CA,3,A,A3,7.66,39.0 -2015-04-23,CA,3,A,A3,7.73,33.0 -2015-04-24,CA,3,A,A3,7.65,37.0 -2015-04-25,CA,3,A,A3,7.91,38.0 -2015-04-26,CA,3,A,A3,7.82,35.0 -2015-04-27,CA,3,A,A3,7.79,38.0 -2015-04-28,CA,3,A,A3,7.77,37.0 -2015-04-29,CA,3,A,A3,7.83,36.0 -2015-04-30,CA,3,A,A3,7.79,35.0 -2015-05-01,CA,3,A,A3,7.89,38.0 -2015-05-02,CA,3,A,A3,7.81,38.0 -2015-05-03,CA,3,A,A3,7.95,36.0 -2015-05-04,CA,3,A,A3,7.68,36.0 -2015-05-05,CA,3,A,A3,7.89,34.0 -2015-05-06,CA,3,A,A3,7.76,35.0 -2015-05-07,CA,3,A,A3,7.72,39.0 -2015-05-08,CA,3,A,A3,7.67,41.0 -2015-05-09,CA,3,A,A3,7.73,43.0 -2015-05-10,CA,3,A,A3,7.8,41.0 -2015-05-11,CA,3,A,A3,7.71,38.0 -2015-05-12,CA,3,A,A3,7.92,34.0 -2015-05-13,CA,3,A,A3,8.06,36.0 -2015-05-14,CA,3,A,A3,7.77,37.0 -2015-05-15,CA,3,A,A3,7.8,40.0 -2015-05-16,CA,3,A,A3,7.78,38.0 -2015-05-17,CA,3,A,A3,7.71,42.0 -2015-05-18,CA,3,A,A3,7.87,37.0 -2015-05-19,CA,3,A,A3,7.74,40.0 -2015-05-20,CA,3,A,A3,7.74,38.0 -2015-05-21,CA,3,A,A3,7.88,40.0 -2015-05-22,CA,3,A,A3,7.56,40.0 -2015-05-23,CA,3,A,A3,7.7,42.0 -2015-05-24,CA,3,A,A3,7.82,38.0 -2015-05-25,CA,3,A,A3,7.82,39.0 -2015-05-26,CA,3,A,A3,7.74,40.0 -2015-05-27,CA,3,A,A3,7.77,37.0 -2015-05-28,CA,3,A,A3,7.9,35.0 -2015-05-29,CA,3,A,A3,7.85,40.0 -2015-05-30,CA,3,A,A3,7.8,37.0 -2015-05-31,CA,3,A,A3,7.87,39.0 -2015-06-01,CA,3,A,A3,7.66,38.0 -2015-06-02,CA,3,A,A3,7.96,35.0 -2015-06-03,CA,3,A,A3,7.82,38.0 -2015-06-04,CA,3,A,A3,7.84,37.0 -2015-06-05,CA,3,A,A3,7.77,38.0 -2015-06-06,CA,3,A,A3,7.7,43.0 -2015-06-07,CA,3,A,A3,7.76,39.0 -2015-06-08,CA,3,A,A3,7.72,37.0 -2015-06-09,CA,3,A,A3,7.81,40.0 -2015-06-10,CA,3,A,A3,7.76,37.0 -2015-06-11,CA,3,A,A3,7.8,43.0 -2015-06-12,CA,3,A,A3,7.91,35.0 -2015-06-13,CA,3,A,A3,7.67,42.0 -2015-06-14,CA,3,A,A3,7.82,36.0 -2015-06-15,CA,3,A,A3,7.79,36.0 -2015-06-16,CA,3,A,A3,7.74,42.0 -2015-06-17,CA,3,A,A3,7.82,39.0 -2015-06-18,CA,3,A,A3,7.83,35.0 -2015-06-19,CA,3,A,A3,7.82,39.0 -2015-06-20,CA,3,A,A3,7.79,39.0 -2015-06-21,CA,3,A,A3,8.0,35.0 -2015-06-22,CA,3,A,A3,7.86,39.0 -2015-06-23,CA,3,A,A3,7.79,39.0 -2015-06-24,CA,3,A,A3,7.78,38.0 -2015-06-25,CA,3,A,A3,7.94,37.0 -2015-06-26,CA,3,A,A3,7.86,37.0 -2015-06-27,CA,3,A,A3,7.78,42.0 -2015-06-28,CA,3,A,A3,7.75,37.0 -2015-06-29,CA,3,A,A3,7.74,39.0 -2015-06-30,CA,3,A,A3,7.93,37.0 -2015-07-01,CA,3,A,A3,7.78,36.0 -2015-07-02,CA,3,A,A3,7.87,38.0 -2015-07-03,CA,3,A,A3,7.87,37.0 -2015-07-04,CA,3,A,A3,7.87,40.0 -2015-07-05,CA,3,A,A3,7.77,38.0 -2015-07-06,CA,3,A,A3,7.72,37.0 -2015-07-07,CA,3,A,A3,7.77,39.0 -2015-07-08,CA,3,A,A3,7.77,39.0 -2015-07-09,CA,3,A,A3,7.83,39.0 -2015-07-10,CA,3,A,A3,7.8,37.0 -2015-07-11,CA,3,A,A3,7.94,36.0 -2015-07-12,CA,3,A,A3,7.81,36.0 -2015-07-13,CA,3,A,A3,7.75,38.0 -2015-07-14,CA,3,A,A3,7.71,39.0 -2015-07-15,CA,3,A,A3,7.69,41.0 -2015-07-16,CA,3,A,A3,7.82,39.0 -2015-07-17,CA,3,A,A3,7.87,38.0 -2015-07-18,CA,3,A,A3,7.72,38.0 -2015-07-19,CA,3,A,A3,7.86,35.0 -2015-07-20,CA,3,A,A3,7.75,37.0 -2015-07-21,CA,3,A,A3,7.77,36.0 -2015-07-22,CA,3,A,A3,7.73,39.0 -2015-07-23,CA,3,A,A3,7.73,42.0 -2015-07-24,CA,3,A,A3,7.64,42.0 -2015-07-25,CA,3,A,A3,7.84,41.0 -2015-07-26,CA,3,A,A3,7.73,37.0 -2015-07-27,CA,3,A,A3,7.82,35.0 -2015-07-28,CA,3,A,A3,7.85,38.0 -2015-07-29,CA,3,A,A3,7.82,36.0 -2015-07-30,CA,3,A,A3,7.79,37.0 -2015-07-31,CA,3,A,A3,7.67,41.0 -2015-08-01,CA,3,A,A3,7.93,37.0 -2015-08-02,CA,3,A,A3,7.71,38.0 -2015-08-03,CA,3,A,A3,7.83,38.0 -2015-08-04,CA,3,A,A3,7.82,37.0 -2015-08-05,CA,3,A,A3,7.94,36.0 -2015-08-06,CA,3,A,A3,7.91,36.0 -2015-08-07,CA,3,A,A3,7.75,39.0 -2015-08-08,CA,3,A,A3,7.7,39.0 -2015-08-09,CA,3,A,A3,7.79,38.0 -2015-08-10,CA,3,A,A3,7.82,37.0 -2015-08-11,CA,3,A,A3,7.81,36.0 -2015-08-12,CA,3,A,A3,7.8,39.0 -2015-08-13,CA,3,A,A3,7.89,35.0 -2015-08-14,CA,3,A,A3,7.85,37.0 -2015-08-15,CA,3,A,A3,7.74,41.0 -2015-08-16,CA,3,A,A3,7.92,35.0 -2015-08-17,CA,3,A,A3,7.87,35.0 -2015-08-18,CA,3,A,A3,7.79,38.0 -2015-08-19,CA,3,A,A3,7.85,39.0 -2015-08-20,CA,3,A,A3,7.79,38.0 -2015-08-21,CA,3,A,A3,7.8,38.0 -2015-08-22,CA,3,A,A3,7.63,42.0 -2015-08-23,CA,3,A,A3,7.83,40.0 -2015-08-24,CA,3,A,A3,7.78,35.0 -2015-08-25,CA,3,A,A3,7.76,42.0 -2015-08-26,CA,3,A,A3,7.67,39.0 -2015-08-27,CA,3,A,A3,7.62,38.0 -2015-08-28,CA,3,A,A3,7.73,41.0 -2015-08-29,CA,3,A,A3,7.85,40.0 -2015-08-30,CA,3,A,A3,7.63,40.0 -2015-08-31,CA,3,A,A3,7.97,35.0 -2015-09-01,CA,3,A,A3,7.77,35.0 -2015-09-02,CA,3,A,A3,7.77,34.0 -2015-09-03,CA,3,A,A3,7.9,35.0 -2015-09-04,CA,3,A,A3,7.71,42.0 -2015-09-05,CA,3,A,A3,7.87,40.0 -2015-09-06,CA,3,A,A3,7.79,39.0 -2015-09-07,CA,3,A,A3,7.86,37.0 -2015-09-08,CA,3,A,A3,7.78,35.0 -2015-09-09,CA,3,A,A3,7.91,38.0 -2015-09-10,CA,3,A,A3,7.87,37.0 -2015-09-11,CA,3,A,A3,7.82,35.0 -2015-09-12,CA,3,A,A3,7.76,38.0 -2015-09-13,CA,3,A,A3,7.82,39.0 -2015-09-14,CA,3,A,A3,7.67,42.0 -2015-09-15,CA,3,A,A3,7.86,40.0 -2015-09-16,CA,3,A,A3,7.79,37.0 -2015-09-17,CA,3,A,A3,7.78,40.0 -2015-09-18,CA,3,A,A3,7.73,37.0 -2015-09-19,CA,3,A,A3,7.77,40.0 -2015-09-20,CA,3,A,A3,7.89,39.0 -2015-09-21,CA,3,A,A3,7.76,40.0 -2015-09-22,CA,3,A,A3,7.8,37.0 -2015-09-23,CA,3,A,A3,7.75,39.0 -2015-09-24,CA,3,A,A3,7.81,38.0 -2015-09-25,CA,3,A,A3,7.76,39.0 -2015-09-26,CA,3,A,A3,7.88,40.0 -2015-09-27,CA,3,A,A3,7.86,40.0 -2015-09-28,CA,3,A,A3,7.76,43.0 -2015-09-29,CA,3,A,A3,7.86,38.0 -2015-09-30,CA,3,A,A3,7.93,38.0 -2015-10-01,CA,3,A,A3,7.68,39.0 -2015-10-02,CA,3,A,A3,7.77,38.0 -2015-10-03,CA,3,A,A3,7.85,41.0 -2015-10-04,CA,3,A,A3,7.69,43.0 -2015-10-05,CA,3,A,A3,7.82,39.0 -2015-10-06,CA,3,A,A3,7.82,36.0 -2015-10-07,CA,3,A,A3,7.78,39.0 -2015-10-08,CA,3,A,A3,7.89,38.0 -2015-10-09,CA,3,A,A3,7.79,34.0 -2015-10-10,CA,3,A,A3,7.77,40.0 -2015-10-11,CA,3,A,A3,7.69,40.0 -2015-10-12,CA,3,A,A3,7.85,35.0 -2015-10-13,CA,3,A,A3,7.65,41.0 -2015-10-14,CA,3,A,A3,7.8,35.0 -2015-10-15,CA,3,A,A3,7.72,39.0 -2015-10-16,CA,3,A,A3,7.82,38.0 -2015-10-17,CA,3,A,A3,7.81,41.0 -2015-10-18,CA,3,A,A3,7.88,38.0 -2015-10-19,CA,3,A,A3,7.58,41.0 -2015-10-20,CA,3,A,A3,7.85,37.0 -2015-10-21,CA,3,A,A3,7.81,39.0 -2015-10-22,CA,3,A,A3,7.71,37.0 -2015-10-23,CA,3,A,A3,7.76,38.0 -2015-10-24,CA,3,A,A3,7.78,38.0 -2015-10-25,CA,3,A,A3,7.83,38.0 -2015-10-26,CA,3,A,A3,7.86,35.0 -2015-10-27,CA,3,A,A3,7.69,43.0 -2015-10-28,CA,3,A,A3,7.68,40.0 -2015-10-29,CA,3,A,A3,7.82,37.0 -2015-10-30,CA,3,A,A3,7.76,41.0 -2015-10-31,CA,3,A,A3,7.8,39.0 -2015-11-01,CA,3,A,A3,7.76,40.0 -2015-11-02,CA,3,A,A3,7.66,39.0 -2015-11-03,CA,3,A,A3,7.81,38.0 -2015-11-04,CA,3,A,A3,7.87,36.0 -2015-11-05,CA,3,A,A3,7.69,38.0 -2015-11-06,CA,3,A,A3,7.82,38.0 -2015-11-07,CA,3,A,A3,7.74,41.0 -2015-11-08,CA,3,A,A3,7.83,39.0 -2015-11-09,CA,3,A,A3,7.8,39.0 -2015-11-10,CA,3,A,A3,7.84,35.0 -2015-11-11,CA,3,A,A3,7.82,36.0 -2015-11-12,CA,3,A,A3,7.7,39.0 -2015-11-13,CA,3,A,A3,7.7,39.0 -2015-11-14,CA,3,A,A3,7.9,40.0 -2015-11-15,CA,3,A,A3,7.68,40.0 -2015-11-16,CA,3,A,A3,7.72,38.0 -2015-11-17,CA,3,A,A3,7.8,40.0 -2015-11-18,CA,3,A,A3,7.79,38.0 -2015-11-19,CA,3,A,A3,7.72,37.0 -2015-11-20,CA,3,A,A3,7.83,41.0 -2015-11-21,CA,3,A,A3,7.83,38.0 -2015-11-22,CA,3,A,A3,7.71,42.0 -2015-11-23,CA,3,A,A3,7.71,36.0 -2015-11-24,CA,3,A,A3,7.92,43.0 -2015-11-25,CA,3,A,A3,8.03,36.0 -2015-11-26,CA,3,A,A3,7.84,36.0 -2015-11-27,CA,3,A,A3,7.9,39.0 -2015-11-28,CA,3,A,A3,7.79,40.0 -2015-11-29,CA,3,A,A3,7.72,39.0 -2015-11-30,CA,3,A,A3,7.74,39.0 -2015-12-01,CA,3,A,A3,7.88,35.0 -2015-12-02,CA,3,A,A3,7.71,37.0 -2015-12-03,CA,3,A,A3,7.92,38.0 -2015-12-04,CA,3,A,A3,7.79,37.0 -2015-12-05,CA,3,A,A3,7.92,38.0 -2015-12-06,CA,3,A,A3,7.84,39.0 -2015-12-07,CA,3,A,A3,7.88,36.0 -2015-12-08,CA,3,A,A3,7.86,37.0 -2015-12-09,CA,3,A,A3,7.97,37.0 -2015-12-10,CA,3,A,A3,7.82,36.0 -2015-12-11,CA,3,A,A3,7.74,39.0 -2015-12-12,CA,3,A,A3,7.81,38.0 -2015-12-13,CA,3,A,A3,7.84,38.0 -2015-12-14,CA,3,A,A3,7.7,41.0 -2015-12-15,CA,3,A,A3,7.85,36.0 -2015-12-16,CA,3,A,A3,7.8,38.0 -2015-12-17,CA,3,A,A3,7.77,38.0 -2015-12-18,CA,3,A,A3,7.78,38.0 -2015-12-19,CA,3,A,A3,7.78,43.0 -2015-12-20,CA,3,A,A3,7.87,39.0 -2015-12-21,CA,3,A,A3,7.73,40.0 -2015-12-22,CA,3,A,A3,7.68,38.0 -2015-12-23,CA,3,A,A3,7.75,41.0 -2015-12-24,CA,3,A,A3,7.74,38.0 -2015-12-25,CA,3,A,A3,7.68,42.0 -2015-12-26,CA,3,A,A3,7.77,41.0 -2015-12-27,CA,3,A,A3,7.81,38.0 -2015-12-28,CA,3,A,A3,7.76,40.0 -2015-12-29,CA,3,A,A3,7.82,37.0 -2015-12-30,CA,3,A,A3,7.82,39.0 -2015-12-31,CA,3,A,A3,7.7,38.0 -2016-01-01,CA,3,A,A3,7.85,38.0 -2016-01-02,CA,3,A,A3,7.86,39.0 -2016-01-03,CA,3,A,A3,7.73,40.0 -2016-01-04,CA,3,A,A3,7.8,40.0 -2016-01-05,CA,3,A,A3,7.87,37.0 -2016-01-06,CA,3,A,A3,7.84,39.0 -2016-01-07,CA,3,A,A3,8.05,35.0 -2016-01-08,CA,3,A,A3,7.8,40.0 -2016-01-09,CA,3,A,A3,7.73,42.0 -2016-01-10,CA,3,A,A3,7.79,40.0 -2016-01-11,CA,3,A,A3,7.95,35.0 -2016-01-12,CA,3,A,A3,7.78,38.0 -2016-01-13,CA,3,A,A3,7.75,38.0 -2016-01-14,CA,3,A,A3,7.69,42.0 -2016-01-15,CA,3,A,A3,7.81,34.0 -2016-01-16,CA,3,A,A3,7.67,37.0 -2016-01-17,CA,3,A,A3,7.73,41.0 -2016-01-18,CA,3,A,A3,7.88,38.0 -2016-01-19,CA,3,A,A3,7.77,38.0 -2016-01-20,CA,3,A,A3,7.89,37.0 -2016-01-21,CA,3,A,A3,7.87,33.0 -2016-01-22,CA,3,A,A3,7.78,42.0 -2016-01-23,CA,3,A,A3,7.85,40.0 -2016-01-24,CA,3,A,A3,7.76,41.0 -2016-01-25,CA,3,A,A3,7.72,40.0 -2016-01-26,CA,3,A,A3,7.77,42.0 -2016-01-27,CA,3,A,A3,7.84,35.0 -2016-01-28,CA,3,A,A3,7.66,43.0 -2016-01-29,CA,3,A,A3,7.68,40.0 -2016-01-30,CA,3,A,A3,7.92,42.0 -2016-01-31,CA,3,A,A3,7.94,36.0 -2016-02-01,CA,3,A,A3,7.82,36.0 -2016-02-02,CA,3,A,A3,7.89,37.0 -2016-02-03,CA,3,A,A3,7.69,38.0 -2016-02-04,CA,3,A,A3,7.85,37.0 -2016-02-05,CA,3,A,A3,7.8,40.0 -2016-02-06,CA,3,A,A3,7.7,41.0 -2016-02-07,CA,3,A,A3,7.71,40.0 -2016-02-08,CA,3,A,A3,7.72,36.0 -2016-02-09,CA,3,A,A3,7.77,35.0 -2016-02-10,CA,3,A,A3,7.77,38.0 -2016-02-11,CA,3,A,A3,7.74,37.0 -2016-02-12,CA,3,A,A3,7.77,38.0 -2016-02-13,CA,3,A,A3,7.83,38.0 -2016-02-14,CA,3,A,A3,7.72,41.0 -2016-02-15,CA,3,A,A3,7.88,36.0 -2016-02-16,CA,3,A,A3,7.82,36.0 -2016-02-17,CA,3,A,A3,7.82,38.0 -2016-02-18,CA,3,A,A3,7.75,38.0 -2016-02-19,CA,3,A,A3,7.75,39.0 -2016-02-20,CA,3,A,A3,7.69,40.0 -2016-02-21,CA,3,A,A3,7.89,40.0 -2016-02-22,CA,3,A,A3,7.68,35.0 -2016-02-23,CA,3,A,A3,7.8,39.0 -2016-02-24,CA,3,A,A3,7.8,38.0 -2016-02-25,CA,3,A,A3,7.73,40.0 -2016-02-26,CA,3,A,A3,7.73,40.0 -2016-02-27,CA,3,A,A3,7.78,37.0 -2016-02-28,CA,3,A,A3,7.78,39.0 -2016-02-29,CA,3,A,A3,7.92,40.0 -2016-03-01,CA,3,A,A3,7.84,38.0 -2016-03-02,CA,3,A,A3,7.78,38.0 -2016-03-03,CA,3,A,A3,7.64,41.0 -2016-03-04,CA,3,A,A3,7.87,40.0 -2016-03-05,CA,3,A,A3,7.8,38.0 -2016-03-06,CA,3,A,A3,7.85,40.0 -2016-03-07,CA,3,A,A3,7.81,39.0 -2016-03-08,CA,3,A,A3,7.72,40.0 -2016-03-09,CA,3,A,A3,7.86,38.0 -2016-03-10,CA,3,A,A3,7.92,40.0 -2016-03-11,CA,3,A,A3,7.74,38.0 -2016-03-12,CA,3,A,A3,7.94,35.0 -2016-03-13,CA,3,A,A3,7.8,38.0 -2016-03-14,CA,3,A,A3,7.69,37.0 -2016-03-15,CA,3,A,A3,7.68,43.0 -2016-03-16,CA,3,A,A3,7.8,43.0 -2016-03-17,CA,3,A,A3,7.82,37.0 -2016-03-18,CA,3,A,A3,7.78,39.0 -2016-03-19,CA,3,A,A3,7.8,45.0 -2016-03-20,CA,3,A,A3,7.78,36.0 -2016-03-21,CA,3,A,A3,7.63,41.0 -2016-03-22,CA,3,A,A3,7.76,38.0 -2016-03-23,CA,3,A,A3,7.84,39.0 -2016-03-24,CA,3,A,A3,7.78,39.0 -2016-03-25,CA,3,A,A3,7.96,34.0 -2016-03-26,CA,3,A,A3,7.65,40.0 -2016-03-27,CA,3,A,A3,7.94,39.0 -2016-03-28,CA,3,A,A3,7.75,35.0 -2016-03-29,CA,3,A,A3,7.76,39.0 -2016-03-30,CA,3,A,A3,7.83,38.0 -2016-03-31,CA,3,A,A3,7.65,42.0 -2016-04-01,CA,3,A,A3,7.78,39.0 -2016-04-02,CA,3,A,A3,7.88,39.0 -2016-04-03,CA,3,A,A3,7.82,40.0 -2016-04-04,CA,3,A,A3,7.82,34.0 -2016-04-05,CA,3,A,A3,7.73,40.0 -2016-04-06,CA,3,A,A3,7.77,40.0 -2016-04-07,CA,3,A,A3,7.74,39.0 -2016-04-08,CA,3,A,A3,7.8,40.0 -2016-04-09,CA,3,A,A3,7.94,42.0 -2016-04-10,CA,3,A,A3,7.83,41.0 -2016-04-11,CA,3,A,A3,7.7,38.0 -2016-04-12,CA,3,A,A3,7.93,36.0 -2016-04-13,CA,3,A,A3,7.59,37.0 -2016-04-14,CA,3,A,A3,7.72,39.0 -2016-04-15,CA,3,A,A3,7.72,41.0 -2016-04-16,CA,3,A,A3,7.88,42.0 -2016-04-17,CA,3,A,A3,7.81,40.0 -2016-04-18,CA,3,A,A3,7.8,35.0 -2016-04-19,CA,3,A,A3,7.85,35.0 -2016-04-20,CA,3,A,A3,7.84,39.0 -2016-04-21,CA,3,A,A3,7.83,38.0 -2016-04-22,CA,3,A,A3,7.72,38.0 -2016-04-23,CA,3,A,A3,7.84,40.0 -2016-04-24,CA,3,A,A3,7.66,42.0 -2016-04-25,CA,3,A,A3,7.73,40.0 -2016-04-26,CA,3,A,A3,7.81,40.0 -2016-04-27,CA,3,A,A3,7.69,38.0 -2016-04-28,CA,3,A,A3,7.82,36.0 -2016-04-29,CA,3,A,A3,7.82,40.0 -2016-04-30,CA,3,A,A3,7.9,37.0 -2016-05-01,CA,3,A,A3,7.83,39.0 -2016-05-02,CA,3,A,A3,7.77,34.0 -2016-05-03,CA,3,A,A3,7.87,38.0 -2016-05-04,CA,3,A,A3,7.93,33.0 -2016-05-05,CA,3,A,A3,7.79,39.0 -2016-05-06,CA,3,A,A3,7.88,36.0 -2016-05-07,CA,3,A,A3,7.71,43.0 -2016-05-08,CA,3,A,A3,7.63,44.0 -2016-05-09,CA,3,A,A3,7.78,34.0 -2016-05-10,CA,3,A,A3,7.67,38.0 -2016-05-11,CA,3,A,A3,8.0,35.0 -2016-05-12,CA,3,A,A3,7.7,39.0 -2016-05-13,CA,3,A,A3,7.94,38.0 -2016-05-14,CA,3,A,A3,7.84,40.0 -2016-05-15,CA,3,A,A3,7.88,39.0 -2016-05-16,CA,3,A,A3,7.82,38.0 -2016-05-17,CA,3,A,A3,7.84,36.0 -2016-05-18,CA,3,A,A3,7.76,40.0 -2016-05-19,CA,3,A,A3,7.86,41.0 -2016-05-20,CA,3,A,A3,7.82,37.0 -2016-05-21,CA,3,A,A3,7.75,40.0 -2016-05-22,CA,3,A,A3,7.75,39.0 -2016-05-23,CA,3,A,A3,7.79,36.0 -2016-05-24,CA,3,A,A3,7.71,40.0 -2016-05-25,CA,3,A,A3,7.75,42.0 -2016-05-26,CA,3,A,A3,7.75,39.0 -2016-05-27,CA,3,A,A3,7.79,41.0 -2016-05-28,CA,3,A,A3,7.65,42.0 -2016-05-29,CA,3,A,A3,7.65,43.0 -2016-05-30,CA,3,A,A3,7.8,36.0 -2016-05-31,CA,3,A,A3,7.81,36.0 -2016-06-01,CA,3,A,A3,7.8,40.0 -2016-06-02,CA,3,A,A3,7.76,39.0 -2016-06-03,CA,3,A,A3,7.85,37.0 -2016-06-04,CA,3,A,A3,7.75,42.0 -2016-06-05,CA,3,A,A3,7.73,40.0 -2016-06-06,CA,3,A,A3,7.72,34.0 -2016-06-07,CA,3,A,A3,7.8,41.0 -2016-06-08,CA,3,A,A3,7.9,34.0 -2016-06-09,CA,3,A,A3,7.77,37.0 -2016-06-10,CA,3,A,A3,7.64,38.0 -2016-06-11,CA,3,A,A3,7.87,41.0 -2016-06-12,CA,3,A,A3,7.71,38.0 -2016-06-13,CA,3,A,A3,7.78,38.0 -2016-06-14,CA,3,A,A3,7.72,41.0 -2016-06-15,CA,3,A,A3,7.65,41.0 -2016-06-16,CA,3,A,A3,7.88,36.0 -2016-06-17,CA,3,A,A3,7.78,36.0 -2016-06-18,CA,3,A,A3,7.76,40.0 -2016-06-19,CA,3,A,A3,7.83,40.0 -2016-06-20,CA,3,A,A3,7.87,36.0 -2016-06-21,CA,3,A,A3,7.9,36.0 -2016-06-22,CA,3,A,A3,7.81,40.0 -2016-06-23,CA,3,A,A3,7.66,41.0 -2016-06-24,CA,3,A,A3,7.73,42.0 -2016-06-25,CA,3,A,A3,7.78,37.0 -2016-06-26,CA,3,A,A3,7.84,40.0 -2016-06-27,CA,3,A,A3,7.88,38.0 -2016-06-28,CA,3,A,A3,7.93,36.0 -2016-06-29,CA,3,A,A3,7.81,40.0 -2016-06-30,CA,3,A,A3,7.84,40.0 -2016-07-01,CA,3,A,A3,7.88,38.0 -2016-07-02,CA,3,A,A3,7.83,40.0 -2016-07-03,CA,3,A,A3,7.82,40.0 -2016-07-04,CA,3,A,A3,7.84,39.0 -2016-07-05,CA,3,A,A3,7.86,36.0 -2016-07-06,CA,3,A,A3,7.74,40.0 -2016-07-07,CA,3,A,A3,7.8,37.0 -2016-07-08,CA,3,A,A3,7.93,39.0 -2016-07-09,CA,3,A,A3,7.84,40.0 -2016-07-10,CA,3,A,A3,7.68,40.0 -2016-07-11,CA,3,A,A3,7.73,37.0 -2016-07-12,CA,3,A,A3,7.86,32.0 -2016-07-13,CA,3,A,A3,7.8,39.0 -2016-07-14,CA,3,A,A3,7.76,40.0 -2016-07-15,CA,3,A,A3,7.9,36.0 -2016-07-16,CA,3,A,A3,7.72,37.0 -2016-07-17,CA,3,A,A3,7.82,40.0 -2016-07-18,CA,3,A,A3,7.87,35.0 -2016-07-19,CA,3,A,A3,7.84,39.0 -2016-07-20,CA,3,A,A3,7.78,38.0 -2016-07-21,CA,3,A,A3,7.7,40.0 -2016-07-22,CA,3,A,A3,7.73,41.0 -2016-07-23,CA,3,A,A3,7.86,41.0 -2016-07-24,CA,3,A,A3,7.94,36.0 -2016-07-25,CA,3,A,A3,7.76,37.0 -2016-07-26,CA,3,A,A3,7.73,38.0 -2016-07-27,CA,3,A,A3,7.7,39.0 -2015-01-05,CA,3,B,B1,3.11,219.0 -2015-01-06,CA,3,B,B1,3.15,210.0 -2015-01-07,CA,3,B,B1,3.12,223.0 -2015-01-08,CA,3,B,B1,3.17,195.0 -2015-01-09,CA,3,B,B1,3.16,183.0 -2015-01-10,CA,3,B,B1,3.1,207.0 -2015-01-11,CA,3,B,B1,3.21,178.0 -2015-01-12,CA,3,B,B1,3.12,215.0 -2015-01-13,CA,3,B,B1,3.2,177.0 -2015-01-14,CA,3,B,B1,3.09,208.0 -2015-01-15,CA,3,B,B1,3.14,205.0 -2015-01-16,CA,3,B,B1,3.19,194.0 -2015-01-17,CA,3,B,B1,3.22,195.0 -2015-01-18,CA,3,B,B1,3.08,206.0 -2015-01-19,CA,3,B,B1,3.16,190.0 -2015-01-20,CA,3,B,B1,3.11,215.0 -2015-01-21,CA,3,B,B1,3.15,216.0 -2015-01-22,CA,3,B,B1,3.08,240.0 -2015-01-23,CA,3,B,B1,3.13,227.0 -2015-01-24,CA,3,B,B1,3.15,202.0 -2015-01-25,CA,3,B,B1,3.13,225.0 -2015-01-26,CA,3,B,B1,3.22,185.0 -2015-01-27,CA,3,B,B1,3.12,195.0 -2015-01-28,CA,3,B,B1,3.16,177.0 -2015-01-29,CA,3,B,B1,3.21,203.0 -2015-01-30,CA,3,B,B1,3.17,213.0 -2015-01-31,CA,3,B,B1,3.14,205.0 -2015-02-01,CA,3,B,B1,3.09,236.0 -2015-02-02,CA,3,B,B1,3.12,188.0 -2015-02-03,CA,3,B,B1,3.14,187.0 -2015-02-04,CA,3,B,B1,3.17,180.0 -2015-02-05,CA,3,B,B1,3.16,205.0 -2015-02-06,CA,3,B,B1,3.14,175.0 -2015-02-07,CA,3,B,B1,3.12,213.0 -2015-02-08,CA,3,B,B1,3.17,190.0 -2015-02-09,CA,3,B,B1,3.14,187.0 -2015-02-10,CA,3,B,B1,3.18,182.0 -2015-02-11,CA,3,B,B1,3.18,187.0 -2015-02-12,CA,3,B,B1,3.16,206.0 -2015-02-13,CA,3,B,B1,3.17,198.0 -2015-02-14,CA,3,B,B1,3.15,224.0 -2015-02-15,CA,3,B,B1,3.15,193.0 -2015-02-16,CA,3,B,B1,3.11,220.0 -2015-02-17,CA,3,B,B1,3.16,192.0 -2015-02-18,CA,3,B,B1,3.1,213.0 -2015-02-19,CA,3,B,B1,3.15,194.0 -2015-02-20,CA,3,B,B1,3.15,200.0 -2015-02-21,CA,3,B,B1,3.13,224.0 -2015-02-22,CA,3,B,B1,3.14,208.0 -2015-02-23,CA,3,B,B1,3.12,216.0 -2015-02-24,CA,3,B,B1,3.17,192.0 -2015-02-25,CA,3,B,B1,3.14,216.0 -2015-02-26,CA,3,B,B1,3.17,191.0 -2015-02-27,CA,3,B,B1,3.16,207.0 -2015-02-28,CA,3,B,B1,3.17,191.0 -2015-03-01,CA,3,B,B1,3.12,223.0 -2015-03-02,CA,3,B,B1,3.14,186.0 -2015-03-03,CA,3,B,B1,3.08,207.0 -2015-03-04,CA,3,B,B1,3.15,189.0 -2015-03-05,CA,3,B,B1,3.12,199.0 -2015-03-06,CA,3,B,B1,3.19,186.0 -2015-03-07,CA,3,B,B1,3.11,242.0 -2015-03-08,CA,3,B,B1,3.09,224.0 -2015-03-09,CA,3,B,B1,3.11,208.0 -2015-03-10,CA,3,B,B1,3.14,199.0 -2015-03-11,CA,3,B,B1,3.15,185.0 -2015-03-12,CA,3,B,B1,3.18,202.0 -2015-03-13,CA,3,B,B1,3.11,204.0 -2015-03-14,CA,3,B,B1,3.14,211.0 -2015-03-15,CA,3,B,B1,3.16,208.0 -2015-03-16,CA,3,B,B1,3.12,209.0 -2015-03-17,CA,3,B,B1,3.11,215.0 -2015-03-18,CA,3,B,B1,3.14,208.0 -2015-03-19,CA,3,B,B1,3.15,203.0 -2015-03-20,CA,3,B,B1,3.13,206.0 -2015-03-21,CA,3,B,B1,3.1,209.0 -2015-03-22,CA,3,B,B1,3.17,208.0 -2015-03-23,CA,3,B,B1,3.17,205.0 -2015-03-24,CA,3,B,B1,3.15,205.0 -2015-03-25,CA,3,B,B1,3.17,204.0 -2015-03-26,CA,3,B,B1,3.1,221.0 -2015-03-27,CA,3,B,B1,3.1,226.0 -2015-03-28,CA,3,B,B1,3.15,231.0 -2015-03-29,CA,3,B,B1,3.15,188.0 -2015-03-30,CA,3,B,B1,3.1,207.0 -2015-03-31,CA,3,B,B1,3.12,186.0 -2015-04-01,CA,3,B,B1,3.14,218.0 -2015-04-02,CA,3,B,B1,3.18,202.0 -2015-04-03,CA,3,B,B1,3.12,218.0 -2015-04-04,CA,3,B,B1,3.15,216.0 -2015-04-05,CA,3,B,B1,3.17,225.0 -2015-04-06,CA,3,B,B1,3.12,202.0 -2015-04-07,CA,3,B,B1,3.15,200.0 -2015-04-08,CA,3,B,B1,3.12,219.0 -2015-04-09,CA,3,B,B1,3.18,195.0 -2015-04-10,CA,3,B,B1,3.13,213.0 -2015-04-11,CA,3,B,B1,3.14,225.0 -2015-04-12,CA,3,B,B1,3.13,210.0 -2015-04-13,CA,3,B,B1,3.11,206.0 -2015-04-14,CA,3,B,B1,3.1,209.0 -2015-04-15,CA,3,B,B1,3.17,197.0 -2015-04-16,CA,3,B,B1,3.09,225.0 -2015-04-17,CA,3,B,B1,3.14,215.0 -2015-04-18,CA,3,B,B1,3.13,210.0 -2015-04-19,CA,3,B,B1,3.16,207.0 -2015-04-20,CA,3,B,B1,3.19,194.0 -2015-04-21,CA,3,B,B1,3.13,206.0 -2015-04-22,CA,3,B,B1,3.14,194.0 -2015-04-23,CA,3,B,B1,3.12,216.0 -2015-04-24,CA,3,B,B1,3.12,221.0 -2015-04-25,CA,3,B,B1,3.17,186.0 -2015-04-26,CA,3,B,B1,3.11,225.0 -2015-04-27,CA,3,B,B1,3.16,196.0 -2015-04-28,CA,3,B,B1,3.08,215.0 -2015-04-29,CA,3,B,B1,3.09,232.0 -2015-04-30,CA,3,B,B1,3.11,209.0 -2015-05-01,CA,3,B,B1,3.1,213.0 -2015-05-02,CA,3,B,B1,3.12,216.0 -2015-05-03,CA,3,B,B1,3.13,204.0 -2015-05-04,CA,3,B,B1,3.13,216.0 -2015-05-05,CA,3,B,B1,3.1,221.0 -2015-05-06,CA,3,B,B1,3.17,208.0 -2015-05-07,CA,3,B,B1,3.11,204.0 -2015-05-08,CA,3,B,B1,3.18,200.0 -2015-05-09,CA,3,B,B1,3.14,196.0 -2015-05-10,CA,3,B,B1,3.18,209.0 -2015-05-11,CA,3,B,B1,3.11,216.0 -2015-05-12,CA,3,B,B1,3.17,191.0 -2015-05-13,CA,3,B,B1,3.16,206.0 -2015-05-14,CA,3,B,B1,3.16,203.0 -2015-05-15,CA,3,B,B1,3.15,200.0 -2015-05-16,CA,3,B,B1,3.13,205.0 -2015-05-17,CA,3,B,B1,3.13,208.0 -2015-05-18,CA,3,B,B1,3.15,186.0 -2015-05-19,CA,3,B,B1,3.14,206.0 -2015-05-20,CA,3,B,B1,3.14,209.0 -2015-05-21,CA,3,B,B1,3.08,241.0 -2015-05-22,CA,3,B,B1,3.13,215.0 -2015-05-23,CA,3,B,B1,3.12,202.0 -2015-05-24,CA,3,B,B1,3.18,189.0 -2015-05-25,CA,3,B,B1,3.12,214.0 -2015-05-26,CA,3,B,B1,3.17,191.0 -2015-05-27,CA,3,B,B1,3.17,200.0 -2015-05-28,CA,3,B,B1,3.15,201.0 -2015-05-29,CA,3,B,B1,3.13,216.0 -2015-05-30,CA,3,B,B1,3.14,209.0 -2015-05-31,CA,3,B,B1,3.13,210.0 -2015-06-01,CA,3,B,B1,3.15,210.0 -2015-06-02,CA,3,B,B1,3.11,199.0 -2015-06-03,CA,3,B,B1,3.14,211.0 -2015-06-04,CA,3,B,B1,3.11,210.0 -2015-06-05,CA,3,B,B1,3.13,217.0 -2015-06-06,CA,3,B,B1,3.09,225.0 -2015-06-07,CA,3,B,B1,3.18,211.0 -2015-06-08,CA,3,B,B1,3.17,218.0 -2015-06-09,CA,3,B,B1,3.22,187.0 -2015-06-10,CA,3,B,B1,3.12,217.0 -2015-06-11,CA,3,B,B1,3.11,214.0 -2015-06-12,CA,3,B,B1,3.18,212.0 -2015-06-13,CA,3,B,B1,3.14,209.0 -2015-06-14,CA,3,B,B1,3.14,195.0 -2015-06-15,CA,3,B,B1,3.15,174.0 -2015-06-16,CA,3,B,B1,3.16,186.0 -2015-06-17,CA,3,B,B1,3.12,211.0 -2015-06-18,CA,3,B,B1,3.1,211.0 -2015-06-19,CA,3,B,B1,3.11,208.0 -2015-06-20,CA,3,B,B1,3.12,212.0 -2015-06-21,CA,3,B,B1,3.22,190.0 -2015-06-22,CA,3,B,B1,3.12,206.0 -2015-06-23,CA,3,B,B1,3.11,226.0 -2015-06-24,CA,3,B,B1,3.16,202.0 -2015-06-25,CA,3,B,B1,3.1,208.0 -2015-06-26,CA,3,B,B1,3.15,205.0 -2015-06-27,CA,3,B,B1,3.13,206.0 -2015-06-28,CA,3,B,B1,3.1,219.0 -2015-06-29,CA,3,B,B1,3.16,200.0 -2015-06-30,CA,3,B,B1,3.17,179.0 -2015-07-01,CA,3,B,B1,3.13,205.0 -2015-07-02,CA,3,B,B1,3.15,207.0 -2015-07-03,CA,3,B,B1,3.15,216.0 -2015-07-04,CA,3,B,B1,3.14,209.0 -2015-07-05,CA,3,B,B1,3.13,203.0 -2015-07-06,CA,3,B,B1,3.15,178.0 -2015-07-07,CA,3,B,B1,3.16,188.0 -2015-07-08,CA,3,B,B1,3.16,208.0 -2015-07-09,CA,3,B,B1,3.17,206.0 -2015-07-10,CA,3,B,B1,3.17,200.0 -2015-07-11,CA,3,B,B1,3.19,193.0 -2015-07-12,CA,3,B,B1,3.15,206.0 -2015-07-13,CA,3,B,B1,3.19,201.0 -2015-07-14,CA,3,B,B1,3.13,204.0 -2015-07-15,CA,3,B,B1,3.16,190.0 -2015-07-16,CA,3,B,B1,3.16,198.0 -2015-07-17,CA,3,B,B1,3.18,191.0 -2015-07-18,CA,3,B,B1,3.16,205.0 -2015-07-19,CA,3,B,B1,3.14,218.0 -2015-07-20,CA,3,B,B1,3.14,195.0 -2015-07-21,CA,3,B,B1,3.16,207.0 -2015-07-22,CA,3,B,B1,3.19,175.0 -2015-07-23,CA,3,B,B1,3.14,191.0 -2015-07-24,CA,3,B,B1,3.15,197.0 -2015-07-25,CA,3,B,B1,3.12,227.0 -2015-07-26,CA,3,B,B1,3.14,228.0 -2015-07-27,CA,3,B,B1,3.17,187.0 -2015-07-28,CA,3,B,B1,3.18,188.0 -2015-07-29,CA,3,B,B1,3.14,203.0 -2015-07-30,CA,3,B,B1,3.19,173.0 -2015-07-31,CA,3,B,B1,3.07,231.0 -2015-08-01,CA,3,B,B1,3.13,231.0 -2015-08-02,CA,3,B,B1,3.18,183.0 -2015-08-03,CA,3,B,B1,3.15,207.0 -2015-08-04,CA,3,B,B1,3.18,182.0 -2015-08-05,CA,3,B,B1,3.09,224.0 -2015-08-06,CA,3,B,B1,3.13,213.0 -2015-08-07,CA,3,B,B1,3.09,220.0 -2015-08-08,CA,3,B,B1,3.14,215.0 -2015-08-09,CA,3,B,B1,3.11,220.0 -2015-08-10,CA,3,B,B1,3.19,184.0 -2015-08-11,CA,3,B,B1,3.08,221.0 -2015-08-12,CA,3,B,B1,3.14,182.0 -2015-08-13,CA,3,B,B1,3.09,214.0 -2015-08-14,CA,3,B,B1,3.09,216.0 -2015-08-15,CA,3,B,B1,3.15,204.0 -2015-08-16,CA,3,B,B1,3.08,209.0 -2015-08-17,CA,3,B,B1,3.16,208.0 -2015-08-18,CA,3,B,B1,3.11,204.0 -2015-08-19,CA,3,B,B1,3.16,197.0 -2015-08-20,CA,3,B,B1,3.1,221.0 -2015-08-21,CA,3,B,B1,3.23,167.0 -2015-08-22,CA,3,B,B1,3.16,208.0 -2015-08-23,CA,3,B,B1,3.15,208.0 -2015-08-24,CA,3,B,B1,3.14,209.0 -2015-08-25,CA,3,B,B1,3.18,204.0 -2015-08-26,CA,3,B,B1,3.13,196.0 -2015-08-27,CA,3,B,B1,3.11,210.0 -2015-08-28,CA,3,B,B1,3.15,213.0 -2015-08-29,CA,3,B,B1,3.13,215.0 -2015-08-30,CA,3,B,B1,3.18,198.0 -2015-08-31,CA,3,B,B1,3.19,172.0 -2015-09-01,CA,3,B,B1,3.14,209.0 -2015-09-02,CA,3,B,B1,3.19,183.0 -2015-09-03,CA,3,B,B1,3.12,208.0 -2015-09-04,CA,3,B,B1,3.15,199.0 -2015-09-05,CA,3,B,B1,3.15,224.0 -2015-09-06,CA,3,B,B1,3.12,215.0 -2015-09-07,CA,3,B,B1,3.14,204.0 -2015-09-08,CA,3,B,B1,3.11,208.0 -2015-09-09,CA,3,B,B1,3.08,221.0 -2015-09-10,CA,3,B,B1,3.18,199.0 -2015-09-11,CA,3,B,B1,3.15,181.0 -2015-09-12,CA,3,B,B1,3.14,218.0 -2015-09-13,CA,3,B,B1,3.2,191.0 -2015-09-14,CA,3,B,B1,3.11,202.0 -2015-09-15,CA,3,B,B1,3.12,201.0 -2015-09-16,CA,3,B,B1,3.16,185.0 -2015-09-17,CA,3,B,B1,3.15,198.0 -2015-09-18,CA,3,B,B1,3.15,194.0 -2015-09-19,CA,3,B,B1,3.11,228.0 -2015-09-20,CA,3,B,B1,3.12,223.0 -2015-09-21,CA,3,B,B1,3.12,201.0 -2015-09-22,CA,3,B,B1,3.13,205.0 -2015-09-23,CA,3,B,B1,3.11,204.0 -2015-09-24,CA,3,B,B1,3.16,200.0 -2015-09-25,CA,3,B,B1,3.1,213.0 -2015-09-26,CA,3,B,B1,3.14,209.0 -2015-09-27,CA,3,B,B1,3.16,199.0 -2015-09-28,CA,3,B,B1,3.15,180.0 -2015-09-29,CA,3,B,B1,3.12,205.0 -2015-09-30,CA,3,B,B1,3.14,194.0 -2015-10-01,CA,3,B,B1,3.19,194.0 -2015-10-02,CA,3,B,B1,3.16,192.0 -2015-10-03,CA,3,B,B1,3.12,221.0 -2015-10-04,CA,3,B,B1,3.14,199.0 -2015-10-05,CA,3,B,B1,3.13,187.0 -2015-10-06,CA,3,B,B1,3.11,224.0 -2015-10-07,CA,3,B,B1,3.19,192.0 -2015-10-08,CA,3,B,B1,3.12,209.0 -2015-10-09,CA,3,B,B1,3.16,204.0 -2015-10-10,CA,3,B,B1,3.17,221.0 -2015-10-11,CA,3,B,B1,3.11,202.0 -2015-10-12,CA,3,B,B1,3.2,190.0 -2015-10-13,CA,3,B,B1,3.16,207.0 -2015-10-14,CA,3,B,B1,3.13,213.0 -2015-10-15,CA,3,B,B1,3.14,205.0 -2015-10-16,CA,3,B,B1,3.13,211.0 -2015-10-17,CA,3,B,B1,3.13,197.0 -2015-10-18,CA,3,B,B1,3.13,208.0 -2015-10-19,CA,3,B,B1,3.09,218.0 -2015-10-20,CA,3,B,B1,3.13,214.0 -2015-10-21,CA,3,B,B1,3.19,173.0 -2015-10-22,CA,3,B,B1,3.13,214.0 -2015-10-23,CA,3,B,B1,3.15,210.0 -2015-10-24,CA,3,B,B1,3.1,221.0 -2015-10-25,CA,3,B,B1,3.14,215.0 -2015-10-26,CA,3,B,B1,3.1,196.0 -2015-10-27,CA,3,B,B1,3.17,202.0 -2015-10-28,CA,3,B,B1,3.06,216.0 -2015-10-29,CA,3,B,B1,3.16,191.0 -2015-10-30,CA,3,B,B1,3.12,197.0 -2015-10-31,CA,3,B,B1,3.17,217.0 -2015-11-01,CA,3,B,B1,3.16,192.0 -2015-11-02,CA,3,B,B1,3.12,213.0 -2015-11-03,CA,3,B,B1,3.12,209.0 -2015-11-04,CA,3,B,B1,3.13,194.0 -2015-11-05,CA,3,B,B1,3.11,192.0 -2015-11-06,CA,3,B,B1,3.16,206.0 -2015-11-07,CA,3,B,B1,3.12,246.0 -2015-11-08,CA,3,B,B1,3.1,235.0 -2015-11-09,CA,3,B,B1,3.17,188.0 -2015-11-10,CA,3,B,B1,3.1,228.0 -2015-11-11,CA,3,B,B1,3.17,191.0 -2015-11-12,CA,3,B,B1,3.17,205.0 -2015-11-13,CA,3,B,B1,3.13,204.0 -2015-11-14,CA,3,B,B1,3.18,208.0 -2015-11-15,CA,3,B,B1,3.14,215.0 -2015-11-16,CA,3,B,B1,3.15,202.0 -2015-11-17,CA,3,B,B1,3.19,191.0 -2015-11-18,CA,3,B,B1,3.15,216.0 -2015-11-19,CA,3,B,B1,3.16,210.0 -2015-11-20,CA,3,B,B1,3.2,201.0 -2015-11-21,CA,3,B,B1,3.21,203.0 -2015-11-22,CA,3,B,B1,3.16,203.0 -2015-11-23,CA,3,B,B1,3.11,227.0 -2015-11-24,CA,3,B,B1,3.09,232.0 -2015-11-25,CA,3,B,B1,3.17,198.0 -2015-11-26,CA,3,B,B1,3.1,229.0 -2015-11-27,CA,3,B,B1,3.19,206.0 -2015-11-28,CA,3,B,B1,3.18,223.0 -2015-11-29,CA,3,B,B1,3.17,211.0 -2015-11-30,CA,3,B,B1,3.15,193.0 -2015-12-01,CA,3,B,B1,3.13,202.0 -2015-12-02,CA,3,B,B1,3.16,190.0 -2015-12-03,CA,3,B,B1,3.17,199.0 -2015-12-04,CA,3,B,B1,3.09,213.0 -2015-12-05,CA,3,B,B1,3.14,217.0 -2015-12-06,CA,3,B,B1,3.12,220.0 -2015-12-07,CA,3,B,B1,3.12,209.0 -2015-12-08,CA,3,B,B1,3.09,212.0 -2015-12-09,CA,3,B,B1,3.09,227.0 -2015-12-10,CA,3,B,B1,3.18,208.0 -2015-12-11,CA,3,B,B1,3.13,206.0 -2015-12-12,CA,3,B,B1,3.15,201.0 -2015-12-13,CA,3,B,B1,3.16,193.0 -2015-12-14,CA,3,B,B1,3.11,213.0 -2015-12-15,CA,3,B,B1,3.09,217.0 -2015-12-16,CA,3,B,B1,3.09,218.0 -2015-12-17,CA,3,B,B1,3.19,196.0 -2015-12-18,CA,3,B,B1,3.21,184.0 -2015-12-19,CA,3,B,B1,3.14,223.0 -2015-12-20,CA,3,B,B1,3.15,214.0 -2015-12-21,CA,3,B,B1,3.14,186.0 -2015-12-22,CA,3,B,B1,3.12,215.0 -2015-12-23,CA,3,B,B1,3.09,213.0 -2015-12-24,CA,3,B,B1,3.19,205.0 -2015-12-25,CA,3,B,B1,3.13,200.0 -2015-12-26,CA,3,B,B1,3.19,228.0 -2015-12-27,CA,3,B,B1,3.12,207.0 -2015-12-28,CA,3,B,B1,3.14,203.0 -2015-12-29,CA,3,B,B1,3.18,176.0 -2015-12-30,CA,3,B,B1,3.1,211.0 -2015-12-31,CA,3,B,B1,3.11,208.0 -2016-01-01,CA,3,B,B1,3.12,217.0 -2016-01-02,CA,3,B,B1,3.1,239.0 -2016-01-03,CA,3,B,B1,3.1,231.0 -2016-01-04,CA,3,B,B1,3.14,200.0 -2016-01-05,CA,3,B,B1,3.14,216.0 -2016-01-06,CA,3,B,B1,3.09,206.0 -2016-01-07,CA,3,B,B1,3.15,216.0 -2016-01-08,CA,3,B,B1,3.13,219.0 -2016-01-09,CA,3,B,B1,3.16,216.0 -2016-01-10,CA,3,B,B1,3.12,227.0 -2016-01-11,CA,3,B,B1,3.17,192.0 -2016-01-12,CA,3,B,B1,3.12,227.0 -2016-01-13,CA,3,B,B1,3.09,204.0 -2016-01-14,CA,3,B,B1,3.12,208.0 -2016-01-15,CA,3,B,B1,3.14,218.0 -2016-01-16,CA,3,B,B1,3.16,219.0 -2016-01-17,CA,3,B,B1,3.1,207.0 -2016-01-18,CA,3,B,B1,3.13,210.0 -2016-01-19,CA,3,B,B1,3.14,211.0 -2016-01-20,CA,3,B,B1,3.07,226.0 -2016-01-21,CA,3,B,B1,3.14,218.0 -2016-01-22,CA,3,B,B1,3.13,209.0 -2016-01-23,CA,3,B,B1,3.2,208.0 -2016-01-24,CA,3,B,B1,3.09,213.0 -2016-01-25,CA,3,B,B1,3.16,206.0 -2016-01-26,CA,3,B,B1,3.14,207.0 -2016-01-27,CA,3,B,B1,3.21,201.0 -2016-01-28,CA,3,B,B1,3.11,214.0 -2016-01-29,CA,3,B,B1,3.12,223.0 -2016-01-30,CA,3,B,B1,3.15,199.0 -2016-01-31,CA,3,B,B1,3.13,227.0 -2016-02-01,CA,3,B,B1,3.11,208.0 -2016-02-02,CA,3,B,B1,3.16,197.0 -2016-02-03,CA,3,B,B1,3.16,198.0 -2016-02-04,CA,3,B,B1,3.1,227.0 -2016-02-05,CA,3,B,B1,3.16,188.0 -2016-02-06,CA,3,B,B1,3.15,224.0 -2016-02-07,CA,3,B,B1,3.16,220.0 -2016-02-08,CA,3,B,B1,3.14,212.0 -2016-02-09,CA,3,B,B1,3.13,194.0 -2016-02-10,CA,3,B,B1,3.15,216.0 -2016-02-11,CA,3,B,B1,3.16,199.0 -2016-02-12,CA,3,B,B1,3.17,206.0 -2016-02-13,CA,3,B,B1,3.12,208.0 -2016-02-14,CA,3,B,B1,3.2,207.0 -2016-02-15,CA,3,B,B1,3.13,209.0 -2016-02-16,CA,3,B,B1,3.16,198.0 -2016-02-17,CA,3,B,B1,3.17,213.0 -2016-02-18,CA,3,B,B1,3.09,219.0 -2016-02-19,CA,3,B,B1,3.2,201.0 -2016-02-20,CA,3,B,B1,3.16,207.0 -2016-02-21,CA,3,B,B1,3.12,210.0 -2016-02-22,CA,3,B,B1,3.13,203.0 -2016-02-23,CA,3,B,B1,3.17,195.0 -2016-02-24,CA,3,B,B1,3.13,196.0 -2016-02-25,CA,3,B,B1,3.17,212.0 -2016-02-26,CA,3,B,B1,3.18,177.0 -2016-02-27,CA,3,B,B1,3.14,234.0 -2016-02-28,CA,3,B,B1,3.1,215.0 -2016-02-29,CA,3,B,B1,3.14,203.0 -2016-03-01,CA,3,B,B1,3.16,206.0 -2016-03-02,CA,3,B,B1,3.17,203.0 -2016-03-03,CA,3,B,B1,3.16,190.0 -2016-03-04,CA,3,B,B1,3.16,192.0 -2016-03-05,CA,3,B,B1,3.09,242.0 -2016-03-06,CA,3,B,B1,3.18,208.0 -2016-03-07,CA,3,B,B1,3.14,206.0 -2016-03-08,CA,3,B,B1,3.16,196.0 -2016-03-09,CA,3,B,B1,3.2,192.0 -2016-03-10,CA,3,B,B1,3.15,192.0 -2016-03-11,CA,3,B,B1,3.17,199.0 -2016-03-12,CA,3,B,B1,3.12,212.0 -2016-03-13,CA,3,B,B1,3.17,201.0 -2016-03-14,CA,3,B,B1,3.1,228.0 -2016-03-15,CA,3,B,B1,3.12,214.0 -2016-03-16,CA,3,B,B1,3.11,213.0 -2016-03-17,CA,3,B,B1,3.1,220.0 -2016-03-18,CA,3,B,B1,3.14,213.0 -2016-03-19,CA,3,B,B1,3.11,227.0 -2016-03-20,CA,3,B,B1,3.11,225.0 -2016-03-21,CA,3,B,B1,3.15,203.0 -2016-03-22,CA,3,B,B1,3.11,231.0 -2016-03-23,CA,3,B,B1,3.15,188.0 -2016-03-24,CA,3,B,B1,3.12,224.0 -2016-03-25,CA,3,B,B1,3.11,211.0 -2016-03-26,CA,3,B,B1,3.17,212.0 -2016-03-27,CA,3,B,B1,3.17,210.0 -2016-03-28,CA,3,B,B1,3.15,202.0 -2016-03-29,CA,3,B,B1,3.17,215.0 -2016-03-30,CA,3,B,B1,3.13,214.0 -2016-03-31,CA,3,B,B1,3.16,222.0 -2016-04-01,CA,3,B,B1,3.19,197.0 -2016-04-02,CA,3,B,B1,3.13,222.0 -2016-04-03,CA,3,B,B1,3.15,197.0 -2016-04-04,CA,3,B,B1,3.15,204.0 -2016-04-05,CA,3,B,B1,3.14,199.0 -2016-04-06,CA,3,B,B1,3.14,200.0 -2016-04-07,CA,3,B,B1,3.09,217.0 -2016-04-08,CA,3,B,B1,3.12,215.0 -2016-04-09,CA,3,B,B1,3.1,230.0 -2016-04-10,CA,3,B,B1,3.14,210.0 -2016-04-11,CA,3,B,B1,3.14,197.0 -2016-04-12,CA,3,B,B1,3.14,211.0 -2016-04-13,CA,3,B,B1,3.18,198.0 -2016-04-14,CA,3,B,B1,3.13,204.0 -2016-04-15,CA,3,B,B1,3.15,194.0 -2016-04-16,CA,3,B,B1,3.14,221.0 -2016-04-17,CA,3,B,B1,3.15,215.0 -2016-04-18,CA,3,B,B1,3.14,195.0 -2016-04-19,CA,3,B,B1,3.14,193.0 -2016-04-20,CA,3,B,B1,3.08,222.0 -2016-04-21,CA,3,B,B1,3.12,220.0 -2016-04-22,CA,3,B,B1,3.16,211.0 -2016-04-23,CA,3,B,B1,3.13,238.0 -2016-04-24,CA,3,B,B1,3.15,210.0 -2016-04-25,CA,3,B,B1,3.11,210.0 -2016-04-26,CA,3,B,B1,3.13,198.0 -2016-04-27,CA,3,B,B1,3.15,221.0 -2016-04-28,CA,3,B,B1,3.14,183.0 -2016-04-29,CA,3,B,B1,3.17,216.0 -2016-04-30,CA,3,B,B1,3.19,201.0 -2016-05-01,CA,3,B,B1,3.11,212.0 -2016-05-02,CA,3,B,B1,3.1,245.0 -2016-05-03,CA,3,B,B1,3.15,202.0 -2016-05-04,CA,3,B,B1,3.18,188.0 -2016-05-05,CA,3,B,B1,3.19,202.0 -2016-05-06,CA,3,B,B1,3.11,217.0 -2016-05-07,CA,3,B,B1,3.18,212.0 -2016-05-08,CA,3,B,B1,3.13,216.0 -2016-05-09,CA,3,B,B1,3.22,179.0 -2016-05-10,CA,3,B,B1,3.1,210.0 -2016-05-11,CA,3,B,B1,3.12,199.0 -2016-05-12,CA,3,B,B1,3.1,224.0 -2016-05-13,CA,3,B,B1,3.14,187.0 -2016-05-14,CA,3,B,B1,3.17,207.0 -2016-05-15,CA,3,B,B1,3.16,196.0 -2016-05-16,CA,3,B,B1,3.18,200.0 -2016-05-17,CA,3,B,B1,3.14,209.0 -2016-05-18,CA,3,B,B1,3.19,178.0 -2016-05-19,CA,3,B,B1,3.21,190.0 -2016-05-20,CA,3,B,B1,3.14,200.0 -2016-05-21,CA,3,B,B1,3.17,192.0 -2016-05-22,CA,3,B,B1,3.14,221.0 -2016-05-23,CA,3,B,B1,3.18,204.0 -2016-05-24,CA,3,B,B1,3.15,181.0 -2016-05-25,CA,3,B,B1,3.13,196.0 -2016-05-26,CA,3,B,B1,3.14,209.0 -2016-05-27,CA,3,B,B1,3.18,192.0 -2016-05-28,CA,3,B,B1,3.15,218.0 -2016-05-29,CA,3,B,B1,3.13,223.0 -2016-05-30,CA,3,B,B1,3.08,221.0 -2016-05-31,CA,3,B,B1,3.1,220.0 -2016-06-01,CA,3,B,B1,3.13,212.0 -2016-06-02,CA,3,B,B1,3.1,211.0 -2016-06-03,CA,3,B,B1,3.17,185.0 -2016-06-04,CA,3,B,B1,3.07,233.0 -2016-06-05,CA,3,B,B1,3.16,200.0 -2016-06-06,CA,3,B,B1,3.15,202.0 -2016-06-07,CA,3,B,B1,3.13,216.0 -2016-06-08,CA,3,B,B1,3.16,211.0 -2016-06-09,CA,3,B,B1,3.17,196.0 -2016-06-10,CA,3,B,B1,3.13,219.0 -2016-06-11,CA,3,B,B1,3.15,220.0 -2016-06-12,CA,3,B,B1,3.11,207.0 -2016-06-13,CA,3,B,B1,3.16,193.0 -2016-06-14,CA,3,B,B1,3.14,208.0 -2016-06-15,CA,3,B,B1,3.17,203.0 -2016-06-16,CA,3,B,B1,3.17,194.0 -2016-06-17,CA,3,B,B1,3.15,183.0 -2016-06-18,CA,3,B,B1,3.19,197.0 -2016-06-19,CA,3,B,B1,3.17,200.0 -2016-06-20,CA,3,B,B1,3.15,188.0 -2016-06-21,CA,3,B,B1,3.11,212.0 -2016-06-22,CA,3,B,B1,3.14,210.0 -2016-06-23,CA,3,B,B1,3.12,218.0 -2016-06-24,CA,3,B,B1,3.1,217.0 -2016-06-25,CA,3,B,B1,3.18,199.0 -2016-06-26,CA,3,B,B1,3.14,194.0 -2016-06-27,CA,3,B,B1,3.15,185.0 -2016-06-28,CA,3,B,B1,3.17,205.0 -2016-06-29,CA,3,B,B1,3.15,179.0 -2016-06-30,CA,3,B,B1,3.14,210.0 -2016-07-01,CA,3,B,B1,3.17,189.0 -2016-07-02,CA,3,B,B1,3.15,223.0 -2016-07-03,CA,3,B,B1,3.13,214.0 -2016-07-04,CA,3,B,B1,3.12,215.0 -2016-07-05,CA,3,B,B1,3.14,203.0 -2016-07-06,CA,3,B,B1,3.14,200.0 -2016-07-07,CA,3,B,B1,3.15,204.0 -2016-07-08,CA,3,B,B1,3.16,216.0 -2016-07-09,CA,3,B,B1,3.08,212.0 -2016-07-10,CA,3,B,B1,3.14,223.0 -2016-07-11,CA,3,B,B1,3.15,223.0 -2016-07-12,CA,3,B,B1,3.19,192.0 -2016-07-13,CA,3,B,B1,3.15,208.0 -2016-07-14,CA,3,B,B1,3.15,197.0 -2016-07-15,CA,3,B,B1,3.11,220.0 -2016-07-16,CA,3,B,B1,3.14,224.0 -2016-07-17,CA,3,B,B1,3.14,200.0 -2016-07-18,CA,3,B,B1,3.09,209.0 -2016-07-19,CA,3,B,B1,3.15,207.0 -2016-07-20,CA,3,B,B1,3.11,210.0 -2016-07-21,CA,3,B,B1,3.15,193.0 -2016-07-22,CA,3,B,B1,3.16,201.0 -2016-07-23,CA,3,B,B1,3.15,208.0 -2016-07-24,CA,3,B,B1,3.13,215.0 -2016-07-25,CA,3,B,B1,3.16,192.0 -2016-07-26,CA,3,B,B1,3.19,196.0 -2016-07-27,CA,3,B,B1,3.16,199.0 -2015-01-05,CA,3,B,B2,6.72,227.0 -2015-01-06,CA,3,B,B2,6.74,233.0 -2015-01-07,CA,3,B,B2,6.83,220.0 -2015-01-08,CA,3,B,B2,6.82,234.0 -2015-01-09,CA,3,B,B2,6.81,249.0 -2015-01-10,CA,3,B,B2,6.82,241.0 -2015-01-11,CA,3,B,B2,6.86,213.0 -2015-01-12,CA,3,B,B2,6.79,233.0 -2015-01-13,CA,3,B,B2,6.91,210.0 -2015-01-14,CA,3,B,B2,6.75,236.0 -2015-01-15,CA,3,B,B2,6.79,232.0 -2015-01-16,CA,3,B,B2,6.7,259.0 -2015-01-17,CA,3,B,B2,6.77,261.0 -2015-01-18,CA,3,B,B2,7.01,212.0 -2015-01-19,CA,3,B,B2,6.81,231.0 -2015-01-20,CA,3,B,B2,6.92,235.0 -2015-01-21,CA,3,B,B2,6.88,229.0 -2015-01-22,CA,3,B,B2,6.8,236.0 -2015-01-23,CA,3,B,B2,6.84,245.0 -2015-01-24,CA,3,B,B2,6.89,252.0 -2015-01-25,CA,3,B,B2,6.89,250.0 -2015-01-26,CA,3,B,B2,6.76,259.0 -2015-01-27,CA,3,B,B2,6.88,216.0 -2015-01-28,CA,3,B,B2,6.83,214.0 -2015-01-29,CA,3,B,B2,6.79,250.0 -2015-01-30,CA,3,B,B2,6.89,248.0 -2015-01-31,CA,3,B,B2,6.82,244.0 -2015-02-01,CA,3,B,B2,6.85,218.0 -2015-02-02,CA,3,B,B2,6.82,225.0 -2015-02-03,CA,3,B,B2,6.81,235.0 -2015-02-04,CA,3,B,B2,6.75,228.0 -2015-02-05,CA,3,B,B2,6.8,218.0 -2015-02-06,CA,3,B,B2,6.85,231.0 -2015-02-07,CA,3,B,B2,7.03,252.0 -2015-02-08,CA,3,B,B2,6.9,242.0 -2015-02-09,CA,3,B,B2,6.84,227.0 -2015-02-10,CA,3,B,B2,6.92,223.0 -2015-02-11,CA,3,B,B2,6.8,216.0 -2015-02-12,CA,3,B,B2,6.78,243.0 -2015-02-13,CA,3,B,B2,6.88,216.0 -2015-02-14,CA,3,B,B2,6.96,248.0 -2015-02-15,CA,3,B,B2,6.96,251.0 -2015-02-16,CA,3,B,B2,6.91,244.0 -2015-02-17,CA,3,B,B2,6.81,229.0 -2015-02-18,CA,3,B,B2,6.83,237.0 -2015-02-19,CA,3,B,B2,6.86,218.0 -2015-02-20,CA,3,B,B2,6.72,237.0 -2015-02-21,CA,3,B,B2,6.81,247.0 -2015-02-22,CA,3,B,B2,7.07,215.0 -2015-02-23,CA,3,B,B2,6.9,225.0 -2015-02-24,CA,3,B,B2,6.92,223.0 -2015-02-25,CA,3,B,B2,6.86,250.0 -2015-02-26,CA,3,B,B2,6.73,256.0 -2015-02-27,CA,3,B,B2,6.78,243.0 -2015-02-28,CA,3,B,B2,6.85,229.0 -2015-03-01,CA,3,B,B2,6.82,232.0 -2015-03-02,CA,3,B,B2,6.92,252.0 -2015-03-03,CA,3,B,B2,6.74,246.0 -2015-03-04,CA,3,B,B2,6.71,231.0 -2015-03-05,CA,3,B,B2,6.89,240.0 -2015-03-06,CA,3,B,B2,6.88,233.0 -2015-03-07,CA,3,B,B2,6.9,228.0 -2015-03-08,CA,3,B,B2,6.79,238.0 -2015-03-09,CA,3,B,B2,6.82,227.0 -2015-03-10,CA,3,B,B2,6.87,231.0 -2015-03-11,CA,3,B,B2,6.79,229.0 -2015-03-12,CA,3,B,B2,6.85,233.0 -2015-03-13,CA,3,B,B2,6.87,239.0 -2015-03-14,CA,3,B,B2,6.65,260.0 -2015-03-15,CA,3,B,B2,6.87,236.0 -2015-03-16,CA,3,B,B2,6.95,197.0 -2015-03-17,CA,3,B,B2,6.77,241.0 -2015-03-18,CA,3,B,B2,6.82,225.0 -2015-03-19,CA,3,B,B2,6.82,215.0 -2015-03-20,CA,3,B,B2,6.84,240.0 -2015-03-21,CA,3,B,B2,6.84,258.0 -2015-03-22,CA,3,B,B2,6.85,251.0 -2015-03-23,CA,3,B,B2,6.78,227.0 -2015-03-24,CA,3,B,B2,6.95,223.0 -2015-03-25,CA,3,B,B2,6.86,246.0 -2015-03-26,CA,3,B,B2,6.91,236.0 -2015-03-27,CA,3,B,B2,6.94,231.0 -2015-03-28,CA,3,B,B2,6.88,255.0 -2015-03-29,CA,3,B,B2,6.85,232.0 -2015-03-30,CA,3,B,B2,6.88,224.0 -2015-03-31,CA,3,B,B2,6.77,240.0 -2015-04-01,CA,3,B,B2,6.84,259.0 -2015-04-02,CA,3,B,B2,6.79,226.0 -2015-04-03,CA,3,B,B2,6.8,240.0 -2015-04-04,CA,3,B,B2,6.87,220.0 -2015-04-05,CA,3,B,B2,6.89,233.0 -2015-04-06,CA,3,B,B2,6.81,250.0 -2015-04-07,CA,3,B,B2,6.95,242.0 -2015-04-08,CA,3,B,B2,6.76,238.0 -2015-04-09,CA,3,B,B2,6.82,256.0 -2015-04-10,CA,3,B,B2,6.9,237.0 -2015-04-11,CA,3,B,B2,6.84,249.0 -2015-04-12,CA,3,B,B2,6.79,238.0 -2015-04-13,CA,3,B,B2,6.89,235.0 -2015-04-14,CA,3,B,B2,6.9,220.0 -2015-04-15,CA,3,B,B2,6.79,225.0 -2015-04-16,CA,3,B,B2,6.76,234.0 -2015-04-17,CA,3,B,B2,6.83,234.0 -2015-04-18,CA,3,B,B2,6.85,241.0 -2015-04-19,CA,3,B,B2,6.8,230.0 -2015-04-20,CA,3,B,B2,6.78,246.0 -2015-04-21,CA,3,B,B2,6.83,217.0 -2015-04-22,CA,3,B,B2,6.91,243.0 -2015-04-23,CA,3,B,B2,6.68,249.0 -2015-04-24,CA,3,B,B2,6.89,250.0 -2015-04-25,CA,3,B,B2,6.79,256.0 -2015-04-26,CA,3,B,B2,6.84,233.0 -2015-04-27,CA,3,B,B2,6.84,253.0 -2015-04-28,CA,3,B,B2,6.84,227.0 -2015-04-29,CA,3,B,B2,6.87,237.0 -2015-04-30,CA,3,B,B2,6.76,234.0 -2015-05-01,CA,3,B,B2,6.88,243.0 -2015-05-02,CA,3,B,B2,6.8,250.0 -2015-05-03,CA,3,B,B2,6.9,247.0 -2015-05-04,CA,3,B,B2,6.84,229.0 -2015-05-05,CA,3,B,B2,6.75,198.0 -2015-05-06,CA,3,B,B2,6.74,240.0 -2015-05-07,CA,3,B,B2,6.83,235.0 -2015-05-08,CA,3,B,B2,6.77,243.0 -2015-05-09,CA,3,B,B2,6.88,220.0 -2015-05-10,CA,3,B,B2,6.89,229.0 -2015-05-11,CA,3,B,B2,6.79,242.0 -2015-05-12,CA,3,B,B2,6.91,242.0 -2015-05-13,CA,3,B,B2,6.93,238.0 -2015-05-14,CA,3,B,B2,6.95,225.0 -2015-05-15,CA,3,B,B2,6.9,230.0 -2015-05-16,CA,3,B,B2,7.0,225.0 -2015-05-17,CA,3,B,B2,6.83,234.0 -2015-05-18,CA,3,B,B2,6.82,208.0 -2015-05-19,CA,3,B,B2,6.81,229.0 -2015-05-20,CA,3,B,B2,6.96,242.0 -2015-05-21,CA,3,B,B2,6.96,223.0 -2015-05-22,CA,3,B,B2,6.91,220.0 -2015-05-23,CA,3,B,B2,6.84,242.0 -2015-05-24,CA,3,B,B2,6.87,245.0 -2015-05-25,CA,3,B,B2,6.8,226.0 -2015-05-26,CA,3,B,B2,6.97,236.0 -2015-05-27,CA,3,B,B2,6.75,245.0 -2015-05-28,CA,3,B,B2,6.78,231.0 -2015-05-29,CA,3,B,B2,6.98,231.0 -2015-05-30,CA,3,B,B2,6.93,245.0 -2015-05-31,CA,3,B,B2,6.98,230.0 -2015-06-01,CA,3,B,B2,6.83,231.0 -2015-06-02,CA,3,B,B2,6.74,240.0 -2015-06-03,CA,3,B,B2,6.79,232.0 -2015-06-04,CA,3,B,B2,6.85,234.0 -2015-06-05,CA,3,B,B2,6.73,248.0 -2015-06-06,CA,3,B,B2,6.82,238.0 -2015-06-07,CA,3,B,B2,6.84,237.0 -2015-06-08,CA,3,B,B2,6.87,242.0 -2015-06-09,CA,3,B,B2,6.88,242.0 -2015-06-10,CA,3,B,B2,6.77,239.0 -2015-06-11,CA,3,B,B2,6.9,216.0 -2015-06-12,CA,3,B,B2,6.94,220.0 -2015-06-13,CA,3,B,B2,6.94,251.0 -2015-06-14,CA,3,B,B2,6.78,241.0 -2015-06-15,CA,3,B,B2,6.81,218.0 -2015-06-16,CA,3,B,B2,6.82,213.0 -2015-06-17,CA,3,B,B2,6.92,223.0 -2015-06-18,CA,3,B,B2,6.83,229.0 -2015-06-19,CA,3,B,B2,6.83,245.0 -2015-06-20,CA,3,B,B2,6.8,238.0 -2015-06-21,CA,3,B,B2,6.84,237.0 -2015-06-22,CA,3,B,B2,7.09,205.0 -2015-06-23,CA,3,B,B2,6.83,231.0 -2015-06-24,CA,3,B,B2,6.83,216.0 -2015-06-25,CA,3,B,B2,6.9,255.0 -2015-06-26,CA,3,B,B2,6.8,248.0 -2015-06-27,CA,3,B,B2,6.81,243.0 -2015-06-28,CA,3,B,B2,6.95,217.0 -2015-06-29,CA,3,B,B2,6.84,206.0 -2015-06-30,CA,3,B,B2,6.91,234.0 -2015-07-01,CA,3,B,B2,6.79,233.0 -2015-07-02,CA,3,B,B2,6.76,249.0 -2015-07-03,CA,3,B,B2,6.82,220.0 -2015-07-04,CA,3,B,B2,6.88,240.0 -2015-07-05,CA,3,B,B2,6.82,244.0 -2015-07-06,CA,3,B,B2,6.81,227.0 -2015-07-07,CA,3,B,B2,6.87,216.0 -2015-07-08,CA,3,B,B2,6.83,225.0 -2015-07-09,CA,3,B,B2,6.76,246.0 -2015-07-10,CA,3,B,B2,6.72,233.0 -2015-07-11,CA,3,B,B2,6.8,237.0 -2015-07-12,CA,3,B,B2,6.93,223.0 -2015-07-13,CA,3,B,B2,6.82,226.0 -2015-07-14,CA,3,B,B2,6.9,234.0 -2015-07-15,CA,3,B,B2,6.84,253.0 -2015-07-16,CA,3,B,B2,6.86,240.0 -2015-07-17,CA,3,B,B2,6.86,229.0 -2015-07-18,CA,3,B,B2,6.98,240.0 -2015-07-19,CA,3,B,B2,6.89,225.0 -2015-07-20,CA,3,B,B2,6.79,214.0 -2015-07-21,CA,3,B,B2,6.88,202.0 -2015-07-22,CA,3,B,B2,6.84,232.0 -2015-07-23,CA,3,B,B2,6.95,234.0 -2015-07-24,CA,3,B,B2,6.93,222.0 -2015-07-25,CA,3,B,B2,6.7,258.0 -2015-07-26,CA,3,B,B2,6.84,234.0 -2015-07-27,CA,3,B,B2,6.83,236.0 -2015-07-28,CA,3,B,B2,6.78,228.0 -2015-07-29,CA,3,B,B2,6.94,232.0 -2015-07-30,CA,3,B,B2,6.79,259.0 -2015-07-31,CA,3,B,B2,6.86,234.0 -2015-08-01,CA,3,B,B2,6.84,217.0 -2015-08-02,CA,3,B,B2,6.89,226.0 -2015-08-03,CA,3,B,B2,6.78,220.0 -2015-08-04,CA,3,B,B2,6.83,229.0 -2015-08-05,CA,3,B,B2,6.76,230.0 -2015-08-06,CA,3,B,B2,6.89,211.0 -2015-08-07,CA,3,B,B2,6.95,232.0 -2015-08-08,CA,3,B,B2,6.88,243.0 -2015-08-09,CA,3,B,B2,6.82,251.0 -2015-08-10,CA,3,B,B2,6.85,223.0 -2015-08-11,CA,3,B,B2,6.86,215.0 -2015-08-12,CA,3,B,B2,6.81,249.0 -2015-08-13,CA,3,B,B2,6.93,246.0 -2015-08-14,CA,3,B,B2,6.93,232.0 -2015-08-15,CA,3,B,B2,6.72,262.0 -2015-08-16,CA,3,B,B2,6.82,247.0 -2015-08-17,CA,3,B,B2,6.87,241.0 -2015-08-18,CA,3,B,B2,6.89,215.0 -2015-08-19,CA,3,B,B2,6.78,244.0 -2015-08-20,CA,3,B,B2,6.71,227.0 -2015-08-21,CA,3,B,B2,6.91,232.0 -2015-08-22,CA,3,B,B2,6.9,256.0 -2015-08-23,CA,3,B,B2,6.94,236.0 -2015-08-24,CA,3,B,B2,6.74,230.0 -2015-08-25,CA,3,B,B2,6.81,236.0 -2015-08-26,CA,3,B,B2,6.88,242.0 -2015-08-27,CA,3,B,B2,6.89,233.0 -2015-08-28,CA,3,B,B2,6.82,233.0 -2015-08-29,CA,3,B,B2,6.83,254.0 -2015-08-30,CA,3,B,B2,6.81,244.0 -2015-08-31,CA,3,B,B2,6.93,230.0 -2015-09-01,CA,3,B,B2,6.84,233.0 -2015-09-02,CA,3,B,B2,6.81,226.0 -2015-09-03,CA,3,B,B2,6.96,222.0 -2015-09-04,CA,3,B,B2,6.83,225.0 -2015-09-05,CA,3,B,B2,6.89,259.0 -2015-09-06,CA,3,B,B2,6.96,220.0 -2015-09-07,CA,3,B,B2,6.77,216.0 -2015-09-08,CA,3,B,B2,6.6,247.0 -2015-09-09,CA,3,B,B2,6.85,251.0 -2015-09-10,CA,3,B,B2,6.81,236.0 -2015-09-11,CA,3,B,B2,6.88,235.0 -2015-09-12,CA,3,B,B2,6.95,227.0 -2015-09-13,CA,3,B,B2,6.88,242.0 -2015-09-14,CA,3,B,B2,7.01,227.0 -2015-09-15,CA,3,B,B2,6.79,224.0 -2015-09-16,CA,3,B,B2,6.83,221.0 -2015-09-17,CA,3,B,B2,6.76,238.0 -2015-09-18,CA,3,B,B2,6.94,226.0 -2015-09-19,CA,3,B,B2,6.82,239.0 -2015-09-20,CA,3,B,B2,6.92,240.0 -2015-09-21,CA,3,B,B2,6.76,239.0 -2015-09-22,CA,3,B,B2,6.89,201.0 -2015-09-23,CA,3,B,B2,6.97,217.0 -2015-09-24,CA,3,B,B2,6.88,224.0 -2015-09-25,CA,3,B,B2,6.91,216.0 -2015-09-26,CA,3,B,B2,6.79,245.0 -2015-09-27,CA,3,B,B2,6.94,221.0 -2015-09-28,CA,3,B,B2,6.92,231.0 -2015-09-29,CA,3,B,B2,6.86,241.0 -2015-09-30,CA,3,B,B2,6.95,224.0 -2015-10-01,CA,3,B,B2,6.78,233.0 -2015-10-02,CA,3,B,B2,6.83,228.0 -2015-10-03,CA,3,B,B2,6.76,256.0 -2015-10-04,CA,3,B,B2,6.91,230.0 -2015-10-05,CA,3,B,B2,6.92,225.0 -2015-10-06,CA,3,B,B2,6.7,238.0 -2015-10-07,CA,3,B,B2,6.8,240.0 -2015-10-08,CA,3,B,B2,6.83,237.0 -2015-10-09,CA,3,B,B2,6.73,241.0 -2015-10-10,CA,3,B,B2,6.91,238.0 -2015-10-11,CA,3,B,B2,6.85,256.0 -2015-10-12,CA,3,B,B2,6.87,222.0 -2015-10-13,CA,3,B,B2,6.85,224.0 -2015-10-14,CA,3,B,B2,6.87,233.0 -2015-10-15,CA,3,B,B2,6.84,245.0 -2015-10-16,CA,3,B,B2,6.86,235.0 -2015-10-17,CA,3,B,B2,6.79,256.0 -2015-10-18,CA,3,B,B2,6.94,227.0 -2015-10-19,CA,3,B,B2,6.94,226.0 -2015-10-20,CA,3,B,B2,6.79,252.0 -2015-10-21,CA,3,B,B2,6.88,244.0 -2015-10-22,CA,3,B,B2,6.91,207.0 -2015-10-23,CA,3,B,B2,6.91,220.0 -2015-10-24,CA,3,B,B2,6.96,239.0 -2015-10-25,CA,3,B,B2,6.74,238.0 -2015-10-26,CA,3,B,B2,6.9,243.0 -2015-10-27,CA,3,B,B2,6.93,248.0 -2015-10-28,CA,3,B,B2,6.78,240.0 -2015-10-29,CA,3,B,B2,6.73,236.0 -2015-10-30,CA,3,B,B2,6.91,232.0 -2015-10-31,CA,3,B,B2,6.84,264.0 -2015-11-01,CA,3,B,B2,6.8,236.0 -2015-11-02,CA,3,B,B2,6.75,247.0 -2015-11-03,CA,3,B,B2,6.74,223.0 -2015-11-04,CA,3,B,B2,6.79,248.0 -2015-11-05,CA,3,B,B2,6.8,221.0 -2015-11-06,CA,3,B,B2,6.93,229.0 -2015-11-07,CA,3,B,B2,6.8,232.0 -2015-11-08,CA,3,B,B2,6.84,218.0 -2015-11-09,CA,3,B,B2,6.83,210.0 -2015-11-10,CA,3,B,B2,6.97,235.0 -2015-11-11,CA,3,B,B2,6.88,239.0 -2015-11-12,CA,3,B,B2,6.83,248.0 -2015-11-13,CA,3,B,B2,6.82,251.0 -2015-11-14,CA,3,B,B2,6.84,238.0 -2015-11-15,CA,3,B,B2,6.82,242.0 -2015-11-16,CA,3,B,B2,6.86,209.0 -2015-11-17,CA,3,B,B2,6.83,243.0 -2015-11-18,CA,3,B,B2,6.9,229.0 -2015-11-19,CA,3,B,B2,6.69,218.0 -2015-11-20,CA,3,B,B2,6.87,219.0 -2015-11-21,CA,3,B,B2,6.86,254.0 -2015-11-22,CA,3,B,B2,6.88,226.0 -2015-11-23,CA,3,B,B2,6.86,234.0 -2015-11-24,CA,3,B,B2,6.81,245.0 -2015-11-25,CA,3,B,B2,6.86,233.0 -2015-11-26,CA,3,B,B2,6.81,238.0 -2015-11-27,CA,3,B,B2,6.89,218.0 -2015-11-28,CA,3,B,B2,6.76,254.0 -2015-11-29,CA,3,B,B2,6.94,215.0 -2015-11-30,CA,3,B,B2,6.9,226.0 -2015-12-01,CA,3,B,B2,6.75,224.0 -2015-12-02,CA,3,B,B2,6.92,243.0 -2015-12-03,CA,3,B,B2,6.84,231.0 -2015-12-04,CA,3,B,B2,6.8,213.0 -2015-12-05,CA,3,B,B2,6.9,249.0 -2015-12-06,CA,3,B,B2,6.85,219.0 -2015-12-07,CA,3,B,B2,6.81,234.0 -2015-12-08,CA,3,B,B2,6.77,233.0 -2015-12-09,CA,3,B,B2,6.81,222.0 -2015-12-10,CA,3,B,B2,6.98,233.0 -2015-12-11,CA,3,B,B2,6.99,229.0 -2015-12-12,CA,3,B,B2,6.86,230.0 -2015-12-13,CA,3,B,B2,6.85,245.0 -2015-12-14,CA,3,B,B2,6.85,246.0 -2015-12-15,CA,3,B,B2,6.86,236.0 -2015-12-16,CA,3,B,B2,6.87,246.0 -2015-12-17,CA,3,B,B2,6.91,222.0 -2015-12-18,CA,3,B,B2,6.88,224.0 -2015-12-19,CA,3,B,B2,6.7,255.0 -2015-12-20,CA,3,B,B2,6.88,238.0 -2015-12-21,CA,3,B,B2,6.76,229.0 -2015-12-22,CA,3,B,B2,6.91,215.0 -2015-12-23,CA,3,B,B2,6.91,252.0 -2015-12-24,CA,3,B,B2,6.83,204.0 -2015-12-25,CA,3,B,B2,6.86,249.0 -2015-12-26,CA,3,B,B2,6.73,264.0 -2015-12-27,CA,3,B,B2,6.93,249.0 -2015-12-28,CA,3,B,B2,6.94,216.0 -2015-12-29,CA,3,B,B2,6.79,222.0 -2015-12-30,CA,3,B,B2,6.81,214.0 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-2016-01-30,CA,3,B,B2,7.05,235.0 -2016-01-31,CA,3,B,B2,6.84,227.0 -2016-02-01,CA,3,B,B2,6.8,252.0 -2016-02-02,CA,3,B,B2,6.84,248.0 -2016-02-03,CA,3,B,B2,6.82,243.0 -2016-02-04,CA,3,B,B2,6.69,252.0 -2016-02-05,CA,3,B,B2,6.9,246.0 -2016-02-06,CA,3,B,B2,6.74,247.0 -2016-02-07,CA,3,B,B2,6.82,247.0 -2016-02-08,CA,3,B,B2,6.77,224.0 -2016-02-09,CA,3,B,B2,6.76,239.0 -2016-02-10,CA,3,B,B2,6.82,239.0 -2016-02-11,CA,3,B,B2,6.87,216.0 -2016-02-12,CA,3,B,B2,6.85,250.0 -2016-02-13,CA,3,B,B2,6.87,225.0 -2016-02-14,CA,3,B,B2,6.93,225.0 -2016-02-15,CA,3,B,B2,6.75,244.0 -2016-02-16,CA,3,B,B2,6.9,223.0 -2016-02-17,CA,3,B,B2,6.94,227.0 -2016-02-18,CA,3,B,B2,6.78,234.0 -2016-02-19,CA,3,B,B2,6.76,249.0 -2016-02-20,CA,3,B,B2,6.98,224.0 -2016-02-21,CA,3,B,B2,6.94,226.0 -2016-02-22,CA,3,B,B2,6.81,247.0 -2016-02-23,CA,3,B,B2,6.79,241.0 -2016-02-24,CA,3,B,B2,6.72,218.0 -2016-02-25,CA,3,B,B2,6.9,237.0 -2016-02-26,CA,3,B,B2,6.93,232.0 -2016-02-27,CA,3,B,B2,6.93,248.0 -2016-02-28,CA,3,B,B2,6.84,228.0 -2016-02-29,CA,3,B,B2,6.79,230.0 -2016-03-01,CA,3,B,B2,6.88,214.0 -2016-03-02,CA,3,B,B2,6.83,230.0 -2016-03-03,CA,3,B,B2,6.88,249.0 -2016-03-04,CA,3,B,B2,6.76,257.0 -2016-03-05,CA,3,B,B2,6.79,253.0 -2016-03-06,CA,3,B,B2,6.81,208.0 -2016-03-07,CA,3,B,B2,7.01,221.0 -2016-03-08,CA,3,B,B2,6.69,229.0 -2016-03-09,CA,3,B,B2,6.83,265.0 -2016-03-10,CA,3,B,B2,6.97,228.0 -2016-03-11,CA,3,B,B2,6.96,229.0 -2016-03-12,CA,3,B,B2,6.86,237.0 -2016-03-13,CA,3,B,B2,6.77,227.0 -2016-03-14,CA,3,B,B2,6.86,220.0 -2016-03-15,CA,3,B,B2,6.88,238.0 -2016-03-16,CA,3,B,B2,6.84,228.0 -2016-03-17,CA,3,B,B2,6.79,237.0 -2016-03-18,CA,3,B,B2,6.83,229.0 -2016-03-19,CA,3,B,B2,6.83,255.0 -2016-03-20,CA,3,B,B2,6.97,246.0 -2016-03-21,CA,3,B,B2,6.75,231.0 -2016-03-22,CA,3,B,B2,6.89,238.0 -2016-03-23,CA,3,B,B2,6.85,225.0 -2016-03-24,CA,3,B,B2,6.79,258.0 -2016-03-25,CA,3,B,B2,6.69,231.0 -2016-03-26,CA,3,B,B2,6.83,234.0 -2016-03-27,CA,3,B,B2,6.87,234.0 -2016-03-28,CA,3,B,B2,6.89,225.0 -2016-03-29,CA,3,B,B2,6.87,240.0 -2016-03-30,CA,3,B,B2,6.86,238.0 -2016-03-31,CA,3,B,B2,6.82,235.0 -2016-04-01,CA,3,B,B2,6.78,234.0 -2016-04-02,CA,3,B,B2,6.83,235.0 -2016-04-03,CA,3,B,B2,6.74,245.0 -2016-04-04,CA,3,B,B2,7.0,226.0 -2016-04-05,CA,3,B,B2,7.01,236.0 -2016-04-06,CA,3,B,B2,6.92,234.0 -2016-04-07,CA,3,B,B2,6.89,219.0 -2016-04-08,CA,3,B,B2,6.89,247.0 -2016-04-09,CA,3,B,B2,6.96,247.0 -2016-04-10,CA,3,B,B2,6.83,257.0 -2016-04-11,CA,3,B,B2,6.93,227.0 -2016-04-12,CA,3,B,B2,6.82,220.0 -2016-04-13,CA,3,B,B2,6.85,237.0 -2016-04-14,CA,3,B,B2,6.83,229.0 -2016-04-15,CA,3,B,B2,6.75,240.0 -2016-04-16,CA,3,B,B2,6.81,238.0 -2016-04-17,CA,3,B,B2,6.79,225.0 -2016-04-18,CA,3,B,B2,6.87,224.0 -2016-04-19,CA,3,B,B2,6.84,228.0 -2016-04-20,CA,3,B,B2,6.9,253.0 -2016-04-21,CA,3,B,B2,6.84,235.0 -2016-04-22,CA,3,B,B2,6.8,230.0 -2016-04-23,CA,3,B,B2,6.9,226.0 -2016-04-24,CA,3,B,B2,6.88,249.0 -2016-04-25,CA,3,B,B2,6.88,245.0 -2016-04-26,CA,3,B,B2,6.86,227.0 -2016-04-27,CA,3,B,B2,6.84,222.0 -2016-04-28,CA,3,B,B2,6.83,225.0 -2016-04-29,CA,3,B,B2,6.91,228.0 -2016-04-30,CA,3,B,B2,6.91,253.0 -2016-05-01,CA,3,B,B2,6.8,246.0 -2016-05-02,CA,3,B,B2,6.82,216.0 -2016-05-03,CA,3,B,B2,6.85,264.0 -2016-05-04,CA,3,B,B2,6.9,228.0 -2016-05-05,CA,3,B,B2,6.74,230.0 -2016-05-06,CA,3,B,B2,6.85,214.0 -2016-05-07,CA,3,B,B2,6.91,251.0 -2016-05-08,CA,3,B,B2,6.8,248.0 -2016-05-09,CA,3,B,B2,6.79,244.0 -2016-05-10,CA,3,B,B2,6.77,239.0 -2016-05-11,CA,3,B,B2,6.84,233.0 -2016-05-12,CA,3,B,B2,6.77,236.0 -2016-05-13,CA,3,B,B2,6.98,245.0 -2016-05-14,CA,3,B,B2,6.97,239.0 -2016-05-15,CA,3,B,B2,6.86,236.0 -2016-05-16,CA,3,B,B2,6.77,205.0 -2016-05-17,CA,3,B,B2,6.84,240.0 -2016-05-18,CA,3,B,B2,6.88,229.0 -2016-05-19,CA,3,B,B2,6.89,242.0 -2016-05-20,CA,3,B,B2,6.78,255.0 -2016-05-21,CA,3,B,B2,6.85,258.0 -2016-05-22,CA,3,B,B2,6.8,245.0 -2016-05-23,CA,3,B,B2,6.88,237.0 -2016-05-24,CA,3,B,B2,6.81,233.0 -2016-05-25,CA,3,B,B2,6.63,254.0 -2016-05-26,CA,3,B,B2,6.83,234.0 -2016-05-27,CA,3,B,B2,6.81,240.0 -2016-05-28,CA,3,B,B2,6.77,259.0 -2016-05-29,CA,3,B,B2,6.76,226.0 -2016-05-30,CA,3,B,B2,6.8,234.0 -2016-05-31,CA,3,B,B2,6.94,228.0 -2016-06-01,CA,3,B,B2,6.84,249.0 -2016-06-02,CA,3,B,B2,6.82,259.0 -2016-06-03,CA,3,B,B2,6.8,223.0 -2016-06-04,CA,3,B,B2,6.84,233.0 -2016-06-05,CA,3,B,B2,6.87,231.0 -2016-06-06,CA,3,B,B2,6.82,233.0 -2016-06-07,CA,3,B,B2,6.69,229.0 -2016-06-08,CA,3,B,B2,6.9,249.0 -2016-06-09,CA,3,B,B2,6.95,219.0 -2016-06-10,CA,3,B,B2,6.9,228.0 -2016-06-11,CA,3,B,B2,6.81,240.0 -2016-06-12,CA,3,B,B2,6.86,221.0 -2016-06-13,CA,3,B,B2,6.82,249.0 -2016-06-14,CA,3,B,B2,6.96,227.0 -2016-06-15,CA,3,B,B2,6.84,225.0 -2016-06-16,CA,3,B,B2,6.92,249.0 -2016-06-17,CA,3,B,B2,6.74,229.0 -2016-06-18,CA,3,B,B2,6.94,235.0 -2016-06-19,CA,3,B,B2,6.81,225.0 -2016-06-20,CA,3,B,B2,6.85,214.0 -2016-06-21,CA,3,B,B2,6.79,251.0 -2016-06-22,CA,3,B,B2,6.77,246.0 -2016-06-23,CA,3,B,B2,6.74,240.0 -2016-06-24,CA,3,B,B2,6.78,240.0 -2016-06-25,CA,3,B,B2,6.76,264.0 -2016-06-26,CA,3,B,B2,6.91,221.0 -2016-06-27,CA,3,B,B2,6.91,217.0 -2016-06-28,CA,3,B,B2,6.9,245.0 -2016-06-29,CA,3,B,B2,6.86,234.0 -2016-06-30,CA,3,B,B2,6.85,240.0 -2016-07-01,CA,3,B,B2,6.83,241.0 -2016-07-02,CA,3,B,B2,6.68,254.0 -2016-07-03,CA,3,B,B2,6.83,223.0 -2016-07-04,CA,3,B,B2,6.91,221.0 -2016-07-05,CA,3,B,B2,6.9,232.0 -2016-07-06,CA,3,B,B2,6.86,235.0 -2016-07-07,CA,3,B,B2,6.89,215.0 -2016-07-08,CA,3,B,B2,6.91,232.0 -2016-07-09,CA,3,B,B2,6.93,224.0 -2016-07-10,CA,3,B,B2,6.77,233.0 -2016-07-11,CA,3,B,B2,6.88,216.0 -2016-07-12,CA,3,B,B2,6.89,238.0 -2016-07-13,CA,3,B,B2,6.87,217.0 -2016-07-14,CA,3,B,B2,6.83,221.0 -2016-07-15,CA,3,B,B2,6.82,232.0 -2016-07-16,CA,3,B,B2,6.77,248.0 -2016-07-17,CA,3,B,B2,6.85,245.0 -2016-07-18,CA,3,B,B2,6.92,224.0 -2016-07-19,CA,3,B,B2,6.88,230.0 -2016-07-20,CA,3,B,B2,6.84,229.0 -2016-07-21,CA,3,B,B2,6.88,227.0 -2016-07-22,CA,3,B,B2,6.75,251.0 -2016-07-23,CA,3,B,B2,6.95,238.0 -2016-07-24,CA,3,B,B2,6.88,255.0 -2016-07-25,CA,3,B,B2,6.92,227.0 -2016-07-26,CA,3,B,B2,6.91,239.0 -2016-07-27,CA,3,B,B2,6.75,221.0 -2015-01-05,FL,1,A,A1,6.13,242.0 -2015-01-06,FL,1,A,A1,6.15,253.0 -2015-01-07,FL,1,A,A1,6.11,270.0 -2015-01-08,FL,1,A,A1,6.1,261.0 -2015-01-09,FL,1,A,A1,6.11,273.0 -2015-01-10,FL,1,A,A1,6.11,274.0 -2015-01-11,FL,1,A,A1,5.99,272.0 -2015-01-12,FL,1,A,A1,6.13,277.0 -2015-01-13,FL,1,A,A1,6.15,268.0 -2015-01-14,FL,1,A,A1,6.15,277.0 -2015-01-15,FL,1,A,A1,6.07,277.0 -2015-01-16,FL,1,A,A1,6.07,259.0 -2015-01-17,FL,1,A,A1,6.01,280.0 -2015-01-18,FL,1,A,A1,6.14,298.0 -2015-01-19,FL,1,A,A1,6.11,266.0 -2015-01-20,FL,1,A,A1,6.04,268.0 -2015-01-21,FL,1,A,A1,6.14,270.0 -2015-01-22,FL,1,A,A1,6.07,270.0 -2015-01-23,FL,1,A,A1,6.13,299.0 -2015-01-24,FL,1,A,A1,6.13,281.0 -2015-01-25,FL,1,A,A1,6.12,294.0 -2015-01-26,FL,1,A,A1,6.22,285.0 -2015-01-27,FL,1,A,A1,6.18,277.0 -2015-01-28,FL,1,A,A1,6.17,263.0 -2015-01-29,FL,1,A,A1,6.19,259.0 -2015-01-30,FL,1,A,A1,6.13,274.0 -2015-01-31,FL,1,A,A1,6.25,294.0 -2015-02-01,FL,1,A,A1,6.2,283.0 -2015-02-02,FL,1,A,A1,6.17,265.0 -2015-02-03,FL,1,A,A1,6.05,290.0 -2015-02-04,FL,1,A,A1,6.11,265.0 -2015-02-05,FL,1,A,A1,6.11,286.0 -2015-02-06,FL,1,A,A1,6.17,313.0 -2015-02-07,FL,1,A,A1,6.18,298.0 -2015-02-08,FL,1,A,A1,6.21,290.0 -2015-02-09,FL,1,A,A1,6.18,269.0 -2015-02-10,FL,1,A,A1,6.23,280.0 -2015-02-11,FL,1,A,A1,6.23,283.0 -2015-02-12,FL,1,A,A1,6.15,271.0 -2015-02-13,FL,1,A,A1,6.2,294.0 -2015-02-14,FL,1,A,A1,6.13,296.0 -2015-02-15,FL,1,A,A1,6.19,280.0 -2015-02-16,FL,1,A,A1,6.18,264.0 -2015-02-17,FL,1,A,A1,6.13,301.0 -2015-02-18,FL,1,A,A1,6.19,268.0 -2015-02-19,FL,1,A,A1,6.11,272.0 -2015-02-20,FL,1,A,A1,6.19,294.0 -2015-02-21,FL,1,A,A1,5.96,298.0 -2015-02-22,FL,1,A,A1,6.24,303.0 -2015-02-23,FL,1,A,A1,6.22,282.0 -2015-02-24,FL,1,A,A1,6.03,306.0 -2015-02-25,FL,1,A,A1,6.05,278.0 -2015-02-26,FL,1,A,A1,6.12,303.0 -2015-02-27,FL,1,A,A1,6.08,279.0 -2015-02-28,FL,1,A,A1,6.11,308.0 -2015-03-01,FL,1,A,A1,6.18,296.0 -2015-03-02,FL,1,A,A1,6.11,282.0 -2015-03-03,FL,1,A,A1,6.11,301.0 -2015-03-04,FL,1,A,A1,6.1,289.0 -2015-03-05,FL,1,A,A1,6.26,286.0 -2015-03-06,FL,1,A,A1,6.17,288.0 -2015-03-07,FL,1,A,A1,6.08,311.0 -2015-03-08,FL,1,A,A1,6.23,276.0 -2015-03-09,FL,1,A,A1,6.18,279.0 -2015-03-10,FL,1,A,A1,6.01,286.0 -2015-03-11,FL,1,A,A1,6.13,272.0 -2015-03-12,FL,1,A,A1,6.12,292.0 -2015-03-13,FL,1,A,A1,6.02,304.0 -2015-03-14,FL,1,A,A1,6.05,320.0 -2015-03-15,FL,1,A,A1,6.17,298.0 -2015-03-16,FL,1,A,A1,6.16,307.0 -2015-03-17,FL,1,A,A1,6.18,284.0 -2015-03-18,FL,1,A,A1,6.05,289.0 -2015-03-19,FL,1,A,A1,6.07,296.0 -2015-03-20,FL,1,A,A1,6.25,281.0 -2015-03-21,FL,1,A,A1,6.14,310.0 -2015-03-22,FL,1,A,A1,6.2,282.0 -2015-03-23,FL,1,A,A1,6.06,289.0 -2015-03-24,FL,1,A,A1,6.23,300.0 -2015-03-25,FL,1,A,A1,6.1,281.0 -2015-03-26,FL,1,A,A1,6.04,301.0 -2015-03-27,FL,1,A,A1,6.15,283.0 -2015-03-28,FL,1,A,A1,6.2,286.0 -2015-03-29,FL,1,A,A1,6.08,301.0 -2015-03-30,FL,1,A,A1,6.18,296.0 -2015-03-31,FL,1,A,A1,6.17,312.0 -2015-04-01,FL,1,A,A1,6.2,279.0 -2015-04-02,FL,1,A,A1,6.15,291.0 -2015-04-03,FL,1,A,A1,6.2,289.0 -2015-04-04,FL,1,A,A1,6.15,330.0 -2015-04-05,FL,1,A,A1,6.1,308.0 -2015-04-06,FL,1,A,A1,6.18,290.0 -2015-04-07,FL,1,A,A1,6.16,285.0 -2015-04-08,FL,1,A,A1,6.19,292.0 -2015-04-09,FL,1,A,A1,6.19,288.0 -2015-04-10,FL,1,A,A1,6.07,298.0 -2015-04-11,FL,1,A,A1,6.22,322.0 -2015-04-12,FL,1,A,A1,6.24,292.0 -2015-04-13,FL,1,A,A1,6.14,278.0 -2015-04-14,FL,1,A,A1,6.18,300.0 -2015-04-15,FL,1,A,A1,6.19,291.0 -2015-04-16,FL,1,A,A1,6.2,274.0 -2015-04-17,FL,1,A,A1,6.2,298.0 -2015-04-18,FL,1,A,A1,6.16,323.0 -2015-04-19,FL,1,A,A1,6.2,287.0 -2015-04-20,FL,1,A,A1,6.1,294.0 -2015-04-21,FL,1,A,A1,6.14,300.0 -2015-04-22,FL,1,A,A1,6.18,327.0 -2015-04-23,FL,1,A,A1,6.18,300.0 -2015-04-24,FL,1,A,A1,6.23,287.0 -2015-04-25,FL,1,A,A1,6.12,321.0 -2015-04-26,FL,1,A,A1,6.18,309.0 -2015-04-27,FL,1,A,A1,6.07,309.0 -2015-04-28,FL,1,A,A1,6.02,304.0 -2015-04-29,FL,1,A,A1,6.1,292.0 -2015-04-30,FL,1,A,A1,5.99,323.0 -2015-05-01,FL,1,A,A1,6.13,300.0 -2015-05-02,FL,1,A,A1,6.15,295.0 -2015-05-03,FL,1,A,A1,6.12,302.0 -2015-05-04,FL,1,A,A1,6.13,288.0 -2015-05-05,FL,1,A,A1,6.2,305.0 -2015-05-06,FL,1,A,A1,6.24,293.0 -2015-05-07,FL,1,A,A1,6.14,306.0 -2015-05-08,FL,1,A,A1,6.1,308.0 -2015-05-09,FL,1,A,A1,6.12,304.0 -2015-05-10,FL,1,A,A1,6.12,301.0 -2015-05-11,FL,1,A,A1,6.12,271.0 -2015-05-12,FL,1,A,A1,6.08,293.0 -2015-05-13,FL,1,A,A1,6.05,287.0 -2015-05-14,FL,1,A,A1,6.04,324.0 -2015-05-15,FL,1,A,A1,6.08,323.0 -2015-05-16,FL,1,A,A1,6.12,339.0 -2015-05-17,FL,1,A,A1,6.15,287.0 -2015-05-18,FL,1,A,A1,6.17,299.0 -2015-05-19,FL,1,A,A1,6.1,279.0 -2015-05-20,FL,1,A,A1,6.27,271.0 -2015-05-21,FL,1,A,A1,6.17,307.0 -2015-05-22,FL,1,A,A1,6.28,295.0 -2015-05-23,FL,1,A,A1,6.08,320.0 -2015-05-24,FL,1,A,A1,6.14,329.0 -2015-05-25,FL,1,A,A1,6.05,284.0 -2015-05-26,FL,1,A,A1,6.29,305.0 -2015-05-27,FL,1,A,A1,6.08,285.0 -2015-05-28,FL,1,A,A1,6.03,313.0 -2015-05-29,FL,1,A,A1,6.23,280.0 -2015-05-30,FL,1,A,A1,6.06,321.0 -2015-05-31,FL,1,A,A1,6.03,319.0 -2015-06-01,FL,1,A,A1,6.09,303.0 -2015-06-02,FL,1,A,A1,6.12,299.0 -2015-06-03,FL,1,A,A1,6.12,289.0 -2015-06-04,FL,1,A,A1,6.06,310.0 -2015-06-05,FL,1,A,A1,6.1,306.0 -2015-06-06,FL,1,A,A1,6.19,313.0 -2015-06-07,FL,1,A,A1,6.21,298.0 -2015-06-08,FL,1,A,A1,6.19,286.0 -2015-06-09,FL,1,A,A1,6.15,295.0 -2015-06-10,FL,1,A,A1,6.18,292.0 -2015-06-11,FL,1,A,A1,6.03,301.0 -2015-06-12,FL,1,A,A1,6.02,315.0 -2015-06-13,FL,1,A,A1,6.09,305.0 -2015-06-14,FL,1,A,A1,6.19,308.0 -2015-06-15,FL,1,A,A1,6.14,309.0 -2015-06-16,FL,1,A,A1,6.15,291.0 -2015-06-17,FL,1,A,A1,6.17,316.0 -2015-06-18,FL,1,A,A1,6.09,290.0 -2015-06-19,FL,1,A,A1,6.19,305.0 -2015-06-20,FL,1,A,A1,6.16,303.0 -2015-06-21,FL,1,A,A1,6.22,316.0 -2015-06-22,FL,1,A,A1,6.13,315.0 -2015-06-23,FL,1,A,A1,6.01,288.0 -2015-06-24,FL,1,A,A1,6.14,323.0 -2015-06-25,FL,1,A,A1,6.08,304.0 -2015-06-26,FL,1,A,A1,6.12,308.0 -2015-06-27,FL,1,A,A1,6.17,304.0 -2015-06-28,FL,1,A,A1,6.12,313.0 -2015-06-29,FL,1,A,A1,6.09,300.0 -2015-06-30,FL,1,A,A1,6.09,290.0 -2015-07-01,FL,1,A,A1,6.15,293.0 -2015-07-02,FL,1,A,A1,6.16,323.0 -2015-07-03,FL,1,A,A1,6.12,299.0 -2015-07-04,FL,1,A,A1,6.2,303.0 -2015-07-05,FL,1,A,A1,6.13,320.0 -2015-07-06,FL,1,A,A1,6.2,297.0 -2015-07-07,FL,1,A,A1,6.18,314.0 -2015-07-08,FL,1,A,A1,6.15,304.0 -2015-07-09,FL,1,A,A1,6.24,315.0 -2015-07-10,FL,1,A,A1,6.25,292.0 -2015-07-11,FL,1,A,A1,6.22,296.0 -2015-07-12,FL,1,A,A1,6.09,321.0 -2015-07-13,FL,1,A,A1,6.16,308.0 -2015-07-14,FL,1,A,A1,6.19,282.0 -2015-07-15,FL,1,A,A1,6.17,298.0 -2015-07-16,FL,1,A,A1,6.19,280.0 -2015-07-17,FL,1,A,A1,6.25,300.0 -2015-07-18,FL,1,A,A1,6.03,310.0 -2015-07-19,FL,1,A,A1,6.23,292.0 -2015-07-20,FL,1,A,A1,6.17,278.0 -2015-07-21,FL,1,A,A1,6.1,295.0 -2015-07-22,FL,1,A,A1,6.36,304.0 -2015-07-23,FL,1,A,A1,6.17,309.0 -2015-07-24,FL,1,A,A1,6.14,305.0 -2015-07-25,FL,1,A,A1,6.1,338.0 -2015-07-26,FL,1,A,A1,6.14,302.0 -2015-07-27,FL,1,A,A1,6.19,297.0 -2015-07-28,FL,1,A,A1,6.11,310.0 -2015-07-29,FL,1,A,A1,6.17,310.0 -2015-07-30,FL,1,A,A1,6.19,291.0 -2015-07-31,FL,1,A,A1,6.22,302.0 -2015-08-01,FL,1,A,A1,6.16,305.0 -2015-08-02,FL,1,A,A1,6.16,338.0 -2015-08-03,FL,1,A,A1,6.01,312.0 -2015-08-04,FL,1,A,A1,6.13,295.0 -2015-08-05,FL,1,A,A1,6.16,294.0 -2015-08-06,FL,1,A,A1,6.01,299.0 -2015-08-07,FL,1,A,A1,6.2,298.0 -2015-08-08,FL,1,A,A1,6.21,311.0 -2015-08-09,FL,1,A,A1,6.19,308.0 -2015-08-10,FL,1,A,A1,6.15,307.0 -2015-08-11,FL,1,A,A1,6.16,311.0 -2015-08-12,FL,1,A,A1,6.07,305.0 -2015-08-13,FL,1,A,A1,6.07,324.0 -2015-08-14,FL,1,A,A1,6.11,288.0 -2015-08-15,FL,1,A,A1,6.11,321.0 -2015-08-16,FL,1,A,A1,6.14,287.0 -2015-08-17,FL,1,A,A1,6.16,286.0 -2015-08-18,FL,1,A,A1,6.1,309.0 -2015-08-19,FL,1,A,A1,6.13,303.0 -2015-08-20,FL,1,A,A1,6.11,294.0 -2015-08-21,FL,1,A,A1,6.18,312.0 -2015-08-22,FL,1,A,A1,6.08,317.0 -2015-08-23,FL,1,A,A1,6.12,305.0 -2015-08-24,FL,1,A,A1,6.29,269.0 -2015-08-25,FL,1,A,A1,6.17,288.0 -2015-08-26,FL,1,A,A1,6.06,302.0 -2015-08-27,FL,1,A,A1,6.05,281.0 -2015-08-28,FL,1,A,A1,6.06,290.0 -2015-08-29,FL,1,A,A1,6.14,308.0 -2015-08-30,FL,1,A,A1,6.11,301.0 -2015-08-31,FL,1,A,A1,6.14,312.0 -2015-09-01,FL,1,A,A1,6.13,305.0 -2015-09-02,FL,1,A,A1,6.13,275.0 -2015-09-03,FL,1,A,A1,5.99,319.0 -2015-09-04,FL,1,A,A1,6.24,291.0 -2015-09-05,FL,1,A,A1,6.2,309.0 -2015-09-06,FL,1,A,A1,6.13,283.0 -2015-09-07,FL,1,A,A1,6.13,328.0 -2015-09-08,FL,1,A,A1,6.21,291.0 -2015-09-09,FL,1,A,A1,6.14,297.0 -2015-09-10,FL,1,A,A1,6.22,293.0 -2015-09-11,FL,1,A,A1,6.05,319.0 -2015-09-12,FL,1,A,A1,6.2,303.0 -2015-09-13,FL,1,A,A1,6.16,305.0 -2015-09-14,FL,1,A,A1,6.06,279.0 -2015-09-15,FL,1,A,A1,6.11,280.0 -2015-09-16,FL,1,A,A1,6.24,293.0 -2015-09-17,FL,1,A,A1,6.22,298.0 -2015-09-18,FL,1,A,A1,6.09,292.0 -2015-09-19,FL,1,A,A1,6.17,331.0 -2015-09-20,FL,1,A,A1,6.2,342.0 -2015-09-21,FL,1,A,A1,6.18,271.0 -2015-09-22,FL,1,A,A1,6.07,303.0 -2015-09-23,FL,1,A,A1,6.25,281.0 -2015-09-24,FL,1,A,A1,6.1,301.0 -2015-09-25,FL,1,A,A1,6.1,312.0 -2015-09-26,FL,1,A,A1,6.15,334.0 -2015-09-27,FL,1,A,A1,6.18,296.0 -2015-09-28,FL,1,A,A1,6.21,298.0 -2015-09-29,FL,1,A,A1,6.19,295.0 -2015-09-30,FL,1,A,A1,6.1,312.0 -2015-10-01,FL,1,A,A1,6.16,310.0 -2015-10-02,FL,1,A,A1,6.08,310.0 -2015-10-03,FL,1,A,A1,6.0,314.0 -2015-10-04,FL,1,A,A1,6.14,298.0 -2015-10-05,FL,1,A,A1,6.19,296.0 -2015-10-06,FL,1,A,A1,6.06,308.0 -2015-10-07,FL,1,A,A1,6.19,308.0 -2015-10-08,FL,1,A,A1,6.2,275.0 -2015-10-09,FL,1,A,A1,6.15,315.0 -2015-10-10,FL,1,A,A1,6.1,315.0 -2015-10-11,FL,1,A,A1,6.11,334.0 -2015-10-12,FL,1,A,A1,6.14,291.0 -2015-10-13,FL,1,A,A1,6.17,297.0 -2015-10-14,FL,1,A,A1,6.14,287.0 -2015-10-15,FL,1,A,A1,6.07,328.0 -2015-10-16,FL,1,A,A1,6.14,311.0 -2015-10-17,FL,1,A,A1,6.17,306.0 -2015-10-18,FL,1,A,A1,6.12,314.0 -2015-10-19,FL,1,A,A1,6.18,279.0 -2015-10-20,FL,1,A,A1,6.13,291.0 -2015-10-21,FL,1,A,A1,6.08,311.0 -2015-10-22,FL,1,A,A1,6.09,291.0 -2015-10-23,FL,1,A,A1,6.16,307.0 -2015-10-24,FL,1,A,A1,6.23,337.0 -2015-10-25,FL,1,A,A1,6.03,307.0 -2015-10-26,FL,1,A,A1,6.05,295.0 -2015-10-27,FL,1,A,A1,6.12,310.0 -2015-10-28,FL,1,A,A1,6.06,321.0 -2015-10-29,FL,1,A,A1,6.12,281.0 -2015-10-30,FL,1,A,A1,6.14,297.0 -2015-10-31,FL,1,A,A1,6.14,320.0 -2015-11-01,FL,1,A,A1,6.11,302.0 -2015-11-02,FL,1,A,A1,6.09,295.0 -2015-11-03,FL,1,A,A1,6.09,292.0 -2015-11-04,FL,1,A,A1,6.08,283.0 -2015-11-05,FL,1,A,A1,6.17,303.0 -2015-11-06,FL,1,A,A1,6.16,318.0 -2015-11-07,FL,1,A,A1,6.05,328.0 -2015-11-08,FL,1,A,A1,6.3,303.0 -2015-11-09,FL,1,A,A1,6.15,295.0 -2015-11-10,FL,1,A,A1,6.17,312.0 -2015-11-11,FL,1,A,A1,6.15,293.0 -2015-11-12,FL,1,A,A1,5.99,324.0 -2015-11-13,FL,1,A,A1,6.07,314.0 -2015-11-14,FL,1,A,A1,6.17,307.0 -2015-11-15,FL,1,A,A1,6.17,289.0 -2015-11-16,FL,1,A,A1,6.22,291.0 -2015-11-17,FL,1,A,A1,6.18,301.0 -2015-11-18,FL,1,A,A1,6.1,296.0 -2015-11-19,FL,1,A,A1,6.19,272.0 -2015-11-20,FL,1,A,A1,6.14,299.0 -2015-11-21,FL,1,A,A1,5.98,322.0 -2015-11-22,FL,1,A,A1,6.17,315.0 -2015-11-23,FL,1,A,A1,6.14,300.0 -2015-11-24,FL,1,A,A1,6.13,298.0 -2015-11-25,FL,1,A,A1,6.18,293.0 -2015-11-26,FL,1,A,A1,5.99,292.0 -2015-11-27,FL,1,A,A1,6.24,283.0 -2015-11-28,FL,1,A,A1,6.06,302.0 -2015-11-29,FL,1,A,A1,6.17,296.0 -2015-11-30,FL,1,A,A1,6.17,282.0 -2015-12-01,FL,1,A,A1,6.25,274.0 -2015-12-02,FL,1,A,A1,6.26,289.0 -2015-12-03,FL,1,A,A1,6.2,300.0 -2015-12-04,FL,1,A,A1,6.04,299.0 -2015-12-05,FL,1,A,A1,6.16,310.0 -2015-12-06,FL,1,A,A1,6.2,295.0 -2015-12-07,FL,1,A,A1,6.23,277.0 -2015-12-08,FL,1,A,A1,6.17,297.0 -2015-12-09,FL,1,A,A1,6.12,306.0 -2015-12-10,FL,1,A,A1,6.18,279.0 -2015-12-11,FL,1,A,A1,6.04,321.0 -2015-12-12,FL,1,A,A1,5.96,318.0 -2015-12-13,FL,1,A,A1,6.05,334.0 -2015-12-14,FL,1,A,A1,6.05,308.0 -2015-12-15,FL,1,A,A1,6.09,307.0 -2015-12-16,FL,1,A,A1,6.1,287.0 -2015-12-17,FL,1,A,A1,6.2,288.0 -2015-12-18,FL,1,A,A1,6.13,303.0 -2015-12-19,FL,1,A,A1,6.18,313.0 -2015-12-20,FL,1,A,A1,6.13,318.0 -2015-12-21,FL,1,A,A1,6.15,288.0 -2015-12-22,FL,1,A,A1,6.11,283.0 -2015-12-23,FL,1,A,A1,6.19,270.0 -2015-12-24,FL,1,A,A1,6.14,296.0 -2015-12-25,FL,1,A,A1,6.09,298.0 -2015-12-26,FL,1,A,A1,6.2,314.0 -2015-12-27,FL,1,A,A1,6.07,325.0 -2015-12-28,FL,1,A,A1,6.03,293.0 -2015-12-29,FL,1,A,A1,6.15,277.0 -2015-12-30,FL,1,A,A1,6.11,288.0 -2015-12-31,FL,1,A,A1,6.28,291.0 -2016-01-01,FL,1,A,A1,6.13,303.0 -2016-01-02,FL,1,A,A1,6.11,297.0 -2016-01-03,FL,1,A,A1,6.23,284.0 -2016-01-04,FL,1,A,A1,6.08,318.0 -2016-01-05,FL,1,A,A1,6.23,282.0 -2016-01-06,FL,1,A,A1,6.13,302.0 -2016-01-07,FL,1,A,A1,6.07,293.0 -2016-01-08,FL,1,A,A1,6.21,283.0 -2016-01-09,FL,1,A,A1,6.23,313.0 -2016-01-10,FL,1,A,A1,6.1,306.0 -2016-01-11,FL,1,A,A1,6.11,299.0 -2016-01-12,FL,1,A,A1,6.19,300.0 -2016-01-13,FL,1,A,A1,6.15,302.0 -2016-01-14,FL,1,A,A1,6.16,296.0 -2016-01-15,FL,1,A,A1,6.03,283.0 -2016-01-16,FL,1,A,A1,6.1,301.0 -2016-01-17,FL,1,A,A1,6.15,323.0 -2016-01-18,FL,1,A,A1,6.07,276.0 -2016-01-19,FL,1,A,A1,6.22,321.0 -2016-01-20,FL,1,A,A1,6.12,309.0 -2016-01-21,FL,1,A,A1,6.13,315.0 -2016-01-22,FL,1,A,A1,6.19,300.0 -2016-01-23,FL,1,A,A1,6.11,303.0 -2016-01-24,FL,1,A,A1,6.12,305.0 -2016-01-25,FL,1,A,A1,6.14,278.0 -2016-01-26,FL,1,A,A1,6.12,287.0 -2016-01-27,FL,1,A,A1,6.25,296.0 -2016-01-28,FL,1,A,A1,6.09,306.0 -2016-01-29,FL,1,A,A1,6.09,332.0 -2016-01-30,FL,1,A,A1,6.07,325.0 -2016-01-31,FL,1,A,A1,6.04,313.0 -2016-02-01,FL,1,A,A1,6.1,271.0 -2016-02-02,FL,1,A,A1,6.14,290.0 -2016-02-03,FL,1,A,A1,6.09,306.0 -2016-02-04,FL,1,A,A1,5.94,282.0 -2016-02-05,FL,1,A,A1,6.11,301.0 -2016-02-06,FL,1,A,A1,6.08,315.0 -2016-02-07,FL,1,A,A1,6.14,294.0 -2016-02-08,FL,1,A,A1,6.12,288.0 -2016-02-09,FL,1,A,A1,6.15,291.0 -2016-02-10,FL,1,A,A1,6.08,270.0 -2016-02-11,FL,1,A,A1,6.16,294.0 -2016-02-12,FL,1,A,A1,6.08,280.0 -2016-02-13,FL,1,A,A1,6.08,321.0 -2016-02-14,FL,1,A,A1,6.17,292.0 -2016-02-15,FL,1,A,A1,6.08,297.0 -2016-02-16,FL,1,A,A1,6.12,313.0 -2016-02-17,FL,1,A,A1,6.19,314.0 -2016-02-18,FL,1,A,A1,6.18,305.0 -2016-02-19,FL,1,A,A1,6.19,309.0 -2016-02-20,FL,1,A,A1,6.2,309.0 -2016-02-21,FL,1,A,A1,6.16,308.0 -2016-02-22,FL,1,A,A1,6.15,299.0 -2016-02-23,FL,1,A,A1,6.1,302.0 -2016-02-24,FL,1,A,A1,6.24,289.0 -2016-02-25,FL,1,A,A1,6.14,280.0 -2016-02-26,FL,1,A,A1,6.13,319.0 -2016-02-27,FL,1,A,A1,6.21,313.0 -2016-02-28,FL,1,A,A1,6.13,322.0 -2016-02-29,FL,1,A,A1,6.1,292.0 -2016-03-01,FL,1,A,A1,5.98,316.0 -2016-03-02,FL,1,A,A1,6.17,301.0 -2016-03-03,FL,1,A,A1,6.21,278.0 -2016-03-04,FL,1,A,A1,6.11,325.0 -2016-03-05,FL,1,A,A1,6.23,329.0 -2016-03-06,FL,1,A,A1,6.11,315.0 -2016-03-07,FL,1,A,A1,6.17,294.0 -2016-03-08,FL,1,A,A1,6.08,308.0 -2016-03-09,FL,1,A,A1,6.21,290.0 -2016-03-10,FL,1,A,A1,6.1,290.0 -2016-03-11,FL,1,A,A1,6.09,299.0 -2016-03-12,FL,1,A,A1,6.23,299.0 -2016-03-13,FL,1,A,A1,6.18,305.0 -2016-03-14,FL,1,A,A1,6.18,295.0 -2016-03-15,FL,1,A,A1,6.18,300.0 -2016-03-16,FL,1,A,A1,6.19,316.0 -2016-03-17,FL,1,A,A1,5.99,318.0 -2016-03-18,FL,1,A,A1,6.11,302.0 -2016-03-19,FL,1,A,A1,6.14,311.0 -2016-03-20,FL,1,A,A1,6.14,315.0 -2016-03-21,FL,1,A,A1,6.15,299.0 -2016-03-22,FL,1,A,A1,6.12,285.0 -2016-03-23,FL,1,A,A1,6.09,301.0 -2016-03-24,FL,1,A,A1,6.14,289.0 -2016-03-25,FL,1,A,A1,6.1,292.0 -2016-03-26,FL,1,A,A1,6.32,309.0 -2016-03-27,FL,1,A,A1,6.13,319.0 -2016-03-28,FL,1,A,A1,6.21,319.0 -2016-03-29,FL,1,A,A1,6.03,282.0 -2016-03-30,FL,1,A,A1,6.15,286.0 -2016-03-31,FL,1,A,A1,6.12,314.0 -2016-04-01,FL,1,A,A1,6.06,329.0 -2016-04-02,FL,1,A,A1,6.23,301.0 -2016-04-03,FL,1,A,A1,6.16,292.0 -2016-04-04,FL,1,A,A1,6.11,306.0 -2016-04-05,FL,1,A,A1,6.12,305.0 -2016-04-06,FL,1,A,A1,6.08,315.0 -2016-04-07,FL,1,A,A1,6.1,306.0 -2016-04-08,FL,1,A,A1,6.22,313.0 -2016-04-09,FL,1,A,A1,6.17,306.0 -2016-04-10,FL,1,A,A1,6.05,301.0 -2016-04-11,FL,1,A,A1,6.1,313.0 -2016-04-12,FL,1,A,A1,6.25,289.0 -2016-04-13,FL,1,A,A1,6.08,297.0 -2016-04-14,FL,1,A,A1,6.18,286.0 -2016-04-15,FL,1,A,A1,6.16,304.0 -2016-04-16,FL,1,A,A1,6.16,311.0 -2016-04-17,FL,1,A,A1,6.22,314.0 -2016-04-18,FL,1,A,A1,6.1,300.0 -2016-04-19,FL,1,A,A1,6.09,304.0 -2016-04-20,FL,1,A,A1,6.16,293.0 -2016-04-21,FL,1,A,A1,6.11,291.0 -2016-04-22,FL,1,A,A1,6.2,296.0 -2016-04-23,FL,1,A,A1,6.14,310.0 -2016-04-24,FL,1,A,A1,6.08,320.0 -2016-04-25,FL,1,A,A1,6.18,293.0 -2016-04-26,FL,1,A,A1,6.09,296.0 -2016-04-27,FL,1,A,A1,6.13,324.0 -2016-04-28,FL,1,A,A1,6.19,275.0 -2016-04-29,FL,1,A,A1,6.11,331.0 -2016-04-30,FL,1,A,A1,6.04,327.0 -2016-05-01,FL,1,A,A1,6.11,296.0 -2016-05-02,FL,1,A,A1,6.21,286.0 -2016-05-03,FL,1,A,A1,6.08,293.0 -2016-05-04,FL,1,A,A1,6.17,296.0 -2016-05-05,FL,1,A,A1,6.15,304.0 -2016-05-06,FL,1,A,A1,6.23,287.0 -2016-05-07,FL,1,A,A1,6.13,301.0 -2016-05-08,FL,1,A,A1,5.98,298.0 -2016-05-09,FL,1,A,A1,6.1,303.0 -2016-05-10,FL,1,A,A1,6.13,273.0 -2016-05-11,FL,1,A,A1,6.18,291.0 -2016-05-12,FL,1,A,A1,6.17,324.0 -2016-05-13,FL,1,A,A1,6.13,313.0 -2016-05-14,FL,1,A,A1,6.24,315.0 -2016-05-15,FL,1,A,A1,6.12,300.0 -2016-05-16,FL,1,A,A1,6.18,284.0 -2016-05-17,FL,1,A,A1,6.23,284.0 -2016-05-18,FL,1,A,A1,6.13,306.0 -2016-05-19,FL,1,A,A1,6.08,313.0 -2016-05-20,FL,1,A,A1,6.06,305.0 -2016-05-21,FL,1,A,A1,6.03,328.0 -2016-05-22,FL,1,A,A1,6.2,305.0 -2016-05-23,FL,1,A,A1,6.08,321.0 -2016-05-24,FL,1,A,A1,6.18,291.0 -2016-05-25,FL,1,A,A1,6.18,287.0 -2016-05-26,FL,1,A,A1,6.17,289.0 -2016-05-27,FL,1,A,A1,6.13,316.0 -2016-05-28,FL,1,A,A1,6.1,301.0 -2016-05-29,FL,1,A,A1,6.21,293.0 -2016-05-30,FL,1,A,A1,6.21,293.0 -2016-05-31,FL,1,A,A1,6.0,287.0 -2016-06-01,FL,1,A,A1,6.19,293.0 -2016-06-02,FL,1,A,A1,6.06,286.0 -2016-06-03,FL,1,A,A1,6.1,304.0 -2016-06-04,FL,1,A,A1,6.15,323.0 -2016-06-05,FL,1,A,A1,6.22,322.0 -2016-06-06,FL,1,A,A1,6.12,303.0 -2016-06-07,FL,1,A,A1,6.12,305.0 -2016-06-08,FL,1,A,A1,6.12,314.0 -2016-06-09,FL,1,A,A1,6.27,306.0 -2016-06-10,FL,1,A,A1,6.1,291.0 -2016-06-11,FL,1,A,A1,6.19,310.0 -2016-06-12,FL,1,A,A1,6.3,295.0 -2016-06-13,FL,1,A,A1,6.13,293.0 -2016-06-14,FL,1,A,A1,6.16,291.0 -2016-06-15,FL,1,A,A1,6.16,313.0 -2016-06-16,FL,1,A,A1,6.13,303.0 -2016-06-17,FL,1,A,A1,6.17,309.0 -2016-06-18,FL,1,A,A1,6.21,333.0 -2016-06-19,FL,1,A,A1,6.1,314.0 -2016-06-20,FL,1,A,A1,6.19,293.0 -2016-06-21,FL,1,A,A1,6.1,303.0 -2016-06-22,FL,1,A,A1,6.19,300.0 -2016-06-23,FL,1,A,A1,6.12,304.0 -2016-06-24,FL,1,A,A1,6.14,306.0 -2016-06-25,FL,1,A,A1,6.13,299.0 -2016-06-26,FL,1,A,A1,6.21,308.0 -2016-06-27,FL,1,A,A1,6.17,283.0 -2016-06-28,FL,1,A,A1,6.16,312.0 -2016-06-29,FL,1,A,A1,6.08,315.0 -2016-06-30,FL,1,A,A1,6.11,303.0 -2016-07-01,FL,1,A,A1,6.15,305.0 -2016-07-02,FL,1,A,A1,6.16,296.0 -2016-07-03,FL,1,A,A1,6.16,316.0 -2016-07-04,FL,1,A,A1,6.11,292.0 -2016-07-05,FL,1,A,A1,6.18,311.0 -2016-07-06,FL,1,A,A1,6.16,310.0 -2016-07-07,FL,1,A,A1,6.19,297.0 -2016-07-08,FL,1,A,A1,6.11,301.0 -2016-07-09,FL,1,A,A1,6.25,317.0 -2016-07-10,FL,1,A,A1,6.11,299.0 -2016-07-11,FL,1,A,A1,6.15,313.0 -2016-07-12,FL,1,A,A1,6.18,310.0 -2016-07-13,FL,1,A,A1,6.13,318.0 -2016-07-14,FL,1,A,A1,6.23,304.0 -2016-07-15,FL,1,A,A1,6.34,295.0 -2016-07-16,FL,1,A,A1,6.15,296.0 -2016-07-17,FL,1,A,A1,6.16,282.0 -2016-07-18,FL,1,A,A1,6.17,296.0 -2016-07-19,FL,1,A,A1,6.05,307.0 -2016-07-20,FL,1,A,A1,6.21,317.0 -2016-07-21,FL,1,A,A1,6.13,294.0 -2016-07-22,FL,1,A,A1,6.07,305.0 -2016-07-23,FL,1,A,A1,6.16,308.0 -2016-07-24,FL,1,A,A1,6.14,319.0 -2016-07-25,FL,1,A,A1,6.04,305.0 -2016-07-26,FL,1,A,A1,6.22,300.0 -2016-07-27,FL,1,A,A1,6.13,304.0 -2015-01-05,FL,1,A,A2,6.27,79.0 -2015-01-06,FL,1,A,A2,6.26,69.0 -2015-01-07,FL,1,A,A2,6.2,79.0 -2015-01-08,FL,1,A,A2,6.27,78.0 -2015-01-09,FL,1,A,A2,6.24,74.0 -2015-01-10,FL,1,A,A2,6.17,84.0 -2015-01-11,FL,1,A,A2,6.26,81.0 -2015-01-12,FL,1,A,A2,6.23,67.0 -2015-01-13,FL,1,A,A2,6.32,70.0 -2015-01-14,FL,1,A,A2,6.19,79.0 -2015-01-15,FL,1,A,A2,6.24,67.0 -2015-01-16,FL,1,A,A2,6.22,77.0 -2015-01-17,FL,1,A,A2,6.27,76.0 -2015-01-18,FL,1,A,A2,6.36,75.0 -2015-01-19,FL,1,A,A2,6.28,82.0 -2015-01-20,FL,1,A,A2,6.31,66.0 -2015-01-21,FL,1,A,A2,6.37,74.0 -2015-01-22,FL,1,A,A2,6.17,77.0 -2015-01-23,FL,1,A,A2,6.23,76.0 -2015-01-24,FL,1,A,A2,6.3,78.0 -2015-01-25,FL,1,A,A2,6.25,74.0 -2015-01-26,FL,1,A,A2,6.24,74.0 -2015-01-27,FL,1,A,A2,6.17,77.0 -2015-01-28,FL,1,A,A2,6.23,73.0 -2015-01-29,FL,1,A,A2,6.27,69.0 -2015-01-30,FL,1,A,A2,6.28,71.0 -2015-01-31,FL,1,A,A2,6.29,79.0 -2015-02-01,FL,1,A,A2,6.21,82.0 -2015-02-02,FL,1,A,A2,6.18,70.0 -2015-02-03,FL,1,A,A2,6.27,80.0 -2015-02-04,FL,1,A,A2,6.3,76.0 -2015-02-05,FL,1,A,A2,6.19,79.0 -2015-02-06,FL,1,A,A2,6.22,78.0 -2015-02-07,FL,1,A,A2,6.34,76.0 -2015-02-08,FL,1,A,A2,6.26,72.0 -2015-02-09,FL,1,A,A2,6.2,73.0 -2015-02-10,FL,1,A,A2,6.26,73.0 -2015-02-11,FL,1,A,A2,6.14,75.0 -2015-02-12,FL,1,A,A2,6.27,78.0 -2015-02-13,FL,1,A,A2,6.17,81.0 -2015-02-14,FL,1,A,A2,6.24,78.0 -2015-02-15,FL,1,A,A2,6.27,77.0 -2015-02-16,FL,1,A,A2,6.14,77.0 -2015-02-17,FL,1,A,A2,6.17,78.0 -2015-02-18,FL,1,A,A2,6.28,77.0 -2015-02-19,FL,1,A,A2,6.19,77.0 -2015-02-20,FL,1,A,A2,6.18,84.0 -2015-02-21,FL,1,A,A2,6.25,82.0 -2015-02-22,FL,1,A,A2,6.23,81.0 -2015-02-23,FL,1,A,A2,6.27,66.0 -2015-02-24,FL,1,A,A2,6.24,75.0 -2015-02-25,FL,1,A,A2,6.34,74.0 -2015-02-26,FL,1,A,A2,6.16,79.0 -2015-02-27,FL,1,A,A2,6.18,77.0 -2015-02-28,FL,1,A,A2,6.3,71.0 -2015-03-01,FL,1,A,A2,6.2,80.0 -2015-03-02,FL,1,A,A2,6.19,69.0 -2015-03-03,FL,1,A,A2,6.21,73.0 -2015-03-04,FL,1,A,A2,6.18,82.0 -2015-03-05,FL,1,A,A2,6.3,67.0 -2015-03-06,FL,1,A,A2,6.19,74.0 -2015-03-07,FL,1,A,A2,6.24,77.0 -2015-03-08,FL,1,A,A2,6.26,77.0 -2015-03-09,FL,1,A,A2,6.31,67.0 -2015-03-10,FL,1,A,A2,6.26,77.0 -2015-03-11,FL,1,A,A2,6.13,80.0 -2015-03-12,FL,1,A,A2,6.27,70.0 -2015-03-13,FL,1,A,A2,6.29,76.0 -2015-03-14,FL,1,A,A2,6.31,79.0 -2015-03-15,FL,1,A,A2,6.2,73.0 -2015-03-16,FL,1,A,A2,6.33,68.0 -2015-03-17,FL,1,A,A2,6.2,81.0 -2015-03-18,FL,1,A,A2,6.23,68.0 -2015-03-19,FL,1,A,A2,6.31,77.0 -2015-03-20,FL,1,A,A2,6.29,73.0 -2015-03-21,FL,1,A,A2,6.25,78.0 -2015-03-22,FL,1,A,A2,6.29,72.0 -2015-03-23,FL,1,A,A2,6.25,78.0 -2015-03-24,FL,1,A,A2,6.17,78.0 -2015-03-25,FL,1,A,A2,6.21,78.0 -2015-03-26,FL,1,A,A2,6.15,80.0 -2015-03-27,FL,1,A,A2,6.26,79.0 -2015-03-28,FL,1,A,A2,6.15,81.0 -2015-03-29,FL,1,A,A2,6.27,77.0 -2015-03-30,FL,1,A,A2,6.23,73.0 -2015-03-31,FL,1,A,A2,6.12,80.0 -2015-04-01,FL,1,A,A2,6.18,79.0 -2015-04-02,FL,1,A,A2,6.26,82.0 -2015-04-03,FL,1,A,A2,6.17,80.0 -2015-04-04,FL,1,A,A2,6.32,75.0 -2015-04-05,FL,1,A,A2,6.11,83.0 -2015-04-06,FL,1,A,A2,6.41,66.0 -2015-04-07,FL,1,A,A2,6.2,76.0 -2015-04-08,FL,1,A,A2,6.41,66.0 -2015-04-09,FL,1,A,A2,6.27,75.0 -2015-04-10,FL,1,A,A2,6.11,84.0 -2015-04-11,FL,1,A,A2,6.29,79.0 -2015-04-12,FL,1,A,A2,6.16,80.0 -2015-04-13,FL,1,A,A2,6.18,74.0 -2015-04-14,FL,1,A,A2,6.22,76.0 -2015-04-15,FL,1,A,A2,6.38,65.0 -2015-04-16,FL,1,A,A2,6.16,87.0 -2015-04-17,FL,1,A,A2,6.24,75.0 -2015-04-18,FL,1,A,A2,6.2,82.0 -2015-04-19,FL,1,A,A2,6.25,74.0 -2015-04-20,FL,1,A,A2,6.22,74.0 -2015-04-21,FL,1,A,A2,6.29,68.0 -2015-04-22,FL,1,A,A2,6.23,76.0 -2015-04-23,FL,1,A,A2,6.28,79.0 -2015-04-24,FL,1,A,A2,6.21,79.0 -2015-04-25,FL,1,A,A2,6.24,84.0 -2015-04-26,FL,1,A,A2,6.19,84.0 -2015-04-27,FL,1,A,A2,6.24,71.0 -2015-04-28,FL,1,A,A2,6.25,82.0 -2015-04-29,FL,1,A,A2,6.18,81.0 -2015-04-30,FL,1,A,A2,6.16,84.0 -2015-05-01,FL,1,A,A2,6.23,76.0 -2015-05-02,FL,1,A,A2,6.28,81.0 -2015-05-03,FL,1,A,A2,6.29,75.0 -2015-05-04,FL,1,A,A2,6.31,74.0 -2015-05-05,FL,1,A,A2,6.09,86.0 -2015-05-06,FL,1,A,A2,6.12,78.0 -2015-05-07,FL,1,A,A2,6.19,81.0 -2015-05-08,FL,1,A,A2,6.23,74.0 -2015-05-09,FL,1,A,A2,6.24,80.0 -2015-05-10,FL,1,A,A2,6.34,77.0 -2015-05-11,FL,1,A,A2,6.16,72.0 -2015-05-12,FL,1,A,A2,6.18,72.0 -2015-05-13,FL,1,A,A2,6.2,80.0 -2015-05-14,FL,1,A,A2,6.23,73.0 -2015-05-15,FL,1,A,A2,6.24,79.0 -2015-05-16,FL,1,A,A2,6.36,73.0 -2015-05-17,FL,1,A,A2,6.27,77.0 -2015-05-18,FL,1,A,A2,6.2,77.0 -2015-05-19,FL,1,A,A2,6.18,77.0 -2015-05-20,FL,1,A,A2,6.29,70.0 -2015-05-21,FL,1,A,A2,6.16,76.0 -2015-05-22,FL,1,A,A2,6.3,80.0 -2015-05-23,FL,1,A,A2,6.25,80.0 -2015-05-24,FL,1,A,A2,6.31,74.0 -2015-05-25,FL,1,A,A2,6.27,71.0 -2015-05-26,FL,1,A,A2,6.28,73.0 -2015-05-27,FL,1,A,A2,6.19,76.0 -2015-05-28,FL,1,A,A2,6.31,68.0 -2015-05-29,FL,1,A,A2,6.19,81.0 -2015-05-30,FL,1,A,A2,6.32,73.0 -2015-05-31,FL,1,A,A2,6.41,69.0 -2015-06-01,FL,1,A,A2,6.26,72.0 -2015-06-02,FL,1,A,A2,6.21,78.0 -2015-06-03,FL,1,A,A2,6.25,78.0 -2015-06-04,FL,1,A,A2,6.25,71.0 -2015-06-05,FL,1,A,A2,6.28,77.0 -2015-06-06,FL,1,A,A2,6.26,77.0 -2015-06-07,FL,1,A,A2,6.28,74.0 -2015-06-08,FL,1,A,A2,6.23,69.0 -2015-06-09,FL,1,A,A2,6.19,69.0 -2015-06-10,FL,1,A,A2,6.18,81.0 -2015-06-11,FL,1,A,A2,6.38,72.0 -2015-06-12,FL,1,A,A2,6.23,74.0 -2015-06-13,FL,1,A,A2,6.26,80.0 -2015-06-14,FL,1,A,A2,6.26,78.0 -2015-06-15,FL,1,A,A2,6.27,69.0 -2015-06-16,FL,1,A,A2,6.38,75.0 -2015-06-17,FL,1,A,A2,6.29,69.0 -2015-06-18,FL,1,A,A2,6.21,72.0 -2015-06-19,FL,1,A,A2,6.2,79.0 -2015-06-20,FL,1,A,A2,6.28,76.0 -2015-06-21,FL,1,A,A2,6.19,82.0 -2015-06-22,FL,1,A,A2,6.18,73.0 -2015-06-23,FL,1,A,A2,6.21,73.0 -2015-06-24,FL,1,A,A2,6.24,68.0 -2015-06-25,FL,1,A,A2,6.29,72.0 -2015-06-26,FL,1,A,A2,6.27,74.0 -2015-06-27,FL,1,A,A2,6.25,78.0 -2015-06-28,FL,1,A,A2,6.28,72.0 -2015-06-29,FL,1,A,A2,6.22,65.0 -2015-06-30,FL,1,A,A2,6.23,77.0 -2015-07-01,FL,1,A,A2,6.19,74.0 -2015-07-02,FL,1,A,A2,6.17,77.0 -2015-07-03,FL,1,A,A2,6.33,72.0 -2015-07-04,FL,1,A,A2,6.13,85.0 -2015-07-05,FL,1,A,A2,6.27,79.0 -2015-07-06,FL,1,A,A2,6.21,76.0 -2015-07-07,FL,1,A,A2,6.27,73.0 -2015-07-08,FL,1,A,A2,6.3,71.0 -2015-07-09,FL,1,A,A2,6.22,85.0 -2015-07-10,FL,1,A,A2,6.14,73.0 -2015-07-11,FL,1,A,A2,6.29,78.0 -2015-07-12,FL,1,A,A2,6.29,75.0 -2015-07-13,FL,1,A,A2,6.41,68.0 -2015-07-14,FL,1,A,A2,6.24,82.0 -2015-07-15,FL,1,A,A2,6.21,76.0 -2015-07-16,FL,1,A,A2,6.26,78.0 -2015-07-17,FL,1,A,A2,6.21,84.0 -2015-07-18,FL,1,A,A2,6.2,74.0 -2015-07-19,FL,1,A,A2,6.31,71.0 -2015-07-20,FL,1,A,A2,6.14,78.0 -2015-07-21,FL,1,A,A2,6.19,88.0 -2015-07-22,FL,1,A,A2,6.24,77.0 -2015-07-23,FL,1,A,A2,6.22,79.0 -2015-07-24,FL,1,A,A2,6.3,70.0 -2015-07-25,FL,1,A,A2,6.24,77.0 -2015-07-26,FL,1,A,A2,6.28,69.0 -2015-07-27,FL,1,A,A2,6.23,77.0 -2015-07-28,FL,1,A,A2,6.29,71.0 -2015-07-29,FL,1,A,A2,6.18,79.0 -2015-07-30,FL,1,A,A2,6.12,83.0 -2015-07-31,FL,1,A,A2,6.17,87.0 -2015-08-01,FL,1,A,A2,6.26,83.0 -2015-08-02,FL,1,A,A2,6.19,74.0 -2015-08-03,FL,1,A,A2,6.29,73.0 -2015-08-04,FL,1,A,A2,6.16,84.0 -2015-08-05,FL,1,A,A2,6.23,71.0 -2015-08-06,FL,1,A,A2,6.29,73.0 -2015-08-07,FL,1,A,A2,6.29,72.0 -2015-08-08,FL,1,A,A2,6.27,75.0 -2015-08-09,FL,1,A,A2,6.21,72.0 -2015-08-10,FL,1,A,A2,6.29,64.0 -2015-08-11,FL,1,A,A2,6.28,69.0 -2015-08-12,FL,1,A,A2,6.25,73.0 -2015-08-13,FL,1,A,A2,6.28,67.0 -2015-08-14,FL,1,A,A2,6.21,81.0 -2015-08-15,FL,1,A,A2,6.14,88.0 -2015-08-16,FL,1,A,A2,6.2,79.0 -2015-08-17,FL,1,A,A2,6.23,73.0 -2015-08-18,FL,1,A,A2,6.26,71.0 -2015-08-19,FL,1,A,A2,6.21,77.0 -2015-08-20,FL,1,A,A2,6.35,72.0 -2015-08-21,FL,1,A,A2,6.23,78.0 -2015-08-22,FL,1,A,A2,6.24,80.0 -2015-08-23,FL,1,A,A2,6.09,82.0 -2015-08-24,FL,1,A,A2,6.26,76.0 -2015-08-25,FL,1,A,A2,6.22,77.0 -2015-08-26,FL,1,A,A2,6.23,80.0 -2015-08-27,FL,1,A,A2,6.22,82.0 -2015-08-28,FL,1,A,A2,6.4,76.0 -2015-08-29,FL,1,A,A2,6.18,82.0 -2015-08-30,FL,1,A,A2,6.35,76.0 -2015-08-31,FL,1,A,A2,6.29,66.0 -2015-09-01,FL,1,A,A2,6.26,76.0 -2015-09-02,FL,1,A,A2,6.18,78.0 -2015-09-03,FL,1,A,A2,6.3,79.0 -2015-09-04,FL,1,A,A2,6.26,73.0 -2015-09-05,FL,1,A,A2,6.26,76.0 -2015-09-06,FL,1,A,A2,6.12,77.0 -2015-09-07,FL,1,A,A2,6.32,70.0 -2015-09-08,FL,1,A,A2,6.23,74.0 -2015-09-09,FL,1,A,A2,6.22,81.0 -2015-09-10,FL,1,A,A2,6.33,73.0 -2015-09-11,FL,1,A,A2,6.32,72.0 -2015-09-12,FL,1,A,A2,6.25,78.0 -2015-09-13,FL,1,A,A2,6.27,71.0 -2015-09-14,FL,1,A,A2,6.28,68.0 -2015-09-15,FL,1,A,A2,6.25,73.0 -2015-09-16,FL,1,A,A2,6.21,73.0 -2015-09-17,FL,1,A,A2,6.22,73.0 -2015-09-18,FL,1,A,A2,6.28,68.0 -2015-09-19,FL,1,A,A2,6.28,75.0 -2015-09-20,FL,1,A,A2,6.24,80.0 -2015-09-21,FL,1,A,A2,6.2,72.0 -2015-09-22,FL,1,A,A2,6.32,69.0 -2015-09-23,FL,1,A,A2,6.22,72.0 -2015-09-24,FL,1,A,A2,6.24,75.0 -2015-09-25,FL,1,A,A2,6.22,81.0 -2015-09-26,FL,1,A,A2,6.27,73.0 -2015-09-27,FL,1,A,A2,6.18,74.0 -2015-09-28,FL,1,A,A2,6.32,70.0 -2015-09-29,FL,1,A,A2,6.18,77.0 -2015-09-30,FL,1,A,A2,6.33,62.0 -2015-10-01,FL,1,A,A2,6.18,82.0 -2015-10-02,FL,1,A,A2,6.27,75.0 -2015-10-03,FL,1,A,A2,6.3,81.0 -2015-10-04,FL,1,A,A2,6.29,78.0 -2015-10-05,FL,1,A,A2,6.4,72.0 -2015-10-06,FL,1,A,A2,6.16,85.0 -2015-10-07,FL,1,A,A2,6.26,74.0 -2015-10-08,FL,1,A,A2,6.31,65.0 -2015-10-09,FL,1,A,A2,6.27,82.0 -2015-10-10,FL,1,A,A2,6.16,83.0 -2015-10-11,FL,1,A,A2,6.18,80.0 -2015-10-12,FL,1,A,A2,6.25,78.0 -2015-10-13,FL,1,A,A2,6.24,76.0 -2015-10-14,FL,1,A,A2,6.16,84.0 -2015-10-15,FL,1,A,A2,6.2,76.0 -2015-10-16,FL,1,A,A2,6.32,74.0 -2015-10-17,FL,1,A,A2,6.24,85.0 -2015-10-18,FL,1,A,A2,6.32,72.0 -2015-10-19,FL,1,A,A2,6.14,89.0 -2015-10-20,FL,1,A,A2,6.34,71.0 -2015-10-21,FL,1,A,A2,6.29,75.0 -2015-10-22,FL,1,A,A2,6.18,75.0 -2015-10-23,FL,1,A,A2,6.19,80.0 -2015-10-24,FL,1,A,A2,6.15,83.0 -2015-10-25,FL,1,A,A2,6.13,78.0 -2015-10-26,FL,1,A,A2,6.18,74.0 -2015-10-27,FL,1,A,A2,6.29,69.0 -2015-10-28,FL,1,A,A2,6.33,77.0 -2015-10-29,FL,1,A,A2,6.2,77.0 -2015-10-30,FL,1,A,A2,6.25,77.0 -2015-10-31,FL,1,A,A2,6.24,75.0 -2015-11-01,FL,1,A,A2,6.26,72.0 -2015-11-02,FL,1,A,A2,6.29,77.0 -2015-11-03,FL,1,A,A2,6.29,70.0 -2015-11-04,FL,1,A,A2,6.14,82.0 -2015-11-05,FL,1,A,A2,6.22,81.0 -2015-11-06,FL,1,A,A2,6.17,78.0 -2015-11-07,FL,1,A,A2,6.18,82.0 -2015-11-08,FL,1,A,A2,6.21,80.0 -2015-11-09,FL,1,A,A2,6.27,74.0 -2015-11-10,FL,1,A,A2,6.23,77.0 -2015-11-11,FL,1,A,A2,6.25,76.0 -2015-11-12,FL,1,A,A2,6.28,78.0 -2015-11-13,FL,1,A,A2,6.28,75.0 -2015-11-14,FL,1,A,A2,6.22,85.0 -2015-11-15,FL,1,A,A2,6.19,76.0 -2015-11-16,FL,1,A,A2,6.22,67.0 -2015-11-17,FL,1,A,A2,6.25,75.0 -2015-11-18,FL,1,A,A2,6.21,79.0 -2015-11-19,FL,1,A,A2,6.29,78.0 -2015-11-20,FL,1,A,A2,6.24,72.0 -2015-11-21,FL,1,A,A2,6.2,78.0 -2015-11-22,FL,1,A,A2,6.22,77.0 -2015-11-23,FL,1,A,A2,6.37,71.0 -2015-11-24,FL,1,A,A2,6.25,76.0 -2015-11-25,FL,1,A,A2,6.35,70.0 -2015-11-26,FL,1,A,A2,6.28,78.0 -2015-11-27,FL,1,A,A2,6.32,68.0 -2015-11-28,FL,1,A,A2,6.15,91.0 -2015-11-29,FL,1,A,A2,6.27,75.0 -2015-11-30,FL,1,A,A2,6.22,73.0 -2015-12-01,FL,1,A,A2,6.2,76.0 -2015-12-02,FL,1,A,A2,6.33,71.0 -2015-12-03,FL,1,A,A2,6.27,75.0 -2015-12-04,FL,1,A,A2,6.19,80.0 -2015-12-05,FL,1,A,A2,6.16,76.0 -2015-12-06,FL,1,A,A2,6.16,75.0 -2015-12-07,FL,1,A,A2,6.25,72.0 -2015-12-08,FL,1,A,A2,6.26,73.0 -2015-12-09,FL,1,A,A2,6.36,72.0 -2015-12-10,FL,1,A,A2,6.23,73.0 -2015-12-11,FL,1,A,A2,6.21,77.0 -2015-12-12,FL,1,A,A2,6.21,80.0 -2015-12-13,FL,1,A,A2,6.16,84.0 -2015-12-14,FL,1,A,A2,6.36,71.0 -2015-12-15,FL,1,A,A2,6.22,76.0 -2015-12-16,FL,1,A,A2,6.28,71.0 -2015-12-17,FL,1,A,A2,6.18,85.0 -2015-12-18,FL,1,A,A2,6.29,73.0 -2015-12-19,FL,1,A,A2,6.25,78.0 -2015-12-20,FL,1,A,A2,6.21,79.0 -2015-12-21,FL,1,A,A2,6.23,68.0 -2015-12-22,FL,1,A,A2,6.24,78.0 -2015-12-23,FL,1,A,A2,6.29,75.0 -2015-12-24,FL,1,A,A2,6.34,74.0 -2015-12-25,FL,1,A,A2,6.27,81.0 -2015-12-26,FL,1,A,A2,6.15,83.0 -2015-12-27,FL,1,A,A2,6.22,81.0 -2015-12-28,FL,1,A,A2,6.26,74.0 -2015-12-29,FL,1,A,A2,6.16,77.0 -2015-12-30,FL,1,A,A2,6.32,76.0 -2015-12-31,FL,1,A,A2,6.2,76.0 -2016-01-01,FL,1,A,A2,6.31,75.0 -2016-01-02,FL,1,A,A2,6.16,83.0 -2016-01-03,FL,1,A,A2,6.15,80.0 -2016-01-04,FL,1,A,A2,6.2,70.0 -2016-01-05,FL,1,A,A2,6.32,76.0 -2016-01-06,FL,1,A,A2,6.24,70.0 -2016-01-07,FL,1,A,A2,6.26,71.0 -2016-01-08,FL,1,A,A2,6.21,77.0 -2016-01-09,FL,1,A,A2,6.2,80.0 -2016-01-10,FL,1,A,A2,6.26,73.0 -2016-01-11,FL,1,A,A2,6.21,73.0 -2016-01-12,FL,1,A,A2,6.18,77.0 -2016-01-13,FL,1,A,A2,6.13,79.0 -2016-01-14,FL,1,A,A2,6.33,64.0 -2016-01-15,FL,1,A,A2,6.11,85.0 -2016-01-16,FL,1,A,A2,6.28,82.0 -2016-01-17,FL,1,A,A2,6.16,78.0 -2016-01-18,FL,1,A,A2,6.21,77.0 -2016-01-19,FL,1,A,A2,6.29,80.0 -2016-01-20,FL,1,A,A2,6.26,75.0 -2016-01-21,FL,1,A,A2,6.28,69.0 -2016-01-22,FL,1,A,A2,6.21,76.0 -2016-01-23,FL,1,A,A2,6.18,87.0 -2016-01-24,FL,1,A,A2,6.27,74.0 -2016-01-25,FL,1,A,A2,6.26,67.0 -2016-01-26,FL,1,A,A2,6.28,73.0 -2016-01-27,FL,1,A,A2,6.21,79.0 -2016-01-28,FL,1,A,A2,6.38,67.0 -2016-01-29,FL,1,A,A2,6.34,70.0 -2016-01-30,FL,1,A,A2,6.23,81.0 -2016-01-31,FL,1,A,A2,6.3,67.0 -2016-02-01,FL,1,A,A2,6.22,78.0 -2016-02-02,FL,1,A,A2,6.2,81.0 -2016-02-03,FL,1,A,A2,6.18,75.0 -2016-02-04,FL,1,A,A2,6.17,75.0 -2016-02-05,FL,1,A,A2,6.24,72.0 -2016-02-06,FL,1,A,A2,6.34,78.0 -2016-02-07,FL,1,A,A2,6.37,75.0 -2016-02-08,FL,1,A,A2,6.25,78.0 -2016-02-09,FL,1,A,A2,6.29,75.0 -2016-02-10,FL,1,A,A2,6.2,72.0 -2016-02-11,FL,1,A,A2,6.16,75.0 -2016-02-12,FL,1,A,A2,6.21,78.0 -2016-02-13,FL,1,A,A2,6.23,80.0 -2016-02-14,FL,1,A,A2,6.28,79.0 -2016-02-15,FL,1,A,A2,6.21,74.0 -2016-02-16,FL,1,A,A2,6.31,66.0 -2016-02-17,FL,1,A,A2,6.16,78.0 -2016-02-18,FL,1,A,A2,6.24,80.0 -2016-02-19,FL,1,A,A2,6.23,79.0 -2016-02-20,FL,1,A,A2,6.15,85.0 -2016-02-21,FL,1,A,A2,6.32,74.0 -2016-02-22,FL,1,A,A2,6.27,65.0 -2016-02-23,FL,1,A,A2,6.21,76.0 -2016-02-24,FL,1,A,A2,6.09,74.0 -2016-02-25,FL,1,A,A2,6.26,70.0 -2016-02-26,FL,1,A,A2,6.27,79.0 -2016-02-27,FL,1,A,A2,6.19,76.0 -2016-02-28,FL,1,A,A2,6.26,72.0 -2016-02-29,FL,1,A,A2,6.08,79.0 -2016-03-01,FL,1,A,A2,6.28,70.0 -2016-03-02,FL,1,A,A2,6.1,86.0 -2016-03-03,FL,1,A,A2,6.2,78.0 -2016-03-04,FL,1,A,A2,6.22,76.0 -2016-03-05,FL,1,A,A2,6.34,70.0 -2016-03-06,FL,1,A,A2,6.25,78.0 -2016-03-07,FL,1,A,A2,6.23,73.0 -2016-03-08,FL,1,A,A2,6.22,71.0 -2016-03-09,FL,1,A,A2,6.32,79.0 -2016-03-10,FL,1,A,A2,6.22,79.0 -2016-03-11,FL,1,A,A2,6.21,75.0 -2016-03-12,FL,1,A,A2,6.16,78.0 -2016-03-13,FL,1,A,A2,6.2,80.0 -2016-03-14,FL,1,A,A2,6.2,81.0 -2016-03-15,FL,1,A,A2,6.35,61.0 -2016-03-16,FL,1,A,A2,6.22,72.0 -2016-03-17,FL,1,A,A2,6.3,71.0 -2016-03-18,FL,1,A,A2,6.44,69.0 -2016-03-19,FL,1,A,A2,6.18,81.0 -2016-03-20,FL,1,A,A2,6.31,69.0 -2016-03-21,FL,1,A,A2,6.38,66.0 -2016-03-22,FL,1,A,A2,6.13,87.0 -2016-03-23,FL,1,A,A2,6.26,77.0 -2016-03-24,FL,1,A,A2,6.18,76.0 -2016-03-25,FL,1,A,A2,6.3,79.0 -2016-03-26,FL,1,A,A2,6.25,72.0 -2016-03-27,FL,1,A,A2,6.29,72.0 -2016-03-28,FL,1,A,A2,6.2,69.0 -2016-03-29,FL,1,A,A2,6.19,72.0 -2016-03-30,FL,1,A,A2,6.11,82.0 -2016-03-31,FL,1,A,A2,6.25,78.0 -2016-04-01,FL,1,A,A2,6.22,82.0 -2016-04-02,FL,1,A,A2,6.26,81.0 -2016-04-03,FL,1,A,A2,6.21,78.0 -2016-04-04,FL,1,A,A2,6.29,77.0 -2016-04-05,FL,1,A,A2,6.23,79.0 -2016-04-06,FL,1,A,A2,6.26,67.0 -2016-04-07,FL,1,A,A2,6.29,76.0 -2016-04-08,FL,1,A,A2,6.18,79.0 -2016-04-09,FL,1,A,A2,6.24,76.0 -2016-04-10,FL,1,A,A2,6.31,77.0 -2016-04-11,FL,1,A,A2,6.35,73.0 -2016-04-12,FL,1,A,A2,6.29,75.0 -2016-04-13,FL,1,A,A2,6.22,79.0 -2016-04-14,FL,1,A,A2,6.35,71.0 -2016-04-15,FL,1,A,A2,6.26,75.0 -2016-04-16,FL,1,A,A2,6.4,67.0 -2016-04-17,FL,1,A,A2,6.19,72.0 -2016-04-18,FL,1,A,A2,6.15,72.0 -2016-04-19,FL,1,A,A2,6.31,79.0 -2016-04-20,FL,1,A,A2,6.1,90.0 -2016-04-21,FL,1,A,A2,6.25,74.0 -2016-04-22,FL,1,A,A2,6.29,73.0 -2016-04-23,FL,1,A,A2,6.25,78.0 -2016-04-24,FL,1,A,A2,6.34,75.0 -2016-04-25,FL,1,A,A2,6.16,77.0 -2016-04-26,FL,1,A,A2,6.33,68.0 -2016-04-27,FL,1,A,A2,6.31,69.0 -2016-04-28,FL,1,A,A2,6.24,73.0 -2016-04-29,FL,1,A,A2,6.14,78.0 -2016-04-30,FL,1,A,A2,6.36,74.0 -2016-05-01,FL,1,A,A2,6.21,74.0 -2016-05-02,FL,1,A,A2,6.43,62.0 -2016-05-03,FL,1,A,A2,6.36,71.0 -2016-05-04,FL,1,A,A2,6.22,78.0 -2016-05-05,FL,1,A,A2,6.26,70.0 -2016-05-06,FL,1,A,A2,6.23,78.0 -2016-05-07,FL,1,A,A2,6.31,71.0 -2016-05-08,FL,1,A,A2,6.29,74.0 -2016-05-09,FL,1,A,A2,6.35,70.0 -2016-05-10,FL,1,A,A2,6.1,79.0 -2016-05-11,FL,1,A,A2,6.2,78.0 -2016-05-12,FL,1,A,A2,6.34,63.0 -2016-05-13,FL,1,A,A2,6.22,81.0 -2016-05-14,FL,1,A,A2,6.14,84.0 -2016-05-15,FL,1,A,A2,6.34,70.0 -2016-05-16,FL,1,A,A2,6.26,69.0 -2016-05-17,FL,1,A,A2,6.36,70.0 -2016-05-18,FL,1,A,A2,6.19,75.0 -2016-05-19,FL,1,A,A2,6.2,70.0 -2016-05-20,FL,1,A,A2,6.29,73.0 -2016-05-21,FL,1,A,A2,6.25,83.0 -2016-05-22,FL,1,A,A2,6.2,78.0 -2016-05-23,FL,1,A,A2,6.23,75.0 -2016-05-24,FL,1,A,A2,6.23,76.0 -2016-05-25,FL,1,A,A2,6.2,80.0 -2016-05-26,FL,1,A,A2,6.3,73.0 -2016-05-27,FL,1,A,A2,6.18,79.0 -2016-05-28,FL,1,A,A2,6.3,72.0 -2016-05-29,FL,1,A,A2,6.24,74.0 -2016-05-30,FL,1,A,A2,6.19,74.0 -2016-05-31,FL,1,A,A2,6.27,73.0 -2016-06-01,FL,1,A,A2,6.31,74.0 -2016-06-02,FL,1,A,A2,6.25,75.0 -2016-06-03,FL,1,A,A2,6.36,71.0 -2016-06-04,FL,1,A,A2,6.22,74.0 -2016-06-05,FL,1,A,A2,6.32,73.0 -2016-06-06,FL,1,A,A2,6.22,75.0 -2016-06-07,FL,1,A,A2,6.21,78.0 -2016-06-08,FL,1,A,A2,6.36,78.0 -2016-06-09,FL,1,A,A2,6.28,77.0 -2016-06-10,FL,1,A,A2,6.26,72.0 -2016-06-11,FL,1,A,A2,6.26,81.0 -2016-06-12,FL,1,A,A2,6.21,75.0 -2016-06-13,FL,1,A,A2,6.26,74.0 -2016-06-14,FL,1,A,A2,6.26,74.0 -2016-06-15,FL,1,A,A2,6.31,76.0 -2016-06-16,FL,1,A,A2,6.28,75.0 -2016-06-17,FL,1,A,A2,6.25,78.0 -2016-06-18,FL,1,A,A2,6.3,78.0 -2016-06-19,FL,1,A,A2,6.22,72.0 -2016-06-20,FL,1,A,A2,6.22,76.0 -2016-06-21,FL,1,A,A2,6.18,73.0 -2016-06-22,FL,1,A,A2,6.31,71.0 -2016-06-23,FL,1,A,A2,6.22,77.0 -2016-06-24,FL,1,A,A2,6.26,76.0 -2016-06-25,FL,1,A,A2,6.18,82.0 -2016-06-26,FL,1,A,A2,6.24,75.0 -2016-06-27,FL,1,A,A2,6.24,70.0 -2016-06-28,FL,1,A,A2,6.26,70.0 -2016-06-29,FL,1,A,A2,6.24,83.0 -2016-06-30,FL,1,A,A2,6.31,70.0 -2016-07-01,FL,1,A,A2,6.26,74.0 -2016-07-02,FL,1,A,A2,6.28,79.0 -2016-07-03,FL,1,A,A2,6.21,78.0 -2016-07-04,FL,1,A,A2,6.24,73.0 -2016-07-05,FL,1,A,A2,6.09,73.0 -2016-07-06,FL,1,A,A2,6.22,77.0 -2016-07-07,FL,1,A,A2,6.27,74.0 -2016-07-08,FL,1,A,A2,6.26,79.0 -2016-07-09,FL,1,A,A2,6.27,76.0 -2016-07-10,FL,1,A,A2,6.09,83.0 -2016-07-11,FL,1,A,A2,6.14,74.0 -2016-07-12,FL,1,A,A2,6.19,70.0 -2016-07-13,FL,1,A,A2,6.18,76.0 -2016-07-14,FL,1,A,A2,6.16,81.0 -2016-07-15,FL,1,A,A2,6.29,72.0 -2016-07-16,FL,1,A,A2,6.29,81.0 -2016-07-17,FL,1,A,A2,6.17,81.0 -2016-07-18,FL,1,A,A2,6.26,70.0 -2016-07-19,FL,1,A,A2,6.32,70.0 -2016-07-20,FL,1,A,A2,6.29,66.0 -2016-07-21,FL,1,A,A2,6.22,78.0 -2016-07-22,FL,1,A,A2,6.22,84.0 -2016-07-23,FL,1,A,A2,6.25,73.0 -2016-07-24,FL,1,A,A2,6.23,80.0 -2016-07-25,FL,1,A,A2,6.22,80.0 -2016-07-26,FL,1,A,A2,6.26,74.0 -2016-07-27,FL,1,A,A2,6.26,72.0 -2015-01-05,FL,1,A,A3,6.22,649.0 -2015-01-06,FL,1,A,A3,6.17,637.0 -2015-01-07,FL,1,A,A3,6.14,580.0 -2015-01-08,FL,1,A,A3,5.99,740.0 -2015-01-09,FL,1,A,A3,6.14,687.0 -2015-01-10,FL,1,A,A3,6.06,737.0 -2015-01-11,FL,1,A,A3,6.19,614.0 -2015-01-12,FL,1,A,A3,6.17,602.0 -2015-01-13,FL,1,A,A3,6.26,637.0 -2015-01-14,FL,1,A,A3,6.08,670.0 -2015-01-15,FL,1,A,A3,6.08,679.0 -2015-01-16,FL,1,A,A3,6.15,667.0 -2015-01-17,FL,1,A,A3,6.11,704.0 -2015-01-18,FL,1,A,A3,6.15,630.0 -2015-01-19,FL,1,A,A3,5.97,770.0 -2015-01-20,FL,1,A,A3,6.2,645.0 -2015-01-21,FL,1,A,A3,6.08,698.0 -2015-01-22,FL,1,A,A3,6.07,656.0 -2015-01-23,FL,1,A,A3,6.04,669.0 -2015-01-24,FL,1,A,A3,6.06,702.0 -2015-01-25,FL,1,A,A3,6.11,687.0 -2015-01-26,FL,1,A,A3,6.14,566.0 -2015-01-27,FL,1,A,A3,6.12,662.0 -2015-01-28,FL,1,A,A3,6.1,632.0 -2015-01-29,FL,1,A,A3,6.15,667.0 -2015-01-30,FL,1,A,A3,6.16,727.0 -2015-01-31,FL,1,A,A3,6.08,745.0 -2015-02-01,FL,1,A,A3,6.06,683.0 -2015-02-02,FL,1,A,A3,6.07,674.0 -2015-02-03,FL,1,A,A3,6.08,612.0 -2015-02-04,FL,1,A,A3,6.14,645.0 -2015-02-05,FL,1,A,A3,6.05,762.0 -2015-02-06,FL,1,A,A3,6.12,588.0 -2015-02-07,FL,1,A,A3,6.08,725.0 -2015-02-08,FL,1,A,A3,6.11,735.0 -2015-02-09,FL,1,A,A3,5.99,710.0 -2015-02-10,FL,1,A,A3,6.04,687.0 -2015-02-11,FL,1,A,A3,6.14,619.0 -2015-02-12,FL,1,A,A3,6.03,639.0 -2015-02-13,FL,1,A,A3,6.11,623.0 -2015-02-14,FL,1,A,A3,6.08,714.0 -2015-02-15,FL,1,A,A3,6.09,710.0 -2015-02-16,FL,1,A,A3,6.13,696.0 -2015-02-17,FL,1,A,A3,6.15,636.0 -2015-02-18,FL,1,A,A3,6.0,666.0 -2015-02-19,FL,1,A,A3,6.21,633.0 -2015-02-20,FL,1,A,A3,6.08,673.0 -2015-02-21,FL,1,A,A3,6.22,630.0 -2015-02-22,FL,1,A,A3,6.1,738.0 -2015-02-23,FL,1,A,A3,6.15,686.0 -2015-02-24,FL,1,A,A3,6.16,617.0 -2015-02-25,FL,1,A,A3,5.99,759.0 -2015-02-26,FL,1,A,A3,6.12,713.0 -2015-02-27,FL,1,A,A3,6.17,656.0 -2015-02-28,FL,1,A,A3,6.15,621.0 -2015-03-01,FL,1,A,A3,6.03,761.0 -2015-03-02,FL,1,A,A3,6.04,597.0 -2015-03-03,FL,1,A,A3,6.07,609.0 -2015-03-04,FL,1,A,A3,6.05,696.0 -2015-03-05,FL,1,A,A3,6.14,653.0 -2015-03-06,FL,1,A,A3,6.16,627.0 -2015-03-07,FL,1,A,A3,6.09,698.0 -2015-03-08,FL,1,A,A3,6.07,717.0 -2015-03-09,FL,1,A,A3,6.12,655.0 -2015-03-10,FL,1,A,A3,6.14,634.0 -2015-03-11,FL,1,A,A3,6.1,654.0 -2015-03-12,FL,1,A,A3,6.02,736.0 -2015-03-13,FL,1,A,A3,6.2,616.0 -2015-03-14,FL,1,A,A3,6.11,741.0 -2015-03-15,FL,1,A,A3,6.05,693.0 -2015-03-16,FL,1,A,A3,6.16,665.0 -2015-03-17,FL,1,A,A3,6.15,650.0 -2015-03-18,FL,1,A,A3,6.04,689.0 -2015-03-19,FL,1,A,A3,6.09,687.0 -2015-03-20,FL,1,A,A3,6.18,639.0 -2015-03-21,FL,1,A,A3,6.08,785.0 -2015-03-22,FL,1,A,A3,6.05,666.0 -2015-03-23,FL,1,A,A3,6.11,650.0 -2015-03-24,FL,1,A,A3,6.03,724.0 -2015-03-25,FL,1,A,A3,6.13,691.0 -2015-03-26,FL,1,A,A3,6.01,722.0 -2015-03-27,FL,1,A,A3,6.11,726.0 -2015-03-28,FL,1,A,A3,6.03,661.0 -2015-03-29,FL,1,A,A3,6.18,650.0 -2015-03-30,FL,1,A,A3,6.0,739.0 -2015-03-31,FL,1,A,A3,6.02,745.0 -2015-04-01,FL,1,A,A3,6.07,691.0 -2015-04-02,FL,1,A,A3,6.08,727.0 -2015-04-03,FL,1,A,A3,6.03,784.0 -2015-04-04,FL,1,A,A3,6.12,719.0 -2015-04-05,FL,1,A,A3,6.1,696.0 -2015-04-06,FL,1,A,A3,6.08,713.0 -2015-04-07,FL,1,A,A3,6.1,676.0 -2015-04-08,FL,1,A,A3,6.1,714.0 -2015-04-09,FL,1,A,A3,6.01,709.0 -2015-04-10,FL,1,A,A3,6.05,715.0 -2015-04-11,FL,1,A,A3,6.02,723.0 -2015-04-12,FL,1,A,A3,6.11,611.0 -2015-04-13,FL,1,A,A3,6.07,695.0 -2015-04-14,FL,1,A,A3,6.14,691.0 -2015-04-15,FL,1,A,A3,6.09,678.0 -2015-04-16,FL,1,A,A3,6.1,629.0 -2015-04-17,FL,1,A,A3,5.99,753.0 -2015-04-18,FL,1,A,A3,6.09,714.0 -2015-04-19,FL,1,A,A3,6.1,705.0 -2015-04-20,FL,1,A,A3,6.12,636.0 -2015-04-21,FL,1,A,A3,6.11,669.0 -2015-04-22,FL,1,A,A3,6.1,675.0 -2015-04-23,FL,1,A,A3,6.04,689.0 -2015-04-24,FL,1,A,A3,6.1,702.0 -2015-04-25,FL,1,A,A3,5.98,804.0 -2015-04-26,FL,1,A,A3,6.09,735.0 -2015-04-27,FL,1,A,A3,6.09,688.0 -2015-04-28,FL,1,A,A3,6.14,643.0 -2015-04-29,FL,1,A,A3,6.11,714.0 -2015-04-30,FL,1,A,A3,6.11,686.0 -2015-05-01,FL,1,A,A3,5.99,710.0 -2015-05-02,FL,1,A,A3,6.08,780.0 -2015-05-03,FL,1,A,A3,6.14,650.0 -2015-05-04,FL,1,A,A3,6.2,643.0 -2015-05-05,FL,1,A,A3,6.13,706.0 -2015-05-06,FL,1,A,A3,6.1,681.0 -2015-05-07,FL,1,A,A3,6.08,669.0 -2015-05-08,FL,1,A,A3,6.12,698.0 -2015-05-09,FL,1,A,A3,6.01,755.0 -2015-05-10,FL,1,A,A3,6.09,727.0 -2015-05-11,FL,1,A,A3,6.13,688.0 -2015-05-12,FL,1,A,A3,6.04,722.0 -2015-05-13,FL,1,A,A3,6.1,740.0 -2015-05-14,FL,1,A,A3,5.97,801.0 -2015-05-15,FL,1,A,A3,6.02,681.0 -2015-05-16,FL,1,A,A3,6.13,716.0 -2015-05-17,FL,1,A,A3,6.03,728.0 -2015-05-18,FL,1,A,A3,6.04,746.0 -2015-05-19,FL,1,A,A3,6.05,715.0 -2015-05-20,FL,1,A,A3,5.96,801.0 -2015-05-21,FL,1,A,A3,6.13,719.0 -2015-05-22,FL,1,A,A3,6.19,552.0 -2015-05-23,FL,1,A,A3,6.11,776.0 -2015-05-24,FL,1,A,A3,6.04,730.0 -2015-05-25,FL,1,A,A3,6.0,744.0 -2015-05-26,FL,1,A,A3,6.0,792.0 -2015-05-27,FL,1,A,A3,6.1,713.0 -2015-05-28,FL,1,A,A3,6.13,633.0 -2015-05-29,FL,1,A,A3,6.15,723.0 -2015-05-30,FL,1,A,A3,6.11,707.0 -2015-05-31,FL,1,A,A3,6.1,722.0 -2015-06-01,FL,1,A,A3,5.99,674.0 -2015-06-02,FL,1,A,A3,6.1,697.0 -2015-06-03,FL,1,A,A3,6.15,688.0 -2015-06-04,FL,1,A,A3,6.02,719.0 -2015-06-05,FL,1,A,A3,6.04,724.0 -2015-06-06,FL,1,A,A3,6.05,773.0 -2015-06-07,FL,1,A,A3,6.06,724.0 -2015-06-08,FL,1,A,A3,6.19,651.0 -2015-06-09,FL,1,A,A3,6.03,739.0 -2015-06-10,FL,1,A,A3,6.07,680.0 -2015-06-11,FL,1,A,A3,6.07,688.0 -2015-06-12,FL,1,A,A3,6.02,756.0 -2015-06-13,FL,1,A,A3,6.07,731.0 -2015-06-14,FL,1,A,A3,6.07,738.0 -2015-06-15,FL,1,A,A3,6.1,671.0 -2015-06-16,FL,1,A,A3,6.09,702.0 -2015-06-17,FL,1,A,A3,6.09,685.0 -2015-06-18,FL,1,A,A3,6.1,651.0 -2015-06-19,FL,1,A,A3,6.13,661.0 -2015-06-20,FL,1,A,A3,6.05,712.0 -2015-06-21,FL,1,A,A3,6.13,684.0 -2015-06-22,FL,1,A,A3,6.11,685.0 -2015-06-23,FL,1,A,A3,6.14,595.0 -2015-06-24,FL,1,A,A3,6.19,608.0 -2015-06-25,FL,1,A,A3,6.18,627.0 -2015-06-26,FL,1,A,A3,6.08,705.0 -2015-06-27,FL,1,A,A3,6.03,730.0 -2015-06-28,FL,1,A,A3,6.09,734.0 -2015-06-29,FL,1,A,A3,6.04,722.0 -2015-06-30,FL,1,A,A3,6.13,685.0 -2015-07-01,FL,1,A,A3,6.13,626.0 -2015-07-02,FL,1,A,A3,6.16,620.0 -2015-07-03,FL,1,A,A3,6.07,728.0 -2015-07-04,FL,1,A,A3,6.07,764.0 -2015-07-05,FL,1,A,A3,6.08,729.0 -2015-07-06,FL,1,A,A3,6.14,695.0 -2015-07-07,FL,1,A,A3,6.1,702.0 -2015-07-08,FL,1,A,A3,6.07,720.0 -2015-07-09,FL,1,A,A3,6.16,640.0 -2015-07-10,FL,1,A,A3,6.17,665.0 -2015-07-11,FL,1,A,A3,6.09,705.0 -2015-07-12,FL,1,A,A3,6.01,797.0 -2015-07-13,FL,1,A,A3,6.1,694.0 -2015-07-14,FL,1,A,A3,6.16,630.0 -2015-07-15,FL,1,A,A3,6.06,692.0 -2015-07-16,FL,1,A,A3,6.08,685.0 -2015-07-17,FL,1,A,A3,5.99,775.0 -2015-07-18,FL,1,A,A3,5.97,769.0 -2015-07-19,FL,1,A,A3,6.04,785.0 -2015-07-20,FL,1,A,A3,6.07,682.0 -2015-07-21,FL,1,A,A3,6.11,702.0 -2015-07-22,FL,1,A,A3,6.11,663.0 -2015-07-23,FL,1,A,A3,6.1,705.0 -2015-07-24,FL,1,A,A3,6.1,639.0 -2015-07-25,FL,1,A,A3,6.05,793.0 -2015-07-26,FL,1,A,A3,6.09,737.0 -2015-07-27,FL,1,A,A3,5.99,662.0 -2015-07-28,FL,1,A,A3,6.09,714.0 -2015-07-29,FL,1,A,A3,6.15,568.0 -2015-07-30,FL,1,A,A3,6.1,677.0 -2015-07-31,FL,1,A,A3,5.97,869.0 -2015-08-01,FL,1,A,A3,6.07,739.0 -2015-08-02,FL,1,A,A3,6.09,717.0 -2015-08-03,FL,1,A,A3,6.08,673.0 -2015-08-04,FL,1,A,A3,6.15,642.0 -2015-08-05,FL,1,A,A3,6.23,681.0 -2015-08-06,FL,1,A,A3,6.05,744.0 -2015-08-07,FL,1,A,A3,6.06,677.0 -2015-08-08,FL,1,A,A3,6.05,732.0 -2015-08-09,FL,1,A,A3,6.09,669.0 -2015-08-10,FL,1,A,A3,6.1,707.0 -2015-08-11,FL,1,A,A3,6.13,713.0 -2015-08-12,FL,1,A,A3,6.1,688.0 -2015-08-13,FL,1,A,A3,6.12,649.0 -2015-08-14,FL,1,A,A3,6.11,757.0 -2015-08-15,FL,1,A,A3,6.13,727.0 -2015-08-16,FL,1,A,A3,6.08,747.0 -2015-08-17,FL,1,A,A3,6.17,673.0 -2015-08-18,FL,1,A,A3,5.96,732.0 -2015-08-19,FL,1,A,A3,6.08,706.0 -2015-08-20,FL,1,A,A3,6.09,647.0 -2015-08-21,FL,1,A,A3,6.04,765.0 -2015-08-22,FL,1,A,A3,6.12,719.0 -2015-08-23,FL,1,A,A3,6.06,768.0 -2015-08-24,FL,1,A,A3,6.02,676.0 -2015-08-25,FL,1,A,A3,6.11,704.0 -2015-08-26,FL,1,A,A3,6.08,703.0 -2015-08-27,FL,1,A,A3,6.18,650.0 -2015-08-28,FL,1,A,A3,6.13,758.0 -2015-08-29,FL,1,A,A3,6.13,715.0 -2015-08-30,FL,1,A,A3,6.12,710.0 -2015-08-31,FL,1,A,A3,6.1,633.0 -2015-09-01,FL,1,A,A3,6.07,688.0 -2015-09-02,FL,1,A,A3,6.14,723.0 -2015-09-03,FL,1,A,A3,6.13,620.0 -2015-09-04,FL,1,A,A3,6.03,774.0 -2015-09-05,FL,1,A,A3,6.03,783.0 -2015-09-06,FL,1,A,A3,6.1,733.0 -2015-09-07,FL,1,A,A3,6.0,680.0 -2015-09-08,FL,1,A,A3,6.15,691.0 -2015-09-09,FL,1,A,A3,6.16,670.0 -2015-09-10,FL,1,A,A3,6.05,711.0 -2015-09-11,FL,1,A,A3,6.09,722.0 -2015-09-12,FL,1,A,A3,6.12,697.0 -2015-09-13,FL,1,A,A3,6.06,706.0 -2015-09-14,FL,1,A,A3,6.15,682.0 -2015-09-15,FL,1,A,A3,6.24,614.0 -2015-09-16,FL,1,A,A3,6.08,721.0 -2015-09-17,FL,1,A,A3,5.97,772.0 -2015-09-18,FL,1,A,A3,6.12,701.0 -2015-09-19,FL,1,A,A3,6.15,675.0 -2015-09-20,FL,1,A,A3,6.15,680.0 -2015-09-21,FL,1,A,A3,6.02,661.0 -2015-09-22,FL,1,A,A3,6.12,760.0 -2015-09-23,FL,1,A,A3,6.07,674.0 -2015-09-24,FL,1,A,A3,6.07,733.0 -2015-09-25,FL,1,A,A3,6.1,699.0 -2015-09-26,FL,1,A,A3,6.15,703.0 -2015-09-27,FL,1,A,A3,6.09,762.0 -2015-09-28,FL,1,A,A3,6.14,635.0 -2015-09-29,FL,1,A,A3,6.13,625.0 -2015-09-30,FL,1,A,A3,6.18,621.0 -2015-10-01,FL,1,A,A3,6.01,720.0 -2015-10-02,FL,1,A,A3,6.19,642.0 -2015-10-03,FL,1,A,A3,6.09,772.0 -2015-10-04,FL,1,A,A3,6.08,691.0 -2015-10-05,FL,1,A,A3,6.12,634.0 -2015-10-06,FL,1,A,A3,6.04,767.0 -2015-10-07,FL,1,A,A3,6.17,672.0 -2015-10-08,FL,1,A,A3,6.03,761.0 -2015-10-09,FL,1,A,A3,6.09,715.0 -2015-10-10,FL,1,A,A3,6.11,733.0 -2015-10-11,FL,1,A,A3,6.07,688.0 -2015-10-12,FL,1,A,A3,6.21,632.0 -2015-10-13,FL,1,A,A3,6.08,694.0 -2015-10-14,FL,1,A,A3,6.04,697.0 -2015-10-15,FL,1,A,A3,6.12,734.0 -2015-10-16,FL,1,A,A3,6.08,710.0 -2015-10-17,FL,1,A,A3,6.06,738.0 -2015-10-18,FL,1,A,A3,6.08,714.0 -2015-10-19,FL,1,A,A3,5.92,795.0 -2015-10-20,FL,1,A,A3,6.11,704.0 -2015-10-21,FL,1,A,A3,6.09,716.0 -2015-10-22,FL,1,A,A3,6.12,635.0 -2015-10-23,FL,1,A,A3,6.12,683.0 -2015-10-24,FL,1,A,A3,6.04,714.0 -2015-10-25,FL,1,A,A3,6.03,705.0 -2015-10-26,FL,1,A,A3,6.02,708.0 -2015-10-27,FL,1,A,A3,6.14,681.0 -2015-10-28,FL,1,A,A3,6.17,689.0 -2015-10-29,FL,1,A,A3,5.96,747.0 -2015-10-30,FL,1,A,A3,6.1,717.0 -2015-10-31,FL,1,A,A3,6.11,697.0 -2015-11-01,FL,1,A,A3,6.13,685.0 -2015-11-02,FL,1,A,A3,5.94,713.0 -2015-11-03,FL,1,A,A3,6.07,683.0 -2015-11-04,FL,1,A,A3,6.23,606.0 -2015-11-05,FL,1,A,A3,6.16,665.0 -2015-11-06,FL,1,A,A3,5.95,803.0 -2015-11-07,FL,1,A,A3,5.99,809.0 -2015-11-08,FL,1,A,A3,6.17,711.0 -2015-11-09,FL,1,A,A3,6.0,780.0 -2015-11-10,FL,1,A,A3,6.09,665.0 -2015-11-11,FL,1,A,A3,6.18,673.0 -2015-11-12,FL,1,A,A3,6.24,592.0 -2015-11-13,FL,1,A,A3,6.14,696.0 -2015-11-14,FL,1,A,A3,6.15,740.0 -2015-11-15,FL,1,A,A3,6.14,764.0 -2015-11-16,FL,1,A,A3,6.01,755.0 -2015-11-17,FL,1,A,A3,6.02,733.0 -2015-11-18,FL,1,A,A3,6.05,701.0 -2015-11-19,FL,1,A,A3,6.1,645.0 -2015-11-20,FL,1,A,A3,6.17,647.0 -2015-11-21,FL,1,A,A3,6.06,704.0 -2015-11-22,FL,1,A,A3,6.05,693.0 -2015-11-23,FL,1,A,A3,6.07,695.0 -2015-11-24,FL,1,A,A3,6.08,715.0 -2015-11-25,FL,1,A,A3,5.98,759.0 -2015-11-26,FL,1,A,A3,6.09,659.0 -2015-11-27,FL,1,A,A3,6.14,663.0 -2015-11-28,FL,1,A,A3,6.12,692.0 -2015-11-29,FL,1,A,A3,6.06,741.0 -2015-11-30,FL,1,A,A3,6.04,680.0 -2015-12-01,FL,1,A,A3,6.11,678.0 -2015-12-02,FL,1,A,A3,6.15,679.0 -2015-12-03,FL,1,A,A3,6.03,713.0 -2015-12-04,FL,1,A,A3,6.16,696.0 -2015-12-05,FL,1,A,A3,6.17,675.0 -2015-12-06,FL,1,A,A3,6.16,667.0 -2015-12-07,FL,1,A,A3,6.08,711.0 -2015-12-08,FL,1,A,A3,6.18,594.0 -2015-12-09,FL,1,A,A3,6.15,673.0 -2015-12-10,FL,1,A,A3,6.02,719.0 -2015-12-11,FL,1,A,A3,6.17,619.0 -2015-12-12,FL,1,A,A3,6.13,647.0 -2015-12-13,FL,1,A,A3,6.12,641.0 -2015-12-14,FL,1,A,A3,6.17,664.0 -2015-12-15,FL,1,A,A3,6.04,711.0 -2015-12-16,FL,1,A,A3,5.99,738.0 -2015-12-17,FL,1,A,A3,6.11,697.0 -2015-12-18,FL,1,A,A3,6.07,732.0 -2015-12-19,FL,1,A,A3,6.08,726.0 -2015-12-20,FL,1,A,A3,6.12,699.0 -2015-12-21,FL,1,A,A3,6.02,690.0 -2015-12-22,FL,1,A,A3,6.18,689.0 -2015-12-23,FL,1,A,A3,6.24,604.0 -2015-12-24,FL,1,A,A3,5.95,714.0 -2015-12-25,FL,1,A,A3,6.08,654.0 -2015-12-26,FL,1,A,A3,6.1,726.0 -2015-12-27,FL,1,A,A3,6.15,684.0 -2015-12-28,FL,1,A,A3,6.01,713.0 -2015-12-29,FL,1,A,A3,6.03,724.0 -2015-12-30,FL,1,A,A3,6.11,648.0 -2015-12-31,FL,1,A,A3,6.08,633.0 -2016-01-01,FL,1,A,A3,6.07,685.0 -2016-01-02,FL,1,A,A3,6.09,735.0 -2016-01-03,FL,1,A,A3,6.03,723.0 -2016-01-04,FL,1,A,A3,6.08,694.0 -2016-01-05,FL,1,A,A3,6.05,751.0 -2016-01-06,FL,1,A,A3,6.07,698.0 -2016-01-07,FL,1,A,A3,6.11,676.0 -2016-01-08,FL,1,A,A3,6.03,739.0 -2016-01-09,FL,1,A,A3,6.13,697.0 -2016-01-10,FL,1,A,A3,6.13,666.0 -2016-01-11,FL,1,A,A3,6.19,661.0 -2016-01-12,FL,1,A,A3,6.17,652.0 -2016-01-13,FL,1,A,A3,6.13,661.0 -2016-01-14,FL,1,A,A3,6.1,752.0 -2016-01-15,FL,1,A,A3,6.08,688.0 -2016-01-16,FL,1,A,A3,6.03,719.0 -2016-01-17,FL,1,A,A3,6.15,699.0 -2016-01-18,FL,1,A,A3,6.07,716.0 -2016-01-19,FL,1,A,A3,6.11,712.0 -2016-01-20,FL,1,A,A3,6.02,706.0 -2016-01-21,FL,1,A,A3,6.1,680.0 -2016-01-22,FL,1,A,A3,6.12,665.0 -2016-01-23,FL,1,A,A3,6.06,728.0 -2016-01-24,FL,1,A,A3,6.08,710.0 -2016-01-25,FL,1,A,A3,6.1,652.0 -2016-01-26,FL,1,A,A3,6.11,673.0 -2016-01-27,FL,1,A,A3,6.16,635.0 -2016-01-28,FL,1,A,A3,6.09,687.0 -2016-01-29,FL,1,A,A3,5.99,782.0 -2016-01-30,FL,1,A,A3,6.14,639.0 -2016-01-31,FL,1,A,A3,6.19,613.0 -2016-02-01,FL,1,A,A3,6.05,725.0 -2016-02-02,FL,1,A,A3,6.12,663.0 -2016-02-03,FL,1,A,A3,6.12,674.0 -2016-02-04,FL,1,A,A3,6.09,722.0 -2016-02-05,FL,1,A,A3,6.08,647.0 -2016-02-06,FL,1,A,A3,6.08,699.0 -2016-02-07,FL,1,A,A3,6.12,692.0 -2016-02-08,FL,1,A,A3,6.1,634.0 -2016-02-09,FL,1,A,A3,6.04,738.0 -2016-02-10,FL,1,A,A3,6.01,735.0 -2016-02-11,FL,1,A,A3,6.14,720.0 -2016-02-12,FL,1,A,A3,6.18,697.0 -2016-02-13,FL,1,A,A3,6.1,716.0 -2016-02-14,FL,1,A,A3,6.06,751.0 -2016-02-15,FL,1,A,A3,6.16,622.0 -2016-02-16,FL,1,A,A3,5.99,783.0 -2016-02-17,FL,1,A,A3,6.07,744.0 -2016-02-18,FL,1,A,A3,5.99,751.0 -2016-02-19,FL,1,A,A3,6.15,656.0 -2016-02-20,FL,1,A,A3,6.15,679.0 -2016-02-21,FL,1,A,A3,5.97,747.0 -2016-02-22,FL,1,A,A3,6.08,645.0 -2016-02-23,FL,1,A,A3,6.1,631.0 -2016-02-24,FL,1,A,A3,5.99,715.0 -2016-02-25,FL,1,A,A3,6.1,673.0 -2016-02-26,FL,1,A,A3,6.08,662.0 -2016-02-27,FL,1,A,A3,6.01,761.0 -2016-02-28,FL,1,A,A3,6.07,761.0 -2016-02-29,FL,1,A,A3,6.17,657.0 -2016-03-01,FL,1,A,A3,6.2,678.0 -2016-03-02,FL,1,A,A3,5.92,763.0 -2016-03-03,FL,1,A,A3,6.07,690.0 -2016-03-04,FL,1,A,A3,6.13,628.0 -2016-03-05,FL,1,A,A3,6.1,679.0 -2016-03-06,FL,1,A,A3,6.15,713.0 -2016-03-07,FL,1,A,A3,6.1,679.0 -2016-03-08,FL,1,A,A3,6.06,727.0 -2016-03-09,FL,1,A,A3,6.17,653.0 -2016-03-10,FL,1,A,A3,6.09,677.0 -2016-03-11,FL,1,A,A3,6.0,732.0 -2016-03-12,FL,1,A,A3,6.13,678.0 -2016-03-13,FL,1,A,A3,6.03,665.0 -2016-03-14,FL,1,A,A3,6.2,609.0 -2016-03-15,FL,1,A,A3,6.1,642.0 -2016-03-16,FL,1,A,A3,6.04,728.0 -2016-03-17,FL,1,A,A3,6.1,655.0 -2016-03-18,FL,1,A,A3,6.0,759.0 -2016-03-19,FL,1,A,A3,6.08,747.0 -2016-03-20,FL,1,A,A3,6.12,743.0 -2016-03-21,FL,1,A,A3,6.06,705.0 -2016-03-22,FL,1,A,A3,6.08,700.0 -2016-03-23,FL,1,A,A3,6.11,659.0 -2016-03-24,FL,1,A,A3,6.09,705.0 -2016-03-25,FL,1,A,A3,6.07,695.0 -2016-03-26,FL,1,A,A3,6.17,709.0 -2016-03-27,FL,1,A,A3,6.13,689.0 -2016-03-28,FL,1,A,A3,6.11,618.0 -2016-03-29,FL,1,A,A3,6.08,713.0 -2016-03-30,FL,1,A,A3,6.06,732.0 -2016-03-31,FL,1,A,A3,6.12,652.0 -2016-04-01,FL,1,A,A3,6.07,704.0 -2016-04-02,FL,1,A,A3,6.08,749.0 -2016-04-03,FL,1,A,A3,6.03,743.0 -2016-04-04,FL,1,A,A3,6.1,662.0 -2016-04-05,FL,1,A,A3,6.12,623.0 -2016-04-06,FL,1,A,A3,6.08,652.0 -2016-04-07,FL,1,A,A3,6.05,729.0 -2016-04-08,FL,1,A,A3,6.16,678.0 -2016-04-09,FL,1,A,A3,6.21,652.0 -2016-04-10,FL,1,A,A3,6.23,584.0 -2016-04-11,FL,1,A,A3,6.15,663.0 -2016-04-12,FL,1,A,A3,6.08,684.0 -2016-04-13,FL,1,A,A3,6.15,638.0 -2016-04-14,FL,1,A,A3,5.96,748.0 -2016-04-15,FL,1,A,A3,6.23,614.0 -2016-04-16,FL,1,A,A3,6.07,730.0 -2016-04-17,FL,1,A,A3,6.07,739.0 -2016-04-18,FL,1,A,A3,6.07,680.0 -2016-04-19,FL,1,A,A3,6.21,649.0 -2016-04-20,FL,1,A,A3,6.04,706.0 -2016-04-21,FL,1,A,A3,6.08,698.0 -2016-04-22,FL,1,A,A3,6.1,566.0 -2016-04-23,FL,1,A,A3,6.11,755.0 -2016-04-24,FL,1,A,A3,6.2,655.0 -2016-04-25,FL,1,A,A3,6.21,594.0 -2016-04-26,FL,1,A,A3,5.93,748.0 -2016-04-27,FL,1,A,A3,6.06,696.0 -2016-04-28,FL,1,A,A3,6.18,672.0 -2016-04-29,FL,1,A,A3,6.25,620.0 -2016-04-30,FL,1,A,A3,6.12,676.0 -2016-05-01,FL,1,A,A3,6.09,731.0 -2016-05-02,FL,1,A,A3,6.11,681.0 -2016-05-03,FL,1,A,A3,6.05,694.0 -2016-05-04,FL,1,A,A3,6.05,684.0 -2016-05-05,FL,1,A,A3,6.23,614.0 -2016-05-06,FL,1,A,A3,6.15,654.0 -2016-05-07,FL,1,A,A3,6.04,754.0 -2016-05-08,FL,1,A,A3,6.19,678.0 -2016-05-09,FL,1,A,A3,6.05,736.0 -2016-05-10,FL,1,A,A3,6.18,659.0 -2016-05-11,FL,1,A,A3,6.11,703.0 -2016-05-12,FL,1,A,A3,6.04,698.0 -2016-05-13,FL,1,A,A3,6.1,704.0 -2016-05-14,FL,1,A,A3,6.11,675.0 -2016-05-15,FL,1,A,A3,6.11,685.0 -2016-05-16,FL,1,A,A3,6.14,707.0 -2016-05-17,FL,1,A,A3,6.09,663.0 -2016-05-18,FL,1,A,A3,6.02,719.0 -2016-05-19,FL,1,A,A3,6.05,749.0 -2016-05-20,FL,1,A,A3,6.0,741.0 -2016-05-21,FL,1,A,A3,5.89,838.0 -2016-05-22,FL,1,A,A3,6.11,739.0 -2016-05-23,FL,1,A,A3,6.09,715.0 -2016-05-24,FL,1,A,A3,6.03,712.0 -2016-05-25,FL,1,A,A3,6.14,650.0 -2016-05-26,FL,1,A,A3,5.95,763.0 -2016-05-27,FL,1,A,A3,6.18,650.0 -2016-05-28,FL,1,A,A3,6.13,741.0 -2016-05-29,FL,1,A,A3,6.07,723.0 -2016-05-30,FL,1,A,A3,6.16,573.0 -2016-05-31,FL,1,A,A3,6.05,702.0 -2016-06-01,FL,1,A,A3,6.1,651.0 -2016-06-02,FL,1,A,A3,6.06,737.0 -2016-06-03,FL,1,A,A3,6.14,752.0 -2016-06-04,FL,1,A,A3,6.24,673.0 -2016-06-05,FL,1,A,A3,6.22,656.0 -2016-06-06,FL,1,A,A3,6.17,630.0 -2016-06-07,FL,1,A,A3,6.16,608.0 -2016-06-08,FL,1,A,A3,6.02,693.0 -2016-06-09,FL,1,A,A3,6.19,654.0 -2016-06-10,FL,1,A,A3,5.99,797.0 -2016-06-11,FL,1,A,A3,6.12,751.0 -2016-06-12,FL,1,A,A3,5.96,754.0 -2016-06-13,FL,1,A,A3,6.09,700.0 -2016-06-14,FL,1,A,A3,6.0,750.0 -2016-06-15,FL,1,A,A3,6.08,637.0 -2016-06-16,FL,1,A,A3,6.14,683.0 -2016-06-17,FL,1,A,A3,6.05,795.0 -2016-06-18,FL,1,A,A3,6.18,679.0 -2016-06-19,FL,1,A,A3,6.02,715.0 -2016-06-20,FL,1,A,A3,6.12,646.0 -2016-06-21,FL,1,A,A3,5.95,828.0 -2016-06-22,FL,1,A,A3,6.2,662.0 -2016-06-23,FL,1,A,A3,6.09,708.0 -2016-06-24,FL,1,A,A3,6.01,735.0 -2016-06-25,FL,1,A,A3,6.06,801.0 -2016-06-26,FL,1,A,A3,6.11,733.0 -2016-06-27,FL,1,A,A3,6.07,695.0 -2016-06-28,FL,1,A,A3,6.21,688.0 -2016-06-29,FL,1,A,A3,6.11,702.0 -2016-06-30,FL,1,A,A3,6.09,665.0 -2016-07-01,FL,1,A,A3,6.07,699.0 -2016-07-02,FL,1,A,A3,6.12,728.0 -2016-07-03,FL,1,A,A3,6.11,721.0 -2016-07-04,FL,1,A,A3,6.02,751.0 -2016-07-05,FL,1,A,A3,6.13,718.0 -2016-07-06,FL,1,A,A3,6.12,661.0 -2016-07-07,FL,1,A,A3,6.1,611.0 -2016-07-08,FL,1,A,A3,6.1,714.0 -2016-07-09,FL,1,A,A3,6.06,730.0 -2016-07-10,FL,1,A,A3,6.21,717.0 -2016-07-11,FL,1,A,A3,6.11,711.0 -2016-07-12,FL,1,A,A3,6.09,702.0 -2016-07-13,FL,1,A,A3,6.05,734.0 -2016-07-14,FL,1,A,A3,6.15,746.0 -2016-07-15,FL,1,A,A3,6.11,667.0 -2016-07-16,FL,1,A,A3,6.19,685.0 -2016-07-17,FL,1,A,A3,6.08,710.0 -2016-07-18,FL,1,A,A3,6.17,594.0 -2016-07-19,FL,1,A,A3,6.19,636.0 -2016-07-20,FL,1,A,A3,6.25,658.0 -2016-07-21,FL,1,A,A3,6.0,706.0 -2016-07-22,FL,1,A,A3,6.03,690.0 -2016-07-23,FL,1,A,A3,6.05,693.0 -2016-07-24,FL,1,A,A3,6.09,719.0 -2016-07-25,FL,1,A,A3,6.11,720.0 -2016-07-26,FL,1,A,A3,6.06,703.0 -2016-07-27,FL,1,A,A3,6.1,741.0 -2015-01-05,FL,1,B,B1,7.96,14.0 -2015-01-06,FL,1,B,B1,7.92,14.0 -2015-01-07,FL,1,B,B1,7.9,15.0 -2015-01-08,FL,1,B,B1,8.04,14.0 -2015-01-09,FL,1,B,B1,7.9,16.0 -2015-01-10,FL,1,B,B1,7.97,15.0 -2015-01-11,FL,1,B,B1,7.78,15.0 -2015-01-12,FL,1,B,B1,7.83,15.0 -2015-01-13,FL,1,B,B1,7.99,15.0 -2015-01-14,FL,1,B,B1,8.03,14.0 -2015-01-15,FL,1,B,B1,7.99,15.0 -2015-01-16,FL,1,B,B1,7.99,14.0 -2015-01-17,FL,1,B,B1,7.93,15.0 -2015-01-18,FL,1,B,B1,7.9,15.0 -2015-01-19,FL,1,B,B1,7.86,14.0 -2015-01-20,FL,1,B,B1,7.86,15.0 -2015-01-21,FL,1,B,B1,8.0,14.0 -2015-01-22,FL,1,B,B1,7.76,15.0 -2015-01-23,FL,1,B,B1,7.86,16.0 -2015-01-24,FL,1,B,B1,7.84,15.0 -2015-01-25,FL,1,B,B1,7.99,14.0 -2015-01-26,FL,1,B,B1,7.83,15.0 -2015-01-27,FL,1,B,B1,7.79,15.0 -2015-01-28,FL,1,B,B1,7.89,15.0 -2015-01-29,FL,1,B,B1,7.88,15.0 -2015-01-30,FL,1,B,B1,8.04,14.0 -2015-01-31,FL,1,B,B1,7.86,16.0 -2015-02-01,FL,1,B,B1,7.8,15.0 -2015-02-02,FL,1,B,B1,7.82,15.0 -2015-02-03,FL,1,B,B1,8.01,14.0 -2015-02-04,FL,1,B,B1,7.89,15.0 -2015-02-05,FL,1,B,B1,7.89,14.0 -2015-02-06,FL,1,B,B1,7.96,15.0 -2015-02-07,FL,1,B,B1,7.84,16.0 -2015-02-08,FL,1,B,B1,7.97,15.0 -2015-02-09,FL,1,B,B1,7.85,16.0 -2015-02-10,FL,1,B,B1,7.8,15.0 -2015-02-11,FL,1,B,B1,7.93,15.0 -2015-02-12,FL,1,B,B1,7.81,16.0 -2015-02-13,FL,1,B,B1,7.97,15.0 -2015-02-14,FL,1,B,B1,7.87,17.0 -2015-02-15,FL,1,B,B1,7.8,17.0 -2015-02-16,FL,1,B,B1,7.97,14.0 -2015-02-17,FL,1,B,B1,7.99,14.0 -2015-02-18,FL,1,B,B1,8.09,14.0 -2015-02-19,FL,1,B,B1,7.98,15.0 -2015-02-20,FL,1,B,B1,7.86,15.0 -2015-02-21,FL,1,B,B1,7.83,17.0 -2015-02-22,FL,1,B,B1,7.94,15.0 -2015-02-23,FL,1,B,B1,7.95,15.0 -2015-02-24,FL,1,B,B1,7.88,15.0 -2015-02-25,FL,1,B,B1,7.97,16.0 -2015-02-26,FL,1,B,B1,7.87,15.0 -2015-02-27,FL,1,B,B1,8.01,16.0 -2015-02-28,FL,1,B,B1,7.96,16.0 -2015-03-01,FL,1,B,B1,7.88,16.0 -2015-03-02,FL,1,B,B1,7.99,15.0 -2015-03-03,FL,1,B,B1,7.97,16.0 -2015-03-04,FL,1,B,B1,7.93,17.0 -2015-03-05,FL,1,B,B1,7.92,15.0 -2015-03-06,FL,1,B,B1,7.96,15.0 -2015-03-07,FL,1,B,B1,7.97,16.0 -2015-03-08,FL,1,B,B1,7.91,16.0 -2015-03-09,FL,1,B,B1,7.77,16.0 -2015-03-10,FL,1,B,B1,7.79,16.0 -2015-03-11,FL,1,B,B1,7.88,16.0 -2015-03-12,FL,1,B,B1,8.04,15.0 -2015-03-13,FL,1,B,B1,7.77,17.0 -2015-03-14,FL,1,B,B1,8.16,15.0 -2015-03-15,FL,1,B,B1,8.03,17.0 -2015-03-16,FL,1,B,B1,7.93,15.0 -2015-03-17,FL,1,B,B1,7.87,17.0 -2015-03-18,FL,1,B,B1,7.88,17.0 -2015-03-19,FL,1,B,B1,7.73,16.0 -2015-03-20,FL,1,B,B1,8.11,16.0 -2015-03-21,FL,1,B,B1,7.99,17.0 -2015-03-22,FL,1,B,B1,7.86,17.0 -2015-03-23,FL,1,B,B1,7.85,16.0 -2015-03-24,FL,1,B,B1,7.87,16.0 -2015-03-25,FL,1,B,B1,7.77,16.0 -2015-03-26,FL,1,B,B1,7.78,17.0 -2015-03-27,FL,1,B,B1,7.85,16.0 -2015-03-28,FL,1,B,B1,7.89,18.0 -2015-03-29,FL,1,B,B1,7.92,16.0 -2015-03-30,FL,1,B,B1,7.89,15.0 -2015-03-31,FL,1,B,B1,7.93,15.0 -2015-04-01,FL,1,B,B1,7.72,17.0 -2015-04-02,FL,1,B,B1,7.82,17.0 -2015-04-03,FL,1,B,B1,7.82,17.0 -2015-04-04,FL,1,B,B1,7.91,16.0 -2015-04-05,FL,1,B,B1,7.97,17.0 -2015-04-06,FL,1,B,B1,7.86,17.0 -2015-04-07,FL,1,B,B1,7.98,17.0 -2015-04-08,FL,1,B,B1,7.85,16.0 -2015-04-09,FL,1,B,B1,7.92,15.0 -2015-04-10,FL,1,B,B1,8.0,15.0 -2015-04-11,FL,1,B,B1,7.98,17.0 -2015-04-12,FL,1,B,B1,7.91,16.0 -2015-04-13,FL,1,B,B1,7.97,16.0 -2015-04-14,FL,1,B,B1,7.76,16.0 -2015-04-15,FL,1,B,B1,7.9,16.0 -2015-04-16,FL,1,B,B1,7.85,16.0 -2015-04-17,FL,1,B,B1,7.98,16.0 -2015-04-18,FL,1,B,B1,7.84,16.0 -2015-04-19,FL,1,B,B1,7.87,17.0 -2015-04-20,FL,1,B,B1,7.93,16.0 -2015-04-21,FL,1,B,B1,7.89,16.0 -2015-04-22,FL,1,B,B1,7.89,16.0 -2015-04-23,FL,1,B,B1,8.03,15.0 -2015-04-24,FL,1,B,B1,7.81,17.0 -2015-04-25,FL,1,B,B1,7.89,17.0 -2015-04-26,FL,1,B,B1,7.97,17.0 -2015-04-27,FL,1,B,B1,7.91,16.0 -2015-04-28,FL,1,B,B1,7.9,16.0 -2015-04-29,FL,1,B,B1,7.87,16.0 -2015-04-30,FL,1,B,B1,7.93,16.0 -2015-05-01,FL,1,B,B1,7.77,16.0 -2015-05-02,FL,1,B,B1,7.78,18.0 -2015-05-03,FL,1,B,B1,7.96,15.0 -2015-05-04,FL,1,B,B1,7.77,17.0 -2015-05-05,FL,1,B,B1,8.03,18.0 -2015-05-06,FL,1,B,B1,7.9,17.0 -2015-05-07,FL,1,B,B1,7.72,16.0 -2015-05-08,FL,1,B,B1,7.97,17.0 -2015-05-09,FL,1,B,B1,8.11,17.0 -2015-05-10,FL,1,B,B1,7.88,17.0 -2015-05-11,FL,1,B,B1,7.88,17.0 -2015-05-12,FL,1,B,B1,7.99,17.0 -2015-05-13,FL,1,B,B1,7.95,16.0 -2015-05-14,FL,1,B,B1,7.96,16.0 -2015-05-15,FL,1,B,B1,7.91,17.0 -2015-05-16,FL,1,B,B1,7.79,18.0 -2015-05-17,FL,1,B,B1,7.87,16.0 -2015-05-18,FL,1,B,B1,7.96,17.0 -2015-05-19,FL,1,B,B1,8.08,16.0 -2015-05-20,FL,1,B,B1,8.07,16.0 -2015-05-21,FL,1,B,B1,7.85,16.0 -2015-05-22,FL,1,B,B1,7.92,17.0 -2015-05-23,FL,1,B,B1,7.89,17.0 -2015-05-24,FL,1,B,B1,8.08,16.0 -2015-05-25,FL,1,B,B1,7.91,17.0 -2015-05-26,FL,1,B,B1,7.86,17.0 -2015-05-27,FL,1,B,B1,7.99,17.0 -2015-05-28,FL,1,B,B1,7.89,17.0 -2015-05-29,FL,1,B,B1,7.86,17.0 -2015-05-30,FL,1,B,B1,7.94,17.0 -2015-05-31,FL,1,B,B1,8.03,17.0 -2015-06-01,FL,1,B,B1,7.81,18.0 -2015-06-02,FL,1,B,B1,7.86,17.0 -2015-06-03,FL,1,B,B1,7.9,17.0 -2015-06-04,FL,1,B,B1,7.79,18.0 -2015-06-05,FL,1,B,B1,7.92,16.0 -2015-06-06,FL,1,B,B1,7.86,18.0 -2015-06-07,FL,1,B,B1,7.8,17.0 -2015-06-08,FL,1,B,B1,7.98,16.0 -2015-06-09,FL,1,B,B1,7.92,16.0 -2015-06-10,FL,1,B,B1,8.02,17.0 -2015-06-11,FL,1,B,B1,7.91,17.0 -2015-06-12,FL,1,B,B1,7.99,17.0 -2015-06-13,FL,1,B,B1,7.85,17.0 -2015-06-14,FL,1,B,B1,7.83,17.0 -2015-06-15,FL,1,B,B1,7.95,16.0 -2015-06-16,FL,1,B,B1,7.91,17.0 -2015-06-17,FL,1,B,B1,7.86,17.0 -2015-06-18,FL,1,B,B1,7.89,17.0 -2015-06-19,FL,1,B,B1,7.95,17.0 -2015-06-20,FL,1,B,B1,8.01,18.0 -2015-06-21,FL,1,B,B1,7.89,17.0 -2015-06-22,FL,1,B,B1,7.89,18.0 -2015-06-23,FL,1,B,B1,7.96,17.0 -2015-06-24,FL,1,B,B1,8.01,17.0 -2015-06-25,FL,1,B,B1,7.91,17.0 -2015-06-26,FL,1,B,B1,7.83,18.0 -2015-06-27,FL,1,B,B1,7.92,18.0 -2015-06-28,FL,1,B,B1,7.88,18.0 -2015-06-29,FL,1,B,B1,7.84,17.0 -2015-06-30,FL,1,B,B1,7.89,17.0 -2015-07-01,FL,1,B,B1,7.88,18.0 -2015-07-02,FL,1,B,B1,7.96,17.0 -2015-07-03,FL,1,B,B1,7.84,18.0 -2015-07-04,FL,1,B,B1,7.93,17.0 -2015-07-05,FL,1,B,B1,7.96,17.0 -2015-07-06,FL,1,B,B1,7.84,17.0 -2015-07-07,FL,1,B,B1,7.89,16.0 -2015-07-08,FL,1,B,B1,7.99,17.0 -2015-07-09,FL,1,B,B1,8.0,17.0 -2015-07-10,FL,1,B,B1,7.98,18.0 -2015-07-11,FL,1,B,B1,7.98,18.0 -2015-07-12,FL,1,B,B1,7.98,18.0 -2015-07-13,FL,1,B,B1,7.96,16.0 -2015-07-14,FL,1,B,B1,7.87,17.0 -2015-07-15,FL,1,B,B1,7.87,18.0 -2015-07-16,FL,1,B,B1,7.93,17.0 -2015-07-17,FL,1,B,B1,7.88,18.0 -2015-07-18,FL,1,B,B1,7.85,19.0 -2015-07-19,FL,1,B,B1,7.93,18.0 -2015-07-20,FL,1,B,B1,7.9,17.0 -2015-07-21,FL,1,B,B1,7.97,17.0 -2015-07-22,FL,1,B,B1,7.84,18.0 -2015-07-23,FL,1,B,B1,7.99,17.0 -2015-07-24,FL,1,B,B1,7.77,19.0 -2015-07-25,FL,1,B,B1,7.9,18.0 -2015-07-26,FL,1,B,B1,7.96,17.0 -2015-07-27,FL,1,B,B1,8.01,16.0 -2015-07-28,FL,1,B,B1,7.94,18.0 -2015-07-29,FL,1,B,B1,7.93,17.0 -2015-07-30,FL,1,B,B1,7.9,18.0 -2015-07-31,FL,1,B,B1,7.9,17.0 -2015-08-01,FL,1,B,B1,7.93,18.0 -2015-08-02,FL,1,B,B1,7.99,17.0 -2015-08-03,FL,1,B,B1,7.92,18.0 -2015-08-04,FL,1,B,B1,7.86,18.0 -2015-08-05,FL,1,B,B1,7.9,18.0 -2015-08-06,FL,1,B,B1,7.98,17.0 -2015-08-07,FL,1,B,B1,7.81,18.0 -2015-08-08,FL,1,B,B1,7.84,18.0 -2015-08-09,FL,1,B,B1,8.0,18.0 -2015-08-10,FL,1,B,B1,7.71,18.0 -2015-08-11,FL,1,B,B1,7.99,16.0 -2015-08-12,FL,1,B,B1,7.95,17.0 -2015-08-13,FL,1,B,B1,7.77,19.0 -2015-08-14,FL,1,B,B1,7.97,18.0 -2015-08-15,FL,1,B,B1,7.83,19.0 -2015-08-16,FL,1,B,B1,7.76,19.0 -2015-08-17,FL,1,B,B1,8.0,18.0 -2015-08-18,FL,1,B,B1,7.81,18.0 -2015-08-19,FL,1,B,B1,8.07,16.0 -2015-08-20,FL,1,B,B1,7.77,18.0 -2015-08-21,FL,1,B,B1,7.92,18.0 -2015-08-22,FL,1,B,B1,7.95,19.0 -2015-08-23,FL,1,B,B1,7.84,18.0 -2015-08-24,FL,1,B,B1,7.77,18.0 -2015-08-25,FL,1,B,B1,7.9,16.0 -2015-08-26,FL,1,B,B1,7.84,19.0 -2015-08-27,FL,1,B,B1,7.93,18.0 -2015-08-28,FL,1,B,B1,7.96,17.0 -2015-08-29,FL,1,B,B1,8.0,18.0 -2015-08-30,FL,1,B,B1,7.91,18.0 -2015-08-31,FL,1,B,B1,7.93,18.0 -2015-09-01,FL,1,B,B1,7.92,18.0 -2015-09-02,FL,1,B,B1,7.86,17.0 -2015-09-03,FL,1,B,B1,7.87,17.0 -2015-09-04,FL,1,B,B1,7.89,18.0 -2015-09-05,FL,1,B,B1,7.93,18.0 -2015-09-06,FL,1,B,B1,7.91,17.0 -2015-09-07,FL,1,B,B1,7.88,17.0 -2015-09-08,FL,1,B,B1,7.92,17.0 -2015-09-09,FL,1,B,B1,7.88,18.0 -2015-09-10,FL,1,B,B1,7.93,17.0 -2015-09-11,FL,1,B,B1,7.84,18.0 -2015-09-12,FL,1,B,B1,7.85,18.0 -2015-09-13,FL,1,B,B1,7.84,18.0 -2015-09-14,FL,1,B,B1,7.9,16.0 -2015-09-15,FL,1,B,B1,7.96,17.0 -2015-09-16,FL,1,B,B1,7.98,19.0 -2015-09-17,FL,1,B,B1,7.79,19.0 -2015-09-18,FL,1,B,B1,7.9,18.0 -2015-09-19,FL,1,B,B1,7.78,19.0 -2015-09-20,FL,1,B,B1,8.01,18.0 -2015-09-21,FL,1,B,B1,7.89,17.0 -2015-09-22,FL,1,B,B1,7.9,18.0 -2015-09-23,FL,1,B,B1,8.0,18.0 -2015-09-24,FL,1,B,B1,7.93,18.0 -2015-09-25,FL,1,B,B1,7.89,18.0 -2015-09-26,FL,1,B,B1,7.85,19.0 -2015-09-27,FL,1,B,B1,7.92,17.0 -2015-09-28,FL,1,B,B1,7.91,18.0 -2015-09-29,FL,1,B,B1,7.9,18.0 -2015-09-30,FL,1,B,B1,7.91,18.0 -2015-10-01,FL,1,B,B1,7.97,18.0 -2015-10-02,FL,1,B,B1,7.96,17.0 -2015-10-03,FL,1,B,B1,8.09,18.0 -2015-10-04,FL,1,B,B1,7.9,18.0 -2015-10-05,FL,1,B,B1,8.03,18.0 -2015-10-06,FL,1,B,B1,7.92,17.0 -2015-10-07,FL,1,B,B1,7.95,17.0 -2015-10-08,FL,1,B,B1,7.8,18.0 -2015-10-09,FL,1,B,B1,7.95,17.0 -2015-10-10,FL,1,B,B1,7.84,18.0 -2015-10-11,FL,1,B,B1,7.82,18.0 -2015-10-12,FL,1,B,B1,7.96,18.0 -2015-10-13,FL,1,B,B1,7.87,17.0 -2015-10-14,FL,1,B,B1,7.88,19.0 -2015-10-15,FL,1,B,B1,7.88,18.0 -2015-10-16,FL,1,B,B1,7.97,17.0 -2015-10-17,FL,1,B,B1,7.92,18.0 -2015-10-18,FL,1,B,B1,7.92,17.0 -2015-10-19,FL,1,B,B1,7.86,17.0 -2015-10-20,FL,1,B,B1,7.97,17.0 -2015-10-21,FL,1,B,B1,7.82,19.0 -2015-10-22,FL,1,B,B1,7.88,19.0 -2015-10-23,FL,1,B,B1,7.92,18.0 -2015-10-24,FL,1,B,B1,7.88,19.0 -2015-10-25,FL,1,B,B1,7.91,18.0 -2015-10-26,FL,1,B,B1,7.82,19.0 -2015-10-27,FL,1,B,B1,7.86,17.0 -2015-10-28,FL,1,B,B1,7.85,18.0 -2015-10-29,FL,1,B,B1,8.03,16.0 -2015-10-30,FL,1,B,B1,7.82,18.0 -2015-10-31,FL,1,B,B1,8.03,19.0 -2015-11-01,FL,1,B,B1,7.87,18.0 -2015-11-02,FL,1,B,B1,7.88,18.0 -2015-11-03,FL,1,B,B1,7.82,18.0 -2015-11-04,FL,1,B,B1,7.9,18.0 -2015-11-05,FL,1,B,B1,7.92,18.0 -2015-11-06,FL,1,B,B1,7.99,17.0 -2015-11-07,FL,1,B,B1,7.77,19.0 -2015-11-08,FL,1,B,B1,8.1,18.0 -2015-11-09,FL,1,B,B1,7.84,18.0 -2015-11-10,FL,1,B,B1,7.9,19.0 -2015-11-11,FL,1,B,B1,7.83,18.0 -2015-11-12,FL,1,B,B1,7.86,18.0 -2015-11-13,FL,1,B,B1,7.87,18.0 -2015-11-14,FL,1,B,B1,7.87,19.0 -2015-11-15,FL,1,B,B1,7.98,18.0 -2015-11-16,FL,1,B,B1,7.98,17.0 -2015-11-17,FL,1,B,B1,7.94,19.0 -2015-11-18,FL,1,B,B1,7.91,19.0 -2015-11-19,FL,1,B,B1,7.91,18.0 -2015-11-20,FL,1,B,B1,7.95,17.0 -2015-11-21,FL,1,B,B1,7.9,19.0 -2015-11-22,FL,1,B,B1,7.87,19.0 -2015-11-23,FL,1,B,B1,7.74,18.0 -2015-11-24,FL,1,B,B1,7.93,19.0 -2015-11-25,FL,1,B,B1,7.83,18.0 -2015-11-26,FL,1,B,B1,7.87,18.0 -2015-11-27,FL,1,B,B1,7.81,19.0 -2015-11-28,FL,1,B,B1,7.99,19.0 -2015-11-29,FL,1,B,B1,7.94,17.0 -2015-11-30,FL,1,B,B1,8.08,17.0 -2015-12-01,FL,1,B,B1,7.96,18.0 -2015-12-02,FL,1,B,B1,7.87,19.0 -2015-12-03,FL,1,B,B1,7.89,18.0 -2015-12-04,FL,1,B,B1,7.94,18.0 -2015-12-05,FL,1,B,B1,8.03,18.0 -2015-12-06,FL,1,B,B1,7.81,18.0 -2015-12-07,FL,1,B,B1,7.95,17.0 -2015-12-08,FL,1,B,B1,7.97,18.0 -2015-12-09,FL,1,B,B1,7.86,18.0 -2015-12-10,FL,1,B,B1,7.9,18.0 -2015-12-11,FL,1,B,B1,7.98,17.0 -2015-12-12,FL,1,B,B1,7.84,19.0 -2015-12-13,FL,1,B,B1,8.0,17.0 -2015-12-14,FL,1,B,B1,7.94,17.0 -2015-12-15,FL,1,B,B1,7.87,18.0 -2015-12-16,FL,1,B,B1,7.85,18.0 -2015-12-17,FL,1,B,B1,7.92,17.0 -2015-12-18,FL,1,B,B1,7.91,18.0 -2015-12-19,FL,1,B,B1,7.84,19.0 -2015-12-20,FL,1,B,B1,7.91,20.0 -2015-12-21,FL,1,B,B1,7.86,18.0 -2015-12-22,FL,1,B,B1,7.82,18.0 -2015-12-23,FL,1,B,B1,7.94,19.0 -2015-12-24,FL,1,B,B1,7.99,17.0 -2015-12-25,FL,1,B,B1,7.87,17.0 -2015-12-26,FL,1,B,B1,7.79,19.0 -2015-12-27,FL,1,B,B1,7.92,18.0 -2015-12-28,FL,1,B,B1,7.95,17.0 -2015-12-29,FL,1,B,B1,7.88,18.0 -2015-12-30,FL,1,B,B1,7.93,18.0 -2015-12-31,FL,1,B,B1,8.0,17.0 -2016-01-01,FL,1,B,B1,7.92,18.0 -2016-01-02,FL,1,B,B1,7.86,18.0 -2016-01-03,FL,1,B,B1,7.83,18.0 -2016-01-04,FL,1,B,B1,7.87,18.0 -2016-01-05,FL,1,B,B1,7.96,18.0 -2016-01-06,FL,1,B,B1,7.93,18.0 -2016-01-07,FL,1,B,B1,7.97,18.0 -2016-01-08,FL,1,B,B1,7.85,18.0 -2016-01-09,FL,1,B,B1,7.85,18.0 -2016-01-10,FL,1,B,B1,8.03,18.0 -2016-01-11,FL,1,B,B1,7.93,17.0 -2016-01-12,FL,1,B,B1,7.9,17.0 -2016-01-13,FL,1,B,B1,7.74,19.0 -2016-01-14,FL,1,B,B1,7.91,18.0 -2016-01-15,FL,1,B,B1,7.79,18.0 -2016-01-16,FL,1,B,B1,7.8,20.0 -2016-01-17,FL,1,B,B1,7.99,18.0 -2016-01-18,FL,1,B,B1,7.94,17.0 -2016-01-19,FL,1,B,B1,8.09,17.0 -2016-01-20,FL,1,B,B1,7.9,18.0 -2016-01-21,FL,1,B,B1,7.81,18.0 -2016-01-22,FL,1,B,B1,7.95,18.0 -2016-01-23,FL,1,B,B1,7.94,18.0 -2016-01-24,FL,1,B,B1,7.87,19.0 -2016-01-25,FL,1,B,B1,7.87,18.0 -2016-01-26,FL,1,B,B1,7.98,17.0 -2016-01-27,FL,1,B,B1,8.06,18.0 -2016-01-28,FL,1,B,B1,8.03,18.0 -2016-01-29,FL,1,B,B1,7.93,18.0 -2016-01-30,FL,1,B,B1,8.0,19.0 -2016-01-31,FL,1,B,B1,7.95,18.0 -2016-02-01,FL,1,B,B1,7.86,18.0 -2016-02-02,FL,1,B,B1,7.78,19.0 -2016-02-03,FL,1,B,B1,7.88,18.0 -2016-02-04,FL,1,B,B1,7.91,18.0 -2016-02-05,FL,1,B,B1,7.81,19.0 -2016-02-06,FL,1,B,B1,7.95,18.0 -2016-02-07,FL,1,B,B1,7.89,19.0 -2016-02-08,FL,1,B,B1,7.94,18.0 -2016-02-09,FL,1,B,B1,7.78,19.0 -2016-02-10,FL,1,B,B1,7.77,19.0 -2016-02-11,FL,1,B,B1,7.99,18.0 -2016-02-12,FL,1,B,B1,8.11,17.0 -2016-02-13,FL,1,B,B1,7.79,19.0 -2016-02-14,FL,1,B,B1,8.02,18.0 -2016-02-15,FL,1,B,B1,7.81,18.0 -2016-02-16,FL,1,B,B1,7.91,18.0 -2016-02-17,FL,1,B,B1,7.87,18.0 -2016-02-18,FL,1,B,B1,7.96,18.0 -2016-02-19,FL,1,B,B1,7.9,19.0 -2016-02-20,FL,1,B,B1,7.89,17.0 -2016-02-21,FL,1,B,B1,7.94,18.0 -2016-02-22,FL,1,B,B1,7.87,19.0 -2016-02-23,FL,1,B,B1,7.85,18.0 -2016-02-24,FL,1,B,B1,7.95,19.0 -2016-02-25,FL,1,B,B1,7.94,18.0 -2016-02-26,FL,1,B,B1,7.94,19.0 -2016-02-27,FL,1,B,B1,7.76,19.0 -2016-02-28,FL,1,B,B1,7.89,19.0 -2016-02-29,FL,1,B,B1,7.91,18.0 -2016-03-01,FL,1,B,B1,8.04,18.0 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-2016-05-03,FL,1,B,B1,7.84,17.0 -2016-05-04,FL,1,B,B1,7.82,18.0 -2016-05-05,FL,1,B,B1,7.9,19.0 -2016-05-06,FL,1,B,B1,7.87,19.0 -2016-05-07,FL,1,B,B1,7.87,18.0 -2016-05-08,FL,1,B,B1,7.97,18.0 -2016-05-09,FL,1,B,B1,7.85,18.0 -2016-05-10,FL,1,B,B1,7.91,18.0 -2016-05-11,FL,1,B,B1,7.88,18.0 -2016-05-12,FL,1,B,B1,7.89,18.0 -2016-05-13,FL,1,B,B1,7.94,18.0 -2016-05-14,FL,1,B,B1,7.92,18.0 -2016-05-15,FL,1,B,B1,8.02,17.0 -2016-05-16,FL,1,B,B1,7.93,18.0 -2016-05-17,FL,1,B,B1,8.03,18.0 -2016-05-18,FL,1,B,B1,7.96,17.0 -2016-05-19,FL,1,B,B1,7.84,19.0 -2016-05-20,FL,1,B,B1,7.87,18.0 -2016-05-21,FL,1,B,B1,7.9,19.0 -2016-05-22,FL,1,B,B1,7.89,19.0 -2016-05-23,FL,1,B,B1,7.99,17.0 -2016-05-24,FL,1,B,B1,7.89,18.0 -2016-05-25,FL,1,B,B1,8.04,17.0 -2016-05-26,FL,1,B,B1,7.88,19.0 -2016-05-27,FL,1,B,B1,7.85,19.0 -2016-05-28,FL,1,B,B1,7.92,18.0 -2016-05-29,FL,1,B,B1,7.93,19.0 -2016-05-30,FL,1,B,B1,7.87,18.0 -2016-05-31,FL,1,B,B1,7.98,17.0 -2016-06-01,FL,1,B,B1,7.86,18.0 -2016-06-02,FL,1,B,B1,7.87,18.0 -2016-06-03,FL,1,B,B1,7.89,18.0 -2016-06-04,FL,1,B,B1,7.92,18.0 -2016-06-05,FL,1,B,B1,7.91,18.0 -2016-06-06,FL,1,B,B1,7.84,18.0 -2016-06-07,FL,1,B,B1,7.88,18.0 -2016-06-08,FL,1,B,B1,7.96,18.0 -2016-06-09,FL,1,B,B1,7.91,19.0 -2016-06-10,FL,1,B,B1,7.89,18.0 -2016-06-11,FL,1,B,B1,7.9,18.0 -2016-06-12,FL,1,B,B1,7.91,18.0 -2016-06-13,FL,1,B,B1,7.98,18.0 -2016-06-14,FL,1,B,B1,7.91,18.0 -2016-06-15,FL,1,B,B1,8.06,18.0 -2016-06-16,FL,1,B,B1,7.93,18.0 -2016-06-17,FL,1,B,B1,7.89,19.0 -2016-06-18,FL,1,B,B1,7.9,19.0 -2016-06-19,FL,1,B,B1,8.02,18.0 -2016-06-20,FL,1,B,B1,8.04,19.0 -2016-06-21,FL,1,B,B1,7.94,18.0 -2016-06-22,FL,1,B,B1,7.83,19.0 -2016-06-23,FL,1,B,B1,7.81,19.0 -2016-06-24,FL,1,B,B1,8.01,17.0 -2016-06-25,FL,1,B,B1,7.77,19.0 -2016-06-26,FL,1,B,B1,7.82,17.0 -2016-06-27,FL,1,B,B1,7.85,19.0 -2016-06-28,FL,1,B,B1,8.08,17.0 -2016-06-29,FL,1,B,B1,7.83,18.0 -2016-06-30,FL,1,B,B1,8.01,17.0 -2016-07-01,FL,1,B,B1,7.91,18.0 -2016-07-02,FL,1,B,B1,7.92,19.0 -2016-07-03,FL,1,B,B1,7.89,20.0 -2016-07-04,FL,1,B,B1,7.9,18.0 -2016-07-05,FL,1,B,B1,7.8,19.0 -2016-07-06,FL,1,B,B1,7.79,18.0 -2016-07-07,FL,1,B,B1,8.01,19.0 -2016-07-08,FL,1,B,B1,7.96,18.0 -2016-07-09,FL,1,B,B1,7.81,20.0 -2016-07-10,FL,1,B,B1,7.84,19.0 -2016-07-11,FL,1,B,B1,7.99,17.0 -2016-07-12,FL,1,B,B1,7.98,18.0 -2016-07-13,FL,1,B,B1,7.79,19.0 -2016-07-14,FL,1,B,B1,7.84,18.0 -2016-07-15,FL,1,B,B1,7.81,18.0 -2016-07-16,FL,1,B,B1,8.1,19.0 -2016-07-17,FL,1,B,B1,7.94,19.0 -2016-07-18,FL,1,B,B1,7.69,19.0 -2016-07-19,FL,1,B,B1,8.09,18.0 -2016-07-20,FL,1,B,B1,7.84,18.0 -2016-07-21,FL,1,B,B1,7.99,18.0 -2016-07-22,FL,1,B,B1,7.89,18.0 -2016-07-23,FL,1,B,B1,7.98,19.0 -2016-07-24,FL,1,B,B1,7.95,17.0 -2016-07-25,FL,1,B,B1,7.82,18.0 -2016-07-26,FL,1,B,B1,7.88,18.0 -2016-07-27,FL,1,B,B1,7.95,18.0 -2015-01-05,FL,1,B,B2,3.84,50.0 -2015-01-06,FL,1,B,B2,3.8,54.0 -2015-01-07,FL,1,B,B2,3.85,51.0 -2015-01-08,FL,1,B,B2,3.88,51.0 -2015-01-09,FL,1,B,B2,3.84,55.0 -2015-01-10,FL,1,B,B2,3.87,56.0 -2015-01-11,FL,1,B,B2,3.85,51.0 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-2015-02-12,FL,1,B,B2,3.92,47.0 -2015-02-13,FL,1,B,B2,3.92,51.0 -2015-02-14,FL,1,B,B2,3.79,55.0 -2015-02-15,FL,1,B,B2,3.86,51.0 -2015-02-16,FL,1,B,B2,3.87,55.0 -2015-02-17,FL,1,B,B2,3.9,51.0 -2015-02-18,FL,1,B,B2,3.79,58.0 -2015-02-19,FL,1,B,B2,3.78,60.0 -2015-02-20,FL,1,B,B2,3.85,48.0 -2015-02-21,FL,1,B,B2,3.88,54.0 -2015-02-22,FL,1,B,B2,3.8,52.0 -2015-02-23,FL,1,B,B2,3.87,50.0 -2015-02-24,FL,1,B,B2,3.84,56.0 -2015-02-25,FL,1,B,B2,3.76,58.0 -2015-02-26,FL,1,B,B2,3.89,51.0 -2015-02-27,FL,1,B,B2,3.88,49.0 -2015-02-28,FL,1,B,B2,3.95,54.0 -2015-03-01,FL,1,B,B2,3.88,48.0 -2015-03-02,FL,1,B,B2,3.81,54.0 -2015-03-03,FL,1,B,B2,3.88,47.0 -2015-03-04,FL,1,B,B2,3.86,55.0 -2015-03-05,FL,1,B,B2,3.92,51.0 -2015-03-06,FL,1,B,B2,3.88,52.0 -2015-03-07,FL,1,B,B2,3.9,54.0 -2015-03-08,FL,1,B,B2,3.85,52.0 -2015-03-09,FL,1,B,B2,3.79,61.0 -2015-03-10,FL,1,B,B2,3.84,55.0 -2015-03-11,FL,1,B,B2,3.87,51.0 -2015-03-12,FL,1,B,B2,3.87,49.0 -2015-03-13,FL,1,B,B2,3.9,49.0 -2015-03-14,FL,1,B,B2,3.87,56.0 -2015-03-15,FL,1,B,B2,3.86,53.0 -2015-03-16,FL,1,B,B2,3.84,53.0 -2015-03-17,FL,1,B,B2,3.86,51.0 -2015-03-18,FL,1,B,B2,3.89,48.0 -2015-03-19,FL,1,B,B2,3.88,54.0 -2015-03-20,FL,1,B,B2,3.86,55.0 -2015-03-21,FL,1,B,B2,3.85,53.0 -2015-03-22,FL,1,B,B2,3.85,49.0 -2015-03-23,FL,1,B,B2,3.9,50.0 -2015-03-24,FL,1,B,B2,3.86,54.0 -2015-03-25,FL,1,B,B2,3.83,51.0 -2015-03-26,FL,1,B,B2,3.87,53.0 -2015-03-27,FL,1,B,B2,3.83,57.0 -2015-03-28,FL,1,B,B2,3.86,49.0 -2015-03-29,FL,1,B,B2,3.91,53.0 -2015-03-30,FL,1,B,B2,3.79,54.0 -2015-03-31,FL,1,B,B2,3.82,57.0 -2015-04-01,FL,1,B,B2,3.83,56.0 -2015-04-02,FL,1,B,B2,3.82,50.0 -2015-04-03,FL,1,B,B2,3.86,52.0 -2015-04-04,FL,1,B,B2,3.81,56.0 -2015-04-05,FL,1,B,B2,3.83,51.0 -2015-04-06,FL,1,B,B2,3.86,50.0 -2015-04-07,FL,1,B,B2,3.88,59.0 -2015-04-08,FL,1,B,B2,3.95,50.0 -2015-04-09,FL,1,B,B2,3.86,52.0 -2015-04-10,FL,1,B,B2,3.92,53.0 -2015-04-11,FL,1,B,B2,3.84,56.0 -2015-04-12,FL,1,B,B2,3.84,54.0 -2015-04-13,FL,1,B,B2,3.89,49.0 -2015-04-14,FL,1,B,B2,3.83,53.0 -2015-04-15,FL,1,B,B2,3.84,51.0 -2015-04-16,FL,1,B,B2,3.9,54.0 -2015-04-17,FL,1,B,B2,3.88,50.0 -2015-04-18,FL,1,B,B2,3.79,61.0 -2015-04-19,FL,1,B,B2,3.93,53.0 -2015-04-20,FL,1,B,B2,3.82,52.0 -2015-04-21,FL,1,B,B2,3.83,54.0 -2015-04-22,FL,1,B,B2,3.87,54.0 -2015-04-23,FL,1,B,B2,3.84,55.0 -2015-04-24,FL,1,B,B2,3.86,54.0 -2015-04-25,FL,1,B,B2,3.78,54.0 -2015-04-26,FL,1,B,B2,3.88,52.0 -2015-04-27,FL,1,B,B2,3.92,51.0 -2015-04-28,FL,1,B,B2,3.87,54.0 -2015-04-29,FL,1,B,B2,3.95,53.0 -2015-04-30,FL,1,B,B2,3.81,53.0 -2015-05-01,FL,1,B,B2,3.85,53.0 -2015-05-02,FL,1,B,B2,3.87,53.0 -2015-05-03,FL,1,B,B2,3.87,55.0 -2015-05-04,FL,1,B,B2,3.94,50.0 -2015-05-05,FL,1,B,B2,3.83,51.0 -2015-05-06,FL,1,B,B2,3.85,49.0 -2015-05-07,FL,1,B,B2,3.85,54.0 -2015-05-08,FL,1,B,B2,3.86,53.0 -2015-05-09,FL,1,B,B2,3.82,52.0 -2015-05-10,FL,1,B,B2,3.97,49.0 -2015-05-11,FL,1,B,B2,3.83,50.0 -2015-05-12,FL,1,B,B2,3.85,55.0 -2015-05-13,FL,1,B,B2,3.86,59.0 -2015-05-14,FL,1,B,B2,3.84,50.0 -2015-05-15,FL,1,B,B2,3.83,55.0 -2015-05-16,FL,1,B,B2,3.86,54.0 -2015-05-17,FL,1,B,B2,3.88,57.0 -2015-05-18,FL,1,B,B2,3.86,52.0 -2015-05-19,FL,1,B,B2,3.87,46.0 -2015-05-20,FL,1,B,B2,3.83,57.0 -2015-05-21,FL,1,B,B2,3.82,53.0 -2015-05-22,FL,1,B,B2,3.86,53.0 -2015-05-23,FL,1,B,B2,3.84,54.0 -2015-05-24,FL,1,B,B2,3.89,53.0 -2015-05-25,FL,1,B,B2,3.84,55.0 -2015-05-26,FL,1,B,B2,3.91,53.0 -2015-05-27,FL,1,B,B2,3.87,54.0 -2015-05-28,FL,1,B,B2,3.91,52.0 -2015-05-29,FL,1,B,B2,3.9,50.0 -2015-05-30,FL,1,B,B2,3.88,56.0 -2015-05-31,FL,1,B,B2,3.81,54.0 -2015-06-01,FL,1,B,B2,3.78,58.0 -2015-06-02,FL,1,B,B2,3.82,57.0 -2015-06-03,FL,1,B,B2,3.81,57.0 -2015-06-04,FL,1,B,B2,3.85,52.0 -2015-06-05,FL,1,B,B2,3.85,58.0 -2015-06-06,FL,1,B,B2,3.89,53.0 -2015-06-07,FL,1,B,B2,3.82,54.0 -2015-06-08,FL,1,B,B2,3.88,52.0 -2015-06-09,FL,1,B,B2,3.87,52.0 -2015-06-10,FL,1,B,B2,3.9,50.0 -2015-06-11,FL,1,B,B2,3.82,54.0 -2015-06-12,FL,1,B,B2,3.84,57.0 -2015-06-13,FL,1,B,B2,3.91,56.0 -2015-06-14,FL,1,B,B2,3.88,56.0 -2015-06-15,FL,1,B,B2,3.87,49.0 -2015-06-16,FL,1,B,B2,3.87,54.0 -2015-06-17,FL,1,B,B2,3.92,50.0 -2015-06-18,FL,1,B,B2,3.85,53.0 -2015-06-19,FL,1,B,B2,3.89,52.0 -2015-06-20,FL,1,B,B2,3.86,54.0 -2015-06-21,FL,1,B,B2,3.86,52.0 -2015-06-22,FL,1,B,B2,3.88,51.0 -2015-06-23,FL,1,B,B2,3.83,55.0 -2015-06-24,FL,1,B,B2,3.89,52.0 -2015-06-25,FL,1,B,B2,3.86,51.0 -2015-06-26,FL,1,B,B2,3.85,55.0 -2015-06-27,FL,1,B,B2,3.89,54.0 -2015-06-28,FL,1,B,B2,3.9,55.0 -2015-06-29,FL,1,B,B2,3.92,52.0 -2015-06-30,FL,1,B,B2,3.9,52.0 -2015-07-01,FL,1,B,B2,3.92,54.0 -2015-07-02,FL,1,B,B2,3.81,57.0 -2015-07-03,FL,1,B,B2,3.84,56.0 -2015-07-04,FL,1,B,B2,3.88,56.0 -2015-07-05,FL,1,B,B2,3.89,58.0 -2015-07-06,FL,1,B,B2,3.84,56.0 -2015-07-07,FL,1,B,B2,3.89,52.0 -2015-07-08,FL,1,B,B2,3.89,52.0 -2015-07-09,FL,1,B,B2,3.82,50.0 -2015-07-10,FL,1,B,B2,3.85,55.0 -2015-07-11,FL,1,B,B2,3.84,58.0 -2015-07-12,FL,1,B,B2,3.87,53.0 -2015-07-13,FL,1,B,B2,3.82,53.0 -2015-07-14,FL,1,B,B2,3.88,54.0 -2015-07-15,FL,1,B,B2,3.87,52.0 -2015-07-16,FL,1,B,B2,3.85,54.0 -2015-07-17,FL,1,B,B2,3.83,52.0 -2015-07-18,FL,1,B,B2,3.8,58.0 -2015-07-19,FL,1,B,B2,3.9,54.0 -2015-07-20,FL,1,B,B2,3.92,50.0 -2015-07-21,FL,1,B,B2,3.94,50.0 -2015-07-22,FL,1,B,B2,3.88,51.0 -2015-07-23,FL,1,B,B2,3.82,52.0 -2015-07-24,FL,1,B,B2,3.85,58.0 -2015-07-25,FL,1,B,B2,3.8,54.0 -2015-07-26,FL,1,B,B2,3.87,60.0 -2015-07-27,FL,1,B,B2,3.87,55.0 -2015-07-28,FL,1,B,B2,3.93,54.0 -2015-07-29,FL,1,B,B2,3.86,52.0 -2015-07-30,FL,1,B,B2,3.87,53.0 -2015-07-31,FL,1,B,B2,3.83,57.0 -2015-08-01,FL,1,B,B2,3.82,57.0 -2015-08-02,FL,1,B,B2,3.8,56.0 -2015-08-03,FL,1,B,B2,3.88,55.0 -2015-08-04,FL,1,B,B2,3.87,54.0 -2015-08-05,FL,1,B,B2,3.79,54.0 -2015-08-06,FL,1,B,B2,3.87,51.0 -2015-08-07,FL,1,B,B2,3.97,52.0 -2015-08-08,FL,1,B,B2,3.97,53.0 -2015-08-09,FL,1,B,B2,3.84,64.0 -2015-08-10,FL,1,B,B2,3.84,51.0 -2015-08-11,FL,1,B,B2,3.92,50.0 -2015-08-12,FL,1,B,B2,3.92,49.0 -2015-08-13,FL,1,B,B2,3.95,49.0 -2015-08-14,FL,1,B,B2,3.88,54.0 -2015-08-15,FL,1,B,B2,3.88,53.0 -2015-08-16,FL,1,B,B2,3.81,59.0 -2015-08-17,FL,1,B,B2,3.85,51.0 -2015-08-18,FL,1,B,B2,3.87,50.0 -2015-08-19,FL,1,B,B2,3.84,56.0 -2015-08-20,FL,1,B,B2,3.87,51.0 -2015-08-21,FL,1,B,B2,3.86,60.0 -2015-08-22,FL,1,B,B2,3.82,57.0 -2015-08-23,FL,1,B,B2,3.78,60.0 -2015-08-24,FL,1,B,B2,3.82,54.0 -2015-08-25,FL,1,B,B2,3.82,54.0 -2015-08-26,FL,1,B,B2,3.82,57.0 -2015-08-27,FL,1,B,B2,3.9,50.0 -2015-08-28,FL,1,B,B2,3.87,55.0 -2015-08-29,FL,1,B,B2,3.87,54.0 -2015-08-30,FL,1,B,B2,3.89,57.0 -2015-08-31,FL,1,B,B2,3.85,51.0 -2015-09-01,FL,1,B,B2,3.9,52.0 -2015-09-02,FL,1,B,B2,3.84,52.0 -2015-09-03,FL,1,B,B2,3.97,56.0 -2015-09-04,FL,1,B,B2,3.92,48.0 -2015-09-05,FL,1,B,B2,3.88,54.0 -2015-09-06,FL,1,B,B2,3.85,53.0 -2015-09-07,FL,1,B,B2,3.88,51.0 -2015-09-08,FL,1,B,B2,3.85,52.0 -2015-09-09,FL,1,B,B2,3.78,60.0 -2015-09-10,FL,1,B,B2,3.9,47.0 -2015-09-11,FL,1,B,B2,3.85,53.0 -2015-09-12,FL,1,B,B2,3.91,48.0 -2015-09-13,FL,1,B,B2,3.89,55.0 -2015-09-14,FL,1,B,B2,3.86,52.0 -2015-09-15,FL,1,B,B2,3.83,52.0 -2015-09-16,FL,1,B,B2,3.84,54.0 -2015-09-17,FL,1,B,B2,3.88,49.0 -2015-09-18,FL,1,B,B2,3.85,53.0 -2015-09-19,FL,1,B,B2,3.82,54.0 -2015-09-20,FL,1,B,B2,3.93,49.0 -2015-09-21,FL,1,B,B2,3.85,49.0 -2015-09-22,FL,1,B,B2,3.89,52.0 -2015-09-23,FL,1,B,B2,3.88,49.0 -2015-09-24,FL,1,B,B2,3.88,52.0 -2015-09-25,FL,1,B,B2,3.91,53.0 -2015-09-26,FL,1,B,B2,3.84,56.0 -2015-09-27,FL,1,B,B2,3.83,53.0 -2015-09-28,FL,1,B,B2,3.84,56.0 -2015-09-29,FL,1,B,B2,3.86,56.0 -2015-09-30,FL,1,B,B2,3.84,51.0 -2015-10-01,FL,1,B,B2,3.86,51.0 -2015-10-02,FL,1,B,B2,3.81,63.0 -2015-10-03,FL,1,B,B2,3.85,57.0 -2015-10-04,FL,1,B,B2,3.88,56.0 -2015-10-05,FL,1,B,B2,3.86,49.0 -2015-10-06,FL,1,B,B2,3.9,53.0 -2015-10-07,FL,1,B,B2,3.86,53.0 -2015-10-08,FL,1,B,B2,3.94,53.0 -2015-10-09,FL,1,B,B2,3.82,53.0 -2015-10-10,FL,1,B,B2,3.89,54.0 -2015-10-11,FL,1,B,B2,3.89,53.0 -2015-10-12,FL,1,B,B2,3.87,52.0 -2015-10-13,FL,1,B,B2,3.82,55.0 -2015-10-14,FL,1,B,B2,3.88,55.0 -2015-10-15,FL,1,B,B2,3.84,53.0 -2015-10-16,FL,1,B,B2,3.92,54.0 -2015-10-17,FL,1,B,B2,3.92,56.0 -2015-10-18,FL,1,B,B2,3.82,58.0 -2015-10-19,FL,1,B,B2,3.87,52.0 -2015-10-20,FL,1,B,B2,3.81,52.0 -2015-10-21,FL,1,B,B2,3.87,53.0 -2015-10-22,FL,1,B,B2,3.87,53.0 -2015-10-23,FL,1,B,B2,3.89,56.0 -2015-10-24,FL,1,B,B2,3.84,57.0 -2015-10-25,FL,1,B,B2,3.91,49.0 -2015-10-26,FL,1,B,B2,3.91,49.0 -2015-10-27,FL,1,B,B2,3.92,55.0 -2015-10-28,FL,1,B,B2,3.8,56.0 -2015-10-29,FL,1,B,B2,3.83,57.0 -2015-10-30,FL,1,B,B2,3.83,53.0 -2015-10-31,FL,1,B,B2,3.88,56.0 -2015-11-01,FL,1,B,B2,3.88,54.0 -2015-11-02,FL,1,B,B2,3.88,56.0 -2015-11-03,FL,1,B,B2,3.88,50.0 -2015-11-04,FL,1,B,B2,3.9,47.0 -2015-11-05,FL,1,B,B2,3.88,50.0 -2015-11-06,FL,1,B,B2,3.91,55.0 -2015-11-07,FL,1,B,B2,3.87,56.0 -2015-11-08,FL,1,B,B2,3.84,54.0 -2015-11-09,FL,1,B,B2,3.79,59.0 -2015-11-10,FL,1,B,B2,3.88,50.0 -2015-11-11,FL,1,B,B2,3.89,50.0 -2015-11-12,FL,1,B,B2,3.76,60.0 -2015-11-13,FL,1,B,B2,3.87,55.0 -2015-11-14,FL,1,B,B2,3.86,56.0 -2015-11-15,FL,1,B,B2,3.89,50.0 -2015-11-16,FL,1,B,B2,3.86,50.0 -2015-11-17,FL,1,B,B2,3.88,52.0 -2015-11-18,FL,1,B,B2,3.96,48.0 -2015-11-19,FL,1,B,B2,3.85,54.0 -2015-11-20,FL,1,B,B2,3.94,49.0 -2015-11-21,FL,1,B,B2,3.88,54.0 -2015-11-22,FL,1,B,B2,3.88,58.0 -2015-11-23,FL,1,B,B2,3.84,52.0 -2015-11-24,FL,1,B,B2,3.84,53.0 -2015-11-25,FL,1,B,B2,3.85,53.0 -2015-11-26,FL,1,B,B2,3.92,47.0 -2015-11-27,FL,1,B,B2,3.93,55.0 -2015-11-28,FL,1,B,B2,3.88,54.0 -2015-11-29,FL,1,B,B2,3.87,53.0 -2015-11-30,FL,1,B,B2,3.84,53.0 -2015-12-01,FL,1,B,B2,3.88,49.0 -2015-12-02,FL,1,B,B2,3.89,52.0 -2015-12-03,FL,1,B,B2,3.85,55.0 -2015-12-04,FL,1,B,B2,3.9,49.0 -2015-12-05,FL,1,B,B2,3.86,52.0 -2015-12-06,FL,1,B,B2,3.91,51.0 -2015-12-07,FL,1,B,B2,3.92,52.0 -2015-12-08,FL,1,B,B2,3.83,56.0 -2015-12-09,FL,1,B,B2,3.88,53.0 -2015-12-10,FL,1,B,B2,3.81,57.0 -2015-12-11,FL,1,B,B2,3.93,53.0 -2015-12-12,FL,1,B,B2,3.88,56.0 -2015-12-13,FL,1,B,B2,3.86,53.0 -2015-12-14,FL,1,B,B2,3.88,46.0 -2015-12-15,FL,1,B,B2,3.86,55.0 -2015-12-16,FL,1,B,B2,3.84,50.0 -2015-12-17,FL,1,B,B2,3.88,57.0 -2015-12-18,FL,1,B,B2,3.86,53.0 -2015-12-19,FL,1,B,B2,3.86,55.0 -2015-12-20,FL,1,B,B2,3.88,50.0 -2015-12-21,FL,1,B,B2,3.9,52.0 -2015-12-22,FL,1,B,B2,3.86,55.0 -2015-12-23,FL,1,B,B2,3.83,56.0 -2015-12-24,FL,1,B,B2,3.9,53.0 -2015-12-25,FL,1,B,B2,3.88,56.0 -2015-12-26,FL,1,B,B2,3.89,55.0 -2015-12-27,FL,1,B,B2,3.79,56.0 -2015-12-28,FL,1,B,B2,3.84,57.0 -2015-12-29,FL,1,B,B2,3.85,56.0 -2015-12-30,FL,1,B,B2,3.88,51.0 -2015-12-31,FL,1,B,B2,3.86,51.0 -2016-01-01,FL,1,B,B2,3.92,50.0 -2016-01-02,FL,1,B,B2,3.9,54.0 -2016-01-03,FL,1,B,B2,3.88,56.0 -2016-01-04,FL,1,B,B2,3.86,58.0 -2016-01-05,FL,1,B,B2,3.85,55.0 -2016-01-06,FL,1,B,B2,3.84,52.0 -2016-01-07,FL,1,B,B2,3.89,52.0 -2016-01-08,FL,1,B,B2,3.82,58.0 -2016-01-09,FL,1,B,B2,3.84,59.0 -2016-01-10,FL,1,B,B2,3.95,47.0 -2016-01-11,FL,1,B,B2,3.91,51.0 -2016-01-12,FL,1,B,B2,3.86,51.0 -2016-01-13,FL,1,B,B2,3.87,56.0 -2016-01-14,FL,1,B,B2,3.91,54.0 -2016-01-15,FL,1,B,B2,3.84,49.0 -2016-01-16,FL,1,B,B2,3.9,52.0 -2016-01-17,FL,1,B,B2,3.83,54.0 -2016-01-18,FL,1,B,B2,3.87,51.0 -2016-01-19,FL,1,B,B2,3.88,56.0 -2016-01-20,FL,1,B,B2,3.79,52.0 -2016-01-21,FL,1,B,B2,3.92,52.0 -2016-01-22,FL,1,B,B2,3.9,51.0 -2016-01-23,FL,1,B,B2,3.89,52.0 -2016-01-24,FL,1,B,B2,3.88,53.0 -2016-01-25,FL,1,B,B2,3.83,51.0 -2016-01-26,FL,1,B,B2,3.79,54.0 -2016-01-27,FL,1,B,B2,3.84,55.0 -2016-01-28,FL,1,B,B2,3.84,54.0 -2016-01-29,FL,1,B,B2,3.84,54.0 -2016-01-30,FL,1,B,B2,3.91,56.0 -2016-01-31,FL,1,B,B2,3.91,52.0 -2016-02-01,FL,1,B,B2,3.93,49.0 -2016-02-02,FL,1,B,B2,3.84,54.0 -2016-02-03,FL,1,B,B2,3.84,55.0 -2016-02-04,FL,1,B,B2,3.84,53.0 -2016-02-05,FL,1,B,B2,3.93,52.0 -2016-02-06,FL,1,B,B2,3.83,56.0 -2016-02-07,FL,1,B,B2,3.84,52.0 -2016-02-08,FL,1,B,B2,3.82,56.0 -2016-02-09,FL,1,B,B2,3.89,55.0 -2016-02-10,FL,1,B,B2,3.85,56.0 -2016-02-11,FL,1,B,B2,3.84,55.0 -2016-02-12,FL,1,B,B2,3.83,55.0 -2016-02-13,FL,1,B,B2,3.88,52.0 -2016-02-14,FL,1,B,B2,3.91,56.0 -2016-02-15,FL,1,B,B2,3.84,56.0 -2016-02-16,FL,1,B,B2,3.92,52.0 -2016-02-17,FL,1,B,B2,3.85,52.0 -2016-02-18,FL,1,B,B2,3.86,53.0 -2016-02-19,FL,1,B,B2,3.83,56.0 -2016-02-20,FL,1,B,B2,3.81,57.0 -2016-02-21,FL,1,B,B2,3.84,53.0 -2016-02-22,FL,1,B,B2,3.83,52.0 -2016-02-23,FL,1,B,B2,3.82,54.0 -2016-02-24,FL,1,B,B2,3.86,54.0 -2016-02-25,FL,1,B,B2,3.88,55.0 -2016-02-26,FL,1,B,B2,3.85,54.0 -2016-02-27,FL,1,B,B2,3.87,57.0 -2016-02-28,FL,1,B,B2,3.9,51.0 -2016-02-29,FL,1,B,B2,3.83,53.0 -2016-03-01,FL,1,B,B2,3.88,52.0 -2016-03-02,FL,1,B,B2,3.84,51.0 -2016-03-03,FL,1,B,B2,3.81,58.0 -2016-03-04,FL,1,B,B2,3.86,52.0 -2016-03-05,FL,1,B,B2,3.82,62.0 -2016-03-06,FL,1,B,B2,3.81,59.0 -2016-03-07,FL,1,B,B2,3.81,53.0 -2016-03-08,FL,1,B,B2,3.9,54.0 -2016-03-09,FL,1,B,B2,3.9,50.0 -2016-03-10,FL,1,B,B2,3.9,49.0 -2016-03-11,FL,1,B,B2,3.85,52.0 -2016-03-12,FL,1,B,B2,3.84,60.0 -2016-03-13,FL,1,B,B2,3.88,50.0 -2016-03-14,FL,1,B,B2,3.88,58.0 -2016-03-15,FL,1,B,B2,3.86,53.0 -2016-03-16,FL,1,B,B2,3.9,52.0 -2016-03-17,FL,1,B,B2,3.82,51.0 -2016-03-18,FL,1,B,B2,3.85,55.0 -2016-03-19,FL,1,B,B2,3.82,56.0 -2016-03-20,FL,1,B,B2,3.82,51.0 -2016-03-21,FL,1,B,B2,3.94,51.0 -2016-03-22,FL,1,B,B2,3.84,54.0 -2016-03-23,FL,1,B,B2,3.81,51.0 -2016-03-24,FL,1,B,B2,3.85,53.0 -2016-03-25,FL,1,B,B2,3.93,48.0 -2016-03-26,FL,1,B,B2,3.84,55.0 -2016-03-27,FL,1,B,B2,3.83,57.0 -2016-03-28,FL,1,B,B2,3.91,49.0 -2016-03-29,FL,1,B,B2,3.81,54.0 -2016-03-30,FL,1,B,B2,3.89,53.0 -2016-03-31,FL,1,B,B2,3.91,56.0 -2016-04-01,FL,1,B,B2,3.89,50.0 -2016-04-02,FL,1,B,B2,3.85,50.0 -2016-04-03,FL,1,B,B2,3.9,49.0 -2016-04-04,FL,1,B,B2,3.85,53.0 -2016-04-05,FL,1,B,B2,3.86,54.0 -2016-04-06,FL,1,B,B2,3.76,51.0 -2016-04-07,FL,1,B,B2,3.8,54.0 -2016-04-08,FL,1,B,B2,3.82,54.0 -2016-04-09,FL,1,B,B2,3.88,54.0 -2016-04-10,FL,1,B,B2,3.81,57.0 -2016-04-11,FL,1,B,B2,3.88,51.0 -2016-04-12,FL,1,B,B2,3.85,59.0 -2016-04-13,FL,1,B,B2,3.82,53.0 -2016-04-14,FL,1,B,B2,3.92,55.0 -2016-04-15,FL,1,B,B2,3.86,55.0 -2016-04-16,FL,1,B,B2,3.86,60.0 -2016-04-17,FL,1,B,B2,3.87,55.0 -2016-04-18,FL,1,B,B2,3.93,50.0 -2016-04-19,FL,1,B,B2,3.85,54.0 -2016-04-20,FL,1,B,B2,3.86,49.0 -2016-04-21,FL,1,B,B2,3.91,57.0 -2016-04-22,FL,1,B,B2,3.89,53.0 -2016-04-23,FL,1,B,B2,3.79,55.0 -2016-04-24,FL,1,B,B2,3.85,51.0 -2016-04-25,FL,1,B,B2,3.84,57.0 -2016-04-26,FL,1,B,B2,3.81,56.0 -2016-04-27,FL,1,B,B2,3.89,48.0 -2016-04-28,FL,1,B,B2,3.85,56.0 -2016-04-29,FL,1,B,B2,3.87,52.0 -2016-04-30,FL,1,B,B2,3.86,57.0 -2016-05-01,FL,1,B,B2,3.85,54.0 -2016-05-02,FL,1,B,B2,3.84,52.0 -2016-05-03,FL,1,B,B2,3.84,55.0 -2016-05-04,FL,1,B,B2,3.92,50.0 -2016-05-05,FL,1,B,B2,3.84,54.0 -2016-05-06,FL,1,B,B2,3.9,50.0 -2016-05-07,FL,1,B,B2,3.82,56.0 -2016-05-08,FL,1,B,B2,3.88,51.0 -2016-05-09,FL,1,B,B2,3.89,51.0 -2016-05-10,FL,1,B,B2,3.8,53.0 -2016-05-11,FL,1,B,B2,3.87,55.0 -2016-05-12,FL,1,B,B2,3.84,56.0 -2016-05-13,FL,1,B,B2,3.88,52.0 -2016-05-14,FL,1,B,B2,3.81,58.0 -2016-05-15,FL,1,B,B2,3.92,56.0 -2016-05-16,FL,1,B,B2,3.79,54.0 -2016-05-17,FL,1,B,B2,3.85,57.0 -2016-05-18,FL,1,B,B2,3.89,55.0 -2016-05-19,FL,1,B,B2,3.87,54.0 -2016-05-20,FL,1,B,B2,3.85,59.0 -2016-05-21,FL,1,B,B2,3.93,56.0 -2016-05-22,FL,1,B,B2,3.9,54.0 -2016-05-23,FL,1,B,B2,3.85,53.0 -2016-05-24,FL,1,B,B2,3.82,57.0 -2016-05-25,FL,1,B,B2,3.89,54.0 -2016-05-26,FL,1,B,B2,3.92,49.0 -2016-05-27,FL,1,B,B2,3.86,51.0 -2016-05-28,FL,1,B,B2,3.91,57.0 -2016-05-29,FL,1,B,B2,3.81,50.0 -2016-05-30,FL,1,B,B2,3.94,52.0 -2016-05-31,FL,1,B,B2,3.93,54.0 -2016-06-01,FL,1,B,B2,3.85,58.0 -2016-06-02,FL,1,B,B2,3.83,54.0 -2016-06-03,FL,1,B,B2,3.93,49.0 -2016-06-04,FL,1,B,B2,3.93,55.0 -2016-06-05,FL,1,B,B2,3.86,56.0 -2016-06-06,FL,1,B,B2,3.81,53.0 -2016-06-07,FL,1,B,B2,3.87,52.0 -2016-06-08,FL,1,B,B2,3.82,59.0 -2016-06-09,FL,1,B,B2,3.82,54.0 -2016-06-10,FL,1,B,B2,3.81,55.0 -2016-06-11,FL,1,B,B2,3.89,52.0 -2016-06-12,FL,1,B,B2,3.84,49.0 -2016-06-13,FL,1,B,B2,3.86,55.0 -2016-06-14,FL,1,B,B2,3.87,53.0 -2016-06-15,FL,1,B,B2,3.83,56.0 -2016-06-16,FL,1,B,B2,3.88,55.0 -2016-06-17,FL,1,B,B2,3.86,56.0 -2016-06-18,FL,1,B,B2,3.91,60.0 -2016-06-19,FL,1,B,B2,3.86,57.0 -2016-06-20,FL,1,B,B2,3.81,53.0 -2016-06-21,FL,1,B,B2,3.82,51.0 -2016-06-22,FL,1,B,B2,3.85,55.0 -2016-06-23,FL,1,B,B2,3.87,50.0 -2016-06-24,FL,1,B,B2,3.85,51.0 -2016-06-25,FL,1,B,B2,3.81,55.0 -2016-06-26,FL,1,B,B2,3.93,53.0 -2016-06-27,FL,1,B,B2,3.86,50.0 -2016-06-28,FL,1,B,B2,3.88,55.0 -2016-06-29,FL,1,B,B2,3.84,55.0 -2016-06-30,FL,1,B,B2,3.85,52.0 -2016-07-01,FL,1,B,B2,3.95,48.0 -2016-07-02,FL,1,B,B2,3.87,53.0 -2016-07-03,FL,1,B,B2,3.86,52.0 -2016-07-04,FL,1,B,B2,3.9,48.0 -2016-07-05,FL,1,B,B2,3.89,50.0 -2016-07-06,FL,1,B,B2,3.89,53.0 -2016-07-07,FL,1,B,B2,3.92,54.0 -2016-07-08,FL,1,B,B2,3.84,53.0 -2016-07-09,FL,1,B,B2,3.91,59.0 -2016-07-10,FL,1,B,B2,3.87,50.0 -2016-07-11,FL,1,B,B2,3.75,54.0 -2016-07-12,FL,1,B,B2,3.85,53.0 -2016-07-13,FL,1,B,B2,3.89,53.0 -2016-07-14,FL,1,B,B2,3.85,53.0 -2016-07-15,FL,1,B,B2,3.83,59.0 -2016-07-16,FL,1,B,B2,3.89,54.0 -2016-07-17,FL,1,B,B2,3.94,54.0 -2016-07-18,FL,1,B,B2,3.89,51.0 -2016-07-19,FL,1,B,B2,3.87,48.0 -2016-07-20,FL,1,B,B2,3.9,52.0 -2016-07-21,FL,1,B,B2,3.84,49.0 -2016-07-22,FL,1,B,B2,3.86,55.0 -2016-07-23,FL,1,B,B2,3.88,55.0 -2016-07-24,FL,1,B,B2,3.86,53.0 -2016-07-25,FL,1,B,B2,3.97,50.0 -2016-07-26,FL,1,B,B2,3.87,53.0 -2016-07-27,FL,1,B,B2,3.86,56.0 -2015-01-05,FL,2,A,A1,6.33,5.0 -2015-01-06,FL,2,A,A1,6.13,6.0 -2015-01-07,FL,2,A,A1,6.38,5.0 -2015-01-08,FL,2,A,A1,6.17,5.0 -2015-01-09,FL,2,A,A1,6.29,6.0 -2015-01-10,FL,2,A,A1,6.25,5.0 -2015-01-11,FL,2,A,A1,6.2,6.0 -2015-01-12,FL,2,A,A1,6.25,5.0 -2015-01-13,FL,2,A,A1,6.25,6.0 -2015-01-14,FL,2,A,A1,6.18,6.0 -2015-01-15,FL,2,A,A1,6.24,6.0 -2015-01-16,FL,2,A,A1,6.31,5.0 -2015-01-17,FL,2,A,A1,6.21,6.0 -2015-01-18,FL,2,A,A1,6.28,5.0 -2015-01-19,FL,2,A,A1,6.27,5.0 -2015-01-20,FL,2,A,A1,6.26,5.0 -2015-01-21,FL,2,A,A1,6.41,5.0 -2015-01-22,FL,2,A,A1,6.21,6.0 -2015-01-23,FL,2,A,A1,6.18,5.0 -2015-01-24,FL,2,A,A1,6.35,5.0 -2015-01-25,FL,2,A,A1,6.21,5.0 -2015-01-26,FL,2,A,A1,6.34,5.0 -2015-01-27,FL,2,A,A1,6.24,6.0 -2015-01-28,FL,2,A,A1,6.38,6.0 -2015-01-29,FL,2,A,A1,6.32,5.0 -2015-01-30,FL,2,A,A1,6.34,5.0 -2015-01-31,FL,2,A,A1,6.24,6.0 -2015-02-01,FL,2,A,A1,6.15,6.0 -2015-02-02,FL,2,A,A1,6.28,5.0 -2015-02-03,FL,2,A,A1,6.35,6.0 -2015-02-04,FL,2,A,A1,6.32,5.0 -2015-02-05,FL,2,A,A1,6.16,5.0 -2015-02-06,FL,2,A,A1,6.31,5.0 -2015-02-07,FL,2,A,A1,6.33,6.0 -2015-02-08,FL,2,A,A1,6.25,6.0 -2015-02-09,FL,2,A,A1,6.19,6.0 -2015-02-10,FL,2,A,A1,6.29,6.0 -2015-02-11,FL,2,A,A1,6.3,5.0 -2015-02-12,FL,2,A,A1,6.33,5.0 -2015-02-13,FL,2,A,A1,6.36,5.0 -2015-02-14,FL,2,A,A1,6.36,6.0 -2015-02-15,FL,2,A,A1,6.36,6.0 -2015-02-16,FL,2,A,A1,6.3,5.0 -2015-02-17,FL,2,A,A1,6.25,5.0 -2015-02-18,FL,2,A,A1,6.23,6.0 -2015-02-19,FL,2,A,A1,6.37,5.0 -2015-02-20,FL,2,A,A1,6.36,5.0 -2015-02-21,FL,2,A,A1,6.29,6.0 -2015-02-22,FL,2,A,A1,6.34,6.0 -2015-02-23,FL,2,A,A1,6.3,5.0 -2015-02-24,FL,2,A,A1,6.31,5.0 -2015-02-25,FL,2,A,A1,6.21,6.0 -2015-02-26,FL,2,A,A1,6.29,5.0 -2015-02-27,FL,2,A,A1,6.34,5.0 -2015-02-28,FL,2,A,A1,6.26,6.0 -2015-03-01,FL,2,A,A1,6.2,5.0 -2015-03-02,FL,2,A,A1,6.23,6.0 -2015-03-03,FL,2,A,A1,6.24,5.0 -2015-03-04,FL,2,A,A1,6.27,6.0 -2015-03-05,FL,2,A,A1,6.21,6.0 -2015-03-06,FL,2,A,A1,6.2,6.0 -2015-03-07,FL,2,A,A1,6.27,6.0 -2015-03-08,FL,2,A,A1,6.22,6.0 -2015-03-09,FL,2,A,A1,6.3,6.0 -2015-03-10,FL,2,A,A1,6.25,5.0 -2015-03-11,FL,2,A,A1,6.26,6.0 -2015-03-12,FL,2,A,A1,6.32,6.0 -2015-03-13,FL,2,A,A1,6.2,6.0 -2015-03-14,FL,2,A,A1,6.23,6.0 -2015-03-15,FL,2,A,A1,6.26,6.0 -2015-03-16,FL,2,A,A1,6.17,5.0 -2015-03-17,FL,2,A,A1,6.36,5.0 -2015-03-18,FL,2,A,A1,6.27,6.0 -2015-03-19,FL,2,A,A1,6.09,6.0 -2015-03-20,FL,2,A,A1,6.32,6.0 -2015-03-21,FL,2,A,A1,6.29,6.0 -2015-03-22,FL,2,A,A1,6.25,6.0 -2015-03-23,FL,2,A,A1,6.31,6.0 -2015-03-24,FL,2,A,A1,6.37,5.0 -2015-03-25,FL,2,A,A1,6.26,6.0 -2015-03-26,FL,2,A,A1,6.34,6.0 -2015-03-27,FL,2,A,A1,6.29,6.0 -2015-03-28,FL,2,A,A1,6.21,6.0 -2015-03-29,FL,2,A,A1,6.16,5.0 -2015-03-30,FL,2,A,A1,6.4,5.0 -2015-03-31,FL,2,A,A1,6.28,5.0 -2015-04-01,FL,2,A,A1,6.28,6.0 -2015-04-02,FL,2,A,A1,6.25,6.0 -2015-04-03,FL,2,A,A1,6.3,6.0 -2015-04-04,FL,2,A,A1,6.3,6.0 -2015-04-05,FL,2,A,A1,6.22,6.0 -2015-04-06,FL,2,A,A1,6.3,5.0 -2015-04-07,FL,2,A,A1,6.31,6.0 -2015-04-08,FL,2,A,A1,6.27,6.0 -2015-04-09,FL,2,A,A1,6.24,6.0 -2015-04-10,FL,2,A,A1,6.19,6.0 -2015-04-11,FL,2,A,A1,6.29,6.0 -2015-04-12,FL,2,A,A1,6.23,6.0 -2015-04-13,FL,2,A,A1,6.3,5.0 -2015-04-14,FL,2,A,A1,6.25,6.0 -2015-04-15,FL,2,A,A1,6.27,5.0 -2015-04-16,FL,2,A,A1,6.28,5.0 -2015-04-17,FL,2,A,A1,6.35,6.0 -2015-04-18,FL,2,A,A1,6.31,6.0 -2015-04-19,FL,2,A,A1,6.23,6.0 -2015-04-20,FL,2,A,A1,6.2,5.0 -2015-04-21,FL,2,A,A1,6.26,6.0 -2015-04-22,FL,2,A,A1,6.23,6.0 -2015-04-23,FL,2,A,A1,6.16,5.0 -2015-04-24,FL,2,A,A1,6.35,5.0 -2015-04-25,FL,2,A,A1,6.31,6.0 -2015-04-26,FL,2,A,A1,6.21,6.0 -2015-04-27,FL,2,A,A1,6.37,5.0 -2015-04-28,FL,2,A,A1,6.35,5.0 -2015-04-29,FL,2,A,A1,6.31,5.0 -2015-04-30,FL,2,A,A1,6.36,5.0 -2015-05-01,FL,2,A,A1,6.23,5.0 -2015-05-02,FL,2,A,A1,6.27,6.0 -2015-05-03,FL,2,A,A1,6.33,6.0 -2015-05-04,FL,2,A,A1,6.23,6.0 -2015-05-05,FL,2,A,A1,6.21,5.0 -2015-05-06,FL,2,A,A1,6.32,6.0 -2015-05-07,FL,2,A,A1,6.23,6.0 -2015-05-08,FL,2,A,A1,6.16,6.0 -2015-05-09,FL,2,A,A1,6.2,6.0 -2015-05-10,FL,2,A,A1,6.17,6.0 -2015-05-11,FL,2,A,A1,6.16,6.0 -2015-05-12,FL,2,A,A1,6.3,5.0 -2015-05-13,FL,2,A,A1,6.25,6.0 -2015-05-14,FL,2,A,A1,6.17,6.0 -2015-05-15,FL,2,A,A1,6.27,5.0 -2015-05-16,FL,2,A,A1,6.32,6.0 -2015-05-17,FL,2,A,A1,6.22,6.0 -2015-05-18,FL,2,A,A1,6.15,6.0 -2015-05-19,FL,2,A,A1,6.3,5.0 -2015-05-20,FL,2,A,A1,6.13,6.0 -2015-05-21,FL,2,A,A1,6.36,6.0 -2015-05-22,FL,2,A,A1,6.33,6.0 -2015-05-23,FL,2,A,A1,6.3,6.0 -2015-05-24,FL,2,A,A1,6.24,6.0 -2015-05-25,FL,2,A,A1,6.28,5.0 -2015-05-26,FL,2,A,A1,6.26,6.0 -2015-05-27,FL,2,A,A1,6.16,5.0 -2015-05-28,FL,2,A,A1,6.29,5.0 -2015-05-29,FL,2,A,A1,6.39,6.0 -2015-05-30,FL,2,A,A1,6.22,6.0 -2015-05-31,FL,2,A,A1,6.25,6.0 -2015-06-01,FL,2,A,A1,6.32,6.0 -2015-06-02,FL,2,A,A1,6.3,6.0 -2015-06-03,FL,2,A,A1,6.26,5.0 -2015-06-04,FL,2,A,A1,6.34,5.0 -2015-06-05,FL,2,A,A1,6.24,6.0 -2015-06-06,FL,2,A,A1,6.33,5.0 -2015-06-07,FL,2,A,A1,6.24,5.0 -2015-06-08,FL,2,A,A1,6.26,5.0 -2015-06-09,FL,2,A,A1,6.2,6.0 -2015-06-10,FL,2,A,A1,6.26,6.0 -2015-06-11,FL,2,A,A1,6.36,5.0 -2015-06-12,FL,2,A,A1,6.33,5.0 -2015-06-13,FL,2,A,A1,6.27,5.0 -2015-06-14,FL,2,A,A1,6.26,5.0 -2015-06-15,FL,2,A,A1,6.36,5.0 -2015-06-16,FL,2,A,A1,6.38,6.0 -2015-06-17,FL,2,A,A1,6.24,6.0 -2015-06-18,FL,2,A,A1,6.26,6.0 -2015-06-19,FL,2,A,A1,6.34,6.0 -2015-06-20,FL,2,A,A1,6.27,6.0 -2015-06-21,FL,2,A,A1,6.24,6.0 -2015-06-22,FL,2,A,A1,6.28,5.0 -2015-06-23,FL,2,A,A1,6.33,5.0 -2015-06-24,FL,2,A,A1,6.34,6.0 -2015-06-25,FL,2,A,A1,6.39,5.0 -2015-06-26,FL,2,A,A1,6.27,6.0 -2015-06-27,FL,2,A,A1,6.18,5.0 -2015-06-28,FL,2,A,A1,6.23,6.0 -2015-06-29,FL,2,A,A1,6.32,5.0 -2015-06-30,FL,2,A,A1,6.27,6.0 -2015-07-01,FL,2,A,A1,6.26,6.0 -2015-07-02,FL,2,A,A1,6.29,5.0 -2015-07-03,FL,2,A,A1,6.21,6.0 -2015-07-04,FL,2,A,A1,6.14,6.0 -2015-07-05,FL,2,A,A1,6.28,6.0 -2015-07-06,FL,2,A,A1,6.28,5.0 -2015-07-07,FL,2,A,A1,6.18,6.0 -2015-07-08,FL,2,A,A1,6.36,5.0 -2015-07-09,FL,2,A,A1,6.29,5.0 -2015-07-10,FL,2,A,A1,6.26,5.0 -2015-07-11,FL,2,A,A1,6.11,6.0 -2015-07-12,FL,2,A,A1,6.27,5.0 -2015-07-13,FL,2,A,A1,6.31,5.0 -2015-07-14,FL,2,A,A1,6.23,6.0 -2015-07-15,FL,2,A,A1,6.25,5.0 -2015-07-16,FL,2,A,A1,6.24,6.0 -2015-07-17,FL,2,A,A1,6.35,5.0 -2015-07-18,FL,2,A,A1,6.24,6.0 -2015-07-19,FL,2,A,A1,6.25,6.0 -2015-07-20,FL,2,A,A1,6.22,5.0 -2015-07-21,FL,2,A,A1,6.24,6.0 -2015-07-22,FL,2,A,A1,6.26,5.0 -2015-07-23,FL,2,A,A1,6.19,5.0 -2015-07-24,FL,2,A,A1,6.3,5.0 -2015-07-25,FL,2,A,A1,6.27,6.0 -2015-07-26,FL,2,A,A1,6.35,5.0 -2015-07-27,FL,2,A,A1,6.31,5.0 -2015-07-28,FL,2,A,A1,6.18,6.0 -2015-07-29,FL,2,A,A1,6.24,5.0 -2015-07-30,FL,2,A,A1,6.29,6.0 -2015-07-31,FL,2,A,A1,6.27,6.0 -2015-08-01,FL,2,A,A1,6.42,5.0 -2015-08-02,FL,2,A,A1,6.26,5.0 -2015-08-03,FL,2,A,A1,6.31,5.0 -2015-08-04,FL,2,A,A1,6.31,5.0 -2015-08-05,FL,2,A,A1,6.26,6.0 -2015-08-06,FL,2,A,A1,6.35,6.0 -2015-08-07,FL,2,A,A1,6.22,6.0 -2015-08-08,FL,2,A,A1,6.26,6.0 -2015-08-09,FL,2,A,A1,6.24,5.0 -2015-08-10,FL,2,A,A1,6.26,6.0 -2015-08-11,FL,2,A,A1,6.12,6.0 -2015-08-12,FL,2,A,A1,6.31,5.0 -2015-08-13,FL,2,A,A1,6.2,6.0 -2015-08-14,FL,2,A,A1,6.2,6.0 -2015-08-15,FL,2,A,A1,6.33,5.0 -2015-08-16,FL,2,A,A1,6.28,6.0 -2015-08-17,FL,2,A,A1,6.24,5.0 -2015-08-18,FL,2,A,A1,6.21,5.0 -2015-08-19,FL,2,A,A1,6.33,6.0 -2015-08-20,FL,2,A,A1,6.35,6.0 -2015-08-21,FL,2,A,A1,6.28,5.0 -2015-08-22,FL,2,A,A1,6.26,6.0 -2015-08-23,FL,2,A,A1,6.19,5.0 -2015-08-24,FL,2,A,A1,6.19,5.0 -2015-08-25,FL,2,A,A1,6.25,5.0 -2015-08-26,FL,2,A,A1,6.21,6.0 -2015-08-27,FL,2,A,A1,6.39,5.0 -2015-08-28,FL,2,A,A1,6.28,5.0 -2015-08-29,FL,2,A,A1,6.34,6.0 -2015-08-30,FL,2,A,A1,6.34,6.0 -2015-08-31,FL,2,A,A1,6.13,6.0 -2015-09-01,FL,2,A,A1,6.2,6.0 -2015-09-02,FL,2,A,A1,6.24,6.0 -2015-09-03,FL,2,A,A1,6.43,5.0 -2015-09-04,FL,2,A,A1,6.24,6.0 -2015-09-05,FL,2,A,A1,6.24,6.0 -2015-09-06,FL,2,A,A1,6.32,6.0 -2015-09-07,FL,2,A,A1,6.34,5.0 -2015-09-08,FL,2,A,A1,6.26,5.0 -2015-09-09,FL,2,A,A1,6.36,5.0 -2015-09-10,FL,2,A,A1,6.27,6.0 -2015-09-11,FL,2,A,A1,6.2,6.0 -2015-09-12,FL,2,A,A1,6.22,6.0 -2015-09-13,FL,2,A,A1,6.22,6.0 -2015-09-14,FL,2,A,A1,6.14,5.0 -2015-09-15,FL,2,A,A1,6.24,6.0 -2015-09-16,FL,2,A,A1,6.27,6.0 -2015-09-17,FL,2,A,A1,6.2,6.0 -2015-09-18,FL,2,A,A1,6.21,6.0 -2015-09-19,FL,2,A,A1,6.33,6.0 -2015-09-20,FL,2,A,A1,6.29,5.0 -2015-09-21,FL,2,A,A1,6.3,5.0 -2015-09-22,FL,2,A,A1,6.28,5.0 -2015-09-23,FL,2,A,A1,6.26,5.0 -2015-09-24,FL,2,A,A1,6.35,5.0 -2015-09-25,FL,2,A,A1,6.35,5.0 -2015-09-26,FL,2,A,A1,6.25,5.0 -2015-09-27,FL,2,A,A1,6.35,5.0 -2015-09-28,FL,2,A,A1,6.28,6.0 -2015-09-29,FL,2,A,A1,6.3,6.0 -2015-09-30,FL,2,A,A1,6.34,5.0 -2015-10-01,FL,2,A,A1,6.32,6.0 -2015-10-02,FL,2,A,A1,6.25,5.0 -2015-10-03,FL,2,A,A1,6.26,6.0 -2015-10-04,FL,2,A,A1,6.21,5.0 -2015-10-05,FL,2,A,A1,6.16,5.0 -2015-10-06,FL,2,A,A1,6.34,5.0 -2015-10-07,FL,2,A,A1,6.33,5.0 -2015-10-08,FL,2,A,A1,6.19,6.0 -2015-10-09,FL,2,A,A1,6.22,6.0 -2015-10-10,FL,2,A,A1,6.28,6.0 -2015-10-11,FL,2,A,A1,6.15,6.0 -2015-10-12,FL,2,A,A1,6.24,6.0 -2015-10-13,FL,2,A,A1,6.24,6.0 -2015-10-14,FL,2,A,A1,6.28,6.0 -2015-10-15,FL,2,A,A1,6.32,5.0 -2015-10-16,FL,2,A,A1,6.18,6.0 -2015-10-17,FL,2,A,A1,6.23,6.0 -2015-10-18,FL,2,A,A1,6.19,6.0 -2015-10-19,FL,2,A,A1,6.24,5.0 -2015-10-20,FL,2,A,A1,6.35,5.0 -2015-10-21,FL,2,A,A1,6.31,6.0 -2015-10-22,FL,2,A,A1,6.4,5.0 -2015-10-23,FL,2,A,A1,6.25,5.0 -2015-10-24,FL,2,A,A1,6.29,5.0 -2015-10-25,FL,2,A,A1,6.3,5.0 -2015-10-26,FL,2,A,A1,6.27,5.0 -2015-10-27,FL,2,A,A1,6.35,5.0 -2015-10-28,FL,2,A,A1,6.25,6.0 -2015-10-29,FL,2,A,A1,6.23,5.0 -2015-10-30,FL,2,A,A1,6.37,6.0 -2015-10-31,FL,2,A,A1,6.24,6.0 -2015-11-01,FL,2,A,A1,6.37,5.0 -2015-11-02,FL,2,A,A1,6.35,5.0 -2015-11-03,FL,2,A,A1,6.22,6.0 -2015-11-04,FL,2,A,A1,6.24,5.0 -2015-11-05,FL,2,A,A1,6.18,6.0 -2015-11-06,FL,2,A,A1,6.32,6.0 -2015-11-07,FL,2,A,A1,6.17,6.0 -2015-11-08,FL,2,A,A1,6.15,6.0 -2015-11-09,FL,2,A,A1,6.33,5.0 -2015-11-10,FL,2,A,A1,6.3,6.0 -2015-11-11,FL,2,A,A1,6.24,6.0 -2015-11-12,FL,2,A,A1,6.18,6.0 -2015-11-13,FL,2,A,A1,6.27,6.0 -2015-11-14,FL,2,A,A1,6.17,6.0 -2015-11-15,FL,2,A,A1,6.22,6.0 -2015-11-16,FL,2,A,A1,6.2,6.0 -2015-11-17,FL,2,A,A1,6.21,6.0 -2015-11-18,FL,2,A,A1,6.28,5.0 -2015-11-19,FL,2,A,A1,6.18,6.0 -2015-11-20,FL,2,A,A1,6.37,5.0 -2015-11-21,FL,2,A,A1,6.31,6.0 -2015-11-22,FL,2,A,A1,6.35,5.0 -2015-11-23,FL,2,A,A1,6.28,6.0 -2015-11-24,FL,2,A,A1,6.36,6.0 -2015-11-25,FL,2,A,A1,6.2,6.0 -2015-11-26,FL,2,A,A1,6.19,6.0 -2015-11-27,FL,2,A,A1,6.36,6.0 -2015-11-28,FL,2,A,A1,6.23,6.0 -2015-11-29,FL,2,A,A1,6.25,6.0 -2015-11-30,FL,2,A,A1,6.32,5.0 -2015-12-01,FL,2,A,A1,6.26,5.0 -2015-12-02,FL,2,A,A1,6.3,5.0 -2015-12-03,FL,2,A,A1,6.18,6.0 -2015-12-04,FL,2,A,A1,6.31,6.0 -2015-12-05,FL,2,A,A1,6.4,5.0 -2015-12-06,FL,2,A,A1,6.29,6.0 -2015-12-07,FL,2,A,A1,6.22,5.0 -2015-12-08,FL,2,A,A1,6.31,5.0 -2015-12-09,FL,2,A,A1,6.34,6.0 -2015-12-10,FL,2,A,A1,6.27,6.0 -2015-12-11,FL,2,A,A1,6.21,6.0 -2015-12-12,FL,2,A,A1,6.32,6.0 -2015-12-13,FL,2,A,A1,6.26,6.0 -2015-12-14,FL,2,A,A1,6.36,5.0 -2015-12-15,FL,2,A,A1,6.29,6.0 -2015-12-16,FL,2,A,A1,6.23,5.0 -2015-12-17,FL,2,A,A1,6.3,5.0 -2015-12-18,FL,2,A,A1,6.27,6.0 -2015-12-19,FL,2,A,A1,6.26,6.0 -2015-12-20,FL,2,A,A1,6.39,5.0 -2015-12-21,FL,2,A,A1,6.41,5.0 -2015-12-22,FL,2,A,A1,6.2,6.0 -2015-12-23,FL,2,A,A1,6.31,6.0 -2015-12-24,FL,2,A,A1,6.36,5.0 -2015-12-25,FL,2,A,A1,6.34,6.0 -2015-12-26,FL,2,A,A1,6.28,6.0 -2015-12-27,FL,2,A,A1,6.23,6.0 -2015-12-28,FL,2,A,A1,6.37,5.0 -2015-12-29,FL,2,A,A1,6.29,6.0 -2015-12-30,FL,2,A,A1,6.34,5.0 -2015-12-31,FL,2,A,A1,6.3,6.0 -2016-01-01,FL,2,A,A1,6.28,6.0 -2016-01-02,FL,2,A,A1,6.24,6.0 -2016-01-03,FL,2,A,A1,6.26,6.0 -2016-01-04,FL,2,A,A1,6.26,6.0 -2016-01-05,FL,2,A,A1,6.25,6.0 -2016-01-06,FL,2,A,A1,6.35,5.0 -2016-01-07,FL,2,A,A1,6.26,6.0 -2016-01-08,FL,2,A,A1,6.37,6.0 -2016-01-09,FL,2,A,A1,6.26,6.0 -2016-01-10,FL,2,A,A1,6.27,6.0 -2016-01-11,FL,2,A,A1,6.22,5.0 -2016-01-12,FL,2,A,A1,6.32,5.0 -2016-01-13,FL,2,A,A1,6.38,6.0 -2016-01-14,FL,2,A,A1,6.42,5.0 -2016-01-15,FL,2,A,A1,6.4,5.0 -2016-01-16,FL,2,A,A1,6.33,6.0 -2016-01-17,FL,2,A,A1,6.26,6.0 -2016-01-18,FL,2,A,A1,6.29,6.0 -2016-01-19,FL,2,A,A1,6.37,5.0 -2016-01-20,FL,2,A,A1,6.15,6.0 -2016-01-21,FL,2,A,A1,6.26,6.0 -2016-01-22,FL,2,A,A1,6.13,6.0 -2016-01-23,FL,2,A,A1,6.21,6.0 -2016-01-24,FL,2,A,A1,6.24,5.0 -2016-01-25,FL,2,A,A1,6.31,5.0 -2016-01-26,FL,2,A,A1,6.17,5.0 -2016-01-27,FL,2,A,A1,6.27,6.0 -2016-01-28,FL,2,A,A1,6.34,5.0 -2016-01-29,FL,2,A,A1,6.32,5.0 -2016-01-30,FL,2,A,A1,6.27,6.0 -2016-01-31,FL,2,A,A1,6.39,6.0 -2016-02-01,FL,2,A,A1,6.31,5.0 -2016-02-02,FL,2,A,A1,6.2,6.0 -2016-02-03,FL,2,A,A1,6.35,6.0 -2016-02-04,FL,2,A,A1,6.3,6.0 -2016-02-05,FL,2,A,A1,6.25,6.0 -2016-02-06,FL,2,A,A1,6.31,6.0 -2016-02-07,FL,2,A,A1,6.27,6.0 -2016-02-08,FL,2,A,A1,6.39,5.0 -2016-02-09,FL,2,A,A1,6.36,5.0 -2016-02-10,FL,2,A,A1,6.37,6.0 -2016-02-11,FL,2,A,A1,6.24,5.0 -2016-02-12,FL,2,A,A1,6.27,6.0 -2016-02-13,FL,2,A,A1,6.31,6.0 -2016-02-14,FL,2,A,A1,6.24,6.0 -2016-02-15,FL,2,A,A1,6.32,5.0 -2016-02-16,FL,2,A,A1,6.28,5.0 -2016-02-17,FL,2,A,A1,6.38,5.0 -2016-02-18,FL,2,A,A1,6.3,6.0 -2016-02-19,FL,2,A,A1,6.38,5.0 -2016-02-20,FL,2,A,A1,6.29,6.0 -2016-02-21,FL,2,A,A1,6.33,5.0 -2016-02-22,FL,2,A,A1,6.34,5.0 -2016-02-23,FL,2,A,A1,6.35,5.0 -2016-02-24,FL,2,A,A1,6.35,5.0 -2016-02-25,FL,2,A,A1,6.24,6.0 -2016-02-26,FL,2,A,A1,6.23,6.0 -2016-02-27,FL,2,A,A1,6.24,6.0 -2016-02-28,FL,2,A,A1,6.3,6.0 -2016-02-29,FL,2,A,A1,6.35,5.0 -2016-03-01,FL,2,A,A1,6.27,6.0 -2016-03-02,FL,2,A,A1,6.27,5.0 -2016-03-03,FL,2,A,A1,6.35,5.0 -2016-03-04,FL,2,A,A1,6.26,6.0 -2016-03-05,FL,2,A,A1,6.13,6.0 -2016-03-06,FL,2,A,A1,6.27,6.0 -2016-03-07,FL,2,A,A1,6.23,5.0 -2016-03-08,FL,2,A,A1,6.28,6.0 -2016-03-09,FL,2,A,A1,6.31,5.0 -2016-03-10,FL,2,A,A1,6.32,6.0 -2016-03-11,FL,2,A,A1,6.3,5.0 -2016-03-12,FL,2,A,A1,6.24,6.0 -2016-03-13,FL,2,A,A1,6.23,6.0 -2016-03-14,FL,2,A,A1,6.42,5.0 -2016-03-15,FL,2,A,A1,6.23,5.0 -2016-03-16,FL,2,A,A1,6.26,6.0 -2016-03-17,FL,2,A,A1,6.29,6.0 -2016-03-18,FL,2,A,A1,6.15,6.0 -2016-03-19,FL,2,A,A1,6.23,6.0 -2016-03-20,FL,2,A,A1,6.14,6.0 -2016-03-21,FL,2,A,A1,6.25,6.0 -2016-03-22,FL,2,A,A1,6.31,6.0 -2016-03-23,FL,2,A,A1,6.2,6.0 -2016-03-24,FL,2,A,A1,6.24,6.0 -2016-03-25,FL,2,A,A1,6.19,6.0 -2016-03-26,FL,2,A,A1,6.2,6.0 -2016-03-27,FL,2,A,A1,6.32,6.0 -2016-03-28,FL,2,A,A1,6.34,5.0 -2016-03-29,FL,2,A,A1,6.28,6.0 -2016-03-30,FL,2,A,A1,6.39,5.0 -2016-03-31,FL,2,A,A1,6.32,5.0 -2016-04-01,FL,2,A,A1,6.44,5.0 -2016-04-02,FL,2,A,A1,6.31,6.0 -2016-04-03,FL,2,A,A1,6.29,6.0 -2016-04-04,FL,2,A,A1,6.27,5.0 -2016-04-05,FL,2,A,A1,6.22,6.0 -2016-04-06,FL,2,A,A1,6.31,6.0 -2016-04-07,FL,2,A,A1,6.24,6.0 -2016-04-08,FL,2,A,A1,6.35,5.0 -2016-04-09,FL,2,A,A1,6.21,6.0 -2016-04-10,FL,2,A,A1,6.34,6.0 -2016-04-11,FL,2,A,A1,6.19,5.0 -2016-04-12,FL,2,A,A1,6.17,6.0 -2016-04-13,FL,2,A,A1,6.3,5.0 -2016-04-14,FL,2,A,A1,6.2,6.0 -2016-04-15,FL,2,A,A1,6.27,6.0 -2016-04-16,FL,2,A,A1,6.33,5.0 -2016-04-17,FL,2,A,A1,6.24,6.0 -2016-04-18,FL,2,A,A1,6.24,6.0 -2016-04-19,FL,2,A,A1,6.27,5.0 -2016-04-20,FL,2,A,A1,6.25,5.0 -2016-04-21,FL,2,A,A1,6.2,5.0 -2016-04-22,FL,2,A,A1,6.28,5.0 -2016-04-23,FL,2,A,A1,6.25,6.0 -2016-04-24,FL,2,A,A1,6.24,6.0 -2016-04-25,FL,2,A,A1,6.4,6.0 -2016-04-26,FL,2,A,A1,6.43,5.0 -2016-04-27,FL,2,A,A1,6.29,5.0 -2016-04-28,FL,2,A,A1,6.25,6.0 -2016-04-29,FL,2,A,A1,6.24,6.0 -2016-04-30,FL,2,A,A1,6.25,6.0 -2016-05-01,FL,2,A,A1,6.28,6.0 -2016-05-02,FL,2,A,A1,6.39,5.0 -2016-05-03,FL,2,A,A1,6.26,5.0 -2016-05-04,FL,2,A,A1,6.33,5.0 -2016-05-05,FL,2,A,A1,6.24,5.0 -2016-05-06,FL,2,A,A1,6.2,6.0 -2016-05-07,FL,2,A,A1,6.33,6.0 -2016-05-08,FL,2,A,A1,6.18,5.0 -2016-05-09,FL,2,A,A1,6.21,5.0 -2016-05-10,FL,2,A,A1,6.31,5.0 -2016-05-11,FL,2,A,A1,6.24,6.0 -2016-05-12,FL,2,A,A1,6.33,5.0 -2016-05-13,FL,2,A,A1,6.36,5.0 -2016-05-14,FL,2,A,A1,6.27,6.0 -2016-05-15,FL,2,A,A1,6.29,5.0 -2016-05-16,FL,2,A,A1,6.31,6.0 -2016-05-17,FL,2,A,A1,6.31,6.0 -2016-05-18,FL,2,A,A1,6.25,6.0 -2016-05-19,FL,2,A,A1,6.16,5.0 -2016-05-20,FL,2,A,A1,6.21,6.0 -2016-05-21,FL,2,A,A1,6.22,5.0 -2016-05-22,FL,2,A,A1,6.13,6.0 -2016-05-23,FL,2,A,A1,6.3,5.0 -2016-05-24,FL,2,A,A1,6.31,5.0 -2016-05-25,FL,2,A,A1,6.28,6.0 -2016-05-26,FL,2,A,A1,6.34,5.0 -2016-05-27,FL,2,A,A1,6.36,5.0 -2016-05-28,FL,2,A,A1,6.18,6.0 -2016-05-29,FL,2,A,A1,6.25,6.0 -2016-05-30,FL,2,A,A1,6.31,6.0 -2016-05-31,FL,2,A,A1,6.3,6.0 -2016-06-01,FL,2,A,A1,6.24,6.0 -2016-06-02,FL,2,A,A1,6.36,5.0 -2016-06-03,FL,2,A,A1,6.21,6.0 -2016-06-04,FL,2,A,A1,6.27,6.0 -2016-06-05,FL,2,A,A1,6.4,5.0 -2016-06-06,FL,2,A,A1,6.32,6.0 -2016-06-07,FL,2,A,A1,6.31,5.0 -2016-06-08,FL,2,A,A1,6.32,5.0 -2016-06-09,FL,2,A,A1,6.28,5.0 -2016-06-10,FL,2,A,A1,6.35,5.0 -2016-06-11,FL,2,A,A1,6.19,6.0 -2016-06-12,FL,2,A,A1,6.3,6.0 -2016-06-13,FL,2,A,A1,6.1,6.0 -2016-06-14,FL,2,A,A1,6.36,5.0 -2016-06-15,FL,2,A,A1,6.25,5.0 -2016-06-16,FL,2,A,A1,6.39,5.0 -2016-06-17,FL,2,A,A1,6.23,6.0 -2016-06-18,FL,2,A,A1,6.29,6.0 -2016-06-19,FL,2,A,A1,6.25,6.0 -2016-06-20,FL,2,A,A1,6.27,6.0 -2016-06-21,FL,2,A,A1,6.27,6.0 -2016-06-22,FL,2,A,A1,6.3,5.0 -2016-06-23,FL,2,A,A1,6.38,6.0 -2016-06-24,FL,2,A,A1,6.21,5.0 -2016-06-25,FL,2,A,A1,6.31,6.0 -2016-06-26,FL,2,A,A1,6.27,6.0 -2016-06-27,FL,2,A,A1,6.29,5.0 -2016-06-28,FL,2,A,A1,6.25,5.0 -2016-06-29,FL,2,A,A1,6.27,5.0 -2016-06-30,FL,2,A,A1,6.24,6.0 -2016-07-01,FL,2,A,A1,6.28,6.0 -2016-07-02,FL,2,A,A1,6.29,6.0 -2016-07-03,FL,2,A,A1,6.28,5.0 -2016-07-04,FL,2,A,A1,6.24,5.0 -2016-07-05,FL,2,A,A1,6.28,6.0 -2016-07-06,FL,2,A,A1,6.15,5.0 -2016-07-07,FL,2,A,A1,6.32,6.0 -2016-07-08,FL,2,A,A1,6.26,5.0 -2016-07-09,FL,2,A,A1,6.29,6.0 -2016-07-10,FL,2,A,A1,6.31,6.0 -2016-07-11,FL,2,A,A1,6.25,6.0 -2016-07-12,FL,2,A,A1,6.24,5.0 -2016-07-13,FL,2,A,A1,6.23,6.0 -2016-07-14,FL,2,A,A1,6.23,6.0 -2016-07-15,FL,2,A,A1,6.26,6.0 -2016-07-16,FL,2,A,A1,6.21,6.0 -2016-07-17,FL,2,A,A1,6.34,5.0 -2016-07-18,FL,2,A,A1,6.24,5.0 -2016-07-19,FL,2,A,A1,6.31,5.0 -2016-07-20,FL,2,A,A1,6.18,6.0 -2016-07-21,FL,2,A,A1,6.25,5.0 -2016-07-22,FL,2,A,A1,6.28,6.0 -2016-07-23,FL,2,A,A1,6.25,5.0 -2016-07-24,FL,2,A,A1,6.36,5.0 -2016-07-25,FL,2,A,A1,6.17,6.0 -2016-07-26,FL,2,A,A1,6.29,5.0 -2016-07-27,FL,2,A,A1,6.26,6.0 -2015-01-05,FL,2,A,A2,2.66,515.0 -2015-01-06,FL,2,A,A2,2.69,566.0 -2015-01-07,FL,2,A,A2,2.64,626.0 -2015-01-08,FL,2,A,A2,2.65,592.0 -2015-01-09,FL,2,A,A2,2.64,541.0 -2015-01-10,FL,2,A,A2,2.66,582.0 -2015-01-11,FL,2,A,A2,2.65,561.0 -2015-01-12,FL,2,A,A2,2.65,540.0 -2015-01-13,FL,2,A,A2,2.65,536.0 -2015-01-14,FL,2,A,A2,2.67,551.0 -2015-01-15,FL,2,A,A2,2.69,541.0 -2015-01-16,FL,2,A,A2,2.6,598.0 -2015-01-17,FL,2,A,A2,2.65,596.0 -2015-01-18,FL,2,A,A2,2.67,540.0 -2015-01-19,FL,2,A,A2,2.63,591.0 -2015-01-20,FL,2,A,A2,2.64,556.0 -2015-01-21,FL,2,A,A2,2.64,608.0 -2015-01-22,FL,2,A,A2,2.63,569.0 -2015-01-23,FL,2,A,A2,2.62,584.0 -2015-01-24,FL,2,A,A2,2.63,621.0 -2015-01-25,FL,2,A,A2,2.68,562.0 -2015-01-26,FL,2,A,A2,2.66,554.0 -2015-01-27,FL,2,A,A2,2.62,569.0 -2015-01-28,FL,2,A,A2,2.64,580.0 -2015-01-29,FL,2,A,A2,2.66,535.0 -2015-01-30,FL,2,A,A2,2.66,525.0 -2015-01-31,FL,2,A,A2,2.66,514.0 -2015-02-01,FL,2,A,A2,2.64,574.0 -2015-02-02,FL,2,A,A2,2.63,567.0 -2015-02-03,FL,2,A,A2,2.62,582.0 -2015-02-04,FL,2,A,A2,2.66,514.0 -2015-02-05,FL,2,A,A2,2.62,616.0 -2015-02-06,FL,2,A,A2,2.67,549.0 -2015-02-07,FL,2,A,A2,2.65,614.0 -2015-02-08,FL,2,A,A2,2.64,593.0 -2015-02-09,FL,2,A,A2,2.7,469.0 -2015-02-10,FL,2,A,A2,2.63,551.0 -2015-02-11,FL,2,A,A2,2.7,543.0 -2015-02-12,FL,2,A,A2,2.68,509.0 -2015-02-13,FL,2,A,A2,2.69,525.0 -2015-02-14,FL,2,A,A2,2.65,609.0 -2015-02-15,FL,2,A,A2,2.66,549.0 -2015-02-16,FL,2,A,A2,2.61,569.0 -2015-02-17,FL,2,A,A2,2.68,474.0 -2015-02-18,FL,2,A,A2,2.7,554.0 -2015-02-19,FL,2,A,A2,2.66,583.0 -2015-02-20,FL,2,A,A2,2.62,603.0 -2015-02-21,FL,2,A,A2,2.69,632.0 -2015-02-22,FL,2,A,A2,2.66,592.0 -2015-02-23,FL,2,A,A2,2.64,574.0 -2015-02-24,FL,2,A,A2,2.61,610.0 -2015-02-25,FL,2,A,A2,2.64,530.0 -2015-02-26,FL,2,A,A2,2.66,518.0 -2015-02-27,FL,2,A,A2,2.71,563.0 -2015-02-28,FL,2,A,A2,2.64,551.0 -2015-03-01,FL,2,A,A2,2.69,513.0 -2015-03-02,FL,2,A,A2,2.66,577.0 -2015-03-03,FL,2,A,A2,2.66,610.0 -2015-03-04,FL,2,A,A2,2.68,609.0 -2015-03-05,FL,2,A,A2,2.64,530.0 -2015-03-06,FL,2,A,A2,2.68,539.0 -2015-03-07,FL,2,A,A2,2.62,648.0 -2015-03-08,FL,2,A,A2,2.67,572.0 -2015-03-09,FL,2,A,A2,2.71,516.0 -2015-03-10,FL,2,A,A2,2.64,557.0 -2015-03-11,FL,2,A,A2,2.7,515.0 -2015-03-12,FL,2,A,A2,2.67,578.0 -2015-03-13,FL,2,A,A2,2.72,537.0 -2015-03-14,FL,2,A,A2,2.67,566.0 -2015-03-15,FL,2,A,A2,2.66,549.0 -2015-03-16,FL,2,A,A2,2.66,485.0 -2015-03-17,FL,2,A,A2,2.62,603.0 -2015-03-18,FL,2,A,A2,2.68,525.0 -2015-03-19,FL,2,A,A2,2.63,552.0 -2015-03-20,FL,2,A,A2,2.7,547.0 -2015-03-21,FL,2,A,A2,2.68,531.0 -2015-03-22,FL,2,A,A2,2.7,539.0 -2015-03-23,FL,2,A,A2,2.68,469.0 -2015-03-24,FL,2,A,A2,2.67,559.0 -2015-03-25,FL,2,A,A2,2.65,532.0 -2015-03-26,FL,2,A,A2,2.64,548.0 -2015-03-27,FL,2,A,A2,2.71,523.0 -2015-03-28,FL,2,A,A2,2.69,537.0 -2015-03-29,FL,2,A,A2,2.62,596.0 -2015-03-30,FL,2,A,A2,2.64,548.0 -2015-03-31,FL,2,A,A2,2.7,512.0 -2015-04-01,FL,2,A,A2,2.66,516.0 -2015-04-02,FL,2,A,A2,2.66,523.0 -2015-04-03,FL,2,A,A2,2.65,587.0 -2015-04-04,FL,2,A,A2,2.64,571.0 -2015-04-05,FL,2,A,A2,2.6,602.0 -2015-04-06,FL,2,A,A2,2.64,548.0 -2015-04-07,FL,2,A,A2,2.64,580.0 -2015-04-08,FL,2,A,A2,2.63,615.0 -2015-04-09,FL,2,A,A2,2.66,580.0 -2015-04-10,FL,2,A,A2,2.66,596.0 -2015-04-11,FL,2,A,A2,2.72,538.0 -2015-04-12,FL,2,A,A2,2.64,621.0 -2015-04-13,FL,2,A,A2,2.62,528.0 -2015-04-14,FL,2,A,A2,2.69,564.0 -2015-04-15,FL,2,A,A2,2.66,573.0 -2015-04-16,FL,2,A,A2,2.64,582.0 -2015-04-17,FL,2,A,A2,2.64,575.0 -2015-04-18,FL,2,A,A2,2.72,494.0 -2015-04-19,FL,2,A,A2,2.63,591.0 -2015-04-20,FL,2,A,A2,2.66,597.0 -2015-04-21,FL,2,A,A2,2.67,587.0 -2015-04-22,FL,2,A,A2,2.66,491.0 -2015-04-23,FL,2,A,A2,2.7,524.0 -2015-04-24,FL,2,A,A2,2.66,547.0 -2015-04-25,FL,2,A,A2,2.73,540.0 -2015-04-26,FL,2,A,A2,2.65,583.0 -2015-04-27,FL,2,A,A2,2.68,580.0 -2015-04-28,FL,2,A,A2,2.64,566.0 -2015-04-29,FL,2,A,A2,2.67,544.0 -2015-04-30,FL,2,A,A2,2.63,566.0 -2015-05-01,FL,2,A,A2,2.68,535.0 -2015-05-02,FL,2,A,A2,2.65,601.0 -2015-05-03,FL,2,A,A2,2.69,502.0 -2015-05-04,FL,2,A,A2,2.7,505.0 -2015-05-05,FL,2,A,A2,2.68,522.0 -2015-05-06,FL,2,A,A2,2.67,570.0 -2015-05-07,FL,2,A,A2,2.66,602.0 -2015-05-08,FL,2,A,A2,2.66,580.0 -2015-05-09,FL,2,A,A2,2.68,605.0 -2015-05-10,FL,2,A,A2,2.63,603.0 -2015-05-11,FL,2,A,A2,2.66,535.0 -2015-05-12,FL,2,A,A2,2.63,552.0 -2015-05-13,FL,2,A,A2,2.67,522.0 -2015-05-14,FL,2,A,A2,2.66,555.0 -2015-05-15,FL,2,A,A2,2.7,537.0 -2015-05-16,FL,2,A,A2,2.7,547.0 -2015-05-17,FL,2,A,A2,2.64,578.0 -2015-05-18,FL,2,A,A2,2.68,504.0 -2015-05-19,FL,2,A,A2,2.66,499.0 -2015-05-20,FL,2,A,A2,2.66,572.0 -2015-05-21,FL,2,A,A2,2.68,518.0 -2015-05-22,FL,2,A,A2,2.65,542.0 -2015-05-23,FL,2,A,A2,2.62,638.0 -2015-05-24,FL,2,A,A2,2.69,501.0 -2015-05-25,FL,2,A,A2,2.67,527.0 -2015-05-26,FL,2,A,A2,2.68,575.0 -2015-05-27,FL,2,A,A2,2.63,568.0 -2015-05-28,FL,2,A,A2,2.61,592.0 -2015-05-29,FL,2,A,A2,2.71,517.0 -2015-05-30,FL,2,A,A2,2.64,629.0 -2015-05-31,FL,2,A,A2,2.68,560.0 -2015-06-01,FL,2,A,A2,2.65,567.0 -2015-06-02,FL,2,A,A2,2.66,555.0 -2015-06-03,FL,2,A,A2,2.63,607.0 -2015-06-04,FL,2,A,A2,2.66,553.0 -2015-06-05,FL,2,A,A2,2.67,546.0 -2015-06-06,FL,2,A,A2,2.69,561.0 -2015-06-07,FL,2,A,A2,2.65,603.0 -2015-06-08,FL,2,A,A2,2.69,515.0 -2015-06-09,FL,2,A,A2,2.64,554.0 -2015-06-10,FL,2,A,A2,2.65,616.0 -2015-06-11,FL,2,A,A2,2.62,589.0 -2015-06-12,FL,2,A,A2,2.67,509.0 -2015-06-13,FL,2,A,A2,2.66,605.0 -2015-06-14,FL,2,A,A2,2.65,587.0 -2015-06-15,FL,2,A,A2,2.68,556.0 -2015-06-16,FL,2,A,A2,2.63,580.0 -2015-06-17,FL,2,A,A2,2.64,529.0 -2015-06-18,FL,2,A,A2,2.66,596.0 -2015-06-19,FL,2,A,A2,2.63,566.0 -2015-06-20,FL,2,A,A2,2.65,578.0 -2015-06-21,FL,2,A,A2,2.66,586.0 -2015-06-22,FL,2,A,A2,2.66,583.0 -2015-06-23,FL,2,A,A2,2.69,462.0 -2015-06-24,FL,2,A,A2,2.63,575.0 -2015-06-25,FL,2,A,A2,2.64,567.0 -2015-06-26,FL,2,A,A2,2.7,495.0 -2015-06-27,FL,2,A,A2,2.71,523.0 -2015-06-28,FL,2,A,A2,2.67,532.0 -2015-06-29,FL,2,A,A2,2.66,519.0 -2015-06-30,FL,2,A,A2,2.65,575.0 -2015-07-01,FL,2,A,A2,2.66,581.0 -2015-07-02,FL,2,A,A2,2.67,551.0 -2015-07-03,FL,2,A,A2,2.66,595.0 -2015-07-04,FL,2,A,A2,2.68,573.0 -2015-07-05,FL,2,A,A2,2.67,541.0 -2015-07-06,FL,2,A,A2,2.68,557.0 -2015-07-07,FL,2,A,A2,2.67,545.0 -2015-07-08,FL,2,A,A2,2.64,581.0 -2015-07-09,FL,2,A,A2,2.63,538.0 -2015-07-10,FL,2,A,A2,2.64,589.0 -2015-07-11,FL,2,A,A2,2.63,608.0 -2015-07-12,FL,2,A,A2,2.67,541.0 -2015-07-13,FL,2,A,A2,2.62,603.0 -2015-07-14,FL,2,A,A2,2.64,569.0 -2015-07-15,FL,2,A,A2,2.69,507.0 -2015-07-16,FL,2,A,A2,2.66,512.0 -2015-07-17,FL,2,A,A2,2.67,582.0 -2015-07-18,FL,2,A,A2,2.63,605.0 -2015-07-19,FL,2,A,A2,2.68,585.0 -2015-07-20,FL,2,A,A2,2.68,551.0 -2015-07-21,FL,2,A,A2,2.64,574.0 -2015-07-22,FL,2,A,A2,2.65,571.0 -2015-07-23,FL,2,A,A2,2.64,586.0 -2015-07-24,FL,2,A,A2,2.68,549.0 -2015-07-25,FL,2,A,A2,2.69,513.0 -2015-07-26,FL,2,A,A2,2.64,602.0 -2015-07-27,FL,2,A,A2,2.64,563.0 -2015-07-28,FL,2,A,A2,2.63,605.0 -2015-07-29,FL,2,A,A2,2.67,524.0 -2015-07-30,FL,2,A,A2,2.67,524.0 -2015-07-31,FL,2,A,A2,2.64,597.0 -2015-08-01,FL,2,A,A2,2.64,618.0 -2015-08-02,FL,2,A,A2,2.63,589.0 -2015-08-03,FL,2,A,A2,2.66,584.0 -2015-08-04,FL,2,A,A2,2.69,578.0 -2015-08-05,FL,2,A,A2,2.62,677.0 -2015-08-06,FL,2,A,A2,2.65,527.0 -2015-08-07,FL,2,A,A2,2.61,582.0 -2015-08-08,FL,2,A,A2,2.68,573.0 -2015-08-09,FL,2,A,A2,2.7,547.0 -2015-08-10,FL,2,A,A2,2.64,550.0 -2015-08-11,FL,2,A,A2,2.64,500.0 -2015-08-12,FL,2,A,A2,2.66,534.0 -2015-08-13,FL,2,A,A2,2.63,585.0 -2015-08-14,FL,2,A,A2,2.61,637.0 -2015-08-15,FL,2,A,A2,2.69,588.0 -2015-08-16,FL,2,A,A2,2.68,573.0 -2015-08-17,FL,2,A,A2,2.67,558.0 -2015-08-18,FL,2,A,A2,2.67,533.0 -2015-08-19,FL,2,A,A2,2.69,567.0 -2015-08-20,FL,2,A,A2,2.64,555.0 -2015-08-21,FL,2,A,A2,2.66,592.0 -2015-08-22,FL,2,A,A2,2.7,593.0 -2015-08-23,FL,2,A,A2,2.69,524.0 -2015-08-24,FL,2,A,A2,2.67,520.0 -2015-08-25,FL,2,A,A2,2.66,568.0 -2015-08-26,FL,2,A,A2,2.65,537.0 -2015-08-27,FL,2,A,A2,2.68,565.0 -2015-08-28,FL,2,A,A2,2.62,582.0 -2015-08-29,FL,2,A,A2,2.65,613.0 -2015-08-30,FL,2,A,A2,2.69,525.0 -2015-08-31,FL,2,A,A2,2.67,513.0 -2015-09-01,FL,2,A,A2,2.64,636.0 -2015-09-02,FL,2,A,A2,2.68,502.0 -2015-09-03,FL,2,A,A2,2.72,503.0 -2015-09-04,FL,2,A,A2,2.69,571.0 -2015-09-05,FL,2,A,A2,2.61,647.0 -2015-09-06,FL,2,A,A2,2.61,586.0 -2015-09-07,FL,2,A,A2,2.63,605.0 -2015-09-08,FL,2,A,A2,2.7,488.0 -2015-09-09,FL,2,A,A2,2.69,569.0 -2015-09-10,FL,2,A,A2,2.62,549.0 -2015-09-11,FL,2,A,A2,2.63,573.0 -2015-09-12,FL,2,A,A2,2.65,635.0 -2015-09-13,FL,2,A,A2,2.65,575.0 -2015-09-14,FL,2,A,A2,2.64,584.0 -2015-09-15,FL,2,A,A2,2.66,566.0 -2015-09-16,FL,2,A,A2,2.69,532.0 -2015-09-17,FL,2,A,A2,2.6,588.0 -2015-09-18,FL,2,A,A2,2.63,579.0 -2015-09-19,FL,2,A,A2,2.64,603.0 -2015-09-20,FL,2,A,A2,2.64,549.0 -2015-09-21,FL,2,A,A2,2.68,514.0 -2015-09-22,FL,2,A,A2,2.65,588.0 -2015-09-23,FL,2,A,A2,2.69,530.0 -2015-09-24,FL,2,A,A2,2.67,539.0 -2015-09-25,FL,2,A,A2,2.66,571.0 -2015-09-26,FL,2,A,A2,2.64,666.0 -2015-09-27,FL,2,A,A2,2.67,572.0 -2015-09-28,FL,2,A,A2,2.68,500.0 -2015-09-29,FL,2,A,A2,2.67,538.0 -2015-09-30,FL,2,A,A2,2.65,578.0 -2015-10-01,FL,2,A,A2,2.62,569.0 -2015-10-02,FL,2,A,A2,2.67,537.0 -2015-10-03,FL,2,A,A2,2.68,547.0 -2015-10-04,FL,2,A,A2,2.65,558.0 -2015-10-05,FL,2,A,A2,2.66,586.0 -2015-10-06,FL,2,A,A2,2.65,546.0 -2015-10-07,FL,2,A,A2,2.64,569.0 -2015-10-08,FL,2,A,A2,2.67,567.0 -2015-10-09,FL,2,A,A2,2.62,632.0 -2015-10-10,FL,2,A,A2,2.68,578.0 -2015-10-11,FL,2,A,A2,2.67,528.0 -2015-10-12,FL,2,A,A2,2.64,551.0 -2015-10-13,FL,2,A,A2,2.67,547.0 -2015-10-14,FL,2,A,A2,2.63,572.0 -2015-10-15,FL,2,A,A2,2.64,621.0 -2015-10-16,FL,2,A,A2,2.7,537.0 -2015-10-17,FL,2,A,A2,2.68,554.0 -2015-10-18,FL,2,A,A2,2.68,492.0 -2015-10-19,FL,2,A,A2,2.64,578.0 -2015-10-20,FL,2,A,A2,2.66,567.0 -2015-10-21,FL,2,A,A2,2.66,535.0 -2015-10-22,FL,2,A,A2,2.65,528.0 -2015-10-23,FL,2,A,A2,2.67,547.0 -2015-10-24,FL,2,A,A2,2.65,627.0 -2015-10-25,FL,2,A,A2,2.66,535.0 -2015-10-26,FL,2,A,A2,2.6,633.0 -2015-10-27,FL,2,A,A2,2.69,561.0 -2015-10-28,FL,2,A,A2,2.65,532.0 -2015-10-29,FL,2,A,A2,2.66,555.0 -2015-10-30,FL,2,A,A2,2.65,613.0 -2015-10-31,FL,2,A,A2,2.69,580.0 -2015-11-01,FL,2,A,A2,2.65,570.0 -2015-11-02,FL,2,A,A2,2.62,556.0 -2015-11-03,FL,2,A,A2,2.66,580.0 -2015-11-04,FL,2,A,A2,2.62,600.0 -2015-11-05,FL,2,A,A2,2.69,531.0 -2015-11-06,FL,2,A,A2,2.65,579.0 -2015-11-07,FL,2,A,A2,2.66,572.0 -2015-11-08,FL,2,A,A2,2.64,546.0 -2015-11-09,FL,2,A,A2,2.66,542.0 -2015-11-10,FL,2,A,A2,2.64,567.0 -2015-11-11,FL,2,A,A2,2.63,533.0 -2015-11-12,FL,2,A,A2,2.63,574.0 -2015-11-13,FL,2,A,A2,2.65,599.0 -2015-11-14,FL,2,A,A2,2.68,546.0 -2015-11-15,FL,2,A,A2,2.64,564.0 -2015-11-16,FL,2,A,A2,2.64,602.0 -2015-11-17,FL,2,A,A2,2.7,537.0 -2015-11-18,FL,2,A,A2,2.64,573.0 -2015-11-19,FL,2,A,A2,2.63,572.0 -2015-11-20,FL,2,A,A2,2.66,540.0 -2015-11-21,FL,2,A,A2,2.65,626.0 -2015-11-22,FL,2,A,A2,2.69,501.0 -2015-11-23,FL,2,A,A2,2.6,606.0 -2015-11-24,FL,2,A,A2,2.67,524.0 -2015-11-25,FL,2,A,A2,2.64,571.0 -2015-11-26,FL,2,A,A2,2.66,513.0 -2015-11-27,FL,2,A,A2,2.65,583.0 -2015-11-28,FL,2,A,A2,2.69,566.0 -2015-11-29,FL,2,A,A2,2.67,520.0 -2015-11-30,FL,2,A,A2,2.69,488.0 -2015-12-01,FL,2,A,A2,2.64,510.0 -2015-12-02,FL,2,A,A2,2.67,538.0 -2015-12-03,FL,2,A,A2,2.65,543.0 -2015-12-04,FL,2,A,A2,2.66,568.0 -2015-12-05,FL,2,A,A2,2.65,608.0 -2015-12-06,FL,2,A,A2,2.68,539.0 -2015-12-07,FL,2,A,A2,2.63,539.0 -2015-12-08,FL,2,A,A2,2.62,581.0 -2015-12-09,FL,2,A,A2,2.65,567.0 -2015-12-10,FL,2,A,A2,2.62,585.0 -2015-12-11,FL,2,A,A2,2.68,548.0 -2015-12-12,FL,2,A,A2,2.64,600.0 -2015-12-13,FL,2,A,A2,2.67,553.0 -2015-12-14,FL,2,A,A2,2.64,539.0 -2015-12-15,FL,2,A,A2,2.59,643.0 -2015-12-16,FL,2,A,A2,2.67,557.0 -2015-12-17,FL,2,A,A2,2.64,567.0 -2015-12-18,FL,2,A,A2,2.61,648.0 -2015-12-19,FL,2,A,A2,2.64,582.0 -2015-12-20,FL,2,A,A2,2.66,596.0 -2015-12-21,FL,2,A,A2,2.66,546.0 -2015-12-22,FL,2,A,A2,2.62,612.0 -2015-12-23,FL,2,A,A2,2.63,543.0 -2015-12-24,FL,2,A,A2,2.63,625.0 -2015-12-25,FL,2,A,A2,2.61,593.0 -2015-12-26,FL,2,A,A2,2.66,589.0 -2015-12-27,FL,2,A,A2,2.64,597.0 -2015-12-28,FL,2,A,A2,2.64,549.0 -2015-12-29,FL,2,A,A2,2.69,524.0 -2015-12-30,FL,2,A,A2,2.63,547.0 -2015-12-31,FL,2,A,A2,2.66,556.0 -2016-01-01,FL,2,A,A2,2.67,568.0 -2016-01-02,FL,2,A,A2,2.68,555.0 -2016-01-03,FL,2,A,A2,2.66,601.0 -2016-01-04,FL,2,A,A2,2.59,640.0 -2016-01-05,FL,2,A,A2,2.65,547.0 -2016-01-06,FL,2,A,A2,2.65,545.0 -2016-01-07,FL,2,A,A2,2.65,556.0 -2016-01-08,FL,2,A,A2,2.61,594.0 -2016-01-09,FL,2,A,A2,2.62,649.0 -2016-01-10,FL,2,A,A2,2.68,564.0 -2016-01-11,FL,2,A,A2,2.68,512.0 -2016-01-12,FL,2,A,A2,2.68,532.0 -2016-01-13,FL,2,A,A2,2.7,504.0 -2016-01-14,FL,2,A,A2,2.68,543.0 -2016-01-15,FL,2,A,A2,2.67,532.0 -2016-01-16,FL,2,A,A2,2.67,595.0 -2016-01-17,FL,2,A,A2,2.69,519.0 -2016-01-18,FL,2,A,A2,2.62,605.0 -2016-01-19,FL,2,A,A2,2.64,529.0 -2016-01-20,FL,2,A,A2,2.67,541.0 -2016-01-21,FL,2,A,A2,2.67,602.0 -2016-01-22,FL,2,A,A2,2.63,571.0 -2016-01-23,FL,2,A,A2,2.67,570.0 -2016-01-24,FL,2,A,A2,2.65,627.0 -2016-01-25,FL,2,A,A2,2.64,575.0 -2016-01-26,FL,2,A,A2,2.62,611.0 -2016-01-27,FL,2,A,A2,2.62,621.0 -2016-01-28,FL,2,A,A2,2.65,526.0 -2016-01-29,FL,2,A,A2,2.68,530.0 -2016-01-30,FL,2,A,A2,2.64,655.0 -2016-01-31,FL,2,A,A2,2.7,534.0 -2016-02-01,FL,2,A,A2,2.66,537.0 -2016-02-02,FL,2,A,A2,2.64,561.0 -2016-02-03,FL,2,A,A2,2.65,526.0 -2016-02-04,FL,2,A,A2,2.67,479.0 -2016-02-05,FL,2,A,A2,2.71,573.0 -2016-02-06,FL,2,A,A2,2.61,557.0 -2016-02-07,FL,2,A,A2,2.63,557.0 -2016-02-08,FL,2,A,A2,2.65,522.0 -2016-02-09,FL,2,A,A2,2.59,631.0 -2016-02-10,FL,2,A,A2,2.64,574.0 -2016-02-11,FL,2,A,A2,2.69,517.0 -2016-02-12,FL,2,A,A2,2.68,558.0 -2016-02-13,FL,2,A,A2,2.71,550.0 -2016-02-14,FL,2,A,A2,2.66,569.0 -2016-02-15,FL,2,A,A2,2.64,547.0 -2016-02-16,FL,2,A,A2,2.66,516.0 -2016-02-17,FL,2,A,A2,2.65,558.0 -2016-02-18,FL,2,A,A2,2.67,575.0 -2016-02-19,FL,2,A,A2,2.64,583.0 -2016-02-20,FL,2,A,A2,2.63,628.0 -2016-02-21,FL,2,A,A2,2.67,557.0 -2016-02-22,FL,2,A,A2,2.65,526.0 -2016-02-23,FL,2,A,A2,2.62,584.0 -2016-02-24,FL,2,A,A2,2.61,596.0 -2016-02-25,FL,2,A,A2,2.6,614.0 -2016-02-26,FL,2,A,A2,2.65,566.0 -2016-02-27,FL,2,A,A2,2.69,548.0 -2016-02-28,FL,2,A,A2,2.67,572.0 -2016-02-29,FL,2,A,A2,2.67,522.0 -2016-03-01,FL,2,A,A2,2.64,578.0 -2016-03-02,FL,2,A,A2,2.66,556.0 -2016-03-03,FL,2,A,A2,2.64,581.0 -2016-03-04,FL,2,A,A2,2.65,568.0 -2016-03-05,FL,2,A,A2,2.65,608.0 -2016-03-06,FL,2,A,A2,2.68,584.0 -2016-03-07,FL,2,A,A2,2.61,614.0 -2016-03-08,FL,2,A,A2,2.65,599.0 -2016-03-09,FL,2,A,A2,2.62,643.0 -2016-03-10,FL,2,A,A2,2.65,536.0 -2016-03-11,FL,2,A,A2,2.65,536.0 -2016-03-12,FL,2,A,A2,2.68,611.0 -2016-03-13,FL,2,A,A2,2.63,589.0 -2016-03-14,FL,2,A,A2,2.65,564.0 -2016-03-15,FL,2,A,A2,2.7,548.0 -2016-03-16,FL,2,A,A2,2.63,598.0 -2016-03-17,FL,2,A,A2,2.64,577.0 -2016-03-18,FL,2,A,A2,2.64,591.0 -2016-03-19,FL,2,A,A2,2.63,652.0 -2016-03-20,FL,2,A,A2,2.68,557.0 -2016-03-21,FL,2,A,A2,2.68,515.0 -2016-03-22,FL,2,A,A2,2.63,630.0 -2016-03-23,FL,2,A,A2,2.64,563.0 -2016-03-24,FL,2,A,A2,2.66,525.0 -2016-03-25,FL,2,A,A2,2.67,555.0 -2016-03-26,FL,2,A,A2,2.66,620.0 -2016-03-27,FL,2,A,A2,2.76,496.0 -2016-03-28,FL,2,A,A2,2.67,551.0 -2016-03-29,FL,2,A,A2,2.67,489.0 -2016-03-30,FL,2,A,A2,2.66,524.0 -2016-03-31,FL,2,A,A2,2.68,534.0 -2016-04-01,FL,2,A,A2,2.66,576.0 -2016-04-02,FL,2,A,A2,2.68,563.0 -2016-04-03,FL,2,A,A2,2.61,626.0 -2016-04-04,FL,2,A,A2,2.76,526.0 -2016-04-05,FL,2,A,A2,2.67,509.0 -2016-04-06,FL,2,A,A2,2.61,586.0 -2016-04-07,FL,2,A,A2,2.64,554.0 -2016-04-08,FL,2,A,A2,2.68,530.0 -2016-04-09,FL,2,A,A2,2.7,555.0 -2016-04-10,FL,2,A,A2,2.63,674.0 -2016-04-11,FL,2,A,A2,2.61,638.0 -2016-04-12,FL,2,A,A2,2.65,547.0 -2016-04-13,FL,2,A,A2,2.64,580.0 -2016-04-14,FL,2,A,A2,2.72,515.0 -2016-04-15,FL,2,A,A2,2.64,586.0 -2016-04-16,FL,2,A,A2,2.65,576.0 -2016-04-17,FL,2,A,A2,2.66,559.0 -2016-04-18,FL,2,A,A2,2.62,640.0 -2016-04-19,FL,2,A,A2,2.72,535.0 -2016-04-20,FL,2,A,A2,2.66,558.0 -2016-04-21,FL,2,A,A2,2.7,509.0 -2016-04-22,FL,2,A,A2,2.67,552.0 -2016-04-23,FL,2,A,A2,2.71,567.0 -2016-04-24,FL,2,A,A2,2.7,576.0 -2016-04-25,FL,2,A,A2,2.68,503.0 -2016-04-26,FL,2,A,A2,2.68,475.0 -2016-04-27,FL,2,A,A2,2.6,614.0 -2016-04-28,FL,2,A,A2,2.62,523.0 -2016-04-29,FL,2,A,A2,2.59,635.0 -2016-04-30,FL,2,A,A2,2.66,587.0 -2016-05-01,FL,2,A,A2,2.6,624.0 -2016-05-02,FL,2,A,A2,2.67,536.0 -2016-05-03,FL,2,A,A2,2.65,570.0 -2016-05-04,FL,2,A,A2,2.67,540.0 -2016-05-05,FL,2,A,A2,2.65,494.0 -2016-05-06,FL,2,A,A2,2.66,578.0 -2016-05-07,FL,2,A,A2,2.7,490.0 -2016-05-08,FL,2,A,A2,2.63,589.0 -2016-05-09,FL,2,A,A2,2.65,515.0 -2016-05-10,FL,2,A,A2,2.64,548.0 -2016-05-11,FL,2,A,A2,2.61,627.0 -2016-05-12,FL,2,A,A2,2.67,554.0 -2016-05-13,FL,2,A,A2,2.67,538.0 -2016-05-14,FL,2,A,A2,2.68,579.0 -2016-05-15,FL,2,A,A2,2.68,544.0 -2016-05-16,FL,2,A,A2,2.65,558.0 -2016-05-17,FL,2,A,A2,2.64,544.0 -2016-05-18,FL,2,A,A2,2.68,589.0 -2016-05-19,FL,2,A,A2,2.66,539.0 -2016-05-20,FL,2,A,A2,2.68,557.0 -2016-05-21,FL,2,A,A2,2.65,556.0 -2016-05-22,FL,2,A,A2,2.67,541.0 -2016-05-23,FL,2,A,A2,2.67,512.0 -2016-05-24,FL,2,A,A2,2.62,642.0 -2016-05-25,FL,2,A,A2,2.64,605.0 -2016-05-26,FL,2,A,A2,2.67,547.0 -2016-05-27,FL,2,A,A2,2.65,548.0 -2016-05-28,FL,2,A,A2,2.69,494.0 -2016-05-29,FL,2,A,A2,2.68,561.0 -2016-05-30,FL,2,A,A2,2.66,545.0 -2016-05-31,FL,2,A,A2,2.68,531.0 -2016-06-01,FL,2,A,A2,2.65,511.0 -2016-06-02,FL,2,A,A2,2.62,574.0 -2016-06-03,FL,2,A,A2,2.6,640.0 -2016-06-04,FL,2,A,A2,2.63,623.0 -2016-06-05,FL,2,A,A2,2.63,574.0 -2016-06-06,FL,2,A,A2,2.7,544.0 -2016-06-07,FL,2,A,A2,2.62,589.0 -2016-06-08,FL,2,A,A2,2.64,613.0 -2016-06-09,FL,2,A,A2,2.61,586.0 -2016-06-10,FL,2,A,A2,2.69,497.0 -2016-06-11,FL,2,A,A2,2.64,616.0 -2016-06-12,FL,2,A,A2,2.67,578.0 -2016-06-13,FL,2,A,A2,2.63,630.0 -2016-06-14,FL,2,A,A2,2.63,636.0 -2016-06-15,FL,2,A,A2,2.59,564.0 -2016-06-16,FL,2,A,A2,2.63,557.0 -2016-06-17,FL,2,A,A2,2.66,586.0 -2016-06-18,FL,2,A,A2,2.64,653.0 -2016-06-19,FL,2,A,A2,2.71,596.0 -2016-06-20,FL,2,A,A2,2.67,578.0 -2016-06-21,FL,2,A,A2,2.67,509.0 -2016-06-22,FL,2,A,A2,2.65,541.0 -2016-06-23,FL,2,A,A2,2.62,630.0 -2016-06-24,FL,2,A,A2,2.67,527.0 -2016-06-25,FL,2,A,A2,2.63,564.0 -2016-06-26,FL,2,A,A2,2.64,534.0 -2016-06-27,FL,2,A,A2,2.7,567.0 -2016-06-28,FL,2,A,A2,2.7,549.0 -2016-06-29,FL,2,A,A2,2.65,583.0 -2016-06-30,FL,2,A,A2,2.67,586.0 -2016-07-01,FL,2,A,A2,2.63,551.0 -2016-07-02,FL,2,A,A2,2.65,598.0 -2016-07-03,FL,2,A,A2,2.69,551.0 -2016-07-04,FL,2,A,A2,2.66,587.0 -2016-07-05,FL,2,A,A2,2.66,577.0 -2016-07-06,FL,2,A,A2,2.69,546.0 -2016-07-07,FL,2,A,A2,2.65,634.0 -2016-07-08,FL,2,A,A2,2.65,558.0 -2016-07-09,FL,2,A,A2,2.64,599.0 -2016-07-10,FL,2,A,A2,2.68,531.0 -2016-07-11,FL,2,A,A2,2.61,621.0 -2016-07-12,FL,2,A,A2,2.67,530.0 -2016-07-13,FL,2,A,A2,2.65,516.0 -2016-07-14,FL,2,A,A2,2.65,553.0 -2016-07-15,FL,2,A,A2,2.67,571.0 -2016-07-16,FL,2,A,A2,2.62,591.0 -2016-07-17,FL,2,A,A2,2.68,533.0 -2016-07-18,FL,2,A,A2,2.62,564.0 -2016-07-19,FL,2,A,A2,2.65,570.0 -2016-07-20,FL,2,A,A2,2.68,512.0 -2016-07-21,FL,2,A,A2,2.66,490.0 -2016-07-22,FL,2,A,A2,2.68,548.0 -2016-07-23,FL,2,A,A2,2.65,616.0 -2016-07-24,FL,2,A,A2,2.72,494.0 -2016-07-25,FL,2,A,A2,2.69,560.0 -2016-07-26,FL,2,A,A2,2.66,518.0 -2016-07-27,FL,2,A,A2,2.65,616.0 -2015-01-05,FL,2,A,A3,3.48,7.0 -2015-01-06,FL,2,A,A3,3.47,7.0 -2015-01-07,FL,2,A,A3,3.48,7.0 -2015-01-08,FL,2,A,A3,3.47,7.0 -2015-01-09,FL,2,A,A3,3.55,6.0 -2015-01-10,FL,2,A,A3,3.47,7.0 -2015-01-11,FL,2,A,A3,3.48,7.0 -2015-01-12,FL,2,A,A3,3.5,6.0 -2015-01-13,FL,2,A,A3,3.46,6.0 -2015-01-14,FL,2,A,A3,3.53,6.0 -2015-01-15,FL,2,A,A3,3.53,5.0 -2015-01-16,FL,2,A,A3,3.46,7.0 -2015-01-17,FL,2,A,A3,3.51,7.0 -2015-01-18,FL,2,A,A3,3.5,7.0 -2015-01-19,FL,2,A,A3,3.51,6.0 -2015-01-20,FL,2,A,A3,3.51,7.0 -2015-01-21,FL,2,A,A3,3.52,6.0 -2015-01-22,FL,2,A,A3,3.51,6.0 -2015-01-23,FL,2,A,A3,3.51,6.0 -2015-01-24,FL,2,A,A3,3.48,7.0 -2015-01-25,FL,2,A,A3,3.51,7.0 -2015-01-26,FL,2,A,A3,3.54,6.0 -2015-01-27,FL,2,A,A3,3.47,6.0 -2015-01-28,FL,2,A,A3,3.48,7.0 -2015-01-29,FL,2,A,A3,3.58,7.0 -2015-01-30,FL,2,A,A3,3.48,6.0 -2015-01-31,FL,2,A,A3,3.49,7.0 -2015-02-01,FL,2,A,A3,3.46,7.0 -2015-02-02,FL,2,A,A3,3.51,7.0 -2015-02-03,FL,2,A,A3,3.52,7.0 -2015-02-04,FL,2,A,A3,3.53,6.0 -2015-02-05,FL,2,A,A3,3.43,7.0 -2015-02-06,FL,2,A,A3,3.49,7.0 -2015-02-07,FL,2,A,A3,3.54,7.0 -2015-02-08,FL,2,A,A3,3.55,6.0 -2015-02-09,FL,2,A,A3,3.51,7.0 -2015-02-10,FL,2,A,A3,3.55,6.0 -2015-02-11,FL,2,A,A3,3.55,7.0 -2015-02-12,FL,2,A,A3,3.52,7.0 -2015-02-13,FL,2,A,A3,3.51,7.0 -2015-02-14,FL,2,A,A3,3.49,8.0 -2015-02-15,FL,2,A,A3,3.45,6.0 -2015-02-16,FL,2,A,A3,3.47,6.0 -2015-02-17,FL,2,A,A3,3.5,7.0 -2015-02-18,FL,2,A,A3,3.49,7.0 -2015-02-19,FL,2,A,A3,3.49,7.0 -2015-02-20,FL,2,A,A3,3.51,7.0 -2015-02-21,FL,2,A,A3,3.49,7.0 -2015-02-22,FL,2,A,A3,3.46,7.0 -2015-02-23,FL,2,A,A3,3.49,7.0 -2015-02-24,FL,2,A,A3,3.47,7.0 -2015-02-25,FL,2,A,A3,3.48,7.0 -2015-02-26,FL,2,A,A3,3.52,6.0 -2015-02-27,FL,2,A,A3,3.49,7.0 -2015-02-28,FL,2,A,A3,3.48,7.0 -2015-03-01,FL,2,A,A3,3.43,6.0 -2015-03-02,FL,2,A,A3,3.46,7.0 -2015-03-03,FL,2,A,A3,3.51,6.0 -2015-03-04,FL,2,A,A3,3.41,7.0 -2015-03-05,FL,2,A,A3,3.47,7.0 -2015-03-06,FL,2,A,A3,3.49,7.0 -2015-03-07,FL,2,A,A3,3.45,7.0 -2015-03-08,FL,2,A,A3,3.49,7.0 -2015-03-09,FL,2,A,A3,3.5,6.0 -2015-03-10,FL,2,A,A3,3.58,6.0 -2015-03-11,FL,2,A,A3,3.57,6.0 -2015-03-12,FL,2,A,A3,3.49,7.0 -2015-03-13,FL,2,A,A3,3.52,7.0 -2015-03-14,FL,2,A,A3,3.5,6.0 -2015-03-15,FL,2,A,A3,3.45,6.0 -2015-03-16,FL,2,A,A3,3.51,7.0 -2015-03-17,FL,2,A,A3,3.47,6.0 -2015-03-18,FL,2,A,A3,3.44,7.0 -2015-03-19,FL,2,A,A3,3.46,7.0 -2015-03-20,FL,2,A,A3,3.44,7.0 -2015-03-21,FL,2,A,A3,3.56,7.0 -2015-03-22,FL,2,A,A3,3.46,7.0 -2015-03-23,FL,2,A,A3,3.45,7.0 -2015-03-24,FL,2,A,A3,3.55,7.0 -2015-03-25,FL,2,A,A3,3.46,7.0 -2015-03-26,FL,2,A,A3,3.5,7.0 -2015-03-27,FL,2,A,A3,3.48,7.0 -2015-03-28,FL,2,A,A3,3.51,7.0 -2015-03-29,FL,2,A,A3,3.48,7.0 -2015-03-30,FL,2,A,A3,3.47,6.0 -2015-03-31,FL,2,A,A3,3.43,7.0 -2015-04-01,FL,2,A,A3,3.54,7.0 -2015-04-02,FL,2,A,A3,3.48,7.0 -2015-04-03,FL,2,A,A3,3.46,7.0 -2015-04-04,FL,2,A,A3,3.46,7.0 -2015-04-05,FL,2,A,A3,3.52,7.0 -2015-04-06,FL,2,A,A3,3.5,6.0 -2015-04-07,FL,2,A,A3,3.49,7.0 -2015-04-08,FL,2,A,A3,3.45,7.0 -2015-04-09,FL,2,A,A3,3.54,6.0 -2015-04-10,FL,2,A,A3,3.47,7.0 -2015-04-11,FL,2,A,A3,3.45,7.0 -2015-04-12,FL,2,A,A3,3.5,7.0 -2015-04-13,FL,2,A,A3,3.43,7.0 -2015-04-14,FL,2,A,A3,3.48,7.0 -2015-04-15,FL,2,A,A3,3.49,7.0 -2015-04-16,FL,2,A,A3,3.47,7.0 -2015-04-17,FL,2,A,A3,3.45,7.0 -2015-04-18,FL,2,A,A3,3.5,7.0 -2015-04-19,FL,2,A,A3,3.45,7.0 -2015-04-20,FL,2,A,A3,3.5,6.0 -2015-04-21,FL,2,A,A3,3.51,6.0 -2015-04-22,FL,2,A,A3,3.48,7.0 -2015-04-23,FL,2,A,A3,3.55,7.0 -2015-04-24,FL,2,A,A3,3.51,7.0 -2015-04-25,FL,2,A,A3,3.41,7.0 -2015-04-26,FL,2,A,A3,3.51,7.0 -2015-04-27,FL,2,A,A3,3.51,6.0 -2015-04-28,FL,2,A,A3,3.47,7.0 -2015-04-29,FL,2,A,A3,3.48,7.0 -2015-04-30,FL,2,A,A3,3.43,7.0 -2015-05-01,FL,2,A,A3,3.47,7.0 -2015-05-02,FL,2,A,A3,3.47,7.0 -2015-05-03,FL,2,A,A3,3.5,7.0 -2015-05-04,FL,2,A,A3,3.5,7.0 -2015-05-05,FL,2,A,A3,3.52,6.0 -2015-05-06,FL,2,A,A3,3.53,7.0 -2015-05-07,FL,2,A,A3,3.48,6.0 -2015-05-08,FL,2,A,A3,3.55,7.0 -2015-05-09,FL,2,A,A3,3.57,6.0 -2015-05-10,FL,2,A,A3,3.49,7.0 -2015-05-11,FL,2,A,A3,3.45,7.0 -2015-05-12,FL,2,A,A3,3.46,7.0 -2015-05-13,FL,2,A,A3,3.47,8.0 -2015-05-14,FL,2,A,A3,3.53,6.0 -2015-05-15,FL,2,A,A3,3.45,7.0 -2015-05-16,FL,2,A,A3,3.45,7.0 -2015-05-17,FL,2,A,A3,3.48,7.0 -2015-05-18,FL,2,A,A3,3.53,6.0 -2015-05-19,FL,2,A,A3,3.38,8.0 -2015-05-20,FL,2,A,A3,3.49,7.0 -2015-05-21,FL,2,A,A3,3.53,6.0 -2015-05-22,FL,2,A,A3,3.49,7.0 -2015-05-23,FL,2,A,A3,3.5,7.0 -2015-05-24,FL,2,A,A3,3.43,7.0 -2015-05-25,FL,2,A,A3,3.44,7.0 -2015-05-26,FL,2,A,A3,3.46,7.0 -2015-05-27,FL,2,A,A3,3.51,7.0 -2015-05-28,FL,2,A,A3,3.45,7.0 -2015-05-29,FL,2,A,A3,3.48,7.0 -2015-05-30,FL,2,A,A3,3.42,7.0 -2015-05-31,FL,2,A,A3,3.46,7.0 -2015-06-01,FL,2,A,A3,3.46,7.0 -2015-06-02,FL,2,A,A3,3.5,7.0 -2015-06-03,FL,2,A,A3,3.49,6.0 -2015-06-04,FL,2,A,A3,3.47,7.0 -2015-06-05,FL,2,A,A3,3.46,7.0 -2015-06-06,FL,2,A,A3,3.51,7.0 -2015-06-07,FL,2,A,A3,3.45,6.0 -2015-06-08,FL,2,A,A3,3.49,7.0 -2015-06-09,FL,2,A,A3,3.47,7.0 -2015-06-10,FL,2,A,A3,3.51,6.0 -2015-06-11,FL,2,A,A3,3.45,7.0 -2015-06-12,FL,2,A,A3,3.48,7.0 -2015-06-13,FL,2,A,A3,3.38,8.0 -2015-06-14,FL,2,A,A3,3.48,7.0 -2015-06-15,FL,2,A,A3,3.53,7.0 -2015-06-16,FL,2,A,A3,3.43,6.0 -2015-06-17,FL,2,A,A3,3.47,7.0 -2015-06-18,FL,2,A,A3,3.45,7.0 -2015-06-19,FL,2,A,A3,3.48,7.0 -2015-06-20,FL,2,A,A3,3.51,7.0 -2015-06-21,FL,2,A,A3,3.47,7.0 -2015-06-22,FL,2,A,A3,3.42,6.0 -2015-06-23,FL,2,A,A3,3.48,7.0 -2015-06-24,FL,2,A,A3,3.5,7.0 -2015-06-25,FL,2,A,A3,3.45,7.0 -2015-06-26,FL,2,A,A3,3.48,7.0 -2015-06-27,FL,2,A,A3,3.49,8.0 -2015-06-28,FL,2,A,A3,3.43,7.0 -2015-06-29,FL,2,A,A3,3.52,7.0 -2015-06-30,FL,2,A,A3,3.43,7.0 -2015-07-01,FL,2,A,A3,3.5,7.0 -2015-07-02,FL,2,A,A3,3.44,7.0 -2015-07-03,FL,2,A,A3,3.52,6.0 -2015-07-04,FL,2,A,A3,3.49,7.0 -2015-07-05,FL,2,A,A3,3.44,7.0 -2015-07-06,FL,2,A,A3,3.48,6.0 -2015-07-07,FL,2,A,A3,3.41,7.0 -2015-07-08,FL,2,A,A3,3.48,7.0 -2015-07-09,FL,2,A,A3,3.47,7.0 -2015-07-10,FL,2,A,A3,3.45,7.0 -2015-07-11,FL,2,A,A3,3.5,7.0 -2015-07-12,FL,2,A,A3,3.46,7.0 -2015-07-13,FL,2,A,A3,3.49,7.0 -2015-07-14,FL,2,A,A3,3.53,7.0 -2015-07-15,FL,2,A,A3,3.51,7.0 -2015-07-16,FL,2,A,A3,3.47,7.0 -2015-07-17,FL,2,A,A3,3.47,7.0 -2015-07-18,FL,2,A,A3,3.5,7.0 -2015-07-19,FL,2,A,A3,3.4,7.0 -2015-07-20,FL,2,A,A3,3.48,7.0 -2015-07-21,FL,2,A,A3,3.42,7.0 -2015-07-22,FL,2,A,A3,3.48,7.0 -2015-07-23,FL,2,A,A3,3.48,6.0 -2015-07-24,FL,2,A,A3,3.54,6.0 -2015-07-25,FL,2,A,A3,3.46,7.0 -2015-07-26,FL,2,A,A3,3.44,7.0 -2015-07-27,FL,2,A,A3,3.49,7.0 -2015-07-28,FL,2,A,A3,3.53,7.0 -2015-07-29,FL,2,A,A3,3.51,7.0 -2015-07-30,FL,2,A,A3,3.44,7.0 -2015-07-31,FL,2,A,A3,3.49,7.0 -2015-08-01,FL,2,A,A3,3.46,7.0 -2015-08-02,FL,2,A,A3,3.51,6.0 -2015-08-03,FL,2,A,A3,3.52,6.0 -2015-08-04,FL,2,A,A3,3.47,7.0 -2015-08-05,FL,2,A,A3,3.49,6.0 -2015-08-06,FL,2,A,A3,3.47,7.0 -2015-08-07,FL,2,A,A3,3.46,7.0 -2015-08-08,FL,2,A,A3,3.48,7.0 -2015-08-09,FL,2,A,A3,3.43,7.0 -2015-08-10,FL,2,A,A3,3.45,6.0 -2015-08-11,FL,2,A,A3,3.51,6.0 -2015-08-12,FL,2,A,A3,3.44,8.0 -2015-08-13,FL,2,A,A3,3.52,7.0 -2015-08-14,FL,2,A,A3,3.45,7.0 -2015-08-15,FL,2,A,A3,3.49,7.0 -2015-08-16,FL,2,A,A3,3.53,6.0 -2015-08-17,FL,2,A,A3,3.51,6.0 -2015-08-18,FL,2,A,A3,3.44,6.0 -2015-08-19,FL,2,A,A3,3.47,7.0 -2015-08-20,FL,2,A,A3,3.52,6.0 -2015-08-21,FL,2,A,A3,3.47,7.0 -2015-08-22,FL,2,A,A3,3.48,7.0 -2015-08-23,FL,2,A,A3,3.48,7.0 -2015-08-24,FL,2,A,A3,3.41,7.0 -2015-08-25,FL,2,A,A3,3.53,7.0 -2015-08-26,FL,2,A,A3,3.5,7.0 -2015-08-27,FL,2,A,A3,3.48,7.0 -2015-08-28,FL,2,A,A3,3.44,7.0 -2015-08-29,FL,2,A,A3,3.47,8.0 -2015-08-30,FL,2,A,A3,3.48,7.0 -2015-08-31,FL,2,A,A3,3.52,6.0 -2015-09-01,FL,2,A,A3,3.5,7.0 -2015-09-02,FL,2,A,A3,3.47,7.0 -2015-09-03,FL,2,A,A3,3.45,7.0 -2015-09-04,FL,2,A,A3,3.49,7.0 -2015-09-05,FL,2,A,A3,3.45,7.0 -2015-09-06,FL,2,A,A3,3.51,7.0 -2015-09-07,FL,2,A,A3,3.47,7.0 -2015-09-08,FL,2,A,A3,3.53,7.0 -2015-09-09,FL,2,A,A3,3.45,7.0 -2015-09-10,FL,2,A,A3,3.46,7.0 -2015-09-11,FL,2,A,A3,3.5,7.0 -2015-09-12,FL,2,A,A3,3.47,8.0 -2015-09-13,FL,2,A,A3,3.45,7.0 -2015-09-14,FL,2,A,A3,3.43,7.0 -2015-09-15,FL,2,A,A3,3.51,7.0 -2015-09-16,FL,2,A,A3,3.42,7.0 -2015-09-17,FL,2,A,A3,3.51,7.0 -2015-09-18,FL,2,A,A3,3.53,7.0 -2015-09-19,FL,2,A,A3,3.51,7.0 -2015-09-20,FL,2,A,A3,3.42,7.0 -2015-09-21,FL,2,A,A3,3.5,6.0 -2015-09-22,FL,2,A,A3,3.48,7.0 -2015-09-23,FL,2,A,A3,3.48,6.0 -2015-09-24,FL,2,A,A3,3.51,6.0 -2015-09-25,FL,2,A,A3,3.52,6.0 -2015-09-26,FL,2,A,A3,3.47,6.0 -2015-09-27,FL,2,A,A3,3.45,7.0 -2015-09-28,FL,2,A,A3,3.48,7.0 -2015-09-29,FL,2,A,A3,3.54,7.0 -2015-09-30,FL,2,A,A3,3.49,7.0 -2015-10-01,FL,2,A,A3,3.48,7.0 -2015-10-02,FL,2,A,A3,3.5,7.0 -2015-10-03,FL,2,A,A3,3.53,7.0 -2015-10-04,FL,2,A,A3,3.46,7.0 -2015-10-05,FL,2,A,A3,3.51,7.0 -2015-10-06,FL,2,A,A3,3.42,7.0 -2015-10-07,FL,2,A,A3,3.56,6.0 -2015-10-08,FL,2,A,A3,3.51,6.0 -2015-10-09,FL,2,A,A3,3.53,7.0 -2015-10-10,FL,2,A,A3,3.42,7.0 -2015-10-11,FL,2,A,A3,3.5,7.0 -2015-10-12,FL,2,A,A3,3.5,7.0 -2015-10-13,FL,2,A,A3,3.45,7.0 -2015-10-14,FL,2,A,A3,3.49,8.0 -2015-10-15,FL,2,A,A3,3.53,6.0 -2015-10-16,FL,2,A,A3,3.46,7.0 -2015-10-17,FL,2,A,A3,3.56,7.0 -2015-10-18,FL,2,A,A3,3.53,7.0 -2015-10-19,FL,2,A,A3,3.48,6.0 -2015-10-20,FL,2,A,A3,3.5,7.0 -2015-10-21,FL,2,A,A3,3.51,6.0 -2015-10-22,FL,2,A,A3,3.48,7.0 -2015-10-23,FL,2,A,A3,3.49,7.0 -2015-10-24,FL,2,A,A3,3.53,6.0 -2015-10-25,FL,2,A,A3,3.48,7.0 -2015-10-26,FL,2,A,A3,3.52,6.0 -2015-10-27,FL,2,A,A3,3.52,7.0 -2015-10-28,FL,2,A,A3,3.47,7.0 -2015-10-29,FL,2,A,A3,3.52,7.0 -2015-10-30,FL,2,A,A3,3.5,7.0 -2015-10-31,FL,2,A,A3,3.49,7.0 -2015-11-01,FL,2,A,A3,3.51,7.0 -2015-11-02,FL,2,A,A3,3.5,6.0 -2015-11-03,FL,2,A,A3,3.47,8.0 -2015-11-04,FL,2,A,A3,3.45,6.0 -2015-11-05,FL,2,A,A3,3.48,6.0 -2015-11-06,FL,2,A,A3,3.46,6.0 -2015-11-07,FL,2,A,A3,3.52,6.0 -2015-11-08,FL,2,A,A3,3.41,7.0 -2015-11-09,FL,2,A,A3,3.51,6.0 -2015-11-10,FL,2,A,A3,3.45,7.0 -2015-11-11,FL,2,A,A3,3.52,7.0 -2015-11-12,FL,2,A,A3,3.45,7.0 -2015-11-13,FL,2,A,A3,3.44,7.0 -2015-11-14,FL,2,A,A3,3.49,7.0 -2015-11-15,FL,2,A,A3,3.49,7.0 -2015-11-16,FL,2,A,A3,3.45,6.0 -2015-11-17,FL,2,A,A3,3.45,7.0 -2015-11-18,FL,2,A,A3,3.4,7.0 -2015-11-19,FL,2,A,A3,3.51,7.0 -2015-11-20,FL,2,A,A3,3.46,7.0 -2015-11-21,FL,2,A,A3,3.47,7.0 -2015-11-22,FL,2,A,A3,3.5,7.0 -2015-11-23,FL,2,A,A3,3.44,7.0 -2015-11-24,FL,2,A,A3,3.54,6.0 -2015-11-25,FL,2,A,A3,3.47,7.0 -2015-11-26,FL,2,A,A3,3.46,7.0 -2015-11-27,FL,2,A,A3,3.5,7.0 -2015-11-28,FL,2,A,A3,3.48,7.0 -2015-11-29,FL,2,A,A3,3.48,6.0 -2015-11-30,FL,2,A,A3,3.43,7.0 -2015-12-01,FL,2,A,A3,3.46,7.0 -2015-12-02,FL,2,A,A3,3.53,7.0 -2015-12-03,FL,2,A,A3,3.48,7.0 -2015-12-04,FL,2,A,A3,3.5,6.0 -2015-12-05,FL,2,A,A3,3.48,7.0 -2015-12-06,FL,2,A,A3,3.5,7.0 -2015-12-07,FL,2,A,A3,3.5,6.0 -2015-12-08,FL,2,A,A3,3.51,6.0 -2015-12-09,FL,2,A,A3,3.46,7.0 -2015-12-10,FL,2,A,A3,3.52,7.0 -2015-12-11,FL,2,A,A3,3.49,7.0 -2015-12-12,FL,2,A,A3,3.5,7.0 -2015-12-13,FL,2,A,A3,3.5,7.0 -2015-12-14,FL,2,A,A3,3.57,7.0 -2015-12-15,FL,2,A,A3,3.54,7.0 -2015-12-16,FL,2,A,A3,3.57,7.0 -2015-12-17,FL,2,A,A3,3.47,7.0 -2015-12-18,FL,2,A,A3,3.45,7.0 -2015-12-19,FL,2,A,A3,3.47,7.0 -2015-12-20,FL,2,A,A3,3.45,6.0 -2015-12-21,FL,2,A,A3,3.47,6.0 -2015-12-22,FL,2,A,A3,3.46,6.0 -2015-12-23,FL,2,A,A3,3.51,6.0 -2015-12-24,FL,2,A,A3,3.47,7.0 -2015-12-25,FL,2,A,A3,3.52,7.0 -2015-12-26,FL,2,A,A3,3.47,7.0 -2015-12-27,FL,2,A,A3,3.48,7.0 -2015-12-28,FL,2,A,A3,3.48,6.0 -2015-12-29,FL,2,A,A3,3.5,7.0 -2015-12-30,FL,2,A,A3,3.51,7.0 -2015-12-31,FL,2,A,A3,3.44,7.0 -2016-01-01,FL,2,A,A3,3.49,7.0 -2016-01-02,FL,2,A,A3,3.48,7.0 -2016-01-03,FL,2,A,A3,3.45,7.0 -2016-01-04,FL,2,A,A3,3.48,7.0 -2016-01-05,FL,2,A,A3,3.45,7.0 -2016-01-06,FL,2,A,A3,3.46,7.0 -2016-01-07,FL,2,A,A3,3.47,6.0 -2016-01-08,FL,2,A,A3,3.48,7.0 -2016-01-09,FL,2,A,A3,3.46,7.0 -2016-01-10,FL,2,A,A3,3.53,7.0 -2016-01-11,FL,2,A,A3,3.49,6.0 -2016-01-12,FL,2,A,A3,3.52,6.0 -2016-01-13,FL,2,A,A3,3.48,6.0 -2016-01-14,FL,2,A,A3,3.55,6.0 -2016-01-15,FL,2,A,A3,3.48,7.0 -2016-01-16,FL,2,A,A3,3.48,8.0 -2016-01-17,FL,2,A,A3,3.47,6.0 -2016-01-18,FL,2,A,A3,3.47,7.0 -2016-01-19,FL,2,A,A3,3.49,7.0 -2016-01-20,FL,2,A,A3,3.52,6.0 -2016-01-21,FL,2,A,A3,3.54,6.0 -2016-01-22,FL,2,A,A3,3.47,7.0 -2016-01-23,FL,2,A,A3,3.51,8.0 -2016-01-24,FL,2,A,A3,3.49,7.0 -2016-01-25,FL,2,A,A3,3.44,7.0 -2016-01-26,FL,2,A,A3,3.47,7.0 -2016-01-27,FL,2,A,A3,3.52,7.0 -2016-01-28,FL,2,A,A3,3.45,7.0 -2016-01-29,FL,2,A,A3,3.46,7.0 -2016-01-30,FL,2,A,A3,3.4,7.0 -2016-01-31,FL,2,A,A3,3.52,7.0 -2016-02-01,FL,2,A,A3,3.5,7.0 -2016-02-02,FL,2,A,A3,3.48,7.0 -2016-02-03,FL,2,A,A3,3.51,6.0 -2016-02-04,FL,2,A,A3,3.51,7.0 -2016-02-05,FL,2,A,A3,3.55,7.0 -2016-02-06,FL,2,A,A3,3.47,7.0 -2016-02-07,FL,2,A,A3,3.46,7.0 -2016-02-08,FL,2,A,A3,3.55,7.0 -2016-02-09,FL,2,A,A3,3.48,6.0 -2016-02-10,FL,2,A,A3,3.48,7.0 -2016-02-11,FL,2,A,A3,3.45,7.0 -2016-02-12,FL,2,A,A3,3.51,7.0 -2016-02-13,FL,2,A,A3,3.5,7.0 -2016-02-14,FL,2,A,A3,3.47,7.0 -2016-02-15,FL,2,A,A3,3.45,7.0 -2016-02-16,FL,2,A,A3,3.46,7.0 -2016-02-17,FL,2,A,A3,3.41,7.0 -2016-02-18,FL,2,A,A3,3.47,7.0 -2016-02-19,FL,2,A,A3,3.49,7.0 -2016-02-20,FL,2,A,A3,3.49,7.0 -2016-02-21,FL,2,A,A3,3.52,6.0 -2016-02-22,FL,2,A,A3,3.42,7.0 -2016-02-23,FL,2,A,A3,3.53,7.0 -2016-02-24,FL,2,A,A3,3.5,7.0 -2016-02-25,FL,2,A,A3,3.49,6.0 -2016-02-26,FL,2,A,A3,3.5,7.0 -2016-02-27,FL,2,A,A3,3.51,7.0 -2016-02-28,FL,2,A,A3,3.46,7.0 -2016-02-29,FL,2,A,A3,3.5,6.0 -2016-03-01,FL,2,A,A3,3.46,7.0 -2016-03-02,FL,2,A,A3,3.47,7.0 -2016-03-03,FL,2,A,A3,3.47,7.0 -2016-03-04,FL,2,A,A3,3.51,6.0 -2016-03-05,FL,2,A,A3,3.5,6.0 -2016-03-06,FL,2,A,A3,3.51,6.0 -2016-03-07,FL,2,A,A3,3.5,6.0 -2016-03-08,FL,2,A,A3,3.5,6.0 -2016-03-09,FL,2,A,A3,3.46,7.0 -2016-03-10,FL,2,A,A3,3.52,6.0 -2016-03-11,FL,2,A,A3,3.5,6.0 -2016-03-12,FL,2,A,A3,3.48,8.0 -2016-03-13,FL,2,A,A3,3.44,7.0 -2016-03-14,FL,2,A,A3,3.43,7.0 -2016-03-15,FL,2,A,A3,3.53,7.0 -2016-03-16,FL,2,A,A3,3.52,6.0 -2016-03-17,FL,2,A,A3,3.44,7.0 -2016-03-18,FL,2,A,A3,3.54,6.0 -2016-03-19,FL,2,A,A3,3.51,7.0 -2016-03-20,FL,2,A,A3,3.43,7.0 -2016-03-21,FL,2,A,A3,3.5,6.0 -2016-03-22,FL,2,A,A3,3.52,6.0 -2016-03-23,FL,2,A,A3,3.45,7.0 -2016-03-24,FL,2,A,A3,3.51,7.0 -2016-03-25,FL,2,A,A3,3.42,7.0 -2016-03-26,FL,2,A,A3,3.57,7.0 -2016-03-27,FL,2,A,A3,3.47,6.0 -2016-03-28,FL,2,A,A3,3.44,6.0 -2016-03-29,FL,2,A,A3,3.51,7.0 -2016-03-30,FL,2,A,A3,3.49,7.0 -2016-03-31,FL,2,A,A3,3.47,7.0 -2016-04-01,FL,2,A,A3,3.5,7.0 -2016-04-02,FL,2,A,A3,3.44,7.0 -2016-04-03,FL,2,A,A3,3.54,6.0 -2016-04-04,FL,2,A,A3,3.48,7.0 -2016-04-05,FL,2,A,A3,3.44,7.0 -2016-04-06,FL,2,A,A3,3.47,7.0 -2016-04-07,FL,2,A,A3,3.45,7.0 -2016-04-08,FL,2,A,A3,3.44,7.0 -2016-04-09,FL,2,A,A3,3.44,7.0 -2016-04-10,FL,2,A,A3,3.45,7.0 -2016-04-11,FL,2,A,A3,3.49,6.0 -2016-04-12,FL,2,A,A3,3.52,7.0 -2016-04-13,FL,2,A,A3,3.54,6.0 -2016-04-14,FL,2,A,A3,3.39,7.0 -2016-04-15,FL,2,A,A3,3.42,7.0 -2016-04-16,FL,2,A,A3,3.46,7.0 -2016-04-17,FL,2,A,A3,3.53,6.0 -2016-04-18,FL,2,A,A3,3.46,6.0 -2016-04-19,FL,2,A,A3,3.5,6.0 -2016-04-20,FL,2,A,A3,3.44,8.0 -2016-04-21,FL,2,A,A3,3.46,7.0 -2016-04-22,FL,2,A,A3,3.47,7.0 -2016-04-23,FL,2,A,A3,3.44,8.0 -2016-04-24,FL,2,A,A3,3.5,7.0 -2016-04-25,FL,2,A,A3,3.48,7.0 -2016-04-26,FL,2,A,A3,3.52,7.0 -2016-04-27,FL,2,A,A3,3.48,7.0 -2016-04-28,FL,2,A,A3,3.4,7.0 -2016-04-29,FL,2,A,A3,3.46,7.0 -2016-04-30,FL,2,A,A3,3.53,7.0 -2016-05-01,FL,2,A,A3,3.45,7.0 -2016-05-02,FL,2,A,A3,3.44,7.0 -2016-05-03,FL,2,A,A3,3.41,6.0 -2016-05-04,FL,2,A,A3,3.48,7.0 -2016-05-05,FL,2,A,A3,3.5,7.0 -2016-05-06,FL,2,A,A3,3.54,6.0 -2016-05-07,FL,2,A,A3,3.46,7.0 -2016-05-08,FL,2,A,A3,3.46,7.0 -2016-05-09,FL,2,A,A3,3.47,7.0 -2016-05-10,FL,2,A,A3,3.5,7.0 -2016-05-11,FL,2,A,A3,3.42,7.0 -2016-05-12,FL,2,A,A3,3.43,7.0 -2016-05-13,FL,2,A,A3,3.49,6.0 -2016-05-14,FL,2,A,A3,3.49,7.0 -2016-05-15,FL,2,A,A3,3.48,7.0 -2016-05-16,FL,2,A,A3,3.5,7.0 -2016-05-17,FL,2,A,A3,3.52,7.0 -2016-05-18,FL,2,A,A3,3.52,7.0 -2016-05-19,FL,2,A,A3,3.46,7.0 -2016-05-20,FL,2,A,A3,3.42,7.0 -2016-05-21,FL,2,A,A3,3.58,7.0 -2016-05-22,FL,2,A,A3,3.5,7.0 -2016-05-23,FL,2,A,A3,3.52,6.0 -2016-05-24,FL,2,A,A3,3.53,7.0 -2016-05-25,FL,2,A,A3,3.46,7.0 -2016-05-26,FL,2,A,A3,3.47,7.0 -2016-05-27,FL,2,A,A3,3.49,7.0 -2016-05-28,FL,2,A,A3,3.5,6.0 -2016-05-29,FL,2,A,A3,3.43,7.0 -2016-05-30,FL,2,A,A3,3.46,7.0 -2016-05-31,FL,2,A,A3,3.47,7.0 -2016-06-01,FL,2,A,A3,3.47,7.0 -2016-06-02,FL,2,A,A3,3.51,6.0 -2016-06-03,FL,2,A,A3,3.47,7.0 -2016-06-04,FL,2,A,A3,3.52,7.0 -2016-06-05,FL,2,A,A3,3.43,7.0 -2016-06-06,FL,2,A,A3,3.45,6.0 -2016-06-07,FL,2,A,A3,3.49,6.0 -2016-06-08,FL,2,A,A3,3.47,6.0 -2016-06-09,FL,2,A,A3,3.47,6.0 -2016-06-10,FL,2,A,A3,3.47,7.0 -2016-06-11,FL,2,A,A3,3.45,7.0 -2016-06-12,FL,2,A,A3,3.47,7.0 -2016-06-13,FL,2,A,A3,3.5,6.0 -2016-06-14,FL,2,A,A3,3.49,7.0 -2016-06-15,FL,2,A,A3,3.5,7.0 -2016-06-16,FL,2,A,A3,3.48,7.0 -2016-06-17,FL,2,A,A3,3.52,6.0 -2016-06-18,FL,2,A,A3,3.46,7.0 -2016-06-19,FL,2,A,A3,3.51,7.0 -2016-06-20,FL,2,A,A3,3.48,7.0 -2016-06-21,FL,2,A,A3,3.47,7.0 -2016-06-22,FL,2,A,A3,3.53,7.0 -2016-06-23,FL,2,A,A3,3.41,7.0 -2016-06-24,FL,2,A,A3,3.48,6.0 -2016-06-25,FL,2,A,A3,3.46,6.0 -2016-06-26,FL,2,A,A3,3.45,7.0 -2016-06-27,FL,2,A,A3,3.43,7.0 -2016-06-28,FL,2,A,A3,3.49,7.0 -2016-06-29,FL,2,A,A3,3.51,6.0 -2016-06-30,FL,2,A,A3,3.46,7.0 -2016-07-01,FL,2,A,A3,3.49,7.0 -2016-07-02,FL,2,A,A3,3.52,7.0 -2016-07-03,FL,2,A,A3,3.51,7.0 -2016-07-04,FL,2,A,A3,3.48,6.0 -2016-07-05,FL,2,A,A3,3.52,7.0 -2016-07-06,FL,2,A,A3,3.45,7.0 -2016-07-07,FL,2,A,A3,3.48,6.0 -2016-07-08,FL,2,A,A3,3.52,7.0 -2016-07-09,FL,2,A,A3,3.54,7.0 -2016-07-10,FL,2,A,A3,3.42,7.0 -2016-07-11,FL,2,A,A3,3.48,7.0 -2016-07-12,FL,2,A,A3,3.45,7.0 -2016-07-13,FL,2,A,A3,3.43,7.0 -2016-07-14,FL,2,A,A3,3.5,7.0 -2016-07-15,FL,2,A,A3,3.45,7.0 -2016-07-16,FL,2,A,A3,3.57,7.0 -2016-07-17,FL,2,A,A3,3.45,7.0 -2016-07-18,FL,2,A,A3,3.44,7.0 -2016-07-19,FL,2,A,A3,3.5,7.0 -2016-07-20,FL,2,A,A3,3.44,7.0 -2016-07-21,FL,2,A,A3,3.47,7.0 -2016-07-22,FL,2,A,A3,3.45,7.0 -2016-07-23,FL,2,A,A3,3.49,8.0 -2016-07-24,FL,2,A,A3,3.56,6.0 -2016-07-25,FL,2,A,A3,3.46,7.0 -2016-07-26,FL,2,A,A3,3.51,7.0 -2016-07-27,FL,2,A,A3,3.53,6.0 -2015-01-05,FL,2,B,B1,2.47,60.0 -2015-01-06,FL,2,B,B1,2.53,57.0 -2015-01-07,FL,2,B,B1,2.53,64.0 -2015-01-08,FL,2,B,B1,2.52,60.0 -2015-01-09,FL,2,B,B1,2.5,64.0 -2015-01-10,FL,2,B,B1,2.5,63.0 -2015-01-11,FL,2,B,B1,2.5,56.0 -2015-01-12,FL,2,B,B1,2.5,58.0 -2015-01-13,FL,2,B,B1,2.5,58.0 -2015-01-14,FL,2,B,B1,2.46,60.0 -2015-01-15,FL,2,B,B1,2.54,58.0 -2015-01-16,FL,2,B,B1,2.49,63.0 -2015-01-17,FL,2,B,B1,2.51,63.0 -2015-01-18,FL,2,B,B1,2.48,65.0 -2015-01-19,FL,2,B,B1,2.47,67.0 -2015-01-20,FL,2,B,B1,2.5,62.0 -2015-01-21,FL,2,B,B1,2.47,64.0 -2015-01-22,FL,2,B,B1,2.51,59.0 -2015-01-23,FL,2,B,B1,2.49,66.0 -2015-01-24,FL,2,B,B1,2.51,60.0 -2015-01-25,FL,2,B,B1,2.5,62.0 -2015-01-26,FL,2,B,B1,2.5,60.0 -2015-01-27,FL,2,B,B1,2.49,63.0 -2015-01-28,FL,2,B,B1,2.49,60.0 -2015-01-29,FL,2,B,B1,2.54,60.0 -2015-01-30,FL,2,B,B1,2.54,64.0 -2015-01-31,FL,2,B,B1,2.53,67.0 -2015-02-01,FL,2,B,B1,2.52,58.0 -2015-02-02,FL,2,B,B1,2.5,59.0 -2015-02-03,FL,2,B,B1,2.49,60.0 -2015-02-04,FL,2,B,B1,2.47,62.0 -2015-02-05,FL,2,B,B1,2.5,65.0 -2015-02-06,FL,2,B,B1,2.51,60.0 -2015-02-07,FL,2,B,B1,2.5,64.0 -2015-02-08,FL,2,B,B1,2.51,62.0 -2015-02-09,FL,2,B,B1,2.46,69.0 -2015-02-10,FL,2,B,B1,2.52,59.0 -2015-02-11,FL,2,B,B1,2.49,64.0 -2015-02-12,FL,2,B,B1,2.51,58.0 -2015-02-13,FL,2,B,B1,2.46,66.0 -2015-02-14,FL,2,B,B1,2.51,64.0 -2015-02-15,FL,2,B,B1,2.53,63.0 -2015-02-16,FL,2,B,B1,2.51,62.0 -2015-02-17,FL,2,B,B1,2.5,66.0 -2015-02-18,FL,2,B,B1,2.47,59.0 -2015-02-19,FL,2,B,B1,2.5,65.0 -2015-02-20,FL,2,B,B1,2.48,64.0 -2015-02-21,FL,2,B,B1,2.52,60.0 -2015-02-22,FL,2,B,B1,2.52,65.0 -2015-02-23,FL,2,B,B1,2.47,63.0 -2015-02-24,FL,2,B,B1,2.49,65.0 -2015-02-25,FL,2,B,B1,2.47,63.0 -2015-02-26,FL,2,B,B1,2.48,60.0 -2015-02-27,FL,2,B,B1,2.46,67.0 -2015-02-28,FL,2,B,B1,2.5,67.0 -2015-03-01,FL,2,B,B1,2.48,55.0 -2015-03-02,FL,2,B,B1,2.49,62.0 -2015-03-03,FL,2,B,B1,2.49,62.0 -2015-03-04,FL,2,B,B1,2.48,66.0 -2015-03-05,FL,2,B,B1,2.54,58.0 -2015-03-06,FL,2,B,B1,2.52,63.0 -2015-03-07,FL,2,B,B1,2.49,65.0 -2015-03-08,FL,2,B,B1,2.49,67.0 -2015-03-09,FL,2,B,B1,2.51,60.0 -2015-03-10,FL,2,B,B1,2.45,62.0 -2015-03-11,FL,2,B,B1,2.51,62.0 -2015-03-12,FL,2,B,B1,2.48,59.0 -2015-03-13,FL,2,B,B1,2.48,62.0 -2015-03-14,FL,2,B,B1,2.5,65.0 -2015-03-15,FL,2,B,B1,2.53,54.0 -2015-03-16,FL,2,B,B1,2.53,54.0 -2015-03-17,FL,2,B,B1,2.54,54.0 -2015-03-18,FL,2,B,B1,2.52,60.0 -2015-03-19,FL,2,B,B1,2.49,59.0 -2015-03-20,FL,2,B,B1,2.53,58.0 -2015-03-21,FL,2,B,B1,2.5,65.0 -2015-03-22,FL,2,B,B1,2.47,66.0 -2015-03-23,FL,2,B,B1,2.53,57.0 -2015-03-24,FL,2,B,B1,2.54,54.0 -2015-03-25,FL,2,B,B1,2.52,60.0 -2015-03-26,FL,2,B,B1,2.54,64.0 -2015-03-27,FL,2,B,B1,2.5,64.0 -2015-03-28,FL,2,B,B1,2.5,64.0 -2015-03-29,FL,2,B,B1,2.54,61.0 -2015-03-30,FL,2,B,B1,2.52,60.0 -2015-03-31,FL,2,B,B1,2.49,67.0 -2015-04-01,FL,2,B,B1,2.49,62.0 -2015-04-02,FL,2,B,B1,2.47,64.0 -2015-04-03,FL,2,B,B1,2.48,62.0 -2015-04-04,FL,2,B,B1,2.58,61.0 -2015-04-05,FL,2,B,B1,2.48,69.0 -2015-04-06,FL,2,B,B1,2.48,61.0 -2015-04-07,FL,2,B,B1,2.54,60.0 -2015-04-08,FL,2,B,B1,2.49,59.0 -2015-04-09,FL,2,B,B1,2.51,58.0 -2015-04-10,FL,2,B,B1,2.49,65.0 -2015-04-11,FL,2,B,B1,2.47,70.0 -2015-04-12,FL,2,B,B1,2.52,56.0 -2015-04-13,FL,2,B,B1,2.5,59.0 -2015-04-14,FL,2,B,B1,2.48,62.0 -2015-04-15,FL,2,B,B1,2.5,68.0 -2015-04-16,FL,2,B,B1,2.47,64.0 -2015-04-17,FL,2,B,B1,2.51,58.0 -2015-04-18,FL,2,B,B1,2.51,64.0 -2015-04-19,FL,2,B,B1,2.52,61.0 -2015-04-20,FL,2,B,B1,2.52,58.0 -2015-04-21,FL,2,B,B1,2.5,65.0 -2015-04-22,FL,2,B,B1,2.52,61.0 -2015-04-23,FL,2,B,B1,2.52,63.0 -2015-04-24,FL,2,B,B1,2.55,58.0 -2015-04-25,FL,2,B,B1,2.49,63.0 -2015-04-26,FL,2,B,B1,2.52,58.0 -2015-04-27,FL,2,B,B1,2.52,58.0 -2015-04-28,FL,2,B,B1,2.45,67.0 -2015-04-29,FL,2,B,B1,2.49,59.0 -2015-04-30,FL,2,B,B1,2.52,55.0 -2015-05-01,FL,2,B,B1,2.5,63.0 -2015-05-02,FL,2,B,B1,2.52,66.0 -2015-05-03,FL,2,B,B1,2.55,60.0 -2015-05-04,FL,2,B,B1,2.49,65.0 -2015-05-05,FL,2,B,B1,2.46,67.0 -2015-05-06,FL,2,B,B1,2.5,67.0 -2015-05-07,FL,2,B,B1,2.51,61.0 -2015-05-08,FL,2,B,B1,2.48,61.0 -2015-05-09,FL,2,B,B1,2.49,67.0 -2015-05-10,FL,2,B,B1,2.51,65.0 -2015-05-11,FL,2,B,B1,2.5,59.0 -2015-05-12,FL,2,B,B1,2.49,65.0 -2015-05-13,FL,2,B,B1,2.48,60.0 -2015-05-14,FL,2,B,B1,2.52,65.0 -2015-05-15,FL,2,B,B1,2.51,65.0 -2015-05-16,FL,2,B,B1,2.52,63.0 -2015-05-17,FL,2,B,B1,2.52,57.0 -2015-05-18,FL,2,B,B1,2.5,69.0 -2015-05-19,FL,2,B,B1,2.52,63.0 -2015-05-20,FL,2,B,B1,2.45,65.0 -2015-05-21,FL,2,B,B1,2.47,64.0 -2015-05-22,FL,2,B,B1,2.48,65.0 -2015-05-23,FL,2,B,B1,2.49,63.0 -2015-05-24,FL,2,B,B1,2.51,60.0 -2015-05-25,FL,2,B,B1,2.55,55.0 -2015-05-26,FL,2,B,B1,2.49,67.0 -2015-05-27,FL,2,B,B1,2.48,63.0 -2015-05-28,FL,2,B,B1,2.52,68.0 -2015-05-29,FL,2,B,B1,2.54,61.0 -2015-05-30,FL,2,B,B1,2.5,70.0 -2015-05-31,FL,2,B,B1,2.5,60.0 -2015-06-01,FL,2,B,B1,2.51,60.0 -2015-06-02,FL,2,B,B1,2.53,57.0 -2015-06-03,FL,2,B,B1,2.52,62.0 -2015-06-04,FL,2,B,B1,2.48,69.0 -2015-06-05,FL,2,B,B1,2.51,63.0 -2015-06-06,FL,2,B,B1,2.47,70.0 -2015-06-07,FL,2,B,B1,2.53,60.0 -2015-06-08,FL,2,B,B1,2.49,61.0 -2015-06-09,FL,2,B,B1,2.5,59.0 -2015-06-10,FL,2,B,B1,2.54,61.0 -2015-06-11,FL,2,B,B1,2.47,64.0 -2015-06-12,FL,2,B,B1,2.53,58.0 -2015-06-13,FL,2,B,B1,2.5,68.0 -2015-06-14,FL,2,B,B1,2.53,58.0 -2015-06-15,FL,2,B,B1,2.46,65.0 -2015-06-16,FL,2,B,B1,2.5,61.0 -2015-06-17,FL,2,B,B1,2.45,68.0 -2015-06-18,FL,2,B,B1,2.47,61.0 -2015-06-19,FL,2,B,B1,2.53,60.0 -2015-06-20,FL,2,B,B1,2.52,63.0 -2015-06-21,FL,2,B,B1,2.49,66.0 -2015-06-22,FL,2,B,B1,2.53,59.0 -2015-06-23,FL,2,B,B1,2.53,56.0 -2015-06-24,FL,2,B,B1,2.51,60.0 -2015-06-25,FL,2,B,B1,2.5,60.0 -2015-06-26,FL,2,B,B1,2.52,60.0 -2015-06-27,FL,2,B,B1,2.53,58.0 -2015-06-28,FL,2,B,B1,2.44,74.0 -2015-06-29,FL,2,B,B1,2.55,56.0 -2015-06-30,FL,2,B,B1,2.5,59.0 -2015-07-01,FL,2,B,B1,2.51,64.0 -2015-07-02,FL,2,B,B1,2.52,64.0 -2015-07-03,FL,2,B,B1,2.48,71.0 -2015-07-04,FL,2,B,B1,2.48,68.0 -2015-07-05,FL,2,B,B1,2.51,65.0 -2015-07-06,FL,2,B,B1,2.51,59.0 -2015-07-07,FL,2,B,B1,2.53,59.0 -2015-07-08,FL,2,B,B1,2.49,60.0 -2015-07-09,FL,2,B,B1,2.5,59.0 -2015-07-10,FL,2,B,B1,2.52,69.0 -2015-07-11,FL,2,B,B1,2.49,70.0 -2015-07-12,FL,2,B,B1,2.5,61.0 -2015-07-13,FL,2,B,B1,2.51,61.0 -2015-07-14,FL,2,B,B1,2.49,62.0 -2015-07-15,FL,2,B,B1,2.5,67.0 -2015-07-16,FL,2,B,B1,2.49,63.0 -2015-07-17,FL,2,B,B1,2.58,58.0 -2015-07-18,FL,2,B,B1,2.53,54.0 -2015-07-19,FL,2,B,B1,2.51,60.0 -2015-07-20,FL,2,B,B1,2.5,58.0 -2015-07-21,FL,2,B,B1,2.52,59.0 -2015-07-22,FL,2,B,B1,2.47,64.0 -2015-07-23,FL,2,B,B1,2.51,61.0 -2015-07-24,FL,2,B,B1,2.47,67.0 -2015-07-25,FL,2,B,B1,2.53,63.0 -2015-07-26,FL,2,B,B1,2.48,66.0 -2015-07-27,FL,2,B,B1,2.53,61.0 -2015-07-28,FL,2,B,B1,2.48,60.0 -2015-07-29,FL,2,B,B1,2.51,62.0 -2015-07-30,FL,2,B,B1,2.47,65.0 -2015-07-31,FL,2,B,B1,2.53,56.0 -2015-08-01,FL,2,B,B1,2.52,63.0 -2015-08-02,FL,2,B,B1,2.47,67.0 -2015-08-03,FL,2,B,B1,2.49,66.0 -2015-08-04,FL,2,B,B1,2.53,59.0 -2015-08-05,FL,2,B,B1,2.5,61.0 -2015-08-06,FL,2,B,B1,2.5,61.0 -2015-08-07,FL,2,B,B1,2.51,62.0 -2015-08-08,FL,2,B,B1,2.49,68.0 -2015-08-09,FL,2,B,B1,2.54,58.0 -2015-08-10,FL,2,B,B1,2.48,62.0 -2015-08-11,FL,2,B,B1,2.51,58.0 -2015-08-12,FL,2,B,B1,2.51,60.0 -2015-08-13,FL,2,B,B1,2.48,64.0 -2015-08-14,FL,2,B,B1,2.53,60.0 -2015-08-15,FL,2,B,B1,2.51,60.0 -2015-08-16,FL,2,B,B1,2.5,64.0 -2015-08-17,FL,2,B,B1,2.53,59.0 -2015-08-18,FL,2,B,B1,2.5,61.0 -2015-08-19,FL,2,B,B1,2.52,63.0 -2015-08-20,FL,2,B,B1,2.48,68.0 -2015-08-21,FL,2,B,B1,2.5,63.0 -2015-08-22,FL,2,B,B1,2.51,60.0 -2015-08-23,FL,2,B,B1,2.48,65.0 -2015-08-24,FL,2,B,B1,2.57,56.0 -2015-08-25,FL,2,B,B1,2.49,64.0 -2015-08-26,FL,2,B,B1,2.48,64.0 -2015-08-27,FL,2,B,B1,2.47,61.0 -2015-08-28,FL,2,B,B1,2.51,61.0 -2015-08-29,FL,2,B,B1,2.47,72.0 -2015-08-30,FL,2,B,B1,2.49,59.0 -2015-08-31,FL,2,B,B1,2.46,65.0 -2015-09-01,FL,2,B,B1,2.5,60.0 -2015-09-02,FL,2,B,B1,2.54,60.0 -2015-09-03,FL,2,B,B1,2.47,61.0 -2015-09-04,FL,2,B,B1,2.51,63.0 -2015-09-05,FL,2,B,B1,2.47,72.0 -2015-09-06,FL,2,B,B1,2.45,64.0 -2015-09-07,FL,2,B,B1,2.52,56.0 -2015-09-08,FL,2,B,B1,2.48,61.0 -2015-09-09,FL,2,B,B1,2.57,52.0 -2015-09-10,FL,2,B,B1,2.48,67.0 -2015-09-11,FL,2,B,B1,2.47,70.0 -2015-09-12,FL,2,B,B1,2.48,75.0 -2015-09-13,FL,2,B,B1,2.52,57.0 -2015-09-14,FL,2,B,B1,2.48,69.0 -2015-09-15,FL,2,B,B1,2.45,65.0 -2015-09-16,FL,2,B,B1,2.53,59.0 -2015-09-17,FL,2,B,B1,2.48,63.0 -2015-09-18,FL,2,B,B1,2.47,68.0 -2015-09-19,FL,2,B,B1,2.5,63.0 -2015-09-20,FL,2,B,B1,2.5,66.0 -2015-09-21,FL,2,B,B1,2.5,64.0 -2015-09-22,FL,2,B,B1,2.48,58.0 -2015-09-23,FL,2,B,B1,2.53,55.0 -2015-09-24,FL,2,B,B1,2.49,61.0 -2015-09-25,FL,2,B,B1,2.5,62.0 -2015-09-26,FL,2,B,B1,2.51,62.0 -2015-09-27,FL,2,B,B1,2.5,66.0 -2015-09-28,FL,2,B,B1,2.47,69.0 -2015-09-29,FL,2,B,B1,2.5,58.0 -2015-09-30,FL,2,B,B1,2.49,60.0 -2015-10-01,FL,2,B,B1,2.53,57.0 -2015-10-02,FL,2,B,B1,2.51,59.0 -2015-10-03,FL,2,B,B1,2.52,67.0 -2015-10-04,FL,2,B,B1,2.5,60.0 -2015-10-05,FL,2,B,B1,2.51,60.0 -2015-10-06,FL,2,B,B1,2.5,66.0 -2015-10-07,FL,2,B,B1,2.48,71.0 -2015-10-08,FL,2,B,B1,2.49,61.0 -2015-10-09,FL,2,B,B1,2.53,58.0 -2015-10-10,FL,2,B,B1,2.48,66.0 -2015-10-11,FL,2,B,B1,2.5,71.0 -2015-10-12,FL,2,B,B1,2.53,58.0 -2015-10-13,FL,2,B,B1,2.52,57.0 -2015-10-14,FL,2,B,B1,2.55,54.0 -2015-10-15,FL,2,B,B1,2.51,56.0 -2015-10-16,FL,2,B,B1,2.51,57.0 -2015-10-17,FL,2,B,B1,2.47,70.0 -2015-10-18,FL,2,B,B1,2.49,66.0 -2015-10-19,FL,2,B,B1,2.53,60.0 -2015-10-20,FL,2,B,B1,2.46,66.0 -2015-10-21,FL,2,B,B1,2.55,57.0 -2015-10-22,FL,2,B,B1,2.54,55.0 -2015-10-23,FL,2,B,B1,2.53,58.0 -2015-10-24,FL,2,B,B1,2.47,71.0 -2015-10-25,FL,2,B,B1,2.52,58.0 -2015-10-26,FL,2,B,B1,2.47,59.0 -2015-10-27,FL,2,B,B1,2.49,56.0 -2015-10-28,FL,2,B,B1,2.52,68.0 -2015-10-29,FL,2,B,B1,2.47,65.0 -2015-10-30,FL,2,B,B1,2.53,60.0 -2015-10-31,FL,2,B,B1,2.48,69.0 -2015-11-01,FL,2,B,B1,2.52,61.0 -2015-11-02,FL,2,B,B1,2.5,56.0 -2015-11-03,FL,2,B,B1,2.47,59.0 -2015-11-04,FL,2,B,B1,2.48,64.0 -2015-11-05,FL,2,B,B1,2.51,60.0 -2015-11-06,FL,2,B,B1,2.52,58.0 -2015-11-07,FL,2,B,B1,2.5,65.0 -2015-11-08,FL,2,B,B1,2.48,67.0 -2015-11-09,FL,2,B,B1,2.5,58.0 -2015-11-10,FL,2,B,B1,2.46,69.0 -2015-11-11,FL,2,B,B1,2.51,64.0 -2015-11-12,FL,2,B,B1,2.53,53.0 -2015-11-13,FL,2,B,B1,2.53,57.0 -2015-11-14,FL,2,B,B1,2.48,67.0 -2015-11-15,FL,2,B,B1,2.51,65.0 -2015-11-16,FL,2,B,B1,2.5,62.0 -2015-11-17,FL,2,B,B1,2.5,59.0 -2015-11-18,FL,2,B,B1,2.5,59.0 -2015-11-19,FL,2,B,B1,2.49,58.0 -2015-11-20,FL,2,B,B1,2.52,59.0 -2015-11-21,FL,2,B,B1,2.48,68.0 -2015-11-22,FL,2,B,B1,2.52,65.0 -2015-11-23,FL,2,B,B1,2.49,61.0 -2015-11-24,FL,2,B,B1,2.5,66.0 -2015-11-25,FL,2,B,B1,2.51,63.0 -2015-11-26,FL,2,B,B1,2.51,56.0 -2015-11-27,FL,2,B,B1,2.49,64.0 -2015-11-28,FL,2,B,B1,2.49,69.0 -2015-11-29,FL,2,B,B1,2.51,62.0 -2015-11-30,FL,2,B,B1,2.44,71.0 -2015-12-01,FL,2,B,B1,2.49,63.0 -2015-12-02,FL,2,B,B1,2.52,61.0 -2015-12-03,FL,2,B,B1,2.51,59.0 -2015-12-04,FL,2,B,B1,2.46,71.0 -2015-12-05,FL,2,B,B1,2.48,67.0 -2015-12-06,FL,2,B,B1,2.51,61.0 -2015-12-07,FL,2,B,B1,2.47,62.0 -2015-12-08,FL,2,B,B1,2.49,66.0 -2015-12-09,FL,2,B,B1,2.49,66.0 -2015-12-10,FL,2,B,B1,2.49,65.0 -2015-12-11,FL,2,B,B1,2.51,62.0 -2015-12-12,FL,2,B,B1,2.54,66.0 -2015-12-13,FL,2,B,B1,2.5,61.0 -2015-12-14,FL,2,B,B1,2.48,59.0 -2015-12-15,FL,2,B,B1,2.49,62.0 -2015-12-16,FL,2,B,B1,2.51,57.0 -2015-12-17,FL,2,B,B1,2.56,56.0 -2015-12-18,FL,2,B,B1,2.53,62.0 -2015-12-19,FL,2,B,B1,2.54,62.0 -2015-12-20,FL,2,B,B1,2.53,60.0 -2015-12-21,FL,2,B,B1,2.48,63.0 -2015-12-22,FL,2,B,B1,2.53,59.0 -2015-12-23,FL,2,B,B1,2.52,62.0 -2015-12-24,FL,2,B,B1,2.54,59.0 -2015-12-25,FL,2,B,B1,2.52,56.0 -2015-12-26,FL,2,B,B1,2.51,63.0 -2015-12-27,FL,2,B,B1,2.51,59.0 -2015-12-28,FL,2,B,B1,2.49,61.0 -2015-12-29,FL,2,B,B1,2.47,61.0 -2015-12-30,FL,2,B,B1,2.5,61.0 -2015-12-31,FL,2,B,B1,2.52,59.0 -2016-01-01,FL,2,B,B1,2.53,58.0 -2016-01-02,FL,2,B,B1,2.47,67.0 -2016-01-03,FL,2,B,B1,2.54,62.0 -2016-01-04,FL,2,B,B1,2.5,64.0 -2016-01-05,FL,2,B,B1,2.51,61.0 -2016-01-06,FL,2,B,B1,2.45,67.0 -2016-01-07,FL,2,B,B1,2.47,66.0 -2016-01-08,FL,2,B,B1,2.52,62.0 -2016-01-09,FL,2,B,B1,2.52,64.0 -2016-01-10,FL,2,B,B1,2.48,62.0 -2016-01-11,FL,2,B,B1,2.48,61.0 -2016-01-12,FL,2,B,B1,2.51,59.0 -2016-01-13,FL,2,B,B1,2.5,61.0 -2016-01-14,FL,2,B,B1,2.5,63.0 -2016-01-15,FL,2,B,B1,2.5,59.0 -2016-01-16,FL,2,B,B1,2.52,61.0 -2016-01-17,FL,2,B,B1,2.5,63.0 -2016-01-18,FL,2,B,B1,2.48,64.0 -2016-01-19,FL,2,B,B1,2.46,66.0 -2016-01-20,FL,2,B,B1,2.49,69.0 -2016-01-21,FL,2,B,B1,2.49,66.0 -2016-01-22,FL,2,B,B1,2.48,67.0 -2016-01-23,FL,2,B,B1,2.51,67.0 -2016-01-24,FL,2,B,B1,2.49,63.0 -2016-01-25,FL,2,B,B1,2.5,60.0 -2016-01-26,FL,2,B,B1,2.51,67.0 -2016-01-27,FL,2,B,B1,2.51,57.0 -2016-01-28,FL,2,B,B1,2.49,65.0 -2016-01-29,FL,2,B,B1,2.55,59.0 -2016-01-30,FL,2,B,B1,2.52,58.0 -2016-01-31,FL,2,B,B1,2.53,55.0 -2016-02-01,FL,2,B,B1,2.53,57.0 -2016-02-02,FL,2,B,B1,2.48,59.0 -2016-02-03,FL,2,B,B1,2.53,60.0 -2016-02-04,FL,2,B,B1,2.52,64.0 -2016-02-05,FL,2,B,B1,2.51,61.0 -2016-02-06,FL,2,B,B1,2.5,62.0 -2016-02-07,FL,2,B,B1,2.51,64.0 -2016-02-08,FL,2,B,B1,2.48,58.0 -2016-02-09,FL,2,B,B1,2.49,61.0 -2016-02-10,FL,2,B,B1,2.54,56.0 -2016-02-11,FL,2,B,B1,2.54,63.0 -2016-02-12,FL,2,B,B1,2.5,66.0 -2016-02-13,FL,2,B,B1,2.49,62.0 -2016-02-14,FL,2,B,B1,2.49,60.0 -2016-02-15,FL,2,B,B1,2.51,59.0 -2016-02-16,FL,2,B,B1,2.52,60.0 -2016-02-17,FL,2,B,B1,2.5,58.0 -2016-02-18,FL,2,B,B1,2.52,58.0 -2016-02-19,FL,2,B,B1,2.5,59.0 -2016-02-20,FL,2,B,B1,2.49,63.0 -2016-02-21,FL,2,B,B1,2.53,65.0 -2016-02-22,FL,2,B,B1,2.5,65.0 -2016-02-23,FL,2,B,B1,2.45,65.0 -2016-02-24,FL,2,B,B1,2.47,67.0 -2016-02-25,FL,2,B,B1,2.49,60.0 -2016-02-26,FL,2,B,B1,2.51,61.0 -2016-02-27,FL,2,B,B1,2.48,68.0 -2016-02-28,FL,2,B,B1,2.49,63.0 -2016-02-29,FL,2,B,B1,2.51,58.0 -2016-03-01,FL,2,B,B1,2.5,65.0 -2016-03-02,FL,2,B,B1,2.49,65.0 -2016-03-03,FL,2,B,B1,2.5,62.0 -2016-03-04,FL,2,B,B1,2.49,66.0 -2016-03-05,FL,2,B,B1,2.51,65.0 -2016-03-06,FL,2,B,B1,2.54,54.0 -2016-03-07,FL,2,B,B1,2.5,61.0 -2016-03-08,FL,2,B,B1,2.49,62.0 -2016-03-09,FL,2,B,B1,2.49,63.0 -2016-03-10,FL,2,B,B1,2.52,58.0 -2016-03-11,FL,2,B,B1,2.52,62.0 -2016-03-12,FL,2,B,B1,2.52,58.0 -2016-03-13,FL,2,B,B1,2.5,63.0 -2016-03-14,FL,2,B,B1,2.53,60.0 -2016-03-15,FL,2,B,B1,2.48,70.0 -2016-03-16,FL,2,B,B1,2.53,60.0 -2016-03-17,FL,2,B,B1,2.51,59.0 -2016-03-18,FL,2,B,B1,2.5,59.0 -2016-03-19,FL,2,B,B1,2.49,65.0 -2016-03-20,FL,2,B,B1,2.53,62.0 -2016-03-21,FL,2,B,B1,2.5,62.0 -2016-03-22,FL,2,B,B1,2.49,65.0 -2016-03-23,FL,2,B,B1,2.49,70.0 -2016-03-24,FL,2,B,B1,2.52,60.0 -2016-03-25,FL,2,B,B1,2.53,58.0 -2016-03-26,FL,2,B,B1,2.49,63.0 -2016-03-27,FL,2,B,B1,2.54,64.0 -2016-03-28,FL,2,B,B1,2.5,58.0 -2016-03-29,FL,2,B,B1,2.48,66.0 -2016-03-30,FL,2,B,B1,2.5,55.0 -2016-03-31,FL,2,B,B1,2.47,62.0 -2016-04-01,FL,2,B,B1,2.52,58.0 -2016-04-02,FL,2,B,B1,2.49,61.0 -2016-04-03,FL,2,B,B1,2.53,61.0 -2016-04-04,FL,2,B,B1,2.53,58.0 -2016-04-05,FL,2,B,B1,2.5,61.0 -2016-04-06,FL,2,B,B1,2.55,59.0 -2016-04-07,FL,2,B,B1,2.51,62.0 -2016-04-08,FL,2,B,B1,2.5,69.0 -2016-04-09,FL,2,B,B1,2.51,62.0 -2016-04-10,FL,2,B,B1,2.52,60.0 -2016-04-11,FL,2,B,B1,2.53,54.0 -2016-04-12,FL,2,B,B1,2.5,65.0 -2016-04-13,FL,2,B,B1,2.5,64.0 -2016-04-14,FL,2,B,B1,2.49,60.0 -2016-04-15,FL,2,B,B1,2.51,57.0 -2016-04-16,FL,2,B,B1,2.49,66.0 -2016-04-17,FL,2,B,B1,2.51,64.0 -2016-04-18,FL,2,B,B1,2.53,58.0 -2016-04-19,FL,2,B,B1,2.52,61.0 -2016-04-20,FL,2,B,B1,2.48,65.0 -2016-04-21,FL,2,B,B1,2.5,62.0 -2016-04-22,FL,2,B,B1,2.49,63.0 -2016-04-23,FL,2,B,B1,2.48,68.0 -2016-04-24,FL,2,B,B1,2.51,61.0 -2016-04-25,FL,2,B,B1,2.52,60.0 -2016-04-26,FL,2,B,B1,2.46,71.0 -2016-04-27,FL,2,B,B1,2.46,68.0 -2016-04-28,FL,2,B,B1,2.49,63.0 -2016-04-29,FL,2,B,B1,2.51,61.0 -2016-04-30,FL,2,B,B1,2.48,66.0 -2016-05-01,FL,2,B,B1,2.51,62.0 -2016-05-02,FL,2,B,B1,2.56,56.0 -2016-05-03,FL,2,B,B1,2.52,63.0 -2016-05-04,FL,2,B,B1,2.51,63.0 -2016-05-05,FL,2,B,B1,2.56,58.0 -2016-05-06,FL,2,B,B1,2.49,66.0 -2016-05-07,FL,2,B,B1,2.51,63.0 -2016-05-08,FL,2,B,B1,2.47,69.0 -2016-05-09,FL,2,B,B1,2.54,58.0 -2016-05-10,FL,2,B,B1,2.5,63.0 -2016-05-11,FL,2,B,B1,2.49,63.0 -2016-05-12,FL,2,B,B1,2.49,60.0 -2016-05-13,FL,2,B,B1,2.5,60.0 -2016-05-14,FL,2,B,B1,2.52,61.0 -2016-05-15,FL,2,B,B1,2.53,56.0 -2016-05-16,FL,2,B,B1,2.52,54.0 -2016-05-17,FL,2,B,B1,2.51,67.0 -2016-05-18,FL,2,B,B1,2.53,58.0 -2016-05-19,FL,2,B,B1,2.48,65.0 -2016-05-20,FL,2,B,B1,2.48,65.0 -2016-05-21,FL,2,B,B1,2.45,72.0 -2016-05-22,FL,2,B,B1,2.51,58.0 -2016-05-23,FL,2,B,B1,2.48,66.0 -2016-05-24,FL,2,B,B1,2.51,59.0 -2016-05-25,FL,2,B,B1,2.45,69.0 -2016-05-26,FL,2,B,B1,2.53,54.0 -2016-05-27,FL,2,B,B1,2.5,64.0 -2016-05-28,FL,2,B,B1,2.51,65.0 -2016-05-29,FL,2,B,B1,2.54,61.0 -2016-05-30,FL,2,B,B1,2.48,61.0 -2016-05-31,FL,2,B,B1,2.47,65.0 -2016-06-01,FL,2,B,B1,2.5,62.0 -2016-06-02,FL,2,B,B1,2.51,60.0 -2016-06-03,FL,2,B,B1,2.45,65.0 -2016-06-04,FL,2,B,B1,2.47,70.0 -2016-06-05,FL,2,B,B1,2.51,62.0 -2016-06-06,FL,2,B,B1,2.52,55.0 -2016-06-07,FL,2,B,B1,2.5,60.0 -2016-06-08,FL,2,B,B1,2.51,62.0 -2016-06-09,FL,2,B,B1,2.49,57.0 -2016-06-10,FL,2,B,B1,2.52,64.0 -2016-06-11,FL,2,B,B1,2.47,66.0 -2016-06-12,FL,2,B,B1,2.5,66.0 -2016-06-13,FL,2,B,B1,2.54,55.0 -2016-06-14,FL,2,B,B1,2.5,59.0 -2016-06-15,FL,2,B,B1,2.52,63.0 -2016-06-16,FL,2,B,B1,2.5,64.0 -2016-06-17,FL,2,B,B1,2.53,58.0 -2016-06-18,FL,2,B,B1,2.52,58.0 -2016-06-19,FL,2,B,B1,2.56,58.0 -2016-06-20,FL,2,B,B1,2.49,61.0 -2016-06-21,FL,2,B,B1,2.44,66.0 -2016-06-22,FL,2,B,B1,2.5,59.0 -2016-06-23,FL,2,B,B1,2.53,57.0 -2016-06-24,FL,2,B,B1,2.51,63.0 -2016-06-25,FL,2,B,B1,2.51,62.0 -2016-06-26,FL,2,B,B1,2.53,61.0 -2016-06-27,FL,2,B,B1,2.5,66.0 -2016-06-28,FL,2,B,B1,2.53,62.0 -2016-06-29,FL,2,B,B1,2.52,59.0 -2016-06-30,FL,2,B,B1,2.5,59.0 -2016-07-01,FL,2,B,B1,2.51,57.0 -2016-07-02,FL,2,B,B1,2.51,63.0 -2016-07-03,FL,2,B,B1,2.5,68.0 -2016-07-04,FL,2,B,B1,2.53,61.0 -2016-07-05,FL,2,B,B1,2.5,58.0 -2016-07-06,FL,2,B,B1,2.52,59.0 -2016-07-07,FL,2,B,B1,2.56,61.0 -2016-07-08,FL,2,B,B1,2.49,62.0 -2016-07-09,FL,2,B,B1,2.5,65.0 -2016-07-10,FL,2,B,B1,2.5,61.0 -2016-07-11,FL,2,B,B1,2.5,59.0 -2016-07-12,FL,2,B,B1,2.48,57.0 -2016-07-13,FL,2,B,B1,2.55,59.0 -2016-07-14,FL,2,B,B1,2.47,67.0 -2016-07-15,FL,2,B,B1,2.49,71.0 -2016-07-16,FL,2,B,B1,2.5,66.0 -2016-07-17,FL,2,B,B1,2.48,62.0 -2016-07-18,FL,2,B,B1,2.55,62.0 -2016-07-19,FL,2,B,B1,2.46,70.0 -2016-07-20,FL,2,B,B1,2.48,63.0 -2016-07-21,FL,2,B,B1,2.51,67.0 -2016-07-22,FL,2,B,B1,2.51,65.0 -2016-07-23,FL,2,B,B1,2.47,70.0 -2016-07-24,FL,2,B,B1,2.53,61.0 -2016-07-25,FL,2,B,B1,2.53,54.0 -2016-07-26,FL,2,B,B1,2.5,61.0 -2016-07-27,FL,2,B,B1,2.5,68.0 -2015-01-05,FL,2,B,B2,7.85,726.0 -2015-01-06,FL,2,B,B2,8.03,718.0 -2015-01-07,FL,2,B,B2,8.07,659.0 -2015-01-08,FL,2,B,B2,7.87,708.0 -2015-01-09,FL,2,B,B2,7.96,692.0 -2015-01-10,FL,2,B,B2,7.91,683.0 -2015-01-11,FL,2,B,B2,7.98,686.0 -2015-01-12,FL,2,B,B2,7.95,666.0 -2015-01-13,FL,2,B,B2,7.94,675.0 -2015-01-14,FL,2,B,B2,7.84,700.0 -2015-01-15,FL,2,B,B2,7.92,737.0 -2015-01-16,FL,2,B,B2,7.87,693.0 -2015-01-17,FL,2,B,B2,7.97,729.0 -2015-01-18,FL,2,B,B2,7.9,779.0 -2015-01-19,FL,2,B,B2,7.95,709.0 -2015-01-20,FL,2,B,B2,7.92,731.0 -2015-01-21,FL,2,B,B2,7.79,756.0 -2015-01-22,FL,2,B,B2,7.86,735.0 -2015-01-23,FL,2,B,B2,7.91,726.0 -2015-01-24,FL,2,B,B2,7.87,794.0 -2015-01-25,FL,2,B,B2,7.89,803.0 -2015-01-26,FL,2,B,B2,7.95,747.0 -2015-01-27,FL,2,B,B2,8.03,694.0 -2015-01-28,FL,2,B,B2,7.9,795.0 -2015-01-29,FL,2,B,B2,7.86,767.0 -2015-01-30,FL,2,B,B2,8.04,748.0 -2015-01-31,FL,2,B,B2,7.91,848.0 -2015-02-01,FL,2,B,B2,8.01,775.0 -2015-02-02,FL,2,B,B2,7.96,758.0 -2015-02-03,FL,2,B,B2,7.97,767.0 -2015-02-04,FL,2,B,B2,7.95,758.0 -2015-02-05,FL,2,B,B2,7.98,794.0 -2015-02-06,FL,2,B,B2,7.99,738.0 -2015-02-07,FL,2,B,B2,7.8,867.0 -2015-02-08,FL,2,B,B2,7.96,777.0 -2015-02-09,FL,2,B,B2,7.91,788.0 -2015-02-10,FL,2,B,B2,7.93,790.0 -2015-02-11,FL,2,B,B2,8.02,761.0 -2015-02-12,FL,2,B,B2,7.93,759.0 -2015-02-13,FL,2,B,B2,8.01,746.0 -2015-02-14,FL,2,B,B2,8.02,746.0 -2015-02-15,FL,2,B,B2,7.88,787.0 -2015-02-16,FL,2,B,B2,8.06,749.0 -2015-02-17,FL,2,B,B2,8.12,804.0 -2015-02-18,FL,2,B,B2,7.9,798.0 -2015-02-19,FL,2,B,B2,7.97,795.0 -2015-02-20,FL,2,B,B2,8.0,793.0 -2015-02-21,FL,2,B,B2,8.01,885.0 -2015-02-22,FL,2,B,B2,8.01,822.0 -2015-02-23,FL,2,B,B2,7.92,839.0 -2015-02-24,FL,2,B,B2,8.14,787.0 -2015-02-25,FL,2,B,B2,7.82,831.0 -2015-02-26,FL,2,B,B2,7.92,793.0 -2015-02-27,FL,2,B,B2,7.98,762.0 -2015-02-28,FL,2,B,B2,7.94,834.0 -2015-03-01,FL,2,B,B2,8.0,802.0 -2015-03-02,FL,2,B,B2,7.99,788.0 -2015-03-03,FL,2,B,B2,8.0,799.0 -2015-03-04,FL,2,B,B2,8.07,795.0 -2015-03-05,FL,2,B,B2,7.89,881.0 -2015-03-06,FL,2,B,B2,8.0,825.0 -2015-03-07,FL,2,B,B2,8.01,814.0 -2015-03-08,FL,2,B,B2,8.03,822.0 -2015-03-09,FL,2,B,B2,7.78,879.0 -2015-03-10,FL,2,B,B2,7.87,893.0 -2015-03-11,FL,2,B,B2,7.85,880.0 -2015-03-12,FL,2,B,B2,7.99,824.0 -2015-03-13,FL,2,B,B2,7.82,891.0 -2015-03-14,FL,2,B,B2,8.12,802.0 -2015-03-15,FL,2,B,B2,7.95,848.0 -2015-03-16,FL,2,B,B2,8.01,834.0 -2015-03-17,FL,2,B,B2,7.89,898.0 -2015-03-18,FL,2,B,B2,8.01,832.0 -2015-03-19,FL,2,B,B2,8.08,792.0 -2015-03-20,FL,2,B,B2,7.87,864.0 -2015-03-21,FL,2,B,B2,7.99,871.0 -2015-03-22,FL,2,B,B2,7.93,830.0 -2015-03-23,FL,2,B,B2,7.88,885.0 -2015-03-24,FL,2,B,B2,7.98,869.0 -2015-03-25,FL,2,B,B2,7.93,856.0 -2015-03-26,FL,2,B,B2,8.01,815.0 -2015-03-27,FL,2,B,B2,8.04,819.0 -2015-03-28,FL,2,B,B2,7.89,863.0 -2015-03-29,FL,2,B,B2,7.95,822.0 -2015-03-30,FL,2,B,B2,7.93,855.0 -2015-03-31,FL,2,B,B2,8.09,815.0 -2015-04-01,FL,2,B,B2,7.98,845.0 -2015-04-02,FL,2,B,B2,7.81,888.0 -2015-04-03,FL,2,B,B2,7.76,860.0 -2015-04-04,FL,2,B,B2,7.84,919.0 -2015-04-05,FL,2,B,B2,7.82,863.0 -2015-04-06,FL,2,B,B2,7.96,839.0 -2015-04-07,FL,2,B,B2,7.99,831.0 -2015-04-08,FL,2,B,B2,7.85,872.0 -2015-04-09,FL,2,B,B2,7.89,857.0 -2015-04-10,FL,2,B,B2,7.8,861.0 -2015-04-11,FL,2,B,B2,8.02,874.0 -2015-04-12,FL,2,B,B2,8.04,753.0 -2015-04-13,FL,2,B,B2,7.87,857.0 -2015-04-14,FL,2,B,B2,7.88,839.0 -2015-04-15,FL,2,B,B2,8.0,812.0 -2015-04-16,FL,2,B,B2,7.89,903.0 -2015-04-17,FL,2,B,B2,7.84,888.0 -2015-04-18,FL,2,B,B2,7.87,906.0 -2015-04-19,FL,2,B,B2,8.06,892.0 -2015-04-20,FL,2,B,B2,7.94,842.0 -2015-04-21,FL,2,B,B2,8.06,859.0 -2015-04-22,FL,2,B,B2,7.95,847.0 -2015-04-23,FL,2,B,B2,7.99,818.0 -2015-04-24,FL,2,B,B2,7.97,818.0 -2015-04-25,FL,2,B,B2,7.86,900.0 -2015-04-26,FL,2,B,B2,8.06,876.0 -2015-04-27,FL,2,B,B2,7.9,824.0 -2015-04-28,FL,2,B,B2,7.86,868.0 -2015-04-29,FL,2,B,B2,7.88,832.0 -2015-04-30,FL,2,B,B2,7.92,860.0 -2015-05-01,FL,2,B,B2,7.92,862.0 -2015-05-02,FL,2,B,B2,7.97,871.0 -2015-05-03,FL,2,B,B2,7.92,866.0 -2015-05-04,FL,2,B,B2,8.02,798.0 -2015-05-05,FL,2,B,B2,8.02,841.0 -2015-05-06,FL,2,B,B2,7.83,920.0 -2015-05-07,FL,2,B,B2,8.0,842.0 -2015-05-08,FL,2,B,B2,7.89,869.0 -2015-05-09,FL,2,B,B2,8.0,822.0 -2015-05-10,FL,2,B,B2,7.89,901.0 -2015-05-11,FL,2,B,B2,8.13,760.0 -2015-05-12,FL,2,B,B2,7.95,851.0 -2015-05-13,FL,2,B,B2,7.89,823.0 -2015-05-14,FL,2,B,B2,7.96,842.0 -2015-05-15,FL,2,B,B2,8.11,831.0 -2015-05-16,FL,2,B,B2,7.79,934.0 -2015-05-17,FL,2,B,B2,7.98,862.0 -2015-05-18,FL,2,B,B2,7.96,841.0 -2015-05-19,FL,2,B,B2,7.95,863.0 -2015-05-20,FL,2,B,B2,7.73,906.0 -2015-05-21,FL,2,B,B2,7.86,854.0 -2015-05-22,FL,2,B,B2,8.01,795.0 -2015-05-23,FL,2,B,B2,8.03,852.0 -2015-05-24,FL,2,B,B2,7.87,882.0 -2015-05-25,FL,2,B,B2,8.1,851.0 -2015-05-26,FL,2,B,B2,7.83,870.0 -2015-05-27,FL,2,B,B2,7.98,859.0 -2015-05-28,FL,2,B,B2,7.88,868.0 -2015-05-29,FL,2,B,B2,7.9,901.0 -2015-05-30,FL,2,B,B2,7.86,912.0 -2015-05-31,FL,2,B,B2,8.13,853.0 -2015-06-01,FL,2,B,B2,7.99,778.0 -2015-06-02,FL,2,B,B2,7.97,830.0 -2015-06-03,FL,2,B,B2,7.97,869.0 -2015-06-04,FL,2,B,B2,7.95,843.0 -2015-06-05,FL,2,B,B2,7.98,891.0 -2015-06-06,FL,2,B,B2,7.94,903.0 -2015-06-07,FL,2,B,B2,8.07,799.0 -2015-06-08,FL,2,B,B2,8.0,795.0 -2015-06-09,FL,2,B,B2,7.92,860.0 -2015-06-10,FL,2,B,B2,7.92,862.0 -2015-06-11,FL,2,B,B2,8.1,787.0 -2015-06-12,FL,2,B,B2,7.92,891.0 -2015-06-13,FL,2,B,B2,7.91,900.0 -2015-06-14,FL,2,B,B2,7.96,868.0 -2015-06-15,FL,2,B,B2,7.96,834.0 -2015-06-16,FL,2,B,B2,7.93,837.0 -2015-06-17,FL,2,B,B2,7.95,868.0 -2015-06-18,FL,2,B,B2,7.93,853.0 -2015-06-19,FL,2,B,B2,8.0,831.0 -2015-06-20,FL,2,B,B2,7.85,887.0 -2015-06-21,FL,2,B,B2,7.88,883.0 -2015-06-22,FL,2,B,B2,8.02,830.0 -2015-06-23,FL,2,B,B2,7.92,882.0 -2015-06-24,FL,2,B,B2,7.84,882.0 -2015-06-25,FL,2,B,B2,7.95,855.0 -2015-06-26,FL,2,B,B2,8.09,851.0 -2015-06-27,FL,2,B,B2,7.98,894.0 -2015-06-28,FL,2,B,B2,7.91,896.0 -2015-06-29,FL,2,B,B2,7.85,865.0 -2015-06-30,FL,2,B,B2,7.93,870.0 -2015-07-01,FL,2,B,B2,7.87,861.0 -2015-07-02,FL,2,B,B2,7.95,857.0 -2015-07-03,FL,2,B,B2,7.97,904.0 -2015-07-04,FL,2,B,B2,7.97,873.0 -2015-07-05,FL,2,B,B2,7.87,861.0 -2015-07-06,FL,2,B,B2,8.1,845.0 -2015-07-07,FL,2,B,B2,8.02,834.0 -2015-07-08,FL,2,B,B2,7.99,839.0 -2015-07-09,FL,2,B,B2,8.15,793.0 -2015-07-10,FL,2,B,B2,7.87,842.0 -2015-07-11,FL,2,B,B2,7.75,937.0 -2015-07-12,FL,2,B,B2,7.89,899.0 -2015-07-13,FL,2,B,B2,7.89,863.0 -2015-07-14,FL,2,B,B2,7.96,903.0 -2015-07-15,FL,2,B,B2,7.89,870.0 -2015-07-16,FL,2,B,B2,7.84,900.0 -2015-07-17,FL,2,B,B2,8.03,826.0 -2015-07-18,FL,2,B,B2,7.9,857.0 -2015-07-19,FL,2,B,B2,7.9,829.0 -2015-07-20,FL,2,B,B2,8.03,834.0 -2015-07-21,FL,2,B,B2,7.95,877.0 -2015-07-22,FL,2,B,B2,8.02,856.0 -2015-07-23,FL,2,B,B2,7.92,838.0 -2015-07-24,FL,2,B,B2,8.03,811.0 -2015-07-25,FL,2,B,B2,7.93,895.0 -2015-07-26,FL,2,B,B2,7.92,861.0 -2015-07-27,FL,2,B,B2,7.86,852.0 -2015-07-28,FL,2,B,B2,7.95,833.0 -2015-07-29,FL,2,B,B2,7.91,886.0 -2015-07-30,FL,2,B,B2,8.03,816.0 -2015-07-31,FL,2,B,B2,7.95,811.0 -2015-08-01,FL,2,B,B2,7.9,855.0 -2015-08-02,FL,2,B,B2,7.93,877.0 -2015-08-03,FL,2,B,B2,7.98,862.0 -2015-08-04,FL,2,B,B2,7.9,854.0 -2015-08-05,FL,2,B,B2,7.79,862.0 -2015-08-06,FL,2,B,B2,7.98,855.0 -2015-08-07,FL,2,B,B2,8.04,820.0 -2015-08-08,FL,2,B,B2,8.01,841.0 -2015-08-09,FL,2,B,B2,7.92,892.0 -2015-08-10,FL,2,B,B2,7.82,863.0 -2015-08-11,FL,2,B,B2,7.85,825.0 -2015-08-12,FL,2,B,B2,8.0,835.0 -2015-08-13,FL,2,B,B2,7.91,831.0 -2015-08-14,FL,2,B,B2,8.03,843.0 -2015-08-15,FL,2,B,B2,7.87,867.0 -2015-08-16,FL,2,B,B2,7.92,876.0 -2015-08-17,FL,2,B,B2,8.01,808.0 -2015-08-18,FL,2,B,B2,8.03,848.0 -2015-08-19,FL,2,B,B2,7.89,875.0 -2015-08-20,FL,2,B,B2,7.99,818.0 -2015-08-21,FL,2,B,B2,7.91,873.0 -2015-08-22,FL,2,B,B2,7.91,848.0 -2015-08-23,FL,2,B,B2,7.94,915.0 -2015-08-24,FL,2,B,B2,8.02,803.0 -2015-08-25,FL,2,B,B2,7.96,833.0 -2015-08-26,FL,2,B,B2,7.98,828.0 -2015-08-27,FL,2,B,B2,7.86,911.0 -2015-08-28,FL,2,B,B2,8.06,825.0 -2015-08-29,FL,2,B,B2,7.88,862.0 -2015-08-30,FL,2,B,B2,8.0,832.0 -2015-08-31,FL,2,B,B2,8.07,759.0 -2015-09-01,FL,2,B,B2,8.09,829.0 -2015-09-02,FL,2,B,B2,7.96,864.0 -2015-09-03,FL,2,B,B2,7.99,818.0 -2015-09-04,FL,2,B,B2,7.94,856.0 -2015-09-05,FL,2,B,B2,7.82,920.0 -2015-09-06,FL,2,B,B2,7.87,880.0 -2015-09-07,FL,2,B,B2,7.97,818.0 -2015-09-08,FL,2,B,B2,7.99,861.0 -2015-09-09,FL,2,B,B2,7.87,858.0 -2015-09-10,FL,2,B,B2,8.09,833.0 -2015-09-11,FL,2,B,B2,7.93,849.0 -2015-09-12,FL,2,B,B2,8.05,849.0 -2015-09-13,FL,2,B,B2,7.75,922.0 -2015-09-14,FL,2,B,B2,8.05,804.0 -2015-09-15,FL,2,B,B2,7.88,854.0 -2015-09-16,FL,2,B,B2,7.95,843.0 -2015-09-17,FL,2,B,B2,7.85,908.0 -2015-09-18,FL,2,B,B2,7.83,935.0 -2015-09-19,FL,2,B,B2,7.91,875.0 -2015-09-20,FL,2,B,B2,7.9,817.0 -2015-09-21,FL,2,B,B2,7.87,833.0 -2015-09-22,FL,2,B,B2,7.89,881.0 -2015-09-23,FL,2,B,B2,7.98,837.0 -2015-09-24,FL,2,B,B2,7.92,872.0 -2015-09-25,FL,2,B,B2,8.06,834.0 -2015-09-26,FL,2,B,B2,7.91,888.0 -2015-09-27,FL,2,B,B2,8.06,823.0 -2015-09-28,FL,2,B,B2,7.88,857.0 -2015-09-29,FL,2,B,B2,7.76,933.0 -2015-09-30,FL,2,B,B2,7.98,868.0 -2015-10-01,FL,2,B,B2,7.87,884.0 -2015-10-02,FL,2,B,B2,8.11,808.0 -2015-10-03,FL,2,B,B2,8.01,867.0 -2015-10-04,FL,2,B,B2,7.98,878.0 -2015-10-05,FL,2,B,B2,7.92,817.0 -2015-10-06,FL,2,B,B2,7.91,891.0 -2015-10-07,FL,2,B,B2,7.92,873.0 -2015-10-08,FL,2,B,B2,7.81,862.0 -2015-10-09,FL,2,B,B2,8.05,848.0 -2015-10-10,FL,2,B,B2,7.88,887.0 -2015-10-11,FL,2,B,B2,7.96,867.0 -2015-10-12,FL,2,B,B2,7.88,877.0 -2015-10-13,FL,2,B,B2,7.9,887.0 -2015-10-14,FL,2,B,B2,7.89,832.0 -2015-10-15,FL,2,B,B2,7.92,839.0 -2015-10-16,FL,2,B,B2,7.81,940.0 -2015-10-17,FL,2,B,B2,7.86,859.0 -2015-10-18,FL,2,B,B2,7.96,833.0 -2015-10-19,FL,2,B,B2,7.92,869.0 -2015-10-20,FL,2,B,B2,7.72,932.0 -2015-10-21,FL,2,B,B2,7.93,864.0 -2015-10-22,FL,2,B,B2,7.92,879.0 -2015-10-23,FL,2,B,B2,8.05,819.0 -2015-10-24,FL,2,B,B2,7.82,911.0 -2015-10-25,FL,2,B,B2,7.85,912.0 -2015-10-26,FL,2,B,B2,8.0,827.0 -2015-10-27,FL,2,B,B2,7.81,901.0 -2015-10-28,FL,2,B,B2,7.97,844.0 -2015-10-29,FL,2,B,B2,7.92,836.0 -2015-10-30,FL,2,B,B2,7.96,871.0 -2015-10-31,FL,2,B,B2,7.97,900.0 -2015-11-01,FL,2,B,B2,7.96,828.0 -2015-11-02,FL,2,B,B2,7.94,874.0 -2015-11-03,FL,2,B,B2,7.88,901.0 -2015-11-04,FL,2,B,B2,7.96,846.0 -2015-11-05,FL,2,B,B2,8.07,796.0 -2015-11-06,FL,2,B,B2,7.86,832.0 -2015-11-07,FL,2,B,B2,7.92,889.0 -2015-11-08,FL,2,B,B2,7.96,832.0 -2015-11-09,FL,2,B,B2,7.88,838.0 -2015-11-10,FL,2,B,B2,7.95,876.0 -2015-11-11,FL,2,B,B2,7.76,905.0 -2015-11-12,FL,2,B,B2,7.97,856.0 -2015-11-13,FL,2,B,B2,7.91,854.0 -2015-11-14,FL,2,B,B2,8.07,808.0 -2015-11-15,FL,2,B,B2,7.88,854.0 -2015-11-16,FL,2,B,B2,7.97,790.0 -2015-11-17,FL,2,B,B2,7.98,839.0 -2015-11-18,FL,2,B,B2,7.93,850.0 -2015-11-19,FL,2,B,B2,8.01,848.0 -2015-11-20,FL,2,B,B2,7.95,830.0 -2015-11-21,FL,2,B,B2,7.86,864.0 -2015-11-22,FL,2,B,B2,7.91,857.0 -2015-11-23,FL,2,B,B2,7.93,857.0 -2015-11-24,FL,2,B,B2,7.88,901.0 -2015-11-25,FL,2,B,B2,7.93,859.0 -2015-11-26,FL,2,B,B2,7.94,874.0 -2015-11-27,FL,2,B,B2,8.03,824.0 -2015-11-28,FL,2,B,B2,8.02,913.0 -2015-11-29,FL,2,B,B2,8.03,860.0 -2015-11-30,FL,2,B,B2,7.92,858.0 -2015-12-01,FL,2,B,B2,7.92,805.0 -2015-12-02,FL,2,B,B2,7.9,865.0 -2015-12-03,FL,2,B,B2,8.02,862.0 -2015-12-04,FL,2,B,B2,7.91,850.0 -2015-12-05,FL,2,B,B2,7.85,898.0 -2015-12-06,FL,2,B,B2,7.96,881.0 -2015-12-07,FL,2,B,B2,7.97,818.0 -2015-12-08,FL,2,B,B2,7.91,849.0 -2015-12-09,FL,2,B,B2,7.93,866.0 -2015-12-10,FL,2,B,B2,7.72,865.0 -2015-12-11,FL,2,B,B2,7.92,850.0 -2015-12-12,FL,2,B,B2,7.89,937.0 -2015-12-13,FL,2,B,B2,7.84,886.0 -2015-12-14,FL,2,B,B2,7.92,864.0 -2015-12-15,FL,2,B,B2,8.06,801.0 -2015-12-16,FL,2,B,B2,7.88,841.0 -2015-12-17,FL,2,B,B2,7.93,827.0 -2015-12-18,FL,2,B,B2,7.95,880.0 -2015-12-19,FL,2,B,B2,7.88,942.0 -2015-12-20,FL,2,B,B2,7.77,942.0 -2015-12-21,FL,2,B,B2,7.99,821.0 -2015-12-22,FL,2,B,B2,7.91,886.0 -2015-12-23,FL,2,B,B2,7.95,855.0 -2015-12-24,FL,2,B,B2,8.02,801.0 -2015-12-25,FL,2,B,B2,7.87,846.0 -2015-12-26,FL,2,B,B2,7.94,896.0 -2015-12-27,FL,2,B,B2,8.04,889.0 -2015-12-28,FL,2,B,B2,7.88,831.0 -2015-12-29,FL,2,B,B2,7.83,910.0 -2015-12-30,FL,2,B,B2,8.0,856.0 -2015-12-31,FL,2,B,B2,7.95,838.0 -2016-01-01,FL,2,B,B2,7.96,842.0 -2016-01-02,FL,2,B,B2,8.03,877.0 -2016-01-03,FL,2,B,B2,8.0,868.0 -2016-01-04,FL,2,B,B2,7.99,828.0 -2016-01-05,FL,2,B,B2,7.94,821.0 -2016-01-06,FL,2,B,B2,8.02,810.0 -2016-01-07,FL,2,B,B2,8.05,851.0 -2016-01-08,FL,2,B,B2,8.0,839.0 -2016-01-09,FL,2,B,B2,7.9,857.0 -2016-01-10,FL,2,B,B2,7.84,878.0 -2016-01-11,FL,2,B,B2,7.9,863.0 -2016-01-12,FL,2,B,B2,8.14,830.0 -2016-01-13,FL,2,B,B2,7.8,874.0 -2016-01-14,FL,2,B,B2,7.99,845.0 -2016-01-15,FL,2,B,B2,7.98,822.0 -2016-01-16,FL,2,B,B2,7.74,941.0 -2016-01-17,FL,2,B,B2,7.85,876.0 -2016-01-18,FL,2,B,B2,7.88,886.0 -2016-01-19,FL,2,B,B2,7.88,856.0 -2016-01-20,FL,2,B,B2,8.17,780.0 -2016-01-21,FL,2,B,B2,7.92,835.0 -2016-01-22,FL,2,B,B2,7.9,895.0 -2016-01-23,FL,2,B,B2,8.11,812.0 -2016-01-24,FL,2,B,B2,8.0,867.0 -2016-01-25,FL,2,B,B2,7.9,904.0 -2016-01-26,FL,2,B,B2,7.85,839.0 -2016-01-27,FL,2,B,B2,7.98,831.0 -2016-01-28,FL,2,B,B2,7.88,852.0 -2016-01-29,FL,2,B,B2,7.89,874.0 -2016-01-30,FL,2,B,B2,7.92,867.0 -2016-01-31,FL,2,B,B2,8.02,869.0 -2016-02-01,FL,2,B,B2,7.91,829.0 -2016-02-02,FL,2,B,B2,7.96,786.0 -2016-02-03,FL,2,B,B2,8.04,809.0 -2016-02-04,FL,2,B,B2,7.94,874.0 -2016-02-05,FL,2,B,B2,7.94,878.0 -2016-02-06,FL,2,B,B2,7.97,903.0 -2016-02-07,FL,2,B,B2,7.89,864.0 -2016-02-08,FL,2,B,B2,7.92,884.0 -2016-02-09,FL,2,B,B2,7.99,811.0 -2016-02-10,FL,2,B,B2,8.02,815.0 -2016-02-11,FL,2,B,B2,8.03,776.0 -2016-02-12,FL,2,B,B2,8.1,813.0 -2016-02-13,FL,2,B,B2,7.87,903.0 -2016-02-14,FL,2,B,B2,7.86,847.0 -2016-02-15,FL,2,B,B2,7.97,803.0 -2016-02-16,FL,2,B,B2,8.05,786.0 -2016-02-17,FL,2,B,B2,7.91,859.0 -2016-02-18,FL,2,B,B2,7.92,857.0 -2016-02-19,FL,2,B,B2,7.95,847.0 -2016-02-20,FL,2,B,B2,8.05,853.0 -2016-02-21,FL,2,B,B2,8.02,870.0 -2016-02-22,FL,2,B,B2,8.03,814.0 -2016-02-23,FL,2,B,B2,7.92,853.0 -2016-02-24,FL,2,B,B2,7.77,884.0 -2016-02-25,FL,2,B,B2,7.97,807.0 -2016-02-26,FL,2,B,B2,7.94,870.0 -2016-02-27,FL,2,B,B2,7.95,874.0 -2016-02-28,FL,2,B,B2,8.05,859.0 -2016-02-29,FL,2,B,B2,7.84,883.0 -2016-03-01,FL,2,B,B2,7.83,886.0 -2016-03-02,FL,2,B,B2,7.81,865.0 -2016-03-03,FL,2,B,B2,7.87,894.0 -2016-03-04,FL,2,B,B2,7.88,877.0 -2016-03-05,FL,2,B,B2,7.88,860.0 -2016-03-06,FL,2,B,B2,8.02,848.0 -2016-03-07,FL,2,B,B2,7.97,859.0 -2016-03-08,FL,2,B,B2,7.98,844.0 -2016-03-09,FL,2,B,B2,8.01,863.0 -2016-03-10,FL,2,B,B2,8.02,773.0 -2016-03-11,FL,2,B,B2,7.91,904.0 -2016-03-12,FL,2,B,B2,7.88,876.0 -2016-03-13,FL,2,B,B2,7.98,831.0 -2016-03-14,FL,2,B,B2,7.96,832.0 -2016-03-15,FL,2,B,B2,7.83,853.0 -2016-03-16,FL,2,B,B2,7.96,873.0 -2016-03-17,FL,2,B,B2,7.87,893.0 -2016-03-18,FL,2,B,B2,7.97,882.0 -2016-03-19,FL,2,B,B2,7.82,879.0 -2016-03-20,FL,2,B,B2,7.99,859.0 -2016-03-21,FL,2,B,B2,7.94,805.0 -2016-03-22,FL,2,B,B2,8.01,774.0 -2016-03-23,FL,2,B,B2,7.86,890.0 -2016-03-24,FL,2,B,B2,7.97,820.0 -2016-03-25,FL,2,B,B2,7.93,861.0 -2016-03-26,FL,2,B,B2,7.89,897.0 -2016-03-27,FL,2,B,B2,8.05,767.0 -2016-03-28,FL,2,B,B2,7.9,847.0 -2016-03-29,FL,2,B,B2,7.79,874.0 -2016-03-30,FL,2,B,B2,7.92,875.0 -2016-03-31,FL,2,B,B2,8.01,850.0 -2016-04-01,FL,2,B,B2,8.03,841.0 -2016-04-02,FL,2,B,B2,7.95,872.0 -2016-04-03,FL,2,B,B2,8.04,807.0 -2016-04-04,FL,2,B,B2,7.89,899.0 -2016-04-05,FL,2,B,B2,7.83,877.0 -2016-04-06,FL,2,B,B2,8.06,832.0 -2016-04-07,FL,2,B,B2,7.92,826.0 -2016-04-08,FL,2,B,B2,7.97,851.0 -2016-04-09,FL,2,B,B2,7.97,885.0 -2016-04-10,FL,2,B,B2,8.05,872.0 -2016-04-11,FL,2,B,B2,7.97,861.0 -2016-04-12,FL,2,B,B2,7.88,880.0 -2016-04-13,FL,2,B,B2,8.06,816.0 -2016-04-14,FL,2,B,B2,7.82,883.0 -2016-04-15,FL,2,B,B2,7.87,880.0 -2016-04-16,FL,2,B,B2,7.84,898.0 -2016-04-17,FL,2,B,B2,7.92,882.0 -2016-04-18,FL,2,B,B2,8.04,818.0 -2016-04-19,FL,2,B,B2,7.9,876.0 -2016-04-20,FL,2,B,B2,7.85,908.0 -2016-04-21,FL,2,B,B2,8.03,830.0 -2016-04-22,FL,2,B,B2,7.8,860.0 -2016-04-23,FL,2,B,B2,7.95,901.0 -2016-04-24,FL,2,B,B2,8.05,833.0 -2016-04-25,FL,2,B,B2,8.08,840.0 -2016-04-26,FL,2,B,B2,7.93,855.0 -2016-04-27,FL,2,B,B2,7.95,863.0 -2016-04-28,FL,2,B,B2,7.81,906.0 -2016-04-29,FL,2,B,B2,7.98,807.0 -2016-04-30,FL,2,B,B2,7.97,872.0 -2016-05-01,FL,2,B,B2,7.94,885.0 -2016-05-02,FL,2,B,B2,7.92,863.0 -2016-05-03,FL,2,B,B2,7.99,827.0 -2016-05-04,FL,2,B,B2,7.92,845.0 -2016-05-05,FL,2,B,B2,7.96,828.0 -2016-05-06,FL,2,B,B2,8.08,797.0 -2016-05-07,FL,2,B,B2,8.03,862.0 -2016-05-08,FL,2,B,B2,7.94,848.0 -2016-05-09,FL,2,B,B2,7.97,792.0 -2016-05-10,FL,2,B,B2,7.92,882.0 -2016-05-11,FL,2,B,B2,7.89,845.0 -2016-05-12,FL,2,B,B2,7.97,871.0 -2016-05-13,FL,2,B,B2,8.0,852.0 -2016-05-14,FL,2,B,B2,8.04,853.0 -2016-05-15,FL,2,B,B2,7.98,826.0 -2016-05-16,FL,2,B,B2,7.88,857.0 -2016-05-17,FL,2,B,B2,7.82,872.0 -2016-05-18,FL,2,B,B2,7.93,849.0 -2016-05-19,FL,2,B,B2,7.81,863.0 -2016-05-20,FL,2,B,B2,7.98,859.0 -2016-05-21,FL,2,B,B2,7.85,922.0 -2016-05-22,FL,2,B,B2,7.97,898.0 -2016-05-23,FL,2,B,B2,8.04,789.0 -2016-05-24,FL,2,B,B2,7.8,872.0 -2016-05-25,FL,2,B,B2,8.04,822.0 -2016-05-26,FL,2,B,B2,7.87,876.0 -2016-05-27,FL,2,B,B2,7.96,876.0 -2016-05-28,FL,2,B,B2,7.97,871.0 -2016-05-29,FL,2,B,B2,7.9,859.0 -2016-05-30,FL,2,B,B2,7.98,879.0 -2016-05-31,FL,2,B,B2,7.94,854.0 -2016-06-01,FL,2,B,B2,8.02,825.0 -2016-06-02,FL,2,B,B2,7.94,853.0 -2016-06-03,FL,2,B,B2,7.91,859.0 -2016-06-04,FL,2,B,B2,7.8,935.0 -2016-06-05,FL,2,B,B2,7.88,943.0 -2016-06-06,FL,2,B,B2,7.93,809.0 -2016-06-07,FL,2,B,B2,8.01,898.0 -2016-06-08,FL,2,B,B2,8.02,802.0 -2016-06-09,FL,2,B,B2,7.91,886.0 -2016-06-10,FL,2,B,B2,7.99,854.0 -2016-06-11,FL,2,B,B2,7.9,897.0 -2016-06-12,FL,2,B,B2,7.96,899.0 -2016-06-13,FL,2,B,B2,7.87,879.0 -2016-06-14,FL,2,B,B2,7.92,885.0 -2016-06-15,FL,2,B,B2,8.08,796.0 -2016-06-16,FL,2,B,B2,7.83,911.0 -2016-06-17,FL,2,B,B2,8.03,834.0 -2016-06-18,FL,2,B,B2,7.85,890.0 -2016-06-19,FL,2,B,B2,7.91,831.0 -2016-06-20,FL,2,B,B2,7.82,859.0 -2016-06-21,FL,2,B,B2,7.94,849.0 -2016-06-22,FL,2,B,B2,7.92,862.0 -2016-06-23,FL,2,B,B2,8.02,793.0 -2016-06-24,FL,2,B,B2,7.93,830.0 -2016-06-25,FL,2,B,B2,7.84,917.0 -2016-06-26,FL,2,B,B2,7.89,896.0 -2016-06-27,FL,2,B,B2,7.93,809.0 -2016-06-28,FL,2,B,B2,8.02,828.0 -2016-06-29,FL,2,B,B2,8.01,801.0 -2016-06-30,FL,2,B,B2,7.82,884.0 -2016-07-01,FL,2,B,B2,8.1,824.0 -2016-07-02,FL,2,B,B2,7.91,897.0 -2016-07-03,FL,2,B,B2,7.91,862.0 -2016-07-04,FL,2,B,B2,8.06,810.0 -2016-07-05,FL,2,B,B2,7.92,825.0 -2016-07-06,FL,2,B,B2,7.93,850.0 -2016-07-07,FL,2,B,B2,8.02,850.0 -2016-07-08,FL,2,B,B2,7.92,862.0 -2016-07-09,FL,2,B,B2,7.93,856.0 -2016-07-10,FL,2,B,B2,8.07,855.0 -2016-07-11,FL,2,B,B2,8.02,797.0 -2016-07-12,FL,2,B,B2,7.99,852.0 -2016-07-13,FL,2,B,B2,8.21,781.0 -2016-07-14,FL,2,B,B2,7.85,867.0 -2016-07-15,FL,2,B,B2,7.97,833.0 -2016-07-16,FL,2,B,B2,7.93,937.0 -2016-07-17,FL,2,B,B2,7.8,888.0 -2016-07-18,FL,2,B,B2,7.94,847.0 -2016-07-19,FL,2,B,B2,7.89,842.0 -2016-07-20,FL,2,B,B2,7.96,850.0 -2016-07-21,FL,2,B,B2,7.97,830.0 -2016-07-22,FL,2,B,B2,7.92,862.0 -2016-07-23,FL,2,B,B2,8.05,868.0 -2016-07-24,FL,2,B,B2,8.03,868.0 -2016-07-25,FL,2,B,B2,7.92,859.0 -2016-07-26,FL,2,B,B2,7.96,856.0 -2016-07-27,FL,2,B,B2,7.94,880.0 -2015-01-05,FL,3,A,A1,4.07,54.0 -2015-01-06,FL,3,A,A1,4.18,50.0 -2015-01-07,FL,3,A,A1,4.12,58.0 -2015-01-08,FL,3,A,A1,4.07,60.0 -2015-01-09,FL,3,A,A1,4.09,57.0 -2015-01-10,FL,3,A,A1,4.17,58.0 -2015-01-11,FL,3,A,A1,4.13,48.0 -2015-01-12,FL,3,A,A1,4.1,56.0 -2015-01-13,FL,3,A,A1,4.13,49.0 -2015-01-14,FL,3,A,A1,4.12,49.0 -2015-01-15,FL,3,A,A1,4.14,57.0 -2015-01-16,FL,3,A,A1,4.12,61.0 -2015-01-17,FL,3,A,A1,4.14,60.0 -2015-01-18,FL,3,A,A1,4.1,56.0 -2015-01-19,FL,3,A,A1,4.1,54.0 -2015-01-20,FL,3,A,A1,4.08,57.0 -2015-01-21,FL,3,A,A1,4.12,50.0 -2015-01-22,FL,3,A,A1,4.15,60.0 -2015-01-23,FL,3,A,A1,4.1,55.0 -2015-01-24,FL,3,A,A1,4.13,58.0 -2015-01-25,FL,3,A,A1,4.13,52.0 -2015-01-26,FL,3,A,A1,4.13,48.0 -2015-01-27,FL,3,A,A1,4.09,58.0 -2015-01-28,FL,3,A,A1,4.1,52.0 -2015-01-29,FL,3,A,A1,4.14,51.0 -2015-01-30,FL,3,A,A1,4.14,50.0 -2015-01-31,FL,3,A,A1,4.17,54.0 -2015-02-01,FL,3,A,A1,4.08,61.0 -2015-02-02,FL,3,A,A1,4.07,60.0 -2015-02-03,FL,3,A,A1,4.22,50.0 -2015-02-04,FL,3,A,A1,4.14,57.0 -2015-02-05,FL,3,A,A1,4.18,55.0 -2015-02-06,FL,3,A,A1,4.15,56.0 -2015-02-07,FL,3,A,A1,4.13,56.0 -2015-02-08,FL,3,A,A1,4.12,58.0 -2015-02-09,FL,3,A,A1,4.19,49.0 -2015-02-10,FL,3,A,A1,4.15,51.0 -2015-02-11,FL,3,A,A1,4.03,55.0 -2015-02-12,FL,3,A,A1,4.05,64.0 -2015-02-13,FL,3,A,A1,4.08,57.0 -2015-02-14,FL,3,A,A1,4.07,61.0 -2015-02-15,FL,3,A,A1,4.01,60.0 -2015-02-16,FL,3,A,A1,4.16,50.0 -2015-02-17,FL,3,A,A1,4.09,56.0 -2015-02-18,FL,3,A,A1,4.16,50.0 -2015-02-19,FL,3,A,A1,4.06,53.0 -2015-02-20,FL,3,A,A1,4.11,55.0 -2015-02-21,FL,3,A,A1,4.22,51.0 -2015-02-22,FL,3,A,A1,4.09,60.0 -2015-02-23,FL,3,A,A1,4.15,54.0 -2015-02-24,FL,3,A,A1,4.08,59.0 -2015-02-25,FL,3,A,A1,4.1,54.0 -2015-02-26,FL,3,A,A1,4.08,60.0 -2015-02-27,FL,3,A,A1,4.14,53.0 -2015-02-28,FL,3,A,A1,4.19,54.0 -2015-03-01,FL,3,A,A1,4.09,58.0 -2015-03-02,FL,3,A,A1,4.16,52.0 -2015-03-03,FL,3,A,A1,4.13,57.0 -2015-03-04,FL,3,A,A1,4.19,51.0 -2015-03-05,FL,3,A,A1,4.13,56.0 -2015-03-06,FL,3,A,A1,4.18,54.0 -2015-03-07,FL,3,A,A1,4.12,61.0 -2015-03-08,FL,3,A,A1,4.17,55.0 -2015-03-09,FL,3,A,A1,4.1,52.0 -2015-03-10,FL,3,A,A1,4.02,60.0 -2015-03-11,FL,3,A,A1,4.11,58.0 -2015-03-12,FL,3,A,A1,4.09,59.0 -2015-03-13,FL,3,A,A1,4.08,56.0 -2015-03-14,FL,3,A,A1,4.14,60.0 -2015-03-15,FL,3,A,A1,4.15,53.0 -2015-03-16,FL,3,A,A1,4.14,54.0 -2015-03-17,FL,3,A,A1,4.08,58.0 -2015-03-18,FL,3,A,A1,4.04,56.0 -2015-03-19,FL,3,A,A1,4.05,60.0 -2015-03-20,FL,3,A,A1,4.1,60.0 -2015-03-21,FL,3,A,A1,4.15,59.0 -2015-03-22,FL,3,A,A1,4.09,53.0 -2015-03-23,FL,3,A,A1,4.07,60.0 -2015-03-24,FL,3,A,A1,4.17,51.0 -2015-03-25,FL,3,A,A1,4.12,56.0 -2015-03-26,FL,3,A,A1,4.12,57.0 -2015-03-27,FL,3,A,A1,4.08,59.0 -2015-03-28,FL,3,A,A1,4.13,58.0 -2015-03-29,FL,3,A,A1,4.07,57.0 -2015-03-30,FL,3,A,A1,4.14,48.0 -2015-03-31,FL,3,A,A1,4.12,55.0 -2015-04-01,FL,3,A,A1,4.14,53.0 -2015-04-02,FL,3,A,A1,4.03,60.0 -2015-04-03,FL,3,A,A1,4.09,56.0 -2015-04-04,FL,3,A,A1,4.19,62.0 -2015-04-05,FL,3,A,A1,4.11,59.0 -2015-04-06,FL,3,A,A1,4.12,53.0 -2015-04-07,FL,3,A,A1,4.09,58.0 -2015-04-08,FL,3,A,A1,4.07,55.0 -2015-04-09,FL,3,A,A1,4.13,57.0 -2015-04-10,FL,3,A,A1,4.13,50.0 -2015-04-11,FL,3,A,A1,4.05,62.0 -2015-04-12,FL,3,A,A1,4.13,57.0 -2015-04-13,FL,3,A,A1,4.07,54.0 -2015-04-14,FL,3,A,A1,4.08,59.0 -2015-04-15,FL,3,A,A1,4.19,56.0 -2015-04-16,FL,3,A,A1,4.17,53.0 -2015-04-17,FL,3,A,A1,4.12,56.0 -2015-04-18,FL,3,A,A1,4.15,62.0 -2015-04-19,FL,3,A,A1,4.14,58.0 -2015-04-20,FL,3,A,A1,4.14,55.0 -2015-04-21,FL,3,A,A1,4.12,56.0 -2015-04-22,FL,3,A,A1,4.18,55.0 -2015-04-23,FL,3,A,A1,4.1,58.0 -2015-04-24,FL,3,A,A1,4.1,60.0 -2015-04-25,FL,3,A,A1,4.15,61.0 -2015-04-26,FL,3,A,A1,4.14,55.0 -2015-04-27,FL,3,A,A1,4.1,57.0 -2015-04-28,FL,3,A,A1,4.15,55.0 -2015-04-29,FL,3,A,A1,4.05,60.0 -2015-04-30,FL,3,A,A1,4.18,48.0 -2015-05-01,FL,3,A,A1,4.13,53.0 -2015-05-02,FL,3,A,A1,4.14,58.0 -2015-05-03,FL,3,A,A1,4.14,53.0 -2015-05-04,FL,3,A,A1,4.11,59.0 -2015-05-05,FL,3,A,A1,4.13,56.0 -2015-05-06,FL,3,A,A1,4.07,57.0 -2015-05-07,FL,3,A,A1,4.17,52.0 -2015-05-08,FL,3,A,A1,4.09,56.0 -2015-05-09,FL,3,A,A1,4.05,56.0 -2015-05-10,FL,3,A,A1,4.13,56.0 -2015-05-11,FL,3,A,A1,4.14,51.0 -2015-05-12,FL,3,A,A1,4.13,54.0 -2015-05-13,FL,3,A,A1,4.11,58.0 -2015-05-14,FL,3,A,A1,4.09,49.0 -2015-05-15,FL,3,A,A1,4.08,59.0 -2015-05-16,FL,3,A,A1,4.08,55.0 -2015-05-17,FL,3,A,A1,4.1,59.0 -2015-05-18,FL,3,A,A1,4.03,61.0 -2015-05-19,FL,3,A,A1,4.2,55.0 -2015-05-20,FL,3,A,A1,4.1,56.0 -2015-05-21,FL,3,A,A1,4.05,55.0 -2015-05-22,FL,3,A,A1,4.11,55.0 -2015-05-23,FL,3,A,A1,4.13,61.0 -2015-05-24,FL,3,A,A1,4.17,58.0 -2015-05-25,FL,3,A,A1,4.16,46.0 -2015-05-26,FL,3,A,A1,4.09,56.0 -2015-05-27,FL,3,A,A1,4.05,58.0 -2015-05-28,FL,3,A,A1,4.13,56.0 -2015-05-29,FL,3,A,A1,4.12,52.0 -2015-05-30,FL,3,A,A1,4.12,60.0 -2015-05-31,FL,3,A,A1,4.15,54.0 -2015-06-01,FL,3,A,A1,4.08,54.0 -2015-06-02,FL,3,A,A1,4.07,58.0 -2015-06-03,FL,3,A,A1,4.12,53.0 -2015-06-04,FL,3,A,A1,4.16,56.0 -2015-06-05,FL,3,A,A1,4.05,61.0 -2015-06-06,FL,3,A,A1,4.08,59.0 -2015-06-07,FL,3,A,A1,4.16,50.0 -2015-06-08,FL,3,A,A1,4.19,53.0 -2015-06-09,FL,3,A,A1,4.18,53.0 -2015-06-10,FL,3,A,A1,4.11,56.0 -2015-06-11,FL,3,A,A1,4.16,53.0 -2015-06-12,FL,3,A,A1,4.16,51.0 -2015-06-13,FL,3,A,A1,4.12,58.0 -2015-06-14,FL,3,A,A1,4.1,56.0 -2015-06-15,FL,3,A,A1,4.07,57.0 -2015-06-16,FL,3,A,A1,4.1,55.0 -2015-06-17,FL,3,A,A1,4.15,56.0 -2015-06-18,FL,3,A,A1,4.05,57.0 -2015-06-19,FL,3,A,A1,4.07,63.0 -2015-06-20,FL,3,A,A1,4.05,58.0 -2015-06-21,FL,3,A,A1,4.11,57.0 -2015-06-22,FL,3,A,A1,4.22,53.0 -2015-06-23,FL,3,A,A1,4.16,55.0 -2015-06-24,FL,3,A,A1,4.17,49.0 -2015-06-25,FL,3,A,A1,4.07,62.0 -2015-06-26,FL,3,A,A1,4.08,58.0 -2015-06-27,FL,3,A,A1,4.12,58.0 -2015-06-28,FL,3,A,A1,4.1,55.0 -2015-06-29,FL,3,A,A1,4.14,51.0 -2015-06-30,FL,3,A,A1,4.13,54.0 -2015-07-01,FL,3,A,A1,4.07,60.0 -2015-07-02,FL,3,A,A1,4.03,60.0 -2015-07-03,FL,3,A,A1,4.05,56.0 -2015-07-04,FL,3,A,A1,4.1,61.0 -2015-07-05,FL,3,A,A1,4.11,57.0 -2015-07-06,FL,3,A,A1,4.17,50.0 -2015-07-07,FL,3,A,A1,4.1,57.0 -2015-07-08,FL,3,A,A1,4.14,61.0 -2015-07-09,FL,3,A,A1,4.09,58.0 -2015-07-10,FL,3,A,A1,4.02,62.0 -2015-07-11,FL,3,A,A1,4.13,59.0 -2015-07-12,FL,3,A,A1,4.13,58.0 -2015-07-13,FL,3,A,A1,4.06,60.0 -2015-07-14,FL,3,A,A1,4.05,60.0 -2015-07-15,FL,3,A,A1,4.2,49.0 -2015-07-16,FL,3,A,A1,4.14,58.0 -2015-07-17,FL,3,A,A1,4.04,57.0 -2015-07-18,FL,3,A,A1,4.21,50.0 -2015-07-19,FL,3,A,A1,4.1,69.0 -2015-07-20,FL,3,A,A1,4.17,52.0 -2015-07-21,FL,3,A,A1,4.11,56.0 -2015-07-22,FL,3,A,A1,4.13,57.0 -2015-07-23,FL,3,A,A1,4.12,51.0 -2015-07-24,FL,3,A,A1,4.2,56.0 -2015-07-25,FL,3,A,A1,4.17,60.0 -2015-07-26,FL,3,A,A1,4.08,53.0 -2015-07-27,FL,3,A,A1,4.08,59.0 -2015-07-28,FL,3,A,A1,4.13,54.0 -2015-07-29,FL,3,A,A1,4.12,53.0 -2015-07-30,FL,3,A,A1,4.21,45.0 -2015-07-31,FL,3,A,A1,4.15,58.0 -2015-08-01,FL,3,A,A1,4.11,59.0 -2015-08-02,FL,3,A,A1,4.1,57.0 -2015-08-03,FL,3,A,A1,4.08,55.0 -2015-08-04,FL,3,A,A1,4.14,51.0 -2015-08-05,FL,3,A,A1,4.14,55.0 -2015-08-06,FL,3,A,A1,4.19,53.0 -2015-08-07,FL,3,A,A1,4.18,52.0 -2015-08-08,FL,3,A,A1,4.15,57.0 -2015-08-09,FL,3,A,A1,4.09,54.0 -2015-08-10,FL,3,A,A1,4.11,54.0 -2015-08-11,FL,3,A,A1,4.08,61.0 -2015-08-12,FL,3,A,A1,4.1,60.0 -2015-08-13,FL,3,A,A1,4.07,58.0 -2015-08-14,FL,3,A,A1,4.16,53.0 -2015-08-15,FL,3,A,A1,4.1,57.0 -2015-08-16,FL,3,A,A1,4.1,53.0 -2015-08-17,FL,3,A,A1,4.09,53.0 -2015-08-18,FL,3,A,A1,4.11,53.0 -2015-08-19,FL,3,A,A1,4.11,53.0 -2015-08-20,FL,3,A,A1,4.11,58.0 -2015-08-21,FL,3,A,A1,4.14,61.0 -2015-08-22,FL,3,A,A1,4.14,61.0 -2015-08-23,FL,3,A,A1,4.11,58.0 -2015-08-24,FL,3,A,A1,4.16,52.0 -2015-08-25,FL,3,A,A1,4.17,54.0 -2015-08-26,FL,3,A,A1,4.17,54.0 -2015-08-27,FL,3,A,A1,4.19,58.0 -2015-08-28,FL,3,A,A1,4.11,58.0 -2015-08-29,FL,3,A,A1,4.11,62.0 -2015-08-30,FL,3,A,A1,4.17,61.0 -2015-08-31,FL,3,A,A1,4.11,55.0 -2015-09-01,FL,3,A,A1,4.1,52.0 -2015-09-02,FL,3,A,A1,3.99,57.0 -2015-09-03,FL,3,A,A1,4.08,61.0 -2015-09-04,FL,3,A,A1,4.15,51.0 -2015-09-05,FL,3,A,A1,4.1,60.0 -2015-09-06,FL,3,A,A1,4.09,60.0 -2015-09-07,FL,3,A,A1,4.12,58.0 -2015-09-08,FL,3,A,A1,4.04,58.0 -2015-09-09,FL,3,A,A1,4.23,51.0 -2015-09-10,FL,3,A,A1,4.04,60.0 -2015-09-11,FL,3,A,A1,4.11,52.0 -2015-09-12,FL,3,A,A1,4.1,59.0 -2015-09-13,FL,3,A,A1,4.13,55.0 -2015-09-14,FL,3,A,A1,4.1,52.0 -2015-09-15,FL,3,A,A1,4.13,53.0 -2015-09-16,FL,3,A,A1,4.1,59.0 -2015-09-17,FL,3,A,A1,4.06,54.0 -2015-09-18,FL,3,A,A1,4.07,59.0 -2015-09-19,FL,3,A,A1,4.11,58.0 -2015-09-20,FL,3,A,A1,4.19,52.0 -2015-09-21,FL,3,A,A1,4.13,58.0 -2015-09-22,FL,3,A,A1,4.15,52.0 -2015-09-23,FL,3,A,A1,4.11,56.0 -2015-09-24,FL,3,A,A1,4.08,54.0 -2015-09-25,FL,3,A,A1,4.14,56.0 -2015-09-26,FL,3,A,A1,4.2,58.0 -2015-09-27,FL,3,A,A1,4.06,65.0 -2015-09-28,FL,3,A,A1,4.11,56.0 -2015-09-29,FL,3,A,A1,4.06,58.0 -2015-09-30,FL,3,A,A1,4.14,56.0 -2015-10-01,FL,3,A,A1,4.12,49.0 -2015-10-02,FL,3,A,A1,4.12,58.0 -2015-10-03,FL,3,A,A1,4.13,56.0 -2015-10-04,FL,3,A,A1,4.06,61.0 -2015-10-05,FL,3,A,A1,4.2,50.0 -2015-10-06,FL,3,A,A1,4.07,53.0 -2015-10-07,FL,3,A,A1,4.11,60.0 -2015-10-08,FL,3,A,A1,4.08,54.0 -2015-10-09,FL,3,A,A1,4.21,50.0 -2015-10-10,FL,3,A,A1,4.19,55.0 -2015-10-11,FL,3,A,A1,4.19,56.0 -2015-10-12,FL,3,A,A1,4.13,50.0 -2015-10-13,FL,3,A,A1,4.11,62.0 -2015-10-14,FL,3,A,A1,4.08,58.0 -2015-10-15,FL,3,A,A1,4.16,54.0 -2015-10-16,FL,3,A,A1,4.0,63.0 -2015-10-17,FL,3,A,A1,4.12,59.0 -2015-10-18,FL,3,A,A1,4.15,54.0 -2015-10-19,FL,3,A,A1,4.09,59.0 -2015-10-20,FL,3,A,A1,4.18,54.0 -2015-10-21,FL,3,A,A1,4.12,58.0 -2015-10-22,FL,3,A,A1,4.14,55.0 -2015-10-23,FL,3,A,A1,4.14,53.0 -2015-10-24,FL,3,A,A1,4.18,59.0 -2015-10-25,FL,3,A,A1,4.12,58.0 -2015-10-26,FL,3,A,A1,4.13,55.0 -2015-10-27,FL,3,A,A1,4.15,55.0 -2015-10-28,FL,3,A,A1,4.13,52.0 -2015-10-29,FL,3,A,A1,4.13,57.0 -2015-10-30,FL,3,A,A1,4.05,60.0 -2015-10-31,FL,3,A,A1,4.13,62.0 -2015-11-01,FL,3,A,A1,4.1,58.0 -2015-11-02,FL,3,A,A1,4.15,53.0 -2015-11-03,FL,3,A,A1,4.13,54.0 -2015-11-04,FL,3,A,A1,4.07,53.0 -2015-11-05,FL,3,A,A1,4.18,55.0 -2015-11-06,FL,3,A,A1,4.14,57.0 -2015-11-07,FL,3,A,A1,4.02,63.0 -2015-11-08,FL,3,A,A1,4.12,53.0 -2015-11-09,FL,3,A,A1,4.06,61.0 -2015-11-10,FL,3,A,A1,4.17,53.0 -2015-11-11,FL,3,A,A1,4.16,55.0 -2015-11-12,FL,3,A,A1,4.16,52.0 -2015-11-13,FL,3,A,A1,4.15,58.0 -2015-11-14,FL,3,A,A1,4.2,51.0 -2015-11-15,FL,3,A,A1,4.11,61.0 -2015-11-16,FL,3,A,A1,4.11,53.0 -2015-11-17,FL,3,A,A1,4.16,50.0 -2015-11-18,FL,3,A,A1,4.08,54.0 -2015-11-19,FL,3,A,A1,4.13,57.0 -2015-11-20,FL,3,A,A1,4.14,56.0 -2015-11-21,FL,3,A,A1,4.06,60.0 -2015-11-22,FL,3,A,A1,4.12,54.0 -2015-11-23,FL,3,A,A1,4.13,50.0 -2015-11-24,FL,3,A,A1,4.13,55.0 -2015-11-25,FL,3,A,A1,4.13,54.0 -2015-11-26,FL,3,A,A1,4.14,53.0 -2015-11-27,FL,3,A,A1,4.11,54.0 -2015-11-28,FL,3,A,A1,4.13,57.0 -2015-11-29,FL,3,A,A1,4.17,52.0 -2015-11-30,FL,3,A,A1,4.11,56.0 -2015-12-01,FL,3,A,A1,4.06,51.0 -2015-12-02,FL,3,A,A1,4.16,60.0 -2015-12-03,FL,3,A,A1,4.16,50.0 -2015-12-04,FL,3,A,A1,4.09,57.0 -2015-12-05,FL,3,A,A1,4.14,54.0 -2015-12-06,FL,3,A,A1,4.15,56.0 -2015-12-07,FL,3,A,A1,4.14,52.0 -2015-12-08,FL,3,A,A1,4.09,53.0 -2015-12-09,FL,3,A,A1,4.11,56.0 -2015-12-10,FL,3,A,A1,4.13,51.0 -2015-12-11,FL,3,A,A1,4.03,59.0 -2015-12-12,FL,3,A,A1,4.19,54.0 -2015-12-13,FL,3,A,A1,4.08,60.0 -2015-12-14,FL,3,A,A1,4.17,49.0 -2015-12-15,FL,3,A,A1,4.07,58.0 -2015-12-16,FL,3,A,A1,4.17,55.0 -2015-12-17,FL,3,A,A1,4.14,57.0 -2015-12-18,FL,3,A,A1,4.14,58.0 -2015-12-19,FL,3,A,A1,4.09,59.0 -2015-12-20,FL,3,A,A1,4.05,62.0 -2015-12-21,FL,3,A,A1,4.05,58.0 -2015-12-22,FL,3,A,A1,4.11,55.0 -2015-12-23,FL,3,A,A1,4.12,59.0 -2015-12-24,FL,3,A,A1,4.22,49.0 -2015-12-25,FL,3,A,A1,4.18,48.0 -2015-12-26,FL,3,A,A1,4.08,63.0 -2015-12-27,FL,3,A,A1,4.14,57.0 -2015-12-28,FL,3,A,A1,4.03,61.0 -2015-12-29,FL,3,A,A1,4.13,55.0 -2015-12-30,FL,3,A,A1,4.16,58.0 -2015-12-31,FL,3,A,A1,4.17,52.0 -2016-01-01,FL,3,A,A1,4.09,61.0 -2016-01-02,FL,3,A,A1,4.15,57.0 -2016-01-03,FL,3,A,A1,4.12,58.0 -2016-01-04,FL,3,A,A1,4.09,54.0 -2016-01-05,FL,3,A,A1,4.06,57.0 -2016-01-06,FL,3,A,A1,4.14,60.0 -2016-01-07,FL,3,A,A1,4.09,57.0 -2016-01-08,FL,3,A,A1,4.08,59.0 -2016-01-09,FL,3,A,A1,4.12,58.0 -2016-01-10,FL,3,A,A1,4.07,60.0 -2016-01-11,FL,3,A,A1,4.14,53.0 -2016-01-12,FL,3,A,A1,4.15,56.0 -2016-01-13,FL,3,A,A1,4.09,49.0 -2016-01-14,FL,3,A,A1,4.18,51.0 -2016-01-15,FL,3,A,A1,4.06,54.0 -2016-01-16,FL,3,A,A1,4.15,61.0 -2016-01-17,FL,3,A,A1,4.05,58.0 -2016-01-18,FL,3,A,A1,4.12,54.0 -2016-01-19,FL,3,A,A1,4.06,58.0 -2016-01-20,FL,3,A,A1,4.06,59.0 -2016-01-21,FL,3,A,A1,4.16,52.0 -2016-01-22,FL,3,A,A1,4.15,55.0 -2016-01-23,FL,3,A,A1,4.15,61.0 -2016-01-24,FL,3,A,A1,4.11,58.0 -2016-01-25,FL,3,A,A1,4.07,57.0 -2016-01-26,FL,3,A,A1,4.11,56.0 -2016-01-27,FL,3,A,A1,4.14,56.0 -2016-01-28,FL,3,A,A1,4.08,57.0 -2016-01-29,FL,3,A,A1,4.1,56.0 -2016-01-30,FL,3,A,A1,4.11,58.0 -2016-01-31,FL,3,A,A1,4.14,53.0 -2016-02-01,FL,3,A,A1,4.19,53.0 -2016-02-02,FL,3,A,A1,4.18,52.0 -2016-02-03,FL,3,A,A1,4.04,54.0 -2016-02-04,FL,3,A,A1,4.14,59.0 -2016-02-05,FL,3,A,A1,4.16,53.0 -2016-02-06,FL,3,A,A1,4.12,61.0 -2016-02-07,FL,3,A,A1,4.13,56.0 -2016-02-08,FL,3,A,A1,4.17,50.0 -2016-02-09,FL,3,A,A1,4.14,53.0 -2016-02-10,FL,3,A,A1,4.12,53.0 -2016-02-11,FL,3,A,A1,4.11,56.0 -2016-02-12,FL,3,A,A1,4.16,54.0 -2016-02-13,FL,3,A,A1,4.04,65.0 -2016-02-14,FL,3,A,A1,4.16,57.0 -2016-02-15,FL,3,A,A1,4.15,48.0 -2016-02-16,FL,3,A,A1,4.16,51.0 -2016-02-17,FL,3,A,A1,4.12,51.0 -2016-02-18,FL,3,A,A1,4.1,58.0 -2016-02-19,FL,3,A,A1,4.09,55.0 -2016-02-20,FL,3,A,A1,4.11,53.0 -2016-02-21,FL,3,A,A1,4.15,55.0 -2016-02-22,FL,3,A,A1,4.11,53.0 -2016-02-23,FL,3,A,A1,4.09,62.0 -2016-02-24,FL,3,A,A1,4.11,57.0 -2016-02-25,FL,3,A,A1,4.05,61.0 -2016-02-26,FL,3,A,A1,4.11,52.0 -2016-02-27,FL,3,A,A1,4.1,55.0 -2016-02-28,FL,3,A,A1,4.08,58.0 -2016-02-29,FL,3,A,A1,4.15,55.0 -2016-03-01,FL,3,A,A1,4.11,60.0 -2016-03-02,FL,3,A,A1,4.14,54.0 -2016-03-03,FL,3,A,A1,4.16,55.0 -2016-03-04,FL,3,A,A1,4.05,58.0 -2016-03-05,FL,3,A,A1,4.08,63.0 -2016-03-06,FL,3,A,A1,4.17,57.0 -2016-03-07,FL,3,A,A1,4.13,53.0 -2016-03-08,FL,3,A,A1,4.06,55.0 -2016-03-09,FL,3,A,A1,4.14,58.0 -2016-03-10,FL,3,A,A1,4.05,61.0 -2016-03-11,FL,3,A,A1,4.13,57.0 -2016-03-12,FL,3,A,A1,4.1,56.0 -2016-03-13,FL,3,A,A1,4.07,60.0 -2016-03-14,FL,3,A,A1,4.13,55.0 -2016-03-15,FL,3,A,A1,4.09,59.0 -2016-03-16,FL,3,A,A1,4.11,57.0 -2016-03-17,FL,3,A,A1,4.14,52.0 -2016-03-18,FL,3,A,A1,4.1,57.0 -2016-03-19,FL,3,A,A1,4.13,58.0 -2016-03-20,FL,3,A,A1,4.12,59.0 -2016-03-21,FL,3,A,A1,4.14,46.0 -2016-03-22,FL,3,A,A1,4.09,54.0 -2016-03-23,FL,3,A,A1,4.06,54.0 -2016-03-24,FL,3,A,A1,4.19,51.0 -2016-03-25,FL,3,A,A1,4.22,51.0 -2016-03-26,FL,3,A,A1,4.1,61.0 -2016-03-27,FL,3,A,A1,4.09,57.0 -2016-03-28,FL,3,A,A1,4.11,54.0 -2016-03-29,FL,3,A,A1,4.09,55.0 -2016-03-30,FL,3,A,A1,4.1,55.0 -2016-03-31,FL,3,A,A1,4.15,56.0 -2016-04-01,FL,3,A,A1,4.15,59.0 -2016-04-02,FL,3,A,A1,4.09,57.0 -2016-04-03,FL,3,A,A1,4.12,53.0 -2016-04-04,FL,3,A,A1,4.12,52.0 -2016-04-05,FL,3,A,A1,4.06,60.0 -2016-04-06,FL,3,A,A1,4.04,59.0 -2016-04-07,FL,3,A,A1,4.08,55.0 -2016-04-08,FL,3,A,A1,4.11,54.0 -2016-04-09,FL,3,A,A1,4.14,52.0 -2016-04-10,FL,3,A,A1,4.13,55.0 -2016-04-11,FL,3,A,A1,4.0,62.0 -2016-04-12,FL,3,A,A1,4.11,57.0 -2016-04-13,FL,3,A,A1,4.12,56.0 -2016-04-14,FL,3,A,A1,4.19,52.0 -2016-04-15,FL,3,A,A1,4.1,54.0 -2016-04-16,FL,3,A,A1,4.15,57.0 -2016-04-17,FL,3,A,A1,4.06,60.0 -2016-04-18,FL,3,A,A1,4.15,55.0 -2016-04-19,FL,3,A,A1,4.1,58.0 -2016-04-20,FL,3,A,A1,4.13,53.0 -2016-04-21,FL,3,A,A1,4.09,56.0 -2016-04-22,FL,3,A,A1,4.07,54.0 -2016-04-23,FL,3,A,A1,4.17,59.0 -2016-04-24,FL,3,A,A1,4.13,59.0 -2016-04-25,FL,3,A,A1,4.18,56.0 -2016-04-26,FL,3,A,A1,4.1,54.0 -2016-04-27,FL,3,A,A1,4.08,52.0 -2016-04-28,FL,3,A,A1,4.1,57.0 -2016-04-29,FL,3,A,A1,4.1,59.0 -2016-04-30,FL,3,A,A1,4.12,59.0 -2016-05-01,FL,3,A,A1,4.16,58.0 -2016-05-02,FL,3,A,A1,4.09,53.0 -2016-05-03,FL,3,A,A1,4.04,57.0 -2016-05-04,FL,3,A,A1,4.15,58.0 -2016-05-05,FL,3,A,A1,4.05,55.0 -2016-05-06,FL,3,A,A1,4.11,52.0 -2016-05-07,FL,3,A,A1,4.09,55.0 -2016-05-08,FL,3,A,A1,4.16,57.0 -2016-05-09,FL,3,A,A1,4.1,54.0 -2016-05-10,FL,3,A,A1,4.13,57.0 -2016-05-11,FL,3,A,A1,4.1,56.0 -2016-05-12,FL,3,A,A1,4.11,56.0 -2016-05-13,FL,3,A,A1,4.06,54.0 -2016-05-14,FL,3,A,A1,4.09,59.0 -2016-05-15,FL,3,A,A1,4.03,59.0 -2016-05-16,FL,3,A,A1,4.11,53.0 -2016-05-17,FL,3,A,A1,4.12,56.0 -2016-05-18,FL,3,A,A1,4.1,60.0 -2016-05-19,FL,3,A,A1,4.13,49.0 -2016-05-20,FL,3,A,A1,4.15,48.0 -2016-05-21,FL,3,A,A1,4.14,53.0 -2016-05-22,FL,3,A,A1,4.11,56.0 -2016-05-23,FL,3,A,A1,4.09,56.0 -2016-05-24,FL,3,A,A1,4.14,49.0 -2016-05-25,FL,3,A,A1,4.17,55.0 -2016-05-26,FL,3,A,A1,4.18,52.0 -2016-05-27,FL,3,A,A1,4.08,61.0 -2016-05-28,FL,3,A,A1,4.16,58.0 -2016-05-29,FL,3,A,A1,4.07,57.0 -2016-05-30,FL,3,A,A1,4.1,54.0 -2016-05-31,FL,3,A,A1,4.06,54.0 -2016-06-01,FL,3,A,A1,4.15,56.0 -2016-06-02,FL,3,A,A1,4.11,57.0 -2016-06-03,FL,3,A,A1,4.16,55.0 -2016-06-04,FL,3,A,A1,4.17,57.0 -2016-06-05,FL,3,A,A1,4.12,58.0 -2016-06-06,FL,3,A,A1,4.14,50.0 -2016-06-07,FL,3,A,A1,4.1,54.0 -2016-06-08,FL,3,A,A1,4.07,55.0 -2016-06-09,FL,3,A,A1,4.16,53.0 -2016-06-10,FL,3,A,A1,4.1,54.0 -2016-06-11,FL,3,A,A1,4.12,62.0 -2016-06-12,FL,3,A,A1,4.07,62.0 -2016-06-13,FL,3,A,A1,4.1,51.0 -2016-06-14,FL,3,A,A1,4.1,52.0 -2016-06-15,FL,3,A,A1,4.11,56.0 -2016-06-16,FL,3,A,A1,4.08,58.0 -2016-06-17,FL,3,A,A1,4.08,58.0 -2016-06-18,FL,3,A,A1,4.1,59.0 -2016-06-19,FL,3,A,A1,4.11,55.0 -2016-06-20,FL,3,A,A1,4.08,56.0 -2016-06-21,FL,3,A,A1,4.09,58.0 -2016-06-22,FL,3,A,A1,4.14,51.0 -2016-06-23,FL,3,A,A1,4.16,50.0 -2016-06-24,FL,3,A,A1,4.04,61.0 -2016-06-25,FL,3,A,A1,4.09,59.0 -2016-06-26,FL,3,A,A1,4.16,51.0 -2016-06-27,FL,3,A,A1,4.16,49.0 -2016-06-28,FL,3,A,A1,4.14,55.0 -2016-06-29,FL,3,A,A1,4.14,53.0 -2016-06-30,FL,3,A,A1,4.22,47.0 -2016-07-01,FL,3,A,A1,4.12,56.0 -2016-07-02,FL,3,A,A1,4.22,55.0 -2016-07-03,FL,3,A,A1,4.17,48.0 -2016-07-04,FL,3,A,A1,4.24,51.0 -2016-07-05,FL,3,A,A1,4.08,60.0 -2016-07-06,FL,3,A,A1,4.06,59.0 -2016-07-07,FL,3,A,A1,4.11,59.0 -2016-07-08,FL,3,A,A1,4.05,63.0 -2016-07-09,FL,3,A,A1,4.09,56.0 -2016-07-10,FL,3,A,A1,4.07,57.0 -2016-07-11,FL,3,A,A1,4.15,50.0 -2016-07-12,FL,3,A,A1,4.15,56.0 -2016-07-13,FL,3,A,A1,4.12,58.0 -2016-07-14,FL,3,A,A1,4.15,56.0 -2016-07-15,FL,3,A,A1,4.13,61.0 -2016-07-16,FL,3,A,A1,4.04,63.0 -2016-07-17,FL,3,A,A1,4.07,53.0 -2016-07-18,FL,3,A,A1,4.13,57.0 -2016-07-19,FL,3,A,A1,4.08,56.0 -2016-07-20,FL,3,A,A1,4.1,51.0 -2016-07-21,FL,3,A,A1,4.08,53.0 -2016-07-22,FL,3,A,A1,4.16,56.0 -2016-07-23,FL,3,A,A1,4.13,63.0 -2016-07-24,FL,3,A,A1,4.16,54.0 -2016-07-25,FL,3,A,A1,4.13,58.0 -2016-07-26,FL,3,A,A1,4.13,55.0 -2016-07-27,FL,3,A,A1,4.15,55.0 -2015-01-05,FL,3,A,A2,7.82,16.0 -2015-01-06,FL,3,A,A2,7.7,16.0 -2015-01-07,FL,3,A,A2,7.71,15.0 -2015-01-08,FL,3,A,A2,7.77,16.0 -2015-01-09,FL,3,A,A2,7.59,18.0 -2015-01-10,FL,3,A,A2,7.78,16.0 -2015-01-11,FL,3,A,A2,7.6,17.0 -2015-01-12,FL,3,A,A2,7.72,15.0 -2015-01-13,FL,3,A,A2,7.68,16.0 -2015-01-14,FL,3,A,A2,7.83,15.0 -2015-01-15,FL,3,A,A2,7.75,15.0 -2015-01-16,FL,3,A,A2,7.63,17.0 -2015-01-17,FL,3,A,A2,7.8,16.0 -2015-01-18,FL,3,A,A2,7.64,17.0 -2015-01-19,FL,3,A,A2,7.68,16.0 -2015-01-20,FL,3,A,A2,7.69,16.0 -2015-01-21,FL,3,A,A2,7.69,16.0 -2015-01-22,FL,3,A,A2,7.72,15.0 -2015-01-23,FL,3,A,A2,7.73,17.0 -2015-01-24,FL,3,A,A2,7.71,17.0 -2015-01-25,FL,3,A,A2,7.75,16.0 -2015-01-26,FL,3,A,A2,7.68,16.0 -2015-01-27,FL,3,A,A2,7.66,18.0 -2015-01-28,FL,3,A,A2,7.72,16.0 -2015-01-29,FL,3,A,A2,7.72,15.0 -2015-01-30,FL,3,A,A2,7.73,16.0 -2015-01-31,FL,3,A,A2,7.87,16.0 -2015-02-01,FL,3,A,A2,7.67,17.0 -2015-02-02,FL,3,A,A2,7.69,16.0 -2015-02-03,FL,3,A,A2,7.66,17.0 -2015-02-04,FL,3,A,A2,7.8,15.0 -2015-02-05,FL,3,A,A2,7.8,16.0 -2015-02-06,FL,3,A,A2,7.75,16.0 -2015-02-07,FL,3,A,A2,7.73,17.0 -2015-02-08,FL,3,A,A2,7.82,16.0 -2015-02-09,FL,3,A,A2,7.69,16.0 -2015-02-10,FL,3,A,A2,7.77,16.0 -2015-02-11,FL,3,A,A2,7.8,16.0 -2015-02-12,FL,3,A,A2,7.8,17.0 -2015-02-13,FL,3,A,A2,7.7,17.0 -2015-02-14,FL,3,A,A2,7.67,16.0 -2015-02-15,FL,3,A,A2,7.58,18.0 -2015-02-16,FL,3,A,A2,7.85,15.0 -2015-02-17,FL,3,A,A2,7.61,16.0 -2015-02-18,FL,3,A,A2,7.61,17.0 -2015-02-19,FL,3,A,A2,7.67,16.0 -2015-02-20,FL,3,A,A2,7.72,17.0 -2015-02-21,FL,3,A,A2,7.86,17.0 -2015-02-22,FL,3,A,A2,7.71,16.0 -2015-02-23,FL,3,A,A2,7.56,17.0 -2015-02-24,FL,3,A,A2,7.62,18.0 -2015-02-25,FL,3,A,A2,7.74,16.0 -2015-02-26,FL,3,A,A2,7.67,18.0 -2015-02-27,FL,3,A,A2,7.79,16.0 -2015-02-28,FL,3,A,A2,7.74,17.0 -2015-03-01,FL,3,A,A2,7.73,17.0 -2015-03-02,FL,3,A,A2,7.77,17.0 -2015-03-03,FL,3,A,A2,7.7,16.0 -2015-03-04,FL,3,A,A2,7.81,16.0 -2015-03-05,FL,3,A,A2,7.48,17.0 -2015-03-06,FL,3,A,A2,7.8,15.0 -2015-03-07,FL,3,A,A2,7.74,18.0 -2015-03-08,FL,3,A,A2,7.6,19.0 -2015-03-09,FL,3,A,A2,7.81,14.0 -2015-03-10,FL,3,A,A2,7.76,16.0 -2015-03-11,FL,3,A,A2,7.85,15.0 -2015-03-12,FL,3,A,A2,7.69,15.0 -2015-03-13,FL,3,A,A2,7.76,16.0 -2015-03-14,FL,3,A,A2,7.77,16.0 -2015-03-15,FL,3,A,A2,7.62,17.0 -2015-03-16,FL,3,A,A2,7.85,15.0 -2015-03-17,FL,3,A,A2,7.46,18.0 -2015-03-18,FL,3,A,A2,7.77,17.0 -2015-03-19,FL,3,A,A2,7.64,18.0 -2015-03-20,FL,3,A,A2,7.77,16.0 -2015-03-21,FL,3,A,A2,7.7,18.0 -2015-03-22,FL,3,A,A2,7.66,17.0 -2015-03-23,FL,3,A,A2,7.75,17.0 -2015-03-24,FL,3,A,A2,7.71,16.0 -2015-03-25,FL,3,A,A2,7.79,16.0 -2015-03-26,FL,3,A,A2,7.58,18.0 -2015-03-27,FL,3,A,A2,7.74,16.0 -2015-03-28,FL,3,A,A2,7.81,17.0 -2015-03-29,FL,3,A,A2,7.65,18.0 -2015-03-30,FL,3,A,A2,7.66,16.0 -2015-03-31,FL,3,A,A2,7.77,16.0 -2015-04-01,FL,3,A,A2,7.67,16.0 -2015-04-02,FL,3,A,A2,7.66,18.0 -2015-04-03,FL,3,A,A2,7.8,17.0 -2015-04-04,FL,3,A,A2,7.71,17.0 -2015-04-05,FL,3,A,A2,7.68,18.0 -2015-04-06,FL,3,A,A2,7.77,14.0 -2015-04-07,FL,3,A,A2,7.6,17.0 -2015-04-08,FL,3,A,A2,7.7,17.0 -2015-04-09,FL,3,A,A2,7.66,16.0 -2015-04-10,FL,3,A,A2,7.74,15.0 -2015-04-11,FL,3,A,A2,7.75,15.0 -2015-04-12,FL,3,A,A2,7.68,17.0 -2015-04-13,FL,3,A,A2,7.75,16.0 -2015-04-14,FL,3,A,A2,7.79,17.0 -2015-04-15,FL,3,A,A2,7.77,16.0 -2015-04-16,FL,3,A,A2,7.73,17.0 -2015-04-17,FL,3,A,A2,7.69,17.0 -2015-04-18,FL,3,A,A2,7.69,17.0 -2015-04-19,FL,3,A,A2,7.86,15.0 -2015-04-20,FL,3,A,A2,7.73,17.0 -2015-04-21,FL,3,A,A2,7.72,17.0 -2015-04-22,FL,3,A,A2,7.72,16.0 -2015-04-23,FL,3,A,A2,7.68,17.0 -2015-04-24,FL,3,A,A2,7.63,18.0 -2015-04-25,FL,3,A,A2,7.72,18.0 -2015-04-26,FL,3,A,A2,7.69,17.0 -2015-04-27,FL,3,A,A2,7.76,15.0 -2015-04-28,FL,3,A,A2,7.76,17.0 -2015-04-29,FL,3,A,A2,7.82,16.0 -2015-04-30,FL,3,A,A2,7.76,17.0 -2015-05-01,FL,3,A,A2,7.71,17.0 -2015-05-02,FL,3,A,A2,7.77,16.0 -2015-05-03,FL,3,A,A2,7.71,16.0 -2015-05-04,FL,3,A,A2,7.59,18.0 -2015-05-05,FL,3,A,A2,7.81,16.0 -2015-05-06,FL,3,A,A2,7.84,16.0 -2015-05-07,FL,3,A,A2,7.83,16.0 -2015-05-08,FL,3,A,A2,7.7,16.0 -2015-05-09,FL,3,A,A2,7.63,18.0 -2015-05-10,FL,3,A,A2,7.72,17.0 -2015-05-11,FL,3,A,A2,7.79,15.0 -2015-05-12,FL,3,A,A2,7.73,16.0 -2015-05-13,FL,3,A,A2,7.75,16.0 -2015-05-14,FL,3,A,A2,7.82,18.0 -2015-05-15,FL,3,A,A2,7.76,16.0 -2015-05-16,FL,3,A,A2,7.68,16.0 -2015-05-17,FL,3,A,A2,7.73,16.0 -2015-05-18,FL,3,A,A2,7.79,14.0 -2015-05-19,FL,3,A,A2,7.74,16.0 -2015-05-20,FL,3,A,A2,7.72,17.0 -2015-05-21,FL,3,A,A2,7.66,18.0 -2015-05-22,FL,3,A,A2,7.76,16.0 -2015-05-23,FL,3,A,A2,7.71,18.0 -2015-05-24,FL,3,A,A2,7.65,16.0 -2015-05-25,FL,3,A,A2,7.75,16.0 -2015-05-26,FL,3,A,A2,7.76,16.0 -2015-05-27,FL,3,A,A2,7.66,18.0 -2015-05-28,FL,3,A,A2,7.72,17.0 -2015-05-29,FL,3,A,A2,7.68,16.0 -2015-05-30,FL,3,A,A2,7.66,18.0 -2015-05-31,FL,3,A,A2,7.8,16.0 -2015-06-01,FL,3,A,A2,7.72,17.0 -2015-06-02,FL,3,A,A2,7.69,17.0 -2015-06-03,FL,3,A,A2,7.59,17.0 -2015-06-04,FL,3,A,A2,7.69,18.0 -2015-06-05,FL,3,A,A2,7.77,16.0 -2015-06-06,FL,3,A,A2,7.86,16.0 -2015-06-07,FL,3,A,A2,7.7,16.0 -2015-06-08,FL,3,A,A2,7.71,16.0 -2015-06-09,FL,3,A,A2,7.69,15.0 -2015-06-10,FL,3,A,A2,7.78,16.0 -2015-06-11,FL,3,A,A2,7.6,19.0 -2015-06-12,FL,3,A,A2,7.55,17.0 -2015-06-13,FL,3,A,A2,7.71,18.0 -2015-06-14,FL,3,A,A2,7.63,17.0 -2015-06-15,FL,3,A,A2,7.72,17.0 -2015-06-16,FL,3,A,A2,7.91,16.0 -2015-06-17,FL,3,A,A2,7.58,17.0 -2015-06-18,FL,3,A,A2,7.69,17.0 -2015-06-19,FL,3,A,A2,7.71,16.0 -2015-06-20,FL,3,A,A2,7.77,18.0 -2015-06-21,FL,3,A,A2,7.81,16.0 -2015-06-22,FL,3,A,A2,7.77,16.0 -2015-06-23,FL,3,A,A2,7.72,15.0 -2015-06-24,FL,3,A,A2,7.8,15.0 -2015-06-25,FL,3,A,A2,7.69,17.0 -2015-06-26,FL,3,A,A2,7.64,18.0 -2015-06-27,FL,3,A,A2,7.72,17.0 -2015-06-28,FL,3,A,A2,7.61,18.0 -2015-06-29,FL,3,A,A2,7.69,15.0 -2015-06-30,FL,3,A,A2,7.73,16.0 -2015-07-01,FL,3,A,A2,7.76,17.0 -2015-07-02,FL,3,A,A2,7.68,17.0 -2015-07-03,FL,3,A,A2,7.71,18.0 -2015-07-04,FL,3,A,A2,7.69,17.0 -2015-07-05,FL,3,A,A2,7.61,17.0 -2015-07-06,FL,3,A,A2,7.77,14.0 -2015-07-07,FL,3,A,A2,7.65,17.0 -2015-07-08,FL,3,A,A2,7.72,15.0 -2015-07-09,FL,3,A,A2,7.74,15.0 -2015-07-10,FL,3,A,A2,7.78,16.0 -2015-07-11,FL,3,A,A2,7.8,16.0 -2015-07-12,FL,3,A,A2,7.8,17.0 -2015-07-13,FL,3,A,A2,7.79,14.0 -2015-07-14,FL,3,A,A2,7.77,15.0 -2015-07-15,FL,3,A,A2,7.84,16.0 -2015-07-16,FL,3,A,A2,7.85,16.0 -2015-07-17,FL,3,A,A2,7.81,16.0 -2015-07-18,FL,3,A,A2,7.53,19.0 -2015-07-19,FL,3,A,A2,7.86,16.0 -2015-07-20,FL,3,A,A2,7.69,16.0 -2015-07-21,FL,3,A,A2,7.71,17.0 -2015-07-22,FL,3,A,A2,7.82,16.0 -2015-07-23,FL,3,A,A2,7.74,18.0 -2015-07-24,FL,3,A,A2,7.77,16.0 -2015-07-25,FL,3,A,A2,7.57,19.0 -2015-07-26,FL,3,A,A2,7.83,16.0 -2015-07-27,FL,3,A,A2,7.7,16.0 -2015-07-28,FL,3,A,A2,7.71,17.0 -2015-07-29,FL,3,A,A2,7.74,16.0 -2015-07-30,FL,3,A,A2,7.76,17.0 -2015-07-31,FL,3,A,A2,7.69,17.0 -2015-08-01,FL,3,A,A2,7.84,16.0 -2015-08-02,FL,3,A,A2,7.73,17.0 -2015-08-03,FL,3,A,A2,7.79,15.0 -2015-08-04,FL,3,A,A2,7.72,17.0 -2015-08-05,FL,3,A,A2,7.7,15.0 -2015-08-06,FL,3,A,A2,7.7,16.0 -2015-08-07,FL,3,A,A2,7.72,17.0 -2015-08-08,FL,3,A,A2,7.66,17.0 -2015-08-09,FL,3,A,A2,7.58,18.0 -2015-08-10,FL,3,A,A2,7.85,17.0 -2015-08-11,FL,3,A,A2,7.78,16.0 -2015-08-12,FL,3,A,A2,7.69,16.0 -2015-08-13,FL,3,A,A2,7.72,16.0 -2015-08-14,FL,3,A,A2,7.59,17.0 -2015-08-15,FL,3,A,A2,7.73,17.0 -2015-08-16,FL,3,A,A2,7.72,16.0 -2015-08-17,FL,3,A,A2,7.88,17.0 -2015-08-18,FL,3,A,A2,7.75,16.0 -2015-08-19,FL,3,A,A2,7.56,17.0 -2015-08-20,FL,3,A,A2,7.68,18.0 -2015-08-21,FL,3,A,A2,7.73,16.0 -2015-08-22,FL,3,A,A2,7.57,18.0 -2015-08-23,FL,3,A,A2,7.69,18.0 -2015-08-24,FL,3,A,A2,7.85,15.0 -2015-08-25,FL,3,A,A2,7.7,17.0 -2015-08-26,FL,3,A,A2,7.75,16.0 -2015-08-27,FL,3,A,A2,7.57,17.0 -2015-08-28,FL,3,A,A2,7.77,16.0 -2015-08-29,FL,3,A,A2,7.66,19.0 -2015-08-30,FL,3,A,A2,7.64,18.0 -2015-08-31,FL,3,A,A2,7.82,15.0 -2015-09-01,FL,3,A,A2,7.77,16.0 -2015-09-02,FL,3,A,A2,7.67,15.0 -2015-09-03,FL,3,A,A2,7.83,14.0 -2015-09-04,FL,3,A,A2,7.78,16.0 -2015-09-05,FL,3,A,A2,7.78,17.0 -2015-09-06,FL,3,A,A2,7.73,17.0 -2015-09-07,FL,3,A,A2,7.72,16.0 -2015-09-08,FL,3,A,A2,7.69,16.0 -2015-09-09,FL,3,A,A2,7.77,17.0 -2015-09-10,FL,3,A,A2,7.68,17.0 -2015-09-11,FL,3,A,A2,7.67,17.0 -2015-09-12,FL,3,A,A2,7.77,17.0 -2015-09-13,FL,3,A,A2,7.76,16.0 -2015-09-14,FL,3,A,A2,7.66,16.0 -2015-09-15,FL,3,A,A2,7.76,16.0 -2015-09-16,FL,3,A,A2,7.71,16.0 -2015-09-17,FL,3,A,A2,7.73,16.0 -2015-09-18,FL,3,A,A2,7.63,18.0 -2015-09-19,FL,3,A,A2,7.71,18.0 -2015-09-20,FL,3,A,A2,7.76,17.0 -2015-09-21,FL,3,A,A2,7.76,14.0 -2015-09-22,FL,3,A,A2,7.74,17.0 -2015-09-23,FL,3,A,A2,7.83,17.0 -2015-09-24,FL,3,A,A2,7.8,15.0 -2015-09-25,FL,3,A,A2,7.91,16.0 -2015-09-26,FL,3,A,A2,7.67,19.0 -2015-09-27,FL,3,A,A2,7.74,18.0 -2015-09-28,FL,3,A,A2,7.94,15.0 -2015-09-29,FL,3,A,A2,7.63,18.0 -2015-09-30,FL,3,A,A2,7.75,15.0 -2015-10-01,FL,3,A,A2,7.72,17.0 -2015-10-02,FL,3,A,A2,7.77,16.0 -2015-10-03,FL,3,A,A2,7.85,16.0 -2015-10-04,FL,3,A,A2,7.7,17.0 -2015-10-05,FL,3,A,A2,7.74,15.0 -2015-10-06,FL,3,A,A2,7.76,17.0 -2015-10-07,FL,3,A,A2,7.74,17.0 -2015-10-08,FL,3,A,A2,7.72,17.0 -2015-10-09,FL,3,A,A2,7.67,18.0 -2015-10-10,FL,3,A,A2,7.62,17.0 -2015-10-11,FL,3,A,A2,7.79,14.0 -2015-10-12,FL,3,A,A2,7.8,14.0 -2015-10-13,FL,3,A,A2,7.7,17.0 -2015-10-14,FL,3,A,A2,7.71,16.0 -2015-10-15,FL,3,A,A2,7.89,15.0 -2015-10-16,FL,3,A,A2,7.85,16.0 -2015-10-17,FL,3,A,A2,7.75,17.0 -2015-10-18,FL,3,A,A2,7.77,16.0 -2015-10-19,FL,3,A,A2,7.65,16.0 -2015-10-20,FL,3,A,A2,7.68,16.0 -2015-10-21,FL,3,A,A2,7.68,17.0 -2015-10-22,FL,3,A,A2,7.68,16.0 -2015-10-23,FL,3,A,A2,7.78,15.0 -2015-10-24,FL,3,A,A2,7.82,17.0 -2015-10-25,FL,3,A,A2,7.75,17.0 -2015-10-26,FL,3,A,A2,7.63,18.0 -2015-10-27,FL,3,A,A2,7.79,16.0 -2015-10-28,FL,3,A,A2,7.7,16.0 -2015-10-29,FL,3,A,A2,7.75,16.0 -2015-10-30,FL,3,A,A2,7.66,16.0 -2015-10-31,FL,3,A,A2,7.66,18.0 -2015-11-01,FL,3,A,A2,7.67,19.0 -2015-11-02,FL,3,A,A2,7.72,16.0 -2015-11-03,FL,3,A,A2,7.58,19.0 -2015-11-04,FL,3,A,A2,7.74,16.0 -2015-11-05,FL,3,A,A2,7.77,16.0 -2015-11-06,FL,3,A,A2,7.71,16.0 -2015-11-07,FL,3,A,A2,7.63,18.0 -2015-11-08,FL,3,A,A2,7.7,17.0 -2015-11-09,FL,3,A,A2,7.71,16.0 -2015-11-10,FL,3,A,A2,7.65,17.0 -2015-11-11,FL,3,A,A2,7.86,16.0 -2015-11-12,FL,3,A,A2,7.7,16.0 -2015-11-13,FL,3,A,A2,7.78,16.0 -2015-11-14,FL,3,A,A2,7.81,17.0 -2015-11-15,FL,3,A,A2,7.71,16.0 -2015-11-16,FL,3,A,A2,7.73,17.0 -2015-11-17,FL,3,A,A2,7.57,18.0 -2015-11-18,FL,3,A,A2,7.76,16.0 -2015-11-19,FL,3,A,A2,7.7,17.0 -2015-11-20,FL,3,A,A2,7.74,16.0 -2015-11-21,FL,3,A,A2,7.81,16.0 -2015-11-22,FL,3,A,A2,7.64,16.0 -2015-11-23,FL,3,A,A2,7.68,17.0 -2015-11-24,FL,3,A,A2,7.59,16.0 -2015-11-25,FL,3,A,A2,7.75,17.0 -2015-11-26,FL,3,A,A2,7.81,16.0 -2015-11-27,FL,3,A,A2,7.78,16.0 -2015-11-28,FL,3,A,A2,7.75,15.0 -2015-11-29,FL,3,A,A2,7.85,16.0 -2015-11-30,FL,3,A,A2,7.62,18.0 -2015-12-01,FL,3,A,A2,7.74,16.0 -2015-12-02,FL,3,A,A2,7.86,17.0 -2015-12-03,FL,3,A,A2,7.81,14.0 -2015-12-04,FL,3,A,A2,7.72,16.0 -2015-12-05,FL,3,A,A2,7.77,17.0 -2015-12-06,FL,3,A,A2,7.85,17.0 -2015-12-07,FL,3,A,A2,7.64,16.0 -2015-12-08,FL,3,A,A2,7.66,17.0 -2015-12-09,FL,3,A,A2,7.76,17.0 -2015-12-10,FL,3,A,A2,7.82,16.0 -2015-12-11,FL,3,A,A2,7.77,16.0 -2015-12-12,FL,3,A,A2,7.69,17.0 -2015-12-13,FL,3,A,A2,7.59,20.0 -2015-12-14,FL,3,A,A2,7.88,15.0 -2015-12-15,FL,3,A,A2,7.67,18.0 -2015-12-16,FL,3,A,A2,7.64,18.0 -2015-12-17,FL,3,A,A2,7.75,16.0 -2015-12-18,FL,3,A,A2,7.68,19.0 -2015-12-19,FL,3,A,A2,7.72,17.0 -2015-12-20,FL,3,A,A2,7.65,17.0 -2015-12-21,FL,3,A,A2,7.75,15.0 -2015-12-22,FL,3,A,A2,7.64,17.0 -2015-12-23,FL,3,A,A2,7.67,17.0 -2015-12-24,FL,3,A,A2,7.8,16.0 -2015-12-25,FL,3,A,A2,7.84,15.0 -2015-12-26,FL,3,A,A2,7.85,17.0 -2015-12-27,FL,3,A,A2,7.62,18.0 -2015-12-28,FL,3,A,A2,7.57,18.0 -2015-12-29,FL,3,A,A2,7.68,16.0 -2015-12-30,FL,3,A,A2,7.73,16.0 -2015-12-31,FL,3,A,A2,7.71,16.0 -2016-01-01,FL,3,A,A2,7.72,17.0 -2016-01-02,FL,3,A,A2,7.75,18.0 -2016-01-03,FL,3,A,A2,7.64,18.0 -2016-01-04,FL,3,A,A2,7.9,14.0 -2016-01-05,FL,3,A,A2,7.83,15.0 -2016-01-06,FL,3,A,A2,7.91,15.0 -2016-01-07,FL,3,A,A2,7.82,16.0 -2016-01-08,FL,3,A,A2,7.78,17.0 -2016-01-09,FL,3,A,A2,7.71,16.0 -2016-01-10,FL,3,A,A2,7.73,18.0 -2016-01-11,FL,3,A,A2,7.57,17.0 -2016-01-12,FL,3,A,A2,7.74,16.0 -2016-01-13,FL,3,A,A2,7.89,16.0 -2016-01-14,FL,3,A,A2,7.74,16.0 -2016-01-15,FL,3,A,A2,7.74,15.0 -2016-01-16,FL,3,A,A2,7.5,19.0 -2016-01-17,FL,3,A,A2,7.69,17.0 -2016-01-18,FL,3,A,A2,7.55,18.0 -2016-01-19,FL,3,A,A2,7.68,16.0 -2016-01-20,FL,3,A,A2,7.77,17.0 -2016-01-21,FL,3,A,A2,7.76,15.0 -2016-01-22,FL,3,A,A2,7.76,17.0 -2016-01-23,FL,3,A,A2,7.7,17.0 -2016-01-24,FL,3,A,A2,7.8,15.0 -2016-01-25,FL,3,A,A2,7.66,16.0 -2016-01-26,FL,3,A,A2,7.67,17.0 -2016-01-27,FL,3,A,A2,7.77,16.0 -2016-01-28,FL,3,A,A2,7.69,17.0 -2016-01-29,FL,3,A,A2,7.66,17.0 -2016-01-30,FL,3,A,A2,7.72,18.0 -2016-01-31,FL,3,A,A2,7.7,18.0 -2016-02-01,FL,3,A,A2,7.71,16.0 -2016-02-02,FL,3,A,A2,7.75,17.0 -2016-02-03,FL,3,A,A2,7.71,17.0 -2016-02-04,FL,3,A,A2,7.63,17.0 -2016-02-05,FL,3,A,A2,7.7,16.0 -2016-02-06,FL,3,A,A2,7.59,18.0 -2016-02-07,FL,3,A,A2,7.81,16.0 -2016-02-08,FL,3,A,A2,7.72,15.0 -2016-02-09,FL,3,A,A2,7.72,17.0 -2016-02-10,FL,3,A,A2,7.71,15.0 -2016-02-11,FL,3,A,A2,7.68,17.0 -2016-02-12,FL,3,A,A2,7.6,17.0 -2016-02-13,FL,3,A,A2,7.78,17.0 -2016-02-14,FL,3,A,A2,7.68,16.0 -2016-02-15,FL,3,A,A2,7.7,16.0 -2016-02-16,FL,3,A,A2,7.85,17.0 -2016-02-17,FL,3,A,A2,7.77,16.0 -2016-02-18,FL,3,A,A2,7.81,17.0 -2016-02-19,FL,3,A,A2,7.68,18.0 -2016-02-20,FL,3,A,A2,7.77,16.0 -2016-02-21,FL,3,A,A2,7.67,16.0 -2016-02-22,FL,3,A,A2,7.73,16.0 -2016-02-23,FL,3,A,A2,7.86,16.0 -2016-02-24,FL,3,A,A2,7.81,17.0 -2016-02-25,FL,3,A,A2,7.71,16.0 -2016-02-26,FL,3,A,A2,7.79,17.0 -2016-02-27,FL,3,A,A2,7.56,19.0 -2016-02-28,FL,3,A,A2,7.7,17.0 -2016-02-29,FL,3,A,A2,7.74,18.0 -2016-03-01,FL,3,A,A2,7.61,18.0 -2016-03-02,FL,3,A,A2,7.78,16.0 -2016-03-03,FL,3,A,A2,7.66,18.0 -2016-03-04,FL,3,A,A2,7.68,15.0 -2016-03-05,FL,3,A,A2,7.75,17.0 -2016-03-06,FL,3,A,A2,7.7,17.0 -2016-03-07,FL,3,A,A2,7.66,18.0 -2016-03-08,FL,3,A,A2,7.76,17.0 -2016-03-09,FL,3,A,A2,7.75,16.0 -2016-03-10,FL,3,A,A2,7.59,17.0 -2016-03-11,FL,3,A,A2,7.76,16.0 -2016-03-12,FL,3,A,A2,7.69,17.0 -2016-03-13,FL,3,A,A2,7.86,16.0 -2016-03-14,FL,3,A,A2,7.77,16.0 -2016-03-15,FL,3,A,A2,7.79,16.0 -2016-03-16,FL,3,A,A2,7.68,17.0 -2016-03-17,FL,3,A,A2,7.61,18.0 -2016-03-18,FL,3,A,A2,7.72,17.0 -2016-03-19,FL,3,A,A2,7.74,17.0 -2016-03-20,FL,3,A,A2,7.63,19.0 -2016-03-21,FL,3,A,A2,7.69,16.0 -2016-03-22,FL,3,A,A2,7.72,16.0 -2016-03-23,FL,3,A,A2,7.83,16.0 -2016-03-24,FL,3,A,A2,7.75,17.0 -2016-03-25,FL,3,A,A2,7.72,18.0 -2016-03-26,FL,3,A,A2,7.79,16.0 -2016-03-27,FL,3,A,A2,7.8,17.0 -2016-03-28,FL,3,A,A2,7.71,16.0 -2016-03-29,FL,3,A,A2,7.77,16.0 -2016-03-30,FL,3,A,A2,7.74,17.0 -2016-03-31,FL,3,A,A2,7.79,17.0 -2016-04-01,FL,3,A,A2,7.78,16.0 -2016-04-02,FL,3,A,A2,7.73,18.0 -2016-04-03,FL,3,A,A2,7.71,17.0 -2016-04-04,FL,3,A,A2,7.66,17.0 -2016-04-05,FL,3,A,A2,7.8,14.0 -2016-04-06,FL,3,A,A2,7.74,16.0 -2016-04-07,FL,3,A,A2,7.69,16.0 -2016-04-08,FL,3,A,A2,7.61,19.0 -2016-04-09,FL,3,A,A2,7.79,16.0 -2016-04-10,FL,3,A,A2,7.63,18.0 -2016-04-11,FL,3,A,A2,7.72,17.0 -2016-04-12,FL,3,A,A2,7.74,16.0 -2016-04-13,FL,3,A,A2,7.72,15.0 -2016-04-14,FL,3,A,A2,7.68,17.0 -2016-04-15,FL,3,A,A2,7.76,16.0 -2016-04-16,FL,3,A,A2,7.66,16.0 -2016-04-17,FL,3,A,A2,7.74,17.0 -2016-04-18,FL,3,A,A2,7.71,18.0 -2016-04-19,FL,3,A,A2,7.69,17.0 -2016-04-20,FL,3,A,A2,7.74,17.0 -2016-04-21,FL,3,A,A2,7.65,18.0 -2016-04-22,FL,3,A,A2,7.84,15.0 -2016-04-23,FL,3,A,A2,7.64,18.0 -2016-04-24,FL,3,A,A2,7.5,18.0 -2016-04-25,FL,3,A,A2,7.63,18.0 -2016-04-26,FL,3,A,A2,7.69,17.0 -2016-04-27,FL,3,A,A2,7.8,17.0 -2016-04-28,FL,3,A,A2,7.72,17.0 -2016-04-29,FL,3,A,A2,7.75,15.0 -2016-04-30,FL,3,A,A2,7.83,17.0 -2016-05-01,FL,3,A,A2,7.76,17.0 -2016-05-02,FL,3,A,A2,7.64,18.0 -2016-05-03,FL,3,A,A2,7.64,16.0 -2016-05-04,FL,3,A,A2,7.81,15.0 -2016-05-05,FL,3,A,A2,7.62,18.0 -2016-05-06,FL,3,A,A2,7.77,16.0 -2016-05-07,FL,3,A,A2,7.68,18.0 -2016-05-08,FL,3,A,A2,7.58,18.0 -2016-05-09,FL,3,A,A2,7.75,16.0 -2016-05-10,FL,3,A,A2,7.62,18.0 -2016-05-11,FL,3,A,A2,7.72,17.0 -2016-05-12,FL,3,A,A2,7.7,16.0 -2016-05-13,FL,3,A,A2,7.7,17.0 -2016-05-14,FL,3,A,A2,7.75,17.0 -2016-05-15,FL,3,A,A2,7.64,19.0 -2016-05-16,FL,3,A,A2,7.94,15.0 -2016-05-17,FL,3,A,A2,7.71,17.0 -2016-05-18,FL,3,A,A2,7.71,15.0 -2016-05-19,FL,3,A,A2,7.72,17.0 -2016-05-20,FL,3,A,A2,7.62,17.0 -2016-05-21,FL,3,A,A2,7.52,18.0 -2016-05-22,FL,3,A,A2,7.7,17.0 -2016-05-23,FL,3,A,A2,7.76,16.0 -2016-05-24,FL,3,A,A2,7.64,18.0 -2016-05-25,FL,3,A,A2,7.81,16.0 -2016-05-26,FL,3,A,A2,7.81,17.0 -2016-05-27,FL,3,A,A2,7.81,15.0 -2016-05-28,FL,3,A,A2,7.73,17.0 -2016-05-29,FL,3,A,A2,7.94,14.0 -2016-05-30,FL,3,A,A2,7.74,16.0 -2016-05-31,FL,3,A,A2,7.66,17.0 -2016-06-01,FL,3,A,A2,7.76,15.0 -2016-06-02,FL,3,A,A2,7.76,15.0 -2016-06-03,FL,3,A,A2,7.79,15.0 -2016-06-04,FL,3,A,A2,7.61,18.0 -2016-06-05,FL,3,A,A2,7.78,17.0 -2016-06-06,FL,3,A,A2,7.79,15.0 -2016-06-07,FL,3,A,A2,7.77,15.0 -2016-06-08,FL,3,A,A2,7.7,15.0 -2016-06-09,FL,3,A,A2,7.77,15.0 -2016-06-10,FL,3,A,A2,7.74,15.0 -2016-06-11,FL,3,A,A2,7.7,18.0 -2016-06-12,FL,3,A,A2,7.81,17.0 -2016-06-13,FL,3,A,A2,7.73,16.0 -2016-06-14,FL,3,A,A2,7.66,19.0 -2016-06-15,FL,3,A,A2,7.75,17.0 -2016-06-16,FL,3,A,A2,7.88,15.0 -2016-06-17,FL,3,A,A2,7.66,16.0 -2016-06-18,FL,3,A,A2,7.66,17.0 -2016-06-19,FL,3,A,A2,7.74,15.0 -2016-06-20,FL,3,A,A2,7.78,16.0 -2016-06-21,FL,3,A,A2,7.79,16.0 -2016-06-22,FL,3,A,A2,7.79,15.0 -2016-06-23,FL,3,A,A2,7.69,16.0 -2016-06-24,FL,3,A,A2,7.78,16.0 -2016-06-25,FL,3,A,A2,7.71,16.0 -2016-06-26,FL,3,A,A2,7.6,17.0 -2016-06-27,FL,3,A,A2,7.7,16.0 -2016-06-28,FL,3,A,A2,7.65,17.0 -2016-06-29,FL,3,A,A2,7.71,18.0 -2016-06-30,FL,3,A,A2,7.86,14.0 -2016-07-01,FL,3,A,A2,7.72,18.0 -2016-07-02,FL,3,A,A2,7.7,17.0 -2016-07-03,FL,3,A,A2,7.75,17.0 -2016-07-04,FL,3,A,A2,7.62,17.0 -2016-07-05,FL,3,A,A2,7.71,18.0 -2016-07-06,FL,3,A,A2,7.85,15.0 -2016-07-07,FL,3,A,A2,7.81,15.0 -2016-07-08,FL,3,A,A2,7.72,16.0 -2016-07-09,FL,3,A,A2,7.64,18.0 -2016-07-10,FL,3,A,A2,7.63,17.0 -2016-07-11,FL,3,A,A2,7.7,17.0 -2016-07-12,FL,3,A,A2,7.81,17.0 -2016-07-13,FL,3,A,A2,7.74,15.0 -2016-07-14,FL,3,A,A2,7.73,15.0 -2016-07-15,FL,3,A,A2,7.67,16.0 -2016-07-16,FL,3,A,A2,7.61,18.0 -2016-07-17,FL,3,A,A2,7.89,15.0 -2016-07-18,FL,3,A,A2,7.89,15.0 -2016-07-19,FL,3,A,A2,7.47,18.0 -2016-07-20,FL,3,A,A2,7.69,16.0 -2016-07-21,FL,3,A,A2,7.66,17.0 -2016-07-22,FL,3,A,A2,7.71,16.0 -2016-07-23,FL,3,A,A2,7.83,17.0 -2016-07-24,FL,3,A,A2,7.86,15.0 -2016-07-25,FL,3,A,A2,7.59,17.0 -2016-07-26,FL,3,A,A2,7.65,16.0 -2016-07-27,FL,3,A,A2,7.75,17.0 -2015-01-05,FL,3,A,A3,5.4,917.0 -2015-01-06,FL,3,A,A3,5.34,963.0 -2015-01-07,FL,3,A,A3,5.44,913.0 -2015-01-08,FL,3,A,A3,5.5,974.0 -2015-01-09,FL,3,A,A3,5.39,1031.0 -2015-01-10,FL,3,A,A3,5.4,1076.0 -2015-01-11,FL,3,A,A3,5.42,986.0 -2015-01-12,FL,3,A,A3,5.51,891.0 -2015-01-13,FL,3,A,A3,5.47,959.0 -2015-01-14,FL,3,A,A3,5.35,1069.0 -2015-01-15,FL,3,A,A3,5.43,1009.0 -2015-01-16,FL,3,A,A3,5.46,984.0 -2015-01-17,FL,3,A,A3,5.42,1007.0 -2015-01-18,FL,3,A,A3,5.4,1002.0 -2015-01-19,FL,3,A,A3,5.38,935.0 -2015-01-20,FL,3,A,A3,5.45,964.0 -2015-01-21,FL,3,A,A3,5.34,1109.0 -2015-01-22,FL,3,A,A3,5.27,1067.0 -2015-01-23,FL,3,A,A3,5.42,1035.0 -2015-01-24,FL,3,A,A3,5.32,1084.0 -2015-01-25,FL,3,A,A3,5.34,1086.0 -2015-01-26,FL,3,A,A3,5.37,1038.0 -2015-01-27,FL,3,A,A3,5.39,918.0 -2015-01-28,FL,3,A,A3,5.46,925.0 -2015-01-29,FL,3,A,A3,5.47,1055.0 -2015-01-30,FL,3,A,A3,5.46,1008.0 -2015-01-31,FL,3,A,A3,5.39,1100.0 -2015-02-01,FL,3,A,A3,5.39,1018.0 -2015-02-02,FL,3,A,A3,5.39,1035.0 -2015-02-03,FL,3,A,A3,5.39,890.0 -2015-02-04,FL,3,A,A3,5.36,1036.0 -2015-02-05,FL,3,A,A3,5.37,966.0 -2015-02-06,FL,3,A,A3,5.3,1005.0 -2015-02-07,FL,3,A,A3,5.4,1035.0 -2015-02-08,FL,3,A,A3,5.5,1016.0 -2015-02-09,FL,3,A,A3,5.4,1000.0 -2015-02-10,FL,3,A,A3,5.43,981.0 -2015-02-11,FL,3,A,A3,5.31,1016.0 -2015-02-12,FL,3,A,A3,5.44,808.0 -2015-02-13,FL,3,A,A3,5.37,1057.0 -2015-02-14,FL,3,A,A3,5.33,1045.0 -2015-02-15,FL,3,A,A3,5.43,994.0 -2015-02-16,FL,3,A,A3,5.43,953.0 -2015-02-17,FL,3,A,A3,5.39,1010.0 -2015-02-18,FL,3,A,A3,5.38,1028.0 -2015-02-19,FL,3,A,A3,5.36,1039.0 -2015-02-20,FL,3,A,A3,5.42,983.0 -2015-02-21,FL,3,A,A3,5.41,999.0 -2015-02-22,FL,3,A,A3,5.52,957.0 -2015-02-23,FL,3,A,A3,5.36,965.0 -2015-02-24,FL,3,A,A3,5.42,945.0 -2015-02-25,FL,3,A,A3,5.27,1057.0 -2015-02-26,FL,3,A,A3,5.35,1074.0 -2015-02-27,FL,3,A,A3,5.43,1037.0 -2015-02-28,FL,3,A,A3,5.39,1092.0 -2015-03-01,FL,3,A,A3,5.46,935.0 -2015-03-02,FL,3,A,A3,5.39,1096.0 -2015-03-03,FL,3,A,A3,5.44,1016.0 -2015-03-04,FL,3,A,A3,5.39,1061.0 -2015-03-05,FL,3,A,A3,5.36,1038.0 -2015-03-06,FL,3,A,A3,5.31,1080.0 -2015-03-07,FL,3,A,A3,5.42,969.0 -2015-03-08,FL,3,A,A3,5.4,1082.0 -2015-03-09,FL,3,A,A3,5.43,916.0 -2015-03-10,FL,3,A,A3,5.42,1010.0 -2015-03-11,FL,3,A,A3,5.34,1061.0 -2015-03-12,FL,3,A,A3,5.42,1135.0 -2015-03-13,FL,3,A,A3,5.33,1070.0 -2015-03-14,FL,3,A,A3,5.48,1031.0 -2015-03-15,FL,3,A,A3,5.35,1052.0 -2015-03-16,FL,3,A,A3,5.42,882.0 -2015-03-17,FL,3,A,A3,5.38,974.0 -2015-03-18,FL,3,A,A3,5.43,963.0 -2015-03-19,FL,3,A,A3,5.38,1017.0 -2015-03-20,FL,3,A,A3,5.54,1041.0 -2015-03-21,FL,3,A,A3,5.44,1044.0 -2015-03-22,FL,3,A,A3,5.44,1012.0 -2015-03-23,FL,3,A,A3,5.37,972.0 -2015-03-24,FL,3,A,A3,5.38,1022.0 -2015-03-25,FL,3,A,A3,5.54,947.0 -2015-03-26,FL,3,A,A3,5.39,1128.0 -2015-03-27,FL,3,A,A3,5.43,1011.0 -2015-03-28,FL,3,A,A3,5.4,1035.0 -2015-03-29,FL,3,A,A3,5.35,1071.0 -2015-03-30,FL,3,A,A3,5.26,1002.0 -2015-03-31,FL,3,A,A3,5.39,975.0 -2015-04-01,FL,3,A,A3,5.4,1081.0 -2015-04-02,FL,3,A,A3,5.39,1048.0 -2015-04-03,FL,3,A,A3,5.27,1112.0 -2015-04-04,FL,3,A,A3,5.48,984.0 -2015-04-05,FL,3,A,A3,5.33,1115.0 -2015-04-06,FL,3,A,A3,5.41,989.0 -2015-04-07,FL,3,A,A3,5.35,929.0 -2015-04-08,FL,3,A,A3,5.4,957.0 -2015-04-09,FL,3,A,A3,5.35,1030.0 -2015-04-10,FL,3,A,A3,5.41,1017.0 -2015-04-11,FL,3,A,A3,5.42,981.0 -2015-04-12,FL,3,A,A3,5.38,1132.0 -2015-04-13,FL,3,A,A3,5.4,979.0 -2015-04-14,FL,3,A,A3,5.52,923.0 -2015-04-15,FL,3,A,A3,5.3,989.0 -2015-04-16,FL,3,A,A3,5.4,974.0 -2015-04-17,FL,3,A,A3,5.39,996.0 -2015-04-18,FL,3,A,A3,5.39,1064.0 -2015-04-19,FL,3,A,A3,5.37,996.0 -2015-04-20,FL,3,A,A3,5.4,1039.0 -2015-04-21,FL,3,A,A3,5.42,1012.0 -2015-04-22,FL,3,A,A3,5.42,917.0 -2015-04-23,FL,3,A,A3,5.45,992.0 -2015-04-24,FL,3,A,A3,5.4,986.0 -2015-04-25,FL,3,A,A3,5.48,1096.0 -2015-04-26,FL,3,A,A3,5.41,1024.0 -2015-04-27,FL,3,A,A3,5.36,909.0 -2015-04-28,FL,3,A,A3,5.53,848.0 -2015-04-29,FL,3,A,A3,5.25,989.0 -2015-04-30,FL,3,A,A3,5.41,1083.0 -2015-05-01,FL,3,A,A3,5.35,998.0 -2015-05-02,FL,3,A,A3,5.44,1010.0 -2015-05-03,FL,3,A,A3,5.37,1051.0 -2015-05-04,FL,3,A,A3,5.36,1013.0 -2015-05-05,FL,3,A,A3,5.4,1115.0 -2015-05-06,FL,3,A,A3,5.55,979.0 -2015-05-07,FL,3,A,A3,5.57,888.0 -2015-05-08,FL,3,A,A3,5.41,1054.0 -2015-05-09,FL,3,A,A3,5.43,1054.0 -2015-05-10,FL,3,A,A3,5.38,1056.0 -2015-05-11,FL,3,A,A3,5.43,1024.0 -2015-05-12,FL,3,A,A3,5.42,947.0 -2015-05-13,FL,3,A,A3,5.39,1105.0 -2015-05-14,FL,3,A,A3,5.25,1023.0 -2015-05-15,FL,3,A,A3,5.37,1000.0 -2015-05-16,FL,3,A,A3,5.55,992.0 -2015-05-17,FL,3,A,A3,5.46,1007.0 -2015-05-18,FL,3,A,A3,5.49,920.0 -2015-05-19,FL,3,A,A3,5.37,978.0 -2015-05-20,FL,3,A,A3,5.56,904.0 -2015-05-21,FL,3,A,A3,5.43,1022.0 -2015-05-22,FL,3,A,A3,5.39,990.0 -2015-05-23,FL,3,A,A3,5.34,1046.0 -2015-05-24,FL,3,A,A3,5.23,1140.0 -2015-05-25,FL,3,A,A3,5.42,1081.0 -2015-05-26,FL,3,A,A3,5.4,1096.0 -2015-05-27,FL,3,A,A3,5.41,1015.0 -2015-05-28,FL,3,A,A3,5.42,992.0 -2015-05-29,FL,3,A,A3,5.43,988.0 -2015-05-30,FL,3,A,A3,5.42,1043.0 -2015-05-31,FL,3,A,A3,5.4,1115.0 -2015-06-01,FL,3,A,A3,5.49,1005.0 -2015-06-02,FL,3,A,A3,5.33,1200.0 -2015-06-03,FL,3,A,A3,5.31,1027.0 -2015-06-04,FL,3,A,A3,5.44,1023.0 -2015-06-05,FL,3,A,A3,5.4,1118.0 -2015-06-06,FL,3,A,A3,5.41,1057.0 -2015-06-07,FL,3,A,A3,5.49,1044.0 -2015-06-08,FL,3,A,A3,5.49,974.0 -2015-06-09,FL,3,A,A3,5.45,927.0 -2015-06-10,FL,3,A,A3,5.41,1051.0 -2015-06-11,FL,3,A,A3,5.46,1067.0 -2015-06-12,FL,3,A,A3,5.37,1051.0 -2015-06-13,FL,3,A,A3,5.37,999.0 -2015-06-14,FL,3,A,A3,5.47,1013.0 -2015-06-15,FL,3,A,A3,5.25,1054.0 -2015-06-16,FL,3,A,A3,5.44,1103.0 -2015-06-17,FL,3,A,A3,5.42,1132.0 -2015-06-18,FL,3,A,A3,5.45,1001.0 -2015-06-19,FL,3,A,A3,5.38,1057.0 -2015-06-20,FL,3,A,A3,5.4,1117.0 -2015-06-21,FL,3,A,A3,5.46,1052.0 -2015-06-22,FL,3,A,A3,5.38,1066.0 -2015-06-23,FL,3,A,A3,5.42,1008.0 -2015-06-24,FL,3,A,A3,5.45,1034.0 -2015-06-25,FL,3,A,A3,5.45,971.0 -2015-06-26,FL,3,A,A3,5.34,990.0 -2015-06-27,FL,3,A,A3,5.27,1106.0 -2015-06-28,FL,3,A,A3,5.42,963.0 -2015-06-29,FL,3,A,A3,5.44,1018.0 -2015-06-30,FL,3,A,A3,5.46,902.0 -2015-07-01,FL,3,A,A3,5.29,1015.0 -2015-07-02,FL,3,A,A3,5.37,1050.0 -2015-07-03,FL,3,A,A3,5.46,987.0 -2015-07-04,FL,3,A,A3,5.32,1105.0 -2015-07-05,FL,3,A,A3,5.35,1029.0 -2015-07-06,FL,3,A,A3,5.42,984.0 -2015-07-07,FL,3,A,A3,5.5,975.0 -2015-07-08,FL,3,A,A3,5.39,1009.0 -2015-07-09,FL,3,A,A3,5.4,976.0 -2015-07-10,FL,3,A,A3,5.41,1015.0 -2015-07-11,FL,3,A,A3,5.32,1056.0 -2015-07-12,FL,3,A,A3,5.46,991.0 -2015-07-13,FL,3,A,A3,5.56,1006.0 -2015-07-14,FL,3,A,A3,5.44,958.0 -2015-07-15,FL,3,A,A3,5.47,930.0 -2015-07-16,FL,3,A,A3,5.4,1003.0 -2015-07-17,FL,3,A,A3,5.31,1050.0 -2015-07-18,FL,3,A,A3,5.34,1087.0 -2015-07-19,FL,3,A,A3,5.4,1145.0 -2015-07-20,FL,3,A,A3,5.43,966.0 -2015-07-21,FL,3,A,A3,5.33,1037.0 -2015-07-22,FL,3,A,A3,5.41,1004.0 -2015-07-23,FL,3,A,A3,5.43,1017.0 -2015-07-24,FL,3,A,A3,5.37,1052.0 -2015-07-25,FL,3,A,A3,5.29,1054.0 -2015-07-26,FL,3,A,A3,5.43,1059.0 -2015-07-27,FL,3,A,A3,5.43,987.0 -2015-07-28,FL,3,A,A3,5.37,1037.0 -2015-07-29,FL,3,A,A3,5.4,1087.0 -2015-07-30,FL,3,A,A3,5.39,934.0 -2015-07-31,FL,3,A,A3,5.45,1029.0 -2015-08-01,FL,3,A,A3,5.36,1032.0 -2015-08-02,FL,3,A,A3,5.34,1057.0 -2015-08-03,FL,3,A,A3,5.36,987.0 -2015-08-04,FL,3,A,A3,5.33,1150.0 -2015-08-05,FL,3,A,A3,5.35,1045.0 -2015-08-06,FL,3,A,A3,5.4,996.0 -2015-08-07,FL,3,A,A3,5.42,1049.0 -2015-08-08,FL,3,A,A3,5.33,1142.0 -2015-08-09,FL,3,A,A3,5.47,1016.0 -2015-08-10,FL,3,A,A3,5.36,979.0 -2015-08-11,FL,3,A,A3,5.41,909.0 -2015-08-12,FL,3,A,A3,5.45,1015.0 -2015-08-13,FL,3,A,A3,5.38,1052.0 -2015-08-14,FL,3,A,A3,5.33,1109.0 -2015-08-15,FL,3,A,A3,5.32,1128.0 -2015-08-16,FL,3,A,A3,5.38,962.0 -2015-08-17,FL,3,A,A3,5.46,1042.0 -2015-08-18,FL,3,A,A3,5.45,891.0 -2015-08-19,FL,3,A,A3,5.37,1024.0 -2015-08-20,FL,3,A,A3,5.37,1034.0 -2015-08-21,FL,3,A,A3,5.46,1021.0 -2015-08-22,FL,3,A,A3,5.4,1051.0 -2015-08-23,FL,3,A,A3,5.47,946.0 -2015-08-24,FL,3,A,A3,5.42,967.0 -2015-08-25,FL,3,A,A3,5.4,995.0 -2015-08-26,FL,3,A,A3,5.38,1042.0 -2015-08-27,FL,3,A,A3,5.37,949.0 -2015-08-28,FL,3,A,A3,5.37,1071.0 -2015-08-29,FL,3,A,A3,5.36,1122.0 -2015-08-30,FL,3,A,A3,5.49,1020.0 -2015-08-31,FL,3,A,A3,5.41,1025.0 -2015-09-01,FL,3,A,A3,5.38,1055.0 -2015-09-02,FL,3,A,A3,5.5,1046.0 -2015-09-03,FL,3,A,A3,5.33,1071.0 -2015-09-04,FL,3,A,A3,5.41,1044.0 -2015-09-05,FL,3,A,A3,5.38,1115.0 -2015-09-06,FL,3,A,A3,5.36,1116.0 -2015-09-07,FL,3,A,A3,5.38,993.0 -2015-09-08,FL,3,A,A3,5.33,1023.0 -2015-09-09,FL,3,A,A3,5.39,990.0 -2015-09-10,FL,3,A,A3,5.37,984.0 -2015-09-11,FL,3,A,A3,5.38,1039.0 -2015-09-12,FL,3,A,A3,5.41,1062.0 -2015-09-13,FL,3,A,A3,5.4,1087.0 -2015-09-14,FL,3,A,A3,5.42,1044.0 -2015-09-15,FL,3,A,A3,5.34,1053.0 -2015-09-16,FL,3,A,A3,5.35,1082.0 -2015-09-17,FL,3,A,A3,5.37,1021.0 -2015-09-18,FL,3,A,A3,5.41,1039.0 -2015-09-19,FL,3,A,A3,5.38,990.0 -2015-09-20,FL,3,A,A3,5.41,927.0 -2015-09-21,FL,3,A,A3,5.41,981.0 -2015-09-22,FL,3,A,A3,5.4,1019.0 -2015-09-23,FL,3,A,A3,5.4,1004.0 -2015-09-24,FL,3,A,A3,5.3,1025.0 -2015-09-25,FL,3,A,A3,5.33,1028.0 -2015-09-26,FL,3,A,A3,5.41,1066.0 -2015-09-27,FL,3,A,A3,5.41,1068.0 -2015-09-28,FL,3,A,A3,5.44,1012.0 -2015-09-29,FL,3,A,A3,5.4,1014.0 -2015-09-30,FL,3,A,A3,5.42,1091.0 -2015-10-01,FL,3,A,A3,5.39,1066.0 -2015-10-02,FL,3,A,A3,5.38,1051.0 -2015-10-03,FL,3,A,A3,5.39,1081.0 -2015-10-04,FL,3,A,A3,5.37,994.0 -2015-10-05,FL,3,A,A3,5.41,1047.0 -2015-10-06,FL,3,A,A3,5.46,1045.0 -2015-10-07,FL,3,A,A3,5.32,992.0 -2015-10-08,FL,3,A,A3,5.35,924.0 -2015-10-09,FL,3,A,A3,5.41,1170.0 -2015-10-10,FL,3,A,A3,5.36,1114.0 -2015-10-11,FL,3,A,A3,5.46,1019.0 -2015-10-12,FL,3,A,A3,5.32,1027.0 -2015-10-13,FL,3,A,A3,5.49,938.0 -2015-10-14,FL,3,A,A3,5.35,1026.0 -2015-10-15,FL,3,A,A3,5.41,999.0 -2015-10-16,FL,3,A,A3,5.49,1040.0 -2015-10-17,FL,3,A,A3,5.35,1051.0 -2015-10-18,FL,3,A,A3,5.46,1113.0 -2015-10-19,FL,3,A,A3,5.36,962.0 -2015-10-20,FL,3,A,A3,5.35,1084.0 -2015-10-21,FL,3,A,A3,5.3,1065.0 -2015-10-22,FL,3,A,A3,5.44,1030.0 -2015-10-23,FL,3,A,A3,5.38,1079.0 -2015-10-24,FL,3,A,A3,5.33,1081.0 -2015-10-25,FL,3,A,A3,5.44,931.0 -2015-10-26,FL,3,A,A3,5.39,1079.0 -2015-10-27,FL,3,A,A3,5.35,1010.0 -2015-10-28,FL,3,A,A3,5.39,881.0 -2015-10-29,FL,3,A,A3,5.44,1089.0 -2015-10-30,FL,3,A,A3,5.33,1004.0 -2015-10-31,FL,3,A,A3,5.44,1120.0 -2015-11-01,FL,3,A,A3,5.42,1041.0 -2015-11-02,FL,3,A,A3,5.36,992.0 -2015-11-03,FL,3,A,A3,5.41,1003.0 -2015-11-04,FL,3,A,A3,5.52,962.0 -2015-11-05,FL,3,A,A3,5.35,919.0 -2015-11-06,FL,3,A,A3,5.37,1008.0 -2015-11-07,FL,3,A,A3,5.38,1129.0 -2015-11-08,FL,3,A,A3,5.43,1051.0 -2015-11-09,FL,3,A,A3,5.41,998.0 -2015-11-10,FL,3,A,A3,5.41,960.0 -2015-11-11,FL,3,A,A3,5.31,1052.0 -2015-11-12,FL,3,A,A3,5.32,1062.0 -2015-11-13,FL,3,A,A3,5.44,974.0 -2015-11-14,FL,3,A,A3,5.42,1061.0 -2015-11-15,FL,3,A,A3,5.35,989.0 -2015-11-16,FL,3,A,A3,5.4,949.0 -2015-11-17,FL,3,A,A3,5.4,983.0 -2015-11-18,FL,3,A,A3,5.46,1004.0 -2015-11-19,FL,3,A,A3,5.39,970.0 -2015-11-20,FL,3,A,A3,5.44,1037.0 -2015-11-21,FL,3,A,A3,5.44,1009.0 -2015-11-22,FL,3,A,A3,5.33,1016.0 -2015-11-23,FL,3,A,A3,5.38,1136.0 -2015-11-24,FL,3,A,A3,5.43,968.0 -2015-11-25,FL,3,A,A3,5.41,976.0 -2015-11-26,FL,3,A,A3,5.49,927.0 -2015-11-27,FL,3,A,A3,5.33,1093.0 -2015-11-28,FL,3,A,A3,5.48,985.0 -2015-11-29,FL,3,A,A3,5.4,1026.0 -2015-11-30,FL,3,A,A3,5.38,1063.0 -2015-12-01,FL,3,A,A3,5.36,1050.0 -2015-12-02,FL,3,A,A3,5.44,1091.0 -2015-12-03,FL,3,A,A3,5.42,1124.0 -2015-12-04,FL,3,A,A3,5.39,1028.0 -2015-12-05,FL,3,A,A3,5.4,999.0 -2015-12-06,FL,3,A,A3,5.39,1032.0 -2015-12-07,FL,3,A,A3,5.43,999.0 -2015-12-08,FL,3,A,A3,5.36,1005.0 -2015-12-09,FL,3,A,A3,5.39,1079.0 -2015-12-10,FL,3,A,A3,5.37,879.0 -2015-12-11,FL,3,A,A3,5.39,1078.0 -2015-12-12,FL,3,A,A3,5.49,981.0 -2015-12-13,FL,3,A,A3,5.54,936.0 -2015-12-14,FL,3,A,A3,5.45,957.0 -2015-12-15,FL,3,A,A3,5.45,982.0 -2015-12-16,FL,3,A,A3,5.4,1115.0 -2015-12-17,FL,3,A,A3,5.46,964.0 -2015-12-18,FL,3,A,A3,5.41,1015.0 -2015-12-19,FL,3,A,A3,5.4,1034.0 -2015-12-20,FL,3,A,A3,5.33,1212.0 -2015-12-21,FL,3,A,A3,5.33,988.0 -2015-12-22,FL,3,A,A3,5.43,1036.0 -2015-12-23,FL,3,A,A3,5.38,1040.0 -2015-12-24,FL,3,A,A3,5.39,1023.0 -2015-12-25,FL,3,A,A3,5.34,1067.0 -2015-12-26,FL,3,A,A3,5.32,1095.0 -2015-12-27,FL,3,A,A3,5.41,971.0 -2015-12-28,FL,3,A,A3,5.47,942.0 -2015-12-29,FL,3,A,A3,5.42,982.0 -2015-12-30,FL,3,A,A3,5.48,1104.0 -2015-12-31,FL,3,A,A3,5.52,963.0 -2016-01-01,FL,3,A,A3,5.3,1019.0 -2016-01-02,FL,3,A,A3,5.37,1051.0 -2016-01-03,FL,3,A,A3,5.34,989.0 -2016-01-04,FL,3,A,A3,5.32,1000.0 -2016-01-05,FL,3,A,A3,5.31,999.0 -2016-01-06,FL,3,A,A3,5.43,1034.0 -2016-01-07,FL,3,A,A3,5.49,964.0 -2016-01-08,FL,3,A,A3,5.35,1085.0 -2016-01-09,FL,3,A,A3,5.38,1145.0 -2016-01-10,FL,3,A,A3,5.52,938.0 -2016-01-11,FL,3,A,A3,5.39,979.0 -2016-01-12,FL,3,A,A3,5.37,1053.0 -2016-01-13,FL,3,A,A3,5.38,1024.0 -2016-01-14,FL,3,A,A3,5.49,1029.0 -2016-01-15,FL,3,A,A3,5.41,961.0 -2016-01-16,FL,3,A,A3,5.42,1025.0 -2016-01-17,FL,3,A,A3,5.43,992.0 -2016-01-18,FL,3,A,A3,5.52,965.0 -2016-01-19,FL,3,A,A3,5.35,1003.0 -2016-01-20,FL,3,A,A3,5.35,1040.0 -2016-01-21,FL,3,A,A3,5.39,972.0 -2016-01-22,FL,3,A,A3,5.35,1035.0 -2016-01-23,FL,3,A,A3,5.32,1139.0 -2016-01-24,FL,3,A,A3,5.38,1066.0 -2016-01-25,FL,3,A,A3,5.41,974.0 -2016-01-26,FL,3,A,A3,5.41,1066.0 -2016-01-27,FL,3,A,A3,5.4,1057.0 -2016-01-28,FL,3,A,A3,5.46,1005.0 -2016-01-29,FL,3,A,A3,5.37,1065.0 -2016-01-30,FL,3,A,A3,5.46,1000.0 -2016-01-31,FL,3,A,A3,5.47,993.0 -2016-02-01,FL,3,A,A3,5.44,977.0 -2016-02-02,FL,3,A,A3,5.28,1079.0 -2016-02-03,FL,3,A,A3,5.36,979.0 -2016-02-04,FL,3,A,A3,5.3,1063.0 -2016-02-05,FL,3,A,A3,5.44,1051.0 -2016-02-06,FL,3,A,A3,5.32,1019.0 -2016-02-07,FL,3,A,A3,5.43,997.0 -2016-02-08,FL,3,A,A3,5.41,990.0 -2016-02-09,FL,3,A,A3,5.45,964.0 -2016-02-10,FL,3,A,A3,5.45,1014.0 -2016-02-11,FL,3,A,A3,5.4,1066.0 -2016-02-12,FL,3,A,A3,5.39,1000.0 -2016-02-13,FL,3,A,A3,5.37,1041.0 -2016-02-14,FL,3,A,A3,5.37,1003.0 -2016-02-15,FL,3,A,A3,5.41,1073.0 -2016-02-16,FL,3,A,A3,5.41,1009.0 -2016-02-17,FL,3,A,A3,5.42,912.0 -2016-02-18,FL,3,A,A3,5.41,951.0 -2016-02-19,FL,3,A,A3,5.38,981.0 -2016-02-20,FL,3,A,A3,5.34,1122.0 -2016-02-21,FL,3,A,A3,5.42,890.0 -2016-02-22,FL,3,A,A3,5.44,1032.0 -2016-02-23,FL,3,A,A3,5.51,972.0 -2016-02-24,FL,3,A,A3,5.36,1096.0 -2016-02-25,FL,3,A,A3,5.33,994.0 -2016-02-26,FL,3,A,A3,5.35,1047.0 -2016-02-27,FL,3,A,A3,5.39,1127.0 -2016-02-28,FL,3,A,A3,5.47,1032.0 -2016-02-29,FL,3,A,A3,5.49,861.0 -2016-03-01,FL,3,A,A3,5.39,1086.0 -2016-03-02,FL,3,A,A3,5.45,1040.0 -2016-03-03,FL,3,A,A3,5.39,973.0 -2016-03-04,FL,3,A,A3,5.4,968.0 -2016-03-05,FL,3,A,A3,5.41,1023.0 -2016-03-06,FL,3,A,A3,5.37,1142.0 -2016-03-07,FL,3,A,A3,5.46,1046.0 -2016-03-08,FL,3,A,A3,5.44,954.0 -2016-03-09,FL,3,A,A3,5.43,1020.0 -2016-03-10,FL,3,A,A3,5.41,991.0 -2016-03-11,FL,3,A,A3,5.41,1081.0 -2016-03-12,FL,3,A,A3,5.36,1137.0 -2016-03-13,FL,3,A,A3,5.39,1009.0 -2016-03-14,FL,3,A,A3,5.34,1080.0 -2016-03-15,FL,3,A,A3,5.46,926.0 -2016-03-16,FL,3,A,A3,5.35,1041.0 -2016-03-17,FL,3,A,A3,5.32,1018.0 -2016-03-18,FL,3,A,A3,5.42,1095.0 -2016-03-19,FL,3,A,A3,5.38,1030.0 -2016-03-20,FL,3,A,A3,5.41,868.0 -2016-03-21,FL,3,A,A3,5.36,1019.0 -2016-03-22,FL,3,A,A3,5.44,899.0 -2016-03-23,FL,3,A,A3,5.44,1003.0 -2016-03-24,FL,3,A,A3,5.41,987.0 -2016-03-25,FL,3,A,A3,5.43,1038.0 -2016-03-26,FL,3,A,A3,5.32,1122.0 -2016-03-27,FL,3,A,A3,5.34,948.0 -2016-03-28,FL,3,A,A3,5.42,1023.0 -2016-03-29,FL,3,A,A3,5.36,1063.0 -2016-03-30,FL,3,A,A3,5.44,976.0 -2016-03-31,FL,3,A,A3,5.49,953.0 -2016-04-01,FL,3,A,A3,5.43,1019.0 -2016-04-02,FL,3,A,A3,5.27,1137.0 -2016-04-03,FL,3,A,A3,5.35,1072.0 -2016-04-04,FL,3,A,A3,5.4,998.0 -2016-04-05,FL,3,A,A3,5.36,1025.0 -2016-04-06,FL,3,A,A3,5.45,1035.0 -2016-04-07,FL,3,A,A3,5.31,1031.0 -2016-04-08,FL,3,A,A3,5.43,972.0 -2016-04-09,FL,3,A,A3,5.31,1160.0 -2016-04-10,FL,3,A,A3,5.44,999.0 -2016-04-11,FL,3,A,A3,5.47,995.0 -2016-04-12,FL,3,A,A3,5.46,933.0 -2016-04-13,FL,3,A,A3,5.35,987.0 -2016-04-14,FL,3,A,A3,5.59,939.0 -2016-04-15,FL,3,A,A3,5.45,1073.0 -2016-04-16,FL,3,A,A3,5.33,1161.0 -2016-04-17,FL,3,A,A3,5.32,1115.0 -2016-04-18,FL,3,A,A3,5.37,972.0 -2016-04-19,FL,3,A,A3,5.46,982.0 -2016-04-20,FL,3,A,A3,5.33,1050.0 -2016-04-21,FL,3,A,A3,5.47,968.0 -2016-04-22,FL,3,A,A3,5.41,1058.0 -2016-04-23,FL,3,A,A3,5.44,1081.0 -2016-04-24,FL,3,A,A3,5.34,1024.0 -2016-04-25,FL,3,A,A3,5.48,911.0 -2016-04-26,FL,3,A,A3,5.44,1045.0 -2016-04-27,FL,3,A,A3,5.42,899.0 -2016-04-28,FL,3,A,A3,5.33,1049.0 -2016-04-29,FL,3,A,A3,5.35,1087.0 -2016-04-30,FL,3,A,A3,5.42,1130.0 -2016-05-01,FL,3,A,A3,5.4,1155.0 -2016-05-02,FL,3,A,A3,5.44,964.0 -2016-05-03,FL,3,A,A3,5.4,1031.0 -2016-05-04,FL,3,A,A3,5.39,1030.0 -2016-05-05,FL,3,A,A3,5.48,961.0 -2016-05-06,FL,3,A,A3,5.32,964.0 -2016-05-07,FL,3,A,A3,5.4,1032.0 -2016-05-08,FL,3,A,A3,5.44,1012.0 -2016-05-09,FL,3,A,A3,5.32,1005.0 -2016-05-10,FL,3,A,A3,5.33,1028.0 -2016-05-11,FL,3,A,A3,5.47,968.0 -2016-05-12,FL,3,A,A3,5.37,1034.0 -2016-05-13,FL,3,A,A3,5.39,1100.0 -2016-05-14,FL,3,A,A3,5.43,983.0 -2016-05-15,FL,3,A,A3,5.44,1053.0 -2016-05-16,FL,3,A,A3,5.39,916.0 -2016-05-17,FL,3,A,A3,5.34,1016.0 -2016-05-18,FL,3,A,A3,5.44,1084.0 -2016-05-19,FL,3,A,A3,5.41,1019.0 -2016-05-20,FL,3,A,A3,5.3,1032.0 -2016-05-21,FL,3,A,A3,5.4,1026.0 -2016-05-22,FL,3,A,A3,5.49,995.0 -2016-05-23,FL,3,A,A3,5.29,1104.0 -2016-05-24,FL,3,A,A3,5.41,1006.0 -2016-05-25,FL,3,A,A3,5.43,1005.0 -2016-05-26,FL,3,A,A3,5.52,959.0 -2016-05-27,FL,3,A,A3,5.4,1110.0 -2016-05-28,FL,3,A,A3,5.48,1072.0 -2016-05-29,FL,3,A,A3,5.4,921.0 -2016-05-30,FL,3,A,A3,5.42,1036.0 -2016-05-31,FL,3,A,A3,5.51,1012.0 -2016-06-01,FL,3,A,A3,5.36,1040.0 -2016-06-02,FL,3,A,A3,5.36,1082.0 -2016-06-03,FL,3,A,A3,5.42,971.0 -2016-06-04,FL,3,A,A3,5.33,1110.0 -2016-06-05,FL,3,A,A3,5.4,1025.0 -2016-06-06,FL,3,A,A3,5.38,1008.0 -2016-06-07,FL,3,A,A3,5.45,971.0 -2016-06-08,FL,3,A,A3,5.45,1058.0 -2016-06-09,FL,3,A,A3,5.46,1045.0 -2016-06-10,FL,3,A,A3,5.31,1062.0 -2016-06-11,FL,3,A,A3,5.45,1094.0 -2016-06-12,FL,3,A,A3,5.37,1029.0 -2016-06-13,FL,3,A,A3,5.31,1047.0 -2016-06-14,FL,3,A,A3,5.31,1014.0 -2016-06-15,FL,3,A,A3,5.34,1039.0 -2016-06-16,FL,3,A,A3,5.43,939.0 -2016-06-17,FL,3,A,A3,5.43,961.0 -2016-06-18,FL,3,A,A3,5.45,1079.0 -2016-06-19,FL,3,A,A3,5.47,984.0 -2016-06-20,FL,3,A,A3,5.38,996.0 -2016-06-21,FL,3,A,A3,5.44,983.0 -2016-06-22,FL,3,A,A3,5.38,1039.0 -2016-06-23,FL,3,A,A3,5.45,973.0 -2016-06-24,FL,3,A,A3,5.42,1054.0 -2016-06-25,FL,3,A,A3,5.43,1020.0 -2016-06-26,FL,3,A,A3,5.37,1142.0 -2016-06-27,FL,3,A,A3,5.38,999.0 -2016-06-28,FL,3,A,A3,5.43,1036.0 -2016-06-29,FL,3,A,A3,5.39,972.0 -2016-06-30,FL,3,A,A3,5.44,977.0 -2016-07-01,FL,3,A,A3,5.54,987.0 -2016-07-02,FL,3,A,A3,5.34,1130.0 -2016-07-03,FL,3,A,A3,5.41,1085.0 -2016-07-04,FL,3,A,A3,5.43,1002.0 -2016-07-05,FL,3,A,A3,5.41,1023.0 -2016-07-06,FL,3,A,A3,5.37,1061.0 -2016-07-07,FL,3,A,A3,5.35,1054.0 -2016-07-08,FL,3,A,A3,5.39,973.0 -2016-07-09,FL,3,A,A3,5.36,1038.0 -2016-07-10,FL,3,A,A3,5.48,1083.0 -2016-07-11,FL,3,A,A3,5.36,981.0 -2016-07-12,FL,3,A,A3,5.39,1106.0 -2016-07-13,FL,3,A,A3,5.41,920.0 -2016-07-14,FL,3,A,A3,5.41,1063.0 -2016-07-15,FL,3,A,A3,5.47,1001.0 -2016-07-16,FL,3,A,A3,5.48,1047.0 -2016-07-17,FL,3,A,A3,5.47,1030.0 -2016-07-18,FL,3,A,A3,5.36,1024.0 -2016-07-19,FL,3,A,A3,5.4,988.0 -2016-07-20,FL,3,A,A3,5.32,1072.0 -2016-07-21,FL,3,A,A3,5.31,1068.0 -2016-07-22,FL,3,A,A3,5.34,1141.0 -2016-07-23,FL,3,A,A3,5.34,1131.0 -2016-07-24,FL,3,A,A3,5.48,956.0 -2016-07-25,FL,3,A,A3,5.42,1012.0 -2016-07-26,FL,3,A,A3,5.34,1135.0 -2016-07-27,FL,3,A,A3,5.39,991.0 -2015-01-05,FL,3,B,B1,2.45,1106.0 -2015-01-06,FL,3,B,B1,2.41,1095.0 -2015-01-07,FL,3,B,B1,2.42,1188.0 -2015-01-08,FL,3,B,B1,2.49,1074.0 -2015-01-09,FL,3,B,B1,2.48,1205.0 -2015-01-10,FL,3,B,B1,2.47,1172.0 -2015-01-11,FL,3,B,B1,2.47,1193.0 -2015-01-12,FL,3,B,B1,2.46,1150.0 -2015-01-13,FL,3,B,B1,2.42,1140.0 -2015-01-14,FL,3,B,B1,2.47,1153.0 -2015-01-15,FL,3,B,B1,2.47,1126.0 -2015-01-16,FL,3,B,B1,2.42,1228.0 -2015-01-17,FL,3,B,B1,2.45,1189.0 -2015-01-18,FL,3,B,B1,2.52,1123.0 -2015-01-19,FL,3,B,B1,2.48,1138.0 -2015-01-20,FL,3,B,B1,2.46,1195.0 -2015-01-21,FL,3,B,B1,2.46,1148.0 -2015-01-22,FL,3,B,B1,2.45,1143.0 -2015-01-23,FL,3,B,B1,2.49,1085.0 -2015-01-24,FL,3,B,B1,2.46,1180.0 -2015-01-25,FL,3,B,B1,2.47,1155.0 -2015-01-26,FL,3,B,B1,2.51,1253.0 -2015-01-27,FL,3,B,B1,2.46,1121.0 -2015-01-28,FL,3,B,B1,2.42,1266.0 -2015-01-29,FL,3,B,B1,2.47,1156.0 -2015-01-30,FL,3,B,B1,2.47,1207.0 -2015-01-31,FL,3,B,B1,2.49,1168.0 -2015-02-01,FL,3,B,B1,2.43,1238.0 -2015-02-02,FL,3,B,B1,2.46,1237.0 -2015-02-03,FL,3,B,B1,2.53,1134.0 -2015-02-04,FL,3,B,B1,2.49,1215.0 -2015-02-05,FL,3,B,B1,2.43,1176.0 -2015-02-06,FL,3,B,B1,2.49,1168.0 -2015-02-07,FL,3,B,B1,2.47,1229.0 -2015-02-08,FL,3,B,B1,2.45,1184.0 -2015-02-09,FL,3,B,B1,2.47,1231.0 -2015-02-10,FL,3,B,B1,2.43,1248.0 -2015-02-11,FL,3,B,B1,2.43,1188.0 -2015-02-12,FL,3,B,B1,2.45,1291.0 -2015-02-13,FL,3,B,B1,2.46,1258.0 -2015-02-14,FL,3,B,B1,2.5,1280.0 -2015-02-15,FL,3,B,B1,2.48,1222.0 -2015-02-16,FL,3,B,B1,2.46,1181.0 -2015-02-17,FL,3,B,B1,2.51,1153.0 -2015-02-18,FL,3,B,B1,2.49,1216.0 -2015-02-19,FL,3,B,B1,2.44,1268.0 -2015-02-20,FL,3,B,B1,2.48,1190.0 -2015-02-21,FL,3,B,B1,2.49,1269.0 -2015-02-22,FL,3,B,B1,2.44,1269.0 -2015-02-23,FL,3,B,B1,2.48,1204.0 -2015-02-24,FL,3,B,B1,2.48,1267.0 -2015-02-25,FL,3,B,B1,2.45,1251.0 -2015-02-26,FL,3,B,B1,2.49,1284.0 -2015-02-27,FL,3,B,B1,2.48,1236.0 -2015-02-28,FL,3,B,B1,2.47,1250.0 -2015-03-01,FL,3,B,B1,2.49,1263.0 -2015-03-02,FL,3,B,B1,2.44,1210.0 -2015-03-03,FL,3,B,B1,2.5,1181.0 -2015-03-04,FL,3,B,B1,2.5,1243.0 -2015-03-05,FL,3,B,B1,2.44,1286.0 -2015-03-06,FL,3,B,B1,2.4,1331.0 -2015-03-07,FL,3,B,B1,2.5,1247.0 -2015-03-08,FL,3,B,B1,2.43,1265.0 -2015-03-09,FL,3,B,B1,2.47,1307.0 -2015-03-10,FL,3,B,B1,2.47,1202.0 -2015-03-11,FL,3,B,B1,2.46,1234.0 -2015-03-12,FL,3,B,B1,2.47,1259.0 -2015-03-13,FL,3,B,B1,2.46,1220.0 -2015-03-14,FL,3,B,B1,2.47,1287.0 -2015-03-15,FL,3,B,B1,2.47,1346.0 -2015-03-16,FL,3,B,B1,2.52,1241.0 -2015-03-17,FL,3,B,B1,2.45,1215.0 -2015-03-18,FL,3,B,B1,2.46,1322.0 -2015-03-19,FL,3,B,B1,2.46,1320.0 -2015-03-20,FL,3,B,B1,2.46,1346.0 -2015-03-21,FL,3,B,B1,2.46,1371.0 -2015-03-22,FL,3,B,B1,2.45,1366.0 -2015-03-23,FL,3,B,B1,2.42,1341.0 -2015-03-24,FL,3,B,B1,2.43,1214.0 -2015-03-25,FL,3,B,B1,2.51,1271.0 -2015-03-26,FL,3,B,B1,2.4,1268.0 -2015-03-27,FL,3,B,B1,2.52,1233.0 -2015-03-28,FL,3,B,B1,2.48,1329.0 -2015-03-29,FL,3,B,B1,2.49,1312.0 -2015-03-30,FL,3,B,B1,2.44,1339.0 -2015-03-31,FL,3,B,B1,2.46,1290.0 -2015-04-01,FL,3,B,B1,2.44,1248.0 -2015-04-02,FL,3,B,B1,2.46,1330.0 -2015-04-03,FL,3,B,B1,2.43,1392.0 -2015-04-04,FL,3,B,B1,2.45,1365.0 -2015-04-05,FL,3,B,B1,2.46,1356.0 -2015-04-06,FL,3,B,B1,2.45,1363.0 -2015-04-07,FL,3,B,B1,2.43,1257.0 -2015-04-08,FL,3,B,B1,2.46,1409.0 -2015-04-09,FL,3,B,B1,2.42,1326.0 -2015-04-10,FL,3,B,B1,2.45,1312.0 -2015-04-11,FL,3,B,B1,2.46,1392.0 -2015-04-12,FL,3,B,B1,2.45,1321.0 -2015-04-13,FL,3,B,B1,2.44,1373.0 -2015-04-14,FL,3,B,B1,2.42,1354.0 -2015-04-15,FL,3,B,B1,2.43,1346.0 -2015-04-16,FL,3,B,B1,2.45,1354.0 -2015-04-17,FL,3,B,B1,2.47,1359.0 -2015-04-18,FL,3,B,B1,2.48,1294.0 -2015-04-19,FL,3,B,B1,2.49,1274.0 -2015-04-20,FL,3,B,B1,2.46,1382.0 -2015-04-21,FL,3,B,B1,2.45,1306.0 -2015-04-22,FL,3,B,B1,2.49,1370.0 -2015-04-23,FL,3,B,B1,2.51,1298.0 -2015-04-24,FL,3,B,B1,2.46,1244.0 -2015-04-25,FL,3,B,B1,2.45,1398.0 -2015-04-26,FL,3,B,B1,2.47,1387.0 -2015-04-27,FL,3,B,B1,2.47,1392.0 -2015-04-28,FL,3,B,B1,2.47,1305.0 -2015-04-29,FL,3,B,B1,2.47,1410.0 -2015-04-30,FL,3,B,B1,2.47,1357.0 -2015-05-01,FL,3,B,B1,2.43,1368.0 -2015-05-02,FL,3,B,B1,2.45,1352.0 -2015-05-03,FL,3,B,B1,2.48,1302.0 -2015-05-04,FL,3,B,B1,2.41,1301.0 -2015-05-05,FL,3,B,B1,2.44,1387.0 -2015-05-06,FL,3,B,B1,2.5,1348.0 -2015-05-07,FL,3,B,B1,2.44,1339.0 -2015-05-08,FL,3,B,B1,2.47,1299.0 -2015-05-09,FL,3,B,B1,2.43,1443.0 -2015-05-10,FL,3,B,B1,2.44,1330.0 -2015-05-11,FL,3,B,B1,2.49,1358.0 -2015-05-12,FL,3,B,B1,2.44,1364.0 -2015-05-13,FL,3,B,B1,2.46,1344.0 -2015-05-14,FL,3,B,B1,2.46,1333.0 -2015-05-15,FL,3,B,B1,2.46,1360.0 -2015-05-16,FL,3,B,B1,2.48,1441.0 -2015-05-17,FL,3,B,B1,2.47,1364.0 -2015-05-18,FL,3,B,B1,2.46,1303.0 -2015-05-19,FL,3,B,B1,2.5,1396.0 -2015-05-20,FL,3,B,B1,2.49,1351.0 -2015-05-21,FL,3,B,B1,2.47,1404.0 -2015-05-22,FL,3,B,B1,2.47,1360.0 -2015-05-23,FL,3,B,B1,2.46,1370.0 -2015-05-24,FL,3,B,B1,2.48,1401.0 -2015-05-25,FL,3,B,B1,2.46,1346.0 -2015-05-26,FL,3,B,B1,2.47,1308.0 -2015-05-27,FL,3,B,B1,2.43,1370.0 -2015-05-28,FL,3,B,B1,2.43,1356.0 -2015-05-29,FL,3,B,B1,2.46,1364.0 -2015-05-30,FL,3,B,B1,2.42,1474.0 -2015-05-31,FL,3,B,B1,2.44,1415.0 -2015-06-01,FL,3,B,B1,2.44,1359.0 -2015-06-02,FL,3,B,B1,2.44,1363.0 -2015-06-03,FL,3,B,B1,2.47,1409.0 -2015-06-04,FL,3,B,B1,2.4,1446.0 -2015-06-05,FL,3,B,B1,2.44,1365.0 -2015-06-06,FL,3,B,B1,2.44,1441.0 -2015-06-07,FL,3,B,B1,2.46,1342.0 -2015-06-08,FL,3,B,B1,2.48,1397.0 -2015-06-09,FL,3,B,B1,2.45,1312.0 -2015-06-10,FL,3,B,B1,2.47,1378.0 -2015-06-11,FL,3,B,B1,2.47,1437.0 -2015-06-12,FL,3,B,B1,2.48,1363.0 -2015-06-13,FL,3,B,B1,2.46,1406.0 -2015-06-14,FL,3,B,B1,2.46,1452.0 -2015-06-15,FL,3,B,B1,2.46,1398.0 -2015-06-16,FL,3,B,B1,2.45,1433.0 -2015-06-17,FL,3,B,B1,2.45,1352.0 -2015-06-18,FL,3,B,B1,2.45,1401.0 -2015-06-19,FL,3,B,B1,2.43,1393.0 -2015-06-20,FL,3,B,B1,2.48,1339.0 -2015-06-21,FL,3,B,B1,2.48,1452.0 -2015-06-22,FL,3,B,B1,2.47,1393.0 -2015-06-23,FL,3,B,B1,2.48,1402.0 -2015-06-24,FL,3,B,B1,2.48,1325.0 -2015-06-25,FL,3,B,B1,2.51,1370.0 -2015-06-26,FL,3,B,B1,2.47,1430.0 -2015-06-27,FL,3,B,B1,2.42,1480.0 -2015-06-28,FL,3,B,B1,2.45,1446.0 -2015-06-29,FL,3,B,B1,2.41,1365.0 -2015-06-30,FL,3,B,B1,2.45,1387.0 -2015-07-01,FL,3,B,B1,2.45,1363.0 -2015-07-02,FL,3,B,B1,2.39,1363.0 -2015-07-03,FL,3,B,B1,2.43,1459.0 -2015-07-04,FL,3,B,B1,2.46,1466.0 -2015-07-05,FL,3,B,B1,2.47,1469.0 -2015-07-06,FL,3,B,B1,2.41,1386.0 -2015-07-07,FL,3,B,B1,2.43,1357.0 -2015-07-08,FL,3,B,B1,2.46,1352.0 -2015-07-09,FL,3,B,B1,2.44,1387.0 -2015-07-10,FL,3,B,B1,2.45,1390.0 -2015-07-11,FL,3,B,B1,2.41,1393.0 -2015-07-12,FL,3,B,B1,2.39,1548.0 -2015-07-13,FL,3,B,B1,2.43,1414.0 -2015-07-14,FL,3,B,B1,2.46,1430.0 -2015-07-15,FL,3,B,B1,2.47,1408.0 -2015-07-16,FL,3,B,B1,2.48,1417.0 -2015-07-17,FL,3,B,B1,2.45,1389.0 -2015-07-18,FL,3,B,B1,2.48,1464.0 -2015-07-19,FL,3,B,B1,2.49,1356.0 -2015-07-20,FL,3,B,B1,2.44,1427.0 -2015-07-21,FL,3,B,B1,2.44,1372.0 -2015-07-22,FL,3,B,B1,2.5,1354.0 -2015-07-23,FL,3,B,B1,2.43,1375.0 -2015-07-24,FL,3,B,B1,2.52,1365.0 -2015-07-25,FL,3,B,B1,2.52,1363.0 -2015-07-26,FL,3,B,B1,2.46,1419.0 -2015-07-27,FL,3,B,B1,2.47,1331.0 -2015-07-28,FL,3,B,B1,2.45,1484.0 -2015-07-29,FL,3,B,B1,2.45,1378.0 -2015-07-30,FL,3,B,B1,2.41,1472.0 -2015-07-31,FL,3,B,B1,2.49,1406.0 -2015-08-01,FL,3,B,B1,2.45,1489.0 -2015-08-02,FL,3,B,B1,2.44,1423.0 -2015-08-03,FL,3,B,B1,2.47,1394.0 -2015-08-04,FL,3,B,B1,2.47,1434.0 -2015-08-05,FL,3,B,B1,2.5,1404.0 -2015-08-06,FL,3,B,B1,2.52,1411.0 -2015-08-07,FL,3,B,B1,2.51,1398.0 -2015-08-08,FL,3,B,B1,2.43,1519.0 -2015-08-09,FL,3,B,B1,2.46,1420.0 -2015-08-10,FL,3,B,B1,2.45,1390.0 -2015-08-11,FL,3,B,B1,2.43,1469.0 -2015-08-12,FL,3,B,B1,2.43,1387.0 -2015-08-13,FL,3,B,B1,2.48,1438.0 -2015-08-14,FL,3,B,B1,2.45,1339.0 -2015-08-15,FL,3,B,B1,2.49,1433.0 -2015-08-16,FL,3,B,B1,2.47,1380.0 -2015-08-17,FL,3,B,B1,2.48,1412.0 -2015-08-18,FL,3,B,B1,2.47,1420.0 -2015-08-19,FL,3,B,B1,2.46,1426.0 -2015-08-20,FL,3,B,B1,2.47,1399.0 -2015-08-21,FL,3,B,B1,2.46,1405.0 -2015-08-22,FL,3,B,B1,2.47,1490.0 -2015-08-23,FL,3,B,B1,2.45,1420.0 -2015-08-24,FL,3,B,B1,2.46,1497.0 -2015-08-25,FL,3,B,B1,2.49,1385.0 -2015-08-26,FL,3,B,B1,2.42,1457.0 -2015-08-27,FL,3,B,B1,2.5,1457.0 -2015-08-28,FL,3,B,B1,2.48,1395.0 -2015-08-29,FL,3,B,B1,2.48,1552.0 -2015-08-30,FL,3,B,B1,2.49,1445.0 -2015-08-31,FL,3,B,B1,2.42,1415.0 -2015-09-01,FL,3,B,B1,2.47,1400.0 -2015-09-02,FL,3,B,B1,2.42,1461.0 -2015-09-03,FL,3,B,B1,2.52,1367.0 -2015-09-04,FL,3,B,B1,2.47,1532.0 -2015-09-05,FL,3,B,B1,2.48,1526.0 -2015-09-06,FL,3,B,B1,2.42,1489.0 -2015-09-07,FL,3,B,B1,2.45,1467.0 -2015-09-08,FL,3,B,B1,2.44,1472.0 -2015-09-09,FL,3,B,B1,2.47,1425.0 -2015-09-10,FL,3,B,B1,2.52,1497.0 -2015-09-11,FL,3,B,B1,2.49,1426.0 -2015-09-12,FL,3,B,B1,2.43,1543.0 -2015-09-13,FL,3,B,B1,2.42,1479.0 -2015-09-14,FL,3,B,B1,2.46,1417.0 -2015-09-15,FL,3,B,B1,2.46,1389.0 -2015-09-16,FL,3,B,B1,2.46,1471.0 -2015-09-17,FL,3,B,B1,2.42,1509.0 -2015-09-18,FL,3,B,B1,2.46,1393.0 -2015-09-19,FL,3,B,B1,2.49,1465.0 -2015-09-20,FL,3,B,B1,2.48,1513.0 -2015-09-21,FL,3,B,B1,2.51,1394.0 -2015-09-22,FL,3,B,B1,2.46,1456.0 -2015-09-23,FL,3,B,B1,2.44,1442.0 -2015-09-24,FL,3,B,B1,2.47,1484.0 -2015-09-25,FL,3,B,B1,2.48,1470.0 -2015-09-26,FL,3,B,B1,2.47,1388.0 -2015-09-27,FL,3,B,B1,2.44,1528.0 -2015-09-28,FL,3,B,B1,2.42,1412.0 -2015-09-29,FL,3,B,B1,2.48,1469.0 -2015-09-30,FL,3,B,B1,2.46,1408.0 -2015-10-01,FL,3,B,B1,2.42,1471.0 -2015-10-02,FL,3,B,B1,2.42,1465.0 -2015-10-03,FL,3,B,B1,2.46,1466.0 -2015-10-04,FL,3,B,B1,2.47,1454.0 -2015-10-05,FL,3,B,B1,2.44,1468.0 -2015-10-06,FL,3,B,B1,2.45,1480.0 -2015-10-07,FL,3,B,B1,2.48,1444.0 -2015-10-08,FL,3,B,B1,2.46,1463.0 -2015-10-09,FL,3,B,B1,2.42,1486.0 -2015-10-10,FL,3,B,B1,2.46,1455.0 -2015-10-11,FL,3,B,B1,2.43,1468.0 -2015-10-12,FL,3,B,B1,2.5,1307.0 -2015-10-13,FL,3,B,B1,2.46,1388.0 -2015-10-14,FL,3,B,B1,2.46,1378.0 -2015-10-15,FL,3,B,B1,2.44,1487.0 -2015-10-16,FL,3,B,B1,2.44,1411.0 -2015-10-17,FL,3,B,B1,2.43,1494.0 -2015-10-18,FL,3,B,B1,2.47,1420.0 -2015-10-19,FL,3,B,B1,2.53,1413.0 -2015-10-20,FL,3,B,B1,2.44,1473.0 -2015-10-21,FL,3,B,B1,2.45,1438.0 -2015-10-22,FL,3,B,B1,2.46,1453.0 -2015-10-23,FL,3,B,B1,2.46,1489.0 -2015-10-24,FL,3,B,B1,2.43,1511.0 -2015-10-25,FL,3,B,B1,2.53,1409.0 -2015-10-26,FL,3,B,B1,2.43,1475.0 -2015-10-27,FL,3,B,B1,2.44,1419.0 -2015-10-28,FL,3,B,B1,2.48,1436.0 -2015-10-29,FL,3,B,B1,2.49,1454.0 -2015-10-30,FL,3,B,B1,2.45,1436.0 -2015-10-31,FL,3,B,B1,2.43,1522.0 -2015-11-01,FL,3,B,B1,2.48,1432.0 -2015-11-02,FL,3,B,B1,2.48,1397.0 -2015-11-03,FL,3,B,B1,2.49,1441.0 -2015-11-04,FL,3,B,B1,2.47,1470.0 -2015-11-05,FL,3,B,B1,2.44,1490.0 -2015-11-06,FL,3,B,B1,2.46,1512.0 -2015-11-07,FL,3,B,B1,2.46,1485.0 -2015-11-08,FL,3,B,B1,2.5,1414.0 -2015-11-09,FL,3,B,B1,2.43,1457.0 -2015-11-10,FL,3,B,B1,2.47,1458.0 -2015-11-11,FL,3,B,B1,2.49,1403.0 -2015-11-12,FL,3,B,B1,2.44,1447.0 -2015-11-13,FL,3,B,B1,2.46,1442.0 -2015-11-14,FL,3,B,B1,2.46,1419.0 -2015-11-15,FL,3,B,B1,2.43,1511.0 -2015-11-16,FL,3,B,B1,2.44,1448.0 -2015-11-17,FL,3,B,B1,2.47,1525.0 -2015-11-18,FL,3,B,B1,2.48,1442.0 -2015-11-19,FL,3,B,B1,2.46,1404.0 -2015-11-20,FL,3,B,B1,2.49,1494.0 -2015-11-21,FL,3,B,B1,2.48,1430.0 -2015-11-22,FL,3,B,B1,2.47,1456.0 -2015-11-23,FL,3,B,B1,2.43,1518.0 -2015-11-24,FL,3,B,B1,2.47,1404.0 -2015-11-25,FL,3,B,B1,2.43,1438.0 -2015-11-26,FL,3,B,B1,2.45,1442.0 -2015-11-27,FL,3,B,B1,2.5,1438.0 -2015-11-28,FL,3,B,B1,2.49,1540.0 -2015-11-29,FL,3,B,B1,2.48,1411.0 -2015-11-30,FL,3,B,B1,2.47,1412.0 -2015-12-01,FL,3,B,B1,2.47,1451.0 -2015-12-02,FL,3,B,B1,2.42,1492.0 -2015-12-03,FL,3,B,B1,2.46,1435.0 -2015-12-04,FL,3,B,B1,2.49,1468.0 -2015-12-05,FL,3,B,B1,2.49,1487.0 -2015-12-06,FL,3,B,B1,2.44,1447.0 -2015-12-07,FL,3,B,B1,2.43,1465.0 -2015-12-08,FL,3,B,B1,2.43,1441.0 -2015-12-09,FL,3,B,B1,2.45,1481.0 -2015-12-10,FL,3,B,B1,2.43,1412.0 -2015-12-11,FL,3,B,B1,2.48,1465.0 -2015-12-12,FL,3,B,B1,2.43,1452.0 -2015-12-13,FL,3,B,B1,2.43,1532.0 -2015-12-14,FL,3,B,B1,2.46,1366.0 -2015-12-15,FL,3,B,B1,2.47,1369.0 -2015-12-16,FL,3,B,B1,2.41,1558.0 -2015-12-17,FL,3,B,B1,2.42,1436.0 -2015-12-18,FL,3,B,B1,2.42,1473.0 -2015-12-19,FL,3,B,B1,2.49,1505.0 -2015-12-20,FL,3,B,B1,2.46,1476.0 -2015-12-21,FL,3,B,B1,2.44,1385.0 -2015-12-22,FL,3,B,B1,2.45,1423.0 -2015-12-23,FL,3,B,B1,2.48,1424.0 -2015-12-24,FL,3,B,B1,2.47,1367.0 -2015-12-25,FL,3,B,B1,2.46,1509.0 -2015-12-26,FL,3,B,B1,2.45,1474.0 -2015-12-27,FL,3,B,B1,2.46,1444.0 -2015-12-28,FL,3,B,B1,2.44,1409.0 -2015-12-29,FL,3,B,B1,2.41,1490.0 -2015-12-30,FL,3,B,B1,2.48,1426.0 -2015-12-31,FL,3,B,B1,2.47,1411.0 -2016-01-01,FL,3,B,B1,2.44,1466.0 -2016-01-02,FL,3,B,B1,2.44,1481.0 -2016-01-03,FL,3,B,B1,2.46,1495.0 -2016-01-04,FL,3,B,B1,2.47,1455.0 -2016-01-05,FL,3,B,B1,2.45,1474.0 -2016-01-06,FL,3,B,B1,2.48,1396.0 -2016-01-07,FL,3,B,B1,2.55,1470.0 -2016-01-08,FL,3,B,B1,2.48,1379.0 -2016-01-09,FL,3,B,B1,2.43,1462.0 -2016-01-10,FL,3,B,B1,2.43,1481.0 -2016-01-11,FL,3,B,B1,2.45,1430.0 -2016-01-12,FL,3,B,B1,2.47,1448.0 -2016-01-13,FL,3,B,B1,2.48,1518.0 -2016-01-14,FL,3,B,B1,2.46,1463.0 -2016-01-15,FL,3,B,B1,2.42,1464.0 -2016-01-16,FL,3,B,B1,2.48,1491.0 -2016-01-17,FL,3,B,B1,2.46,1468.0 -2016-01-18,FL,3,B,B1,2.48,1480.0 -2016-01-19,FL,3,B,B1,2.48,1400.0 -2016-01-20,FL,3,B,B1,2.46,1465.0 -2016-01-21,FL,3,B,B1,2.44,1403.0 -2016-01-22,FL,3,B,B1,2.44,1481.0 -2016-01-23,FL,3,B,B1,2.45,1490.0 -2016-01-24,FL,3,B,B1,2.38,1453.0 -2016-01-25,FL,3,B,B1,2.49,1380.0 -2016-01-26,FL,3,B,B1,2.48,1506.0 -2016-01-27,FL,3,B,B1,2.48,1441.0 -2016-01-28,FL,3,B,B1,2.42,1524.0 -2016-01-29,FL,3,B,B1,2.47,1470.0 -2016-01-30,FL,3,B,B1,2.45,1513.0 -2016-01-31,FL,3,B,B1,2.43,1429.0 -2016-02-01,FL,3,B,B1,2.45,1410.0 -2016-02-02,FL,3,B,B1,2.47,1451.0 -2016-02-03,FL,3,B,B1,2.47,1466.0 -2016-02-04,FL,3,B,B1,2.46,1347.0 -2016-02-05,FL,3,B,B1,2.5,1405.0 -2016-02-06,FL,3,B,B1,2.47,1489.0 -2016-02-07,FL,3,B,B1,2.48,1513.0 -2016-02-08,FL,3,B,B1,2.48,1512.0 -2016-02-09,FL,3,B,B1,2.44,1416.0 -2016-02-10,FL,3,B,B1,2.48,1406.0 -2016-02-11,FL,3,B,B1,2.45,1460.0 -2016-02-12,FL,3,B,B1,2.44,1435.0 -2016-02-13,FL,3,B,B1,2.45,1538.0 -2016-02-14,FL,3,B,B1,2.46,1486.0 -2016-02-15,FL,3,B,B1,2.47,1477.0 -2016-02-16,FL,3,B,B1,2.46,1406.0 -2016-02-17,FL,3,B,B1,2.44,1393.0 -2016-02-18,FL,3,B,B1,2.47,1385.0 -2016-02-19,FL,3,B,B1,2.45,1436.0 -2016-02-20,FL,3,B,B1,2.49,1473.0 -2016-02-21,FL,3,B,B1,2.48,1430.0 -2016-02-22,FL,3,B,B1,2.51,1442.0 -2016-02-23,FL,3,B,B1,2.47,1453.0 -2016-02-24,FL,3,B,B1,2.44,1429.0 -2016-02-25,FL,3,B,B1,2.48,1434.0 -2016-02-26,FL,3,B,B1,2.44,1434.0 -2016-02-27,FL,3,B,B1,2.46,1407.0 -2016-02-28,FL,3,B,B1,2.44,1502.0 -2016-02-29,FL,3,B,B1,2.42,1493.0 -2016-03-01,FL,3,B,B1,2.44,1439.0 -2016-03-02,FL,3,B,B1,2.47,1481.0 -2016-03-03,FL,3,B,B1,2.52,1401.0 -2016-03-04,FL,3,B,B1,2.48,1437.0 -2016-03-05,FL,3,B,B1,2.49,1427.0 -2016-03-06,FL,3,B,B1,2.44,1474.0 -2016-03-07,FL,3,B,B1,2.42,1504.0 -2016-03-08,FL,3,B,B1,2.47,1460.0 -2016-03-09,FL,3,B,B1,2.41,1518.0 -2016-03-10,FL,3,B,B1,2.5,1376.0 -2016-03-11,FL,3,B,B1,2.43,1535.0 -2016-03-12,FL,3,B,B1,2.43,1502.0 -2016-03-13,FL,3,B,B1,2.4,1483.0 -2016-03-14,FL,3,B,B1,2.47,1366.0 -2016-03-15,FL,3,B,B1,2.49,1430.0 -2016-03-16,FL,3,B,B1,2.5,1453.0 -2016-03-17,FL,3,B,B1,2.5,1457.0 -2016-03-18,FL,3,B,B1,2.48,1481.0 -2016-03-19,FL,3,B,B1,2.51,1445.0 -2016-03-20,FL,3,B,B1,2.49,1368.0 -2016-03-21,FL,3,B,B1,2.45,1464.0 -2016-03-22,FL,3,B,B1,2.48,1379.0 -2016-03-23,FL,3,B,B1,2.47,1484.0 -2016-03-24,FL,3,B,B1,2.49,1475.0 -2016-03-25,FL,3,B,B1,2.46,1433.0 -2016-03-26,FL,3,B,B1,2.46,1494.0 -2016-03-27,FL,3,B,B1,2.47,1419.0 -2016-03-28,FL,3,B,B1,2.46,1393.0 -2016-03-29,FL,3,B,B1,2.45,1417.0 -2016-03-30,FL,3,B,B1,2.47,1417.0 -2016-03-31,FL,3,B,B1,2.45,1404.0 -2016-04-01,FL,3,B,B1,2.47,1507.0 -2016-04-02,FL,3,B,B1,2.44,1532.0 -2016-04-03,FL,3,B,B1,2.42,1518.0 -2016-04-04,FL,3,B,B1,2.48,1459.0 -2016-04-05,FL,3,B,B1,2.48,1456.0 -2016-04-06,FL,3,B,B1,2.46,1469.0 -2016-04-07,FL,3,B,B1,2.46,1432.0 -2016-04-08,FL,3,B,B1,2.44,1506.0 -2016-04-09,FL,3,B,B1,2.46,1514.0 -2016-04-10,FL,3,B,B1,2.43,1467.0 -2016-04-11,FL,3,B,B1,2.47,1394.0 -2016-04-12,FL,3,B,B1,2.45,1485.0 -2016-04-13,FL,3,B,B1,2.46,1551.0 -2016-04-14,FL,3,B,B1,2.4,1490.0 -2016-04-15,FL,3,B,B1,2.49,1479.0 -2016-04-16,FL,3,B,B1,2.48,1397.0 -2016-04-17,FL,3,B,B1,2.48,1400.0 -2016-04-18,FL,3,B,B1,2.49,1478.0 -2016-04-19,FL,3,B,B1,2.43,1432.0 -2016-04-20,FL,3,B,B1,2.42,1482.0 -2016-04-21,FL,3,B,B1,2.45,1493.0 -2016-04-22,FL,3,B,B1,2.5,1478.0 -2016-04-23,FL,3,B,B1,2.5,1419.0 -2016-04-24,FL,3,B,B1,2.46,1431.0 -2016-04-25,FL,3,B,B1,2.5,1395.0 -2016-04-26,FL,3,B,B1,2.45,1434.0 -2016-04-27,FL,3,B,B1,2.5,1405.0 -2016-04-28,FL,3,B,B1,2.45,1446.0 -2016-04-29,FL,3,B,B1,2.43,1421.0 -2016-04-30,FL,3,B,B1,2.43,1527.0 -2016-05-01,FL,3,B,B1,2.42,1517.0 -2016-05-02,FL,3,B,B1,2.47,1449.0 -2016-05-03,FL,3,B,B1,2.47,1415.0 -2016-05-04,FL,3,B,B1,2.41,1529.0 -2016-05-05,FL,3,B,B1,2.45,1494.0 -2016-05-06,FL,3,B,B1,2.47,1472.0 -2016-05-07,FL,3,B,B1,2.48,1471.0 -2016-05-08,FL,3,B,B1,2.46,1463.0 -2016-05-09,FL,3,B,B1,2.44,1482.0 -2016-05-10,FL,3,B,B1,2.44,1544.0 -2016-05-11,FL,3,B,B1,2.46,1378.0 -2016-05-12,FL,3,B,B1,2.45,1445.0 -2016-05-13,FL,3,B,B1,2.52,1428.0 -2016-05-14,FL,3,B,B1,2.48,1539.0 -2016-05-15,FL,3,B,B1,2.45,1481.0 -2016-05-16,FL,3,B,B1,2.45,1455.0 -2016-05-17,FL,3,B,B1,2.48,1415.0 -2016-05-18,FL,3,B,B1,2.49,1417.0 -2016-05-19,FL,3,B,B1,2.44,1438.0 -2016-05-20,FL,3,B,B1,2.42,1487.0 -2016-05-21,FL,3,B,B1,2.44,1498.0 -2016-05-22,FL,3,B,B1,2.46,1469.0 -2016-05-23,FL,3,B,B1,2.47,1442.0 -2016-05-24,FL,3,B,B1,2.44,1410.0 -2016-05-25,FL,3,B,B1,2.47,1397.0 -2016-05-26,FL,3,B,B1,2.43,1554.0 -2016-05-27,FL,3,B,B1,2.46,1424.0 -2016-05-28,FL,3,B,B1,2.47,1517.0 -2016-05-29,FL,3,B,B1,2.47,1475.0 -2016-05-30,FL,3,B,B1,2.48,1432.0 -2016-05-31,FL,3,B,B1,2.44,1451.0 -2016-06-01,FL,3,B,B1,2.47,1402.0 -2016-06-02,FL,3,B,B1,2.47,1388.0 -2016-06-03,FL,3,B,B1,2.47,1435.0 -2016-06-04,FL,3,B,B1,2.45,1446.0 -2016-06-05,FL,3,B,B1,2.55,1443.0 -2016-06-06,FL,3,B,B1,2.5,1351.0 -2016-06-07,FL,3,B,B1,2.44,1419.0 -2016-06-08,FL,3,B,B1,2.46,1455.0 -2016-06-09,FL,3,B,B1,2.49,1428.0 -2016-06-10,FL,3,B,B1,2.45,1472.0 -2016-06-11,FL,3,B,B1,2.41,1518.0 -2016-06-12,FL,3,B,B1,2.44,1448.0 -2016-06-13,FL,3,B,B1,2.51,1382.0 -2016-06-14,FL,3,B,B1,2.49,1441.0 -2016-06-15,FL,3,B,B1,2.46,1494.0 -2016-06-16,FL,3,B,B1,2.44,1561.0 -2016-06-17,FL,3,B,B1,2.43,1515.0 -2016-06-18,FL,3,B,B1,2.44,1417.0 -2016-06-19,FL,3,B,B1,2.45,1433.0 -2016-06-20,FL,3,B,B1,2.49,1482.0 -2016-06-21,FL,3,B,B1,2.47,1418.0 -2016-06-22,FL,3,B,B1,2.49,1380.0 -2016-06-23,FL,3,B,B1,2.45,1413.0 -2016-06-24,FL,3,B,B1,2.44,1473.0 -2016-06-25,FL,3,B,B1,2.49,1490.0 -2016-06-26,FL,3,B,B1,2.39,1539.0 -2016-06-27,FL,3,B,B1,2.48,1384.0 -2016-06-28,FL,3,B,B1,2.44,1456.0 -2016-06-29,FL,3,B,B1,2.46,1506.0 -2016-06-30,FL,3,B,B1,2.47,1480.0 -2016-07-01,FL,3,B,B1,2.43,1496.0 -2016-07-02,FL,3,B,B1,2.45,1462.0 -2016-07-03,FL,3,B,B1,2.43,1489.0 -2016-07-04,FL,3,B,B1,2.48,1442.0 -2016-07-05,FL,3,B,B1,2.47,1398.0 -2016-07-06,FL,3,B,B1,2.47,1499.0 -2016-07-07,FL,3,B,B1,2.47,1426.0 -2016-07-08,FL,3,B,B1,2.43,1424.0 -2016-07-09,FL,3,B,B1,2.5,1499.0 -2016-07-10,FL,3,B,B1,2.43,1459.0 -2016-07-11,FL,3,B,B1,2.48,1432.0 -2016-07-12,FL,3,B,B1,2.48,1434.0 -2016-07-13,FL,3,B,B1,2.45,1483.0 -2016-07-14,FL,3,B,B1,2.43,1504.0 -2016-07-15,FL,3,B,B1,2.49,1425.0 -2016-07-16,FL,3,B,B1,2.47,1435.0 -2016-07-17,FL,3,B,B1,2.46,1420.0 -2016-07-18,FL,3,B,B1,2.49,1456.0 -2016-07-19,FL,3,B,B1,2.46,1485.0 -2016-07-20,FL,3,B,B1,2.5,1455.0 -2016-07-21,FL,3,B,B1,2.41,1473.0 -2016-07-22,FL,3,B,B1,2.46,1432.0 -2016-07-23,FL,3,B,B1,2.46,1420.0 -2016-07-24,FL,3,B,B1,2.46,1410.0 -2016-07-25,FL,3,B,B1,2.42,1435.0 -2016-07-26,FL,3,B,B1,2.42,1467.0 -2016-07-27,FL,3,B,B1,2.47,1492.0 -2015-01-05,FL,3,B,B2,6.95,16.0 -2015-01-06,FL,3,B,B2,6.78,15.0 -2015-01-07,FL,3,B,B2,6.79,16.0 -2015-01-08,FL,3,B,B2,6.74,18.0 -2015-01-09,FL,3,B,B2,6.87,19.0 -2015-01-10,FL,3,B,B2,6.83,18.0 -2015-01-11,FL,3,B,B2,6.84,17.0 -2015-01-12,FL,3,B,B2,6.94,16.0 -2015-01-13,FL,3,B,B2,6.78,17.0 -2015-01-14,FL,3,B,B2,6.89,15.0 -2015-01-15,FL,3,B,B2,6.79,17.0 -2015-01-16,FL,3,B,B2,6.93,16.0 -2015-01-17,FL,3,B,B2,6.83,17.0 -2015-01-18,FL,3,B,B2,6.85,18.0 -2015-01-19,FL,3,B,B2,6.87,17.0 -2015-01-20,FL,3,B,B2,6.91,17.0 -2015-01-21,FL,3,B,B2,6.87,19.0 -2015-01-22,FL,3,B,B2,6.86,18.0 -2015-01-23,FL,3,B,B2,6.82,19.0 -2015-01-24,FL,3,B,B2,6.85,17.0 -2015-01-25,FL,3,B,B2,6.85,17.0 -2015-01-26,FL,3,B,B2,6.82,19.0 -2015-01-27,FL,3,B,B2,6.81,18.0 -2015-01-28,FL,3,B,B2,6.89,18.0 -2015-01-29,FL,3,B,B2,6.93,17.0 -2015-01-30,FL,3,B,B2,6.84,17.0 -2015-01-31,FL,3,B,B2,6.9,19.0 -2015-02-01,FL,3,B,B2,6.76,18.0 -2015-02-02,FL,3,B,B2,6.76,18.0 -2015-02-03,FL,3,B,B2,6.86,15.0 -2015-02-04,FL,3,B,B2,6.84,18.0 -2015-02-05,FL,3,B,B2,6.86,17.0 -2015-02-06,FL,3,B,B2,6.83,20.0 -2015-02-07,FL,3,B,B2,6.87,16.0 -2015-02-08,FL,3,B,B2,6.76,19.0 -2015-02-09,FL,3,B,B2,6.81,18.0 -2015-02-10,FL,3,B,B2,6.67,17.0 -2015-02-11,FL,3,B,B2,6.81,17.0 -2015-02-12,FL,3,B,B2,6.78,18.0 -2015-02-13,FL,3,B,B2,6.65,17.0 -2015-02-14,FL,3,B,B2,6.83,20.0 -2015-02-15,FL,3,B,B2,6.86,18.0 -2015-02-16,FL,3,B,B2,6.76,19.0 -2015-02-17,FL,3,B,B2,6.82,19.0 -2015-02-18,FL,3,B,B2,6.7,18.0 -2015-02-19,FL,3,B,B2,6.79,19.0 -2015-02-20,FL,3,B,B2,6.82,19.0 -2015-02-21,FL,3,B,B2,6.91,19.0 -2015-02-22,FL,3,B,B2,6.79,19.0 -2015-02-23,FL,3,B,B2,6.78,17.0 -2015-02-24,FL,3,B,B2,6.88,18.0 -2015-02-25,FL,3,B,B2,6.86,18.0 -2015-02-26,FL,3,B,B2,6.91,18.0 -2015-02-27,FL,3,B,B2,6.9,18.0 -2015-02-28,FL,3,B,B2,6.65,21.0 -2015-03-01,FL,3,B,B2,6.74,18.0 -2015-03-02,FL,3,B,B2,6.84,18.0 -2015-03-03,FL,3,B,B2,6.75,19.0 -2015-03-04,FL,3,B,B2,6.89,17.0 -2015-03-05,FL,3,B,B2,6.84,17.0 -2015-03-06,FL,3,B,B2,6.85,18.0 -2015-03-07,FL,3,B,B2,6.85,19.0 -2015-03-08,FL,3,B,B2,6.88,17.0 -2015-03-09,FL,3,B,B2,6.79,18.0 -2015-03-10,FL,3,B,B2,6.76,19.0 -2015-03-11,FL,3,B,B2,6.82,18.0 -2015-03-12,FL,3,B,B2,7.04,17.0 -2015-03-13,FL,3,B,B2,6.76,18.0 -2015-03-14,FL,3,B,B2,6.78,18.0 -2015-03-15,FL,3,B,B2,6.91,17.0 -2015-03-16,FL,3,B,B2,6.85,19.0 -2015-03-17,FL,3,B,B2,6.79,17.0 -2015-03-18,FL,3,B,B2,6.88,18.0 -2015-03-19,FL,3,B,B2,6.84,17.0 -2015-03-20,FL,3,B,B2,6.82,18.0 -2015-03-21,FL,3,B,B2,6.89,18.0 -2015-03-22,FL,3,B,B2,6.96,19.0 -2015-03-23,FL,3,B,B2,6.86,20.0 -2015-03-24,FL,3,B,B2,6.79,19.0 -2015-03-25,FL,3,B,B2,6.92,17.0 -2015-03-26,FL,3,B,B2,6.8,19.0 -2015-03-27,FL,3,B,B2,6.8,18.0 -2015-03-28,FL,3,B,B2,6.88,20.0 -2015-03-29,FL,3,B,B2,6.71,19.0 -2015-03-30,FL,3,B,B2,6.83,19.0 -2015-03-31,FL,3,B,B2,6.81,18.0 -2015-04-01,FL,3,B,B2,6.79,19.0 -2015-04-02,FL,3,B,B2,6.78,19.0 -2015-04-03,FL,3,B,B2,6.89,18.0 -2015-04-04,FL,3,B,B2,6.93,18.0 -2015-04-05,FL,3,B,B2,6.82,17.0 -2015-04-06,FL,3,B,B2,6.88,18.0 -2015-04-07,FL,3,B,B2,6.78,19.0 -2015-04-08,FL,3,B,B2,6.93,18.0 -2015-04-09,FL,3,B,B2,6.81,17.0 -2015-04-10,FL,3,B,B2,6.81,18.0 -2015-04-11,FL,3,B,B2,6.7,20.0 -2015-04-12,FL,3,B,B2,6.81,17.0 -2015-04-13,FL,3,B,B2,6.92,18.0 -2015-04-14,FL,3,B,B2,6.96,18.0 -2015-04-15,FL,3,B,B2,6.74,18.0 -2015-04-16,FL,3,B,B2,6.79,16.0 -2015-04-17,FL,3,B,B2,6.83,18.0 -2015-04-18,FL,3,B,B2,6.83,19.0 -2015-04-19,FL,3,B,B2,6.78,17.0 -2015-04-20,FL,3,B,B2,6.83,17.0 -2015-04-21,FL,3,B,B2,6.85,19.0 -2015-04-22,FL,3,B,B2,6.84,17.0 -2015-04-23,FL,3,B,B2,6.95,18.0 -2015-04-24,FL,3,B,B2,6.82,19.0 -2015-04-25,FL,3,B,B2,6.88,18.0 -2015-04-26,FL,3,B,B2,6.9,20.0 -2015-04-27,FL,3,B,B2,6.71,18.0 -2015-04-28,FL,3,B,B2,6.82,18.0 -2015-04-29,FL,3,B,B2,6.86,18.0 -2015-04-30,FL,3,B,B2,6.96,17.0 -2015-05-01,FL,3,B,B2,6.9,18.0 -2015-05-02,FL,3,B,B2,6.98,17.0 -2015-05-03,FL,3,B,B2,6.75,19.0 -2015-05-04,FL,3,B,B2,6.82,19.0 -2015-05-05,FL,3,B,B2,6.97,17.0 -2015-05-06,FL,3,B,B2,6.87,17.0 -2015-05-07,FL,3,B,B2,6.92,17.0 -2015-05-08,FL,3,B,B2,6.82,17.0 -2015-05-09,FL,3,B,B2,6.96,18.0 -2015-05-10,FL,3,B,B2,6.85,19.0 -2015-05-11,FL,3,B,B2,6.9,18.0 -2015-05-12,FL,3,B,B2,6.86,18.0 -2015-05-13,FL,3,B,B2,6.7,18.0 -2015-05-14,FL,3,B,B2,6.91,18.0 -2015-05-15,FL,3,B,B2,6.95,18.0 -2015-05-16,FL,3,B,B2,6.92,19.0 -2015-05-17,FL,3,B,B2,6.87,18.0 -2015-05-18,FL,3,B,B2,6.77,18.0 -2015-05-19,FL,3,B,B2,6.8,17.0 -2015-05-20,FL,3,B,B2,6.77,18.0 -2015-05-21,FL,3,B,B2,6.92,18.0 -2015-05-22,FL,3,B,B2,6.9,18.0 -2015-05-23,FL,3,B,B2,6.8,19.0 -2015-05-24,FL,3,B,B2,6.93,18.0 -2015-05-25,FL,3,B,B2,6.76,18.0 -2015-05-26,FL,3,B,B2,6.8,17.0 -2015-05-27,FL,3,B,B2,6.76,19.0 -2015-05-28,FL,3,B,B2,6.82,17.0 -2015-05-29,FL,3,B,B2,6.8,18.0 -2015-05-30,FL,3,B,B2,6.8,18.0 -2015-05-31,FL,3,B,B2,6.89,18.0 -2015-06-01,FL,3,B,B2,6.74,18.0 -2015-06-02,FL,3,B,B2,6.81,19.0 -2015-06-03,FL,3,B,B2,6.93,17.0 -2015-06-04,FL,3,B,B2,6.92,18.0 -2015-06-05,FL,3,B,B2,6.94,18.0 -2015-06-06,FL,3,B,B2,6.94,17.0 -2015-06-07,FL,3,B,B2,6.91,18.0 -2015-06-08,FL,3,B,B2,6.9,17.0 -2015-06-09,FL,3,B,B2,6.82,18.0 -2015-06-10,FL,3,B,B2,6.89,17.0 -2015-06-11,FL,3,B,B2,6.86,18.0 -2015-06-12,FL,3,B,B2,6.8,17.0 -2015-06-13,FL,3,B,B2,6.95,17.0 -2015-06-14,FL,3,B,B2,6.68,17.0 -2015-06-15,FL,3,B,B2,6.87,18.0 -2015-06-16,FL,3,B,B2,6.77,18.0 -2015-06-17,FL,3,B,B2,6.85,18.0 -2015-06-18,FL,3,B,B2,6.78,19.0 -2015-06-19,FL,3,B,B2,6.81,18.0 -2015-06-20,FL,3,B,B2,6.87,18.0 -2015-06-21,FL,3,B,B2,6.83,18.0 -2015-06-22,FL,3,B,B2,6.82,19.0 -2015-06-23,FL,3,B,B2,6.76,17.0 -2015-06-24,FL,3,B,B2,6.72,18.0 -2015-06-25,FL,3,B,B2,6.66,20.0 -2015-06-26,FL,3,B,B2,6.82,19.0 -2015-06-27,FL,3,B,B2,6.88,19.0 -2015-06-28,FL,3,B,B2,6.75,20.0 -2015-06-29,FL,3,B,B2,6.81,18.0 -2015-06-30,FL,3,B,B2,6.85,17.0 -2015-07-01,FL,3,B,B2,6.94,17.0 -2015-07-02,FL,3,B,B2,6.92,17.0 -2015-07-03,FL,3,B,B2,6.7,20.0 -2015-07-04,FL,3,B,B2,6.73,19.0 -2015-07-05,FL,3,B,B2,6.84,17.0 -2015-07-06,FL,3,B,B2,7.02,18.0 -2015-07-07,FL,3,B,B2,6.74,18.0 -2015-07-08,FL,3,B,B2,6.86,18.0 -2015-07-09,FL,3,B,B2,6.96,19.0 -2015-07-10,FL,3,B,B2,6.8,18.0 -2015-07-11,FL,3,B,B2,6.82,18.0 -2015-07-12,FL,3,B,B2,6.92,18.0 -2015-07-13,FL,3,B,B2,6.95,16.0 -2015-07-14,FL,3,B,B2,6.76,19.0 -2015-07-15,FL,3,B,B2,6.83,17.0 -2015-07-16,FL,3,B,B2,6.78,18.0 -2015-07-17,FL,3,B,B2,6.84,16.0 -2015-07-18,FL,3,B,B2,6.84,19.0 -2015-07-19,FL,3,B,B2,6.8,19.0 -2015-07-20,FL,3,B,B2,6.88,16.0 -2015-07-21,FL,3,B,B2,6.73,19.0 -2015-07-22,FL,3,B,B2,6.82,18.0 -2015-07-23,FL,3,B,B2,6.82,18.0 -2015-07-24,FL,3,B,B2,6.95,18.0 -2015-07-25,FL,3,B,B2,6.85,19.0 -2015-07-26,FL,3,B,B2,6.9,18.0 -2015-07-27,FL,3,B,B2,6.83,17.0 -2015-07-28,FL,3,B,B2,6.83,17.0 -2015-07-29,FL,3,B,B2,6.73,18.0 -2015-07-30,FL,3,B,B2,6.89,19.0 -2015-07-31,FL,3,B,B2,6.98,18.0 -2015-08-01,FL,3,B,B2,6.75,19.0 -2015-08-02,FL,3,B,B2,6.8,17.0 -2015-08-03,FL,3,B,B2,6.83,18.0 -2015-08-04,FL,3,B,B2,6.86,17.0 -2015-08-05,FL,3,B,B2,6.94,16.0 -2015-08-06,FL,3,B,B2,6.86,18.0 -2015-08-07,FL,3,B,B2,6.82,18.0 -2015-08-08,FL,3,B,B2,6.76,20.0 -2015-08-09,FL,3,B,B2,6.71,18.0 -2015-08-10,FL,3,B,B2,6.74,18.0 -2015-08-11,FL,3,B,B2,6.89,19.0 -2015-08-12,FL,3,B,B2,6.94,17.0 -2015-08-13,FL,3,B,B2,6.97,17.0 -2015-08-14,FL,3,B,B2,6.87,19.0 -2015-08-15,FL,3,B,B2,6.84,18.0 -2015-08-16,FL,3,B,B2,6.74,20.0 -2015-08-17,FL,3,B,B2,7.05,17.0 -2015-08-18,FL,3,B,B2,6.85,17.0 -2015-08-19,FL,3,B,B2,6.81,18.0 -2015-08-20,FL,3,B,B2,6.68,18.0 -2015-08-21,FL,3,B,B2,6.78,17.0 -2015-08-22,FL,3,B,B2,6.81,19.0 -2015-08-23,FL,3,B,B2,6.87,19.0 -2015-08-24,FL,3,B,B2,6.84,18.0 -2015-08-25,FL,3,B,B2,6.88,19.0 -2015-08-26,FL,3,B,B2,6.92,17.0 -2015-08-27,FL,3,B,B2,6.7,18.0 -2015-08-28,FL,3,B,B2,6.79,18.0 -2015-08-29,FL,3,B,B2,6.9,18.0 -2015-08-30,FL,3,B,B2,6.82,19.0 -2015-08-31,FL,3,B,B2,6.81,18.0 -2015-09-01,FL,3,B,B2,6.74,18.0 -2015-09-02,FL,3,B,B2,6.91,18.0 -2015-09-03,FL,3,B,B2,6.84,17.0 -2015-09-04,FL,3,B,B2,6.73,17.0 -2015-09-05,FL,3,B,B2,6.86,20.0 -2015-09-06,FL,3,B,B2,6.88,19.0 -2015-09-07,FL,3,B,B2,6.78,18.0 -2015-09-08,FL,3,B,B2,6.94,19.0 -2015-09-09,FL,3,B,B2,6.82,18.0 -2015-09-10,FL,3,B,B2,6.91,17.0 -2015-09-11,FL,3,B,B2,6.83,18.0 -2015-09-12,FL,3,B,B2,6.86,17.0 -2015-09-13,FL,3,B,B2,6.83,17.0 -2015-09-14,FL,3,B,B2,6.78,18.0 -2015-09-15,FL,3,B,B2,6.83,19.0 -2015-09-16,FL,3,B,B2,6.89,18.0 -2015-09-17,FL,3,B,B2,6.79,19.0 -2015-09-18,FL,3,B,B2,6.87,20.0 -2015-09-19,FL,3,B,B2,6.91,17.0 -2015-09-20,FL,3,B,B2,6.83,18.0 -2015-09-21,FL,3,B,B2,6.86,17.0 -2015-09-22,FL,3,B,B2,6.86,18.0 -2015-09-23,FL,3,B,B2,6.84,18.0 -2015-09-24,FL,3,B,B2,6.9,19.0 -2015-09-25,FL,3,B,B2,6.75,17.0 -2015-09-26,FL,3,B,B2,6.76,19.0 -2015-09-27,FL,3,B,B2,6.83,19.0 -2015-09-28,FL,3,B,B2,6.84,17.0 -2015-09-29,FL,3,B,B2,6.93,17.0 -2015-09-30,FL,3,B,B2,6.88,19.0 -2015-10-01,FL,3,B,B2,6.83,17.0 -2015-10-02,FL,3,B,B2,6.87,19.0 -2015-10-03,FL,3,B,B2,6.85,19.0 -2015-10-04,FL,3,B,B2,6.85,18.0 -2015-10-05,FL,3,B,B2,6.89,17.0 -2015-10-06,FL,3,B,B2,6.93,17.0 -2015-10-07,FL,3,B,B2,6.78,16.0 -2015-10-08,FL,3,B,B2,6.95,17.0 -2015-10-09,FL,3,B,B2,6.98,19.0 -2015-10-10,FL,3,B,B2,6.79,18.0 -2015-10-11,FL,3,B,B2,6.82,19.0 -2015-10-12,FL,3,B,B2,6.69,18.0 -2015-10-13,FL,3,B,B2,6.83,18.0 -2015-10-14,FL,3,B,B2,6.81,18.0 -2015-10-15,FL,3,B,B2,6.87,19.0 -2015-10-16,FL,3,B,B2,6.82,19.0 -2015-10-17,FL,3,B,B2,6.74,20.0 -2015-10-18,FL,3,B,B2,6.75,18.0 -2015-10-19,FL,3,B,B2,6.75,18.0 -2015-10-20,FL,3,B,B2,6.72,17.0 -2015-10-21,FL,3,B,B2,6.79,18.0 -2015-10-22,FL,3,B,B2,6.95,17.0 -2015-10-23,FL,3,B,B2,6.8,19.0 -2015-10-24,FL,3,B,B2,6.87,18.0 -2015-10-25,FL,3,B,B2,6.89,20.0 -2015-10-26,FL,3,B,B2,6.81,17.0 -2015-10-27,FL,3,B,B2,6.82,18.0 -2015-10-28,FL,3,B,B2,6.88,17.0 -2015-10-29,FL,3,B,B2,6.84,18.0 -2015-10-30,FL,3,B,B2,6.88,19.0 -2015-10-31,FL,3,B,B2,7.01,19.0 -2015-11-01,FL,3,B,B2,6.8,17.0 -2015-11-02,FL,3,B,B2,6.87,18.0 -2015-11-03,FL,3,B,B2,6.88,17.0 -2015-11-04,FL,3,B,B2,6.85,18.0 -2015-11-05,FL,3,B,B2,6.81,19.0 -2015-11-06,FL,3,B,B2,6.95,17.0 -2015-11-07,FL,3,B,B2,6.95,18.0 -2015-11-08,FL,3,B,B2,6.86,18.0 -2015-11-09,FL,3,B,B2,6.83,17.0 -2015-11-10,FL,3,B,B2,6.76,17.0 -2015-11-11,FL,3,B,B2,6.86,18.0 -2015-11-12,FL,3,B,B2,6.87,19.0 -2015-11-13,FL,3,B,B2,6.91,17.0 -2015-11-14,FL,3,B,B2,6.82,17.0 -2015-11-15,FL,3,B,B2,6.91,17.0 -2015-11-16,FL,3,B,B2,6.7,18.0 -2015-11-17,FL,3,B,B2,6.82,18.0 -2015-11-18,FL,3,B,B2,6.81,18.0 -2015-11-19,FL,3,B,B2,6.89,18.0 -2015-11-20,FL,3,B,B2,6.71,18.0 -2015-11-21,FL,3,B,B2,6.84,18.0 -2015-11-22,FL,3,B,B2,6.78,18.0 -2015-11-23,FL,3,B,B2,6.87,18.0 -2015-11-24,FL,3,B,B2,6.82,18.0 -2015-11-25,FL,3,B,B2,6.73,19.0 -2015-11-26,FL,3,B,B2,6.87,18.0 -2015-11-27,FL,3,B,B2,6.75,19.0 -2015-11-28,FL,3,B,B2,6.9,19.0 -2015-11-29,FL,3,B,B2,6.86,19.0 -2015-11-30,FL,3,B,B2,6.83,18.0 -2015-12-01,FL,3,B,B2,6.76,18.0 -2015-12-02,FL,3,B,B2,6.85,17.0 -2015-12-03,FL,3,B,B2,6.91,18.0 -2015-12-04,FL,3,B,B2,6.9,17.0 -2015-12-05,FL,3,B,B2,6.91,18.0 -2015-12-06,FL,3,B,B2,6.88,18.0 -2015-12-07,FL,3,B,B2,6.9,18.0 -2015-12-08,FL,3,B,B2,6.79,19.0 -2015-12-09,FL,3,B,B2,6.79,18.0 -2015-12-10,FL,3,B,B2,6.84,17.0 -2015-12-11,FL,3,B,B2,6.78,17.0 -2015-12-12,FL,3,B,B2,6.74,19.0 -2015-12-13,FL,3,B,B2,6.98,18.0 -2015-12-14,FL,3,B,B2,6.89,17.0 -2015-12-15,FL,3,B,B2,6.99,18.0 -2015-12-16,FL,3,B,B2,6.8,16.0 -2015-12-17,FL,3,B,B2,6.79,19.0 -2015-12-18,FL,3,B,B2,6.81,18.0 -2015-12-19,FL,3,B,B2,6.95,18.0 -2015-12-20,FL,3,B,B2,6.83,17.0 -2015-12-21,FL,3,B,B2,6.8,19.0 -2015-12-22,FL,3,B,B2,6.92,18.0 -2015-12-23,FL,3,B,B2,6.86,18.0 -2015-12-24,FL,3,B,B2,6.87,18.0 -2015-12-25,FL,3,B,B2,6.74,19.0 -2015-12-26,FL,3,B,B2,6.86,18.0 -2015-12-27,FL,3,B,B2,6.84,17.0 -2015-12-28,FL,3,B,B2,6.81,17.0 -2015-12-29,FL,3,B,B2,6.9,17.0 -2015-12-30,FL,3,B,B2,6.84,20.0 -2015-12-31,FL,3,B,B2,6.87,18.0 -2016-01-01,FL,3,B,B2,6.95,17.0 -2016-01-02,FL,3,B,B2,6.89,18.0 -2016-01-03,FL,3,B,B2,6.84,19.0 -2016-01-04,FL,3,B,B2,6.82,17.0 -2016-01-05,FL,3,B,B2,6.77,18.0 -2016-01-06,FL,3,B,B2,6.81,18.0 -2016-01-07,FL,3,B,B2,6.97,17.0 -2016-01-08,FL,3,B,B2,6.9,17.0 -2016-01-09,FL,3,B,B2,6.81,18.0 -2016-01-10,FL,3,B,B2,6.9,18.0 -2016-01-11,FL,3,B,B2,6.89,17.0 -2016-01-12,FL,3,B,B2,6.85,17.0 -2016-01-13,FL,3,B,B2,6.76,20.0 -2016-01-14,FL,3,B,B2,6.92,18.0 -2016-01-15,FL,3,B,B2,6.86,20.0 -2016-01-16,FL,3,B,B2,6.89,19.0 -2016-01-17,FL,3,B,B2,6.72,18.0 -2016-01-18,FL,3,B,B2,6.97,17.0 -2016-01-19,FL,3,B,B2,6.81,18.0 -2016-01-20,FL,3,B,B2,6.68,17.0 -2016-01-21,FL,3,B,B2,6.83,18.0 -2016-01-22,FL,3,B,B2,6.72,18.0 -2016-01-23,FL,3,B,B2,6.85,18.0 -2016-01-24,FL,3,B,B2,6.86,18.0 -2016-01-25,FL,3,B,B2,6.91,16.0 -2016-01-26,FL,3,B,B2,6.77,18.0 -2016-01-27,FL,3,B,B2,6.76,19.0 -2016-01-28,FL,3,B,B2,6.82,17.0 -2016-01-29,FL,3,B,B2,6.82,17.0 -2016-01-30,FL,3,B,B2,6.86,20.0 -2016-01-31,FL,3,B,B2,6.84,18.0 -2016-02-01,FL,3,B,B2,6.82,19.0 -2016-02-02,FL,3,B,B2,6.88,18.0 -2016-02-03,FL,3,B,B2,7.0,17.0 -2016-02-04,FL,3,B,B2,6.84,19.0 -2016-02-05,FL,3,B,B2,6.88,17.0 -2016-02-06,FL,3,B,B2,6.85,20.0 -2016-02-07,FL,3,B,B2,6.8,18.0 -2016-02-08,FL,3,B,B2,6.77,17.0 -2016-02-09,FL,3,B,B2,6.88,18.0 -2016-02-10,FL,3,B,B2,6.85,17.0 -2016-02-11,FL,3,B,B2,6.92,17.0 -2016-02-12,FL,3,B,B2,6.89,18.0 -2016-02-13,FL,3,B,B2,6.85,18.0 -2016-02-14,FL,3,B,B2,6.89,18.0 -2016-02-15,FL,3,B,B2,6.92,18.0 -2016-02-16,FL,3,B,B2,6.76,17.0 -2016-02-17,FL,3,B,B2,6.94,18.0 -2016-02-18,FL,3,B,B2,6.81,18.0 -2016-02-19,FL,3,B,B2,6.76,18.0 -2016-02-20,FL,3,B,B2,6.89,19.0 -2016-02-21,FL,3,B,B2,6.76,19.0 -2016-02-22,FL,3,B,B2,6.84,18.0 -2016-02-23,FL,3,B,B2,6.92,17.0 -2016-02-24,FL,3,B,B2,6.76,17.0 -2016-02-25,FL,3,B,B2,6.89,19.0 -2016-02-26,FL,3,B,B2,6.96,18.0 -2016-02-27,FL,3,B,B2,6.94,19.0 -2016-02-28,FL,3,B,B2,6.86,19.0 -2016-02-29,FL,3,B,B2,6.84,17.0 -2016-03-01,FL,3,B,B2,6.77,17.0 -2016-03-02,FL,3,B,B2,6.99,17.0 -2016-03-03,FL,3,B,B2,6.79,19.0 -2016-03-04,FL,3,B,B2,6.82,18.0 -2016-03-05,FL,3,B,B2,6.89,18.0 -2016-03-06,FL,3,B,B2,6.82,19.0 -2016-03-07,FL,3,B,B2,6.9,18.0 -2016-03-08,FL,3,B,B2,6.88,17.0 -2016-03-09,FL,3,B,B2,6.82,18.0 -2016-03-10,FL,3,B,B2,6.83,17.0 -2016-03-11,FL,3,B,B2,6.86,17.0 -2016-03-12,FL,3,B,B2,6.79,19.0 -2016-03-13,FL,3,B,B2,6.81,19.0 -2016-03-14,FL,3,B,B2,6.96,18.0 -2016-03-15,FL,3,B,B2,6.95,18.0 -2016-03-16,FL,3,B,B2,6.81,19.0 -2016-03-17,FL,3,B,B2,6.85,18.0 -2016-03-18,FL,3,B,B2,6.86,17.0 -2016-03-19,FL,3,B,B2,6.85,19.0 -2016-03-20,FL,3,B,B2,6.8,18.0 -2016-03-21,FL,3,B,B2,6.84,17.0 -2016-03-22,FL,3,B,B2,6.75,18.0 -2016-03-23,FL,3,B,B2,6.9,17.0 -2016-03-24,FL,3,B,B2,6.82,19.0 -2016-03-25,FL,3,B,B2,6.85,19.0 -2016-03-26,FL,3,B,B2,6.75,20.0 -2016-03-27,FL,3,B,B2,6.73,17.0 -2016-03-28,FL,3,B,B2,6.91,18.0 -2016-03-29,FL,3,B,B2,6.76,19.0 -2016-03-30,FL,3,B,B2,6.84,18.0 -2016-03-31,FL,3,B,B2,6.82,19.0 -2016-04-01,FL,3,B,B2,6.86,16.0 -2016-04-02,FL,3,B,B2,6.78,18.0 -2016-04-03,FL,3,B,B2,6.73,18.0 -2016-04-04,FL,3,B,B2,6.9,19.0 -2016-04-05,FL,3,B,B2,6.86,18.0 -2016-04-06,FL,3,B,B2,6.8,18.0 -2016-04-07,FL,3,B,B2,6.83,18.0 -2016-04-08,FL,3,B,B2,6.83,18.0 -2016-04-09,FL,3,B,B2,6.92,18.0 -2016-04-10,FL,3,B,B2,6.71,19.0 -2016-04-11,FL,3,B,B2,6.84,19.0 -2016-04-12,FL,3,B,B2,6.8,17.0 -2016-04-13,FL,3,B,B2,6.82,19.0 -2016-04-14,FL,3,B,B2,6.94,17.0 -2016-04-15,FL,3,B,B2,6.84,18.0 -2016-04-16,FL,3,B,B2,6.81,18.0 -2016-04-17,FL,3,B,B2,6.91,19.0 -2016-04-18,FL,3,B,B2,6.91,16.0 -2016-04-19,FL,3,B,B2,6.84,18.0 -2016-04-20,FL,3,B,B2,6.82,18.0 -2016-04-21,FL,3,B,B2,6.82,19.0 -2016-04-22,FL,3,B,B2,6.79,18.0 -2016-04-23,FL,3,B,B2,6.78,18.0 -2016-04-24,FL,3,B,B2,6.84,18.0 -2016-04-25,FL,3,B,B2,6.87,18.0 -2016-04-26,FL,3,B,B2,6.75,17.0 -2016-04-27,FL,3,B,B2,6.9,18.0 -2016-04-28,FL,3,B,B2,6.75,18.0 -2016-04-29,FL,3,B,B2,7.0,18.0 -2016-04-30,FL,3,B,B2,6.83,18.0 -2016-05-01,FL,3,B,B2,6.89,17.0 -2016-05-02,FL,3,B,B2,6.87,18.0 -2016-05-03,FL,3,B,B2,6.65,18.0 -2016-05-04,FL,3,B,B2,6.85,19.0 -2016-05-05,FL,3,B,B2,6.9,17.0 -2016-05-06,FL,3,B,B2,6.74,18.0 -2016-05-07,FL,3,B,B2,6.85,18.0 -2016-05-08,FL,3,B,B2,6.8,17.0 -2016-05-09,FL,3,B,B2,6.9,17.0 -2016-05-10,FL,3,B,B2,6.86,17.0 -2016-05-11,FL,3,B,B2,6.91,17.0 -2016-05-12,FL,3,B,B2,6.88,18.0 -2016-05-13,FL,3,B,B2,6.78,17.0 -2016-05-14,FL,3,B,B2,6.78,18.0 -2016-05-15,FL,3,B,B2,6.78,17.0 -2016-05-16,FL,3,B,B2,6.87,16.0 -2016-05-17,FL,3,B,B2,6.79,18.0 -2016-05-18,FL,3,B,B2,6.88,18.0 -2016-05-19,FL,3,B,B2,6.9,18.0 -2016-05-20,FL,3,B,B2,6.88,18.0 -2016-05-21,FL,3,B,B2,6.85,19.0 -2016-05-22,FL,3,B,B2,6.91,19.0 -2016-05-23,FL,3,B,B2,6.85,18.0 -2016-05-24,FL,3,B,B2,6.92,16.0 -2016-05-25,FL,3,B,B2,6.88,18.0 -2016-05-26,FL,3,B,B2,6.92,19.0 -2016-05-27,FL,3,B,B2,6.93,18.0 -2016-05-28,FL,3,B,B2,6.85,18.0 -2016-05-29,FL,3,B,B2,6.67,18.0 -2016-05-30,FL,3,B,B2,6.87,18.0 -2016-05-31,FL,3,B,B2,6.88,18.0 -2016-06-01,FL,3,B,B2,6.73,18.0 -2016-06-02,FL,3,B,B2,6.85,17.0 -2016-06-03,FL,3,B,B2,6.88,18.0 -2016-06-04,FL,3,B,B2,6.79,18.0 -2016-06-05,FL,3,B,B2,6.88,18.0 -2016-06-06,FL,3,B,B2,6.85,18.0 -2016-06-07,FL,3,B,B2,6.89,19.0 -2016-06-08,FL,3,B,B2,6.74,17.0 -2016-06-09,FL,3,B,B2,6.87,17.0 -2016-06-10,FL,3,B,B2,6.83,19.0 -2016-06-11,FL,3,B,B2,6.87,18.0 -2016-06-12,FL,3,B,B2,6.85,18.0 -2016-06-13,FL,3,B,B2,6.84,17.0 -2016-06-14,FL,3,B,B2,6.85,18.0 -2016-06-15,FL,3,B,B2,6.92,18.0 -2016-06-16,FL,3,B,B2,6.76,17.0 -2016-06-17,FL,3,B,B2,6.85,19.0 -2016-06-18,FL,3,B,B2,6.84,18.0 -2016-06-19,FL,3,B,B2,6.79,18.0 -2016-06-20,FL,3,B,B2,6.83,18.0 -2016-06-21,FL,3,B,B2,6.66,18.0 -2016-06-22,FL,3,B,B2,6.71,18.0 -2016-06-23,FL,3,B,B2,6.84,16.0 -2016-06-24,FL,3,B,B2,6.82,17.0 -2016-06-25,FL,3,B,B2,6.86,19.0 -2016-06-26,FL,3,B,B2,6.93,18.0 -2016-06-27,FL,3,B,B2,6.98,18.0 -2016-06-28,FL,3,B,B2,6.93,17.0 -2016-06-29,FL,3,B,B2,6.71,17.0 -2016-06-30,FL,3,B,B2,6.74,19.0 -2016-07-01,FL,3,B,B2,6.86,17.0 -2016-07-02,FL,3,B,B2,6.8,19.0 -2016-07-03,FL,3,B,B2,6.9,18.0 -2016-07-04,FL,3,B,B2,6.86,19.0 -2016-07-05,FL,3,B,B2,6.92,17.0 -2016-07-06,FL,3,B,B2,6.8,19.0 -2016-07-07,FL,3,B,B2,6.77,19.0 -2016-07-08,FL,3,B,B2,6.76,18.0 -2016-07-09,FL,3,B,B2,6.95,19.0 -2016-07-10,FL,3,B,B2,6.83,17.0 -2016-07-11,FL,3,B,B2,6.83,18.0 -2016-07-12,FL,3,B,B2,6.7,19.0 -2016-07-13,FL,3,B,B2,6.89,19.0 -2016-07-14,FL,3,B,B2,6.83,18.0 -2016-07-15,FL,3,B,B2,6.92,18.0 -2016-07-16,FL,3,B,B2,6.76,18.0 -2016-07-17,FL,3,B,B2,6.74,17.0 -2016-07-18,FL,3,B,B2,6.85,17.0 -2016-07-19,FL,3,B,B2,6.86,18.0 -2016-07-20,FL,3,B,B2,6.95,18.0 -2016-07-21,FL,3,B,B2,6.86,17.0 -2016-07-22,FL,3,B,B2,6.78,18.0 -2016-07-23,FL,3,B,B2,6.86,18.0 -2016-07-24,FL,3,B,B2,6.81,17.0 -2016-07-25,FL,3,B,B2,6.79,18.0 -2016-07-26,FL,3,B,B2,6.86,18.0 -2016-07-27,FL,3,B,B2,6.84,19.0 -2015-01-05,WA,1,A,A1,3.46,5.0 -2015-01-06,WA,1,A,A1,3.57,5.0 -2015-01-07,WA,1,A,A1,3.47,5.0 -2015-01-08,WA,1,A,A1,3.49,5.0 -2015-01-09,WA,1,A,A1,3.49,5.0 -2015-01-10,WA,1,A,A1,3.52,6.0 -2015-01-11,WA,1,A,A1,3.51,5.0 -2015-01-12,WA,1,A,A1,3.5,5.0 -2015-01-13,WA,1,A,A1,3.52,5.0 -2015-01-14,WA,1,A,A1,3.51,5.0 -2015-01-15,WA,1,A,A1,3.55,5.0 -2015-01-16,WA,1,A,A1,3.48,5.0 -2015-01-17,WA,1,A,A1,3.5,5.0 -2015-01-18,WA,1,A,A1,3.48,5.0 -2015-01-19,WA,1,A,A1,3.55,5.0 -2015-01-20,WA,1,A,A1,3.55,5.0 -2015-01-21,WA,1,A,A1,3.48,5.0 -2015-01-22,WA,1,A,A1,3.48,6.0 -2015-01-23,WA,1,A,A1,3.5,5.0 -2015-01-24,WA,1,A,A1,3.47,6.0 -2015-01-25,WA,1,A,A1,3.46,6.0 -2015-01-26,WA,1,A,A1,3.46,5.0 -2015-01-27,WA,1,A,A1,3.5,5.0 -2015-01-28,WA,1,A,A1,3.53,5.0 -2015-01-29,WA,1,A,A1,3.5,5.0 -2015-01-30,WA,1,A,A1,3.5,6.0 -2015-01-31,WA,1,A,A1,3.47,5.0 -2015-02-01,WA,1,A,A1,3.5,6.0 -2015-02-02,WA,1,A,A1,3.51,5.0 -2015-02-03,WA,1,A,A1,3.44,5.0 -2015-02-04,WA,1,A,A1,3.42,6.0 -2015-02-05,WA,1,A,A1,3.57,5.0 -2015-02-06,WA,1,A,A1,3.51,5.0 -2015-02-07,WA,1,A,A1,3.54,5.0 -2015-02-08,WA,1,A,A1,3.5,5.0 -2015-02-09,WA,1,A,A1,3.52,5.0 -2015-02-10,WA,1,A,A1,3.46,6.0 -2015-02-11,WA,1,A,A1,3.48,5.0 -2015-02-12,WA,1,A,A1,3.51,5.0 -2015-02-13,WA,1,A,A1,3.48,6.0 -2015-02-14,WA,1,A,A1,3.5,6.0 -2015-02-15,WA,1,A,A1,3.56,5.0 -2015-02-16,WA,1,A,A1,3.42,5.0 -2015-02-17,WA,1,A,A1,3.51,5.0 -2015-02-18,WA,1,A,A1,3.51,5.0 -2015-02-19,WA,1,A,A1,3.51,6.0 -2015-02-20,WA,1,A,A1,3.47,5.0 -2015-02-21,WA,1,A,A1,3.51,5.0 -2015-02-22,WA,1,A,A1,3.49,5.0 -2015-02-23,WA,1,A,A1,3.51,5.0 -2015-02-24,WA,1,A,A1,3.43,5.0 -2015-02-25,WA,1,A,A1,3.46,5.0 -2015-02-26,WA,1,A,A1,3.46,5.0 -2015-02-27,WA,1,A,A1,3.5,5.0 -2015-02-28,WA,1,A,A1,3.44,5.0 -2015-03-01,WA,1,A,A1,3.52,5.0 -2015-03-02,WA,1,A,A1,3.51,5.0 -2015-03-03,WA,1,A,A1,3.48,5.0 -2015-03-04,WA,1,A,A1,3.49,5.0 -2015-03-05,WA,1,A,A1,3.48,5.0 -2015-03-06,WA,1,A,A1,3.5,6.0 -2015-03-07,WA,1,A,A1,3.49,6.0 -2015-03-08,WA,1,A,A1,3.46,5.0 -2015-03-09,WA,1,A,A1,3.51,5.0 -2015-03-10,WA,1,A,A1,3.55,5.0 -2015-03-11,WA,1,A,A1,3.49,6.0 -2015-03-12,WA,1,A,A1,3.52,5.0 -2015-03-13,WA,1,A,A1,3.46,5.0 -2015-03-14,WA,1,A,A1,3.56,5.0 -2015-03-15,WA,1,A,A1,3.51,5.0 -2015-03-16,WA,1,A,A1,3.51,5.0 -2015-03-17,WA,1,A,A1,3.48,6.0 -2015-03-18,WA,1,A,A1,3.48,6.0 -2015-03-19,WA,1,A,A1,3.54,5.0 -2015-03-20,WA,1,A,A1,3.5,5.0 -2015-03-21,WA,1,A,A1,3.5,5.0 -2015-03-22,WA,1,A,A1,3.46,5.0 -2015-03-23,WA,1,A,A1,3.51,5.0 -2015-03-24,WA,1,A,A1,3.55,5.0 -2015-03-25,WA,1,A,A1,3.49,5.0 -2015-03-26,WA,1,A,A1,3.49,5.0 -2015-03-27,WA,1,A,A1,3.47,6.0 -2015-03-28,WA,1,A,A1,3.53,5.0 -2015-03-29,WA,1,A,A1,3.52,5.0 -2015-03-30,WA,1,A,A1,3.52,5.0 -2015-03-31,WA,1,A,A1,3.53,5.0 -2015-04-01,WA,1,A,A1,3.54,5.0 -2015-04-02,WA,1,A,A1,3.48,5.0 -2015-04-03,WA,1,A,A1,3.52,6.0 -2015-04-04,WA,1,A,A1,3.46,5.0 -2015-04-05,WA,1,A,A1,3.48,5.0 -2015-04-06,WA,1,A,A1,3.49,5.0 -2015-04-07,WA,1,A,A1,3.51,5.0 -2015-04-08,WA,1,A,A1,3.46,5.0 -2015-04-09,WA,1,A,A1,3.45,5.0 -2015-04-10,WA,1,A,A1,3.51,5.0 -2015-04-11,WA,1,A,A1,3.45,6.0 -2015-04-12,WA,1,A,A1,3.47,6.0 -2015-04-13,WA,1,A,A1,3.43,5.0 -2015-04-14,WA,1,A,A1,3.5,5.0 -2015-04-15,WA,1,A,A1,3.54,5.0 -2015-04-16,WA,1,A,A1,3.49,5.0 -2015-04-17,WA,1,A,A1,3.53,5.0 -2015-04-18,WA,1,A,A1,3.5,5.0 -2015-04-19,WA,1,A,A1,3.5,5.0 -2015-04-20,WA,1,A,A1,3.44,5.0 -2015-04-21,WA,1,A,A1,3.51,5.0 -2015-04-22,WA,1,A,A1,3.52,5.0 -2015-04-23,WA,1,A,A1,3.51,5.0 -2015-04-24,WA,1,A,A1,3.46,5.0 -2015-04-25,WA,1,A,A1,3.48,5.0 -2015-04-26,WA,1,A,A1,3.52,5.0 -2015-04-27,WA,1,A,A1,3.42,5.0 -2015-04-28,WA,1,A,A1,3.54,5.0 -2015-04-29,WA,1,A,A1,3.51,5.0 -2015-04-30,WA,1,A,A1,3.55,4.0 -2015-05-01,WA,1,A,A1,3.56,5.0 -2015-05-02,WA,1,A,A1,3.48,5.0 -2015-05-03,WA,1,A,A1,3.52,5.0 -2015-05-04,WA,1,A,A1,3.57,5.0 -2015-05-05,WA,1,A,A1,3.47,5.0 -2015-05-06,WA,1,A,A1,3.48,5.0 -2015-05-07,WA,1,A,A1,3.47,5.0 -2015-05-08,WA,1,A,A1,3.51,5.0 -2015-05-09,WA,1,A,A1,3.43,6.0 -2015-05-10,WA,1,A,A1,3.55,5.0 -2015-05-11,WA,1,A,A1,3.45,6.0 -2015-05-12,WA,1,A,A1,3.48,5.0 -2015-05-13,WA,1,A,A1,3.53,5.0 -2015-05-14,WA,1,A,A1,3.54,5.0 -2015-05-15,WA,1,A,A1,3.56,5.0 -2015-05-16,WA,1,A,A1,3.5,6.0 -2015-05-17,WA,1,A,A1,3.5,5.0 -2015-05-18,WA,1,A,A1,3.48,5.0 -2015-05-19,WA,1,A,A1,3.55,5.0 -2015-05-20,WA,1,A,A1,3.55,5.0 -2015-05-21,WA,1,A,A1,3.45,6.0 -2015-05-22,WA,1,A,A1,3.55,5.0 -2015-05-23,WA,1,A,A1,3.44,5.0 -2015-05-24,WA,1,A,A1,3.53,5.0 -2015-05-25,WA,1,A,A1,3.48,5.0 -2015-05-26,WA,1,A,A1,3.54,5.0 -2015-05-27,WA,1,A,A1,3.52,5.0 -2015-05-28,WA,1,A,A1,3.54,5.0 -2015-05-29,WA,1,A,A1,3.54,5.0 -2015-05-30,WA,1,A,A1,3.51,5.0 -2015-05-31,WA,1,A,A1,3.48,5.0 -2015-06-01,WA,1,A,A1,3.49,5.0 -2015-06-02,WA,1,A,A1,3.53,5.0 -2015-06-03,WA,1,A,A1,3.57,5.0 -2015-06-04,WA,1,A,A1,3.53,5.0 -2015-06-05,WA,1,A,A1,3.54,5.0 -2015-06-06,WA,1,A,A1,3.49,6.0 -2015-06-07,WA,1,A,A1,3.46,5.0 -2015-06-08,WA,1,A,A1,3.54,5.0 -2015-06-09,WA,1,A,A1,3.45,5.0 -2015-06-10,WA,1,A,A1,3.48,5.0 -2015-06-11,WA,1,A,A1,3.51,5.0 -2015-06-12,WA,1,A,A1,3.51,5.0 -2015-06-13,WA,1,A,A1,3.49,6.0 -2015-06-14,WA,1,A,A1,3.46,6.0 -2015-06-15,WA,1,A,A1,3.47,5.0 -2015-06-16,WA,1,A,A1,3.44,5.0 -2015-06-17,WA,1,A,A1,3.48,5.0 -2015-06-18,WA,1,A,A1,3.5,5.0 -2015-06-19,WA,1,A,A1,3.47,5.0 -2015-06-20,WA,1,A,A1,3.56,5.0 -2015-06-21,WA,1,A,A1,3.45,6.0 -2015-06-22,WA,1,A,A1,3.54,5.0 -2015-06-23,WA,1,A,A1,3.53,5.0 -2015-06-24,WA,1,A,A1,3.5,5.0 -2015-06-25,WA,1,A,A1,3.51,5.0 -2015-06-26,WA,1,A,A1,3.53,5.0 -2015-06-27,WA,1,A,A1,3.45,5.0 -2015-06-28,WA,1,A,A1,3.52,5.0 -2015-06-29,WA,1,A,A1,3.51,5.0 -2015-06-30,WA,1,A,A1,3.55,5.0 -2015-07-01,WA,1,A,A1,3.45,5.0 -2015-07-02,WA,1,A,A1,3.5,5.0 -2015-07-03,WA,1,A,A1,3.49,6.0 -2015-07-04,WA,1,A,A1,3.5,5.0 -2015-07-05,WA,1,A,A1,3.44,6.0 -2015-07-06,WA,1,A,A1,3.61,5.0 -2015-07-07,WA,1,A,A1,3.55,5.0 -2015-07-08,WA,1,A,A1,3.47,5.0 -2015-07-09,WA,1,A,A1,3.51,5.0 -2015-07-10,WA,1,A,A1,3.49,5.0 -2015-07-11,WA,1,A,A1,3.5,6.0 -2015-07-12,WA,1,A,A1,3.47,6.0 -2015-07-13,WA,1,A,A1,3.51,5.0 -2015-07-14,WA,1,A,A1,3.53,6.0 -2015-07-15,WA,1,A,A1,3.48,6.0 -2015-07-16,WA,1,A,A1,3.45,5.0 -2015-07-17,WA,1,A,A1,3.5,5.0 -2015-07-18,WA,1,A,A1,3.57,5.0 -2015-07-19,WA,1,A,A1,3.52,5.0 -2015-07-20,WA,1,A,A1,3.51,5.0 -2015-07-21,WA,1,A,A1,3.48,5.0 -2015-07-22,WA,1,A,A1,3.52,5.0 -2015-07-23,WA,1,A,A1,3.43,5.0 -2015-07-24,WA,1,A,A1,3.48,5.0 -2015-07-25,WA,1,A,A1,3.48,6.0 -2015-07-26,WA,1,A,A1,3.47,5.0 -2015-07-27,WA,1,A,A1,3.47,5.0 -2015-07-28,WA,1,A,A1,3.5,5.0 -2015-07-29,WA,1,A,A1,3.54,5.0 -2015-07-30,WA,1,A,A1,3.52,5.0 -2015-07-31,WA,1,A,A1,3.46,6.0 -2015-08-01,WA,1,A,A1,3.52,6.0 -2015-08-02,WA,1,A,A1,3.48,6.0 -2015-08-03,WA,1,A,A1,3.51,5.0 -2015-08-04,WA,1,A,A1,3.49,5.0 -2015-08-05,WA,1,A,A1,3.5,5.0 -2015-08-06,WA,1,A,A1,3.47,5.0 -2015-08-07,WA,1,A,A1,3.46,6.0 -2015-08-08,WA,1,A,A1,3.48,5.0 -2015-08-09,WA,1,A,A1,3.5,5.0 -2015-08-10,WA,1,A,A1,3.5,5.0 -2015-08-11,WA,1,A,A1,3.5,6.0 -2015-08-12,WA,1,A,A1,3.51,5.0 -2015-08-13,WA,1,A,A1,3.5,5.0 -2015-08-14,WA,1,A,A1,3.48,6.0 -2015-08-15,WA,1,A,A1,3.51,5.0 -2015-08-16,WA,1,A,A1,3.55,5.0 -2015-08-17,WA,1,A,A1,3.42,6.0 -2015-08-18,WA,1,A,A1,3.47,5.0 -2015-08-19,WA,1,A,A1,3.52,5.0 -2015-08-20,WA,1,A,A1,3.48,5.0 -2015-08-21,WA,1,A,A1,3.48,5.0 -2015-08-22,WA,1,A,A1,3.54,5.0 -2015-08-23,WA,1,A,A1,3.5,5.0 -2015-08-24,WA,1,A,A1,3.53,5.0 -2015-08-25,WA,1,A,A1,3.51,5.0 -2015-08-26,WA,1,A,A1,3.52,5.0 -2015-08-27,WA,1,A,A1,3.5,5.0 -2015-08-28,WA,1,A,A1,3.48,6.0 -2015-08-29,WA,1,A,A1,3.54,6.0 -2015-08-30,WA,1,A,A1,3.56,5.0 -2015-08-31,WA,1,A,A1,3.54,5.0 -2015-09-01,WA,1,A,A1,3.5,5.0 -2015-09-02,WA,1,A,A1,3.5,5.0 -2015-09-03,WA,1,A,A1,3.51,5.0 -2015-09-04,WA,1,A,A1,3.56,5.0 -2015-09-05,WA,1,A,A1,3.47,6.0 -2015-09-06,WA,1,A,A1,3.58,5.0 -2015-09-07,WA,1,A,A1,3.47,5.0 -2015-09-08,WA,1,A,A1,3.5,5.0 -2015-09-09,WA,1,A,A1,3.5,6.0 -2015-09-10,WA,1,A,A1,3.44,6.0 -2015-09-11,WA,1,A,A1,3.53,5.0 -2015-09-12,WA,1,A,A1,3.44,6.0 -2015-09-13,WA,1,A,A1,3.49,5.0 -2015-09-14,WA,1,A,A1,3.56,5.0 -2015-09-15,WA,1,A,A1,3.47,5.0 -2015-09-16,WA,1,A,A1,3.5,6.0 -2015-09-17,WA,1,A,A1,3.48,5.0 -2015-09-18,WA,1,A,A1,3.41,5.0 -2015-09-19,WA,1,A,A1,3.43,5.0 -2015-09-20,WA,1,A,A1,3.49,6.0 -2015-09-21,WA,1,A,A1,3.53,5.0 -2015-09-22,WA,1,A,A1,3.48,5.0 -2015-09-23,WA,1,A,A1,3.5,5.0 -2015-09-24,WA,1,A,A1,3.52,5.0 -2015-09-25,WA,1,A,A1,3.51,5.0 -2015-09-26,WA,1,A,A1,3.53,5.0 -2015-09-27,WA,1,A,A1,3.47,5.0 -2015-09-28,WA,1,A,A1,3.54,5.0 -2015-09-29,WA,1,A,A1,3.47,5.0 -2015-09-30,WA,1,A,A1,3.56,5.0 -2015-10-01,WA,1,A,A1,3.5,5.0 -2015-10-02,WA,1,A,A1,3.42,5.0 -2015-10-03,WA,1,A,A1,3.5,6.0 -2015-10-04,WA,1,A,A1,3.52,6.0 -2015-10-05,WA,1,A,A1,3.48,5.0 -2015-10-06,WA,1,A,A1,3.52,5.0 -2015-10-07,WA,1,A,A1,3.48,5.0 -2015-10-08,WA,1,A,A1,3.5,5.0 -2015-10-09,WA,1,A,A1,3.48,5.0 -2015-10-10,WA,1,A,A1,3.43,6.0 -2015-10-11,WA,1,A,A1,3.5,5.0 -2015-10-12,WA,1,A,A1,3.53,5.0 -2015-10-13,WA,1,A,A1,3.5,5.0 -2015-10-14,WA,1,A,A1,3.47,5.0 -2015-10-15,WA,1,A,A1,3.54,5.0 -2015-10-16,WA,1,A,A1,3.51,6.0 -2015-10-17,WA,1,A,A1,3.52,6.0 -2015-10-18,WA,1,A,A1,3.5,5.0 -2015-10-19,WA,1,A,A1,3.44,5.0 -2015-10-20,WA,1,A,A1,3.52,5.0 -2015-10-21,WA,1,A,A1,3.41,6.0 -2015-10-22,WA,1,A,A1,3.56,5.0 -2015-10-23,WA,1,A,A1,3.5,5.0 -2015-10-24,WA,1,A,A1,3.5,5.0 -2015-10-25,WA,1,A,A1,3.52,5.0 -2015-10-26,WA,1,A,A1,3.49,5.0 -2015-10-27,WA,1,A,A1,3.56,5.0 -2015-10-28,WA,1,A,A1,3.44,5.0 -2015-10-29,WA,1,A,A1,3.51,5.0 -2015-10-30,WA,1,A,A1,3.46,6.0 -2015-10-31,WA,1,A,A1,3.51,6.0 -2015-11-01,WA,1,A,A1,3.55,5.0 -2015-11-02,WA,1,A,A1,3.46,5.0 -2015-11-03,WA,1,A,A1,3.52,6.0 -2015-11-04,WA,1,A,A1,3.53,5.0 -2015-11-05,WA,1,A,A1,3.5,5.0 -2015-11-06,WA,1,A,A1,3.51,6.0 -2015-11-07,WA,1,A,A1,3.47,6.0 -2015-11-08,WA,1,A,A1,3.51,5.0 -2015-11-09,WA,1,A,A1,3.42,5.0 -2015-11-10,WA,1,A,A1,3.49,5.0 -2015-11-11,WA,1,A,A1,3.56,5.0 -2015-11-12,WA,1,A,A1,3.53,5.0 -2015-11-13,WA,1,A,A1,3.52,5.0 -2015-11-14,WA,1,A,A1,3.54,5.0 -2015-11-15,WA,1,A,A1,3.55,5.0 -2015-11-16,WA,1,A,A1,3.48,6.0 -2015-11-17,WA,1,A,A1,3.46,5.0 -2015-11-18,WA,1,A,A1,3.51,5.0 -2015-11-19,WA,1,A,A1,3.54,5.0 -2015-11-20,WA,1,A,A1,3.49,5.0 -2015-11-21,WA,1,A,A1,3.51,6.0 -2015-11-22,WA,1,A,A1,3.53,5.0 -2015-11-23,WA,1,A,A1,3.46,5.0 -2015-11-24,WA,1,A,A1,3.52,5.0 -2015-11-25,WA,1,A,A1,3.5,5.0 -2015-11-26,WA,1,A,A1,3.43,5.0 -2015-11-27,WA,1,A,A1,3.48,5.0 -2015-11-28,WA,1,A,A1,3.52,6.0 -2015-11-29,WA,1,A,A1,3.44,5.0 -2015-11-30,WA,1,A,A1,3.51,5.0 -2015-12-01,WA,1,A,A1,3.49,5.0 -2015-12-02,WA,1,A,A1,3.55,5.0 -2015-12-03,WA,1,A,A1,3.48,5.0 -2015-12-04,WA,1,A,A1,3.53,5.0 -2015-12-05,WA,1,A,A1,3.57,6.0 -2015-12-06,WA,1,A,A1,3.51,5.0 -2015-12-07,WA,1,A,A1,3.51,6.0 -2015-12-08,WA,1,A,A1,3.53,5.0 -2015-12-09,WA,1,A,A1,3.53,5.0 -2015-12-10,WA,1,A,A1,3.52,5.0 -2015-12-11,WA,1,A,A1,3.53,5.0 -2015-12-12,WA,1,A,A1,3.51,6.0 -2015-12-13,WA,1,A,A1,3.47,5.0 -2015-12-14,WA,1,A,A1,3.55,5.0 -2015-12-15,WA,1,A,A1,3.5,6.0 -2015-12-16,WA,1,A,A1,3.52,5.0 -2015-12-17,WA,1,A,A1,3.51,5.0 -2015-12-18,WA,1,A,A1,3.46,5.0 -2015-12-19,WA,1,A,A1,3.49,5.0 -2015-12-20,WA,1,A,A1,3.47,5.0 -2015-12-21,WA,1,A,A1,3.48,6.0 -2015-12-22,WA,1,A,A1,3.54,5.0 -2015-12-23,WA,1,A,A1,3.43,5.0 -2015-12-24,WA,1,A,A1,3.54,5.0 -2015-12-25,WA,1,A,A1,3.51,5.0 -2015-12-26,WA,1,A,A1,3.53,6.0 -2015-12-27,WA,1,A,A1,3.45,5.0 -2015-12-28,WA,1,A,A1,3.5,5.0 -2015-12-29,WA,1,A,A1,3.5,5.0 -2015-12-30,WA,1,A,A1,3.49,6.0 -2015-12-31,WA,1,A,A1,3.54,5.0 -2016-01-01,WA,1,A,A1,3.47,5.0 -2016-01-02,WA,1,A,A1,3.51,6.0 -2016-01-03,WA,1,A,A1,3.48,5.0 -2016-01-04,WA,1,A,A1,3.55,5.0 -2016-01-05,WA,1,A,A1,3.46,6.0 -2016-01-06,WA,1,A,A1,3.55,6.0 -2016-01-07,WA,1,A,A1,3.47,6.0 -2016-01-08,WA,1,A,A1,3.49,5.0 -2016-01-09,WA,1,A,A1,3.45,6.0 -2016-01-10,WA,1,A,A1,3.52,5.0 -2016-01-11,WA,1,A,A1,3.46,5.0 -2016-01-12,WA,1,A,A1,3.53,5.0 -2016-01-13,WA,1,A,A1,3.48,5.0 -2016-01-14,WA,1,A,A1,3.48,5.0 -2016-01-15,WA,1,A,A1,3.44,5.0 -2016-01-16,WA,1,A,A1,3.48,5.0 -2016-01-17,WA,1,A,A1,3.51,5.0 -2016-01-18,WA,1,A,A1,3.5,5.0 -2016-01-19,WA,1,A,A1,3.52,5.0 -2016-01-20,WA,1,A,A1,3.51,5.0 -2016-01-21,WA,1,A,A1,3.49,5.0 -2016-01-22,WA,1,A,A1,3.56,5.0 -2016-01-23,WA,1,A,A1,3.54,6.0 -2016-01-24,WA,1,A,A1,3.52,5.0 -2016-01-25,WA,1,A,A1,3.44,6.0 -2016-01-26,WA,1,A,A1,3.5,6.0 -2016-01-27,WA,1,A,A1,3.53,5.0 -2016-01-28,WA,1,A,A1,3.44,6.0 -2016-01-29,WA,1,A,A1,3.49,5.0 -2016-01-30,WA,1,A,A1,3.48,6.0 -2016-01-31,WA,1,A,A1,3.49,6.0 -2016-02-01,WA,1,A,A1,3.49,5.0 -2016-02-02,WA,1,A,A1,3.46,5.0 -2016-02-03,WA,1,A,A1,3.48,5.0 -2016-02-04,WA,1,A,A1,3.54,5.0 -2016-02-05,WA,1,A,A1,3.49,6.0 -2016-02-06,WA,1,A,A1,3.53,5.0 -2016-02-07,WA,1,A,A1,3.48,6.0 -2016-02-08,WA,1,A,A1,3.54,5.0 -2016-02-09,WA,1,A,A1,3.52,5.0 -2016-02-10,WA,1,A,A1,3.52,6.0 -2016-02-11,WA,1,A,A1,3.48,5.0 -2016-02-12,WA,1,A,A1,3.52,5.0 -2016-02-13,WA,1,A,A1,3.47,5.0 -2016-02-14,WA,1,A,A1,3.53,5.0 -2016-02-15,WA,1,A,A1,3.51,5.0 -2016-02-16,WA,1,A,A1,3.5,5.0 -2016-02-17,WA,1,A,A1,3.53,6.0 -2016-02-18,WA,1,A,A1,3.45,6.0 -2016-02-19,WA,1,A,A1,3.42,6.0 -2016-02-20,WA,1,A,A1,3.43,6.0 -2016-02-21,WA,1,A,A1,3.51,5.0 -2016-02-22,WA,1,A,A1,3.52,5.0 -2016-02-23,WA,1,A,A1,3.52,5.0 -2016-02-24,WA,1,A,A1,3.48,5.0 -2016-02-25,WA,1,A,A1,3.51,5.0 -2016-02-26,WA,1,A,A1,3.56,5.0 -2016-02-27,WA,1,A,A1,3.49,6.0 -2016-02-28,WA,1,A,A1,3.47,6.0 -2016-02-29,WA,1,A,A1,3.47,5.0 -2016-03-01,WA,1,A,A1,3.47,5.0 -2016-03-02,WA,1,A,A1,3.43,6.0 -2016-03-03,WA,1,A,A1,3.49,5.0 -2016-03-04,WA,1,A,A1,3.43,5.0 -2016-03-05,WA,1,A,A1,3.54,6.0 -2016-03-06,WA,1,A,A1,3.54,5.0 -2016-03-07,WA,1,A,A1,3.48,5.0 -2016-03-08,WA,1,A,A1,3.47,5.0 -2016-03-09,WA,1,A,A1,3.55,6.0 -2016-03-10,WA,1,A,A1,3.44,5.0 -2016-03-11,WA,1,A,A1,3.55,5.0 -2016-03-12,WA,1,A,A1,3.47,6.0 -2016-03-13,WA,1,A,A1,3.42,6.0 -2016-03-14,WA,1,A,A1,3.45,6.0 -2016-03-15,WA,1,A,A1,3.48,5.0 -2016-03-16,WA,1,A,A1,3.47,5.0 -2016-03-17,WA,1,A,A1,3.48,5.0 -2016-03-18,WA,1,A,A1,3.51,5.0 -2016-03-19,WA,1,A,A1,3.5,5.0 -2016-03-20,WA,1,A,A1,3.54,5.0 -2016-03-21,WA,1,A,A1,3.46,5.0 -2016-03-22,WA,1,A,A1,3.47,5.0 -2016-03-23,WA,1,A,A1,3.49,5.0 -2016-03-24,WA,1,A,A1,3.5,6.0 -2016-03-25,WA,1,A,A1,3.5,5.0 -2016-03-26,WA,1,A,A1,3.43,6.0 -2016-03-27,WA,1,A,A1,3.5,5.0 -2016-03-28,WA,1,A,A1,3.48,5.0 -2016-03-29,WA,1,A,A1,3.54,5.0 -2016-03-30,WA,1,A,A1,3.53,5.0 -2016-03-31,WA,1,A,A1,3.49,5.0 -2016-04-01,WA,1,A,A1,3.5,5.0 -2016-04-02,WA,1,A,A1,3.47,6.0 -2016-04-03,WA,1,A,A1,3.53,6.0 -2016-04-04,WA,1,A,A1,3.47,6.0 -2016-04-05,WA,1,A,A1,3.52,6.0 -2016-04-06,WA,1,A,A1,3.48,5.0 -2016-04-07,WA,1,A,A1,3.45,6.0 -2016-04-08,WA,1,A,A1,3.49,5.0 -2016-04-09,WA,1,A,A1,3.44,5.0 -2016-04-10,WA,1,A,A1,3.49,5.0 -2016-04-11,WA,1,A,A1,3.5,5.0 -2016-04-12,WA,1,A,A1,3.5,5.0 -2016-04-13,WA,1,A,A1,3.46,5.0 -2016-04-14,WA,1,A,A1,3.47,5.0 -2016-04-15,WA,1,A,A1,3.49,5.0 -2016-04-16,WA,1,A,A1,3.51,5.0 -2016-04-17,WA,1,A,A1,3.48,6.0 -2016-04-18,WA,1,A,A1,3.53,5.0 -2016-04-19,WA,1,A,A1,3.46,6.0 -2016-04-20,WA,1,A,A1,3.54,5.0 -2016-04-21,WA,1,A,A1,3.52,5.0 -2016-04-22,WA,1,A,A1,3.5,6.0 -2016-04-23,WA,1,A,A1,3.52,5.0 -2016-04-24,WA,1,A,A1,3.43,6.0 -2016-04-25,WA,1,A,A1,3.48,5.0 -2016-04-26,WA,1,A,A1,3.48,5.0 -2016-04-27,WA,1,A,A1,3.5,5.0 -2016-04-28,WA,1,A,A1,3.54,5.0 -2016-04-29,WA,1,A,A1,3.54,6.0 -2016-04-30,WA,1,A,A1,3.49,6.0 -2016-05-01,WA,1,A,A1,3.49,5.0 -2016-05-02,WA,1,A,A1,3.49,5.0 -2016-05-03,WA,1,A,A1,3.49,5.0 -2016-05-04,WA,1,A,A1,3.53,5.0 -2016-05-05,WA,1,A,A1,3.52,5.0 -2016-05-06,WA,1,A,A1,3.47,5.0 -2016-05-07,WA,1,A,A1,3.47,5.0 -2016-05-08,WA,1,A,A1,3.54,5.0 -2016-05-09,WA,1,A,A1,3.49,6.0 -2016-05-10,WA,1,A,A1,3.47,5.0 -2016-05-11,WA,1,A,A1,3.53,5.0 -2016-05-12,WA,1,A,A1,3.47,5.0 -2016-05-13,WA,1,A,A1,3.49,6.0 -2016-05-14,WA,1,A,A1,3.5,6.0 -2016-05-15,WA,1,A,A1,3.48,6.0 -2016-05-16,WA,1,A,A1,3.53,5.0 -2016-05-17,WA,1,A,A1,3.48,6.0 -2016-05-18,WA,1,A,A1,3.49,5.0 -2016-05-19,WA,1,A,A1,3.51,5.0 -2016-05-20,WA,1,A,A1,3.55,5.0 -2016-05-21,WA,1,A,A1,3.48,6.0 -2016-05-22,WA,1,A,A1,3.51,5.0 -2016-05-23,WA,1,A,A1,3.51,5.0 -2016-05-24,WA,1,A,A1,3.47,5.0 -2016-05-25,WA,1,A,A1,3.52,5.0 -2016-05-26,WA,1,A,A1,3.48,5.0 -2016-05-27,WA,1,A,A1,3.49,5.0 -2016-05-28,WA,1,A,A1,3.5,6.0 -2016-05-29,WA,1,A,A1,3.56,6.0 -2016-05-30,WA,1,A,A1,3.49,5.0 -2016-05-31,WA,1,A,A1,3.42,6.0 -2016-06-01,WA,1,A,A1,3.46,6.0 -2016-06-02,WA,1,A,A1,3.48,5.0 -2016-06-03,WA,1,A,A1,3.48,5.0 -2016-06-04,WA,1,A,A1,3.49,5.0 -2016-06-05,WA,1,A,A1,3.52,5.0 -2016-06-06,WA,1,A,A1,3.49,5.0 -2016-06-07,WA,1,A,A1,3.45,6.0 -2016-06-08,WA,1,A,A1,3.49,5.0 -2016-06-09,WA,1,A,A1,3.47,6.0 -2016-06-10,WA,1,A,A1,3.51,5.0 -2016-06-11,WA,1,A,A1,3.44,6.0 -2016-06-12,WA,1,A,A1,3.46,5.0 -2016-06-13,WA,1,A,A1,3.53,5.0 -2016-06-14,WA,1,A,A1,3.51,6.0 -2016-06-15,WA,1,A,A1,3.47,5.0 -2016-06-16,WA,1,A,A1,3.51,6.0 -2016-06-17,WA,1,A,A1,3.51,5.0 -2016-06-18,WA,1,A,A1,3.51,5.0 -2016-06-19,WA,1,A,A1,3.51,5.0 -2016-06-20,WA,1,A,A1,3.51,5.0 -2016-06-21,WA,1,A,A1,3.51,5.0 -2016-06-22,WA,1,A,A1,3.48,5.0 -2016-06-23,WA,1,A,A1,3.5,5.0 -2016-06-24,WA,1,A,A1,3.48,5.0 -2016-06-25,WA,1,A,A1,3.47,6.0 -2016-06-26,WA,1,A,A1,3.51,5.0 -2016-06-27,WA,1,A,A1,3.48,5.0 -2016-06-28,WA,1,A,A1,3.48,5.0 -2016-06-29,WA,1,A,A1,3.49,5.0 -2016-06-30,WA,1,A,A1,3.55,5.0 -2016-07-01,WA,1,A,A1,3.5,5.0 -2016-07-02,WA,1,A,A1,3.47,6.0 -2016-07-03,WA,1,A,A1,3.53,5.0 -2016-07-04,WA,1,A,A1,3.44,5.0 -2016-07-05,WA,1,A,A1,3.52,5.0 -2016-07-06,WA,1,A,A1,3.5,5.0 -2016-07-07,WA,1,A,A1,3.51,5.0 -2016-07-08,WA,1,A,A1,3.5,5.0 -2016-07-09,WA,1,A,A1,3.53,5.0 -2016-07-10,WA,1,A,A1,3.48,6.0 -2016-07-11,WA,1,A,A1,3.51,5.0 -2016-07-12,WA,1,A,A1,3.47,5.0 -2016-07-13,WA,1,A,A1,3.47,5.0 -2016-07-14,WA,1,A,A1,3.51,5.0 -2016-07-15,WA,1,A,A1,3.42,6.0 -2016-07-16,WA,1,A,A1,3.48,5.0 -2016-07-17,WA,1,A,A1,3.57,5.0 -2016-07-18,WA,1,A,A1,3.5,4.0 -2016-07-19,WA,1,A,A1,3.5,5.0 -2016-07-20,WA,1,A,A1,3.55,5.0 -2016-07-21,WA,1,A,A1,3.48,6.0 -2016-07-22,WA,1,A,A1,3.5,5.0 -2016-07-23,WA,1,A,A1,3.5,6.0 -2016-07-24,WA,1,A,A1,3.5,5.0 -2016-07-25,WA,1,A,A1,3.5,5.0 -2016-07-26,WA,1,A,A1,3.52,5.0 -2016-07-27,WA,1,A,A1,3.45,6.0 -2015-01-05,WA,1,A,A2,7.11,118.0 -2015-01-06,WA,1,A,A2,7.06,136.0 -2015-01-07,WA,1,A,A2,7.07,126.0 -2015-01-08,WA,1,A,A2,7.13,124.0 -2015-01-09,WA,1,A,A2,7.05,120.0 -2015-01-10,WA,1,A,A2,7.02,134.0 -2015-01-11,WA,1,A,A2,7.18,130.0 -2015-01-12,WA,1,A,A2,7.05,115.0 -2015-01-13,WA,1,A,A2,7.02,139.0 -2015-01-14,WA,1,A,A2,7.11,132.0 -2015-01-15,WA,1,A,A2,7.19,131.0 -2015-01-16,WA,1,A,A2,7.07,125.0 -2015-01-17,WA,1,A,A2,7.04,138.0 -2015-01-18,WA,1,A,A2,7.12,138.0 -2015-01-19,WA,1,A,A2,7.17,129.0 -2015-01-20,WA,1,A,A2,7.04,134.0 -2015-01-21,WA,1,A,A2,7.14,119.0 -2015-01-22,WA,1,A,A2,7.14,122.0 -2015-01-23,WA,1,A,A2,7.17,136.0 -2015-01-24,WA,1,A,A2,7.13,140.0 -2015-01-25,WA,1,A,A2,6.94,137.0 -2015-01-26,WA,1,A,A2,7.11,129.0 -2015-01-27,WA,1,A,A2,7.05,125.0 -2015-01-28,WA,1,A,A2,7.09,129.0 -2015-01-29,WA,1,A,A2,7.15,134.0 -2015-01-30,WA,1,A,A2,7.04,132.0 -2015-01-31,WA,1,A,A2,7.17,131.0 -2015-02-01,WA,1,A,A2,7.11,137.0 -2015-02-02,WA,1,A,A2,7.08,129.0 -2015-02-03,WA,1,A,A2,7.06,134.0 -2015-02-04,WA,1,A,A2,7.03,134.0 -2015-02-05,WA,1,A,A2,7.0,130.0 -2015-02-06,WA,1,A,A2,7.01,135.0 -2015-02-07,WA,1,A,A2,7.09,134.0 -2015-02-08,WA,1,A,A2,6.97,139.0 -2015-02-09,WA,1,A,A2,7.08,136.0 -2015-02-10,WA,1,A,A2,7.13,127.0 -2015-02-11,WA,1,A,A2,7.07,128.0 -2015-02-12,WA,1,A,A2,7.1,137.0 -2015-02-13,WA,1,A,A2,6.98,142.0 -2015-02-14,WA,1,A,A2,7.14,141.0 -2015-02-15,WA,1,A,A2,7.08,145.0 -2015-02-16,WA,1,A,A2,7.15,128.0 -2015-02-17,WA,1,A,A2,7.11,130.0 -2015-02-18,WA,1,A,A2,6.99,140.0 -2015-02-19,WA,1,A,A2,7.13,139.0 -2015-02-20,WA,1,A,A2,7.11,125.0 -2015-02-21,WA,1,A,A2,7.08,137.0 -2015-02-22,WA,1,A,A2,7.23,135.0 -2015-02-23,WA,1,A,A2,7.16,128.0 -2015-02-24,WA,1,A,A2,6.92,140.0 -2015-02-25,WA,1,A,A2,7.27,137.0 -2015-02-26,WA,1,A,A2,7.14,124.0 -2015-02-27,WA,1,A,A2,7.0,144.0 -2015-02-28,WA,1,A,A2,7.11,135.0 -2015-03-01,WA,1,A,A2,6.96,142.0 -2015-03-02,WA,1,A,A2,7.19,133.0 -2015-03-03,WA,1,A,A2,7.2,128.0 -2015-03-04,WA,1,A,A2,7.06,139.0 -2015-03-05,WA,1,A,A2,7.06,146.0 -2015-03-06,WA,1,A,A2,7.19,143.0 -2015-03-07,WA,1,A,A2,6.99,151.0 -2015-03-08,WA,1,A,A2,6.99,137.0 -2015-03-09,WA,1,A,A2,6.97,138.0 -2015-03-10,WA,1,A,A2,7.06,128.0 -2015-03-11,WA,1,A,A2,7.24,134.0 -2015-03-12,WA,1,A,A2,7.13,138.0 -2015-03-13,WA,1,A,A2,7.11,146.0 -2015-03-14,WA,1,A,A2,7.14,150.0 -2015-03-15,WA,1,A,A2,7.05,149.0 -2015-03-16,WA,1,A,A2,7.13,129.0 -2015-03-17,WA,1,A,A2,7.05,136.0 -2015-03-18,WA,1,A,A2,7.01,142.0 -2015-03-19,WA,1,A,A2,7.17,131.0 -2015-03-20,WA,1,A,A2,7.06,136.0 -2015-03-21,WA,1,A,A2,7.04,127.0 -2015-03-22,WA,1,A,A2,7.05,132.0 -2015-03-23,WA,1,A,A2,7.01,125.0 -2015-03-24,WA,1,A,A2,7.01,138.0 -2015-03-25,WA,1,A,A2,6.96,134.0 -2015-03-26,WA,1,A,A2,7.08,138.0 -2015-03-27,WA,1,A,A2,7.21,130.0 -2015-03-28,WA,1,A,A2,7.16,135.0 -2015-03-29,WA,1,A,A2,7.1,134.0 -2015-03-30,WA,1,A,A2,7.21,135.0 -2015-03-31,WA,1,A,A2,7.14,142.0 -2015-04-01,WA,1,A,A2,7.16,125.0 -2015-04-02,WA,1,A,A2,7.0,137.0 -2015-04-03,WA,1,A,A2,7.07,135.0 -2015-04-04,WA,1,A,A2,7.08,138.0 -2015-04-05,WA,1,A,A2,7.13,136.0 -2015-04-06,WA,1,A,A2,7.14,129.0 -2015-04-07,WA,1,A,A2,7.06,136.0 -2015-04-08,WA,1,A,A2,7.03,133.0 -2015-04-09,WA,1,A,A2,7.21,139.0 -2015-04-10,WA,1,A,A2,7.17,136.0 -2015-04-11,WA,1,A,A2,7.13,141.0 -2015-04-12,WA,1,A,A2,7.09,143.0 -2015-04-13,WA,1,A,A2,7.09,138.0 -2015-04-14,WA,1,A,A2,7.08,131.0 -2015-04-15,WA,1,A,A2,6.97,137.0 -2015-04-16,WA,1,A,A2,7.07,136.0 -2015-04-17,WA,1,A,A2,7.07,124.0 -2015-04-18,WA,1,A,A2,7.11,139.0 -2015-04-19,WA,1,A,A2,7.22,131.0 -2015-04-20,WA,1,A,A2,7.01,136.0 -2015-04-21,WA,1,A,A2,7.22,130.0 -2015-04-22,WA,1,A,A2,7.08,143.0 -2015-04-23,WA,1,A,A2,7.06,142.0 -2015-04-24,WA,1,A,A2,7.01,134.0 -2015-04-25,WA,1,A,A2,7.1,140.0 -2015-04-26,WA,1,A,A2,7.19,133.0 -2015-04-27,WA,1,A,A2,7.13,128.0 -2015-04-28,WA,1,A,A2,6.96,129.0 -2015-04-29,WA,1,A,A2,7.08,141.0 -2015-04-30,WA,1,A,A2,7.13,139.0 -2015-05-01,WA,1,A,A2,7.06,136.0 -2015-05-02,WA,1,A,A2,7.11,141.0 -2015-05-03,WA,1,A,A2,7.12,144.0 -2015-05-04,WA,1,A,A2,7.09,131.0 -2015-05-05,WA,1,A,A2,7.02,130.0 -2015-05-06,WA,1,A,A2,6.99,130.0 -2015-05-07,WA,1,A,A2,7.03,134.0 -2015-05-08,WA,1,A,A2,7.21,131.0 -2015-05-09,WA,1,A,A2,7.13,133.0 -2015-05-10,WA,1,A,A2,7.14,138.0 -2015-05-11,WA,1,A,A2,7.16,120.0 -2015-05-12,WA,1,A,A2,7.12,127.0 -2015-05-13,WA,1,A,A2,7.02,133.0 -2015-05-14,WA,1,A,A2,7.19,126.0 -2015-05-15,WA,1,A,A2,7.22,120.0 -2015-05-16,WA,1,A,A2,7.02,134.0 -2015-05-17,WA,1,A,A2,7.12,120.0 -2015-05-18,WA,1,A,A2,7.11,129.0 -2015-05-19,WA,1,A,A2,7.18,144.0 -2015-05-20,WA,1,A,A2,6.98,140.0 -2015-05-21,WA,1,A,A2,6.99,139.0 -2015-05-22,WA,1,A,A2,7.08,139.0 -2015-05-23,WA,1,A,A2,7.13,133.0 -2015-05-24,WA,1,A,A2,7.19,147.0 -2015-05-25,WA,1,A,A2,7.03,133.0 -2015-05-26,WA,1,A,A2,7.2,132.0 -2015-05-27,WA,1,A,A2,7.22,109.0 -2015-05-28,WA,1,A,A2,7.08,132.0 -2015-05-29,WA,1,A,A2,7.07,134.0 -2015-05-30,WA,1,A,A2,7.03,144.0 -2015-05-31,WA,1,A,A2,7.19,136.0 -2015-06-01,WA,1,A,A2,7.04,135.0 -2015-06-02,WA,1,A,A2,7.11,122.0 -2015-06-03,WA,1,A,A2,7.12,137.0 -2015-06-04,WA,1,A,A2,7.05,134.0 -2015-06-05,WA,1,A,A2,7.11,130.0 -2015-06-06,WA,1,A,A2,7.01,151.0 -2015-06-07,WA,1,A,A2,7.11,130.0 -2015-06-08,WA,1,A,A2,7.14,135.0 -2015-06-09,WA,1,A,A2,7.09,137.0 -2015-06-10,WA,1,A,A2,7.12,128.0 -2015-06-11,WA,1,A,A2,7.06,134.0 -2015-06-12,WA,1,A,A2,7.17,130.0 -2015-06-13,WA,1,A,A2,7.04,144.0 -2015-06-14,WA,1,A,A2,6.93,143.0 -2015-06-15,WA,1,A,A2,6.92,137.0 -2015-06-16,WA,1,A,A2,7.16,133.0 -2015-06-17,WA,1,A,A2,7.19,130.0 -2015-06-18,WA,1,A,A2,7.02,130.0 -2015-06-19,WA,1,A,A2,7.14,121.0 -2015-06-20,WA,1,A,A2,7.04,139.0 -2015-06-21,WA,1,A,A2,7.14,136.0 -2015-06-22,WA,1,A,A2,7.07,129.0 -2015-06-23,WA,1,A,A2,7.12,137.0 -2015-06-24,WA,1,A,A2,7.18,130.0 -2015-06-25,WA,1,A,A2,7.13,138.0 -2015-06-26,WA,1,A,A2,7.01,142.0 -2015-06-27,WA,1,A,A2,7.08,141.0 -2015-06-28,WA,1,A,A2,7.04,144.0 -2015-06-29,WA,1,A,A2,7.0,131.0 -2015-06-30,WA,1,A,A2,7.06,144.0 -2015-07-01,WA,1,A,A2,7.13,130.0 -2015-07-02,WA,1,A,A2,6.97,138.0 -2015-07-03,WA,1,A,A2,7.12,135.0 -2015-07-04,WA,1,A,A2,7.1,142.0 -2015-07-05,WA,1,A,A2,7.15,130.0 -2015-07-06,WA,1,A,A2,7.14,129.0 -2015-07-07,WA,1,A,A2,7.05,137.0 -2015-07-08,WA,1,A,A2,7.12,132.0 -2015-07-09,WA,1,A,A2,7.11,127.0 -2015-07-10,WA,1,A,A2,7.0,148.0 -2015-07-11,WA,1,A,A2,7.13,133.0 -2015-07-12,WA,1,A,A2,7.06,133.0 -2015-07-13,WA,1,A,A2,6.96,136.0 -2015-07-14,WA,1,A,A2,7.33,129.0 -2015-07-15,WA,1,A,A2,7.2,127.0 -2015-07-16,WA,1,A,A2,6.97,130.0 -2015-07-17,WA,1,A,A2,7.14,137.0 -2015-07-18,WA,1,A,A2,6.99,143.0 -2015-07-19,WA,1,A,A2,7.03,143.0 -2015-07-20,WA,1,A,A2,7.11,128.0 -2015-07-21,WA,1,A,A2,7.11,133.0 -2015-07-22,WA,1,A,A2,7.02,131.0 -2015-07-23,WA,1,A,A2,7.23,139.0 -2015-07-24,WA,1,A,A2,7.11,139.0 -2015-07-25,WA,1,A,A2,7.2,139.0 -2015-07-26,WA,1,A,A2,7.11,140.0 -2015-07-27,WA,1,A,A2,7.17,125.0 -2015-07-28,WA,1,A,A2,7.2,119.0 -2015-07-29,WA,1,A,A2,7.15,119.0 -2015-07-30,WA,1,A,A2,7.09,135.0 -2015-07-31,WA,1,A,A2,7.14,143.0 -2015-08-01,WA,1,A,A2,7.04,142.0 -2015-08-02,WA,1,A,A2,7.14,142.0 -2015-08-03,WA,1,A,A2,6.93,141.0 -2015-08-04,WA,1,A,A2,7.03,136.0 -2015-08-05,WA,1,A,A2,7.08,140.0 -2015-08-06,WA,1,A,A2,6.97,141.0 -2015-08-07,WA,1,A,A2,7.13,127.0 -2015-08-08,WA,1,A,A2,7.09,142.0 -2015-08-09,WA,1,A,A2,6.99,143.0 -2015-08-10,WA,1,A,A2,7.0,135.0 -2015-08-11,WA,1,A,A2,7.14,131.0 -2015-08-12,WA,1,A,A2,7.09,128.0 -2015-08-13,WA,1,A,A2,7.12,132.0 -2015-08-14,WA,1,A,A2,7.09,145.0 -2015-08-15,WA,1,A,A2,7.02,137.0 -2015-08-16,WA,1,A,A2,7.12,123.0 -2015-08-17,WA,1,A,A2,7.11,134.0 -2015-08-18,WA,1,A,A2,7.17,123.0 -2015-08-19,WA,1,A,A2,7.08,131.0 -2015-08-20,WA,1,A,A2,6.99,147.0 -2015-08-21,WA,1,A,A2,7.15,133.0 -2015-08-22,WA,1,A,A2,7.11,142.0 -2015-08-23,WA,1,A,A2,7.12,140.0 -2015-08-24,WA,1,A,A2,7.05,129.0 -2015-08-25,WA,1,A,A2,7.01,140.0 -2015-08-26,WA,1,A,A2,7.09,126.0 -2015-08-27,WA,1,A,A2,7.18,135.0 -2015-08-28,WA,1,A,A2,7.13,141.0 -2015-08-29,WA,1,A,A2,7.21,126.0 -2015-08-30,WA,1,A,A2,7.18,133.0 -2015-08-31,WA,1,A,A2,7.08,126.0 -2015-09-01,WA,1,A,A2,6.96,148.0 -2015-09-02,WA,1,A,A2,7.02,139.0 -2015-09-03,WA,1,A,A2,7.1,131.0 -2015-09-04,WA,1,A,A2,7.09,131.0 -2015-09-05,WA,1,A,A2,7.19,133.0 -2015-09-06,WA,1,A,A2,7.15,136.0 -2015-09-07,WA,1,A,A2,7.07,128.0 -2015-09-08,WA,1,A,A2,7.17,127.0 -2015-09-09,WA,1,A,A2,7.1,137.0 -2015-09-10,WA,1,A,A2,7.12,135.0 -2015-09-11,WA,1,A,A2,7.14,126.0 -2015-09-12,WA,1,A,A2,7.05,140.0 -2015-09-13,WA,1,A,A2,7.12,141.0 -2015-09-14,WA,1,A,A2,7.09,130.0 -2015-09-15,WA,1,A,A2,7.17,140.0 -2015-09-16,WA,1,A,A2,7.06,139.0 -2015-09-17,WA,1,A,A2,7.08,142.0 -2015-09-18,WA,1,A,A2,7.21,140.0 -2015-09-19,WA,1,A,A2,7.05,133.0 -2015-09-20,WA,1,A,A2,7.14,144.0 -2015-09-21,WA,1,A,A2,7.08,128.0 -2015-09-22,WA,1,A,A2,7.06,123.0 -2015-09-23,WA,1,A,A2,7.15,131.0 -2015-09-24,WA,1,A,A2,7.17,127.0 -2015-09-25,WA,1,A,A2,7.11,140.0 -2015-09-26,WA,1,A,A2,7.2,142.0 -2015-09-27,WA,1,A,A2,6.95,132.0 -2015-09-28,WA,1,A,A2,7.16,129.0 -2015-09-29,WA,1,A,A2,7.03,128.0 -2015-09-30,WA,1,A,A2,7.14,132.0 -2015-10-01,WA,1,A,A2,7.03,130.0 -2015-10-02,WA,1,A,A2,7.09,138.0 -2015-10-03,WA,1,A,A2,7.02,138.0 -2015-10-04,WA,1,A,A2,7.11,126.0 -2015-10-05,WA,1,A,A2,7.17,134.0 -2015-10-06,WA,1,A,A2,6.95,143.0 -2015-10-07,WA,1,A,A2,7.05,141.0 -2015-10-08,WA,1,A,A2,7.0,144.0 -2015-10-09,WA,1,A,A2,6.93,140.0 -2015-10-10,WA,1,A,A2,7.15,136.0 -2015-10-11,WA,1,A,A2,7.17,137.0 -2015-10-12,WA,1,A,A2,7.17,125.0 -2015-10-13,WA,1,A,A2,7.0,142.0 -2015-10-14,WA,1,A,A2,7.03,143.0 -2015-10-15,WA,1,A,A2,7.07,136.0 -2015-10-16,WA,1,A,A2,7.15,136.0 -2015-10-17,WA,1,A,A2,7.11,149.0 -2015-10-18,WA,1,A,A2,6.94,148.0 -2015-10-19,WA,1,A,A2,7.22,120.0 -2015-10-20,WA,1,A,A2,7.09,135.0 -2015-10-21,WA,1,A,A2,7.06,128.0 -2015-10-22,WA,1,A,A2,6.97,151.0 -2015-10-23,WA,1,A,A2,7.05,139.0 -2015-10-24,WA,1,A,A2,6.98,146.0 -2015-10-25,WA,1,A,A2,7.13,146.0 -2015-10-26,WA,1,A,A2,7.17,130.0 -2015-10-27,WA,1,A,A2,7.12,135.0 -2015-10-28,WA,1,A,A2,7.09,136.0 -2015-10-29,WA,1,A,A2,7.21,122.0 -2015-10-30,WA,1,A,A2,7.08,140.0 -2015-10-31,WA,1,A,A2,7.01,142.0 -2015-11-01,WA,1,A,A2,7.13,139.0 -2015-11-02,WA,1,A,A2,7.03,137.0 -2015-11-03,WA,1,A,A2,7.09,130.0 -2015-11-04,WA,1,A,A2,7.16,135.0 -2015-11-05,WA,1,A,A2,7.18,140.0 -2015-11-06,WA,1,A,A2,7.04,136.0 -2015-11-07,WA,1,A,A2,7.08,138.0 -2015-11-08,WA,1,A,A2,7.08,128.0 -2015-11-09,WA,1,A,A2,7.21,128.0 -2015-11-10,WA,1,A,A2,7.1,143.0 -2015-11-11,WA,1,A,A2,7.19,127.0 -2015-11-12,WA,1,A,A2,7.13,152.0 -2015-11-13,WA,1,A,A2,6.96,141.0 -2015-11-14,WA,1,A,A2,6.95,135.0 -2015-11-15,WA,1,A,A2,7.0,143.0 -2015-11-16,WA,1,A,A2,7.04,135.0 -2015-11-17,WA,1,A,A2,7.02,139.0 -2015-11-18,WA,1,A,A2,7.05,132.0 -2015-11-19,WA,1,A,A2,7.07,139.0 -2015-11-20,WA,1,A,A2,7.18,126.0 -2015-11-21,WA,1,A,A2,7.13,135.0 -2015-11-22,WA,1,A,A2,7.07,143.0 -2015-11-23,WA,1,A,A2,7.08,134.0 -2015-11-24,WA,1,A,A2,7.13,137.0 -2015-11-25,WA,1,A,A2,7.1,140.0 -2015-11-26,WA,1,A,A2,7.08,134.0 -2015-11-27,WA,1,A,A2,7.07,134.0 -2015-11-28,WA,1,A,A2,7.04,144.0 -2015-11-29,WA,1,A,A2,7.15,126.0 -2015-11-30,WA,1,A,A2,7.06,131.0 -2015-12-01,WA,1,A,A2,7.07,134.0 -2015-12-02,WA,1,A,A2,7.19,132.0 -2015-12-03,WA,1,A,A2,6.95,139.0 -2015-12-04,WA,1,A,A2,7.02,140.0 -2015-12-05,WA,1,A,A2,7.18,131.0 -2015-12-06,WA,1,A,A2,7.08,137.0 -2015-12-07,WA,1,A,A2,7.26,122.0 -2015-12-08,WA,1,A,A2,6.96,137.0 -2015-12-09,WA,1,A,A2,7.06,132.0 -2015-12-10,WA,1,A,A2,7.12,136.0 -2015-12-11,WA,1,A,A2,7.15,140.0 -2015-12-12,WA,1,A,A2,7.19,124.0 -2015-12-13,WA,1,A,A2,7.04,148.0 -2015-12-14,WA,1,A,A2,7.22,127.0 -2015-12-15,WA,1,A,A2,6.96,130.0 -2015-12-16,WA,1,A,A2,6.98,138.0 -2015-12-17,WA,1,A,A2,7.22,129.0 -2015-12-18,WA,1,A,A2,7.16,130.0 -2015-12-19,WA,1,A,A2,7.01,136.0 -2015-12-20,WA,1,A,A2,7.18,131.0 -2015-12-21,WA,1,A,A2,7.14,133.0 -2015-12-22,WA,1,A,A2,7.15,130.0 -2015-12-23,WA,1,A,A2,7.09,132.0 -2015-12-24,WA,1,A,A2,7.19,126.0 -2015-12-25,WA,1,A,A2,7.12,135.0 -2015-12-26,WA,1,A,A2,7.16,140.0 -2015-12-27,WA,1,A,A2,7.03,155.0 -2015-12-28,WA,1,A,A2,7.12,135.0 -2015-12-29,WA,1,A,A2,7.15,134.0 -2015-12-30,WA,1,A,A2,7.17,144.0 -2015-12-31,WA,1,A,A2,7.08,132.0 -2016-01-01,WA,1,A,A2,7.04,139.0 -2016-01-02,WA,1,A,A2,6.94,149.0 -2016-01-03,WA,1,A,A2,7.21,142.0 -2016-01-04,WA,1,A,A2,7.07,140.0 -2016-01-05,WA,1,A,A2,7.09,131.0 -2016-01-06,WA,1,A,A2,7.18,123.0 -2016-01-07,WA,1,A,A2,7.02,147.0 -2016-01-08,WA,1,A,A2,7.09,132.0 -2016-01-09,WA,1,A,A2,7.08,140.0 -2016-01-10,WA,1,A,A2,7.1,133.0 -2016-01-11,WA,1,A,A2,7.13,129.0 -2016-01-12,WA,1,A,A2,7.03,142.0 -2016-01-13,WA,1,A,A2,7.12,130.0 -2016-01-14,WA,1,A,A2,7.02,141.0 -2016-01-15,WA,1,A,A2,7.08,138.0 -2016-01-16,WA,1,A,A2,7.12,140.0 -2016-01-17,WA,1,A,A2,7.15,129.0 -2016-01-18,WA,1,A,A2,7.13,122.0 -2016-01-19,WA,1,A,A2,7.21,129.0 -2016-01-20,WA,1,A,A2,7.16,132.0 -2016-01-21,WA,1,A,A2,7.11,122.0 -2016-01-22,WA,1,A,A2,7.08,126.0 -2016-01-23,WA,1,A,A2,7.14,135.0 -2016-01-24,WA,1,A,A2,7.19,135.0 -2016-01-25,WA,1,A,A2,6.99,134.0 -2016-01-26,WA,1,A,A2,6.96,135.0 -2016-01-27,WA,1,A,A2,7.1,138.0 -2016-01-28,WA,1,A,A2,7.18,126.0 -2016-01-29,WA,1,A,A2,7.08,132.0 -2016-01-30,WA,1,A,A2,7.07,133.0 -2016-01-31,WA,1,A,A2,7.18,137.0 -2016-02-01,WA,1,A,A2,7.16,118.0 -2016-02-02,WA,1,A,A2,7.03,133.0 -2016-02-03,WA,1,A,A2,7.09,127.0 -2016-02-04,WA,1,A,A2,7.19,128.0 -2016-02-05,WA,1,A,A2,7.16,132.0 -2016-02-06,WA,1,A,A2,7.21,142.0 -2016-02-07,WA,1,A,A2,7.11,126.0 -2016-02-08,WA,1,A,A2,7.08,127.0 -2016-02-09,WA,1,A,A2,7.09,137.0 -2016-02-10,WA,1,A,A2,7.05,124.0 -2016-02-11,WA,1,A,A2,7.11,138.0 -2016-02-12,WA,1,A,A2,7.06,130.0 -2016-02-13,WA,1,A,A2,7.2,130.0 -2016-02-14,WA,1,A,A2,6.95,146.0 -2016-02-15,WA,1,A,A2,7.1,137.0 -2016-02-16,WA,1,A,A2,7.18,135.0 -2016-02-17,WA,1,A,A2,7.08,127.0 -2016-02-18,WA,1,A,A2,7.09,130.0 -2016-02-19,WA,1,A,A2,7.14,135.0 -2016-02-20,WA,1,A,A2,7.25,133.0 -2016-02-21,WA,1,A,A2,7.09,135.0 -2016-02-22,WA,1,A,A2,7.11,134.0 -2016-02-23,WA,1,A,A2,6.96,138.0 -2016-02-24,WA,1,A,A2,7.02,135.0 -2016-02-25,WA,1,A,A2,7.12,132.0 -2016-02-26,WA,1,A,A2,7.05,142.0 -2016-02-27,WA,1,A,A2,7.12,138.0 -2016-02-28,WA,1,A,A2,7.04,141.0 -2016-02-29,WA,1,A,A2,7.06,137.0 -2016-03-01,WA,1,A,A2,6.92,144.0 -2016-03-02,WA,1,A,A2,7.14,138.0 -2016-03-03,WA,1,A,A2,7.23,133.0 -2016-03-04,WA,1,A,A2,7.07,131.0 -2016-03-05,WA,1,A,A2,6.95,153.0 -2016-03-06,WA,1,A,A2,7.12,137.0 -2016-03-07,WA,1,A,A2,7.08,136.0 -2016-03-08,WA,1,A,A2,7.09,125.0 -2016-03-09,WA,1,A,A2,7.08,139.0 -2016-03-10,WA,1,A,A2,7.13,139.0 -2016-03-11,WA,1,A,A2,7.14,126.0 -2016-03-12,WA,1,A,A2,7.05,146.0 -2016-03-13,WA,1,A,A2,7.15,147.0 -2016-03-14,WA,1,A,A2,7.08,126.0 -2016-03-15,WA,1,A,A2,7.02,146.0 -2016-03-16,WA,1,A,A2,7.0,122.0 -2016-03-17,WA,1,A,A2,7.18,129.0 -2016-03-18,WA,1,A,A2,7.06,133.0 -2016-03-19,WA,1,A,A2,7.01,141.0 -2016-03-20,WA,1,A,A2,7.1,124.0 -2016-03-21,WA,1,A,A2,7.06,127.0 -2016-03-22,WA,1,A,A2,7.05,131.0 -2016-03-23,WA,1,A,A2,7.08,136.0 -2016-03-24,WA,1,A,A2,7.04,141.0 -2016-03-25,WA,1,A,A2,7.2,136.0 -2016-03-26,WA,1,A,A2,7.14,132.0 -2016-03-27,WA,1,A,A2,7.19,129.0 -2016-03-28,WA,1,A,A2,7.22,127.0 -2016-03-29,WA,1,A,A2,7.13,129.0 -2016-03-30,WA,1,A,A2,7.08,135.0 -2016-03-31,WA,1,A,A2,6.99,148.0 -2016-04-01,WA,1,A,A2,7.13,139.0 -2016-04-02,WA,1,A,A2,6.95,136.0 -2016-04-03,WA,1,A,A2,7.18,136.0 -2016-04-04,WA,1,A,A2,7.19,129.0 -2016-04-05,WA,1,A,A2,7.0,135.0 -2016-04-06,WA,1,A,A2,7.06,126.0 -2016-04-07,WA,1,A,A2,7.09,130.0 -2016-04-08,WA,1,A,A2,7.06,135.0 -2016-04-09,WA,1,A,A2,7.16,139.0 -2016-04-10,WA,1,A,A2,7.23,139.0 -2016-04-11,WA,1,A,A2,7.01,126.0 -2016-04-12,WA,1,A,A2,7.15,132.0 -2016-04-13,WA,1,A,A2,7.08,130.0 -2016-04-14,WA,1,A,A2,7.02,152.0 -2016-04-15,WA,1,A,A2,7.09,131.0 -2016-04-16,WA,1,A,A2,7.04,145.0 -2016-04-17,WA,1,A,A2,7.13,137.0 -2016-04-18,WA,1,A,A2,7.1,125.0 -2016-04-19,WA,1,A,A2,7.12,125.0 -2016-04-20,WA,1,A,A2,7.03,145.0 -2016-04-21,WA,1,A,A2,7.14,129.0 -2016-04-22,WA,1,A,A2,7.14,142.0 -2016-04-23,WA,1,A,A2,7.11,140.0 -2016-04-24,WA,1,A,A2,7.15,133.0 -2016-04-25,WA,1,A,A2,7.12,135.0 -2016-04-26,WA,1,A,A2,7.13,137.0 -2016-04-27,WA,1,A,A2,7.11,130.0 -2016-04-28,WA,1,A,A2,7.23,137.0 -2016-04-29,WA,1,A,A2,7.04,137.0 -2016-04-30,WA,1,A,A2,7.07,137.0 -2016-05-01,WA,1,A,A2,7.1,139.0 -2016-05-02,WA,1,A,A2,7.04,136.0 -2016-05-03,WA,1,A,A2,7.0,136.0 -2016-05-04,WA,1,A,A2,7.07,134.0 -2016-05-05,WA,1,A,A2,7.12,130.0 -2016-05-06,WA,1,A,A2,7.03,144.0 -2016-05-07,WA,1,A,A2,7.03,145.0 -2016-05-08,WA,1,A,A2,7.07,138.0 -2016-05-09,WA,1,A,A2,7.2,132.0 -2016-05-10,WA,1,A,A2,7.18,131.0 -2016-05-11,WA,1,A,A2,7.23,137.0 -2016-05-12,WA,1,A,A2,7.14,136.0 -2016-05-13,WA,1,A,A2,7.23,134.0 -2016-05-14,WA,1,A,A2,7.07,146.0 -2016-05-15,WA,1,A,A2,7.05,132.0 -2016-05-16,WA,1,A,A2,7.12,123.0 -2016-05-17,WA,1,A,A2,7.07,137.0 -2016-05-18,WA,1,A,A2,7.14,132.0 -2016-05-19,WA,1,A,A2,7.14,128.0 -2016-05-20,WA,1,A,A2,7.12,140.0 -2016-05-21,WA,1,A,A2,7.16,132.0 -2016-05-22,WA,1,A,A2,6.99,137.0 -2016-05-23,WA,1,A,A2,7.19,131.0 -2016-05-24,WA,1,A,A2,7.18,121.0 -2016-05-25,WA,1,A,A2,7.12,137.0 -2016-05-26,WA,1,A,A2,7.09,131.0 -2016-05-27,WA,1,A,A2,7.21,133.0 -2016-05-28,WA,1,A,A2,7.0,136.0 -2016-05-29,WA,1,A,A2,7.09,131.0 -2016-05-30,WA,1,A,A2,7.11,137.0 -2016-05-31,WA,1,A,A2,7.11,132.0 -2016-06-01,WA,1,A,A2,7.04,135.0 -2016-06-02,WA,1,A,A2,7.15,135.0 -2016-06-03,WA,1,A,A2,7.09,143.0 -2016-06-04,WA,1,A,A2,7.12,151.0 -2016-06-05,WA,1,A,A2,7.09,143.0 -2016-06-06,WA,1,A,A2,6.99,139.0 -2016-06-07,WA,1,A,A2,7.15,125.0 -2016-06-08,WA,1,A,A2,7.14,130.0 -2016-06-09,WA,1,A,A2,7.03,133.0 -2016-06-10,WA,1,A,A2,7.08,143.0 -2016-06-11,WA,1,A,A2,7.08,139.0 -2016-06-12,WA,1,A,A2,6.96,147.0 -2016-06-13,WA,1,A,A2,7.18,128.0 -2016-06-14,WA,1,A,A2,7.18,140.0 -2016-06-15,WA,1,A,A2,7.06,135.0 -2016-06-16,WA,1,A,A2,7.17,126.0 -2016-06-17,WA,1,A,A2,7.03,135.0 -2016-06-18,WA,1,A,A2,7.18,138.0 -2016-06-19,WA,1,A,A2,6.88,143.0 -2016-06-20,WA,1,A,A2,7.06,132.0 -2016-06-21,WA,1,A,A2,7.06,135.0 -2016-06-22,WA,1,A,A2,7.03,139.0 -2016-06-23,WA,1,A,A2,7.04,135.0 -2016-06-24,WA,1,A,A2,7.13,138.0 -2016-06-25,WA,1,A,A2,7.15,147.0 -2016-06-26,WA,1,A,A2,6.99,150.0 -2016-06-27,WA,1,A,A2,7.16,129.0 -2016-06-28,WA,1,A,A2,6.99,150.0 -2016-06-29,WA,1,A,A2,7.14,134.0 -2016-06-30,WA,1,A,A2,7.05,138.0 -2016-07-01,WA,1,A,A2,7.02,137.0 -2016-07-02,WA,1,A,A2,7.23,128.0 -2016-07-03,WA,1,A,A2,7.23,138.0 -2016-07-04,WA,1,A,A2,7.09,138.0 -2016-07-05,WA,1,A,A2,7.02,143.0 -2016-07-06,WA,1,A,A2,7.07,137.0 -2016-07-07,WA,1,A,A2,7.03,125.0 -2016-07-08,WA,1,A,A2,7.09,129.0 -2016-07-09,WA,1,A,A2,7.08,136.0 -2016-07-10,WA,1,A,A2,7.09,137.0 -2016-07-11,WA,1,A,A2,7.08,128.0 -2016-07-12,WA,1,A,A2,7.01,134.0 -2016-07-13,WA,1,A,A2,6.98,132.0 -2016-07-14,WA,1,A,A2,7.16,130.0 -2016-07-15,WA,1,A,A2,7.11,131.0 -2016-07-16,WA,1,A,A2,7.03,142.0 -2016-07-17,WA,1,A,A2,7.09,127.0 -2016-07-18,WA,1,A,A2,7.07,138.0 -2016-07-19,WA,1,A,A2,7.12,134.0 -2016-07-20,WA,1,A,A2,7.1,139.0 -2016-07-21,WA,1,A,A2,6.99,137.0 -2016-07-22,WA,1,A,A2,7.04,132.0 -2016-07-23,WA,1,A,A2,7.08,141.0 -2016-07-24,WA,1,A,A2,7.17,139.0 -2016-07-25,WA,1,A,A2,7.11,125.0 -2016-07-26,WA,1,A,A2,7.02,128.0 -2016-07-27,WA,1,A,A2,7.14,134.0 -2015-01-05,WA,1,A,A3,7.23,23.0 -2015-01-06,WA,1,A,A3,7.32,25.0 -2015-01-07,WA,1,A,A3,7.18,26.0 -2015-01-08,WA,1,A,A3,7.27,23.0 -2015-01-09,WA,1,A,A3,7.4,20.0 -2015-01-10,WA,1,A,A3,7.37,24.0 -2015-01-11,WA,1,A,A3,7.35,22.0 -2015-01-12,WA,1,A,A3,7.27,24.0 -2015-01-13,WA,1,A,A3,7.26,23.0 -2015-01-14,WA,1,A,A3,7.38,23.0 -2015-01-15,WA,1,A,A3,7.34,23.0 -2015-01-16,WA,1,A,A3,7.36,20.0 -2015-01-17,WA,1,A,A3,7.35,25.0 -2015-01-18,WA,1,A,A3,7.24,26.0 -2015-01-19,WA,1,A,A3,7.29,24.0 -2015-01-20,WA,1,A,A3,7.36,22.0 -2015-01-21,WA,1,A,A3,7.26,23.0 -2015-01-22,WA,1,A,A3,7.21,24.0 -2015-01-23,WA,1,A,A3,7.27,23.0 -2015-01-24,WA,1,A,A3,7.29,25.0 -2015-01-25,WA,1,A,A3,7.26,26.0 -2015-01-26,WA,1,A,A3,7.27,22.0 -2015-01-27,WA,1,A,A3,7.12,25.0 -2015-01-28,WA,1,A,A3,7.2,25.0 -2015-01-29,WA,1,A,A3,7.28,25.0 -2015-01-30,WA,1,A,A3,7.34,25.0 -2015-01-31,WA,1,A,A3,7.28,24.0 -2015-02-01,WA,1,A,A3,7.33,25.0 -2015-02-02,WA,1,A,A3,7.31,23.0 -2015-02-03,WA,1,A,A3,7.29,22.0 -2015-02-04,WA,1,A,A3,7.27,24.0 -2015-02-05,WA,1,A,A3,7.12,25.0 -2015-02-06,WA,1,A,A3,7.31,23.0 -2015-02-07,WA,1,A,A3,7.25,23.0 -2015-02-08,WA,1,A,A3,7.4,23.0 -2015-02-09,WA,1,A,A3,7.32,23.0 -2015-02-10,WA,1,A,A3,7.28,24.0 -2015-02-11,WA,1,A,A3,7.4,24.0 -2015-02-12,WA,1,A,A3,7.34,24.0 -2015-02-13,WA,1,A,A3,7.29,22.0 -2015-02-14,WA,1,A,A3,7.27,25.0 -2015-02-15,WA,1,A,A3,7.26,25.0 -2015-02-16,WA,1,A,A3,7.26,23.0 -2015-02-17,WA,1,A,A3,7.34,22.0 -2015-02-18,WA,1,A,A3,7.21,26.0 -2015-02-19,WA,1,A,A3,7.15,25.0 -2015-02-20,WA,1,A,A3,7.26,23.0 -2015-02-21,WA,1,A,A3,7.39,24.0 -2015-02-22,WA,1,A,A3,7.33,23.0 -2015-02-23,WA,1,A,A3,7.23,24.0 -2015-02-24,WA,1,A,A3,7.38,20.0 -2015-02-25,WA,1,A,A3,7.35,23.0 -2015-02-26,WA,1,A,A3,7.29,24.0 -2015-02-27,WA,1,A,A3,7.13,26.0 -2015-02-28,WA,1,A,A3,7.27,24.0 -2015-03-01,WA,1,A,A3,7.2,25.0 -2015-03-02,WA,1,A,A3,7.22,24.0 -2015-03-03,WA,1,A,A3,7.26,23.0 -2015-03-04,WA,1,A,A3,7.29,24.0 -2015-03-05,WA,1,A,A3,7.18,25.0 -2015-03-06,WA,1,A,A3,7.36,22.0 -2015-03-07,WA,1,A,A3,7.22,25.0 -2015-03-08,WA,1,A,A3,7.24,24.0 -2015-03-09,WA,1,A,A3,7.27,22.0 -2015-03-10,WA,1,A,A3,7.24,23.0 -2015-03-11,WA,1,A,A3,7.32,21.0 -2015-03-12,WA,1,A,A3,7.32,23.0 -2015-03-13,WA,1,A,A3,7.24,24.0 -2015-03-14,WA,1,A,A3,7.28,23.0 -2015-03-15,WA,1,A,A3,7.41,23.0 -2015-03-16,WA,1,A,A3,7.31,22.0 -2015-03-17,WA,1,A,A3,7.31,23.0 -2015-03-18,WA,1,A,A3,7.36,22.0 -2015-03-19,WA,1,A,A3,7.22,24.0 -2015-03-20,WA,1,A,A3,7.26,22.0 -2015-03-21,WA,1,A,A3,7.25,27.0 -2015-03-22,WA,1,A,A3,7.32,23.0 -2015-03-23,WA,1,A,A3,7.37,22.0 -2015-03-24,WA,1,A,A3,7.31,24.0 -2015-03-25,WA,1,A,A3,7.19,24.0 -2015-03-26,WA,1,A,A3,7.29,22.0 -2015-03-27,WA,1,A,A3,7.22,25.0 -2015-03-28,WA,1,A,A3,7.25,23.0 -2015-03-29,WA,1,A,A3,7.24,24.0 -2015-03-30,WA,1,A,A3,7.23,24.0 -2015-03-31,WA,1,A,A3,7.28,24.0 -2015-04-01,WA,1,A,A3,7.25,23.0 -2015-04-02,WA,1,A,A3,7.21,23.0 -2015-04-03,WA,1,A,A3,7.3,23.0 -2015-04-04,WA,1,A,A3,7.23,25.0 -2015-04-05,WA,1,A,A3,7.32,21.0 -2015-04-06,WA,1,A,A3,7.21,25.0 -2015-04-07,WA,1,A,A3,7.32,21.0 -2015-04-08,WA,1,A,A3,7.25,27.0 -2015-04-09,WA,1,A,A3,7.32,22.0 -2015-04-10,WA,1,A,A3,7.41,22.0 -2015-04-11,WA,1,A,A3,7.25,26.0 -2015-04-12,WA,1,A,A3,7.19,25.0 -2015-04-13,WA,1,A,A3,7.29,22.0 -2015-04-14,WA,1,A,A3,7.16,27.0 -2015-04-15,WA,1,A,A3,7.39,21.0 -2015-04-16,WA,1,A,A3,7.39,21.0 -2015-04-17,WA,1,A,A3,7.3,23.0 -2015-04-18,WA,1,A,A3,7.34,24.0 -2015-04-19,WA,1,A,A3,7.3,25.0 -2015-04-20,WA,1,A,A3,7.41,24.0 -2015-04-21,WA,1,A,A3,7.22,24.0 -2015-04-22,WA,1,A,A3,7.46,22.0 -2015-04-23,WA,1,A,A3,7.22,23.0 -2015-04-24,WA,1,A,A3,7.35,24.0 -2015-04-25,WA,1,A,A3,7.28,24.0 -2015-04-26,WA,1,A,A3,7.3,24.0 -2015-04-27,WA,1,A,A3,7.3,22.0 -2015-04-28,WA,1,A,A3,7.24,27.0 -2015-04-29,WA,1,A,A3,7.28,23.0 -2015-04-30,WA,1,A,A3,7.17,24.0 -2015-05-01,WA,1,A,A3,7.28,24.0 -2015-05-02,WA,1,A,A3,7.31,27.0 -2015-05-03,WA,1,A,A3,7.3,22.0 -2015-05-04,WA,1,A,A3,7.28,19.0 -2015-05-05,WA,1,A,A3,7.33,21.0 -2015-05-06,WA,1,A,A3,7.25,23.0 -2015-05-07,WA,1,A,A3,7.35,21.0 -2015-05-08,WA,1,A,A3,7.14,26.0 -2015-05-09,WA,1,A,A3,7.3,23.0 -2015-05-10,WA,1,A,A3,7.21,26.0 -2015-05-11,WA,1,A,A3,7.26,22.0 -2015-05-12,WA,1,A,A3,7.35,24.0 -2015-05-13,WA,1,A,A3,7.21,22.0 -2015-05-14,WA,1,A,A3,7.26,23.0 -2015-05-15,WA,1,A,A3,7.35,25.0 -2015-05-16,WA,1,A,A3,7.39,23.0 -2015-05-17,WA,1,A,A3,7.25,25.0 -2015-05-18,WA,1,A,A3,7.23,24.0 -2015-05-19,WA,1,A,A3,7.13,28.0 -2015-05-20,WA,1,A,A3,7.3,24.0 -2015-05-21,WA,1,A,A3,7.31,21.0 -2015-05-22,WA,1,A,A3,7.28,23.0 -2015-05-23,WA,1,A,A3,7.3,23.0 -2015-05-24,WA,1,A,A3,7.24,26.0 -2015-05-25,WA,1,A,A3,7.25,22.0 -2015-05-26,WA,1,A,A3,7.23,22.0 -2015-05-27,WA,1,A,A3,7.31,23.0 -2015-05-28,WA,1,A,A3,7.21,25.0 -2015-05-29,WA,1,A,A3,7.3,25.0 -2015-05-30,WA,1,A,A3,7.31,24.0 -2015-05-31,WA,1,A,A3,7.14,27.0 -2015-06-01,WA,1,A,A3,7.33,22.0 -2015-06-02,WA,1,A,A3,7.22,24.0 -2015-06-03,WA,1,A,A3,7.37,23.0 -2015-06-04,WA,1,A,A3,7.29,23.0 -2015-06-05,WA,1,A,A3,7.28,22.0 -2015-06-06,WA,1,A,A3,7.27,24.0 -2015-06-07,WA,1,A,A3,7.31,24.0 -2015-06-08,WA,1,A,A3,7.27,21.0 -2015-06-09,WA,1,A,A3,7.17,24.0 -2015-06-10,WA,1,A,A3,7.3,23.0 -2015-06-11,WA,1,A,A3,7.22,23.0 -2015-06-12,WA,1,A,A3,7.3,24.0 -2015-06-13,WA,1,A,A3,7.27,26.0 -2015-06-14,WA,1,A,A3,7.28,26.0 -2015-06-15,WA,1,A,A3,7.15,26.0 -2015-06-16,WA,1,A,A3,7.23,25.0 -2015-06-17,WA,1,A,A3,7.2,25.0 -2015-06-18,WA,1,A,A3,7.21,26.0 -2015-06-19,WA,1,A,A3,7.22,26.0 -2015-06-20,WA,1,A,A3,7.2,25.0 -2015-06-21,WA,1,A,A3,7.23,23.0 -2015-06-22,WA,1,A,A3,7.37,22.0 -2015-06-23,WA,1,A,A3,7.27,23.0 -2015-06-24,WA,1,A,A3,7.35,23.0 -2015-06-25,WA,1,A,A3,7.17,24.0 -2015-06-26,WA,1,A,A3,7.43,21.0 -2015-06-27,WA,1,A,A3,7.29,24.0 -2015-06-28,WA,1,A,A3,7.3,22.0 -2015-06-29,WA,1,A,A3,7.28,23.0 -2015-06-30,WA,1,A,A3,7.36,24.0 -2015-07-01,WA,1,A,A3,7.33,20.0 -2015-07-02,WA,1,A,A3,7.24,25.0 -2015-07-03,WA,1,A,A3,7.44,23.0 -2015-07-04,WA,1,A,A3,7.27,23.0 -2015-07-05,WA,1,A,A3,7.38,24.0 -2015-07-06,WA,1,A,A3,7.31,23.0 -2015-07-07,WA,1,A,A3,7.29,21.0 -2015-07-08,WA,1,A,A3,7.23,24.0 -2015-07-09,WA,1,A,A3,7.27,21.0 -2015-07-10,WA,1,A,A3,7.33,22.0 -2015-07-11,WA,1,A,A3,7.31,25.0 -2015-07-12,WA,1,A,A3,7.29,23.0 -2015-07-13,WA,1,A,A3,7.42,21.0 -2015-07-14,WA,1,A,A3,7.35,23.0 -2015-07-15,WA,1,A,A3,7.23,26.0 -2015-07-16,WA,1,A,A3,7.23,24.0 -2015-07-17,WA,1,A,A3,7.31,21.0 -2015-07-18,WA,1,A,A3,7.26,26.0 -2015-07-19,WA,1,A,A3,7.3,24.0 -2015-07-20,WA,1,A,A3,7.28,21.0 -2015-07-21,WA,1,A,A3,7.26,21.0 -2015-07-22,WA,1,A,A3,7.18,23.0 -2015-07-23,WA,1,A,A3,7.16,26.0 -2015-07-24,WA,1,A,A3,7.27,23.0 -2015-07-25,WA,1,A,A3,7.21,25.0 -2015-07-26,WA,1,A,A3,7.37,24.0 -2015-07-27,WA,1,A,A3,7.2,24.0 -2015-07-28,WA,1,A,A3,7.34,23.0 -2015-07-29,WA,1,A,A3,7.16,25.0 -2015-07-30,WA,1,A,A3,7.32,24.0 -2015-07-31,WA,1,A,A3,7.25,24.0 -2015-08-01,WA,1,A,A3,7.23,25.0 -2015-08-02,WA,1,A,A3,7.23,26.0 -2015-08-03,WA,1,A,A3,7.31,23.0 -2015-08-04,WA,1,A,A3,7.24,25.0 -2015-08-05,WA,1,A,A3,7.2,25.0 -2015-08-06,WA,1,A,A3,7.3,25.0 -2015-08-07,WA,1,A,A3,7.23,27.0 -2015-08-08,WA,1,A,A3,7.2,26.0 -2015-08-09,WA,1,A,A3,7.36,22.0 -2015-08-10,WA,1,A,A3,7.36,20.0 -2015-08-11,WA,1,A,A3,7.28,23.0 -2015-08-12,WA,1,A,A3,7.26,25.0 -2015-08-13,WA,1,A,A3,7.36,23.0 -2015-08-14,WA,1,A,A3,7.24,23.0 -2015-08-15,WA,1,A,A3,7.37,24.0 -2015-08-16,WA,1,A,A3,7.18,27.0 -2015-08-17,WA,1,A,A3,7.23,22.0 -2015-08-18,WA,1,A,A3,7.33,21.0 -2015-08-19,WA,1,A,A3,7.3,24.0 -2015-08-20,WA,1,A,A3,7.25,25.0 -2015-08-21,WA,1,A,A3,7.24,23.0 -2015-08-22,WA,1,A,A3,7.29,23.0 -2015-08-23,WA,1,A,A3,7.32,22.0 -2015-08-24,WA,1,A,A3,7.37,21.0 -2015-08-25,WA,1,A,A3,7.37,23.0 -2015-08-26,WA,1,A,A3,7.22,23.0 -2015-08-27,WA,1,A,A3,7.17,26.0 -2015-08-28,WA,1,A,A3,7.35,22.0 -2015-08-29,WA,1,A,A3,7.4,24.0 -2015-08-30,WA,1,A,A3,7.32,23.0 -2015-08-31,WA,1,A,A3,7.25,24.0 -2015-09-01,WA,1,A,A3,7.31,23.0 -2015-09-02,WA,1,A,A3,7.28,24.0 -2015-09-03,WA,1,A,A3,7.21,25.0 -2015-09-04,WA,1,A,A3,7.35,22.0 -2015-09-05,WA,1,A,A3,7.24,25.0 -2015-09-06,WA,1,A,A3,7.32,24.0 -2015-09-07,WA,1,A,A3,7.32,24.0 -2015-09-08,WA,1,A,A3,7.31,24.0 -2015-09-09,WA,1,A,A3,7.31,20.0 -2015-09-10,WA,1,A,A3,7.4,22.0 -2015-09-11,WA,1,A,A3,7.19,26.0 -2015-09-12,WA,1,A,A3,7.2,25.0 -2015-09-13,WA,1,A,A3,7.44,23.0 -2015-09-14,WA,1,A,A3,7.33,21.0 -2015-09-15,WA,1,A,A3,7.38,21.0 -2015-09-16,WA,1,A,A3,7.26,22.0 -2015-09-17,WA,1,A,A3,7.26,23.0 -2015-09-18,WA,1,A,A3,7.2,27.0 -2015-09-19,WA,1,A,A3,7.29,25.0 -2015-09-20,WA,1,A,A3,7.25,23.0 -2015-09-21,WA,1,A,A3,7.3,23.0 -2015-09-22,WA,1,A,A3,7.13,26.0 -2015-09-23,WA,1,A,A3,7.32,22.0 -2015-09-24,WA,1,A,A3,7.28,24.0 -2015-09-25,WA,1,A,A3,7.39,23.0 -2015-09-26,WA,1,A,A3,7.12,27.0 -2015-09-27,WA,1,A,A3,7.39,22.0 -2015-09-28,WA,1,A,A3,7.34,23.0 -2015-09-29,WA,1,A,A3,7.42,20.0 -2015-09-30,WA,1,A,A3,7.21,23.0 -2015-10-01,WA,1,A,A3,7.27,25.0 -2015-10-02,WA,1,A,A3,7.18,26.0 -2015-10-03,WA,1,A,A3,7.19,28.0 -2015-10-04,WA,1,A,A3,7.26,24.0 -2015-10-05,WA,1,A,A3,7.23,23.0 -2015-10-06,WA,1,A,A3,7.33,22.0 -2015-10-07,WA,1,A,A3,7.26,22.0 -2015-10-08,WA,1,A,A3,7.29,22.0 -2015-10-09,WA,1,A,A3,7.31,23.0 -2015-10-10,WA,1,A,A3,7.38,23.0 -2015-10-11,WA,1,A,A3,7.26,24.0 -2015-10-12,WA,1,A,A3,7.32,24.0 -2015-10-13,WA,1,A,A3,7.28,22.0 -2015-10-14,WA,1,A,A3,7.27,22.0 -2015-10-15,WA,1,A,A3,7.28,24.0 -2015-10-16,WA,1,A,A3,7.2,26.0 -2015-10-17,WA,1,A,A3,7.32,25.0 -2015-10-18,WA,1,A,A3,7.34,23.0 -2015-10-19,WA,1,A,A3,7.35,20.0 -2015-10-20,WA,1,A,A3,7.28,21.0 -2015-10-21,WA,1,A,A3,7.19,23.0 -2015-10-22,WA,1,A,A3,7.24,24.0 -2015-10-23,WA,1,A,A3,7.32,21.0 -2015-10-24,WA,1,A,A3,7.28,24.0 -2015-10-25,WA,1,A,A3,7.2,26.0 -2015-10-26,WA,1,A,A3,7.36,22.0 -2015-10-27,WA,1,A,A3,7.29,25.0 -2015-10-28,WA,1,A,A3,7.16,23.0 -2015-10-29,WA,1,A,A3,7.29,23.0 -2015-10-30,WA,1,A,A3,7.47,22.0 -2015-10-31,WA,1,A,A3,7.22,26.0 -2015-11-01,WA,1,A,A3,7.34,24.0 -2015-11-02,WA,1,A,A3,7.41,21.0 -2015-11-03,WA,1,A,A3,7.27,24.0 -2015-11-04,WA,1,A,A3,7.27,23.0 -2015-11-05,WA,1,A,A3,7.28,23.0 -2015-11-06,WA,1,A,A3,7.25,25.0 -2015-11-07,WA,1,A,A3,7.32,25.0 -2015-11-08,WA,1,A,A3,7.46,21.0 -2015-11-09,WA,1,A,A3,7.35,23.0 -2015-11-10,WA,1,A,A3,7.37,23.0 -2015-11-11,WA,1,A,A3,7.18,23.0 -2015-11-12,WA,1,A,A3,7.34,22.0 -2015-11-13,WA,1,A,A3,7.23,25.0 -2015-11-14,WA,1,A,A3,7.25,25.0 -2015-11-15,WA,1,A,A3,7.23,22.0 -2015-11-16,WA,1,A,A3,7.27,23.0 -2015-11-17,WA,1,A,A3,7.35,24.0 -2015-11-18,WA,1,A,A3,7.4,23.0 -2015-11-19,WA,1,A,A3,7.28,24.0 -2015-11-20,WA,1,A,A3,7.36,23.0 -2015-11-21,WA,1,A,A3,7.23,25.0 -2015-11-22,WA,1,A,A3,7.32,24.0 -2015-11-23,WA,1,A,A3,7.29,24.0 -2015-11-24,WA,1,A,A3,7.2,25.0 -2015-11-25,WA,1,A,A3,7.27,24.0 -2015-11-26,WA,1,A,A3,7.3,22.0 -2015-11-27,WA,1,A,A3,7.24,24.0 -2015-11-28,WA,1,A,A3,7.26,27.0 -2015-11-29,WA,1,A,A3,7.27,23.0 -2015-11-30,WA,1,A,A3,7.37,24.0 -2015-12-01,WA,1,A,A3,7.2,23.0 -2015-12-02,WA,1,A,A3,7.19,25.0 -2015-12-03,WA,1,A,A3,7.14,25.0 -2015-12-04,WA,1,A,A3,7.15,25.0 -2015-12-05,WA,1,A,A3,7.38,24.0 -2015-12-06,WA,1,A,A3,7.37,22.0 -2015-12-07,WA,1,A,A3,7.14,26.0 -2015-12-08,WA,1,A,A3,7.31,21.0 -2015-12-09,WA,1,A,A3,7.35,22.0 -2015-12-10,WA,1,A,A3,7.24,23.0 -2015-12-11,WA,1,A,A3,7.28,24.0 -2015-12-12,WA,1,A,A3,7.23,26.0 -2015-12-13,WA,1,A,A3,7.17,26.0 -2015-12-14,WA,1,A,A3,7.33,23.0 -2015-12-15,WA,1,A,A3,7.27,23.0 -2015-12-16,WA,1,A,A3,7.31,21.0 -2015-12-17,WA,1,A,A3,7.28,25.0 -2015-12-18,WA,1,A,A3,7.35,23.0 -2015-12-19,WA,1,A,A3,7.25,26.0 -2015-12-20,WA,1,A,A3,7.16,26.0 -2015-12-21,WA,1,A,A3,7.21,25.0 -2015-12-22,WA,1,A,A3,7.33,23.0 -2015-12-23,WA,1,A,A3,7.32,24.0 -2015-12-24,WA,1,A,A3,7.34,24.0 -2015-12-25,WA,1,A,A3,7.31,23.0 -2015-12-26,WA,1,A,A3,7.28,22.0 -2015-12-27,WA,1,A,A3,7.2,27.0 -2015-12-28,WA,1,A,A3,7.24,24.0 -2015-12-29,WA,1,A,A3,7.29,23.0 -2015-12-30,WA,1,A,A3,7.18,23.0 -2015-12-31,WA,1,A,A3,7.35,21.0 -2016-01-01,WA,1,A,A3,7.29,25.0 -2016-01-02,WA,1,A,A3,7.25,24.0 -2016-01-03,WA,1,A,A3,7.39,23.0 -2016-01-04,WA,1,A,A3,7.35,20.0 -2016-01-05,WA,1,A,A3,7.19,24.0 -2016-01-06,WA,1,A,A3,7.36,22.0 -2016-01-07,WA,1,A,A3,7.22,25.0 -2016-01-08,WA,1,A,A3,7.34,24.0 -2016-01-09,WA,1,A,A3,7.34,22.0 -2016-01-10,WA,1,A,A3,7.3,25.0 -2016-01-11,WA,1,A,A3,7.3,23.0 -2016-01-12,WA,1,A,A3,7.28,23.0 -2016-01-13,WA,1,A,A3,7.24,24.0 -2016-01-14,WA,1,A,A3,7.38,24.0 -2016-01-15,WA,1,A,A3,7.27,26.0 -2016-01-16,WA,1,A,A3,7.24,23.0 -2016-01-17,WA,1,A,A3,7.2,25.0 -2016-01-18,WA,1,A,A3,7.28,25.0 -2016-01-19,WA,1,A,A3,7.25,24.0 -2016-01-20,WA,1,A,A3,7.3,23.0 -2016-01-21,WA,1,A,A3,7.29,24.0 -2016-01-22,WA,1,A,A3,7.33,22.0 -2016-01-23,WA,1,A,A3,7.22,26.0 -2016-01-24,WA,1,A,A3,7.14,27.0 -2016-01-25,WA,1,A,A3,7.33,23.0 -2016-01-26,WA,1,A,A3,7.32,23.0 -2016-01-27,WA,1,A,A3,7.33,24.0 -2016-01-28,WA,1,A,A3,7.3,21.0 -2016-01-29,WA,1,A,A3,7.32,23.0 -2016-01-30,WA,1,A,A3,7.19,26.0 -2016-01-31,WA,1,A,A3,7.35,23.0 -2016-02-01,WA,1,A,A3,7.27,27.0 -2016-02-02,WA,1,A,A3,7.4,21.0 -2016-02-03,WA,1,A,A3,7.31,23.0 -2016-02-04,WA,1,A,A3,7.31,22.0 -2016-02-05,WA,1,A,A3,7.37,20.0 -2016-02-06,WA,1,A,A3,7.33,24.0 -2016-02-07,WA,1,A,A3,7.17,25.0 -2016-02-08,WA,1,A,A3,7.28,21.0 -2016-02-09,WA,1,A,A3,7.31,23.0 -2016-02-10,WA,1,A,A3,7.27,24.0 -2016-02-11,WA,1,A,A3,7.4,21.0 -2016-02-12,WA,1,A,A3,7.22,24.0 -2016-02-13,WA,1,A,A3,7.24,27.0 -2016-02-14,WA,1,A,A3,7.23,24.0 -2016-02-15,WA,1,A,A3,7.32,21.0 -2016-02-16,WA,1,A,A3,7.21,24.0 -2016-02-17,WA,1,A,A3,7.4,21.0 -2016-02-18,WA,1,A,A3,7.32,22.0 -2016-02-19,WA,1,A,A3,7.41,22.0 -2016-02-20,WA,1,A,A3,7.24,26.0 -2016-02-21,WA,1,A,A3,7.41,23.0 -2016-02-22,WA,1,A,A3,7.42,19.0 -2016-02-23,WA,1,A,A3,7.31,22.0 -2016-02-24,WA,1,A,A3,7.17,25.0 -2016-02-25,WA,1,A,A3,7.22,21.0 -2016-02-26,WA,1,A,A3,7.23,25.0 -2016-02-27,WA,1,A,A3,7.23,23.0 -2016-02-28,WA,1,A,A3,7.36,21.0 -2016-02-29,WA,1,A,A3,7.25,23.0 -2016-03-01,WA,1,A,A3,7.3,22.0 -2016-03-02,WA,1,A,A3,7.35,22.0 -2016-03-03,WA,1,A,A3,7.28,23.0 -2016-03-04,WA,1,A,A3,7.34,25.0 -2016-03-05,WA,1,A,A3,7.25,24.0 -2016-03-06,WA,1,A,A3,7.35,23.0 -2016-03-07,WA,1,A,A3,7.19,22.0 -2016-03-08,WA,1,A,A3,7.36,22.0 -2016-03-09,WA,1,A,A3,7.28,22.0 -2016-03-10,WA,1,A,A3,7.3,24.0 -2016-03-11,WA,1,A,A3,7.21,24.0 -2016-03-12,WA,1,A,A3,7.31,25.0 -2016-03-13,WA,1,A,A3,7.2,26.0 -2016-03-14,WA,1,A,A3,7.36,22.0 -2016-03-15,WA,1,A,A3,7.41,22.0 -2016-03-16,WA,1,A,A3,7.26,23.0 -2016-03-17,WA,1,A,A3,7.42,21.0 -2016-03-18,WA,1,A,A3,7.27,21.0 -2016-03-19,WA,1,A,A3,7.22,24.0 -2016-03-20,WA,1,A,A3,7.39,22.0 -2016-03-21,WA,1,A,A3,7.32,22.0 -2016-03-22,WA,1,A,A3,7.23,25.0 -2016-03-23,WA,1,A,A3,7.33,24.0 -2016-03-24,WA,1,A,A3,7.26,22.0 -2016-03-25,WA,1,A,A3,7.29,23.0 -2016-03-26,WA,1,A,A3,7.3,27.0 -2016-03-27,WA,1,A,A3,7.25,24.0 -2016-03-28,WA,1,A,A3,7.16,28.0 -2016-03-29,WA,1,A,A3,7.29,22.0 -2016-03-30,WA,1,A,A3,7.22,24.0 -2016-03-31,WA,1,A,A3,7.16,25.0 -2016-04-01,WA,1,A,A3,7.31,21.0 -2016-04-02,WA,1,A,A3,7.3,23.0 -2016-04-03,WA,1,A,A3,7.16,26.0 -2016-04-04,WA,1,A,A3,7.27,23.0 -2016-04-05,WA,1,A,A3,7.16,25.0 -2016-04-06,WA,1,A,A3,7.21,25.0 -2016-04-07,WA,1,A,A3,7.43,22.0 -2016-04-08,WA,1,A,A3,7.35,22.0 -2016-04-09,WA,1,A,A3,7.23,29.0 -2016-04-10,WA,1,A,A3,7.28,23.0 -2016-04-11,WA,1,A,A3,7.15,25.0 -2016-04-12,WA,1,A,A3,7.24,25.0 -2016-04-13,WA,1,A,A3,7.23,24.0 -2016-04-14,WA,1,A,A3,7.3,25.0 -2016-04-15,WA,1,A,A3,7.36,23.0 -2016-04-16,WA,1,A,A3,7.26,25.0 -2016-04-17,WA,1,A,A3,7.41,21.0 -2016-04-18,WA,1,A,A3,7.28,22.0 -2016-04-19,WA,1,A,A3,7.36,23.0 -2016-04-20,WA,1,A,A3,7.33,24.0 -2016-04-21,WA,1,A,A3,7.28,25.0 -2016-04-22,WA,1,A,A3,7.32,27.0 -2016-04-23,WA,1,A,A3,7.3,24.0 -2016-04-24,WA,1,A,A3,7.36,23.0 -2016-04-25,WA,1,A,A3,7.16,26.0 -2016-04-26,WA,1,A,A3,7.36,23.0 -2016-04-27,WA,1,A,A3,7.34,22.0 -2016-04-28,WA,1,A,A3,7.24,23.0 -2016-04-29,WA,1,A,A3,7.37,23.0 -2016-04-30,WA,1,A,A3,7.33,25.0 -2016-05-01,WA,1,A,A3,7.3,23.0 -2016-05-02,WA,1,A,A3,7.33,22.0 -2016-05-03,WA,1,A,A3,7.32,23.0 -2016-05-04,WA,1,A,A3,7.35,23.0 -2016-05-05,WA,1,A,A3,7.35,20.0 -2016-05-06,WA,1,A,A3,7.3,23.0 -2016-05-07,WA,1,A,A3,7.14,26.0 -2016-05-08,WA,1,A,A3,7.32,22.0 -2016-05-09,WA,1,A,A3,7.34,22.0 -2016-05-10,WA,1,A,A3,7.14,25.0 -2016-05-11,WA,1,A,A3,7.31,21.0 -2016-05-12,WA,1,A,A3,7.24,26.0 -2016-05-13,WA,1,A,A3,7.37,22.0 -2016-05-14,WA,1,A,A3,7.2,26.0 -2016-05-15,WA,1,A,A3,7.25,24.0 -2016-05-16,WA,1,A,A3,7.42,22.0 -2016-05-17,WA,1,A,A3,7.35,22.0 -2016-05-18,WA,1,A,A3,7.21,24.0 -2016-05-19,WA,1,A,A3,7.27,25.0 -2016-05-20,WA,1,A,A3,7.24,27.0 -2016-05-21,WA,1,A,A3,7.28,26.0 -2016-05-22,WA,1,A,A3,7.28,23.0 -2016-05-23,WA,1,A,A3,7.22,26.0 -2016-05-24,WA,1,A,A3,7.24,23.0 -2016-05-25,WA,1,A,A3,7.28,26.0 -2016-05-26,WA,1,A,A3,7.31,23.0 -2016-05-27,WA,1,A,A3,7.28,26.0 -2016-05-28,WA,1,A,A3,7.22,26.0 -2016-05-29,WA,1,A,A3,7.28,23.0 -2016-05-30,WA,1,A,A3,7.29,23.0 -2016-05-31,WA,1,A,A3,7.17,26.0 -2016-06-01,WA,1,A,A3,7.37,21.0 -2016-06-02,WA,1,A,A3,7.18,25.0 -2016-06-03,WA,1,A,A3,7.32,24.0 -2016-06-04,WA,1,A,A3,7.18,26.0 -2016-06-05,WA,1,A,A3,7.46,21.0 -2016-06-06,WA,1,A,A3,7.25,24.0 -2016-06-07,WA,1,A,A3,7.22,27.0 -2016-06-08,WA,1,A,A3,7.29,21.0 -2016-06-09,WA,1,A,A3,7.32,23.0 -2016-06-10,WA,1,A,A3,7.34,22.0 -2016-06-11,WA,1,A,A3,7.28,22.0 -2016-06-12,WA,1,A,A3,7.32,24.0 -2016-06-13,WA,1,A,A3,7.29,23.0 -2016-06-14,WA,1,A,A3,7.34,22.0 -2016-06-15,WA,1,A,A3,7.21,24.0 -2016-06-16,WA,1,A,A3,7.42,22.0 -2016-06-17,WA,1,A,A3,7.19,24.0 -2016-06-18,WA,1,A,A3,7.21,25.0 -2016-06-19,WA,1,A,A3,7.23,24.0 -2016-06-20,WA,1,A,A3,7.34,22.0 -2016-06-21,WA,1,A,A3,7.3,22.0 -2016-06-22,WA,1,A,A3,7.36,22.0 -2016-06-23,WA,1,A,A3,7.3,21.0 -2016-06-24,WA,1,A,A3,7.3,23.0 -2016-06-25,WA,1,A,A3,7.3,23.0 -2016-06-26,WA,1,A,A3,7.34,24.0 -2016-06-27,WA,1,A,A3,7.33,22.0 -2016-06-28,WA,1,A,A3,7.35,21.0 -2016-06-29,WA,1,A,A3,7.22,25.0 -2016-06-30,WA,1,A,A3,7.22,25.0 -2016-07-01,WA,1,A,A3,7.19,26.0 -2016-07-02,WA,1,A,A3,7.27,24.0 -2016-07-03,WA,1,A,A3,7.28,23.0 -2016-07-04,WA,1,A,A3,7.3,25.0 -2016-07-05,WA,1,A,A3,7.19,25.0 -2016-07-06,WA,1,A,A3,7.19,25.0 -2016-07-07,WA,1,A,A3,7.32,21.0 -2016-07-08,WA,1,A,A3,7.29,22.0 -2016-07-09,WA,1,A,A3,7.19,26.0 -2016-07-10,WA,1,A,A3,7.43,21.0 -2016-07-11,WA,1,A,A3,7.35,23.0 -2016-07-12,WA,1,A,A3,7.27,24.0 -2016-07-13,WA,1,A,A3,7.28,25.0 -2016-07-14,WA,1,A,A3,7.23,25.0 -2016-07-15,WA,1,A,A3,7.28,23.0 -2016-07-16,WA,1,A,A3,7.26,27.0 -2016-07-17,WA,1,A,A3,7.25,24.0 -2016-07-18,WA,1,A,A3,7.48,19.0 -2016-07-19,WA,1,A,A3,7.21,25.0 -2016-07-20,WA,1,A,A3,7.23,24.0 -2016-07-21,WA,1,A,A3,7.28,24.0 -2016-07-22,WA,1,A,A3,7.23,24.0 -2016-07-23,WA,1,A,A3,7.39,22.0 -2016-07-24,WA,1,A,A3,7.24,25.0 -2016-07-25,WA,1,A,A3,7.34,22.0 -2016-07-26,WA,1,A,A3,7.23,25.0 -2016-07-27,WA,1,A,A3,7.34,23.0 -2015-01-05,WA,1,B,B1,3.63,7.0 -2015-01-06,WA,1,B,B1,3.68,7.0 -2015-01-07,WA,1,B,B1,3.65,7.0 -2015-01-08,WA,1,B,B1,3.63,7.0 -2015-01-09,WA,1,B,B1,3.73,7.0 -2015-01-10,WA,1,B,B1,3.64,7.0 -2015-01-11,WA,1,B,B1,3.65,7.0 -2015-01-12,WA,1,B,B1,3.6,8.0 -2015-01-13,WA,1,B,B1,3.66,7.0 -2015-01-14,WA,1,B,B1,3.65,7.0 -2015-01-15,WA,1,B,B1,3.61,8.0 -2015-01-16,WA,1,B,B1,3.66,7.0 -2015-01-17,WA,1,B,B1,3.62,8.0 -2015-01-18,WA,1,B,B1,3.69,7.0 -2015-01-19,WA,1,B,B1,3.68,7.0 -2015-01-20,WA,1,B,B1,3.62,8.0 -2015-01-21,WA,1,B,B1,3.65,7.0 -2015-01-22,WA,1,B,B1,3.73,7.0 -2015-01-23,WA,1,B,B1,3.59,8.0 -2015-01-24,WA,1,B,B1,3.7,7.0 -2015-01-25,WA,1,B,B1,3.68,8.0 -2015-01-26,WA,1,B,B1,3.67,7.0 -2015-01-27,WA,1,B,B1,3.69,8.0 -2015-01-28,WA,1,B,B1,3.62,8.0 -2015-01-29,WA,1,B,B1,3.66,8.0 -2015-01-30,WA,1,B,B1,3.66,8.0 -2015-01-31,WA,1,B,B1,3.63,8.0 -2015-02-01,WA,1,B,B1,3.61,8.0 -2015-02-02,WA,1,B,B1,3.67,7.0 -2015-02-03,WA,1,B,B1,3.71,7.0 -2015-02-04,WA,1,B,B1,3.71,7.0 -2015-02-05,WA,1,B,B1,3.63,8.0 -2015-02-06,WA,1,B,B1,3.7,7.0 -2015-02-07,WA,1,B,B1,3.64,8.0 -2015-02-08,WA,1,B,B1,3.69,8.0 -2015-02-09,WA,1,B,B1,3.69,7.0 -2015-02-10,WA,1,B,B1,3.64,8.0 -2015-02-11,WA,1,B,B1,3.72,7.0 -2015-02-12,WA,1,B,B1,3.68,8.0 -2015-02-13,WA,1,B,B1,3.69,7.0 -2015-02-14,WA,1,B,B1,3.69,8.0 -2015-02-15,WA,1,B,B1,3.61,8.0 -2015-02-16,WA,1,B,B1,3.63,8.0 -2015-02-17,WA,1,B,B1,3.63,8.0 -2015-02-18,WA,1,B,B1,3.64,8.0 -2015-02-19,WA,1,B,B1,3.58,8.0 -2015-02-20,WA,1,B,B1,3.65,8.0 -2015-02-21,WA,1,B,B1,3.63,9.0 -2015-02-22,WA,1,B,B1,3.6,8.0 -2015-02-23,WA,1,B,B1,3.59,8.0 -2015-02-24,WA,1,B,B1,3.66,7.0 -2015-02-25,WA,1,B,B1,3.67,8.0 -2015-02-26,WA,1,B,B1,3.66,8.0 -2015-02-27,WA,1,B,B1,3.65,8.0 -2015-02-28,WA,1,B,B1,3.67,8.0 -2015-03-01,WA,1,B,B1,3.67,8.0 -2015-03-02,WA,1,B,B1,3.61,8.0 -2015-03-03,WA,1,B,B1,3.62,8.0 -2015-03-04,WA,1,B,B1,3.62,8.0 -2015-03-05,WA,1,B,B1,3.67,8.0 -2015-03-06,WA,1,B,B1,3.64,7.0 -2015-03-07,WA,1,B,B1,3.74,7.0 -2015-03-08,WA,1,B,B1,3.66,8.0 -2015-03-09,WA,1,B,B1,3.6,8.0 -2015-03-10,WA,1,B,B1,3.63,8.0 -2015-03-11,WA,1,B,B1,3.65,8.0 -2015-03-12,WA,1,B,B1,3.63,8.0 -2015-03-13,WA,1,B,B1,3.66,8.0 -2015-03-14,WA,1,B,B1,3.63,9.0 -2015-03-15,WA,1,B,B1,3.66,8.0 -2015-03-16,WA,1,B,B1,3.61,8.0 -2015-03-17,WA,1,B,B1,3.65,8.0 -2015-03-18,WA,1,B,B1,3.69,8.0 -2015-03-19,WA,1,B,B1,3.57,8.0 -2015-03-20,WA,1,B,B1,3.63,8.0 -2015-03-21,WA,1,B,B1,3.66,8.0 -2015-03-22,WA,1,B,B1,3.64,8.0 -2015-03-23,WA,1,B,B1,3.64,8.0 -2015-03-24,WA,1,B,B1,3.64,8.0 -2015-03-25,WA,1,B,B1,3.7,8.0 -2015-03-26,WA,1,B,B1,3.63,8.0 -2015-03-27,WA,1,B,B1,3.68,8.0 -2015-03-28,WA,1,B,B1,3.69,8.0 -2015-03-29,WA,1,B,B1,3.62,8.0 -2015-03-30,WA,1,B,B1,3.57,8.0 -2015-03-31,WA,1,B,B1,3.65,8.0 -2015-04-01,WA,1,B,B1,3.63,8.0 -2015-04-02,WA,1,B,B1,3.63,7.0 -2015-04-03,WA,1,B,B1,3.69,8.0 -2015-04-04,WA,1,B,B1,3.65,8.0 -2015-04-05,WA,1,B,B1,3.64,8.0 -2015-04-06,WA,1,B,B1,3.63,8.0 -2015-04-07,WA,1,B,B1,3.63,8.0 -2015-04-08,WA,1,B,B1,3.64,8.0 -2015-04-09,WA,1,B,B1,3.66,8.0 -2015-04-10,WA,1,B,B1,3.67,8.0 -2015-04-11,WA,1,B,B1,3.66,8.0 -2015-04-12,WA,1,B,B1,3.66,8.0 -2015-04-13,WA,1,B,B1,3.66,7.0 -2015-04-14,WA,1,B,B1,3.64,8.0 -2015-04-15,WA,1,B,B1,3.69,7.0 -2015-04-16,WA,1,B,B1,3.66,8.0 -2015-04-17,WA,1,B,B1,3.66,8.0 -2015-04-18,WA,1,B,B1,3.66,8.0 -2015-04-19,WA,1,B,B1,3.59,8.0 -2015-04-20,WA,1,B,B1,3.63,8.0 -2015-04-21,WA,1,B,B1,3.7,8.0 -2015-04-22,WA,1,B,B1,3.6,8.0 -2015-04-23,WA,1,B,B1,3.67,8.0 -2015-04-24,WA,1,B,B1,3.64,8.0 -2015-04-25,WA,1,B,B1,3.62,9.0 -2015-04-26,WA,1,B,B1,3.64,8.0 -2015-04-27,WA,1,B,B1,3.57,8.0 -2015-04-28,WA,1,B,B1,3.65,8.0 -2015-04-29,WA,1,B,B1,3.66,8.0 -2015-04-30,WA,1,B,B1,3.63,8.0 -2015-05-01,WA,1,B,B1,3.66,8.0 -2015-05-02,WA,1,B,B1,3.7,8.0 -2015-05-03,WA,1,B,B1,3.69,8.0 -2015-05-04,WA,1,B,B1,3.64,8.0 -2015-05-05,WA,1,B,B1,3.6,8.0 -2015-05-06,WA,1,B,B1,3.68,8.0 -2015-05-07,WA,1,B,B1,3.65,8.0 -2015-05-08,WA,1,B,B1,3.66,8.0 -2015-05-09,WA,1,B,B1,3.68,8.0 -2015-05-10,WA,1,B,B1,3.65,8.0 -2015-05-11,WA,1,B,B1,3.65,8.0 -2015-05-12,WA,1,B,B1,3.67,7.0 -2015-05-13,WA,1,B,B1,3.6,8.0 -2015-05-14,WA,1,B,B1,3.65,8.0 -2015-05-15,WA,1,B,B1,3.68,8.0 -2015-05-16,WA,1,B,B1,3.62,8.0 -2015-05-17,WA,1,B,B1,3.7,8.0 -2015-05-18,WA,1,B,B1,3.65,8.0 -2015-05-19,WA,1,B,B1,3.69,8.0 -2015-05-20,WA,1,B,B1,3.67,8.0 -2015-05-21,WA,1,B,B1,3.63,8.0 -2015-05-22,WA,1,B,B1,3.55,8.0 -2015-05-23,WA,1,B,B1,3.67,8.0 -2015-05-24,WA,1,B,B1,3.69,8.0 -2015-05-25,WA,1,B,B1,3.66,8.0 -2015-05-26,WA,1,B,B1,3.64,8.0 -2015-05-27,WA,1,B,B1,3.67,8.0 -2015-05-28,WA,1,B,B1,3.66,8.0 -2015-05-29,WA,1,B,B1,3.64,8.0 -2015-05-30,WA,1,B,B1,3.61,8.0 -2015-05-31,WA,1,B,B1,3.72,8.0 -2015-06-01,WA,1,B,B1,3.7,7.0 -2015-06-02,WA,1,B,B1,3.66,8.0 -2015-06-03,WA,1,B,B1,3.68,8.0 -2015-06-04,WA,1,B,B1,3.68,8.0 -2015-06-05,WA,1,B,B1,3.65,8.0 -2015-06-06,WA,1,B,B1,3.65,8.0 -2015-06-07,WA,1,B,B1,3.66,8.0 -2015-06-08,WA,1,B,B1,3.64,8.0 -2015-06-09,WA,1,B,B1,3.67,8.0 -2015-06-10,WA,1,B,B1,3.63,8.0 -2015-06-11,WA,1,B,B1,3.61,8.0 -2015-06-12,WA,1,B,B1,3.61,8.0 -2015-06-13,WA,1,B,B1,3.65,8.0 -2015-06-14,WA,1,B,B1,3.63,8.0 -2015-06-15,WA,1,B,B1,3.66,8.0 -2015-06-16,WA,1,B,B1,3.64,8.0 -2015-06-17,WA,1,B,B1,3.64,8.0 -2015-06-18,WA,1,B,B1,3.61,8.0 -2015-06-19,WA,1,B,B1,3.61,9.0 -2015-06-20,WA,1,B,B1,3.7,8.0 -2015-06-21,WA,1,B,B1,3.67,8.0 -2015-06-22,WA,1,B,B1,3.69,8.0 -2015-06-23,WA,1,B,B1,3.72,8.0 -2015-06-24,WA,1,B,B1,3.73,8.0 -2015-06-25,WA,1,B,B1,3.57,9.0 -2015-06-26,WA,1,B,B1,3.73,7.0 -2015-06-27,WA,1,B,B1,3.61,9.0 -2015-06-28,WA,1,B,B1,3.64,8.0 -2015-06-29,WA,1,B,B1,3.62,8.0 -2015-06-30,WA,1,B,B1,3.61,8.0 -2015-07-01,WA,1,B,B1,3.67,8.0 -2015-07-02,WA,1,B,B1,3.59,8.0 -2015-07-03,WA,1,B,B1,3.61,8.0 -2015-07-04,WA,1,B,B1,3.6,8.0 -2015-07-05,WA,1,B,B1,3.65,8.0 -2015-07-06,WA,1,B,B1,3.64,8.0 -2015-07-07,WA,1,B,B1,3.66,8.0 -2015-07-08,WA,1,B,B1,3.63,7.0 -2015-07-09,WA,1,B,B1,3.63,8.0 -2015-07-10,WA,1,B,B1,3.69,8.0 -2015-07-11,WA,1,B,B1,3.67,8.0 -2015-07-12,WA,1,B,B1,3.63,8.0 -2015-07-13,WA,1,B,B1,3.65,8.0 -2015-07-14,WA,1,B,B1,3.66,8.0 -2015-07-15,WA,1,B,B1,3.68,8.0 -2015-07-16,WA,1,B,B1,3.59,9.0 -2015-07-17,WA,1,B,B1,3.72,8.0 -2015-07-18,WA,1,B,B1,3.69,8.0 -2015-07-19,WA,1,B,B1,3.62,8.0 -2015-07-20,WA,1,B,B1,3.66,8.0 -2015-07-21,WA,1,B,B1,3.63,8.0 -2015-07-22,WA,1,B,B1,3.67,8.0 -2015-07-23,WA,1,B,B1,3.75,7.0 -2015-07-24,WA,1,B,B1,3.64,8.0 -2015-07-25,WA,1,B,B1,3.66,8.0 -2015-07-26,WA,1,B,B1,3.62,8.0 -2015-07-27,WA,1,B,B1,3.63,7.0 -2015-07-28,WA,1,B,B1,3.72,8.0 -2015-07-29,WA,1,B,B1,3.66,8.0 -2015-07-30,WA,1,B,B1,3.6,8.0 -2015-07-31,WA,1,B,B1,3.65,8.0 -2015-08-01,WA,1,B,B1,3.65,8.0 -2015-08-02,WA,1,B,B1,3.62,8.0 -2015-08-03,WA,1,B,B1,3.68,8.0 -2015-08-04,WA,1,B,B1,3.63,8.0 -2015-08-05,WA,1,B,B1,3.63,8.0 -2015-08-06,WA,1,B,B1,3.64,8.0 -2015-08-07,WA,1,B,B1,3.69,8.0 -2015-08-08,WA,1,B,B1,3.62,9.0 -2015-08-09,WA,1,B,B1,3.64,8.0 -2015-08-10,WA,1,B,B1,3.62,8.0 -2015-08-11,WA,1,B,B1,3.65,8.0 -2015-08-12,WA,1,B,B1,3.61,8.0 -2015-08-13,WA,1,B,B1,3.73,8.0 -2015-08-14,WA,1,B,B1,3.65,8.0 -2015-08-15,WA,1,B,B1,3.57,8.0 -2015-08-16,WA,1,B,B1,3.7,8.0 -2015-08-17,WA,1,B,B1,3.65,7.0 -2015-08-18,WA,1,B,B1,3.64,8.0 -2015-08-19,WA,1,B,B1,3.59,8.0 -2015-08-20,WA,1,B,B1,3.65,8.0 -2015-08-21,WA,1,B,B1,3.61,8.0 -2015-08-22,WA,1,B,B1,3.63,8.0 -2015-08-23,WA,1,B,B1,3.63,8.0 -2015-08-24,WA,1,B,B1,3.67,7.0 -2015-08-25,WA,1,B,B1,3.69,8.0 -2015-08-26,WA,1,B,B1,3.57,8.0 -2015-08-27,WA,1,B,B1,3.66,7.0 -2015-08-28,WA,1,B,B1,3.67,7.0 -2015-08-29,WA,1,B,B1,3.65,8.0 -2015-08-30,WA,1,B,B1,3.63,8.0 -2015-08-31,WA,1,B,B1,3.65,8.0 -2015-09-01,WA,1,B,B1,3.6,8.0 -2015-09-02,WA,1,B,B1,3.64,8.0 -2015-09-03,WA,1,B,B1,3.68,8.0 -2015-09-04,WA,1,B,B1,3.69,7.0 -2015-09-05,WA,1,B,B1,3.6,9.0 -2015-09-06,WA,1,B,B1,3.62,8.0 -2015-09-07,WA,1,B,B1,3.7,7.0 -2015-09-08,WA,1,B,B1,3.65,8.0 -2015-09-09,WA,1,B,B1,3.59,8.0 -2015-09-10,WA,1,B,B1,3.63,8.0 -2015-09-11,WA,1,B,B1,3.69,8.0 -2015-09-12,WA,1,B,B1,3.64,8.0 -2015-09-13,WA,1,B,B1,3.67,8.0 -2015-09-14,WA,1,B,B1,3.68,7.0 -2015-09-15,WA,1,B,B1,3.63,8.0 -2015-09-16,WA,1,B,B1,3.72,8.0 -2015-09-17,WA,1,B,B1,3.61,8.0 -2015-09-18,WA,1,B,B1,3.63,8.0 -2015-09-19,WA,1,B,B1,3.65,8.0 -2015-09-20,WA,1,B,B1,3.63,7.0 -2015-09-21,WA,1,B,B1,3.61,8.0 -2015-09-22,WA,1,B,B1,3.67,8.0 -2015-09-23,WA,1,B,B1,3.64,8.0 -2015-09-24,WA,1,B,B1,3.62,8.0 -2015-09-25,WA,1,B,B1,3.66,8.0 -2015-09-26,WA,1,B,B1,3.61,8.0 -2015-09-27,WA,1,B,B1,3.63,8.0 -2015-09-28,WA,1,B,B1,3.67,8.0 -2015-09-29,WA,1,B,B1,3.63,8.0 -2015-09-30,WA,1,B,B1,3.65,8.0 -2015-10-01,WA,1,B,B1,3.72,8.0 -2015-10-02,WA,1,B,B1,3.57,9.0 -2015-10-03,WA,1,B,B1,3.65,8.0 -2015-10-04,WA,1,B,B1,3.67,8.0 -2015-10-05,WA,1,B,B1,3.62,8.0 -2015-10-06,WA,1,B,B1,3.68,8.0 -2015-10-07,WA,1,B,B1,3.66,8.0 -2015-10-08,WA,1,B,B1,3.61,8.0 -2015-10-09,WA,1,B,B1,3.61,8.0 -2015-10-10,WA,1,B,B1,3.59,8.0 -2015-10-11,WA,1,B,B1,3.67,8.0 -2015-10-12,WA,1,B,B1,3.6,8.0 -2015-10-13,WA,1,B,B1,3.66,8.0 -2015-10-14,WA,1,B,B1,3.61,8.0 -2015-10-15,WA,1,B,B1,3.6,8.0 -2015-10-16,WA,1,B,B1,3.66,7.0 -2015-10-17,WA,1,B,B1,3.69,8.0 -2015-10-18,WA,1,B,B1,3.71,8.0 -2015-10-19,WA,1,B,B1,3.69,7.0 -2015-10-20,WA,1,B,B1,3.63,8.0 -2015-10-21,WA,1,B,B1,3.61,8.0 -2015-10-22,WA,1,B,B1,3.67,8.0 -2015-10-23,WA,1,B,B1,3.67,8.0 -2015-10-24,WA,1,B,B1,3.64,9.0 -2015-10-25,WA,1,B,B1,3.63,8.0 -2015-10-26,WA,1,B,B1,3.66,8.0 -2015-10-27,WA,1,B,B1,3.64,8.0 -2015-10-28,WA,1,B,B1,3.63,8.0 -2015-10-29,WA,1,B,B1,3.63,8.0 -2015-10-30,WA,1,B,B1,3.63,7.0 -2015-10-31,WA,1,B,B1,3.69,8.0 -2015-11-01,WA,1,B,B1,3.7,8.0 -2015-11-02,WA,1,B,B1,3.57,8.0 -2015-11-03,WA,1,B,B1,3.58,8.0 -2015-11-04,WA,1,B,B1,3.63,8.0 -2015-11-05,WA,1,B,B1,3.69,7.0 -2015-11-06,WA,1,B,B1,3.61,8.0 -2015-11-07,WA,1,B,B1,3.65,8.0 -2015-11-08,WA,1,B,B1,3.66,8.0 -2015-11-09,WA,1,B,B1,3.63,8.0 -2015-11-10,WA,1,B,B1,3.64,8.0 -2015-11-11,WA,1,B,B1,3.58,9.0 -2015-11-12,WA,1,B,B1,3.66,8.0 -2015-11-13,WA,1,B,B1,3.65,8.0 -2015-11-14,WA,1,B,B1,3.64,8.0 -2015-11-15,WA,1,B,B1,3.62,8.0 -2015-11-16,WA,1,B,B1,3.59,8.0 -2015-11-17,WA,1,B,B1,3.69,8.0 -2015-11-18,WA,1,B,B1,3.65,8.0 -2015-11-19,WA,1,B,B1,3.74,7.0 -2015-11-20,WA,1,B,B1,3.66,7.0 -2015-11-21,WA,1,B,B1,3.64,8.0 -2015-11-22,WA,1,B,B1,3.66,9.0 -2015-11-23,WA,1,B,B1,3.66,8.0 -2015-11-24,WA,1,B,B1,3.66,8.0 -2015-11-25,WA,1,B,B1,3.67,8.0 -2015-11-26,WA,1,B,B1,3.63,8.0 -2015-11-27,WA,1,B,B1,3.67,8.0 -2015-11-28,WA,1,B,B1,3.68,8.0 -2015-11-29,WA,1,B,B1,3.64,8.0 -2015-11-30,WA,1,B,B1,3.63,8.0 -2015-12-01,WA,1,B,B1,3.63,8.0 -2015-12-02,WA,1,B,B1,3.68,8.0 -2015-12-03,WA,1,B,B1,3.71,8.0 -2015-12-04,WA,1,B,B1,3.67,8.0 -2015-12-05,WA,1,B,B1,3.64,8.0 -2015-12-06,WA,1,B,B1,3.64,8.0 -2015-12-07,WA,1,B,B1,3.61,8.0 -2015-12-08,WA,1,B,B1,3.61,8.0 -2015-12-09,WA,1,B,B1,3.66,8.0 -2015-12-10,WA,1,B,B1,3.66,8.0 -2015-12-11,WA,1,B,B1,3.63,8.0 -2015-12-12,WA,1,B,B1,3.67,8.0 -2015-12-13,WA,1,B,B1,3.69,8.0 -2015-12-14,WA,1,B,B1,3.57,8.0 -2015-12-15,WA,1,B,B1,3.7,8.0 -2015-12-16,WA,1,B,B1,3.64,8.0 -2015-12-17,WA,1,B,B1,3.62,8.0 -2015-12-18,WA,1,B,B1,3.63,8.0 -2015-12-19,WA,1,B,B1,3.63,9.0 -2015-12-20,WA,1,B,B1,3.58,9.0 -2015-12-21,WA,1,B,B1,3.71,8.0 -2015-12-22,WA,1,B,B1,3.7,8.0 -2015-12-23,WA,1,B,B1,3.65,8.0 -2015-12-24,WA,1,B,B1,3.65,8.0 -2015-12-25,WA,1,B,B1,3.64,8.0 -2015-12-26,WA,1,B,B1,3.61,8.0 -2015-12-27,WA,1,B,B1,3.59,8.0 -2015-12-28,WA,1,B,B1,3.68,7.0 -2015-12-29,WA,1,B,B1,3.68,8.0 -2015-12-30,WA,1,B,B1,3.62,8.0 -2015-12-31,WA,1,B,B1,3.61,8.0 -2016-01-01,WA,1,B,B1,3.6,8.0 -2016-01-02,WA,1,B,B1,3.63,8.0 -2016-01-03,WA,1,B,B1,3.63,8.0 -2016-01-04,WA,1,B,B1,3.63,8.0 -2016-01-05,WA,1,B,B1,3.64,9.0 -2016-01-06,WA,1,B,B1,3.68,8.0 -2016-01-07,WA,1,B,B1,3.67,8.0 -2016-01-08,WA,1,B,B1,3.64,8.0 -2016-01-09,WA,1,B,B1,3.66,8.0 -2016-01-10,WA,1,B,B1,3.6,8.0 -2016-01-11,WA,1,B,B1,3.66,7.0 -2016-01-12,WA,1,B,B1,3.63,8.0 -2016-01-13,WA,1,B,B1,3.68,8.0 -2016-01-14,WA,1,B,B1,3.66,8.0 -2016-01-15,WA,1,B,B1,3.65,8.0 -2016-01-16,WA,1,B,B1,3.67,8.0 -2016-01-17,WA,1,B,B1,3.65,8.0 -2016-01-18,WA,1,B,B1,3.66,8.0 -2016-01-19,WA,1,B,B1,3.6,8.0 -2016-01-20,WA,1,B,B1,3.63,8.0 -2016-01-21,WA,1,B,B1,3.63,7.0 -2016-01-22,WA,1,B,B1,3.63,9.0 -2016-01-23,WA,1,B,B1,3.68,8.0 -2016-01-24,WA,1,B,B1,3.61,8.0 -2016-01-25,WA,1,B,B1,3.64,8.0 -2016-01-26,WA,1,B,B1,3.69,8.0 -2016-01-27,WA,1,B,B1,3.72,8.0 -2016-01-28,WA,1,B,B1,3.65,8.0 -2016-01-29,WA,1,B,B1,3.65,7.0 -2016-01-30,WA,1,B,B1,3.61,8.0 -2016-01-31,WA,1,B,B1,3.62,8.0 -2016-02-01,WA,1,B,B1,3.59,8.0 -2016-02-02,WA,1,B,B1,3.66,8.0 -2016-02-03,WA,1,B,B1,3.64,8.0 -2016-02-04,WA,1,B,B1,3.62,8.0 -2016-02-05,WA,1,B,B1,3.69,8.0 -2016-02-06,WA,1,B,B1,3.65,8.0 -2016-02-07,WA,1,B,B1,3.64,8.0 -2016-02-08,WA,1,B,B1,3.65,8.0 -2016-02-09,WA,1,B,B1,3.64,8.0 -2016-02-10,WA,1,B,B1,3.67,8.0 -2016-02-11,WA,1,B,B1,3.62,8.0 -2016-02-12,WA,1,B,B1,3.73,7.0 -2016-02-13,WA,1,B,B1,3.72,8.0 -2016-02-14,WA,1,B,B1,3.6,9.0 -2016-02-15,WA,1,B,B1,3.66,7.0 -2016-02-16,WA,1,B,B1,3.69,8.0 -2016-02-17,WA,1,B,B1,3.68,8.0 -2016-02-18,WA,1,B,B1,3.61,7.0 -2016-02-19,WA,1,B,B1,3.65,9.0 -2016-02-20,WA,1,B,B1,3.62,8.0 -2016-02-21,WA,1,B,B1,3.61,9.0 -2016-02-22,WA,1,B,B1,3.65,8.0 -2016-02-23,WA,1,B,B1,3.64,8.0 -2016-02-24,WA,1,B,B1,3.62,8.0 -2016-02-25,WA,1,B,B1,3.62,8.0 -2016-02-26,WA,1,B,B1,3.59,8.0 -2016-02-27,WA,1,B,B1,3.69,8.0 -2016-02-28,WA,1,B,B1,3.62,8.0 -2016-02-29,WA,1,B,B1,3.63,8.0 -2016-03-01,WA,1,B,B1,3.68,9.0 -2016-03-02,WA,1,B,B1,3.62,8.0 -2016-03-03,WA,1,B,B1,3.68,8.0 -2016-03-04,WA,1,B,B1,3.6,8.0 -2016-03-05,WA,1,B,B1,3.65,8.0 -2016-03-06,WA,1,B,B1,3.65,8.0 -2016-03-07,WA,1,B,B1,3.65,7.0 -2016-03-08,WA,1,B,B1,3.61,9.0 -2016-03-09,WA,1,B,B1,3.71,8.0 -2016-03-10,WA,1,B,B1,3.56,8.0 -2016-03-11,WA,1,B,B1,3.66,8.0 -2016-03-12,WA,1,B,B1,3.7,8.0 -2016-03-13,WA,1,B,B1,3.62,8.0 -2016-03-14,WA,1,B,B1,3.62,8.0 -2016-03-15,WA,1,B,B1,3.69,8.0 -2016-03-16,WA,1,B,B1,3.64,8.0 -2016-03-17,WA,1,B,B1,3.6,8.0 -2016-03-18,WA,1,B,B1,3.63,8.0 -2016-03-19,WA,1,B,B1,3.65,8.0 -2016-03-20,WA,1,B,B1,3.61,8.0 -2016-03-21,WA,1,B,B1,3.63,8.0 -2016-03-22,WA,1,B,B1,3.64,8.0 -2016-03-23,WA,1,B,B1,3.67,7.0 -2016-03-24,WA,1,B,B1,3.69,8.0 -2016-03-25,WA,1,B,B1,3.57,8.0 -2016-03-26,WA,1,B,B1,3.63,8.0 -2016-03-27,WA,1,B,B1,3.58,8.0 -2016-03-28,WA,1,B,B1,3.67,8.0 -2016-03-29,WA,1,B,B1,3.69,8.0 -2016-03-30,WA,1,B,B1,3.65,9.0 -2016-03-31,WA,1,B,B1,3.68,8.0 -2016-04-01,WA,1,B,B1,3.65,8.0 -2016-04-02,WA,1,B,B1,3.61,8.0 -2016-04-03,WA,1,B,B1,3.67,8.0 -2016-04-04,WA,1,B,B1,3.62,9.0 -2016-04-05,WA,1,B,B1,3.54,9.0 -2016-04-06,WA,1,B,B1,3.58,8.0 -2016-04-07,WA,1,B,B1,3.64,8.0 -2016-04-08,WA,1,B,B1,3.64,8.0 -2016-04-09,WA,1,B,B1,3.65,8.0 -2016-04-10,WA,1,B,B1,3.71,8.0 -2016-04-11,WA,1,B,B1,3.7,8.0 -2016-04-12,WA,1,B,B1,3.68,7.0 -2016-04-13,WA,1,B,B1,3.69,8.0 -2016-04-14,WA,1,B,B1,3.58,8.0 -2016-04-15,WA,1,B,B1,3.7,8.0 -2016-04-16,WA,1,B,B1,3.64,8.0 -2016-04-17,WA,1,B,B1,3.74,8.0 -2016-04-18,WA,1,B,B1,3.63,8.0 -2016-04-19,WA,1,B,B1,3.61,8.0 -2016-04-20,WA,1,B,B1,3.62,9.0 -2016-04-21,WA,1,B,B1,3.59,8.0 -2016-04-22,WA,1,B,B1,3.65,8.0 -2016-04-23,WA,1,B,B1,3.68,8.0 -2016-04-24,WA,1,B,B1,3.64,9.0 -2016-04-25,WA,1,B,B1,3.63,8.0 -2016-04-26,WA,1,B,B1,3.68,8.0 -2016-04-27,WA,1,B,B1,3.58,8.0 -2016-04-28,WA,1,B,B1,3.65,7.0 -2016-04-29,WA,1,B,B1,3.66,7.0 -2016-04-30,WA,1,B,B1,3.59,8.0 -2016-05-01,WA,1,B,B1,3.6,9.0 -2016-05-02,WA,1,B,B1,3.62,8.0 -2016-05-03,WA,1,B,B1,3.58,9.0 -2016-05-04,WA,1,B,B1,3.65,8.0 -2016-05-05,WA,1,B,B1,3.64,8.0 -2016-05-06,WA,1,B,B1,3.71,8.0 -2016-05-07,WA,1,B,B1,3.66,8.0 -2016-05-08,WA,1,B,B1,3.56,8.0 -2016-05-09,WA,1,B,B1,3.65,8.0 -2016-05-10,WA,1,B,B1,3.64,8.0 -2016-05-11,WA,1,B,B1,3.67,8.0 -2016-05-12,WA,1,B,B1,3.62,8.0 -2016-05-13,WA,1,B,B1,3.64,8.0 -2016-05-14,WA,1,B,B1,3.58,9.0 -2016-05-15,WA,1,B,B1,3.67,7.0 -2016-05-16,WA,1,B,B1,3.66,8.0 -2016-05-17,WA,1,B,B1,3.65,8.0 -2016-05-18,WA,1,B,B1,3.69,8.0 -2016-05-19,WA,1,B,B1,3.64,8.0 -2016-05-20,WA,1,B,B1,3.68,8.0 -2016-05-21,WA,1,B,B1,3.69,9.0 -2016-05-22,WA,1,B,B1,3.62,8.0 -2016-05-23,WA,1,B,B1,3.62,8.0 -2016-05-24,WA,1,B,B1,3.64,8.0 -2016-05-25,WA,1,B,B1,3.66,8.0 -2016-05-26,WA,1,B,B1,3.64,8.0 -2016-05-27,WA,1,B,B1,3.66,9.0 -2016-05-28,WA,1,B,B1,3.61,9.0 -2016-05-29,WA,1,B,B1,3.64,8.0 -2016-05-30,WA,1,B,B1,3.67,8.0 -2016-05-31,WA,1,B,B1,3.66,8.0 -2016-06-01,WA,1,B,B1,3.66,7.0 -2016-06-02,WA,1,B,B1,3.66,8.0 -2016-06-03,WA,1,B,B1,3.66,8.0 -2016-06-04,WA,1,B,B1,3.73,8.0 -2016-06-05,WA,1,B,B1,3.56,8.0 -2016-06-06,WA,1,B,B1,3.67,7.0 -2016-06-07,WA,1,B,B1,3.67,8.0 -2016-06-08,WA,1,B,B1,3.67,8.0 -2016-06-09,WA,1,B,B1,3.67,7.0 -2016-06-10,WA,1,B,B1,3.68,8.0 -2016-06-11,WA,1,B,B1,3.69,8.0 -2016-06-12,WA,1,B,B1,3.63,8.0 -2016-06-13,WA,1,B,B1,3.57,8.0 -2016-06-14,WA,1,B,B1,3.68,8.0 -2016-06-15,WA,1,B,B1,3.71,8.0 -2016-06-16,WA,1,B,B1,3.61,8.0 -2016-06-17,WA,1,B,B1,3.64,8.0 -2016-06-18,WA,1,B,B1,3.71,8.0 -2016-06-19,WA,1,B,B1,3.68,7.0 -2016-06-20,WA,1,B,B1,3.61,8.0 -2016-06-21,WA,1,B,B1,3.57,8.0 -2016-06-22,WA,1,B,B1,3.56,8.0 -2016-06-23,WA,1,B,B1,3.7,8.0 -2016-06-24,WA,1,B,B1,3.64,8.0 -2016-06-25,WA,1,B,B1,3.62,8.0 -2016-06-26,WA,1,B,B1,3.59,8.0 -2016-06-27,WA,1,B,B1,3.66,8.0 -2016-06-28,WA,1,B,B1,3.72,8.0 -2016-06-29,WA,1,B,B1,3.63,8.0 -2016-06-30,WA,1,B,B1,3.65,7.0 -2016-07-01,WA,1,B,B1,3.62,8.0 -2016-07-02,WA,1,B,B1,3.75,7.0 -2016-07-03,WA,1,B,B1,3.61,8.0 -2016-07-04,WA,1,B,B1,3.62,8.0 -2016-07-05,WA,1,B,B1,3.62,8.0 -2016-07-06,WA,1,B,B1,3.73,8.0 -2016-07-07,WA,1,B,B1,3.61,8.0 -2016-07-08,WA,1,B,B1,3.63,8.0 -2016-07-09,WA,1,B,B1,3.63,8.0 -2016-07-10,WA,1,B,B1,3.61,8.0 -2016-07-11,WA,1,B,B1,3.64,8.0 -2016-07-12,WA,1,B,B1,3.61,8.0 -2016-07-13,WA,1,B,B1,3.58,8.0 -2016-07-14,WA,1,B,B1,3.65,7.0 -2016-07-15,WA,1,B,B1,3.65,8.0 -2016-07-16,WA,1,B,B1,3.63,8.0 -2016-07-17,WA,1,B,B1,3.6,8.0 -2016-07-18,WA,1,B,B1,3.65,8.0 -2016-07-19,WA,1,B,B1,3.66,8.0 -2016-07-20,WA,1,B,B1,3.67,8.0 -2016-07-21,WA,1,B,B1,3.59,8.0 -2016-07-22,WA,1,B,B1,3.63,8.0 -2016-07-23,WA,1,B,B1,3.67,8.0 -2016-07-24,WA,1,B,B1,3.67,8.0 -2016-07-25,WA,1,B,B1,3.64,8.0 -2016-07-26,WA,1,B,B1,3.68,8.0 -2016-07-27,WA,1,B,B1,3.61,8.0 -2015-01-05,WA,1,B,B2,6.93,7.0 -2015-01-06,WA,1,B,B2,6.93,8.0 -2015-01-07,WA,1,B,B2,7.03,7.0 -2015-01-08,WA,1,B,B2,7.02,7.0 -2015-01-09,WA,1,B,B2,7.01,7.0 -2015-01-10,WA,1,B,B2,7.01,8.0 -2015-01-11,WA,1,B,B2,6.96,8.0 -2015-01-12,WA,1,B,B2,6.82,7.0 -2015-01-13,WA,1,B,B2,7.04,7.0 -2015-01-14,WA,1,B,B2,7.03,8.0 -2015-01-15,WA,1,B,B2,6.97,8.0 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-2015-02-17,WA,1,B,B2,6.87,8.0 -2015-02-18,WA,1,B,B2,7.03,7.0 -2015-02-19,WA,1,B,B2,6.97,8.0 -2015-02-20,WA,1,B,B2,6.94,8.0 -2015-02-21,WA,1,B,B2,7.0,8.0 -2015-02-22,WA,1,B,B2,6.88,8.0 -2015-02-23,WA,1,B,B2,7.02,8.0 -2015-02-24,WA,1,B,B2,6.87,7.0 -2015-02-25,WA,1,B,B2,6.98,7.0 -2015-02-26,WA,1,B,B2,6.9,8.0 -2015-02-27,WA,1,B,B2,7.08,7.0 -2015-02-28,WA,1,B,B2,6.9,8.0 -2015-03-01,WA,1,B,B2,6.96,7.0 -2015-03-02,WA,1,B,B2,6.95,7.0 -2015-03-03,WA,1,B,B2,6.89,8.0 -2015-03-04,WA,1,B,B2,6.85,8.0 -2015-03-05,WA,1,B,B2,6.95,7.0 -2015-03-06,WA,1,B,B2,6.91,8.0 -2015-03-07,WA,1,B,B2,6.79,8.0 -2015-03-08,WA,1,B,B2,6.99,8.0 -2015-03-09,WA,1,B,B2,6.86,7.0 -2015-03-10,WA,1,B,B2,6.84,8.0 -2015-03-11,WA,1,B,B2,6.89,8.0 -2015-03-12,WA,1,B,B2,6.91,8.0 -2015-03-13,WA,1,B,B2,6.86,8.0 -2015-03-14,WA,1,B,B2,6.95,8.0 -2015-03-15,WA,1,B,B2,7.07,8.0 -2015-03-16,WA,1,B,B2,6.99,7.0 -2015-03-17,WA,1,B,B2,6.99,7.0 -2015-03-18,WA,1,B,B2,6.95,8.0 -2015-03-19,WA,1,B,B2,6.97,8.0 -2015-03-20,WA,1,B,B2,6.93,7.0 -2015-03-21,WA,1,B,B2,6.89,7.0 -2015-03-22,WA,1,B,B2,6.91,8.0 -2015-03-23,WA,1,B,B2,6.95,8.0 -2015-03-24,WA,1,B,B2,6.97,7.0 -2015-03-25,WA,1,B,B2,6.96,8.0 -2015-03-26,WA,1,B,B2,6.93,8.0 -2015-03-27,WA,1,B,B2,6.93,7.0 -2015-03-28,WA,1,B,B2,6.85,8.0 -2015-03-29,WA,1,B,B2,6.98,8.0 -2015-03-30,WA,1,B,B2,6.98,7.0 -2015-03-31,WA,1,B,B2,6.96,8.0 -2015-04-01,WA,1,B,B2,6.87,8.0 -2015-04-02,WA,1,B,B2,6.98,8.0 -2015-04-03,WA,1,B,B2,6.91,8.0 -2015-04-04,WA,1,B,B2,7.0,8.0 -2015-04-05,WA,1,B,B2,6.96,8.0 -2015-04-06,WA,1,B,B2,6.8,7.0 -2015-04-07,WA,1,B,B2,6.91,7.0 -2015-04-08,WA,1,B,B2,6.95,7.0 -2015-04-09,WA,1,B,B2,6.85,8.0 -2015-04-10,WA,1,B,B2,6.87,8.0 -2015-04-11,WA,1,B,B2,6.92,8.0 -2015-04-12,WA,1,B,B2,7.16,8.0 -2015-04-13,WA,1,B,B2,6.96,7.0 -2015-04-14,WA,1,B,B2,6.92,7.0 -2015-04-15,WA,1,B,B2,6.98,7.0 -2015-04-16,WA,1,B,B2,6.96,7.0 -2015-04-17,WA,1,B,B2,7.01,7.0 -2015-04-18,WA,1,B,B2,6.92,7.0 -2015-04-19,WA,1,B,B2,6.99,8.0 -2015-04-20,WA,1,B,B2,6.98,7.0 -2015-04-21,WA,1,B,B2,6.96,7.0 -2015-04-22,WA,1,B,B2,6.9,8.0 -2015-04-23,WA,1,B,B2,7.04,8.0 -2015-04-24,WA,1,B,B2,6.92,8.0 -2015-04-25,WA,1,B,B2,7.03,8.0 -2015-04-26,WA,1,B,B2,6.86,8.0 -2015-04-27,WA,1,B,B2,7.03,8.0 -2015-04-28,WA,1,B,B2,6.89,8.0 -2015-04-29,WA,1,B,B2,6.87,8.0 -2015-04-30,WA,1,B,B2,7.01,8.0 -2015-05-01,WA,1,B,B2,6.95,7.0 -2015-05-02,WA,1,B,B2,7.04,8.0 -2015-05-03,WA,1,B,B2,6.72,8.0 -2015-05-04,WA,1,B,B2,6.97,7.0 -2015-05-05,WA,1,B,B2,6.87,8.0 -2015-05-06,WA,1,B,B2,7.06,8.0 -2015-05-07,WA,1,B,B2,6.92,8.0 -2015-05-08,WA,1,B,B2,6.85,7.0 -2015-05-09,WA,1,B,B2,6.97,8.0 -2015-05-10,WA,1,B,B2,6.98,8.0 -2015-05-11,WA,1,B,B2,6.85,8.0 -2015-05-12,WA,1,B,B2,6.93,7.0 -2015-05-13,WA,1,B,B2,7.12,7.0 -2015-05-14,WA,1,B,B2,6.93,8.0 -2015-05-15,WA,1,B,B2,7.02,8.0 -2015-05-16,WA,1,B,B2,6.9,8.0 -2015-05-17,WA,1,B,B2,6.86,8.0 -2015-05-18,WA,1,B,B2,6.86,8.0 -2015-05-19,WA,1,B,B2,6.94,7.0 -2015-05-20,WA,1,B,B2,6.96,7.0 -2015-05-21,WA,1,B,B2,6.89,8.0 -2015-05-22,WA,1,B,B2,6.83,8.0 -2015-05-23,WA,1,B,B2,7.04,8.0 -2015-05-24,WA,1,B,B2,6.85,8.0 -2015-05-25,WA,1,B,B2,6.89,8.0 -2015-05-26,WA,1,B,B2,6.85,8.0 -2015-05-27,WA,1,B,B2,6.94,7.0 -2015-05-28,WA,1,B,B2,6.92,8.0 -2015-05-29,WA,1,B,B2,6.93,8.0 -2015-05-30,WA,1,B,B2,6.91,9.0 -2015-05-31,WA,1,B,B2,6.97,8.0 -2015-06-01,WA,1,B,B2,7.08,8.0 -2015-06-02,WA,1,B,B2,6.96,8.0 -2015-06-03,WA,1,B,B2,6.84,8.0 -2015-06-04,WA,1,B,B2,6.95,8.0 -2015-06-05,WA,1,B,B2,6.93,8.0 -2015-06-06,WA,1,B,B2,6.95,8.0 -2015-06-07,WA,1,B,B2,7.02,7.0 -2015-06-08,WA,1,B,B2,6.97,7.0 -2015-06-09,WA,1,B,B2,7.0,7.0 -2015-06-10,WA,1,B,B2,7.0,7.0 -2015-06-11,WA,1,B,B2,6.98,7.0 -2015-06-12,WA,1,B,B2,6.96,7.0 -2015-06-13,WA,1,B,B2,6.9,8.0 -2015-06-14,WA,1,B,B2,6.86,8.0 -2015-06-15,WA,1,B,B2,6.92,8.0 -2015-06-16,WA,1,B,B2,6.99,8.0 -2015-06-17,WA,1,B,B2,6.93,7.0 -2015-06-18,WA,1,B,B2,6.95,7.0 -2015-06-19,WA,1,B,B2,6.99,8.0 -2015-06-20,WA,1,B,B2,6.92,8.0 -2015-06-21,WA,1,B,B2,6.88,8.0 -2015-06-22,WA,1,B,B2,6.94,7.0 -2015-06-23,WA,1,B,B2,6.91,8.0 -2015-06-24,WA,1,B,B2,7.1,7.0 -2015-06-25,WA,1,B,B2,6.97,7.0 -2015-06-26,WA,1,B,B2,7.01,8.0 -2015-06-27,WA,1,B,B2,6.89,8.0 -2015-06-28,WA,1,B,B2,6.97,8.0 -2015-06-29,WA,1,B,B2,6.9,7.0 -2015-06-30,WA,1,B,B2,6.93,7.0 -2015-07-01,WA,1,B,B2,6.85,8.0 -2015-07-02,WA,1,B,B2,6.94,8.0 -2015-07-03,WA,1,B,B2,6.91,8.0 -2015-07-04,WA,1,B,B2,6.9,8.0 -2015-07-05,WA,1,B,B2,6.9,8.0 -2015-07-06,WA,1,B,B2,6.92,8.0 -2015-07-07,WA,1,B,B2,6.85,8.0 -2015-07-08,WA,1,B,B2,6.97,8.0 -2015-07-09,WA,1,B,B2,6.97,8.0 -2015-07-10,WA,1,B,B2,7.02,7.0 -2015-07-11,WA,1,B,B2,7.04,8.0 -2015-07-12,WA,1,B,B2,6.96,8.0 -2015-07-13,WA,1,B,B2,6.88,8.0 -2015-07-14,WA,1,B,B2,7.02,8.0 -2015-07-15,WA,1,B,B2,6.87,8.0 -2015-07-16,WA,1,B,B2,6.94,8.0 -2015-07-17,WA,1,B,B2,6.83,9.0 -2015-07-18,WA,1,B,B2,6.87,8.0 -2015-07-19,WA,1,B,B2,6.99,8.0 -2015-07-20,WA,1,B,B2,6.86,7.0 -2015-07-21,WA,1,B,B2,6.82,8.0 -2015-07-22,WA,1,B,B2,6.95,7.0 -2015-07-23,WA,1,B,B2,6.99,7.0 -2015-07-24,WA,1,B,B2,7.03,8.0 -2015-07-25,WA,1,B,B2,6.99,8.0 -2015-07-26,WA,1,B,B2,6.88,8.0 -2015-07-27,WA,1,B,B2,7.07,7.0 -2015-07-28,WA,1,B,B2,7.02,7.0 -2015-07-29,WA,1,B,B2,6.84,8.0 -2015-07-30,WA,1,B,B2,6.97,8.0 -2015-07-31,WA,1,B,B2,6.95,7.0 -2015-08-01,WA,1,B,B2,7.0,8.0 -2015-08-02,WA,1,B,B2,6.89,8.0 -2015-08-03,WA,1,B,B2,7.06,8.0 -2015-08-04,WA,1,B,B2,6.96,8.0 -2015-08-05,WA,1,B,B2,6.92,7.0 -2015-08-06,WA,1,B,B2,6.83,8.0 -2015-08-07,WA,1,B,B2,6.94,7.0 -2015-08-08,WA,1,B,B2,7.0,8.0 -2015-08-09,WA,1,B,B2,6.8,9.0 -2015-08-10,WA,1,B,B2,6.88,8.0 -2015-08-11,WA,1,B,B2,6.96,8.0 -2015-08-12,WA,1,B,B2,6.93,8.0 -2015-08-13,WA,1,B,B2,6.84,8.0 -2015-08-14,WA,1,B,B2,6.89,8.0 -2015-08-15,WA,1,B,B2,6.89,8.0 -2015-08-16,WA,1,B,B2,6.9,7.0 -2015-08-17,WA,1,B,B2,6.89,8.0 -2015-08-18,WA,1,B,B2,7.02,8.0 -2015-08-19,WA,1,B,B2,6.92,7.0 -2015-08-20,WA,1,B,B2,6.87,8.0 -2015-08-21,WA,1,B,B2,6.87,8.0 -2015-08-22,WA,1,B,B2,7.01,8.0 -2015-08-23,WA,1,B,B2,6.97,8.0 -2015-08-24,WA,1,B,B2,6.9,7.0 -2015-08-25,WA,1,B,B2,7.04,7.0 -2015-08-26,WA,1,B,B2,6.97,7.0 -2015-08-27,WA,1,B,B2,6.97,8.0 -2015-08-28,WA,1,B,B2,6.83,8.0 -2015-08-29,WA,1,B,B2,6.88,8.0 -2015-08-30,WA,1,B,B2,6.9,8.0 -2015-08-31,WA,1,B,B2,6.93,7.0 -2015-09-01,WA,1,B,B2,6.92,8.0 -2015-09-02,WA,1,B,B2,6.91,8.0 -2015-09-03,WA,1,B,B2,6.96,8.0 -2015-09-04,WA,1,B,B2,7.04,8.0 -2015-09-05,WA,1,B,B2,7.0,8.0 -2015-09-06,WA,1,B,B2,6.95,8.0 -2015-09-07,WA,1,B,B2,6.81,8.0 -2015-09-08,WA,1,B,B2,6.92,7.0 -2015-09-09,WA,1,B,B2,6.86,7.0 -2015-09-10,WA,1,B,B2,6.94,8.0 -2015-09-11,WA,1,B,B2,6.89,8.0 -2015-09-12,WA,1,B,B2,6.96,9.0 -2015-09-13,WA,1,B,B2,7.06,8.0 -2015-09-14,WA,1,B,B2,6.9,7.0 -2015-09-15,WA,1,B,B2,6.84,8.0 -2015-09-16,WA,1,B,B2,7.03,8.0 -2015-09-17,WA,1,B,B2,6.99,7.0 -2015-09-18,WA,1,B,B2,6.86,8.0 -2015-09-19,WA,1,B,B2,6.93,8.0 -2015-09-20,WA,1,B,B2,7.07,8.0 -2015-09-21,WA,1,B,B2,6.94,8.0 -2015-09-22,WA,1,B,B2,6.86,8.0 -2015-09-23,WA,1,B,B2,6.91,8.0 -2015-09-24,WA,1,B,B2,7.08,7.0 -2015-09-25,WA,1,B,B2,6.96,8.0 -2015-09-26,WA,1,B,B2,6.9,8.0 -2015-09-27,WA,1,B,B2,6.98,8.0 -2015-09-28,WA,1,B,B2,6.89,8.0 -2015-09-29,WA,1,B,B2,6.91,8.0 -2015-09-30,WA,1,B,B2,6.92,8.0 -2015-10-01,WA,1,B,B2,6.89,8.0 -2015-10-02,WA,1,B,B2,7.01,7.0 -2015-10-03,WA,1,B,B2,6.9,8.0 -2015-10-04,WA,1,B,B2,6.97,8.0 -2015-10-05,WA,1,B,B2,6.96,7.0 -2015-10-06,WA,1,B,B2,6.91,9.0 -2015-10-07,WA,1,B,B2,6.96,7.0 -2015-10-08,WA,1,B,B2,6.94,8.0 -2015-10-09,WA,1,B,B2,6.98,7.0 -2015-10-10,WA,1,B,B2,7.0,8.0 -2015-10-11,WA,1,B,B2,6.88,8.0 -2015-10-12,WA,1,B,B2,6.96,8.0 -2015-10-13,WA,1,B,B2,6.9,8.0 -2015-10-14,WA,1,B,B2,6.99,7.0 -2015-10-15,WA,1,B,B2,6.98,7.0 -2015-10-16,WA,1,B,B2,7.01,8.0 -2015-10-17,WA,1,B,B2,6.8,9.0 -2015-10-18,WA,1,B,B2,6.89,8.0 -2015-10-19,WA,1,B,B2,6.92,9.0 -2015-10-20,WA,1,B,B2,7.04,7.0 -2015-10-21,WA,1,B,B2,7.0,7.0 -2015-10-22,WA,1,B,B2,6.89,8.0 -2015-10-23,WA,1,B,B2,6.87,7.0 -2015-10-24,WA,1,B,B2,6.87,8.0 -2015-10-25,WA,1,B,B2,6.89,7.0 -2015-10-26,WA,1,B,B2,6.99,7.0 -2015-10-27,WA,1,B,B2,7.0,8.0 -2015-10-28,WA,1,B,B2,7.0,8.0 -2015-10-29,WA,1,B,B2,6.93,8.0 -2015-10-30,WA,1,B,B2,6.92,8.0 -2015-10-31,WA,1,B,B2,6.94,8.0 -2015-11-01,WA,1,B,B2,7.0,8.0 -2015-11-02,WA,1,B,B2,6.91,7.0 -2015-11-03,WA,1,B,B2,7.09,7.0 -2015-11-04,WA,1,B,B2,6.92,8.0 -2015-11-05,WA,1,B,B2,6.92,8.0 -2015-11-06,WA,1,B,B2,7.05,7.0 -2015-11-07,WA,1,B,B2,7.04,7.0 -2015-11-08,WA,1,B,B2,6.89,8.0 -2015-11-09,WA,1,B,B2,7.05,8.0 -2015-11-10,WA,1,B,B2,6.9,8.0 -2015-11-11,WA,1,B,B2,6.94,8.0 -2015-11-12,WA,1,B,B2,6.94,8.0 -2015-11-13,WA,1,B,B2,7.03,8.0 -2015-11-14,WA,1,B,B2,6.89,8.0 -2015-11-15,WA,1,B,B2,6.94,8.0 -2015-11-16,WA,1,B,B2,7.08,7.0 -2015-11-17,WA,1,B,B2,6.97,8.0 -2015-11-18,WA,1,B,B2,6.94,8.0 -2015-11-19,WA,1,B,B2,6.87,7.0 -2015-11-20,WA,1,B,B2,6.81,8.0 -2015-11-21,WA,1,B,B2,7.05,8.0 -2015-11-22,WA,1,B,B2,6.89,8.0 -2015-11-23,WA,1,B,B2,7.07,7.0 -2015-11-24,WA,1,B,B2,7.04,7.0 -2015-11-25,WA,1,B,B2,6.94,7.0 -2015-11-26,WA,1,B,B2,6.99,7.0 -2015-11-27,WA,1,B,B2,6.98,8.0 -2015-11-28,WA,1,B,B2,6.91,8.0 -2015-11-29,WA,1,B,B2,6.91,8.0 -2015-11-30,WA,1,B,B2,6.85,7.0 -2015-12-01,WA,1,B,B2,6.88,7.0 -2015-12-02,WA,1,B,B2,6.96,8.0 -2015-12-03,WA,1,B,B2,6.9,8.0 -2015-12-04,WA,1,B,B2,6.85,8.0 -2015-12-05,WA,1,B,B2,7.09,8.0 -2015-12-06,WA,1,B,B2,6.97,8.0 -2015-12-07,WA,1,B,B2,6.9,8.0 -2015-12-08,WA,1,B,B2,7.1,7.0 -2015-12-09,WA,1,B,B2,6.96,8.0 -2015-12-10,WA,1,B,B2,7.06,7.0 -2015-12-11,WA,1,B,B2,6.99,8.0 -2015-12-12,WA,1,B,B2,6.93,8.0 -2015-12-13,WA,1,B,B2,6.85,8.0 -2015-12-14,WA,1,B,B2,7.02,7.0 -2015-12-15,WA,1,B,B2,6.98,7.0 -2015-12-16,WA,1,B,B2,6.81,8.0 -2015-12-17,WA,1,B,B2,6.96,7.0 -2015-12-18,WA,1,B,B2,6.89,8.0 -2015-12-19,WA,1,B,B2,6.97,8.0 -2015-12-20,WA,1,B,B2,6.96,8.0 -2015-12-21,WA,1,B,B2,7.05,7.0 -2015-12-22,WA,1,B,B2,6.83,8.0 -2015-12-23,WA,1,B,B2,6.91,8.0 -2015-12-24,WA,1,B,B2,6.95,7.0 -2015-12-25,WA,1,B,B2,6.98,8.0 -2015-12-26,WA,1,B,B2,6.9,8.0 -2015-12-27,WA,1,B,B2,6.97,8.0 -2015-12-28,WA,1,B,B2,6.96,7.0 -2015-12-29,WA,1,B,B2,6.84,9.0 -2015-12-30,WA,1,B,B2,6.98,8.0 -2015-12-31,WA,1,B,B2,6.9,8.0 -2016-01-01,WA,1,B,B2,7.03,8.0 -2016-01-02,WA,1,B,B2,6.94,8.0 -2016-01-03,WA,1,B,B2,6.97,7.0 -2016-01-04,WA,1,B,B2,6.87,8.0 -2016-01-05,WA,1,B,B2,6.88,8.0 -2016-01-06,WA,1,B,B2,6.88,7.0 -2016-01-07,WA,1,B,B2,6.99,8.0 -2016-01-08,WA,1,B,B2,7.04,7.0 -2016-01-09,WA,1,B,B2,6.93,9.0 -2016-01-10,WA,1,B,B2,6.88,8.0 -2016-01-11,WA,1,B,B2,6.93,8.0 -2016-01-12,WA,1,B,B2,6.91,8.0 -2016-01-13,WA,1,B,B2,6.83,8.0 -2016-01-14,WA,1,B,B2,6.85,7.0 -2016-01-15,WA,1,B,B2,6.96,7.0 -2016-01-16,WA,1,B,B2,7.03,8.0 -2016-01-17,WA,1,B,B2,6.94,8.0 -2016-01-18,WA,1,B,B2,6.88,8.0 -2016-01-19,WA,1,B,B2,6.88,7.0 -2016-01-20,WA,1,B,B2,6.96,7.0 -2016-01-21,WA,1,B,B2,6.95,7.0 -2016-01-22,WA,1,B,B2,6.98,8.0 -2016-01-23,WA,1,B,B2,6.9,8.0 -2016-01-24,WA,1,B,B2,7.04,8.0 -2016-01-25,WA,1,B,B2,7.07,7.0 -2016-01-26,WA,1,B,B2,7.01,7.0 -2016-01-27,WA,1,B,B2,6.95,7.0 -2016-01-28,WA,1,B,B2,6.87,8.0 -2016-01-29,WA,1,B,B2,6.93,8.0 -2016-01-30,WA,1,B,B2,6.94,8.0 -2016-01-31,WA,1,B,B2,6.91,8.0 -2016-02-01,WA,1,B,B2,6.85,8.0 -2016-02-02,WA,1,B,B2,6.93,8.0 -2016-02-03,WA,1,B,B2,6.91,8.0 -2016-02-04,WA,1,B,B2,6.96,7.0 -2016-02-05,WA,1,B,B2,6.94,8.0 -2016-02-06,WA,1,B,B2,6.91,9.0 -2016-02-07,WA,1,B,B2,6.89,7.0 -2016-02-08,WA,1,B,B2,6.96,8.0 -2016-02-09,WA,1,B,B2,7.02,7.0 -2016-02-10,WA,1,B,B2,6.84,8.0 -2016-02-11,WA,1,B,B2,6.97,7.0 -2016-02-12,WA,1,B,B2,6.82,8.0 -2016-02-13,WA,1,B,B2,6.99,8.0 -2016-02-14,WA,1,B,B2,6.89,8.0 -2016-02-15,WA,1,B,B2,7.02,7.0 -2016-02-16,WA,1,B,B2,6.98,8.0 -2016-02-17,WA,1,B,B2,6.95,8.0 -2016-02-18,WA,1,B,B2,7.07,7.0 -2016-02-19,WA,1,B,B2,7.03,8.0 -2016-02-20,WA,1,B,B2,6.87,8.0 -2016-02-21,WA,1,B,B2,6.86,7.0 -2016-02-22,WA,1,B,B2,6.77,7.0 -2016-02-23,WA,1,B,B2,6.93,7.0 -2016-02-24,WA,1,B,B2,6.94,7.0 -2016-02-25,WA,1,B,B2,6.88,8.0 -2016-02-26,WA,1,B,B2,6.85,8.0 -2016-02-27,WA,1,B,B2,6.9,8.0 -2016-02-28,WA,1,B,B2,6.96,8.0 -2016-02-29,WA,1,B,B2,6.9,8.0 -2016-03-01,WA,1,B,B2,6.9,8.0 -2016-03-02,WA,1,B,B2,6.88,8.0 -2016-03-03,WA,1,B,B2,6.95,7.0 -2016-03-04,WA,1,B,B2,6.94,8.0 -2016-03-05,WA,1,B,B2,7.04,8.0 -2016-03-06,WA,1,B,B2,6.99,7.0 -2016-03-07,WA,1,B,B2,6.82,8.0 -2016-03-08,WA,1,B,B2,6.88,8.0 -2016-03-09,WA,1,B,B2,6.98,7.0 -2016-03-10,WA,1,B,B2,7.06,7.0 -2016-03-11,WA,1,B,B2,6.92,8.0 -2016-03-12,WA,1,B,B2,7.0,8.0 -2016-03-13,WA,1,B,B2,7.01,8.0 -2016-03-14,WA,1,B,B2,7.09,8.0 -2016-03-15,WA,1,B,B2,7.0,7.0 -2016-03-16,WA,1,B,B2,6.92,7.0 -2016-03-17,WA,1,B,B2,6.91,8.0 -2016-03-18,WA,1,B,B2,6.94,8.0 -2016-03-19,WA,1,B,B2,6.91,8.0 -2016-03-20,WA,1,B,B2,6.93,8.0 -2016-03-21,WA,1,B,B2,6.85,8.0 -2016-03-22,WA,1,B,B2,6.85,7.0 -2016-03-23,WA,1,B,B2,6.94,8.0 -2016-03-24,WA,1,B,B2,6.9,7.0 -2016-03-25,WA,1,B,B2,6.85,8.0 -2016-03-26,WA,1,B,B2,6.93,8.0 -2016-03-27,WA,1,B,B2,7.03,8.0 -2016-03-28,WA,1,B,B2,7.05,7.0 -2016-03-29,WA,1,B,B2,6.88,8.0 -2016-03-30,WA,1,B,B2,7.02,7.0 -2016-03-31,WA,1,B,B2,6.89,8.0 -2016-04-01,WA,1,B,B2,6.9,8.0 -2016-04-02,WA,1,B,B2,6.98,8.0 -2016-04-03,WA,1,B,B2,7.02,8.0 -2016-04-04,WA,1,B,B2,6.94,8.0 -2016-04-05,WA,1,B,B2,6.9,8.0 -2016-04-06,WA,1,B,B2,6.87,8.0 -2016-04-07,WA,1,B,B2,6.97,7.0 -2016-04-08,WA,1,B,B2,6.95,7.0 -2016-04-09,WA,1,B,B2,6.97,8.0 -2016-04-10,WA,1,B,B2,7.03,8.0 -2016-04-11,WA,1,B,B2,6.86,8.0 -2016-04-12,WA,1,B,B2,6.89,8.0 -2016-04-13,WA,1,B,B2,6.97,7.0 -2016-04-14,WA,1,B,B2,6.97,8.0 -2016-04-15,WA,1,B,B2,6.99,7.0 -2016-04-16,WA,1,B,B2,6.9,8.0 -2016-04-17,WA,1,B,B2,6.97,8.0 -2016-04-18,WA,1,B,B2,6.83,8.0 -2016-04-19,WA,1,B,B2,6.9,8.0 -2016-04-20,WA,1,B,B2,6.93,7.0 -2016-04-21,WA,1,B,B2,7.0,8.0 -2016-04-22,WA,1,B,B2,7.0,8.0 -2016-04-23,WA,1,B,B2,6.88,8.0 -2016-04-24,WA,1,B,B2,6.95,7.0 -2016-04-25,WA,1,B,B2,7.02,7.0 -2016-04-26,WA,1,B,B2,7.15,8.0 -2016-04-27,WA,1,B,B2,6.92,8.0 -2016-04-28,WA,1,B,B2,6.92,7.0 -2016-04-29,WA,1,B,B2,6.89,8.0 -2016-04-30,WA,1,B,B2,6.95,9.0 -2016-05-01,WA,1,B,B2,6.9,8.0 -2016-05-02,WA,1,B,B2,6.86,8.0 -2016-05-03,WA,1,B,B2,6.93,8.0 -2016-05-04,WA,1,B,B2,6.97,8.0 -2016-05-05,WA,1,B,B2,6.88,7.0 -2016-05-06,WA,1,B,B2,6.87,8.0 -2016-05-07,WA,1,B,B2,6.88,9.0 -2016-05-08,WA,1,B,B2,6.8,8.0 -2016-05-09,WA,1,B,B2,6.9,8.0 -2016-05-10,WA,1,B,B2,6.99,8.0 -2016-05-11,WA,1,B,B2,6.97,8.0 -2016-05-12,WA,1,B,B2,6.93,7.0 -2016-05-13,WA,1,B,B2,6.98,8.0 -2016-05-14,WA,1,B,B2,6.95,8.0 -2016-05-15,WA,1,B,B2,6.99,8.0 -2016-05-16,WA,1,B,B2,6.98,7.0 -2016-05-17,WA,1,B,B2,6.86,8.0 -2016-05-18,WA,1,B,B2,6.88,8.0 -2016-05-19,WA,1,B,B2,7.0,7.0 -2016-05-20,WA,1,B,B2,6.96,8.0 -2016-05-21,WA,1,B,B2,6.96,8.0 -2016-05-22,WA,1,B,B2,6.98,8.0 -2016-05-23,WA,1,B,B2,6.9,8.0 -2016-05-24,WA,1,B,B2,6.93,8.0 -2016-05-25,WA,1,B,B2,7.0,7.0 -2016-05-26,WA,1,B,B2,6.95,8.0 -2016-05-27,WA,1,B,B2,6.82,8.0 -2016-05-28,WA,1,B,B2,6.78,9.0 -2016-05-29,WA,1,B,B2,6.93,8.0 -2016-05-30,WA,1,B,B2,6.87,7.0 -2016-05-31,WA,1,B,B2,6.97,7.0 -2016-06-01,WA,1,B,B2,6.78,8.0 -2016-06-02,WA,1,B,B2,6.91,8.0 -2016-06-03,WA,1,B,B2,6.92,8.0 -2016-06-04,WA,1,B,B2,6.91,8.0 -2016-06-05,WA,1,B,B2,6.95,7.0 -2016-06-06,WA,1,B,B2,7.0,8.0 -2016-06-07,WA,1,B,B2,6.92,7.0 -2016-06-08,WA,1,B,B2,6.96,7.0 -2016-06-09,WA,1,B,B2,6.81,8.0 -2016-06-10,WA,1,B,B2,6.96,8.0 -2016-06-11,WA,1,B,B2,7.0,7.0 -2016-06-12,WA,1,B,B2,6.99,8.0 -2016-06-13,WA,1,B,B2,6.87,8.0 -2016-06-14,WA,1,B,B2,6.98,8.0 -2016-06-15,WA,1,B,B2,6.91,7.0 -2016-06-16,WA,1,B,B2,6.98,8.0 -2016-06-17,WA,1,B,B2,7.04,8.0 -2016-06-18,WA,1,B,B2,6.94,8.0 -2016-06-19,WA,1,B,B2,7.09,8.0 -2016-06-20,WA,1,B,B2,7.05,7.0 -2016-06-21,WA,1,B,B2,6.88,7.0 -2016-06-22,WA,1,B,B2,7.0,7.0 -2016-06-23,WA,1,B,B2,6.87,8.0 -2016-06-24,WA,1,B,B2,6.97,8.0 -2016-06-25,WA,1,B,B2,6.8,8.0 -2016-06-26,WA,1,B,B2,7.05,7.0 -2016-06-27,WA,1,B,B2,7.01,7.0 -2016-06-28,WA,1,B,B2,6.92,8.0 -2016-06-29,WA,1,B,B2,6.82,8.0 -2016-06-30,WA,1,B,B2,6.99,8.0 -2016-07-01,WA,1,B,B2,6.9,7.0 -2016-07-02,WA,1,B,B2,7.03,8.0 -2016-07-03,WA,1,B,B2,6.97,7.0 -2016-07-04,WA,1,B,B2,6.83,8.0 -2016-07-05,WA,1,B,B2,7.04,7.0 -2016-07-06,WA,1,B,B2,6.93,8.0 -2016-07-07,WA,1,B,B2,7.04,7.0 -2016-07-08,WA,1,B,B2,6.88,8.0 -2016-07-09,WA,1,B,B2,6.99,8.0 -2016-07-10,WA,1,B,B2,6.99,8.0 -2016-07-11,WA,1,B,B2,6.95,7.0 -2016-07-12,WA,1,B,B2,6.85,7.0 -2016-07-13,WA,1,B,B2,6.93,8.0 -2016-07-14,WA,1,B,B2,6.97,8.0 -2016-07-15,WA,1,B,B2,6.91,8.0 -2016-07-16,WA,1,B,B2,6.93,8.0 -2016-07-17,WA,1,B,B2,6.88,8.0 -2016-07-18,WA,1,B,B2,6.84,8.0 -2016-07-19,WA,1,B,B2,6.92,8.0 -2016-07-20,WA,1,B,B2,6.85,8.0 -2016-07-21,WA,1,B,B2,6.96,8.0 -2016-07-22,WA,1,B,B2,6.92,8.0 -2016-07-23,WA,1,B,B2,6.98,8.0 -2016-07-24,WA,1,B,B2,6.91,8.0 -2016-07-25,WA,1,B,B2,6.95,8.0 -2016-07-26,WA,1,B,B2,6.93,8.0 -2016-07-27,WA,1,B,B2,6.92,8.0 -2015-01-05,WA,2,A,A1,4.69,368.0 -2015-01-06,WA,2,A,A1,4.72,380.0 -2015-01-07,WA,2,A,A1,4.74,362.0 -2015-01-08,WA,2,A,A1,4.72,396.0 -2015-01-09,WA,2,A,A1,4.65,374.0 -2015-01-10,WA,2,A,A1,4.74,411.0 -2015-01-11,WA,2,A,A1,4.68,387.0 -2015-01-12,WA,2,A,A1,4.65,396.0 -2015-01-13,WA,2,A,A1,4.66,373.0 -2015-01-14,WA,2,A,A1,4.68,349.0 -2015-01-15,WA,2,A,A1,4.67,412.0 -2015-01-16,WA,2,A,A1,4.75,373.0 -2015-01-17,WA,2,A,A1,4.76,362.0 -2015-01-18,WA,2,A,A1,4.71,375.0 -2015-01-19,WA,2,A,A1,4.7,359.0 -2015-01-20,WA,2,A,A1,4.74,360.0 -2015-01-21,WA,2,A,A1,4.74,370.0 -2015-01-22,WA,2,A,A1,4.58,411.0 -2015-01-23,WA,2,A,A1,4.7,402.0 -2015-01-24,WA,2,A,A1,4.75,416.0 -2015-01-25,WA,2,A,A1,4.71,392.0 -2015-01-26,WA,2,A,A1,4.72,376.0 -2015-01-27,WA,2,A,A1,4.66,398.0 -2015-01-28,WA,2,A,A1,4.7,400.0 -2015-01-29,WA,2,A,A1,4.76,393.0 -2015-01-30,WA,2,A,A1,4.68,386.0 -2015-01-31,WA,2,A,A1,4.72,386.0 -2015-02-01,WA,2,A,A1,4.66,385.0 -2015-02-02,WA,2,A,A1,4.8,364.0 -2015-02-03,WA,2,A,A1,4.8,401.0 -2015-02-04,WA,2,A,A1,4.77,358.0 -2015-02-05,WA,2,A,A1,4.7,391.0 -2015-02-06,WA,2,A,A1,4.71,391.0 -2015-02-07,WA,2,A,A1,4.82,378.0 -2015-02-08,WA,2,A,A1,4.75,403.0 -2015-02-09,WA,2,A,A1,4.7,374.0 -2015-02-10,WA,2,A,A1,4.64,385.0 -2015-02-11,WA,2,A,A1,4.69,411.0 -2015-02-12,WA,2,A,A1,4.7,396.0 -2015-02-13,WA,2,A,A1,4.78,382.0 -2015-02-14,WA,2,A,A1,4.71,387.0 -2015-02-15,WA,2,A,A1,4.68,404.0 -2015-02-16,WA,2,A,A1,4.72,378.0 -2015-02-17,WA,2,A,A1,4.73,374.0 -2015-02-18,WA,2,A,A1,4.73,391.0 -2015-02-19,WA,2,A,A1,4.76,353.0 -2015-02-20,WA,2,A,A1,4.7,399.0 -2015-02-21,WA,2,A,A1,4.74,386.0 -2015-02-22,WA,2,A,A1,4.73,412.0 -2015-02-23,WA,2,A,A1,4.72,369.0 -2015-02-24,WA,2,A,A1,4.68,407.0 -2015-02-25,WA,2,A,A1,4.69,406.0 -2015-02-26,WA,2,A,A1,4.68,383.0 -2015-02-27,WA,2,A,A1,4.64,412.0 -2015-02-28,WA,2,A,A1,4.76,432.0 -2015-03-01,WA,2,A,A1,4.69,400.0 -2015-03-02,WA,2,A,A1,4.7,394.0 -2015-03-03,WA,2,A,A1,4.66,408.0 -2015-03-04,WA,2,A,A1,4.66,413.0 -2015-03-05,WA,2,A,A1,4.69,419.0 -2015-03-06,WA,2,A,A1,4.64,394.0 -2015-03-07,WA,2,A,A1,4.7,412.0 -2015-03-08,WA,2,A,A1,4.8,405.0 -2015-03-09,WA,2,A,A1,4.77,408.0 -2015-03-10,WA,2,A,A1,4.72,406.0 -2015-03-11,WA,2,A,A1,4.73,389.0 -2015-03-12,WA,2,A,A1,4.73,393.0 -2015-03-13,WA,2,A,A1,4.65,410.0 -2015-03-14,WA,2,A,A1,4.72,422.0 -2015-03-15,WA,2,A,A1,4.72,381.0 -2015-03-16,WA,2,A,A1,4.74,390.0 -2015-03-17,WA,2,A,A1,4.72,424.0 -2015-03-18,WA,2,A,A1,4.68,409.0 -2015-03-19,WA,2,A,A1,4.83,378.0 -2015-03-20,WA,2,A,A1,4.71,412.0 -2015-03-21,WA,2,A,A1,4.67,406.0 -2015-03-22,WA,2,A,A1,4.75,399.0 -2015-03-23,WA,2,A,A1,4.73,394.0 -2015-03-24,WA,2,A,A1,4.7,385.0 -2015-03-25,WA,2,A,A1,4.7,421.0 -2015-03-26,WA,2,A,A1,4.74,410.0 -2015-03-27,WA,2,A,A1,4.77,406.0 -2015-03-28,WA,2,A,A1,4.68,395.0 -2015-03-29,WA,2,A,A1,4.71,413.0 -2015-03-30,WA,2,A,A1,4.65,401.0 -2015-03-31,WA,2,A,A1,4.68,399.0 -2015-04-01,WA,2,A,A1,4.6,424.0 -2015-04-02,WA,2,A,A1,4.65,423.0 -2015-04-03,WA,2,A,A1,4.65,434.0 -2015-04-04,WA,2,A,A1,4.72,424.0 -2015-04-05,WA,2,A,A1,4.67,388.0 -2015-04-06,WA,2,A,A1,4.66,414.0 -2015-04-07,WA,2,A,A1,4.66,449.0 -2015-04-08,WA,2,A,A1,4.79,369.0 -2015-04-09,WA,2,A,A1,4.7,386.0 -2015-04-10,WA,2,A,A1,4.65,427.0 -2015-04-11,WA,2,A,A1,4.63,429.0 -2015-04-12,WA,2,A,A1,4.72,435.0 -2015-04-13,WA,2,A,A1,4.71,386.0 -2015-04-14,WA,2,A,A1,4.71,406.0 -2015-04-15,WA,2,A,A1,4.66,404.0 -2015-04-16,WA,2,A,A1,4.73,398.0 -2015-04-17,WA,2,A,A1,4.7,401.0 -2015-04-18,WA,2,A,A1,4.76,431.0 -2015-04-19,WA,2,A,A1,4.62,427.0 -2015-04-20,WA,2,A,A1,4.68,392.0 -2015-04-21,WA,2,A,A1,4.74,394.0 -2015-04-22,WA,2,A,A1,4.63,398.0 -2015-04-23,WA,2,A,A1,4.61,424.0 -2015-04-24,WA,2,A,A1,4.78,353.0 -2015-04-25,WA,2,A,A1,4.81,401.0 -2015-04-26,WA,2,A,A1,4.66,439.0 -2015-04-27,WA,2,A,A1,4.56,389.0 -2015-04-28,WA,2,A,A1,4.7,405.0 -2015-04-29,WA,2,A,A1,4.8,392.0 -2015-04-30,WA,2,A,A1,4.75,355.0 -2015-05-01,WA,2,A,A1,4.77,373.0 -2015-05-02,WA,2,A,A1,4.66,409.0 -2015-05-03,WA,2,A,A1,4.69,425.0 -2015-05-04,WA,2,A,A1,4.67,378.0 -2015-05-05,WA,2,A,A1,4.69,414.0 -2015-05-06,WA,2,A,A1,4.66,399.0 -2015-05-07,WA,2,A,A1,4.7,418.0 -2015-05-08,WA,2,A,A1,4.64,423.0 -2015-05-09,WA,2,A,A1,4.69,420.0 -2015-05-10,WA,2,A,A1,4.69,374.0 -2015-05-11,WA,2,A,A1,4.79,351.0 -2015-05-12,WA,2,A,A1,4.7,353.0 -2015-05-13,WA,2,A,A1,4.6,421.0 -2015-05-14,WA,2,A,A1,4.71,424.0 -2015-05-15,WA,2,A,A1,4.69,374.0 -2015-05-16,WA,2,A,A1,4.68,395.0 -2015-05-17,WA,2,A,A1,4.73,387.0 -2015-05-18,WA,2,A,A1,4.66,364.0 -2015-05-19,WA,2,A,A1,4.75,388.0 -2015-05-20,WA,2,A,A1,4.72,405.0 -2015-05-21,WA,2,A,A1,4.69,400.0 -2015-05-22,WA,2,A,A1,4.65,427.0 -2015-05-23,WA,2,A,A1,4.74,421.0 -2015-05-24,WA,2,A,A1,4.72,401.0 -2015-05-25,WA,2,A,A1,4.67,418.0 -2015-05-26,WA,2,A,A1,4.72,409.0 -2015-05-27,WA,2,A,A1,4.68,398.0 -2015-05-28,WA,2,A,A1,4.67,412.0 -2015-05-29,WA,2,A,A1,4.7,386.0 -2015-05-30,WA,2,A,A1,4.67,381.0 -2015-05-31,WA,2,A,A1,4.81,407.0 -2015-06-01,WA,2,A,A1,4.7,364.0 -2015-06-02,WA,2,A,A1,4.73,407.0 -2015-06-03,WA,2,A,A1,4.76,380.0 -2015-06-04,WA,2,A,A1,4.65,410.0 -2015-06-05,WA,2,A,A1,4.67,379.0 -2015-06-06,WA,2,A,A1,4.64,446.0 -2015-06-07,WA,2,A,A1,4.69,438.0 -2015-06-08,WA,2,A,A1,4.66,377.0 -2015-06-09,WA,2,A,A1,4.76,380.0 -2015-06-10,WA,2,A,A1,4.73,386.0 -2015-06-11,WA,2,A,A1,4.7,387.0 -2015-06-12,WA,2,A,A1,4.72,380.0 -2015-06-13,WA,2,A,A1,4.67,417.0 -2015-06-14,WA,2,A,A1,4.77,420.0 -2015-06-15,WA,2,A,A1,4.79,377.0 -2015-06-16,WA,2,A,A1,4.73,409.0 -2015-06-17,WA,2,A,A1,4.65,403.0 -2015-06-18,WA,2,A,A1,4.69,384.0 -2015-06-19,WA,2,A,A1,4.72,388.0 -2015-06-20,WA,2,A,A1,4.75,378.0 -2015-06-21,WA,2,A,A1,4.73,386.0 -2015-06-22,WA,2,A,A1,4.67,387.0 -2015-06-23,WA,2,A,A1,4.68,411.0 -2015-06-24,WA,2,A,A1,4.68,383.0 -2015-06-25,WA,2,A,A1,4.79,362.0 -2015-06-26,WA,2,A,A1,4.65,404.0 -2015-06-27,WA,2,A,A1,4.7,460.0 -2015-06-28,WA,2,A,A1,4.7,417.0 -2015-06-29,WA,2,A,A1,4.66,382.0 -2015-06-30,WA,2,A,A1,4.65,389.0 -2015-07-01,WA,2,A,A1,4.64,435.0 -2015-07-02,WA,2,A,A1,4.73,349.0 -2015-07-03,WA,2,A,A1,4.74,389.0 -2015-07-04,WA,2,A,A1,4.74,421.0 -2015-07-05,WA,2,A,A1,4.65,389.0 -2015-07-06,WA,2,A,A1,4.67,393.0 -2015-07-07,WA,2,A,A1,4.73,419.0 -2015-07-08,WA,2,A,A1,4.78,391.0 -2015-07-09,WA,2,A,A1,4.71,398.0 -2015-07-10,WA,2,A,A1,4.71,418.0 -2015-07-11,WA,2,A,A1,4.64,452.0 -2015-07-12,WA,2,A,A1,4.79,392.0 -2015-07-13,WA,2,A,A1,4.68,391.0 -2015-07-14,WA,2,A,A1,4.72,399.0 -2015-07-15,WA,2,A,A1,4.65,378.0 -2015-07-16,WA,2,A,A1,4.69,383.0 -2015-07-17,WA,2,A,A1,4.62,425.0 -2015-07-18,WA,2,A,A1,4.72,409.0 -2015-07-19,WA,2,A,A1,4.75,416.0 -2015-07-20,WA,2,A,A1,4.73,402.0 -2015-07-21,WA,2,A,A1,4.69,368.0 -2015-07-22,WA,2,A,A1,4.76,367.0 -2015-07-23,WA,2,A,A1,4.74,410.0 -2015-07-24,WA,2,A,A1,4.74,366.0 -2015-07-25,WA,2,A,A1,4.68,447.0 -2015-07-26,WA,2,A,A1,4.67,420.0 -2015-07-27,WA,2,A,A1,4.7,395.0 -2015-07-28,WA,2,A,A1,4.68,411.0 -2015-07-29,WA,2,A,A1,4.74,386.0 -2015-07-30,WA,2,A,A1,4.64,413.0 -2015-07-31,WA,2,A,A1,4.74,408.0 -2015-08-01,WA,2,A,A1,4.7,420.0 -2015-08-02,WA,2,A,A1,4.65,444.0 -2015-08-03,WA,2,A,A1,4.71,397.0 -2015-08-04,WA,2,A,A1,4.71,426.0 -2015-08-05,WA,2,A,A1,4.78,374.0 -2015-08-06,WA,2,A,A1,4.69,404.0 -2015-08-07,WA,2,A,A1,4.7,404.0 -2015-08-08,WA,2,A,A1,4.63,426.0 -2015-08-09,WA,2,A,A1,4.64,421.0 -2015-08-10,WA,2,A,A1,4.73,380.0 -2015-08-11,WA,2,A,A1,4.69,404.0 -2015-08-12,WA,2,A,A1,4.78,376.0 -2015-08-13,WA,2,A,A1,4.75,345.0 -2015-08-14,WA,2,A,A1,4.73,396.0 -2015-08-15,WA,2,A,A1,4.74,397.0 -2015-08-16,WA,2,A,A1,4.71,366.0 -2015-08-17,WA,2,A,A1,4.69,379.0 -2015-08-18,WA,2,A,A1,4.75,413.0 -2015-08-19,WA,2,A,A1,4.8,363.0 -2015-08-20,WA,2,A,A1,4.71,413.0 -2015-08-21,WA,2,A,A1,4.72,395.0 -2015-08-22,WA,2,A,A1,4.72,385.0 -2015-08-23,WA,2,A,A1,4.72,397.0 -2015-08-24,WA,2,A,A1,4.64,386.0 -2015-08-25,WA,2,A,A1,4.67,385.0 -2015-08-26,WA,2,A,A1,4.67,407.0 -2015-08-27,WA,2,A,A1,4.63,426.0 -2015-08-28,WA,2,A,A1,4.69,357.0 -2015-08-29,WA,2,A,A1,4.82,364.0 -2015-08-30,WA,2,A,A1,4.73,444.0 -2015-08-31,WA,2,A,A1,4.71,410.0 -2015-09-01,WA,2,A,A1,4.69,394.0 -2015-09-02,WA,2,A,A1,4.78,411.0 -2015-09-03,WA,2,A,A1,4.66,413.0 -2015-09-04,WA,2,A,A1,4.67,393.0 -2015-09-05,WA,2,A,A1,4.73,380.0 -2015-09-06,WA,2,A,A1,4.66,426.0 -2015-09-07,WA,2,A,A1,4.7,392.0 -2015-09-08,WA,2,A,A1,4.74,395.0 -2015-09-09,WA,2,A,A1,4.75,404.0 -2015-09-10,WA,2,A,A1,4.71,405.0 -2015-09-11,WA,2,A,A1,4.75,411.0 -2015-09-12,WA,2,A,A1,4.74,399.0 -2015-09-13,WA,2,A,A1,4.73,433.0 -2015-09-14,WA,2,A,A1,4.72,410.0 -2015-09-15,WA,2,A,A1,4.69,399.0 -2015-09-16,WA,2,A,A1,4.6,419.0 -2015-09-17,WA,2,A,A1,4.74,361.0 -2015-09-18,WA,2,A,A1,4.68,407.0 -2015-09-19,WA,2,A,A1,4.67,429.0 -2015-09-20,WA,2,A,A1,4.73,391.0 -2015-09-21,WA,2,A,A1,4.63,398.0 -2015-09-22,WA,2,A,A1,4.64,417.0 -2015-09-23,WA,2,A,A1,4.75,418.0 -2015-09-24,WA,2,A,A1,4.73,398.0 -2015-09-25,WA,2,A,A1,4.68,424.0 -2015-09-26,WA,2,A,A1,4.76,396.0 -2015-09-27,WA,2,A,A1,4.61,422.0 -2015-09-28,WA,2,A,A1,4.69,369.0 -2015-09-29,WA,2,A,A1,4.71,424.0 -2015-09-30,WA,2,A,A1,4.72,401.0 -2015-10-01,WA,2,A,A1,4.68,441.0 -2015-10-02,WA,2,A,A1,4.75,390.0 -2015-10-03,WA,2,A,A1,4.68,397.0 -2015-10-04,WA,2,A,A1,4.69,418.0 -2015-10-05,WA,2,A,A1,4.72,382.0 -2015-10-06,WA,2,A,A1,4.71,397.0 -2015-10-07,WA,2,A,A1,4.7,377.0 -2015-10-08,WA,2,A,A1,4.67,384.0 -2015-10-09,WA,2,A,A1,4.73,382.0 -2015-10-10,WA,2,A,A1,4.65,435.0 -2015-10-11,WA,2,A,A1,4.72,383.0 -2015-10-12,WA,2,A,A1,4.76,344.0 -2015-10-13,WA,2,A,A1,4.66,403.0 -2015-10-14,WA,2,A,A1,4.67,419.0 -2015-10-15,WA,2,A,A1,4.7,410.0 -2015-10-16,WA,2,A,A1,4.73,401.0 -2015-10-17,WA,2,A,A1,4.72,437.0 -2015-10-18,WA,2,A,A1,4.8,382.0 -2015-10-19,WA,2,A,A1,4.67,436.0 -2015-10-20,WA,2,A,A1,4.66,422.0 -2015-10-21,WA,2,A,A1,4.74,401.0 -2015-10-22,WA,2,A,A1,4.68,414.0 -2015-10-23,WA,2,A,A1,4.74,405.0 -2015-10-24,WA,2,A,A1,4.63,428.0 -2015-10-25,WA,2,A,A1,4.74,381.0 -2015-10-26,WA,2,A,A1,4.78,405.0 -2015-10-27,WA,2,A,A1,4.78,375.0 -2015-10-28,WA,2,A,A1,4.76,386.0 -2015-10-29,WA,2,A,A1,4.7,375.0 -2015-10-30,WA,2,A,A1,4.69,397.0 -2015-10-31,WA,2,A,A1,4.76,405.0 -2015-11-01,WA,2,A,A1,4.67,430.0 -2015-11-02,WA,2,A,A1,4.78,369.0 -2015-11-03,WA,2,A,A1,4.66,403.0 -2015-11-04,WA,2,A,A1,4.73,387.0 -2015-11-05,WA,2,A,A1,4.69,393.0 -2015-11-06,WA,2,A,A1,4.72,391.0 -2015-11-07,WA,2,A,A1,4.72,429.0 -2015-11-08,WA,2,A,A1,4.65,426.0 -2015-11-09,WA,2,A,A1,4.68,403.0 -2015-11-10,WA,2,A,A1,4.73,404.0 -2015-11-11,WA,2,A,A1,4.75,403.0 -2015-11-12,WA,2,A,A1,4.71,423.0 -2015-11-13,WA,2,A,A1,4.76,383.0 -2015-11-14,WA,2,A,A1,4.65,423.0 -2015-11-15,WA,2,A,A1,4.75,377.0 -2015-11-16,WA,2,A,A1,4.66,391.0 -2015-11-17,WA,2,A,A1,4.7,379.0 -2015-11-18,WA,2,A,A1,4.75,418.0 -2015-11-19,WA,2,A,A1,4.63,392.0 -2015-11-20,WA,2,A,A1,4.71,379.0 -2015-11-21,WA,2,A,A1,4.77,419.0 -2015-11-22,WA,2,A,A1,4.77,388.0 -2015-11-23,WA,2,A,A1,4.77,382.0 -2015-11-24,WA,2,A,A1,4.71,399.0 -2015-11-25,WA,2,A,A1,4.67,407.0 -2015-11-26,WA,2,A,A1,4.77,384.0 -2015-11-27,WA,2,A,A1,4.71,424.0 -2015-11-28,WA,2,A,A1,4.75,389.0 -2015-11-29,WA,2,A,A1,4.68,399.0 -2015-11-30,WA,2,A,A1,4.7,388.0 -2015-12-01,WA,2,A,A1,4.73,357.0 -2015-12-02,WA,2,A,A1,4.73,404.0 -2015-12-03,WA,2,A,A1,4.69,396.0 -2015-12-04,WA,2,A,A1,4.74,386.0 -2015-12-05,WA,2,A,A1,4.76,397.0 -2015-12-06,WA,2,A,A1,4.69,369.0 -2015-12-07,WA,2,A,A1,4.7,401.0 -2015-12-08,WA,2,A,A1,4.71,393.0 -2015-12-09,WA,2,A,A1,4.84,334.0 -2015-12-10,WA,2,A,A1,4.68,387.0 -2015-12-11,WA,2,A,A1,4.69,423.0 -2015-12-12,WA,2,A,A1,4.7,430.0 -2015-12-13,WA,2,A,A1,4.69,380.0 -2015-12-14,WA,2,A,A1,4.7,399.0 -2015-12-15,WA,2,A,A1,4.78,378.0 -2015-12-16,WA,2,A,A1,4.73,415.0 -2015-12-17,WA,2,A,A1,4.58,415.0 -2015-12-18,WA,2,A,A1,4.64,429.0 -2015-12-19,WA,2,A,A1,4.74,419.0 -2015-12-20,WA,2,A,A1,4.66,394.0 -2015-12-21,WA,2,A,A1,4.75,369.0 -2015-12-22,WA,2,A,A1,4.73,425.0 -2015-12-23,WA,2,A,A1,4.69,414.0 -2015-12-24,WA,2,A,A1,4.76,409.0 -2015-12-25,WA,2,A,A1,4.61,449.0 -2015-12-26,WA,2,A,A1,4.66,452.0 -2015-12-27,WA,2,A,A1,4.76,409.0 -2015-12-28,WA,2,A,A1,4.71,397.0 -2015-12-29,WA,2,A,A1,4.77,414.0 -2015-12-30,WA,2,A,A1,4.72,374.0 -2015-12-31,WA,2,A,A1,4.74,388.0 -2016-01-01,WA,2,A,A1,4.69,416.0 -2016-01-02,WA,2,A,A1,4.71,425.0 -2016-01-03,WA,2,A,A1,4.66,407.0 -2016-01-04,WA,2,A,A1,4.66,412.0 -2016-01-05,WA,2,A,A1,4.73,427.0 -2016-01-06,WA,2,A,A1,4.76,373.0 -2016-01-07,WA,2,A,A1,4.63,402.0 -2016-01-08,WA,2,A,A1,4.67,402.0 -2016-01-09,WA,2,A,A1,4.68,422.0 -2016-01-10,WA,2,A,A1,4.7,417.0 -2016-01-11,WA,2,A,A1,4.69,406.0 -2016-01-12,WA,2,A,A1,4.67,407.0 -2016-01-13,WA,2,A,A1,4.67,397.0 -2016-01-14,WA,2,A,A1,4.63,411.0 -2016-01-15,WA,2,A,A1,4.72,396.0 -2016-01-16,WA,2,A,A1,4.68,445.0 -2016-01-17,WA,2,A,A1,4.7,385.0 -2016-01-18,WA,2,A,A1,4.69,400.0 -2016-01-19,WA,2,A,A1,4.71,360.0 -2016-01-20,WA,2,A,A1,4.72,406.0 -2016-01-21,WA,2,A,A1,4.62,411.0 -2016-01-22,WA,2,A,A1,4.78,395.0 -2016-01-23,WA,2,A,A1,4.73,435.0 -2016-01-24,WA,2,A,A1,4.76,403.0 -2016-01-25,WA,2,A,A1,4.77,384.0 -2016-01-26,WA,2,A,A1,4.74,412.0 -2016-01-27,WA,2,A,A1,4.71,400.0 -2016-01-28,WA,2,A,A1,4.77,384.0 -2016-01-29,WA,2,A,A1,4.71,392.0 -2016-01-30,WA,2,A,A1,4.72,413.0 -2016-01-31,WA,2,A,A1,4.76,394.0 -2016-02-01,WA,2,A,A1,4.77,362.0 -2016-02-02,WA,2,A,A1,4.78,401.0 -2016-02-03,WA,2,A,A1,4.69,385.0 -2016-02-04,WA,2,A,A1,4.68,415.0 -2016-02-05,WA,2,A,A1,4.69,413.0 -2016-02-06,WA,2,A,A1,4.74,391.0 -2016-02-07,WA,2,A,A1,4.71,421.0 -2016-02-08,WA,2,A,A1,4.68,394.0 -2016-02-09,WA,2,A,A1,4.65,423.0 -2016-02-10,WA,2,A,A1,4.76,390.0 -2016-02-11,WA,2,A,A1,4.78,379.0 -2016-02-12,WA,2,A,A1,4.71,401.0 -2016-02-13,WA,2,A,A1,4.8,415.0 -2016-02-14,WA,2,A,A1,4.61,444.0 -2016-02-15,WA,2,A,A1,4.7,369.0 -2016-02-16,WA,2,A,A1,4.71,423.0 -2016-02-17,WA,2,A,A1,4.69,391.0 -2016-02-18,WA,2,A,A1,4.72,391.0 -2016-02-19,WA,2,A,A1,4.7,392.0 -2016-02-20,WA,2,A,A1,4.7,404.0 -2016-02-21,WA,2,A,A1,4.71,407.0 -2016-02-22,WA,2,A,A1,4.69,406.0 -2016-02-23,WA,2,A,A1,4.76,368.0 -2016-02-24,WA,2,A,A1,4.66,412.0 -2016-02-25,WA,2,A,A1,4.67,403.0 -2016-02-26,WA,2,A,A1,4.73,370.0 -2016-02-27,WA,2,A,A1,4.77,437.0 -2016-02-28,WA,2,A,A1,4.72,419.0 -2016-02-29,WA,2,A,A1,4.81,391.0 -2016-03-01,WA,2,A,A1,4.71,378.0 -2016-03-02,WA,2,A,A1,4.72,429.0 -2016-03-03,WA,2,A,A1,4.61,397.0 -2016-03-04,WA,2,A,A1,4.73,417.0 -2016-03-05,WA,2,A,A1,4.73,419.0 -2016-03-06,WA,2,A,A1,4.7,391.0 -2016-03-07,WA,2,A,A1,4.73,372.0 -2016-03-08,WA,2,A,A1,4.74,404.0 -2016-03-09,WA,2,A,A1,4.72,400.0 -2016-03-10,WA,2,A,A1,4.71,392.0 -2016-03-11,WA,2,A,A1,4.64,413.0 -2016-03-12,WA,2,A,A1,4.72,392.0 -2016-03-13,WA,2,A,A1,4.66,365.0 -2016-03-14,WA,2,A,A1,4.72,396.0 -2016-03-15,WA,2,A,A1,4.66,403.0 -2016-03-16,WA,2,A,A1,4.72,414.0 -2016-03-17,WA,2,A,A1,4.65,402.0 -2016-03-18,WA,2,A,A1,4.74,403.0 -2016-03-19,WA,2,A,A1,4.72,412.0 -2016-03-20,WA,2,A,A1,4.73,421.0 -2016-03-21,WA,2,A,A1,4.66,417.0 -2016-03-22,WA,2,A,A1,4.66,393.0 -2016-03-23,WA,2,A,A1,4.63,371.0 -2016-03-24,WA,2,A,A1,4.76,388.0 -2016-03-25,WA,2,A,A1,4.71,398.0 -2016-03-26,WA,2,A,A1,4.76,405.0 -2016-03-27,WA,2,A,A1,4.76,401.0 -2016-03-28,WA,2,A,A1,4.73,374.0 -2016-03-29,WA,2,A,A1,4.75,374.0 -2016-03-30,WA,2,A,A1,4.81,389.0 -2016-03-31,WA,2,A,A1,4.71,389.0 -2016-04-01,WA,2,A,A1,4.8,392.0 -2016-04-02,WA,2,A,A1,4.72,396.0 -2016-04-03,WA,2,A,A1,4.63,428.0 -2016-04-04,WA,2,A,A1,4.71,404.0 -2016-04-05,WA,2,A,A1,4.69,394.0 -2016-04-06,WA,2,A,A1,4.72,382.0 -2016-04-07,WA,2,A,A1,4.73,379.0 -2016-04-08,WA,2,A,A1,4.78,409.0 -2016-04-09,WA,2,A,A1,4.77,397.0 -2016-04-10,WA,2,A,A1,4.72,386.0 -2016-04-11,WA,2,A,A1,4.73,369.0 -2016-04-12,WA,2,A,A1,4.73,393.0 -2016-04-13,WA,2,A,A1,4.69,390.0 -2016-04-14,WA,2,A,A1,4.68,415.0 -2016-04-15,WA,2,A,A1,4.68,409.0 -2016-04-16,WA,2,A,A1,4.66,422.0 -2016-04-17,WA,2,A,A1,4.65,402.0 -2016-04-18,WA,2,A,A1,4.67,394.0 -2016-04-19,WA,2,A,A1,4.72,417.0 -2016-04-20,WA,2,A,A1,4.73,420.0 -2016-04-21,WA,2,A,A1,4.77,401.0 -2016-04-22,WA,2,A,A1,4.71,376.0 -2016-04-23,WA,2,A,A1,4.7,392.0 -2016-04-24,WA,2,A,A1,4.74,379.0 -2016-04-25,WA,2,A,A1,4.71,408.0 -2016-04-26,WA,2,A,A1,4.66,399.0 -2016-04-27,WA,2,A,A1,4.68,416.0 -2016-04-28,WA,2,A,A1,4.62,438.0 -2016-04-29,WA,2,A,A1,4.64,397.0 -2016-04-30,WA,2,A,A1,4.62,408.0 -2016-05-01,WA,2,A,A1,4.65,396.0 -2016-05-02,WA,2,A,A1,4.7,410.0 -2016-05-03,WA,2,A,A1,4.66,411.0 -2016-05-04,WA,2,A,A1,4.71,409.0 -2016-05-05,WA,2,A,A1,4.65,384.0 -2016-05-06,WA,2,A,A1,4.69,448.0 -2016-05-07,WA,2,A,A1,4.69,432.0 -2016-05-08,WA,2,A,A1,4.63,411.0 -2016-05-09,WA,2,A,A1,4.75,335.0 -2016-05-10,WA,2,A,A1,4.69,405.0 -2016-05-11,WA,2,A,A1,4.68,433.0 -2016-05-12,WA,2,A,A1,4.71,363.0 -2016-05-13,WA,2,A,A1,4.71,409.0 -2016-05-14,WA,2,A,A1,4.65,441.0 -2016-05-15,WA,2,A,A1,4.79,414.0 -2016-05-16,WA,2,A,A1,4.7,418.0 -2016-05-17,WA,2,A,A1,4.69,407.0 -2016-05-18,WA,2,A,A1,4.78,389.0 -2016-05-19,WA,2,A,A1,4.72,362.0 -2016-05-20,WA,2,A,A1,4.67,407.0 -2016-05-21,WA,2,A,A1,4.73,429.0 -2016-05-22,WA,2,A,A1,4.69,410.0 -2016-05-23,WA,2,A,A1,4.72,405.0 -2016-05-24,WA,2,A,A1,4.76,373.0 -2016-05-25,WA,2,A,A1,4.71,370.0 -2016-05-26,WA,2,A,A1,4.7,401.0 -2016-05-27,WA,2,A,A1,4.74,408.0 -2016-05-28,WA,2,A,A1,4.68,437.0 -2016-05-29,WA,2,A,A1,4.69,416.0 -2016-05-30,WA,2,A,A1,4.77,378.0 -2016-05-31,WA,2,A,A1,4.7,409.0 -2016-06-01,WA,2,A,A1,4.7,399.0 -2016-06-02,WA,2,A,A1,4.67,386.0 -2016-06-03,WA,2,A,A1,4.7,413.0 -2016-06-04,WA,2,A,A1,4.69,440.0 -2016-06-05,WA,2,A,A1,4.73,439.0 -2016-06-06,WA,2,A,A1,4.68,379.0 -2016-06-07,WA,2,A,A1,4.75,400.0 -2016-06-08,WA,2,A,A1,4.7,408.0 -2016-06-09,WA,2,A,A1,4.77,369.0 -2016-06-10,WA,2,A,A1,4.71,388.0 -2016-06-11,WA,2,A,A1,4.74,455.0 -2016-06-12,WA,2,A,A1,4.72,429.0 -2016-06-13,WA,2,A,A1,4.76,403.0 -2016-06-14,WA,2,A,A1,4.71,354.0 -2016-06-15,WA,2,A,A1,4.66,404.0 -2016-06-16,WA,2,A,A1,4.75,420.0 -2016-06-17,WA,2,A,A1,4.72,426.0 -2016-06-18,WA,2,A,A1,4.72,414.0 -2016-06-19,WA,2,A,A1,4.72,391.0 -2016-06-20,WA,2,A,A1,4.73,423.0 -2016-06-21,WA,2,A,A1,4.75,395.0 -2016-06-22,WA,2,A,A1,4.76,391.0 -2016-06-23,WA,2,A,A1,4.62,424.0 -2016-06-24,WA,2,A,A1,4.74,417.0 -2016-06-25,WA,2,A,A1,4.72,411.0 -2016-06-26,WA,2,A,A1,4.65,428.0 -2016-06-27,WA,2,A,A1,4.64,420.0 -2016-06-28,WA,2,A,A1,4.82,363.0 -2016-06-29,WA,2,A,A1,4.72,381.0 -2016-06-30,WA,2,A,A1,4.64,361.0 -2016-07-01,WA,2,A,A1,4.6,410.0 -2016-07-02,WA,2,A,A1,4.69,405.0 -2016-07-03,WA,2,A,A1,4.69,383.0 -2016-07-04,WA,2,A,A1,4.71,404.0 -2016-07-05,WA,2,A,A1,4.63,424.0 -2016-07-06,WA,2,A,A1,4.69,423.0 -2016-07-07,WA,2,A,A1,4.7,386.0 -2016-07-08,WA,2,A,A1,4.74,372.0 -2016-07-09,WA,2,A,A1,4.65,444.0 -2016-07-10,WA,2,A,A1,4.74,363.0 -2016-07-11,WA,2,A,A1,4.77,372.0 -2016-07-12,WA,2,A,A1,4.73,407.0 -2016-07-13,WA,2,A,A1,4.66,396.0 -2016-07-14,WA,2,A,A1,4.68,408.0 -2016-07-15,WA,2,A,A1,4.7,411.0 -2016-07-16,WA,2,A,A1,4.64,426.0 -2016-07-17,WA,2,A,A1,4.65,441.0 -2016-07-18,WA,2,A,A1,4.67,393.0 -2016-07-19,WA,2,A,A1,4.71,408.0 -2016-07-20,WA,2,A,A1,4.7,387.0 -2016-07-21,WA,2,A,A1,4.74,375.0 -2016-07-22,WA,2,A,A1,4.7,391.0 -2016-07-23,WA,2,A,A1,4.68,443.0 -2016-07-24,WA,2,A,A1,4.67,406.0 -2016-07-25,WA,2,A,A1,4.71,365.0 -2016-07-26,WA,2,A,A1,4.71,389.0 -2016-07-27,WA,2,A,A1,4.74,355.0 -2015-01-05,WA,2,A,A2,7.94,121.0 -2015-01-06,WA,2,A,A2,7.79,117.0 -2015-01-07,WA,2,A,A2,7.98,123.0 -2015-01-08,WA,2,A,A2,7.94,118.0 -2015-01-09,WA,2,A,A2,7.91,119.0 -2015-01-10,WA,2,A,A2,7.99,118.0 -2015-01-11,WA,2,A,A2,7.95,122.0 -2015-01-12,WA,2,A,A2,7.91,116.0 -2015-01-13,WA,2,A,A2,7.81,112.0 -2015-01-14,WA,2,A,A2,7.9,123.0 -2015-01-15,WA,2,A,A2,7.93,123.0 -2015-01-16,WA,2,A,A2,7.98,116.0 -2015-01-17,WA,2,A,A2,7.91,132.0 -2015-01-18,WA,2,A,A2,8.0,129.0 -2015-01-19,WA,2,A,A2,7.88,108.0 -2015-01-20,WA,2,A,A2,7.88,120.0 -2015-01-21,WA,2,A,A2,7.76,126.0 -2015-01-22,WA,2,A,A2,7.94,131.0 -2015-01-23,WA,2,A,A2,7.84,128.0 -2015-01-24,WA,2,A,A2,7.79,120.0 -2015-01-25,WA,2,A,A2,7.97,123.0 -2015-01-26,WA,2,A,A2,8.0,111.0 -2015-01-27,WA,2,A,A2,8.05,117.0 -2015-01-28,WA,2,A,A2,7.81,128.0 -2015-01-29,WA,2,A,A2,7.92,121.0 -2015-01-30,WA,2,A,A2,7.92,124.0 -2015-01-31,WA,2,A,A2,7.92,125.0 -2015-02-01,WA,2,A,A2,7.81,121.0 -2015-02-02,WA,2,A,A2,7.89,124.0 -2015-02-03,WA,2,A,A2,7.81,120.0 -2015-02-04,WA,2,A,A2,7.84,127.0 -2015-02-05,WA,2,A,A2,8.02,110.0 -2015-02-06,WA,2,A,A2,7.8,128.0 -2015-02-07,WA,2,A,A2,7.96,128.0 -2015-02-08,WA,2,A,A2,7.84,117.0 -2015-02-09,WA,2,A,A2,7.76,126.0 -2015-02-10,WA,2,A,A2,7.87,117.0 -2015-02-11,WA,2,A,A2,7.81,133.0 -2015-02-12,WA,2,A,A2,7.93,117.0 -2015-02-13,WA,2,A,A2,8.02,124.0 -2015-02-14,WA,2,A,A2,7.98,115.0 -2015-02-15,WA,2,A,A2,7.91,125.0 -2015-02-16,WA,2,A,A2,7.99,110.0 -2015-02-17,WA,2,A,A2,8.0,119.0 -2015-02-18,WA,2,A,A2,7.9,123.0 -2015-02-19,WA,2,A,A2,7.87,134.0 -2015-02-20,WA,2,A,A2,7.98,113.0 -2015-02-21,WA,2,A,A2,7.9,129.0 -2015-02-22,WA,2,A,A2,7.88,121.0 -2015-02-23,WA,2,A,A2,7.99,106.0 -2015-02-24,WA,2,A,A2,7.81,123.0 -2015-02-25,WA,2,A,A2,7.82,129.0 -2015-02-26,WA,2,A,A2,7.8,128.0 -2015-02-27,WA,2,A,A2,7.9,124.0 -2015-02-28,WA,2,A,A2,7.7,135.0 -2015-03-01,WA,2,A,A2,7.77,136.0 -2015-03-02,WA,2,A,A2,7.82,124.0 -2015-03-03,WA,2,A,A2,8.14,110.0 -2015-03-04,WA,2,A,A2,7.9,130.0 -2015-03-05,WA,2,A,A2,8.09,104.0 -2015-03-06,WA,2,A,A2,7.96,129.0 -2015-03-07,WA,2,A,A2,7.92,124.0 -2015-03-08,WA,2,A,A2,8.02,126.0 -2015-03-09,WA,2,A,A2,7.79,108.0 -2015-03-10,WA,2,A,A2,8.12,119.0 -2015-03-11,WA,2,A,A2,8.02,118.0 -2015-03-12,WA,2,A,A2,7.83,126.0 -2015-03-13,WA,2,A,A2,7.82,134.0 -2015-03-14,WA,2,A,A2,8.02,117.0 -2015-03-15,WA,2,A,A2,7.91,127.0 -2015-03-16,WA,2,A,A2,7.97,136.0 -2015-03-17,WA,2,A,A2,7.93,113.0 -2015-03-18,WA,2,A,A2,7.92,127.0 -2015-03-19,WA,2,A,A2,7.88,126.0 -2015-03-20,WA,2,A,A2,7.93,113.0 -2015-03-21,WA,2,A,A2,7.77,136.0 -2015-03-22,WA,2,A,A2,7.9,122.0 -2015-03-23,WA,2,A,A2,7.77,118.0 -2015-03-24,WA,2,A,A2,7.87,113.0 -2015-03-25,WA,2,A,A2,7.94,119.0 -2015-03-26,WA,2,A,A2,8.01,119.0 -2015-03-27,WA,2,A,A2,7.84,127.0 -2015-03-28,WA,2,A,A2,7.85,123.0 -2015-03-29,WA,2,A,A2,7.93,137.0 -2015-03-30,WA,2,A,A2,7.93,129.0 -2015-03-31,WA,2,A,A2,7.93,108.0 -2015-04-01,WA,2,A,A2,7.89,133.0 -2015-04-02,WA,2,A,A2,7.99,124.0 -2015-04-03,WA,2,A,A2,7.87,127.0 -2015-04-04,WA,2,A,A2,7.92,132.0 -2015-04-05,WA,2,A,A2,7.9,127.0 -2015-04-06,WA,2,A,A2,7.73,130.0 -2015-04-07,WA,2,A,A2,7.98,124.0 -2015-04-08,WA,2,A,A2,8.06,115.0 -2015-04-09,WA,2,A,A2,7.83,121.0 -2015-04-10,WA,2,A,A2,7.84,115.0 -2015-04-11,WA,2,A,A2,7.81,124.0 -2015-04-12,WA,2,A,A2,7.88,124.0 -2015-04-13,WA,2,A,A2,7.94,119.0 -2015-04-14,WA,2,A,A2,7.9,127.0 -2015-04-15,WA,2,A,A2,7.92,127.0 -2015-04-16,WA,2,A,A2,7.89,130.0 -2015-04-17,WA,2,A,A2,7.74,137.0 -2015-04-18,WA,2,A,A2,8.02,127.0 -2015-04-19,WA,2,A,A2,7.95,135.0 -2015-04-20,WA,2,A,A2,7.86,121.0 -2015-04-21,WA,2,A,A2,7.79,127.0 -2015-04-22,WA,2,A,A2,7.83,132.0 -2015-04-23,WA,2,A,A2,7.9,113.0 -2015-04-24,WA,2,A,A2,7.88,129.0 -2015-04-25,WA,2,A,A2,7.84,129.0 -2015-04-26,WA,2,A,A2,7.95,126.0 -2015-04-27,WA,2,A,A2,7.89,134.0 -2015-04-28,WA,2,A,A2,7.88,130.0 -2015-04-29,WA,2,A,A2,7.92,120.0 -2015-04-30,WA,2,A,A2,7.82,121.0 -2015-05-01,WA,2,A,A2,7.85,125.0 -2015-05-02,WA,2,A,A2,8.05,119.0 -2015-05-03,WA,2,A,A2,7.7,133.0 -2015-05-04,WA,2,A,A2,7.77,121.0 -2015-05-05,WA,2,A,A2,7.98,124.0 -2015-05-06,WA,2,A,A2,7.98,127.0 -2015-05-07,WA,2,A,A2,8.29,105.0 -2015-05-08,WA,2,A,A2,7.94,117.0 -2015-05-09,WA,2,A,A2,8.04,126.0 -2015-05-10,WA,2,A,A2,7.76,132.0 -2015-05-11,WA,2,A,A2,7.9,113.0 -2015-05-12,WA,2,A,A2,8.1,106.0 -2015-05-13,WA,2,A,A2,7.86,118.0 -2015-05-14,WA,2,A,A2,7.85,122.0 -2015-05-15,WA,2,A,A2,7.9,126.0 -2015-05-16,WA,2,A,A2,7.97,129.0 -2015-05-17,WA,2,A,A2,8.02,114.0 -2015-05-18,WA,2,A,A2,7.94,115.0 -2015-05-19,WA,2,A,A2,7.94,120.0 -2015-05-20,WA,2,A,A2,8.0,117.0 -2015-05-21,WA,2,A,A2,7.88,118.0 -2015-05-22,WA,2,A,A2,7.89,116.0 -2015-05-23,WA,2,A,A2,7.84,122.0 -2015-05-24,WA,2,A,A2,7.98,120.0 -2015-05-25,WA,2,A,A2,8.13,111.0 -2015-05-26,WA,2,A,A2,7.9,126.0 -2015-05-27,WA,2,A,A2,7.88,125.0 -2015-05-28,WA,2,A,A2,8.04,110.0 -2015-05-29,WA,2,A,A2,7.9,124.0 -2015-05-30,WA,2,A,A2,7.86,141.0 -2015-05-31,WA,2,A,A2,7.93,125.0 -2015-06-01,WA,2,A,A2,8.02,110.0 -2015-06-02,WA,2,A,A2,7.81,112.0 -2015-06-03,WA,2,A,A2,7.88,127.0 -2015-06-04,WA,2,A,A2,7.93,123.0 -2015-06-05,WA,2,A,A2,7.87,126.0 -2015-06-06,WA,2,A,A2,7.98,130.0 -2015-06-07,WA,2,A,A2,7.9,127.0 -2015-06-08,WA,2,A,A2,7.88,114.0 -2015-06-09,WA,2,A,A2,7.96,123.0 -2015-06-10,WA,2,A,A2,7.83,130.0 -2015-06-11,WA,2,A,A2,7.76,128.0 -2015-06-12,WA,2,A,A2,8.11,118.0 -2015-06-13,WA,2,A,A2,7.93,131.0 -2015-06-14,WA,2,A,A2,7.94,127.0 -2015-06-15,WA,2,A,A2,7.93,133.0 -2015-06-16,WA,2,A,A2,7.91,115.0 -2015-06-17,WA,2,A,A2,7.97,124.0 -2015-06-18,WA,2,A,A2,7.89,128.0 -2015-06-19,WA,2,A,A2,7.98,126.0 -2015-06-20,WA,2,A,A2,7.95,136.0 -2015-06-21,WA,2,A,A2,7.94,124.0 -2015-06-22,WA,2,A,A2,7.99,111.0 -2015-06-23,WA,2,A,A2,7.86,134.0 -2015-06-24,WA,2,A,A2,7.84,123.0 -2015-06-25,WA,2,A,A2,7.91,124.0 -2015-06-26,WA,2,A,A2,8.03,125.0 -2015-06-27,WA,2,A,A2,7.87,132.0 -2015-06-28,WA,2,A,A2,7.88,129.0 -2015-06-29,WA,2,A,A2,8.03,120.0 -2015-06-30,WA,2,A,A2,7.99,125.0 -2015-07-01,WA,2,A,A2,7.82,122.0 -2015-07-02,WA,2,A,A2,7.87,119.0 -2015-07-03,WA,2,A,A2,7.88,131.0 -2015-07-04,WA,2,A,A2,7.99,121.0 -2015-07-05,WA,2,A,A2,7.85,127.0 -2015-07-06,WA,2,A,A2,7.9,115.0 -2015-07-07,WA,2,A,A2,7.85,110.0 -2015-07-08,WA,2,A,A2,7.98,117.0 -2015-07-09,WA,2,A,A2,7.81,129.0 -2015-07-10,WA,2,A,A2,7.94,120.0 -2015-07-11,WA,2,A,A2,7.82,132.0 -2015-07-12,WA,2,A,A2,7.97,122.0 -2015-07-13,WA,2,A,A2,7.86,127.0 -2015-07-14,WA,2,A,A2,8.04,112.0 -2015-07-15,WA,2,A,A2,8.0,105.0 -2015-07-16,WA,2,A,A2,7.97,122.0 -2015-07-17,WA,2,A,A2,8.03,110.0 -2015-07-18,WA,2,A,A2,7.84,135.0 -2015-07-19,WA,2,A,A2,7.86,129.0 -2015-07-20,WA,2,A,A2,7.92,134.0 -2015-07-21,WA,2,A,A2,7.99,120.0 -2015-07-22,WA,2,A,A2,7.87,130.0 -2015-07-23,WA,2,A,A2,7.84,129.0 -2015-07-24,WA,2,A,A2,7.88,127.0 -2015-07-25,WA,2,A,A2,7.88,135.0 -2015-07-26,WA,2,A,A2,8.01,121.0 -2015-07-27,WA,2,A,A2,8.02,120.0 -2015-07-28,WA,2,A,A2,7.76,128.0 -2015-07-29,WA,2,A,A2,7.92,112.0 -2015-07-30,WA,2,A,A2,7.93,127.0 -2015-07-31,WA,2,A,A2,7.88,121.0 -2015-08-01,WA,2,A,A2,7.81,123.0 -2015-08-02,WA,2,A,A2,8.07,113.0 -2015-08-03,WA,2,A,A2,7.78,125.0 -2015-08-04,WA,2,A,A2,7.97,126.0 -2015-08-05,WA,2,A,A2,7.99,118.0 -2015-08-06,WA,2,A,A2,7.74,123.0 -2015-08-07,WA,2,A,A2,7.94,121.0 -2015-08-08,WA,2,A,A2,7.85,136.0 -2015-08-09,WA,2,A,A2,7.95,123.0 -2015-08-10,WA,2,A,A2,7.86,122.0 -2015-08-11,WA,2,A,A2,8.02,107.0 -2015-08-12,WA,2,A,A2,7.82,120.0 -2015-08-13,WA,2,A,A2,8.01,119.0 -2015-08-14,WA,2,A,A2,7.74,127.0 -2015-08-15,WA,2,A,A2,7.85,129.0 -2015-08-16,WA,2,A,A2,7.88,132.0 -2015-08-17,WA,2,A,A2,7.98,111.0 -2015-08-18,WA,2,A,A2,7.7,132.0 -2015-08-19,WA,2,A,A2,7.96,126.0 -2015-08-20,WA,2,A,A2,7.82,128.0 -2015-08-21,WA,2,A,A2,7.91,129.0 -2015-08-22,WA,2,A,A2,7.93,121.0 -2015-08-23,WA,2,A,A2,7.9,120.0 -2015-08-24,WA,2,A,A2,7.9,120.0 -2015-08-25,WA,2,A,A2,7.79,125.0 -2015-08-26,WA,2,A,A2,7.95,124.0 -2015-08-27,WA,2,A,A2,7.97,127.0 -2015-08-28,WA,2,A,A2,7.9,130.0 -2015-08-29,WA,2,A,A2,8.09,128.0 -2015-08-30,WA,2,A,A2,7.94,122.0 -2015-08-31,WA,2,A,A2,7.94,123.0 -2015-09-01,WA,2,A,A2,7.88,117.0 -2015-09-02,WA,2,A,A2,7.76,127.0 -2015-09-03,WA,2,A,A2,7.84,125.0 -2015-09-04,WA,2,A,A2,7.96,129.0 -2015-09-05,WA,2,A,A2,7.99,125.0 -2015-09-06,WA,2,A,A2,7.9,140.0 -2015-09-07,WA,2,A,A2,7.93,127.0 -2015-09-08,WA,2,A,A2,7.94,128.0 -2015-09-09,WA,2,A,A2,8.1,122.0 -2015-09-10,WA,2,A,A2,7.96,116.0 -2015-09-11,WA,2,A,A2,7.95,116.0 -2015-09-12,WA,2,A,A2,7.89,117.0 -2015-09-13,WA,2,A,A2,7.8,128.0 -2015-09-14,WA,2,A,A2,7.96,109.0 -2015-09-15,WA,2,A,A2,8.04,118.0 -2015-09-16,WA,2,A,A2,7.93,126.0 -2015-09-17,WA,2,A,A2,7.84,118.0 -2015-09-18,WA,2,A,A2,7.88,120.0 -2015-09-19,WA,2,A,A2,7.92,130.0 -2015-09-20,WA,2,A,A2,7.87,134.0 -2015-09-21,WA,2,A,A2,7.89,120.0 -2015-09-22,WA,2,A,A2,7.95,120.0 -2015-09-23,WA,2,A,A2,7.95,112.0 -2015-09-24,WA,2,A,A2,7.92,131.0 -2015-09-25,WA,2,A,A2,7.77,131.0 -2015-09-26,WA,2,A,A2,7.78,130.0 -2015-09-27,WA,2,A,A2,7.73,137.0 -2015-09-28,WA,2,A,A2,8.02,111.0 -2015-09-29,WA,2,A,A2,7.91,126.0 -2015-09-30,WA,2,A,A2,7.98,116.0 -2015-10-01,WA,2,A,A2,8.01,127.0 -2015-10-02,WA,2,A,A2,7.87,131.0 -2015-10-03,WA,2,A,A2,7.84,131.0 -2015-10-04,WA,2,A,A2,7.9,119.0 -2015-10-05,WA,2,A,A2,7.83,125.0 -2015-10-06,WA,2,A,A2,7.89,131.0 -2015-10-07,WA,2,A,A2,7.96,122.0 -2015-10-08,WA,2,A,A2,7.98,122.0 -2015-10-09,WA,2,A,A2,7.89,129.0 -2015-10-10,WA,2,A,A2,7.85,128.0 -2015-10-11,WA,2,A,A2,7.8,138.0 -2015-10-12,WA,2,A,A2,7.98,118.0 -2015-10-13,WA,2,A,A2,7.83,123.0 -2015-10-14,WA,2,A,A2,7.85,137.0 -2015-10-15,WA,2,A,A2,7.9,129.0 -2015-10-16,WA,2,A,A2,7.99,118.0 -2015-10-17,WA,2,A,A2,8.06,133.0 -2015-10-18,WA,2,A,A2,8.01,124.0 -2015-10-19,WA,2,A,A2,7.79,128.0 -2015-10-20,WA,2,A,A2,7.94,119.0 -2015-10-21,WA,2,A,A2,7.86,124.0 -2015-10-22,WA,2,A,A2,7.91,124.0 -2015-10-23,WA,2,A,A2,7.92,130.0 -2015-10-24,WA,2,A,A2,7.83,140.0 -2015-10-25,WA,2,A,A2,8.03,113.0 -2015-10-26,WA,2,A,A2,7.87,117.0 -2015-10-27,WA,2,A,A2,7.85,123.0 -2015-10-28,WA,2,A,A2,7.91,129.0 -2015-10-29,WA,2,A,A2,7.91,123.0 -2015-10-30,WA,2,A,A2,7.94,128.0 -2015-10-31,WA,2,A,A2,7.98,120.0 -2015-11-01,WA,2,A,A2,7.94,118.0 -2015-11-02,WA,2,A,A2,7.85,131.0 -2015-11-03,WA,2,A,A2,7.93,118.0 -2015-11-04,WA,2,A,A2,7.85,123.0 -2015-11-05,WA,2,A,A2,7.89,127.0 -2015-11-06,WA,2,A,A2,8.02,121.0 -2015-11-07,WA,2,A,A2,7.88,129.0 -2015-11-08,WA,2,A,A2,7.9,133.0 -2015-11-09,WA,2,A,A2,7.93,125.0 -2015-11-10,WA,2,A,A2,7.9,116.0 -2015-11-11,WA,2,A,A2,7.87,124.0 -2015-11-12,WA,2,A,A2,7.79,128.0 -2015-11-13,WA,2,A,A2,7.89,132.0 -2015-11-14,WA,2,A,A2,7.92,124.0 -2015-11-15,WA,2,A,A2,7.75,126.0 -2015-11-16,WA,2,A,A2,7.92,127.0 -2015-11-17,WA,2,A,A2,8.02,129.0 -2015-11-18,WA,2,A,A2,7.97,121.0 -2015-11-19,WA,2,A,A2,7.81,127.0 -2015-11-20,WA,2,A,A2,8.06,130.0 -2015-11-21,WA,2,A,A2,7.88,134.0 -2015-11-22,WA,2,A,A2,7.94,120.0 -2015-11-23,WA,2,A,A2,7.9,121.0 -2015-11-24,WA,2,A,A2,7.95,122.0 -2015-11-25,WA,2,A,A2,7.93,119.0 -2015-11-26,WA,2,A,A2,7.92,118.0 -2015-11-27,WA,2,A,A2,7.92,124.0 -2015-11-28,WA,2,A,A2,7.97,123.0 -2015-11-29,WA,2,A,A2,7.88,119.0 -2015-11-30,WA,2,A,A2,8.02,119.0 -2015-12-01,WA,2,A,A2,7.88,120.0 -2015-12-02,WA,2,A,A2,7.97,120.0 -2015-12-03,WA,2,A,A2,8.06,112.0 -2015-12-04,WA,2,A,A2,7.83,125.0 -2015-12-05,WA,2,A,A2,7.85,125.0 -2015-12-06,WA,2,A,A2,7.97,133.0 -2015-12-07,WA,2,A,A2,7.84,119.0 -2015-12-08,WA,2,A,A2,7.9,128.0 -2015-12-09,WA,2,A,A2,7.9,124.0 -2015-12-10,WA,2,A,A2,7.85,115.0 -2015-12-11,WA,2,A,A2,7.86,130.0 -2015-12-12,WA,2,A,A2,7.86,126.0 -2015-12-13,WA,2,A,A2,7.89,121.0 -2015-12-14,WA,2,A,A2,7.96,119.0 -2015-12-15,WA,2,A,A2,8.06,107.0 -2015-12-16,WA,2,A,A2,7.84,128.0 -2015-12-17,WA,2,A,A2,7.9,126.0 -2015-12-18,WA,2,A,A2,8.04,127.0 -2015-12-19,WA,2,A,A2,7.84,136.0 -2015-12-20,WA,2,A,A2,7.99,121.0 -2015-12-21,WA,2,A,A2,7.79,131.0 -2015-12-22,WA,2,A,A2,7.96,126.0 -2015-12-23,WA,2,A,A2,7.86,119.0 -2015-12-24,WA,2,A,A2,7.87,114.0 -2015-12-25,WA,2,A,A2,7.88,126.0 -2015-12-26,WA,2,A,A2,7.9,129.0 -2015-12-27,WA,2,A,A2,7.89,130.0 -2015-12-28,WA,2,A,A2,7.84,114.0 -2015-12-29,WA,2,A,A2,7.86,127.0 -2015-12-30,WA,2,A,A2,7.92,135.0 -2015-12-31,WA,2,A,A2,7.87,123.0 -2016-01-01,WA,2,A,A2,7.76,123.0 -2016-01-02,WA,2,A,A2,7.89,143.0 -2016-01-03,WA,2,A,A2,7.96,116.0 -2016-01-04,WA,2,A,A2,8.0,118.0 -2016-01-05,WA,2,A,A2,7.87,124.0 -2016-01-06,WA,2,A,A2,7.85,133.0 -2016-01-07,WA,2,A,A2,7.93,107.0 -2016-01-08,WA,2,A,A2,7.95,123.0 -2016-01-09,WA,2,A,A2,7.97,135.0 -2016-01-10,WA,2,A,A2,7.87,124.0 -2016-01-11,WA,2,A,A2,7.91,129.0 -2016-01-12,WA,2,A,A2,8.02,127.0 -2016-01-13,WA,2,A,A2,7.76,132.0 -2016-01-14,WA,2,A,A2,7.99,130.0 -2016-01-15,WA,2,A,A2,7.87,131.0 -2016-01-16,WA,2,A,A2,7.99,133.0 -2016-01-17,WA,2,A,A2,7.86,133.0 -2016-01-18,WA,2,A,A2,7.93,129.0 -2016-01-19,WA,2,A,A2,7.98,118.0 -2016-01-20,WA,2,A,A2,7.97,119.0 -2016-01-21,WA,2,A,A2,7.9,117.0 -2016-01-22,WA,2,A,A2,7.89,117.0 -2016-01-23,WA,2,A,A2,7.9,125.0 -2016-01-24,WA,2,A,A2,7.96,139.0 -2016-01-25,WA,2,A,A2,7.88,109.0 -2016-01-26,WA,2,A,A2,8.05,111.0 -2016-01-27,WA,2,A,A2,7.83,128.0 -2016-01-28,WA,2,A,A2,8.0,119.0 -2016-01-29,WA,2,A,A2,8.09,115.0 -2016-01-30,WA,2,A,A2,7.83,134.0 -2016-01-31,WA,2,A,A2,7.91,125.0 -2016-02-01,WA,2,A,A2,7.9,115.0 -2016-02-02,WA,2,A,A2,7.9,120.0 -2016-02-03,WA,2,A,A2,8.0,119.0 -2016-02-04,WA,2,A,A2,7.91,118.0 -2016-02-05,WA,2,A,A2,8.02,109.0 -2016-02-06,WA,2,A,A2,7.81,137.0 -2016-02-07,WA,2,A,A2,7.81,140.0 -2016-02-08,WA,2,A,A2,7.94,122.0 -2016-02-09,WA,2,A,A2,8.04,122.0 -2016-02-10,WA,2,A,A2,7.99,123.0 -2016-02-11,WA,2,A,A2,7.94,127.0 -2016-02-12,WA,2,A,A2,8.0,122.0 -2016-02-13,WA,2,A,A2,7.88,141.0 -2016-02-14,WA,2,A,A2,7.96,129.0 -2016-02-15,WA,2,A,A2,8.06,117.0 -2016-02-16,WA,2,A,A2,7.8,135.0 -2016-02-17,WA,2,A,A2,7.84,126.0 -2016-02-18,WA,2,A,A2,7.84,129.0 -2016-02-19,WA,2,A,A2,7.86,134.0 -2016-02-20,WA,2,A,A2,7.94,129.0 -2016-02-21,WA,2,A,A2,7.8,135.0 -2016-02-22,WA,2,A,A2,7.77,126.0 -2016-02-23,WA,2,A,A2,8.05,120.0 -2016-02-24,WA,2,A,A2,7.87,115.0 -2016-02-25,WA,2,A,A2,7.93,125.0 -2016-02-26,WA,2,A,A2,7.82,134.0 -2016-02-27,WA,2,A,A2,8.03,115.0 -2016-02-28,WA,2,A,A2,7.83,129.0 -2016-02-29,WA,2,A,A2,7.85,125.0 -2016-03-01,WA,2,A,A2,7.85,118.0 -2016-03-02,WA,2,A,A2,7.86,119.0 -2016-03-03,WA,2,A,A2,7.84,119.0 -2016-03-04,WA,2,A,A2,7.85,129.0 -2016-03-05,WA,2,A,A2,7.87,136.0 -2016-03-06,WA,2,A,A2,7.98,125.0 -2016-03-07,WA,2,A,A2,7.91,126.0 -2016-03-08,WA,2,A,A2,7.95,120.0 -2016-03-09,WA,2,A,A2,7.83,127.0 -2016-03-10,WA,2,A,A2,7.85,123.0 -2016-03-11,WA,2,A,A2,7.84,121.0 -2016-03-12,WA,2,A,A2,8.02,117.0 -2016-03-13,WA,2,A,A2,7.87,137.0 -2016-03-14,WA,2,A,A2,7.97,109.0 -2016-03-15,WA,2,A,A2,7.84,133.0 -2016-03-16,WA,2,A,A2,7.93,123.0 -2016-03-17,WA,2,A,A2,7.89,128.0 -2016-03-18,WA,2,A,A2,7.93,124.0 -2016-03-19,WA,2,A,A2,7.89,135.0 -2016-03-20,WA,2,A,A2,7.86,132.0 -2016-03-21,WA,2,A,A2,7.89,131.0 -2016-03-22,WA,2,A,A2,7.99,110.0 -2016-03-23,WA,2,A,A2,7.78,120.0 -2016-03-24,WA,2,A,A2,7.92,119.0 -2016-03-25,WA,2,A,A2,7.98,108.0 -2016-03-26,WA,2,A,A2,8.1,129.0 -2016-03-27,WA,2,A,A2,7.93,114.0 -2016-03-28,WA,2,A,A2,8.02,110.0 -2016-03-29,WA,2,A,A2,7.96,125.0 -2016-03-30,WA,2,A,A2,8.02,122.0 -2016-03-31,WA,2,A,A2,7.98,119.0 -2016-04-01,WA,2,A,A2,8.02,114.0 -2016-04-02,WA,2,A,A2,7.91,128.0 -2016-04-03,WA,2,A,A2,7.81,135.0 -2016-04-04,WA,2,A,A2,8.09,113.0 -2016-04-05,WA,2,A,A2,7.89,139.0 -2016-04-06,WA,2,A,A2,7.97,128.0 -2016-04-07,WA,2,A,A2,7.92,124.0 -2016-04-08,WA,2,A,A2,7.88,125.0 -2016-04-09,WA,2,A,A2,7.92,125.0 -2016-04-10,WA,2,A,A2,7.98,125.0 -2016-04-11,WA,2,A,A2,7.94,120.0 -2016-04-12,WA,2,A,A2,7.91,122.0 -2016-04-13,WA,2,A,A2,7.82,135.0 -2016-04-14,WA,2,A,A2,7.78,131.0 -2016-04-15,WA,2,A,A2,7.8,124.0 -2016-04-16,WA,2,A,A2,7.98,116.0 -2016-04-17,WA,2,A,A2,7.78,126.0 -2016-04-18,WA,2,A,A2,7.91,125.0 -2016-04-19,WA,2,A,A2,7.9,121.0 -2016-04-20,WA,2,A,A2,7.82,131.0 -2016-04-21,WA,2,A,A2,7.88,130.0 -2016-04-22,WA,2,A,A2,7.96,123.0 -2016-04-23,WA,2,A,A2,7.9,127.0 -2016-04-24,WA,2,A,A2,7.79,140.0 -2016-04-25,WA,2,A,A2,7.9,117.0 -2016-04-26,WA,2,A,A2,7.91,123.0 -2016-04-27,WA,2,A,A2,7.93,125.0 -2016-04-28,WA,2,A,A2,7.79,125.0 -2016-04-29,WA,2,A,A2,7.97,116.0 -2016-04-30,WA,2,A,A2,7.88,132.0 -2016-05-01,WA,2,A,A2,7.81,127.0 -2016-05-02,WA,2,A,A2,7.97,128.0 -2016-05-03,WA,2,A,A2,7.79,119.0 -2016-05-04,WA,2,A,A2,8.0,123.0 -2016-05-05,WA,2,A,A2,7.98,103.0 -2016-05-06,WA,2,A,A2,7.92,118.0 -2016-05-07,WA,2,A,A2,7.93,135.0 -2016-05-08,WA,2,A,A2,7.95,122.0 -2016-05-09,WA,2,A,A2,7.75,128.0 -2016-05-10,WA,2,A,A2,7.96,125.0 -2016-05-11,WA,2,A,A2,7.91,112.0 -2016-05-12,WA,2,A,A2,7.83,126.0 -2016-05-13,WA,2,A,A2,8.04,117.0 -2016-05-14,WA,2,A,A2,7.94,125.0 -2016-05-15,WA,2,A,A2,8.01,115.0 -2016-05-16,WA,2,A,A2,8.03,126.0 -2016-05-17,WA,2,A,A2,7.95,122.0 -2016-05-18,WA,2,A,A2,7.93,116.0 -2016-05-19,WA,2,A,A2,7.91,128.0 -2016-05-20,WA,2,A,A2,7.8,139.0 -2016-05-21,WA,2,A,A2,7.91,138.0 -2016-05-22,WA,2,A,A2,7.86,123.0 -2016-05-23,WA,2,A,A2,7.83,126.0 -2016-05-24,WA,2,A,A2,7.86,125.0 -2016-05-25,WA,2,A,A2,7.97,131.0 -2016-05-26,WA,2,A,A2,7.92,130.0 -2016-05-27,WA,2,A,A2,7.8,126.0 -2016-05-28,WA,2,A,A2,7.84,118.0 -2016-05-29,WA,2,A,A2,7.84,122.0 -2016-05-30,WA,2,A,A2,7.87,128.0 -2016-05-31,WA,2,A,A2,7.78,124.0 -2016-06-01,WA,2,A,A2,7.87,131.0 -2016-06-02,WA,2,A,A2,8.0,117.0 -2016-06-03,WA,2,A,A2,8.11,120.0 -2016-06-04,WA,2,A,A2,7.94,129.0 -2016-06-05,WA,2,A,A2,7.84,126.0 -2016-06-06,WA,2,A,A2,8.01,117.0 -2016-06-07,WA,2,A,A2,7.84,120.0 -2016-06-08,WA,2,A,A2,7.87,121.0 -2016-06-09,WA,2,A,A2,7.78,118.0 -2016-06-10,WA,2,A,A2,7.88,124.0 -2016-06-11,WA,2,A,A2,7.9,128.0 -2016-06-12,WA,2,A,A2,7.96,128.0 -2016-06-13,WA,2,A,A2,8.04,111.0 -2016-06-14,WA,2,A,A2,7.84,123.0 -2016-06-15,WA,2,A,A2,7.94,115.0 -2016-06-16,WA,2,A,A2,7.99,129.0 -2016-06-17,WA,2,A,A2,7.88,121.0 -2016-06-18,WA,2,A,A2,7.96,129.0 -2016-06-19,WA,2,A,A2,7.93,129.0 -2016-06-20,WA,2,A,A2,8.02,124.0 -2016-06-21,WA,2,A,A2,7.97,121.0 -2016-06-22,WA,2,A,A2,7.95,126.0 -2016-06-23,WA,2,A,A2,7.9,117.0 -2016-06-24,WA,2,A,A2,7.88,117.0 -2016-06-25,WA,2,A,A2,7.9,120.0 -2016-06-26,WA,2,A,A2,7.96,119.0 -2016-06-27,WA,2,A,A2,7.95,130.0 -2016-06-28,WA,2,A,A2,7.88,121.0 -2016-06-29,WA,2,A,A2,7.93,121.0 -2016-06-30,WA,2,A,A2,7.98,124.0 -2016-07-01,WA,2,A,A2,7.86,117.0 -2016-07-02,WA,2,A,A2,7.65,143.0 -2016-07-03,WA,2,A,A2,7.86,122.0 -2016-07-04,WA,2,A,A2,8.01,120.0 -2016-07-05,WA,2,A,A2,7.91,126.0 -2016-07-06,WA,2,A,A2,7.94,121.0 -2016-07-07,WA,2,A,A2,7.86,115.0 -2016-07-08,WA,2,A,A2,7.96,114.0 -2016-07-09,WA,2,A,A2,7.83,130.0 -2016-07-10,WA,2,A,A2,7.77,133.0 -2016-07-11,WA,2,A,A2,8.05,117.0 -2016-07-12,WA,2,A,A2,7.82,128.0 -2016-07-13,WA,2,A,A2,7.94,105.0 -2016-07-14,WA,2,A,A2,7.92,128.0 -2016-07-15,WA,2,A,A2,7.95,120.0 -2016-07-16,WA,2,A,A2,7.84,134.0 -2016-07-17,WA,2,A,A2,8.04,117.0 -2016-07-18,WA,2,A,A2,7.9,112.0 -2016-07-19,WA,2,A,A2,7.91,119.0 -2016-07-20,WA,2,A,A2,7.78,118.0 -2016-07-21,WA,2,A,A2,7.99,119.0 -2016-07-22,WA,2,A,A2,7.88,128.0 -2016-07-23,WA,2,A,A2,7.99,124.0 -2016-07-24,WA,2,A,A2,8.01,127.0 -2016-07-25,WA,2,A,A2,7.81,127.0 -2016-07-26,WA,2,A,A2,8.04,115.0 -2016-07-27,WA,2,A,A2,7.91,121.0 -2015-01-05,WA,2,A,A3,6.92,366.0 -2015-01-06,WA,2,A,A3,6.95,372.0 -2015-01-07,WA,2,A,A3,6.8,388.0 -2015-01-08,WA,2,A,A3,6.96,351.0 -2015-01-09,WA,2,A,A3,6.89,390.0 -2015-01-10,WA,2,A,A3,6.89,360.0 -2015-01-11,WA,2,A,A3,6.97,352.0 -2015-01-12,WA,2,A,A3,6.98,379.0 -2015-01-13,WA,2,A,A3,6.84,384.0 -2015-01-14,WA,2,A,A3,6.85,382.0 -2015-01-15,WA,2,A,A3,6.91,362.0 -2015-01-16,WA,2,A,A3,7.02,325.0 -2015-01-17,WA,2,A,A3,6.9,350.0 -2015-01-18,WA,2,A,A3,6.88,377.0 -2015-01-19,WA,2,A,A3,6.95,328.0 -2015-01-20,WA,2,A,A3,6.87,340.0 -2015-01-21,WA,2,A,A3,6.96,372.0 -2015-01-22,WA,2,A,A3,6.79,399.0 -2015-01-23,WA,2,A,A3,7.13,347.0 -2015-01-24,WA,2,A,A3,6.81,408.0 -2015-01-25,WA,2,A,A3,6.88,333.0 -2015-01-26,WA,2,A,A3,6.83,362.0 -2015-01-27,WA,2,A,A3,6.81,347.0 -2015-01-28,WA,2,A,A3,6.85,386.0 -2015-01-29,WA,2,A,A3,6.9,373.0 -2015-01-30,WA,2,A,A3,6.96,321.0 -2015-01-31,WA,2,A,A3,6.86,357.0 -2015-02-01,WA,2,A,A3,6.93,339.0 -2015-02-02,WA,2,A,A3,6.85,338.0 -2015-02-03,WA,2,A,A3,6.92,368.0 -2015-02-04,WA,2,A,A3,6.81,378.0 -2015-02-05,WA,2,A,A3,6.85,333.0 -2015-02-06,WA,2,A,A3,6.97,354.0 -2015-02-07,WA,2,A,A3,6.78,422.0 -2015-02-08,WA,2,A,A3,6.82,372.0 -2015-02-09,WA,2,A,A3,6.98,346.0 -2015-02-10,WA,2,A,A3,7.06,343.0 -2015-02-11,WA,2,A,A3,6.89,367.0 -2015-02-12,WA,2,A,A3,6.94,346.0 -2015-02-13,WA,2,A,A3,6.89,382.0 -2015-02-14,WA,2,A,A3,6.95,366.0 -2015-02-15,WA,2,A,A3,6.92,368.0 -2015-02-16,WA,2,A,A3,6.87,352.0 -2015-02-17,WA,2,A,A3,6.89,351.0 -2015-02-18,WA,2,A,A3,6.81,355.0 -2015-02-19,WA,2,A,A3,7.01,384.0 -2015-02-20,WA,2,A,A3,6.92,355.0 -2015-02-21,WA,2,A,A3,6.9,387.0 -2015-02-22,WA,2,A,A3,6.83,373.0 -2015-02-23,WA,2,A,A3,6.93,386.0 -2015-02-24,WA,2,A,A3,6.95,313.0 -2015-02-25,WA,2,A,A3,6.8,368.0 -2015-02-26,WA,2,A,A3,6.79,328.0 -2015-02-27,WA,2,A,A3,6.95,359.0 -2015-02-28,WA,2,A,A3,6.84,395.0 -2015-03-01,WA,2,A,A3,6.81,422.0 -2015-03-02,WA,2,A,A3,6.91,372.0 -2015-03-03,WA,2,A,A3,6.88,355.0 -2015-03-04,WA,2,A,A3,6.9,342.0 -2015-03-05,WA,2,A,A3,6.94,339.0 -2015-03-06,WA,2,A,A3,6.98,358.0 -2015-03-07,WA,2,A,A3,6.77,405.0 -2015-03-08,WA,2,A,A3,6.95,347.0 -2015-03-09,WA,2,A,A3,6.96,334.0 -2015-03-10,WA,2,A,A3,6.86,390.0 -2015-03-11,WA,2,A,A3,6.81,373.0 -2015-03-12,WA,2,A,A3,6.87,387.0 -2015-03-13,WA,2,A,A3,6.91,362.0 -2015-03-14,WA,2,A,A3,6.95,391.0 -2015-03-15,WA,2,A,A3,6.94,348.0 -2015-03-16,WA,2,A,A3,6.9,370.0 -2015-03-17,WA,2,A,A3,6.97,365.0 -2015-03-18,WA,2,A,A3,6.8,374.0 -2015-03-19,WA,2,A,A3,6.81,356.0 -2015-03-20,WA,2,A,A3,6.85,389.0 -2015-03-21,WA,2,A,A3,6.94,397.0 -2015-03-22,WA,2,A,A3,6.95,367.0 -2015-03-23,WA,2,A,A3,6.91,354.0 -2015-03-24,WA,2,A,A3,7.02,350.0 -2015-03-25,WA,2,A,A3,6.94,347.0 -2015-03-26,WA,2,A,A3,6.88,372.0 -2015-03-27,WA,2,A,A3,6.88,377.0 -2015-03-28,WA,2,A,A3,6.89,415.0 -2015-03-29,WA,2,A,A3,6.87,389.0 -2015-03-30,WA,2,A,A3,6.9,319.0 -2015-03-31,WA,2,A,A3,6.81,368.0 -2015-04-01,WA,2,A,A3,6.95,374.0 -2015-04-02,WA,2,A,A3,6.87,376.0 -2015-04-03,WA,2,A,A3,6.92,374.0 -2015-04-04,WA,2,A,A3,6.87,369.0 -2015-04-05,WA,2,A,A3,6.95,324.0 -2015-04-06,WA,2,A,A3,6.9,378.0 -2015-04-07,WA,2,A,A3,6.99,356.0 -2015-04-08,WA,2,A,A3,6.92,341.0 -2015-04-09,WA,2,A,A3,6.91,367.0 -2015-04-10,WA,2,A,A3,6.91,369.0 -2015-04-11,WA,2,A,A3,7.01,371.0 -2015-04-12,WA,2,A,A3,6.97,331.0 -2015-04-13,WA,2,A,A3,6.96,351.0 -2015-04-14,WA,2,A,A3,6.93,350.0 -2015-04-15,WA,2,A,A3,6.96,346.0 -2015-04-16,WA,2,A,A3,6.86,344.0 -2015-04-17,WA,2,A,A3,6.86,383.0 -2015-04-18,WA,2,A,A3,6.86,388.0 -2015-04-19,WA,2,A,A3,6.95,350.0 -2015-04-20,WA,2,A,A3,6.99,331.0 -2015-04-21,WA,2,A,A3,6.88,348.0 -2015-04-22,WA,2,A,A3,6.88,409.0 -2015-04-23,WA,2,A,A3,6.87,388.0 -2015-04-24,WA,2,A,A3,6.9,352.0 -2015-04-25,WA,2,A,A3,6.96,383.0 -2015-04-26,WA,2,A,A3,6.88,362.0 -2015-04-27,WA,2,A,A3,6.98,351.0 -2015-04-28,WA,2,A,A3,6.81,399.0 -2015-04-29,WA,2,A,A3,6.93,363.0 -2015-04-30,WA,2,A,A3,6.83,401.0 -2015-05-01,WA,2,A,A3,6.87,387.0 -2015-05-02,WA,2,A,A3,6.96,372.0 -2015-05-03,WA,2,A,A3,6.89,363.0 -2015-05-04,WA,2,A,A3,6.82,389.0 -2015-05-05,WA,2,A,A3,6.85,392.0 -2015-05-06,WA,2,A,A3,6.98,342.0 -2015-05-07,WA,2,A,A3,6.91,363.0 -2015-05-08,WA,2,A,A3,6.84,368.0 -2015-05-09,WA,2,A,A3,7.0,352.0 -2015-05-10,WA,2,A,A3,6.9,357.0 -2015-05-11,WA,2,A,A3,6.96,362.0 -2015-05-12,WA,2,A,A3,6.92,368.0 -2015-05-13,WA,2,A,A3,6.93,380.0 -2015-05-14,WA,2,A,A3,6.98,360.0 -2015-05-15,WA,2,A,A3,6.83,364.0 -2015-05-16,WA,2,A,A3,6.9,377.0 -2015-05-17,WA,2,A,A3,6.87,376.0 -2015-05-18,WA,2,A,A3,6.96,339.0 -2015-05-19,WA,2,A,A3,6.93,359.0 -2015-05-20,WA,2,A,A3,6.88,373.0 -2015-05-21,WA,2,A,A3,6.95,347.0 -2015-05-22,WA,2,A,A3,6.86,348.0 -2015-05-23,WA,2,A,A3,6.97,355.0 -2015-05-24,WA,2,A,A3,6.94,365.0 -2015-05-25,WA,2,A,A3,6.98,382.0 -2015-05-26,WA,2,A,A3,6.95,348.0 -2015-05-27,WA,2,A,A3,6.93,362.0 -2015-05-28,WA,2,A,A3,6.91,347.0 -2015-05-29,WA,2,A,A3,6.89,386.0 -2015-05-30,WA,2,A,A3,6.89,397.0 -2015-05-31,WA,2,A,A3,6.79,390.0 -2015-06-01,WA,2,A,A3,6.73,394.0 -2015-06-02,WA,2,A,A3,6.88,377.0 -2015-06-03,WA,2,A,A3,6.94,372.0 -2015-06-04,WA,2,A,A3,6.96,329.0 -2015-06-05,WA,2,A,A3,7.02,309.0 -2015-06-06,WA,2,A,A3,6.93,396.0 -2015-06-07,WA,2,A,A3,7.03,363.0 -2015-06-08,WA,2,A,A3,7.02,341.0 -2015-06-09,WA,2,A,A3,6.83,379.0 -2015-06-10,WA,2,A,A3,6.94,368.0 -2015-06-11,WA,2,A,A3,6.88,373.0 -2015-06-12,WA,2,A,A3,6.81,387.0 -2015-06-13,WA,2,A,A3,6.96,366.0 -2015-06-14,WA,2,A,A3,6.86,396.0 -2015-06-15,WA,2,A,A3,6.88,362.0 -2015-06-16,WA,2,A,A3,6.93,355.0 -2015-06-17,WA,2,A,A3,6.91,312.0 -2015-06-18,WA,2,A,A3,6.83,385.0 -2015-06-19,WA,2,A,A3,6.98,356.0 -2015-06-20,WA,2,A,A3,6.91,386.0 -2015-06-21,WA,2,A,A3,6.92,383.0 -2015-06-22,WA,2,A,A3,6.98,345.0 -2015-06-23,WA,2,A,A3,6.86,390.0 -2015-06-24,WA,2,A,A3,7.15,277.0 -2015-06-25,WA,2,A,A3,6.86,364.0 -2015-06-26,WA,2,A,A3,6.97,364.0 -2015-06-27,WA,2,A,A3,6.92,373.0 -2015-06-28,WA,2,A,A3,6.86,358.0 -2015-06-29,WA,2,A,A3,6.96,341.0 -2015-06-30,WA,2,A,A3,6.93,343.0 -2015-07-01,WA,2,A,A3,6.9,406.0 -2015-07-02,WA,2,A,A3,6.88,374.0 -2015-07-03,WA,2,A,A3,6.83,394.0 -2015-07-04,WA,2,A,A3,6.85,386.0 -2015-07-05,WA,2,A,A3,6.93,335.0 -2015-07-06,WA,2,A,A3,6.91,342.0 -2015-07-07,WA,2,A,A3,6.85,369.0 -2015-07-08,WA,2,A,A3,7.02,341.0 -2015-07-09,WA,2,A,A3,6.9,376.0 -2015-07-10,WA,2,A,A3,6.75,383.0 -2015-07-11,WA,2,A,A3,6.82,426.0 -2015-07-12,WA,2,A,A3,6.92,380.0 -2015-07-13,WA,2,A,A3,6.85,369.0 -2015-07-14,WA,2,A,A3,7.04,317.0 -2015-07-15,WA,2,A,A3,6.8,365.0 -2015-07-16,WA,2,A,A3,6.92,367.0 -2015-07-17,WA,2,A,A3,6.87,372.0 -2015-07-18,WA,2,A,A3,6.81,390.0 -2015-07-19,WA,2,A,A3,6.89,354.0 -2015-07-20,WA,2,A,A3,6.89,345.0 -2015-07-21,WA,2,A,A3,6.84,360.0 -2015-07-22,WA,2,A,A3,6.88,362.0 -2015-07-23,WA,2,A,A3,6.88,365.0 -2015-07-24,WA,2,A,A3,6.89,369.0 -2015-07-25,WA,2,A,A3,6.95,393.0 -2015-07-26,WA,2,A,A3,6.83,363.0 -2015-07-27,WA,2,A,A3,6.78,390.0 -2015-07-28,WA,2,A,A3,6.85,346.0 -2015-07-29,WA,2,A,A3,6.96,354.0 -2015-07-30,WA,2,A,A3,6.96,348.0 -2015-07-31,WA,2,A,A3,6.88,348.0 -2015-08-01,WA,2,A,A3,6.8,392.0 -2015-08-02,WA,2,A,A3,6.84,392.0 -2015-08-03,WA,2,A,A3,6.92,343.0 -2015-08-04,WA,2,A,A3,6.94,381.0 -2015-08-05,WA,2,A,A3,6.9,375.0 -2015-08-06,WA,2,A,A3,6.94,370.0 -2015-08-07,WA,2,A,A3,6.93,340.0 -2015-08-08,WA,2,A,A3,6.88,386.0 -2015-08-09,WA,2,A,A3,7.08,336.0 -2015-08-10,WA,2,A,A3,6.85,376.0 -2015-08-11,WA,2,A,A3,6.96,353.0 -2015-08-12,WA,2,A,A3,6.88,383.0 -2015-08-13,WA,2,A,A3,7.04,341.0 -2015-08-14,WA,2,A,A3,6.84,378.0 -2015-08-15,WA,2,A,A3,6.92,385.0 -2015-08-16,WA,2,A,A3,6.87,354.0 -2015-08-17,WA,2,A,A3,6.94,365.0 -2015-08-18,WA,2,A,A3,6.88,349.0 -2015-08-19,WA,2,A,A3,6.89,378.0 -2015-08-20,WA,2,A,A3,6.84,397.0 -2015-08-21,WA,2,A,A3,6.94,354.0 -2015-08-22,WA,2,A,A3,6.87,378.0 -2015-08-23,WA,2,A,A3,6.71,408.0 -2015-08-24,WA,2,A,A3,6.9,364.0 -2015-08-25,WA,2,A,A3,6.91,364.0 -2015-08-26,WA,2,A,A3,6.87,367.0 -2015-08-27,WA,2,A,A3,7.01,395.0 -2015-08-28,WA,2,A,A3,6.87,368.0 -2015-08-29,WA,2,A,A3,6.94,379.0 -2015-08-30,WA,2,A,A3,6.92,373.0 -2015-08-31,WA,2,A,A3,7.04,325.0 -2015-09-01,WA,2,A,A3,6.87,365.0 -2015-09-02,WA,2,A,A3,6.87,375.0 -2015-09-03,WA,2,A,A3,6.97,339.0 -2015-09-04,WA,2,A,A3,6.82,405.0 -2015-09-05,WA,2,A,A3,6.88,367.0 -2015-09-06,WA,2,A,A3,7.0,358.0 -2015-09-07,WA,2,A,A3,6.97,365.0 -2015-09-08,WA,2,A,A3,6.85,366.0 -2015-09-09,WA,2,A,A3,6.81,392.0 -2015-09-10,WA,2,A,A3,6.95,318.0 -2015-09-11,WA,2,A,A3,6.84,383.0 -2015-09-12,WA,2,A,A3,6.9,361.0 -2015-09-13,WA,2,A,A3,6.92,396.0 -2015-09-14,WA,2,A,A3,7.05,301.0 -2015-09-15,WA,2,A,A3,6.83,350.0 -2015-09-16,WA,2,A,A3,7.06,366.0 -2015-09-17,WA,2,A,A3,6.82,363.0 -2015-09-18,WA,2,A,A3,6.76,416.0 -2015-09-19,WA,2,A,A3,6.82,402.0 -2015-09-20,WA,2,A,A3,6.85,383.0 -2015-09-21,WA,2,A,A3,6.88,343.0 -2015-09-22,WA,2,A,A3,6.8,360.0 -2015-09-23,WA,2,A,A3,6.96,345.0 -2015-09-24,WA,2,A,A3,6.84,367.0 -2015-09-25,WA,2,A,A3,6.88,340.0 -2015-09-26,WA,2,A,A3,6.88,372.0 -2015-09-27,WA,2,A,A3,6.95,366.0 -2015-09-28,WA,2,A,A3,6.9,373.0 -2015-09-29,WA,2,A,A3,6.83,360.0 -2015-09-30,WA,2,A,A3,6.84,362.0 -2015-10-01,WA,2,A,A3,6.92,370.0 -2015-10-02,WA,2,A,A3,6.88,371.0 -2015-10-03,WA,2,A,A3,6.95,397.0 -2015-10-04,WA,2,A,A3,6.92,378.0 -2015-10-05,WA,2,A,A3,6.82,388.0 -2015-10-06,WA,2,A,A3,6.82,358.0 -2015-10-07,WA,2,A,A3,6.78,432.0 -2015-10-08,WA,2,A,A3,6.95,358.0 -2015-10-09,WA,2,A,A3,6.82,392.0 -2015-10-10,WA,2,A,A3,6.88,387.0 -2015-10-11,WA,2,A,A3,6.91,383.0 -2015-10-12,WA,2,A,A3,6.96,353.0 -2015-10-13,WA,2,A,A3,7.0,367.0 -2015-10-14,WA,2,A,A3,6.92,377.0 -2015-10-15,WA,2,A,A3,6.94,376.0 -2015-10-16,WA,2,A,A3,6.94,401.0 -2015-10-17,WA,2,A,A3,6.92,368.0 -2015-10-18,WA,2,A,A3,6.81,400.0 -2015-10-19,WA,2,A,A3,6.86,353.0 -2015-10-20,WA,2,A,A3,6.99,376.0 -2015-10-21,WA,2,A,A3,6.88,377.0 -2015-10-22,WA,2,A,A3,6.93,385.0 -2015-10-23,WA,2,A,A3,6.9,371.0 -2015-10-24,WA,2,A,A3,6.78,427.0 -2015-10-25,WA,2,A,A3,6.97,346.0 -2015-10-26,WA,2,A,A3,6.96,306.0 -2015-10-27,WA,2,A,A3,6.83,392.0 -2015-10-28,WA,2,A,A3,6.98,359.0 -2015-10-29,WA,2,A,A3,6.89,357.0 -2015-10-30,WA,2,A,A3,6.91,334.0 -2015-10-31,WA,2,A,A3,6.96,411.0 -2015-11-01,WA,2,A,A3,6.97,363.0 -2015-11-02,WA,2,A,A3,6.96,320.0 -2015-11-03,WA,2,A,A3,6.83,382.0 -2015-11-04,WA,2,A,A3,6.81,381.0 -2015-11-05,WA,2,A,A3,6.89,379.0 -2015-11-06,WA,2,A,A3,6.85,375.0 -2015-11-07,WA,2,A,A3,6.92,389.0 -2015-11-08,WA,2,A,A3,6.84,340.0 -2015-11-09,WA,2,A,A3,6.86,324.0 -2015-11-10,WA,2,A,A3,6.92,352.0 -2015-11-11,WA,2,A,A3,6.8,385.0 -2015-11-12,WA,2,A,A3,6.89,392.0 -2015-11-13,WA,2,A,A3,6.86,378.0 -2015-11-14,WA,2,A,A3,6.75,422.0 -2015-11-15,WA,2,A,A3,6.77,396.0 -2015-11-16,WA,2,A,A3,6.79,364.0 -2015-11-17,WA,2,A,A3,6.91,372.0 -2015-11-18,WA,2,A,A3,6.84,384.0 -2015-11-19,WA,2,A,A3,6.85,385.0 -2015-11-20,WA,2,A,A3,6.91,353.0 -2015-11-21,WA,2,A,A3,6.87,401.0 -2015-11-22,WA,2,A,A3,6.96,385.0 -2015-11-23,WA,2,A,A3,6.92,359.0 -2015-11-24,WA,2,A,A3,7.0,356.0 -2015-11-25,WA,2,A,A3,7.03,321.0 -2015-11-26,WA,2,A,A3,6.9,356.0 -2015-11-27,WA,2,A,A3,6.95,332.0 -2015-11-28,WA,2,A,A3,6.94,399.0 -2015-11-29,WA,2,A,A3,6.81,410.0 -2015-11-30,WA,2,A,A3,6.97,332.0 -2015-12-01,WA,2,A,A3,7.02,342.0 -2015-12-02,WA,2,A,A3,6.84,378.0 -2015-12-03,WA,2,A,A3,6.91,323.0 -2015-12-04,WA,2,A,A3,6.95,357.0 -2015-12-05,WA,2,A,A3,6.87,380.0 -2015-12-06,WA,2,A,A3,6.96,311.0 -2015-12-07,WA,2,A,A3,6.75,387.0 -2015-12-08,WA,2,A,A3,6.98,329.0 -2015-12-09,WA,2,A,A3,6.96,373.0 -2015-12-10,WA,2,A,A3,6.98,315.0 -2015-12-11,WA,2,A,A3,6.84,391.0 -2015-12-12,WA,2,A,A3,6.84,380.0 -2015-12-13,WA,2,A,A3,6.93,368.0 -2015-12-14,WA,2,A,A3,6.91,372.0 -2015-12-15,WA,2,A,A3,6.86,357.0 -2015-12-16,WA,2,A,A3,6.83,352.0 -2015-12-17,WA,2,A,A3,6.86,421.0 -2015-12-18,WA,2,A,A3,6.89,388.0 -2015-12-19,WA,2,A,A3,6.8,399.0 -2015-12-20,WA,2,A,A3,6.97,364.0 -2015-12-21,WA,2,A,A3,6.77,367.0 -2015-12-22,WA,2,A,A3,6.79,374.0 -2015-12-23,WA,2,A,A3,6.97,341.0 -2015-12-24,WA,2,A,A3,7.06,364.0 -2015-12-25,WA,2,A,A3,6.86,366.0 -2015-12-26,WA,2,A,A3,6.85,402.0 -2015-12-27,WA,2,A,A3,6.92,391.0 -2015-12-28,WA,2,A,A3,6.78,375.0 -2015-12-29,WA,2,A,A3,6.83,373.0 -2015-12-30,WA,2,A,A3,6.82,393.0 -2015-12-31,WA,2,A,A3,6.99,349.0 -2016-01-01,WA,2,A,A3,6.92,371.0 -2016-01-02,WA,2,A,A3,6.99,342.0 -2016-01-03,WA,2,A,A3,6.83,371.0 -2016-01-04,WA,2,A,A3,6.82,378.0 -2016-01-05,WA,2,A,A3,6.98,385.0 -2016-01-06,WA,2,A,A3,6.93,354.0 -2016-01-07,WA,2,A,A3,7.02,372.0 -2016-01-08,WA,2,A,A3,7.02,361.0 -2016-01-09,WA,2,A,A3,6.86,401.0 -2016-01-10,WA,2,A,A3,6.97,332.0 -2016-01-11,WA,2,A,A3,6.76,365.0 -2016-01-12,WA,2,A,A3,6.91,371.0 -2016-01-13,WA,2,A,A3,7.0,343.0 -2016-01-14,WA,2,A,A3,6.88,373.0 -2016-01-15,WA,2,A,A3,6.84,421.0 -2016-01-16,WA,2,A,A3,6.96,384.0 -2016-01-17,WA,2,A,A3,6.99,350.0 -2016-01-18,WA,2,A,A3,6.99,341.0 -2016-01-19,WA,2,A,A3,6.95,353.0 -2016-01-20,WA,2,A,A3,6.87,361.0 -2016-01-21,WA,2,A,A3,6.98,343.0 -2016-01-22,WA,2,A,A3,6.9,359.0 -2016-01-23,WA,2,A,A3,6.93,375.0 -2016-01-24,WA,2,A,A3,6.84,366.0 -2016-01-25,WA,2,A,A3,6.83,384.0 -2016-01-26,WA,2,A,A3,6.84,393.0 -2016-01-27,WA,2,A,A3,6.82,400.0 -2016-01-28,WA,2,A,A3,6.96,335.0 -2016-01-29,WA,2,A,A3,6.9,378.0 -2016-01-30,WA,2,A,A3,6.82,394.0 -2016-01-31,WA,2,A,A3,7.03,361.0 -2016-02-01,WA,2,A,A3,6.86,382.0 -2016-02-02,WA,2,A,A3,6.87,348.0 -2016-02-03,WA,2,A,A3,6.95,354.0 -2016-02-04,WA,2,A,A3,6.91,351.0 -2016-02-05,WA,2,A,A3,6.93,374.0 -2016-02-06,WA,2,A,A3,6.82,398.0 -2016-02-07,WA,2,A,A3,6.87,383.0 -2016-02-08,WA,2,A,A3,6.78,409.0 -2016-02-09,WA,2,A,A3,6.85,360.0 -2016-02-10,WA,2,A,A3,6.83,408.0 -2016-02-11,WA,2,A,A3,6.98,358.0 -2016-02-12,WA,2,A,A3,6.81,383.0 -2016-02-13,WA,2,A,A3,7.02,379.0 -2016-02-14,WA,2,A,A3,7.0,340.0 -2016-02-15,WA,2,A,A3,6.9,380.0 -2016-02-16,WA,2,A,A3,6.91,345.0 -2016-02-17,WA,2,A,A3,6.89,337.0 -2016-02-18,WA,2,A,A3,6.85,371.0 -2016-02-19,WA,2,A,A3,6.81,396.0 -2016-02-20,WA,2,A,A3,7.01,352.0 -2016-02-21,WA,2,A,A3,6.96,359.0 -2016-02-22,WA,2,A,A3,6.78,398.0 -2016-02-23,WA,2,A,A3,6.82,373.0 -2016-02-24,WA,2,A,A3,6.9,336.0 -2016-02-25,WA,2,A,A3,6.89,361.0 -2016-02-26,WA,2,A,A3,6.92,364.0 -2016-02-27,WA,2,A,A3,6.98,364.0 -2016-02-28,WA,2,A,A3,6.95,345.0 -2016-02-29,WA,2,A,A3,6.84,382.0 -2016-03-01,WA,2,A,A3,6.94,373.0 -2016-03-02,WA,2,A,A3,6.94,390.0 -2016-03-03,WA,2,A,A3,7.02,345.0 -2016-03-04,WA,2,A,A3,6.88,357.0 -2016-03-05,WA,2,A,A3,6.77,417.0 -2016-03-06,WA,2,A,A3,6.83,392.0 -2016-03-07,WA,2,A,A3,6.86,376.0 -2016-03-08,WA,2,A,A3,6.94,365.0 -2016-03-09,WA,2,A,A3,6.94,349.0 -2016-03-10,WA,2,A,A3,6.89,378.0 -2016-03-11,WA,2,A,A3,6.94,323.0 -2016-03-12,WA,2,A,A3,6.95,386.0 -2016-03-13,WA,2,A,A3,6.88,344.0 -2016-03-14,WA,2,A,A3,6.89,390.0 -2016-03-15,WA,2,A,A3,6.94,399.0 -2016-03-16,WA,2,A,A3,6.84,387.0 -2016-03-17,WA,2,A,A3,6.79,352.0 -2016-03-18,WA,2,A,A3,6.85,372.0 -2016-03-19,WA,2,A,A3,6.91,375.0 -2016-03-20,WA,2,A,A3,6.86,379.0 -2016-03-21,WA,2,A,A3,6.88,366.0 -2016-03-22,WA,2,A,A3,6.87,366.0 -2016-03-23,WA,2,A,A3,6.94,345.0 -2016-03-24,WA,2,A,A3,6.86,360.0 -2016-03-25,WA,2,A,A3,6.82,421.0 -2016-03-26,WA,2,A,A3,6.88,393.0 -2016-03-27,WA,2,A,A3,6.88,361.0 -2016-03-28,WA,2,A,A3,7.04,348.0 -2016-03-29,WA,2,A,A3,6.9,345.0 -2016-03-30,WA,2,A,A3,6.9,400.0 -2016-03-31,WA,2,A,A3,6.88,397.0 -2016-04-01,WA,2,A,A3,6.79,364.0 -2016-04-02,WA,2,A,A3,6.83,399.0 -2016-04-03,WA,2,A,A3,6.74,360.0 -2016-04-04,WA,2,A,A3,7.04,323.0 -2016-04-05,WA,2,A,A3,6.97,348.0 -2016-04-06,WA,2,A,A3,6.87,373.0 -2016-04-07,WA,2,A,A3,6.91,367.0 -2016-04-08,WA,2,A,A3,6.91,416.0 -2016-04-09,WA,2,A,A3,6.98,344.0 -2016-04-10,WA,2,A,A3,6.83,410.0 -2016-04-11,WA,2,A,A3,6.93,364.0 -2016-04-12,WA,2,A,A3,6.88,398.0 -2016-04-13,WA,2,A,A3,6.92,386.0 -2016-04-14,WA,2,A,A3,6.98,375.0 -2016-04-15,WA,2,A,A3,7.07,338.0 -2016-04-16,WA,2,A,A3,6.78,400.0 -2016-04-17,WA,2,A,A3,6.96,365.0 -2016-04-18,WA,2,A,A3,7.04,337.0 -2016-04-19,WA,2,A,A3,6.84,358.0 -2016-04-20,WA,2,A,A3,6.96,367.0 -2016-04-21,WA,2,A,A3,6.93,383.0 -2016-04-22,WA,2,A,A3,6.86,365.0 -2016-04-23,WA,2,A,A3,6.82,396.0 -2016-04-24,WA,2,A,A3,6.96,380.0 -2016-04-25,WA,2,A,A3,6.91,368.0 -2016-04-26,WA,2,A,A3,6.88,360.0 -2016-04-27,WA,2,A,A3,6.83,355.0 -2016-04-28,WA,2,A,A3,7.0,349.0 -2016-04-29,WA,2,A,A3,6.92,361.0 -2016-04-30,WA,2,A,A3,7.04,366.0 -2016-05-01,WA,2,A,A3,7.0,344.0 -2016-05-02,WA,2,A,A3,6.93,361.0 -2016-05-03,WA,2,A,A3,6.88,376.0 -2016-05-04,WA,2,A,A3,6.82,401.0 -2016-05-05,WA,2,A,A3,6.98,353.0 -2016-05-06,WA,2,A,A3,6.92,372.0 -2016-05-07,WA,2,A,A3,6.89,396.0 -2016-05-08,WA,2,A,A3,6.9,413.0 -2016-05-09,WA,2,A,A3,6.87,361.0 -2016-05-10,WA,2,A,A3,6.83,365.0 -2016-05-11,WA,2,A,A3,6.93,355.0 -2016-05-12,WA,2,A,A3,6.95,397.0 -2016-05-13,WA,2,A,A3,6.79,392.0 -2016-05-14,WA,2,A,A3,6.87,377.0 -2016-05-15,WA,2,A,A3,6.79,410.0 -2016-05-16,WA,2,A,A3,6.97,330.0 -2016-05-17,WA,2,A,A3,6.94,380.0 -2016-05-18,WA,2,A,A3,6.84,387.0 -2016-05-19,WA,2,A,A3,6.9,376.0 -2016-05-20,WA,2,A,A3,6.92,360.0 -2016-05-21,WA,2,A,A3,6.88,390.0 -2016-05-22,WA,2,A,A3,6.81,393.0 -2016-05-23,WA,2,A,A3,6.91,368.0 -2016-05-24,WA,2,A,A3,7.01,341.0 -2016-05-25,WA,2,A,A3,6.83,380.0 -2016-05-26,WA,2,A,A3,6.95,376.0 -2016-05-27,WA,2,A,A3,6.88,377.0 -2016-05-28,WA,2,A,A3,6.98,363.0 -2016-05-29,WA,2,A,A3,7.02,355.0 -2016-05-30,WA,2,A,A3,6.79,382.0 -2016-05-31,WA,2,A,A3,6.92,353.0 -2016-06-01,WA,2,A,A3,6.82,411.0 -2016-06-02,WA,2,A,A3,6.85,364.0 -2016-06-03,WA,2,A,A3,6.86,385.0 -2016-06-04,WA,2,A,A3,6.89,392.0 -2016-06-05,WA,2,A,A3,6.89,380.0 -2016-06-06,WA,2,A,A3,6.93,393.0 -2016-06-07,WA,2,A,A3,6.84,353.0 -2016-06-08,WA,2,A,A3,6.86,381.0 -2016-06-09,WA,2,A,A3,6.75,378.0 -2016-06-10,WA,2,A,A3,6.86,406.0 -2016-06-11,WA,2,A,A3,6.96,381.0 -2016-06-12,WA,2,A,A3,6.89,375.0 -2016-06-13,WA,2,A,A3,6.83,330.0 -2016-06-14,WA,2,A,A3,6.86,367.0 -2016-06-15,WA,2,A,A3,6.84,329.0 -2016-06-16,WA,2,A,A3,6.98,355.0 -2016-06-17,WA,2,A,A3,6.84,391.0 -2016-06-18,WA,2,A,A3,6.95,385.0 -2016-06-19,WA,2,A,A3,6.89,375.0 -2016-06-20,WA,2,A,A3,6.95,341.0 -2016-06-21,WA,2,A,A3,6.87,396.0 -2016-06-22,WA,2,A,A3,6.78,387.0 -2016-06-23,WA,2,A,A3,6.82,357.0 -2016-06-24,WA,2,A,A3,6.9,374.0 -2016-06-25,WA,2,A,A3,6.9,377.0 -2016-06-26,WA,2,A,A3,6.98,327.0 -2016-06-27,WA,2,A,A3,6.91,345.0 -2016-06-28,WA,2,A,A3,6.94,359.0 -2016-06-29,WA,2,A,A3,6.91,380.0 -2016-06-30,WA,2,A,A3,6.9,364.0 -2016-07-01,WA,2,A,A3,6.91,335.0 -2016-07-02,WA,2,A,A3,6.96,402.0 -2016-07-03,WA,2,A,A3,6.8,404.0 -2016-07-04,WA,2,A,A3,6.88,373.0 -2016-07-05,WA,2,A,A3,6.89,357.0 -2016-07-06,WA,2,A,A3,6.86,369.0 -2016-07-07,WA,2,A,A3,6.88,392.0 -2016-07-08,WA,2,A,A3,6.86,384.0 -2016-07-09,WA,2,A,A3,6.9,371.0 -2016-07-10,WA,2,A,A3,6.77,380.0 -2016-07-11,WA,2,A,A3,6.86,355.0 -2016-07-12,WA,2,A,A3,6.81,416.0 -2016-07-13,WA,2,A,A3,6.96,339.0 -2016-07-14,WA,2,A,A3,6.95,370.0 -2016-07-15,WA,2,A,A3,6.86,390.0 -2016-07-16,WA,2,A,A3,6.77,397.0 -2016-07-17,WA,2,A,A3,6.89,349.0 -2016-07-18,WA,2,A,A3,6.96,362.0 -2016-07-19,WA,2,A,A3,6.91,385.0 -2016-07-20,WA,2,A,A3,7.11,357.0 -2016-07-21,WA,2,A,A3,6.8,375.0 -2016-07-22,WA,2,A,A3,7.05,356.0 -2016-07-23,WA,2,A,A3,6.85,364.0 -2016-07-24,WA,2,A,A3,6.89,363.0 -2016-07-25,WA,2,A,A3,6.91,366.0 -2016-07-26,WA,2,A,A3,6.8,395.0 -2016-07-27,WA,2,A,A3,6.82,374.0 -2015-01-05,WA,2,B,B1,7.68,98.0 -2015-01-06,WA,2,B,B1,7.67,113.0 -2015-01-07,WA,2,B,B1,7.58,124.0 -2015-01-08,WA,2,B,B1,7.54,131.0 -2015-01-09,WA,2,B,B1,7.67,119.0 -2015-01-10,WA,2,B,B1,7.61,135.0 -2015-01-11,WA,2,B,B1,7.58,119.0 -2015-01-12,WA,2,B,B1,7.61,111.0 -2015-01-13,WA,2,B,B1,7.53,128.0 -2015-01-14,WA,2,B,B1,7.6,126.0 -2015-01-15,WA,2,B,B1,7.68,111.0 -2015-01-16,WA,2,B,B1,7.63,124.0 -2015-01-17,WA,2,B,B1,7.72,121.0 -2015-01-18,WA,2,B,B1,7.49,130.0 -2015-01-19,WA,2,B,B1,7.53,129.0 -2015-01-20,WA,2,B,B1,7.73,123.0 -2015-01-21,WA,2,B,B1,7.66,121.0 -2015-01-22,WA,2,B,B1,7.59,120.0 -2015-01-23,WA,2,B,B1,7.79,116.0 -2015-01-24,WA,2,B,B1,7.64,128.0 -2015-01-25,WA,2,B,B1,7.63,128.0 -2015-01-26,WA,2,B,B1,7.76,103.0 -2015-01-27,WA,2,B,B1,7.56,123.0 -2015-01-28,WA,2,B,B1,7.57,122.0 -2015-01-29,WA,2,B,B1,7.74,121.0 -2015-01-30,WA,2,B,B1,7.69,120.0 -2015-01-31,WA,2,B,B1,7.6,138.0 -2015-02-01,WA,2,B,B1,7.78,110.0 -2015-02-02,WA,2,B,B1,7.72,100.0 -2015-02-03,WA,2,B,B1,7.73,114.0 -2015-02-04,WA,2,B,B1,7.64,126.0 -2015-02-05,WA,2,B,B1,7.66,120.0 -2015-02-06,WA,2,B,B1,7.58,124.0 -2015-02-07,WA,2,B,B1,7.61,125.0 -2015-02-08,WA,2,B,B1,7.6,112.0 -2015-02-09,WA,2,B,B1,7.58,124.0 -2015-02-10,WA,2,B,B1,7.7,116.0 -2015-02-11,WA,2,B,B1,7.62,121.0 -2015-02-12,WA,2,B,B1,7.59,126.0 -2015-02-13,WA,2,B,B1,7.62,121.0 -2015-02-14,WA,2,B,B1,7.84,104.0 -2015-02-15,WA,2,B,B1,7.68,129.0 -2015-02-16,WA,2,B,B1,7.65,120.0 -2015-02-17,WA,2,B,B1,7.83,115.0 -2015-02-18,WA,2,B,B1,7.61,114.0 -2015-02-19,WA,2,B,B1,7.59,119.0 -2015-02-20,WA,2,B,B1,7.62,125.0 -2015-02-21,WA,2,B,B1,7.52,137.0 -2015-02-22,WA,2,B,B1,7.65,128.0 -2015-02-23,WA,2,B,B1,7.56,117.0 -2015-02-24,WA,2,B,B1,7.67,119.0 -2015-02-25,WA,2,B,B1,7.54,126.0 -2015-02-26,WA,2,B,B1,7.62,126.0 -2015-02-27,WA,2,B,B1,7.64,135.0 -2015-02-28,WA,2,B,B1,7.61,122.0 -2015-03-01,WA,2,B,B1,7.61,121.0 -2015-03-02,WA,2,B,B1,7.57,120.0 -2015-03-03,WA,2,B,B1,7.51,133.0 -2015-03-04,WA,2,B,B1,7.73,112.0 -2015-03-05,WA,2,B,B1,7.73,114.0 -2015-03-06,WA,2,B,B1,7.66,127.0 -2015-03-07,WA,2,B,B1,7.69,120.0 -2015-03-08,WA,2,B,B1,7.69,121.0 -2015-03-09,WA,2,B,B1,7.62,121.0 -2015-03-10,WA,2,B,B1,7.48,130.0 -2015-03-11,WA,2,B,B1,7.61,136.0 -2015-03-12,WA,2,B,B1,7.7,116.0 -2015-03-13,WA,2,B,B1,7.63,117.0 -2015-03-14,WA,2,B,B1,7.66,121.0 -2015-03-15,WA,2,B,B1,7.5,134.0 -2015-03-16,WA,2,B,B1,7.61,127.0 -2015-03-17,WA,2,B,B1,7.63,109.0 -2015-03-18,WA,2,B,B1,7.48,140.0 -2015-03-19,WA,2,B,B1,7.62,131.0 -2015-03-20,WA,2,B,B1,7.7,116.0 -2015-03-21,WA,2,B,B1,7.67,122.0 -2015-03-22,WA,2,B,B1,7.58,140.0 -2015-03-23,WA,2,B,B1,7.67,121.0 -2015-03-24,WA,2,B,B1,7.62,121.0 -2015-03-25,WA,2,B,B1,7.56,125.0 -2015-03-26,WA,2,B,B1,7.62,130.0 -2015-03-27,WA,2,B,B1,7.62,131.0 -2015-03-28,WA,2,B,B1,7.61,129.0 -2015-03-29,WA,2,B,B1,7.61,127.0 -2015-03-30,WA,2,B,B1,7.52,131.0 -2015-03-31,WA,2,B,B1,7.6,105.0 -2015-04-01,WA,2,B,B1,7.73,120.0 -2015-04-02,WA,2,B,B1,7.75,119.0 -2015-04-03,WA,2,B,B1,7.66,116.0 -2015-04-04,WA,2,B,B1,7.62,128.0 -2015-04-05,WA,2,B,B1,7.56,120.0 -2015-04-06,WA,2,B,B1,7.65,118.0 -2015-04-07,WA,2,B,B1,7.52,129.0 -2015-04-08,WA,2,B,B1,7.7,119.0 -2015-04-09,WA,2,B,B1,7.78,112.0 -2015-04-10,WA,2,B,B1,7.55,137.0 -2015-04-11,WA,2,B,B1,7.54,129.0 -2015-04-12,WA,2,B,B1,7.56,129.0 -2015-04-13,WA,2,B,B1,7.69,112.0 -2015-04-14,WA,2,B,B1,7.59,126.0 -2015-04-15,WA,2,B,B1,7.77,117.0 -2015-04-16,WA,2,B,B1,7.64,126.0 -2015-04-17,WA,2,B,B1,7.62,132.0 -2015-04-18,WA,2,B,B1,7.59,130.0 -2015-04-19,WA,2,B,B1,7.61,124.0 -2015-04-20,WA,2,B,B1,7.62,121.0 -2015-04-21,WA,2,B,B1,7.69,114.0 -2015-04-22,WA,2,B,B1,7.7,116.0 -2015-04-23,WA,2,B,B1,7.76,109.0 -2015-04-24,WA,2,B,B1,7.7,129.0 -2015-04-25,WA,2,B,B1,7.69,120.0 -2015-04-26,WA,2,B,B1,7.64,114.0 -2015-04-27,WA,2,B,B1,7.73,110.0 -2015-04-28,WA,2,B,B1,7.59,127.0 -2015-04-29,WA,2,B,B1,7.64,123.0 -2015-04-30,WA,2,B,B1,7.7,116.0 -2015-05-01,WA,2,B,B1,7.58,128.0 -2015-05-02,WA,2,B,B1,7.51,149.0 -2015-05-03,WA,2,B,B1,7.76,116.0 -2015-05-04,WA,2,B,B1,7.64,121.0 -2015-05-05,WA,2,B,B1,7.56,125.0 -2015-05-06,WA,2,B,B1,7.66,115.0 -2015-05-07,WA,2,B,B1,7.59,126.0 -2015-05-08,WA,2,B,B1,7.66,125.0 -2015-05-09,WA,2,B,B1,7.59,135.0 -2015-05-10,WA,2,B,B1,7.68,136.0 -2015-05-11,WA,2,B,B1,7.64,123.0 -2015-05-12,WA,2,B,B1,7.58,127.0 -2015-05-13,WA,2,B,B1,7.69,119.0 -2015-05-14,WA,2,B,B1,7.6,112.0 -2015-05-15,WA,2,B,B1,7.71,120.0 -2015-05-16,WA,2,B,B1,7.66,124.0 -2015-05-17,WA,2,B,B1,7.64,130.0 -2015-05-18,WA,2,B,B1,7.55,127.0 -2015-05-19,WA,2,B,B1,7.64,122.0 -2015-05-20,WA,2,B,B1,7.47,141.0 -2015-05-21,WA,2,B,B1,7.55,133.0 -2015-05-22,WA,2,B,B1,7.71,119.0 -2015-05-23,WA,2,B,B1,7.69,127.0 -2015-05-24,WA,2,B,B1,7.5,139.0 -2015-05-25,WA,2,B,B1,7.62,118.0 -2015-05-26,WA,2,B,B1,7.66,117.0 -2015-05-27,WA,2,B,B1,7.69,110.0 -2015-05-28,WA,2,B,B1,7.59,118.0 -2015-05-29,WA,2,B,B1,7.62,120.0 -2015-05-30,WA,2,B,B1,7.75,121.0 -2015-05-31,WA,2,B,B1,7.73,128.0 -2015-06-01,WA,2,B,B1,7.57,120.0 -2015-06-02,WA,2,B,B1,7.73,126.0 -2015-06-03,WA,2,B,B1,7.63,118.0 -2015-06-04,WA,2,B,B1,7.6,123.0 -2015-06-05,WA,2,B,B1,7.64,126.0 -2015-06-06,WA,2,B,B1,7.57,140.0 -2015-06-07,WA,2,B,B1,7.59,125.0 -2015-06-08,WA,2,B,B1,7.66,124.0 -2015-06-09,WA,2,B,B1,7.68,124.0 -2015-06-10,WA,2,B,B1,7.55,127.0 -2015-06-11,WA,2,B,B1,7.63,128.0 -2015-06-12,WA,2,B,B1,7.42,135.0 -2015-06-13,WA,2,B,B1,7.71,122.0 -2015-06-14,WA,2,B,B1,7.65,120.0 -2015-06-15,WA,2,B,B1,7.68,111.0 -2015-06-16,WA,2,B,B1,7.64,116.0 -2015-06-17,WA,2,B,B1,7.63,118.0 -2015-06-18,WA,2,B,B1,7.59,119.0 -2015-06-19,WA,2,B,B1,7.62,122.0 -2015-06-20,WA,2,B,B1,7.72,112.0 -2015-06-21,WA,2,B,B1,7.52,123.0 -2015-06-22,WA,2,B,B1,7.56,111.0 -2015-06-23,WA,2,B,B1,7.56,136.0 -2015-06-24,WA,2,B,B1,7.65,123.0 -2015-06-25,WA,2,B,B1,7.5,128.0 -2015-06-26,WA,2,B,B1,7.58,117.0 -2015-06-27,WA,2,B,B1,7.66,126.0 -2015-06-28,WA,2,B,B1,7.63,123.0 -2015-06-29,WA,2,B,B1,7.71,115.0 -2015-06-30,WA,2,B,B1,7.66,127.0 -2015-07-01,WA,2,B,B1,7.61,115.0 -2015-07-02,WA,2,B,B1,7.67,113.0 -2015-07-03,WA,2,B,B1,7.64,120.0 -2015-07-04,WA,2,B,B1,7.52,120.0 -2015-07-05,WA,2,B,B1,7.67,122.0 -2015-07-06,WA,2,B,B1,7.6,119.0 -2015-07-07,WA,2,B,B1,7.69,127.0 -2015-07-08,WA,2,B,B1,7.67,115.0 -2015-07-09,WA,2,B,B1,7.55,127.0 -2015-07-10,WA,2,B,B1,7.56,127.0 -2015-07-11,WA,2,B,B1,7.71,138.0 -2015-07-12,WA,2,B,B1,7.66,107.0 -2015-07-13,WA,2,B,B1,7.62,115.0 -2015-07-14,WA,2,B,B1,7.54,124.0 -2015-07-15,WA,2,B,B1,7.59,123.0 -2015-07-16,WA,2,B,B1,7.72,123.0 -2015-07-17,WA,2,B,B1,7.62,129.0 -2015-07-18,WA,2,B,B1,7.69,120.0 -2015-07-19,WA,2,B,B1,7.56,120.0 -2015-07-20,WA,2,B,B1,7.55,133.0 -2015-07-21,WA,2,B,B1,7.59,132.0 -2015-07-22,WA,2,B,B1,7.69,116.0 -2015-07-23,WA,2,B,B1,7.55,119.0 -2015-07-24,WA,2,B,B1,7.61,129.0 -2015-07-25,WA,2,B,B1,7.77,109.0 -2015-07-26,WA,2,B,B1,7.64,126.0 -2015-07-27,WA,2,B,B1,7.59,118.0 -2015-07-28,WA,2,B,B1,7.61,123.0 -2015-07-29,WA,2,B,B1,7.68,121.0 -2015-07-30,WA,2,B,B1,7.74,118.0 -2015-07-31,WA,2,B,B1,7.54,122.0 -2015-08-01,WA,2,B,B1,7.66,123.0 -2015-08-02,WA,2,B,B1,7.55,131.0 -2015-08-03,WA,2,B,B1,7.74,113.0 -2015-08-04,WA,2,B,B1,7.56,125.0 -2015-08-05,WA,2,B,B1,7.53,134.0 -2015-08-06,WA,2,B,B1,7.5,130.0 -2015-08-07,WA,2,B,B1,7.65,121.0 -2015-08-08,WA,2,B,B1,7.63,127.0 -2015-08-09,WA,2,B,B1,7.67,132.0 -2015-08-10,WA,2,B,B1,7.55,128.0 -2015-08-11,WA,2,B,B1,7.49,126.0 -2015-08-12,WA,2,B,B1,7.76,122.0 -2015-08-13,WA,2,B,B1,7.5,132.0 -2015-08-14,WA,2,B,B1,7.58,131.0 -2015-08-15,WA,2,B,B1,7.64,124.0 -2015-08-16,WA,2,B,B1,7.58,127.0 -2015-08-17,WA,2,B,B1,7.7,122.0 -2015-08-18,WA,2,B,B1,7.65,119.0 -2015-08-19,WA,2,B,B1,7.69,121.0 -2015-08-20,WA,2,B,B1,7.68,108.0 -2015-08-21,WA,2,B,B1,7.83,108.0 -2015-08-22,WA,2,B,B1,7.68,127.0 -2015-08-23,WA,2,B,B1,7.69,122.0 -2015-08-24,WA,2,B,B1,7.64,112.0 -2015-08-25,WA,2,B,B1,7.64,122.0 -2015-08-26,WA,2,B,B1,7.62,122.0 -2015-08-27,WA,2,B,B1,7.76,112.0 -2015-08-28,WA,2,B,B1,7.6,124.0 -2015-08-29,WA,2,B,B1,7.77,125.0 -2015-08-30,WA,2,B,B1,7.65,123.0 -2015-08-31,WA,2,B,B1,7.74,111.0 -2015-09-01,WA,2,B,B1,7.66,115.0 -2015-09-02,WA,2,B,B1,7.74,117.0 -2015-09-03,WA,2,B,B1,7.48,136.0 -2015-09-04,WA,2,B,B1,7.7,122.0 -2015-09-05,WA,2,B,B1,7.73,125.0 -2015-09-06,WA,2,B,B1,7.66,122.0 -2015-09-07,WA,2,B,B1,7.5,133.0 -2015-09-08,WA,2,B,B1,7.61,120.0 -2015-09-09,WA,2,B,B1,7.67,120.0 -2015-09-10,WA,2,B,B1,7.72,110.0 -2015-09-11,WA,2,B,B1,7.68,130.0 -2015-09-12,WA,2,B,B1,7.62,136.0 -2015-09-13,WA,2,B,B1,7.68,119.0 -2015-09-14,WA,2,B,B1,7.66,115.0 -2015-09-15,WA,2,B,B1,7.81,99.0 -2015-09-16,WA,2,B,B1,7.75,116.0 -2015-09-17,WA,2,B,B1,7.63,120.0 -2015-09-18,WA,2,B,B1,7.56,120.0 -2015-09-19,WA,2,B,B1,7.54,136.0 -2015-09-20,WA,2,B,B1,7.61,129.0 -2015-09-21,WA,2,B,B1,7.61,131.0 -2015-09-22,WA,2,B,B1,7.6,119.0 -2015-09-23,WA,2,B,B1,7.52,127.0 -2015-09-24,WA,2,B,B1,7.67,125.0 -2015-09-25,WA,2,B,B1,7.58,142.0 -2015-09-26,WA,2,B,B1,7.55,133.0 -2015-09-27,WA,2,B,B1,7.59,139.0 -2015-09-28,WA,2,B,B1,7.63,117.0 -2015-09-29,WA,2,B,B1,7.62,121.0 -2015-09-30,WA,2,B,B1,7.57,131.0 -2015-10-01,WA,2,B,B1,7.56,119.0 -2015-10-02,WA,2,B,B1,7.56,135.0 -2015-10-03,WA,2,B,B1,7.71,133.0 -2015-10-04,WA,2,B,B1,7.54,135.0 -2015-10-05,WA,2,B,B1,7.71,111.0 -2015-10-06,WA,2,B,B1,7.5,136.0 -2015-10-07,WA,2,B,B1,7.54,134.0 -2015-10-08,WA,2,B,B1,7.74,118.0 -2015-10-09,WA,2,B,B1,7.63,135.0 -2015-10-10,WA,2,B,B1,7.68,131.0 -2015-10-11,WA,2,B,B1,7.69,116.0 -2015-10-12,WA,2,B,B1,7.73,108.0 -2015-10-13,WA,2,B,B1,7.74,114.0 -2015-10-14,WA,2,B,B1,7.59,127.0 -2015-10-15,WA,2,B,B1,7.61,121.0 -2015-10-16,WA,2,B,B1,7.6,131.0 -2015-10-17,WA,2,B,B1,7.56,130.0 -2015-10-18,WA,2,B,B1,7.69,121.0 -2015-10-19,WA,2,B,B1,7.66,119.0 -2015-10-20,WA,2,B,B1,7.52,131.0 -2015-10-21,WA,2,B,B1,7.45,122.0 -2015-10-22,WA,2,B,B1,7.68,117.0 -2015-10-23,WA,2,B,B1,7.66,112.0 -2015-10-24,WA,2,B,B1,7.68,115.0 -2015-10-25,WA,2,B,B1,7.57,125.0 -2015-10-26,WA,2,B,B1,7.71,102.0 -2015-10-27,WA,2,B,B1,7.6,129.0 -2015-10-28,WA,2,B,B1,7.53,122.0 -2015-10-29,WA,2,B,B1,7.56,130.0 -2015-10-30,WA,2,B,B1,7.72,124.0 -2015-10-31,WA,2,B,B1,7.7,123.0 -2015-11-01,WA,2,B,B1,7.61,128.0 -2015-11-02,WA,2,B,B1,7.68,112.0 -2015-11-03,WA,2,B,B1,7.67,113.0 -2015-11-04,WA,2,B,B1,7.56,121.0 -2015-11-05,WA,2,B,B1,7.56,130.0 -2015-11-06,WA,2,B,B1,7.66,126.0 -2015-11-07,WA,2,B,B1,7.73,126.0 -2015-11-08,WA,2,B,B1,7.62,118.0 -2015-11-09,WA,2,B,B1,7.43,134.0 -2015-11-10,WA,2,B,B1,7.56,122.0 -2015-11-11,WA,2,B,B1,7.56,117.0 -2015-11-12,WA,2,B,B1,7.52,135.0 -2015-11-13,WA,2,B,B1,7.65,118.0 -2015-11-14,WA,2,B,B1,7.62,124.0 -2015-11-15,WA,2,B,B1,7.63,130.0 -2015-11-16,WA,2,B,B1,7.67,116.0 -2015-11-17,WA,2,B,B1,7.61,131.0 -2015-11-18,WA,2,B,B1,7.54,132.0 -2015-11-19,WA,2,B,B1,7.71,123.0 -2015-11-20,WA,2,B,B1,7.7,127.0 -2015-11-21,WA,2,B,B1,7.75,120.0 -2015-11-22,WA,2,B,B1,7.61,127.0 -2015-11-23,WA,2,B,B1,7.64,112.0 -2015-11-24,WA,2,B,B1,7.62,116.0 -2015-11-25,WA,2,B,B1,7.67,117.0 -2015-11-26,WA,2,B,B1,7.6,113.0 -2015-11-27,WA,2,B,B1,7.71,125.0 -2015-11-28,WA,2,B,B1,7.77,121.0 -2015-11-29,WA,2,B,B1,7.49,133.0 -2015-11-30,WA,2,B,B1,7.6,120.0 -2015-12-01,WA,2,B,B1,7.61,126.0 -2015-12-02,WA,2,B,B1,7.62,128.0 -2015-12-03,WA,2,B,B1,7.66,121.0 -2015-12-04,WA,2,B,B1,7.65,114.0 -2015-12-05,WA,2,B,B1,7.65,117.0 -2015-12-06,WA,2,B,B1,7.5,129.0 -2015-12-07,WA,2,B,B1,7.52,132.0 -2015-12-08,WA,2,B,B1,7.76,112.0 -2015-12-09,WA,2,B,B1,7.63,129.0 -2015-12-10,WA,2,B,B1,7.53,132.0 -2015-12-11,WA,2,B,B1,7.67,122.0 -2015-12-12,WA,2,B,B1,7.66,127.0 -2015-12-13,WA,2,B,B1,7.71,115.0 -2015-12-14,WA,2,B,B1,7.51,142.0 -2015-12-15,WA,2,B,B1,7.65,125.0 -2015-12-16,WA,2,B,B1,7.53,135.0 -2015-12-17,WA,2,B,B1,7.7,123.0 -2015-12-18,WA,2,B,B1,7.71,124.0 -2015-12-19,WA,2,B,B1,7.61,122.0 -2015-12-20,WA,2,B,B1,7.57,124.0 -2015-12-21,WA,2,B,B1,7.62,125.0 -2015-12-22,WA,2,B,B1,7.67,125.0 -2015-12-23,WA,2,B,B1,7.52,122.0 -2015-12-24,WA,2,B,B1,7.56,130.0 -2015-12-25,WA,2,B,B1,7.57,127.0 -2015-12-26,WA,2,B,B1,7.59,122.0 -2015-12-27,WA,2,B,B1,7.64,133.0 -2015-12-28,WA,2,B,B1,7.65,119.0 -2015-12-29,WA,2,B,B1,7.59,122.0 -2015-12-30,WA,2,B,B1,7.69,116.0 -2015-12-31,WA,2,B,B1,7.74,113.0 -2016-01-01,WA,2,B,B1,7.85,111.0 -2016-01-02,WA,2,B,B1,7.79,115.0 -2016-01-03,WA,2,B,B1,7.81,116.0 -2016-01-04,WA,2,B,B1,7.66,117.0 -2016-01-05,WA,2,B,B1,7.58,121.0 -2016-01-06,WA,2,B,B1,7.62,139.0 -2016-01-07,WA,2,B,B1,7.63,117.0 -2016-01-08,WA,2,B,B1,7.65,131.0 -2016-01-09,WA,2,B,B1,7.54,134.0 -2016-01-10,WA,2,B,B1,7.77,121.0 -2016-01-11,WA,2,B,B1,7.54,124.0 -2016-01-12,WA,2,B,B1,7.57,128.0 -2016-01-13,WA,2,B,B1,7.52,128.0 -2016-01-14,WA,2,B,B1,7.5,135.0 -2016-01-15,WA,2,B,B1,7.73,119.0 -2016-01-16,WA,2,B,B1,7.66,120.0 -2016-01-17,WA,2,B,B1,7.58,131.0 -2016-01-18,WA,2,B,B1,7.5,129.0 -2016-01-19,WA,2,B,B1,7.82,109.0 -2016-01-20,WA,2,B,B1,7.67,112.0 -2016-01-21,WA,2,B,B1,7.72,125.0 -2016-01-22,WA,2,B,B1,7.57,135.0 -2016-01-23,WA,2,B,B1,7.53,135.0 -2016-01-24,WA,2,B,B1,7.67,130.0 -2016-01-25,WA,2,B,B1,7.75,108.0 -2016-01-26,WA,2,B,B1,7.6,124.0 -2016-01-27,WA,2,B,B1,7.63,127.0 -2016-01-28,WA,2,B,B1,7.56,129.0 -2016-01-29,WA,2,B,B1,7.76,113.0 -2016-01-30,WA,2,B,B1,7.82,124.0 -2016-01-31,WA,2,B,B1,7.63,104.0 -2016-02-01,WA,2,B,B1,7.55,125.0 -2016-02-02,WA,2,B,B1,7.6,120.0 -2016-02-03,WA,2,B,B1,7.48,137.0 -2016-02-04,WA,2,B,B1,7.65,124.0 -2016-02-05,WA,2,B,B1,7.75,117.0 -2016-02-06,WA,2,B,B1,7.53,134.0 -2016-02-07,WA,2,B,B1,7.57,129.0 -2016-02-08,WA,2,B,B1,7.53,126.0 -2016-02-09,WA,2,B,B1,7.64,124.0 -2016-02-10,WA,2,B,B1,7.65,123.0 -2016-02-11,WA,2,B,B1,7.65,115.0 -2016-02-12,WA,2,B,B1,7.68,117.0 -2016-02-13,WA,2,B,B1,7.54,129.0 -2016-02-14,WA,2,B,B1,7.52,119.0 -2016-02-15,WA,2,B,B1,7.77,101.0 -2016-02-16,WA,2,B,B1,7.57,132.0 -2016-02-17,WA,2,B,B1,7.63,115.0 -2016-02-18,WA,2,B,B1,7.69,125.0 -2016-02-19,WA,2,B,B1,7.6,130.0 -2016-02-20,WA,2,B,B1,7.57,125.0 -2016-02-21,WA,2,B,B1,7.65,131.0 -2016-02-22,WA,2,B,B1,7.57,123.0 -2016-02-23,WA,2,B,B1,7.52,130.0 -2016-02-24,WA,2,B,B1,7.67,134.0 -2016-02-25,WA,2,B,B1,7.63,121.0 -2016-02-26,WA,2,B,B1,7.62,125.0 -2016-02-27,WA,2,B,B1,7.64,131.0 -2016-02-28,WA,2,B,B1,7.6,130.0 -2016-02-29,WA,2,B,B1,7.73,121.0 -2016-03-01,WA,2,B,B1,7.54,123.0 -2016-03-02,WA,2,B,B1,7.73,109.0 -2016-03-03,WA,2,B,B1,7.54,132.0 -2016-03-04,WA,2,B,B1,7.67,121.0 -2016-03-05,WA,2,B,B1,7.75,120.0 -2016-03-06,WA,2,B,B1,7.52,128.0 -2016-03-07,WA,2,B,B1,7.71,112.0 -2016-03-08,WA,2,B,B1,7.66,122.0 -2016-03-09,WA,2,B,B1,7.63,127.0 -2016-03-10,WA,2,B,B1,7.53,131.0 -2016-03-11,WA,2,B,B1,7.66,122.0 -2016-03-12,WA,2,B,B1,7.67,131.0 -2016-03-13,WA,2,B,B1,7.54,131.0 -2016-03-14,WA,2,B,B1,7.66,117.0 -2016-03-15,WA,2,B,B1,7.57,130.0 -2016-03-16,WA,2,B,B1,7.6,125.0 -2016-03-17,WA,2,B,B1,7.86,106.0 -2016-03-18,WA,2,B,B1,7.75,123.0 -2016-03-19,WA,2,B,B1,7.55,128.0 -2016-03-20,WA,2,B,B1,7.51,130.0 -2016-03-21,WA,2,B,B1,7.7,130.0 -2016-03-22,WA,2,B,B1,7.56,134.0 -2016-03-23,WA,2,B,B1,7.72,122.0 -2016-03-24,WA,2,B,B1,7.63,117.0 -2016-03-25,WA,2,B,B1,7.7,119.0 -2016-03-26,WA,2,B,B1,7.55,133.0 -2016-03-27,WA,2,B,B1,7.7,117.0 -2016-03-28,WA,2,B,B1,7.58,131.0 -2016-03-29,WA,2,B,B1,7.59,128.0 -2016-03-30,WA,2,B,B1,7.74,110.0 -2016-03-31,WA,2,B,B1,7.69,116.0 -2016-04-01,WA,2,B,B1,7.55,133.0 -2016-04-02,WA,2,B,B1,7.71,125.0 -2016-04-03,WA,2,B,B1,7.67,126.0 -2016-04-04,WA,2,B,B1,7.57,128.0 -2016-04-05,WA,2,B,B1,7.59,128.0 -2016-04-06,WA,2,B,B1,7.59,131.0 -2016-04-07,WA,2,B,B1,7.6,131.0 -2016-04-08,WA,2,B,B1,7.6,132.0 -2016-04-09,WA,2,B,B1,7.55,124.0 -2016-04-10,WA,2,B,B1,7.62,123.0 -2016-04-11,WA,2,B,B1,7.62,120.0 -2016-04-12,WA,2,B,B1,7.62,124.0 -2016-04-13,WA,2,B,B1,7.61,123.0 -2016-04-14,WA,2,B,B1,7.6,136.0 -2016-04-15,WA,2,B,B1,7.63,115.0 -2016-04-16,WA,2,B,B1,7.6,133.0 -2016-04-17,WA,2,B,B1,7.75,122.0 -2016-04-18,WA,2,B,B1,7.54,118.0 -2016-04-19,WA,2,B,B1,7.67,127.0 -2016-04-20,WA,2,B,B1,7.7,113.0 -2016-04-21,WA,2,B,B1,7.56,118.0 -2016-04-22,WA,2,B,B1,7.71,122.0 -2016-04-23,WA,2,B,B1,7.62,126.0 -2016-04-24,WA,2,B,B1,7.65,126.0 -2016-04-25,WA,2,B,B1,7.51,136.0 -2016-04-26,WA,2,B,B1,7.58,121.0 -2016-04-27,WA,2,B,B1,7.61,122.0 -2016-04-28,WA,2,B,B1,7.6,122.0 -2016-04-29,WA,2,B,B1,7.65,127.0 -2016-04-30,WA,2,B,B1,7.73,121.0 -2016-05-01,WA,2,B,B1,7.62,130.0 -2016-05-02,WA,2,B,B1,7.55,119.0 -2016-05-03,WA,2,B,B1,7.56,126.0 -2016-05-04,WA,2,B,B1,7.71,105.0 -2016-05-05,WA,2,B,B1,7.65,123.0 -2016-05-06,WA,2,B,B1,7.65,118.0 -2016-05-07,WA,2,B,B1,7.58,129.0 -2016-05-08,WA,2,B,B1,7.73,115.0 -2016-05-09,WA,2,B,B1,7.69,114.0 -2016-05-10,WA,2,B,B1,7.63,125.0 -2016-05-11,WA,2,B,B1,7.54,118.0 -2016-05-12,WA,2,B,B1,7.52,134.0 -2016-05-13,WA,2,B,B1,7.65,115.0 -2016-05-14,WA,2,B,B1,7.54,139.0 -2016-05-15,WA,2,B,B1,7.6,128.0 -2016-05-16,WA,2,B,B1,7.59,113.0 -2016-05-17,WA,2,B,B1,7.54,124.0 -2016-05-18,WA,2,B,B1,7.47,128.0 -2016-05-19,WA,2,B,B1,7.57,123.0 -2016-05-20,WA,2,B,B1,7.52,138.0 -2016-05-21,WA,2,B,B1,7.69,127.0 -2016-05-22,WA,2,B,B1,7.71,107.0 -2016-05-23,WA,2,B,B1,7.72,119.0 -2016-05-24,WA,2,B,B1,7.65,111.0 -2016-05-25,WA,2,B,B1,7.64,119.0 -2016-05-26,WA,2,B,B1,7.63,121.0 -2016-05-27,WA,2,B,B1,7.55,117.0 -2016-05-28,WA,2,B,B1,7.64,122.0 -2016-05-29,WA,2,B,B1,7.73,114.0 -2016-05-30,WA,2,B,B1,7.57,118.0 -2016-05-31,WA,2,B,B1,7.51,139.0 -2016-06-01,WA,2,B,B1,7.64,115.0 -2016-06-02,WA,2,B,B1,7.63,120.0 -2016-06-03,WA,2,B,B1,7.48,142.0 -2016-06-04,WA,2,B,B1,7.7,122.0 -2016-06-05,WA,2,B,B1,7.62,131.0 -2016-06-06,WA,2,B,B1,7.68,117.0 -2016-06-07,WA,2,B,B1,7.69,111.0 -2016-06-08,WA,2,B,B1,7.54,120.0 -2016-06-09,WA,2,B,B1,7.56,120.0 -2016-06-10,WA,2,B,B1,7.6,116.0 -2016-06-11,WA,2,B,B1,7.68,136.0 -2016-06-12,WA,2,B,B1,7.52,133.0 -2016-06-13,WA,2,B,B1,7.66,118.0 -2016-06-14,WA,2,B,B1,7.61,119.0 -2016-06-15,WA,2,B,B1,7.81,115.0 -2016-06-16,WA,2,B,B1,7.67,125.0 -2016-06-17,WA,2,B,B1,7.73,108.0 -2016-06-18,WA,2,B,B1,7.71,129.0 -2016-06-19,WA,2,B,B1,7.66,115.0 -2016-06-20,WA,2,B,B1,7.62,109.0 -2016-06-21,WA,2,B,B1,7.56,118.0 -2016-06-22,WA,2,B,B1,7.71,120.0 -2016-06-23,WA,2,B,B1,7.76,108.0 -2016-06-24,WA,2,B,B1,7.72,132.0 -2016-06-25,WA,2,B,B1,7.66,128.0 -2016-06-26,WA,2,B,B1,7.55,121.0 -2016-06-27,WA,2,B,B1,7.65,116.0 -2016-06-28,WA,2,B,B1,7.66,123.0 -2016-06-29,WA,2,B,B1,7.55,134.0 -2016-06-30,WA,2,B,B1,7.64,120.0 -2016-07-01,WA,2,B,B1,7.59,129.0 -2016-07-02,WA,2,B,B1,7.73,117.0 -2016-07-03,WA,2,B,B1,7.67,127.0 -2016-07-04,WA,2,B,B1,7.76,108.0 -2016-07-05,WA,2,B,B1,7.68,125.0 -2016-07-06,WA,2,B,B1,7.59,131.0 -2016-07-07,WA,2,B,B1,7.6,132.0 -2016-07-08,WA,2,B,B1,7.55,132.0 -2016-07-09,WA,2,B,B1,7.62,129.0 -2016-07-10,WA,2,B,B1,7.46,134.0 -2016-07-11,WA,2,B,B1,7.59,124.0 -2016-07-12,WA,2,B,B1,7.64,116.0 -2016-07-13,WA,2,B,B1,7.66,116.0 -2016-07-14,WA,2,B,B1,7.6,124.0 -2016-07-15,WA,2,B,B1,7.51,129.0 -2016-07-16,WA,2,B,B1,7.59,123.0 -2016-07-17,WA,2,B,B1,7.54,123.0 -2016-07-18,WA,2,B,B1,7.62,114.0 -2016-07-19,WA,2,B,B1,7.86,109.0 -2016-07-20,WA,2,B,B1,7.63,118.0 -2016-07-21,WA,2,B,B1,7.6,126.0 -2016-07-22,WA,2,B,B1,7.59,132.0 -2016-07-23,WA,2,B,B1,7.62,121.0 -2016-07-24,WA,2,B,B1,7.72,129.0 -2016-07-25,WA,2,B,B1,7.64,117.0 -2016-07-26,WA,2,B,B1,7.73,108.0 -2016-07-27,WA,2,B,B1,7.61,127.0 -2015-01-05,WA,2,B,B2,2.47,46.0 -2015-01-06,WA,2,B,B2,2.47,43.0 -2015-01-07,WA,2,B,B2,2.47,45.0 -2015-01-08,WA,2,B,B2,2.48,43.0 -2015-01-09,WA,2,B,B2,2.47,43.0 -2015-01-10,WA,2,B,B2,2.47,49.0 -2015-01-11,WA,2,B,B2,2.48,45.0 -2015-01-12,WA,2,B,B2,2.51,42.0 -2015-01-13,WA,2,B,B2,2.49,43.0 -2015-01-14,WA,2,B,B2,2.51,43.0 -2015-01-15,WA,2,B,B2,2.43,48.0 -2015-01-16,WA,2,B,B2,2.49,47.0 -2015-01-17,WA,2,B,B2,2.45,49.0 -2015-01-18,WA,2,B,B2,2.5,42.0 -2015-01-19,WA,2,B,B2,2.46,44.0 -2015-01-20,WA,2,B,B2,2.55,39.0 -2015-01-21,WA,2,B,B2,2.5,41.0 -2015-01-22,WA,2,B,B2,2.45,46.0 -2015-01-23,WA,2,B,B2,2.49,45.0 -2015-01-24,WA,2,B,B2,2.54,46.0 -2015-01-25,WA,2,B,B2,2.47,46.0 -2015-01-26,WA,2,B,B2,2.49,39.0 -2015-01-27,WA,2,B,B2,2.49,47.0 -2015-01-28,WA,2,B,B2,2.5,41.0 -2015-01-29,WA,2,B,B2,2.48,47.0 -2015-01-30,WA,2,B,B2,2.46,49.0 -2015-01-31,WA,2,B,B2,2.48,43.0 -2015-02-01,WA,2,B,B2,2.49,47.0 -2015-02-02,WA,2,B,B2,2.48,44.0 -2015-02-03,WA,2,B,B2,2.52,40.0 -2015-02-04,WA,2,B,B2,2.5,40.0 -2015-02-05,WA,2,B,B2,2.47,47.0 -2015-02-06,WA,2,B,B2,2.52,42.0 -2015-02-07,WA,2,B,B2,2.48,44.0 -2015-02-08,WA,2,B,B2,2.48,42.0 -2015-02-09,WA,2,B,B2,2.5,44.0 -2015-02-10,WA,2,B,B2,2.51,44.0 -2015-02-11,WA,2,B,B2,2.46,48.0 -2015-02-12,WA,2,B,B2,2.45,45.0 -2015-02-13,WA,2,B,B2,2.47,51.0 -2015-02-14,WA,2,B,B2,2.47,48.0 -2015-02-15,WA,2,B,B2,2.47,45.0 -2015-02-16,WA,2,B,B2,2.48,41.0 -2015-02-17,WA,2,B,B2,2.5,42.0 -2015-02-18,WA,2,B,B2,2.5,44.0 -2015-02-19,WA,2,B,B2,2.52,41.0 -2015-02-20,WA,2,B,B2,2.5,46.0 -2015-02-21,WA,2,B,B2,2.5,45.0 -2015-02-22,WA,2,B,B2,2.46,48.0 -2015-02-23,WA,2,B,B2,2.5,42.0 -2015-02-24,WA,2,B,B2,2.5,42.0 -2015-02-25,WA,2,B,B2,2.46,42.0 -2015-02-26,WA,2,B,B2,2.49,43.0 -2015-02-27,WA,2,B,B2,2.46,44.0 -2015-02-28,WA,2,B,B2,2.43,52.0 -2015-03-01,WA,2,B,B2,2.46,49.0 -2015-03-02,WA,2,B,B2,2.47,42.0 -2015-03-03,WA,2,B,B2,2.47,45.0 -2015-03-04,WA,2,B,B2,2.5,40.0 -2015-03-05,WA,2,B,B2,2.49,43.0 -2015-03-06,WA,2,B,B2,2.51,47.0 -2015-03-07,WA,2,B,B2,2.5,45.0 -2015-03-08,WA,2,B,B2,2.45,44.0 -2015-03-09,WA,2,B,B2,2.51,43.0 -2015-03-10,WA,2,B,B2,2.48,43.0 -2015-03-11,WA,2,B,B2,2.53,42.0 -2015-03-12,WA,2,B,B2,2.51,43.0 -2015-03-13,WA,2,B,B2,2.47,46.0 -2015-03-14,WA,2,B,B2,2.49,45.0 -2015-03-15,WA,2,B,B2,2.51,44.0 -2015-03-16,WA,2,B,B2,2.48,45.0 -2015-03-17,WA,2,B,B2,2.46,45.0 -2015-03-18,WA,2,B,B2,2.52,43.0 -2015-03-19,WA,2,B,B2,2.46,42.0 -2015-03-20,WA,2,B,B2,2.52,40.0 -2015-03-21,WA,2,B,B2,2.46,49.0 -2015-03-22,WA,2,B,B2,2.47,49.0 -2015-03-23,WA,2,B,B2,2.49,42.0 -2015-03-24,WA,2,B,B2,2.51,45.0 -2015-03-25,WA,2,B,B2,2.53,39.0 -2015-03-26,WA,2,B,B2,2.47,47.0 -2015-03-27,WA,2,B,B2,2.48,42.0 -2015-03-28,WA,2,B,B2,2.45,51.0 -2015-03-29,WA,2,B,B2,2.47,47.0 -2015-03-30,WA,2,B,B2,2.5,40.0 -2015-03-31,WA,2,B,B2,2.49,43.0 -2015-04-01,WA,2,B,B2,2.54,41.0 -2015-04-02,WA,2,B,B2,2.47,45.0 -2015-04-03,WA,2,B,B2,2.51,41.0 -2015-04-04,WA,2,B,B2,2.48,47.0 -2015-04-05,WA,2,B,B2,2.48,46.0 -2015-04-06,WA,2,B,B2,2.49,43.0 -2015-04-07,WA,2,B,B2,2.49,45.0 -2015-04-08,WA,2,B,B2,2.52,45.0 -2015-04-09,WA,2,B,B2,2.52,41.0 -2015-04-10,WA,2,B,B2,2.49,44.0 -2015-04-11,WA,2,B,B2,2.45,51.0 -2015-04-12,WA,2,B,B2,2.49,48.0 -2015-04-13,WA,2,B,B2,2.48,41.0 -2015-04-14,WA,2,B,B2,2.49,43.0 -2015-04-15,WA,2,B,B2,2.5,43.0 -2015-04-16,WA,2,B,B2,2.43,51.0 -2015-04-17,WA,2,B,B2,2.5,42.0 -2015-04-18,WA,2,B,B2,2.44,54.0 -2015-04-19,WA,2,B,B2,2.51,43.0 -2015-04-20,WA,2,B,B2,2.45,48.0 -2015-04-21,WA,2,B,B2,2.52,41.0 -2015-04-22,WA,2,B,B2,2.5,43.0 -2015-04-23,WA,2,B,B2,2.48,41.0 -2015-04-24,WA,2,B,B2,2.44,46.0 -2015-04-25,WA,2,B,B2,2.54,42.0 -2015-04-26,WA,2,B,B2,2.48,45.0 -2015-04-27,WA,2,B,B2,2.53,40.0 -2015-04-28,WA,2,B,B2,2.48,44.0 -2015-04-29,WA,2,B,B2,2.44,46.0 -2015-04-30,WA,2,B,B2,2.49,44.0 -2015-05-01,WA,2,B,B2,2.49,44.0 -2015-05-02,WA,2,B,B2,2.46,49.0 -2015-05-03,WA,2,B,B2,2.48,46.0 -2015-05-04,WA,2,B,B2,2.46,45.0 -2015-05-05,WA,2,B,B2,2.45,48.0 -2015-05-06,WA,2,B,B2,2.48,43.0 -2015-05-07,WA,2,B,B2,2.49,44.0 -2015-05-08,WA,2,B,B2,2.5,42.0 -2015-05-09,WA,2,B,B2,2.49,48.0 -2015-05-10,WA,2,B,B2,2.46,46.0 -2015-05-11,WA,2,B,B2,2.48,43.0 -2015-05-12,WA,2,B,B2,2.49,45.0 -2015-05-13,WA,2,B,B2,2.49,40.0 -2015-05-14,WA,2,B,B2,2.44,48.0 -2015-05-15,WA,2,B,B2,2.51,43.0 -2015-05-16,WA,2,B,B2,2.49,47.0 -2015-05-17,WA,2,B,B2,2.48,47.0 -2015-05-18,WA,2,B,B2,2.48,46.0 -2015-05-19,WA,2,B,B2,2.49,41.0 -2015-05-20,WA,2,B,B2,2.52,43.0 -2015-05-21,WA,2,B,B2,2.5,44.0 -2015-05-22,WA,2,B,B2,2.5,42.0 -2015-05-23,WA,2,B,B2,2.43,52.0 -2015-05-24,WA,2,B,B2,2.47,44.0 -2015-05-25,WA,2,B,B2,2.46,48.0 -2015-05-26,WA,2,B,B2,2.48,48.0 -2015-05-27,WA,2,B,B2,2.51,39.0 -2015-05-28,WA,2,B,B2,2.51,41.0 -2015-05-29,WA,2,B,B2,2.5,44.0 -2015-05-30,WA,2,B,B2,2.42,52.0 -2015-05-31,WA,2,B,B2,2.5,46.0 -2015-06-01,WA,2,B,B2,2.49,44.0 -2015-06-02,WA,2,B,B2,2.46,45.0 -2015-06-03,WA,2,B,B2,2.52,43.0 -2015-06-04,WA,2,B,B2,2.49,45.0 -2015-06-05,WA,2,B,B2,2.5,46.0 -2015-06-06,WA,2,B,B2,2.5,47.0 -2015-06-07,WA,2,B,B2,2.52,40.0 -2015-06-08,WA,2,B,B2,2.47,44.0 -2015-06-09,WA,2,B,B2,2.46,47.0 -2015-06-10,WA,2,B,B2,2.48,49.0 -2015-06-11,WA,2,B,B2,2.49,43.0 -2015-06-12,WA,2,B,B2,2.49,48.0 -2015-06-13,WA,2,B,B2,2.51,45.0 -2015-06-14,WA,2,B,B2,2.51,46.0 -2015-06-15,WA,2,B,B2,2.52,41.0 -2015-06-16,WA,2,B,B2,2.46,46.0 -2015-06-17,WA,2,B,B2,2.45,46.0 -2015-06-18,WA,2,B,B2,2.55,41.0 -2015-06-19,WA,2,B,B2,2.52,43.0 -2015-06-20,WA,2,B,B2,2.53,42.0 -2015-06-21,WA,2,B,B2,2.52,40.0 -2015-06-22,WA,2,B,B2,2.45,48.0 -2015-06-23,WA,2,B,B2,2.45,49.0 -2015-06-24,WA,2,B,B2,2.45,44.0 -2015-06-25,WA,2,B,B2,2.49,44.0 -2015-06-26,WA,2,B,B2,2.53,41.0 -2015-06-27,WA,2,B,B2,2.51,43.0 -2015-06-28,WA,2,B,B2,2.49,43.0 -2015-06-29,WA,2,B,B2,2.48,45.0 -2015-06-30,WA,2,B,B2,2.47,42.0 -2015-07-01,WA,2,B,B2,2.45,51.0 -2015-07-02,WA,2,B,B2,2.46,47.0 -2015-07-03,WA,2,B,B2,2.45,46.0 -2015-07-04,WA,2,B,B2,2.48,49.0 -2015-07-05,WA,2,B,B2,2.51,45.0 -2015-07-06,WA,2,B,B2,2.46,49.0 -2015-07-07,WA,2,B,B2,2.5,39.0 -2015-07-08,WA,2,B,B2,2.55,39.0 -2015-07-09,WA,2,B,B2,2.51,46.0 -2015-07-10,WA,2,B,B2,2.48,46.0 -2015-07-11,WA,2,B,B2,2.46,49.0 -2015-07-12,WA,2,B,B2,2.47,47.0 -2015-07-13,WA,2,B,B2,2.52,41.0 -2015-07-14,WA,2,B,B2,2.45,46.0 -2015-07-15,WA,2,B,B2,2.52,44.0 -2015-07-16,WA,2,B,B2,2.49,46.0 -2015-07-17,WA,2,B,B2,2.49,47.0 -2015-07-18,WA,2,B,B2,2.49,43.0 -2015-07-19,WA,2,B,B2,2.48,48.0 -2015-07-20,WA,2,B,B2,2.45,44.0 -2015-07-21,WA,2,B,B2,2.5,46.0 -2015-07-22,WA,2,B,B2,2.46,47.0 -2015-07-23,WA,2,B,B2,2.47,45.0 -2015-07-24,WA,2,B,B2,2.5,42.0 -2015-07-25,WA,2,B,B2,2.53,38.0 -2015-07-26,WA,2,B,B2,2.43,50.0 -2015-07-27,WA,2,B,B2,2.49,39.0 -2015-07-28,WA,2,B,B2,2.5,41.0 -2015-07-29,WA,2,B,B2,2.5,44.0 -2015-07-30,WA,2,B,B2,2.48,46.0 -2015-07-31,WA,2,B,B2,2.46,43.0 -2015-08-01,WA,2,B,B2,2.5,46.0 -2015-08-02,WA,2,B,B2,2.46,46.0 -2015-08-03,WA,2,B,B2,2.44,47.0 -2015-08-04,WA,2,B,B2,2.45,46.0 -2015-08-05,WA,2,B,B2,2.49,43.0 -2015-08-06,WA,2,B,B2,2.47,46.0 -2015-08-07,WA,2,B,B2,2.47,46.0 -2015-08-08,WA,2,B,B2,2.48,49.0 -2015-08-09,WA,2,B,B2,2.46,45.0 -2015-08-10,WA,2,B,B2,2.52,42.0 -2015-08-11,WA,2,B,B2,2.51,45.0 -2015-08-12,WA,2,B,B2,2.48,45.0 -2015-08-13,WA,2,B,B2,2.45,51.0 -2015-08-14,WA,2,B,B2,2.46,46.0 -2015-08-15,WA,2,B,B2,2.51,45.0 -2015-08-16,WA,2,B,B2,2.51,45.0 -2015-08-17,WA,2,B,B2,2.52,43.0 -2015-08-18,WA,2,B,B2,2.5,43.0 -2015-08-19,WA,2,B,B2,2.46,47.0 -2015-08-20,WA,2,B,B2,2.49,45.0 -2015-08-21,WA,2,B,B2,2.48,46.0 -2015-08-22,WA,2,B,B2,2.48,48.0 -2015-08-23,WA,2,B,B2,2.48,46.0 -2015-08-24,WA,2,B,B2,2.51,42.0 -2015-08-25,WA,2,B,B2,2.46,47.0 -2015-08-26,WA,2,B,B2,2.49,45.0 -2015-08-27,WA,2,B,B2,2.44,52.0 -2015-08-28,WA,2,B,B2,2.47,46.0 -2015-08-29,WA,2,B,B2,2.43,50.0 -2015-08-30,WA,2,B,B2,2.47,48.0 -2015-08-31,WA,2,B,B2,2.45,46.0 -2015-09-01,WA,2,B,B2,2.53,42.0 -2015-09-02,WA,2,B,B2,2.5,42.0 -2015-09-03,WA,2,B,B2,2.49,41.0 -2015-09-04,WA,2,B,B2,2.47,45.0 -2015-09-05,WA,2,B,B2,2.49,46.0 -2015-09-06,WA,2,B,B2,2.52,45.0 -2015-09-07,WA,2,B,B2,2.48,39.0 -2015-09-08,WA,2,B,B2,2.47,48.0 -2015-09-09,WA,2,B,B2,2.5,43.0 -2015-09-10,WA,2,B,B2,2.48,41.0 -2015-09-11,WA,2,B,B2,2.49,44.0 -2015-09-12,WA,2,B,B2,2.48,48.0 -2015-09-13,WA,2,B,B2,2.5,46.0 -2015-09-14,WA,2,B,B2,2.53,43.0 -2015-09-15,WA,2,B,B2,2.46,47.0 -2015-09-16,WA,2,B,B2,2.51,45.0 -2015-09-17,WA,2,B,B2,2.5,47.0 -2015-09-18,WA,2,B,B2,2.46,43.0 -2015-09-19,WA,2,B,B2,2.52,46.0 -2015-09-20,WA,2,B,B2,2.47,49.0 -2015-09-21,WA,2,B,B2,2.49,45.0 -2015-09-22,WA,2,B,B2,2.48,48.0 -2015-09-23,WA,2,B,B2,2.5,46.0 -2015-09-24,WA,2,B,B2,2.48,49.0 -2015-09-25,WA,2,B,B2,2.46,50.0 -2015-09-26,WA,2,B,B2,2.48,48.0 -2015-09-27,WA,2,B,B2,2.5,47.0 -2015-09-28,WA,2,B,B2,2.52,41.0 -2015-09-29,WA,2,B,B2,2.49,41.0 -2015-09-30,WA,2,B,B2,2.48,49.0 -2015-10-01,WA,2,B,B2,2.47,46.0 -2015-10-02,WA,2,B,B2,2.5,46.0 -2015-10-03,WA,2,B,B2,2.46,45.0 -2015-10-04,WA,2,B,B2,2.46,46.0 -2015-10-05,WA,2,B,B2,2.56,41.0 -2015-10-06,WA,2,B,B2,2.46,50.0 -2015-10-07,WA,2,B,B2,2.42,48.0 -2015-10-08,WA,2,B,B2,2.49,39.0 -2015-10-09,WA,2,B,B2,2.46,48.0 -2015-10-10,WA,2,B,B2,2.5,46.0 -2015-10-11,WA,2,B,B2,2.42,50.0 -2015-10-12,WA,2,B,B2,2.49,42.0 -2015-10-13,WA,2,B,B2,2.47,45.0 -2015-10-14,WA,2,B,B2,2.46,43.0 -2015-10-15,WA,2,B,B2,2.51,43.0 -2015-10-16,WA,2,B,B2,2.51,43.0 -2015-10-17,WA,2,B,B2,2.5,46.0 -2015-10-18,WA,2,B,B2,2.47,49.0 -2015-10-19,WA,2,B,B2,2.47,43.0 -2015-10-20,WA,2,B,B2,2.51,43.0 -2015-10-21,WA,2,B,B2,2.41,51.0 -2015-10-22,WA,2,B,B2,2.5,43.0 -2015-10-23,WA,2,B,B2,2.49,44.0 -2015-10-24,WA,2,B,B2,2.47,47.0 -2015-10-25,WA,2,B,B2,2.48,47.0 -2015-10-26,WA,2,B,B2,2.49,43.0 -2015-10-27,WA,2,B,B2,2.47,45.0 -2015-10-28,WA,2,B,B2,2.51,40.0 -2015-10-29,WA,2,B,B2,2.5,44.0 -2015-10-30,WA,2,B,B2,2.51,43.0 -2015-10-31,WA,2,B,B2,2.5,49.0 -2015-11-01,WA,2,B,B2,2.53,41.0 -2015-11-02,WA,2,B,B2,2.47,46.0 -2015-11-03,WA,2,B,B2,2.5,43.0 -2015-11-04,WA,2,B,B2,2.5,44.0 -2015-11-05,WA,2,B,B2,2.48,44.0 -2015-11-06,WA,2,B,B2,2.43,48.0 -2015-11-07,WA,2,B,B2,2.44,50.0 -2015-11-08,WA,2,B,B2,2.48,48.0 -2015-11-09,WA,2,B,B2,2.48,41.0 -2015-11-10,WA,2,B,B2,2.51,41.0 -2015-11-11,WA,2,B,B2,2.49,42.0 -2015-11-12,WA,2,B,B2,2.47,41.0 -2015-11-13,WA,2,B,B2,2.48,45.0 -2015-11-14,WA,2,B,B2,2.49,48.0 -2015-11-15,WA,2,B,B2,2.49,46.0 -2015-11-16,WA,2,B,B2,2.47,49.0 -2015-11-17,WA,2,B,B2,2.52,41.0 -2015-11-18,WA,2,B,B2,2.51,44.0 -2015-11-19,WA,2,B,B2,2.5,40.0 -2015-11-20,WA,2,B,B2,2.5,45.0 -2015-11-21,WA,2,B,B2,2.5,45.0 -2015-11-22,WA,2,B,B2,2.52,43.0 -2015-11-23,WA,2,B,B2,2.48,43.0 -2015-11-24,WA,2,B,B2,2.51,44.0 -2015-11-25,WA,2,B,B2,2.47,44.0 -2015-11-26,WA,2,B,B2,2.48,44.0 -2015-11-27,WA,2,B,B2,2.49,45.0 -2015-11-28,WA,2,B,B2,2.51,44.0 -2015-11-29,WA,2,B,B2,2.5,45.0 -2015-11-30,WA,2,B,B2,2.5,42.0 -2015-12-01,WA,2,B,B2,2.48,47.0 -2015-12-02,WA,2,B,B2,2.45,46.0 -2015-12-03,WA,2,B,B2,2.46,44.0 -2015-12-04,WA,2,B,B2,2.51,46.0 -2015-12-05,WA,2,B,B2,2.41,48.0 -2015-12-06,WA,2,B,B2,2.51,48.0 -2015-12-07,WA,2,B,B2,2.51,43.0 -2015-12-08,WA,2,B,B2,2.48,43.0 -2015-12-09,WA,2,B,B2,2.49,42.0 -2015-12-10,WA,2,B,B2,2.48,42.0 -2015-12-11,WA,2,B,B2,2.47,42.0 -2015-12-12,WA,2,B,B2,2.46,49.0 -2015-12-13,WA,2,B,B2,2.52,45.0 -2015-12-14,WA,2,B,B2,2.48,45.0 -2015-12-15,WA,2,B,B2,2.45,47.0 -2015-12-16,WA,2,B,B2,2.51,41.0 -2015-12-17,WA,2,B,B2,2.49,52.0 -2015-12-18,WA,2,B,B2,2.48,44.0 -2015-12-19,WA,2,B,B2,2.46,51.0 -2015-12-20,WA,2,B,B2,2.47,47.0 -2015-12-21,WA,2,B,B2,2.55,39.0 -2015-12-22,WA,2,B,B2,2.49,43.0 -2015-12-23,WA,2,B,B2,2.51,40.0 -2015-12-24,WA,2,B,B2,2.45,47.0 -2015-12-25,WA,2,B,B2,2.5,43.0 -2015-12-26,WA,2,B,B2,2.5,46.0 -2015-12-27,WA,2,B,B2,2.5,43.0 -2015-12-28,WA,2,B,B2,2.51,46.0 -2015-12-29,WA,2,B,B2,2.49,39.0 -2015-12-30,WA,2,B,B2,2.5,42.0 -2015-12-31,WA,2,B,B2,2.49,42.0 -2016-01-01,WA,2,B,B2,2.47,47.0 -2016-01-02,WA,2,B,B2,2.5,45.0 -2016-01-03,WA,2,B,B2,2.49,43.0 -2016-01-04,WA,2,B,B2,2.46,43.0 -2016-01-05,WA,2,B,B2,2.46,47.0 -2016-01-06,WA,2,B,B2,2.52,42.0 -2016-01-07,WA,2,B,B2,2.49,44.0 -2016-01-08,WA,2,B,B2,2.48,46.0 -2016-01-09,WA,2,B,B2,2.49,47.0 -2016-01-10,WA,2,B,B2,2.47,46.0 -2016-01-11,WA,2,B,B2,2.48,42.0 -2016-01-12,WA,2,B,B2,2.48,40.0 -2016-01-13,WA,2,B,B2,2.49,47.0 -2016-01-14,WA,2,B,B2,2.53,43.0 -2016-01-15,WA,2,B,B2,2.48,46.0 -2016-01-16,WA,2,B,B2,2.45,47.0 -2016-01-17,WA,2,B,B2,2.5,48.0 -2016-01-18,WA,2,B,B2,2.46,45.0 -2016-01-19,WA,2,B,B2,2.46,49.0 -2016-01-20,WA,2,B,B2,2.48,43.0 -2016-01-21,WA,2,B,B2,2.51,45.0 -2016-01-22,WA,2,B,B2,2.5,48.0 -2016-01-23,WA,2,B,B2,2.45,51.0 -2016-01-24,WA,2,B,B2,2.49,43.0 -2016-01-25,WA,2,B,B2,2.47,45.0 -2016-01-26,WA,2,B,B2,2.55,44.0 -2016-01-27,WA,2,B,B2,2.5,39.0 -2016-01-28,WA,2,B,B2,2.52,42.0 -2016-01-29,WA,2,B,B2,2.44,48.0 -2016-01-30,WA,2,B,B2,2.48,50.0 -2016-01-31,WA,2,B,B2,2.49,44.0 -2016-02-01,WA,2,B,B2,2.47,41.0 -2016-02-02,WA,2,B,B2,2.49,43.0 -2016-02-03,WA,2,B,B2,2.46,47.0 -2016-02-04,WA,2,B,B2,2.5,40.0 -2016-02-05,WA,2,B,B2,2.5,41.0 -2016-02-06,WA,2,B,B2,2.51,43.0 -2016-02-07,WA,2,B,B2,2.43,48.0 -2016-02-08,WA,2,B,B2,2.47,44.0 -2016-02-09,WA,2,B,B2,2.49,42.0 -2016-02-10,WA,2,B,B2,2.5,41.0 -2016-02-11,WA,2,B,B2,2.49,46.0 -2016-02-12,WA,2,B,B2,2.54,40.0 -2016-02-13,WA,2,B,B2,2.48,49.0 -2016-02-14,WA,2,B,B2,2.52,45.0 -2016-02-15,WA,2,B,B2,2.48,41.0 -2016-02-16,WA,2,B,B2,2.47,46.0 -2016-02-17,WA,2,B,B2,2.42,48.0 -2016-02-18,WA,2,B,B2,2.5,41.0 -2016-02-19,WA,2,B,B2,2.52,40.0 -2016-02-20,WA,2,B,B2,2.5,44.0 -2016-02-21,WA,2,B,B2,2.52,43.0 -2016-02-22,WA,2,B,B2,2.46,45.0 -2016-02-23,WA,2,B,B2,2.5,41.0 -2016-02-24,WA,2,B,B2,2.48,45.0 -2016-02-25,WA,2,B,B2,2.41,52.0 -2016-02-26,WA,2,B,B2,2.51,42.0 -2016-02-27,WA,2,B,B2,2.49,45.0 -2016-02-28,WA,2,B,B2,2.48,46.0 -2016-02-29,WA,2,B,B2,2.49,45.0 -2016-03-01,WA,2,B,B2,2.47,48.0 -2016-03-02,WA,2,B,B2,2.45,45.0 -2016-03-03,WA,2,B,B2,2.49,44.0 -2016-03-04,WA,2,B,B2,2.5,45.0 -2016-03-05,WA,2,B,B2,2.51,44.0 -2016-03-06,WA,2,B,B2,2.51,45.0 -2016-03-07,WA,2,B,B2,2.46,48.0 -2016-03-08,WA,2,B,B2,2.51,39.0 -2016-03-09,WA,2,B,B2,2.44,48.0 -2016-03-10,WA,2,B,B2,2.52,44.0 -2016-03-11,WA,2,B,B2,2.42,51.0 -2016-03-12,WA,2,B,B2,2.48,45.0 -2016-03-13,WA,2,B,B2,2.47,47.0 -2016-03-14,WA,2,B,B2,2.5,44.0 -2016-03-15,WA,2,B,B2,2.55,38.0 -2016-03-16,WA,2,B,B2,2.51,42.0 -2016-03-17,WA,2,B,B2,2.44,44.0 -2016-03-18,WA,2,B,B2,2.46,46.0 -2016-03-19,WA,2,B,B2,2.48,48.0 -2016-03-20,WA,2,B,B2,2.49,44.0 -2016-03-21,WA,2,B,B2,2.51,41.0 -2016-03-22,WA,2,B,B2,2.45,47.0 -2016-03-23,WA,2,B,B2,2.45,44.0 -2016-03-24,WA,2,B,B2,2.47,45.0 -2016-03-25,WA,2,B,B2,2.48,45.0 -2016-03-26,WA,2,B,B2,2.45,48.0 -2016-03-27,WA,2,B,B2,2.52,41.0 -2016-03-28,WA,2,B,B2,2.51,40.0 -2016-03-29,WA,2,B,B2,2.48,43.0 -2016-03-30,WA,2,B,B2,2.46,49.0 -2016-03-31,WA,2,B,B2,2.45,47.0 -2016-04-01,WA,2,B,B2,2.5,44.0 -2016-04-02,WA,2,B,B2,2.52,45.0 -2016-04-03,WA,2,B,B2,2.45,49.0 -2016-04-04,WA,2,B,B2,2.47,50.0 -2016-04-05,WA,2,B,B2,2.46,47.0 -2016-04-06,WA,2,B,B2,2.48,42.0 -2016-04-07,WA,2,B,B2,2.48,46.0 -2016-04-08,WA,2,B,B2,2.47,47.0 -2016-04-09,WA,2,B,B2,2.48,48.0 -2016-04-10,WA,2,B,B2,2.5,46.0 -2016-04-11,WA,2,B,B2,2.51,43.0 -2016-04-12,WA,2,B,B2,2.48,46.0 -2016-04-13,WA,2,B,B2,2.47,47.0 -2016-04-14,WA,2,B,B2,2.49,45.0 -2016-04-15,WA,2,B,B2,2.45,47.0 -2016-04-16,WA,2,B,B2,2.5,41.0 -2016-04-17,WA,2,B,B2,2.52,43.0 -2016-04-18,WA,2,B,B2,2.53,40.0 -2016-04-19,WA,2,B,B2,2.51,38.0 -2016-04-20,WA,2,B,B2,2.47,44.0 -2016-04-21,WA,2,B,B2,2.49,47.0 -2016-04-22,WA,2,B,B2,2.47,46.0 -2016-04-23,WA,2,B,B2,2.46,47.0 -2016-04-24,WA,2,B,B2,2.47,49.0 -2016-04-25,WA,2,B,B2,2.48,45.0 -2016-04-26,WA,2,B,B2,2.52,43.0 -2016-04-27,WA,2,B,B2,2.47,47.0 -2016-04-28,WA,2,B,B2,2.53,43.0 -2016-04-29,WA,2,B,B2,2.47,46.0 -2016-04-30,WA,2,B,B2,2.49,44.0 -2016-05-01,WA,2,B,B2,2.47,45.0 -2016-05-02,WA,2,B,B2,2.48,43.0 -2016-05-03,WA,2,B,B2,2.49,44.0 -2016-05-04,WA,2,B,B2,2.49,44.0 -2016-05-05,WA,2,B,B2,2.45,45.0 -2016-05-06,WA,2,B,B2,2.5,43.0 -2016-05-07,WA,2,B,B2,2.48,49.0 -2016-05-08,WA,2,B,B2,2.52,41.0 -2016-05-09,WA,2,B,B2,2.51,42.0 -2016-05-10,WA,2,B,B2,2.45,45.0 -2016-05-11,WA,2,B,B2,2.47,48.0 -2016-05-12,WA,2,B,B2,2.5,44.0 -2016-05-13,WA,2,B,B2,2.48,51.0 -2016-05-14,WA,2,B,B2,2.51,45.0 -2016-05-15,WA,2,B,B2,2.5,45.0 -2016-05-16,WA,2,B,B2,2.47,47.0 -2016-05-17,WA,2,B,B2,2.43,48.0 -2016-05-18,WA,2,B,B2,2.54,44.0 -2016-05-19,WA,2,B,B2,2.53,39.0 -2016-05-20,WA,2,B,B2,2.48,46.0 -2016-05-21,WA,2,B,B2,2.49,48.0 -2016-05-22,WA,2,B,B2,2.48,46.0 -2016-05-23,WA,2,B,B2,2.45,44.0 -2016-05-24,WA,2,B,B2,2.49,43.0 -2016-05-25,WA,2,B,B2,2.52,45.0 -2016-05-26,WA,2,B,B2,2.45,43.0 -2016-05-27,WA,2,B,B2,2.42,52.0 -2016-05-28,WA,2,B,B2,2.5,44.0 -2016-05-29,WA,2,B,B2,2.49,44.0 -2016-05-30,WA,2,B,B2,2.55,41.0 -2016-05-31,WA,2,B,B2,2.47,49.0 -2016-06-01,WA,2,B,B2,2.51,46.0 -2016-06-02,WA,2,B,B2,2.49,44.0 -2016-06-03,WA,2,B,B2,2.51,46.0 -2016-06-04,WA,2,B,B2,2.49,46.0 -2016-06-05,WA,2,B,B2,2.46,48.0 -2016-06-06,WA,2,B,B2,2.47,44.0 -2016-06-07,WA,2,B,B2,2.52,40.0 -2016-06-08,WA,2,B,B2,2.47,46.0 -2016-06-09,WA,2,B,B2,2.43,48.0 -2016-06-10,WA,2,B,B2,2.49,43.0 -2016-06-11,WA,2,B,B2,2.48,45.0 -2016-06-12,WA,2,B,B2,2.51,43.0 -2016-06-13,WA,2,B,B2,2.5,44.0 -2016-06-14,WA,2,B,B2,2.49,45.0 -2016-06-15,WA,2,B,B2,2.46,45.0 -2016-06-16,WA,2,B,B2,2.45,44.0 -2016-06-17,WA,2,B,B2,2.49,48.0 -2016-06-18,WA,2,B,B2,2.45,47.0 -2016-06-19,WA,2,B,B2,2.49,45.0 -2016-06-20,WA,2,B,B2,2.51,42.0 -2016-06-21,WA,2,B,B2,2.52,43.0 -2016-06-22,WA,2,B,B2,2.49,45.0 -2016-06-23,WA,2,B,B2,2.49,45.0 -2016-06-24,WA,2,B,B2,2.51,43.0 -2016-06-25,WA,2,B,B2,2.5,42.0 -2016-06-26,WA,2,B,B2,2.53,43.0 -2016-06-27,WA,2,B,B2,2.47,46.0 -2016-06-28,WA,2,B,B2,2.49,45.0 -2016-06-29,WA,2,B,B2,2.43,52.0 -2016-06-30,WA,2,B,B2,2.46,44.0 -2016-07-01,WA,2,B,B2,2.5,39.0 -2016-07-02,WA,2,B,B2,2.53,42.0 -2016-07-03,WA,2,B,B2,2.5,45.0 -2016-07-04,WA,2,B,B2,2.46,44.0 -2016-07-05,WA,2,B,B2,2.46,47.0 -2016-07-06,WA,2,B,B2,2.44,48.0 -2016-07-07,WA,2,B,B2,2.46,45.0 -2016-07-08,WA,2,B,B2,2.51,45.0 -2016-07-09,WA,2,B,B2,2.47,49.0 -2016-07-10,WA,2,B,B2,2.5,46.0 -2016-07-11,WA,2,B,B2,2.47,45.0 -2016-07-12,WA,2,B,B2,2.53,36.0 -2016-07-13,WA,2,B,B2,2.44,49.0 -2016-07-14,WA,2,B,B2,2.48,46.0 -2016-07-15,WA,2,B,B2,2.47,45.0 -2016-07-16,WA,2,B,B2,2.47,50.0 -2016-07-17,WA,2,B,B2,2.52,43.0 -2016-07-18,WA,2,B,B2,2.48,43.0 -2016-07-19,WA,2,B,B2,2.48,48.0 -2016-07-20,WA,2,B,B2,2.49,45.0 -2016-07-21,WA,2,B,B2,2.5,43.0 -2016-07-22,WA,2,B,B2,2.49,42.0 -2016-07-23,WA,2,B,B2,2.51,44.0 -2016-07-24,WA,2,B,B2,2.51,45.0 -2016-07-25,WA,2,B,B2,2.46,43.0 -2016-07-26,WA,2,B,B2,2.46,47.0 -2016-07-27,WA,2,B,B2,2.47,46.0 -2015-01-05,WA,3,A,A1,3.77,12.0 -2015-01-06,WA,3,A,A1,3.64,13.0 -2015-01-07,WA,3,A,A1,3.73,14.0 -2015-01-08,WA,3,A,A1,3.66,13.0 -2015-01-09,WA,3,A,A1,3.68,13.0 -2015-01-10,WA,3,A,A1,3.64,13.0 -2015-01-11,WA,3,A,A1,3.7,12.0 -2015-01-12,WA,3,A,A1,3.67,13.0 -2015-01-13,WA,3,A,A1,3.63,12.0 -2015-01-14,WA,3,A,A1,3.76,12.0 -2015-01-15,WA,3,A,A1,3.73,12.0 -2015-01-16,WA,3,A,A1,3.68,13.0 -2015-01-17,WA,3,A,A1,3.66,14.0 -2015-01-18,WA,3,A,A1,3.67,13.0 -2015-01-19,WA,3,A,A1,3.77,12.0 -2015-01-20,WA,3,A,A1,3.66,13.0 -2015-01-21,WA,3,A,A1,3.7,13.0 -2015-01-22,WA,3,A,A1,3.67,11.0 -2015-01-23,WA,3,A,A1,3.74,13.0 -2015-01-24,WA,3,A,A1,3.62,13.0 -2015-01-25,WA,3,A,A1,3.7,14.0 -2015-01-26,WA,3,A,A1,3.69,13.0 -2015-01-27,WA,3,A,A1,3.74,13.0 -2015-01-28,WA,3,A,A1,3.68,14.0 -2015-01-29,WA,3,A,A1,3.71,12.0 -2015-01-30,WA,3,A,A1,3.66,13.0 -2015-01-31,WA,3,A,A1,3.72,13.0 -2015-02-01,WA,3,A,A1,3.66,14.0 -2015-02-02,WA,3,A,A1,3.75,13.0 -2015-02-03,WA,3,A,A1,3.65,13.0 -2015-02-04,WA,3,A,A1,3.69,14.0 -2015-02-05,WA,3,A,A1,3.75,12.0 -2015-02-06,WA,3,A,A1,3.67,14.0 -2015-02-07,WA,3,A,A1,3.69,14.0 -2015-02-08,WA,3,A,A1,3.73,12.0 -2015-02-09,WA,3,A,A1,3.66,12.0 -2015-02-10,WA,3,A,A1,3.71,14.0 -2015-02-11,WA,3,A,A1,3.7,14.0 -2015-02-12,WA,3,A,A1,3.67,13.0 -2015-02-13,WA,3,A,A1,3.62,13.0 -2015-02-14,WA,3,A,A1,3.65,14.0 -2015-02-15,WA,3,A,A1,3.72,12.0 -2015-02-16,WA,3,A,A1,3.7,13.0 -2015-02-17,WA,3,A,A1,3.69,14.0 -2015-02-18,WA,3,A,A1,3.69,12.0 -2015-02-19,WA,3,A,A1,3.69,13.0 -2015-02-20,WA,3,A,A1,3.69,13.0 -2015-02-21,WA,3,A,A1,3.66,14.0 -2015-02-22,WA,3,A,A1,3.61,14.0 -2015-02-23,WA,3,A,A1,3.79,12.0 -2015-02-24,WA,3,A,A1,3.67,13.0 -2015-02-25,WA,3,A,A1,3.62,14.0 -2015-02-26,WA,3,A,A1,3.71,13.0 -2015-02-27,WA,3,A,A1,3.67,13.0 -2015-02-28,WA,3,A,A1,3.72,14.0 -2015-03-01,WA,3,A,A1,3.68,15.0 -2015-03-02,WA,3,A,A1,3.75,13.0 -2015-03-03,WA,3,A,A1,3.69,13.0 -2015-03-04,WA,3,A,A1,3.73,13.0 -2015-03-05,WA,3,A,A1,3.66,14.0 -2015-03-06,WA,3,A,A1,3.71,13.0 -2015-03-07,WA,3,A,A1,3.62,14.0 -2015-03-08,WA,3,A,A1,3.68,13.0 -2015-03-09,WA,3,A,A1,3.69,13.0 -2015-03-10,WA,3,A,A1,3.74,13.0 -2015-03-11,WA,3,A,A1,3.7,12.0 -2015-03-12,WA,3,A,A1,3.7,13.0 -2015-03-13,WA,3,A,A1,3.66,13.0 -2015-03-14,WA,3,A,A1,3.62,13.0 -2015-03-15,WA,3,A,A1,3.72,14.0 -2015-03-16,WA,3,A,A1,3.7,12.0 -2015-03-17,WA,3,A,A1,3.71,13.0 -2015-03-18,WA,3,A,A1,3.73,14.0 -2015-03-19,WA,3,A,A1,3.68,14.0 -2015-03-20,WA,3,A,A1,3.68,13.0 -2015-03-21,WA,3,A,A1,3.7,14.0 -2015-03-22,WA,3,A,A1,3.69,13.0 -2015-03-23,WA,3,A,A1,3.74,13.0 -2015-03-24,WA,3,A,A1,3.72,14.0 -2015-03-25,WA,3,A,A1,3.68,12.0 -2015-03-26,WA,3,A,A1,3.63,13.0 -2015-03-27,WA,3,A,A1,3.69,14.0 -2015-03-28,WA,3,A,A1,3.68,14.0 -2015-03-29,WA,3,A,A1,3.66,15.0 -2015-03-30,WA,3,A,A1,3.72,12.0 -2015-03-31,WA,3,A,A1,3.75,13.0 -2015-04-01,WA,3,A,A1,3.7,14.0 -2015-04-02,WA,3,A,A1,3.71,13.0 -2015-04-03,WA,3,A,A1,3.65,14.0 -2015-04-04,WA,3,A,A1,3.67,13.0 -2015-04-05,WA,3,A,A1,3.71,14.0 -2015-04-06,WA,3,A,A1,3.69,13.0 -2015-04-07,WA,3,A,A1,3.71,13.0 -2015-04-08,WA,3,A,A1,3.65,13.0 -2015-04-09,WA,3,A,A1,3.64,13.0 -2015-04-10,WA,3,A,A1,3.74,13.0 -2015-04-11,WA,3,A,A1,3.72,15.0 -2015-04-12,WA,3,A,A1,3.68,14.0 -2015-04-13,WA,3,A,A1,3.67,13.0 -2015-04-14,WA,3,A,A1,3.71,12.0 -2015-04-15,WA,3,A,A1,3.7,14.0 -2015-04-16,WA,3,A,A1,3.65,14.0 -2015-04-17,WA,3,A,A1,3.68,14.0 -2015-04-18,WA,3,A,A1,3.65,15.0 -2015-04-19,WA,3,A,A1,3.69,14.0 -2015-04-20,WA,3,A,A1,3.69,14.0 -2015-04-21,WA,3,A,A1,3.69,13.0 -2015-04-22,WA,3,A,A1,3.66,14.0 -2015-04-23,WA,3,A,A1,3.76,13.0 -2015-04-24,WA,3,A,A1,3.69,14.0 -2015-04-25,WA,3,A,A1,3.74,14.0 -2015-04-26,WA,3,A,A1,3.72,14.0 -2015-04-27,WA,3,A,A1,3.74,14.0 -2015-04-28,WA,3,A,A1,3.68,14.0 -2015-04-29,WA,3,A,A1,3.67,14.0 -2015-04-30,WA,3,A,A1,3.7,14.0 -2015-05-01,WA,3,A,A1,3.65,15.0 -2015-05-02,WA,3,A,A1,3.69,14.0 -2015-05-03,WA,3,A,A1,3.73,14.0 -2015-05-04,WA,3,A,A1,3.76,13.0 -2015-05-05,WA,3,A,A1,3.78,14.0 -2015-05-06,WA,3,A,A1,3.75,14.0 -2015-05-07,WA,3,A,A1,3.75,14.0 -2015-05-08,WA,3,A,A1,3.71,14.0 -2015-05-09,WA,3,A,A1,3.71,14.0 -2015-05-10,WA,3,A,A1,3.74,13.0 -2015-05-11,WA,3,A,A1,3.71,13.0 -2015-05-12,WA,3,A,A1,3.71,14.0 -2015-05-13,WA,3,A,A1,3.74,14.0 -2015-05-14,WA,3,A,A1,3.66,13.0 -2015-05-15,WA,3,A,A1,3.72,13.0 -2015-05-16,WA,3,A,A1,3.77,14.0 -2015-05-17,WA,3,A,A1,3.67,14.0 -2015-05-18,WA,3,A,A1,3.67,13.0 -2015-05-19,WA,3,A,A1,3.65,14.0 -2015-05-20,WA,3,A,A1,3.66,14.0 -2015-05-21,WA,3,A,A1,3.74,14.0 -2015-05-22,WA,3,A,A1,3.76,13.0 -2015-05-23,WA,3,A,A1,3.67,14.0 -2015-05-24,WA,3,A,A1,3.74,14.0 -2015-05-25,WA,3,A,A1,3.72,14.0 -2015-05-26,WA,3,A,A1,3.68,14.0 -2015-05-27,WA,3,A,A1,3.7,15.0 -2015-05-28,WA,3,A,A1,3.73,13.0 -2015-05-29,WA,3,A,A1,3.64,13.0 -2015-05-30,WA,3,A,A1,3.64,15.0 -2015-05-31,WA,3,A,A1,3.69,13.0 -2015-06-01,WA,3,A,A1,3.65,14.0 -2015-06-02,WA,3,A,A1,3.71,13.0 -2015-06-03,WA,3,A,A1,3.73,14.0 -2015-06-04,WA,3,A,A1,3.72,14.0 -2015-06-05,WA,3,A,A1,3.77,14.0 -2015-06-06,WA,3,A,A1,3.68,14.0 -2015-06-07,WA,3,A,A1,3.69,14.0 -2015-06-08,WA,3,A,A1,3.69,13.0 -2015-06-09,WA,3,A,A1,3.64,13.0 -2015-06-10,WA,3,A,A1,3.72,14.0 -2015-06-11,WA,3,A,A1,3.68,13.0 -2015-06-12,WA,3,A,A1,3.72,13.0 -2015-06-13,WA,3,A,A1,3.75,14.0 -2015-06-14,WA,3,A,A1,3.67,14.0 -2015-06-15,WA,3,A,A1,3.69,13.0 -2015-06-16,WA,3,A,A1,3.67,14.0 -2015-06-17,WA,3,A,A1,3.71,14.0 -2015-06-18,WA,3,A,A1,3.72,14.0 -2015-06-19,WA,3,A,A1,3.66,13.0 -2015-06-20,WA,3,A,A1,3.78,14.0 -2015-06-21,WA,3,A,A1,3.66,14.0 -2015-06-22,WA,3,A,A1,3.68,14.0 -2015-06-23,WA,3,A,A1,3.72,14.0 -2015-06-24,WA,3,A,A1,3.7,14.0 -2015-06-25,WA,3,A,A1,3.72,15.0 -2015-06-26,WA,3,A,A1,3.6,14.0 -2015-06-27,WA,3,A,A1,3.72,14.0 -2015-06-28,WA,3,A,A1,3.74,13.0 -2015-06-29,WA,3,A,A1,3.63,12.0 -2015-06-30,WA,3,A,A1,3.7,13.0 -2015-07-01,WA,3,A,A1,3.75,13.0 -2015-07-02,WA,3,A,A1,3.67,13.0 -2015-07-03,WA,3,A,A1,3.66,14.0 -2015-07-04,WA,3,A,A1,3.69,14.0 -2015-07-05,WA,3,A,A1,3.62,14.0 -2015-07-06,WA,3,A,A1,3.67,13.0 -2015-07-07,WA,3,A,A1,3.64,14.0 -2015-07-08,WA,3,A,A1,3.68,14.0 -2015-07-09,WA,3,A,A1,3.81,13.0 -2015-07-10,WA,3,A,A1,3.62,14.0 -2015-07-11,WA,3,A,A1,3.69,13.0 -2015-07-12,WA,3,A,A1,3.69,15.0 -2015-07-13,WA,3,A,A1,3.64,13.0 -2015-07-14,WA,3,A,A1,3.67,14.0 -2015-07-15,WA,3,A,A1,3.73,14.0 -2015-07-16,WA,3,A,A1,3.71,14.0 -2015-07-17,WA,3,A,A1,3.78,13.0 -2015-07-18,WA,3,A,A1,3.68,14.0 -2015-07-19,WA,3,A,A1,3.7,14.0 -2015-07-20,WA,3,A,A1,3.64,14.0 -2015-07-21,WA,3,A,A1,3.65,15.0 -2015-07-22,WA,3,A,A1,3.66,14.0 -2015-07-23,WA,3,A,A1,3.62,14.0 -2015-07-24,WA,3,A,A1,3.72,14.0 -2015-07-25,WA,3,A,A1,3.73,13.0 -2015-07-26,WA,3,A,A1,3.72,14.0 -2015-07-27,WA,3,A,A1,3.64,14.0 -2015-07-28,WA,3,A,A1,3.64,14.0 -2015-07-29,WA,3,A,A1,3.75,13.0 -2015-07-30,WA,3,A,A1,3.68,14.0 -2015-07-31,WA,3,A,A1,3.69,15.0 -2015-08-01,WA,3,A,A1,3.73,13.0 -2015-08-02,WA,3,A,A1,3.7,14.0 -2015-08-03,WA,3,A,A1,3.68,14.0 -2015-08-04,WA,3,A,A1,3.65,13.0 -2015-08-05,WA,3,A,A1,3.63,14.0 -2015-08-06,WA,3,A,A1,3.69,14.0 -2015-08-07,WA,3,A,A1,3.72,14.0 -2015-08-08,WA,3,A,A1,3.72,14.0 -2015-08-09,WA,3,A,A1,3.66,14.0 -2015-08-10,WA,3,A,A1,3.64,14.0 -2015-08-11,WA,3,A,A1,3.69,14.0 -2015-08-12,WA,3,A,A1,3.7,15.0 -2015-08-13,WA,3,A,A1,3.74,13.0 -2015-08-14,WA,3,A,A1,3.72,14.0 -2015-08-15,WA,3,A,A1,3.69,15.0 -2015-08-16,WA,3,A,A1,3.6,14.0 -2015-08-17,WA,3,A,A1,3.64,14.0 -2015-08-18,WA,3,A,A1,3.72,15.0 -2015-08-19,WA,3,A,A1,3.71,14.0 -2015-08-20,WA,3,A,A1,3.76,14.0 -2015-08-21,WA,3,A,A1,3.68,15.0 -2015-08-22,WA,3,A,A1,3.61,15.0 -2015-08-23,WA,3,A,A1,3.64,14.0 -2015-08-24,WA,3,A,A1,3.71,13.0 -2015-08-25,WA,3,A,A1,3.68,14.0 -2015-08-26,WA,3,A,A1,3.66,14.0 -2015-08-27,WA,3,A,A1,3.7,13.0 -2015-08-28,WA,3,A,A1,3.69,13.0 -2015-08-29,WA,3,A,A1,3.64,14.0 -2015-08-30,WA,3,A,A1,3.68,14.0 -2015-08-31,WA,3,A,A1,3.69,13.0 -2015-09-01,WA,3,A,A1,3.68,14.0 -2015-09-02,WA,3,A,A1,3.67,15.0 -2015-09-03,WA,3,A,A1,3.68,14.0 -2015-09-04,WA,3,A,A1,3.67,14.0 -2015-09-05,WA,3,A,A1,3.65,15.0 -2015-09-06,WA,3,A,A1,3.7,14.0 -2015-09-07,WA,3,A,A1,3.73,13.0 -2015-09-08,WA,3,A,A1,3.76,14.0 -2015-09-09,WA,3,A,A1,3.72,13.0 -2015-09-10,WA,3,A,A1,3.71,14.0 -2015-09-11,WA,3,A,A1,3.69,15.0 -2015-09-12,WA,3,A,A1,3.72,15.0 -2015-09-13,WA,3,A,A1,3.72,14.0 -2015-09-14,WA,3,A,A1,3.74,14.0 -2015-09-15,WA,3,A,A1,3.72,14.0 -2015-09-16,WA,3,A,A1,3.71,15.0 -2015-09-17,WA,3,A,A1,3.64,15.0 -2015-09-18,WA,3,A,A1,3.68,14.0 -2015-09-19,WA,3,A,A1,3.68,15.0 -2015-09-20,WA,3,A,A1,3.64,15.0 -2015-09-21,WA,3,A,A1,3.7,13.0 -2015-09-22,WA,3,A,A1,3.7,13.0 -2015-09-23,WA,3,A,A1,3.66,15.0 -2015-09-24,WA,3,A,A1,3.71,14.0 -2015-09-25,WA,3,A,A1,3.7,14.0 -2015-09-26,WA,3,A,A1,3.67,15.0 -2015-09-27,WA,3,A,A1,3.7,15.0 -2015-09-28,WA,3,A,A1,3.71,13.0 -2015-09-29,WA,3,A,A1,3.63,14.0 -2015-09-30,WA,3,A,A1,3.68,14.0 -2015-10-01,WA,3,A,A1,3.69,13.0 -2015-10-02,WA,3,A,A1,3.76,15.0 -2015-10-03,WA,3,A,A1,3.71,14.0 -2015-10-04,WA,3,A,A1,3.68,13.0 -2015-10-05,WA,3,A,A1,3.71,15.0 -2015-10-06,WA,3,A,A1,3.62,14.0 -2015-10-07,WA,3,A,A1,3.68,13.0 -2015-10-08,WA,3,A,A1,3.73,12.0 -2015-10-09,WA,3,A,A1,3.7,13.0 -2015-10-10,WA,3,A,A1,3.68,14.0 -2015-10-11,WA,3,A,A1,3.65,14.0 -2015-10-12,WA,3,A,A1,3.66,14.0 -2015-10-13,WA,3,A,A1,3.71,15.0 -2015-10-14,WA,3,A,A1,3.66,14.0 -2015-10-15,WA,3,A,A1,3.69,14.0 -2015-10-16,WA,3,A,A1,3.69,13.0 -2015-10-17,WA,3,A,A1,3.66,15.0 -2015-10-18,WA,3,A,A1,3.6,15.0 -2015-10-19,WA,3,A,A1,3.66,13.0 -2015-10-20,WA,3,A,A1,3.74,14.0 -2015-10-21,WA,3,A,A1,3.74,15.0 -2015-10-22,WA,3,A,A1,3.71,14.0 -2015-10-23,WA,3,A,A1,3.71,14.0 -2015-10-24,WA,3,A,A1,3.69,15.0 -2015-10-25,WA,3,A,A1,3.69,13.0 -2015-10-26,WA,3,A,A1,3.69,14.0 -2015-10-27,WA,3,A,A1,3.73,13.0 -2015-10-28,WA,3,A,A1,3.64,14.0 -2015-10-29,WA,3,A,A1,3.69,14.0 -2015-10-30,WA,3,A,A1,3.79,14.0 -2015-10-31,WA,3,A,A1,3.7,15.0 -2015-11-01,WA,3,A,A1,3.77,14.0 -2015-11-02,WA,3,A,A1,3.63,14.0 -2015-11-03,WA,3,A,A1,3.7,14.0 -2015-11-04,WA,3,A,A1,3.69,13.0 -2015-11-05,WA,3,A,A1,3.74,13.0 -2015-11-06,WA,3,A,A1,3.71,13.0 -2015-11-07,WA,3,A,A1,3.74,15.0 -2015-11-08,WA,3,A,A1,3.67,15.0 -2015-11-09,WA,3,A,A1,3.66,14.0 -2015-11-10,WA,3,A,A1,3.71,14.0 -2015-11-11,WA,3,A,A1,3.75,13.0 -2015-11-12,WA,3,A,A1,3.74,14.0 -2015-11-13,WA,3,A,A1,3.72,14.0 -2015-11-14,WA,3,A,A1,3.73,14.0 -2015-11-15,WA,3,A,A1,3.72,13.0 -2015-11-16,WA,3,A,A1,3.7,14.0 -2015-11-17,WA,3,A,A1,3.73,13.0 -2015-11-18,WA,3,A,A1,3.67,14.0 -2015-11-19,WA,3,A,A1,3.66,15.0 -2015-11-20,WA,3,A,A1,3.7,13.0 -2015-11-21,WA,3,A,A1,3.73,14.0 -2015-11-22,WA,3,A,A1,3.74,14.0 -2015-11-23,WA,3,A,A1,3.68,13.0 -2015-11-24,WA,3,A,A1,3.68,14.0 -2015-11-25,WA,3,A,A1,3.71,14.0 -2015-11-26,WA,3,A,A1,3.71,14.0 -2015-11-27,WA,3,A,A1,3.69,14.0 -2015-11-28,WA,3,A,A1,3.61,14.0 -2015-11-29,WA,3,A,A1,3.73,14.0 -2015-11-30,WA,3,A,A1,3.69,14.0 -2015-12-01,WA,3,A,A1,3.68,14.0 -2015-12-02,WA,3,A,A1,3.68,14.0 -2015-12-03,WA,3,A,A1,3.75,14.0 -2015-12-04,WA,3,A,A1,3.66,14.0 -2015-12-05,WA,3,A,A1,3.71,15.0 -2015-12-06,WA,3,A,A1,3.68,15.0 -2015-12-07,WA,3,A,A1,3.75,14.0 -2015-12-08,WA,3,A,A1,3.74,14.0 -2015-12-09,WA,3,A,A1,3.71,14.0 -2015-12-10,WA,3,A,A1,3.66,14.0 -2015-12-11,WA,3,A,A1,3.63,14.0 -2015-12-12,WA,3,A,A1,3.76,15.0 -2015-12-13,WA,3,A,A1,3.69,15.0 -2015-12-14,WA,3,A,A1,3.7,14.0 -2015-12-15,WA,3,A,A1,3.68,13.0 -2015-12-16,WA,3,A,A1,3.64,13.0 -2015-12-17,WA,3,A,A1,3.75,15.0 -2015-12-18,WA,3,A,A1,3.71,14.0 -2015-12-19,WA,3,A,A1,3.71,15.0 -2015-12-20,WA,3,A,A1,3.7,14.0 -2015-12-21,WA,3,A,A1,3.71,13.0 -2015-12-22,WA,3,A,A1,3.71,14.0 -2015-12-23,WA,3,A,A1,3.66,14.0 -2015-12-24,WA,3,A,A1,3.73,13.0 -2015-12-25,WA,3,A,A1,3.73,14.0 -2015-12-26,WA,3,A,A1,3.69,14.0 -2015-12-27,WA,3,A,A1,3.67,13.0 -2015-12-28,WA,3,A,A1,3.7,14.0 -2015-12-29,WA,3,A,A1,3.65,15.0 -2015-12-30,WA,3,A,A1,3.77,14.0 -2015-12-31,WA,3,A,A1,3.68,13.0 -2016-01-01,WA,3,A,A1,3.71,14.0 -2016-01-02,WA,3,A,A1,3.75,14.0 -2016-01-03,WA,3,A,A1,3.73,13.0 -2016-01-04,WA,3,A,A1,3.72,14.0 -2016-01-05,WA,3,A,A1,3.74,13.0 -2016-01-06,WA,3,A,A1,3.64,14.0 -2016-01-07,WA,3,A,A1,3.72,14.0 -2016-01-08,WA,3,A,A1,3.7,14.0 -2016-01-09,WA,3,A,A1,3.68,14.0 -2016-01-10,WA,3,A,A1,3.69,15.0 -2016-01-11,WA,3,A,A1,3.71,14.0 -2016-01-12,WA,3,A,A1,3.67,15.0 -2016-01-13,WA,3,A,A1,3.75,14.0 -2016-01-14,WA,3,A,A1,3.68,15.0 -2016-01-15,WA,3,A,A1,3.68,14.0 -2016-01-16,WA,3,A,A1,3.71,14.0 -2016-01-17,WA,3,A,A1,3.71,14.0 -2016-01-18,WA,3,A,A1,3.71,14.0 -2016-01-19,WA,3,A,A1,3.76,14.0 -2016-01-20,WA,3,A,A1,3.71,14.0 -2016-01-21,WA,3,A,A1,3.65,14.0 -2016-01-22,WA,3,A,A1,3.71,14.0 -2016-01-23,WA,3,A,A1,3.7,14.0 -2016-01-24,WA,3,A,A1,3.69,15.0 -2016-01-25,WA,3,A,A1,3.68,14.0 -2016-01-26,WA,3,A,A1,3.66,13.0 -2016-01-27,WA,3,A,A1,3.65,14.0 -2016-01-28,WA,3,A,A1,3.71,14.0 -2016-01-29,WA,3,A,A1,3.67,14.0 -2016-01-30,WA,3,A,A1,3.67,14.0 -2016-01-31,WA,3,A,A1,3.7,13.0 -2016-02-01,WA,3,A,A1,3.65,13.0 -2016-02-02,WA,3,A,A1,3.69,14.0 -2016-02-03,WA,3,A,A1,3.67,14.0 -2016-02-04,WA,3,A,A1,3.74,14.0 -2016-02-05,WA,3,A,A1,3.73,14.0 -2016-02-06,WA,3,A,A1,3.71,16.0 -2016-02-07,WA,3,A,A1,3.66,14.0 -2016-02-08,WA,3,A,A1,3.66,13.0 -2016-02-09,WA,3,A,A1,3.65,15.0 -2016-02-10,WA,3,A,A1,3.75,14.0 -2016-02-11,WA,3,A,A1,3.74,14.0 -2016-02-12,WA,3,A,A1,3.73,15.0 -2016-02-13,WA,3,A,A1,3.72,14.0 -2016-02-14,WA,3,A,A1,3.66,14.0 -2016-02-15,WA,3,A,A1,3.7,13.0 -2016-02-16,WA,3,A,A1,3.65,14.0 -2016-02-17,WA,3,A,A1,3.7,13.0 -2016-02-18,WA,3,A,A1,3.64,14.0 -2016-02-19,WA,3,A,A1,3.71,14.0 -2016-02-20,WA,3,A,A1,3.69,14.0 -2016-02-21,WA,3,A,A1,3.72,14.0 -2016-02-22,WA,3,A,A1,3.65,13.0 -2016-02-23,WA,3,A,A1,3.81,13.0 -2016-02-24,WA,3,A,A1,3.73,14.0 -2016-02-25,WA,3,A,A1,3.81,13.0 -2016-02-26,WA,3,A,A1,3.76,14.0 -2016-02-27,WA,3,A,A1,3.63,15.0 -2016-02-28,WA,3,A,A1,3.75,13.0 -2016-02-29,WA,3,A,A1,3.65,13.0 -2016-03-01,WA,3,A,A1,3.73,14.0 -2016-03-02,WA,3,A,A1,3.65,14.0 -2016-03-03,WA,3,A,A1,3.61,13.0 -2016-03-04,WA,3,A,A1,3.65,14.0 -2016-03-05,WA,3,A,A1,3.69,14.0 -2016-03-06,WA,3,A,A1,3.7,14.0 -2016-03-07,WA,3,A,A1,3.74,13.0 -2016-03-08,WA,3,A,A1,3.67,14.0 -2016-03-09,WA,3,A,A1,3.64,14.0 -2016-03-10,WA,3,A,A1,3.64,14.0 -2016-03-11,WA,3,A,A1,3.77,13.0 -2016-03-12,WA,3,A,A1,3.69,14.0 -2016-03-13,WA,3,A,A1,3.67,14.0 -2016-03-14,WA,3,A,A1,3.68,14.0 -2016-03-15,WA,3,A,A1,3.75,14.0 -2016-03-16,WA,3,A,A1,3.65,15.0 -2016-03-17,WA,3,A,A1,3.69,14.0 -2016-03-18,WA,3,A,A1,3.7,13.0 -2016-03-19,WA,3,A,A1,3.74,13.0 -2016-03-20,WA,3,A,A1,3.72,15.0 -2016-03-21,WA,3,A,A1,3.7,13.0 -2016-03-22,WA,3,A,A1,3.72,14.0 -2016-03-23,WA,3,A,A1,3.69,13.0 -2016-03-24,WA,3,A,A1,3.68,14.0 -2016-03-25,WA,3,A,A1,3.64,13.0 -2016-03-26,WA,3,A,A1,3.7,15.0 -2016-03-27,WA,3,A,A1,3.68,14.0 -2016-03-28,WA,3,A,A1,3.71,15.0 -2016-03-29,WA,3,A,A1,3.75,14.0 -2016-03-30,WA,3,A,A1,3.67,14.0 -2016-03-31,WA,3,A,A1,3.71,14.0 -2016-04-01,WA,3,A,A1,3.7,15.0 -2016-04-02,WA,3,A,A1,3.68,14.0 -2016-04-03,WA,3,A,A1,3.76,13.0 -2016-04-04,WA,3,A,A1,3.69,14.0 -2016-04-05,WA,3,A,A1,3.63,13.0 -2016-04-06,WA,3,A,A1,3.69,13.0 -2016-04-07,WA,3,A,A1,3.74,13.0 -2016-04-08,WA,3,A,A1,3.7,15.0 -2016-04-09,WA,3,A,A1,3.71,15.0 -2016-04-10,WA,3,A,A1,3.71,15.0 -2016-04-11,WA,3,A,A1,3.6,13.0 -2016-04-12,WA,3,A,A1,3.76,13.0 -2016-04-13,WA,3,A,A1,3.7,14.0 -2016-04-14,WA,3,A,A1,3.69,14.0 -2016-04-15,WA,3,A,A1,3.68,14.0 -2016-04-16,WA,3,A,A1,3.69,14.0 -2016-04-17,WA,3,A,A1,3.68,14.0 -2016-04-18,WA,3,A,A1,3.75,13.0 -2016-04-19,WA,3,A,A1,3.71,13.0 -2016-04-20,WA,3,A,A1,3.7,16.0 -2016-04-21,WA,3,A,A1,3.68,14.0 -2016-04-22,WA,3,A,A1,3.65,14.0 -2016-04-23,WA,3,A,A1,3.7,14.0 -2016-04-24,WA,3,A,A1,3.75,14.0 -2016-04-25,WA,3,A,A1,3.74,14.0 -2016-04-26,WA,3,A,A1,3.67,13.0 -2016-04-27,WA,3,A,A1,3.7,14.0 -2016-04-28,WA,3,A,A1,3.72,14.0 -2016-04-29,WA,3,A,A1,3.65,13.0 -2016-04-30,WA,3,A,A1,3.67,15.0 -2016-05-01,WA,3,A,A1,3.71,14.0 -2016-05-02,WA,3,A,A1,3.71,13.0 -2016-05-03,WA,3,A,A1,3.72,13.0 -2016-05-04,WA,3,A,A1,3.66,14.0 -2016-05-05,WA,3,A,A1,3.72,15.0 -2016-05-06,WA,3,A,A1,3.69,14.0 -2016-05-07,WA,3,A,A1,3.68,15.0 -2016-05-08,WA,3,A,A1,3.78,14.0 -2016-05-09,WA,3,A,A1,3.66,14.0 -2016-05-10,WA,3,A,A1,3.72,13.0 -2016-05-11,WA,3,A,A1,3.64,14.0 -2016-05-12,WA,3,A,A1,3.68,13.0 -2016-05-13,WA,3,A,A1,3.73,14.0 -2016-05-14,WA,3,A,A1,3.78,14.0 -2016-05-15,WA,3,A,A1,3.69,15.0 -2016-05-16,WA,3,A,A1,3.76,14.0 -2016-05-17,WA,3,A,A1,3.71,13.0 -2016-05-18,WA,3,A,A1,3.69,13.0 -2016-05-19,WA,3,A,A1,3.7,14.0 -2016-05-20,WA,3,A,A1,3.68,14.0 -2016-05-21,WA,3,A,A1,3.69,15.0 -2016-05-22,WA,3,A,A1,3.67,15.0 -2016-05-23,WA,3,A,A1,3.72,14.0 -2016-05-24,WA,3,A,A1,3.71,14.0 -2016-05-25,WA,3,A,A1,3.69,14.0 -2016-05-26,WA,3,A,A1,3.73,14.0 -2016-05-27,WA,3,A,A1,3.71,14.0 -2016-05-28,WA,3,A,A1,3.69,15.0 -2016-05-29,WA,3,A,A1,3.72,13.0 -2016-05-30,WA,3,A,A1,3.71,13.0 -2016-05-31,WA,3,A,A1,3.73,14.0 -2016-06-01,WA,3,A,A1,3.72,14.0 -2016-06-02,WA,3,A,A1,3.69,14.0 -2016-06-03,WA,3,A,A1,3.68,13.0 -2016-06-04,WA,3,A,A1,3.74,14.0 -2016-06-05,WA,3,A,A1,3.68,14.0 -2016-06-06,WA,3,A,A1,3.73,14.0 -2016-06-07,WA,3,A,A1,3.71,14.0 -2016-06-08,WA,3,A,A1,3.74,13.0 -2016-06-09,WA,3,A,A1,3.74,14.0 -2016-06-10,WA,3,A,A1,3.72,14.0 -2016-06-11,WA,3,A,A1,3.65,14.0 -2016-06-12,WA,3,A,A1,3.63,15.0 -2016-06-13,WA,3,A,A1,3.65,14.0 -2016-06-14,WA,3,A,A1,3.65,14.0 -2016-06-15,WA,3,A,A1,3.68,13.0 -2016-06-16,WA,3,A,A1,3.73,14.0 -2016-06-17,WA,3,A,A1,3.68,13.0 -2016-06-18,WA,3,A,A1,3.65,14.0 -2016-06-19,WA,3,A,A1,3.72,14.0 -2016-06-20,WA,3,A,A1,3.7,14.0 -2016-06-21,WA,3,A,A1,3.72,14.0 -2016-06-22,WA,3,A,A1,3.71,13.0 -2016-06-23,WA,3,A,A1,3.7,13.0 -2016-06-24,WA,3,A,A1,3.69,15.0 -2016-06-25,WA,3,A,A1,3.7,14.0 -2016-06-26,WA,3,A,A1,3.71,14.0 -2016-06-27,WA,3,A,A1,3.69,14.0 -2016-06-28,WA,3,A,A1,3.68,14.0 -2016-06-29,WA,3,A,A1,3.73,15.0 -2016-06-30,WA,3,A,A1,3.73,13.0 -2016-07-01,WA,3,A,A1,3.67,14.0 -2016-07-02,WA,3,A,A1,3.7,15.0 -2016-07-03,WA,3,A,A1,3.64,15.0 -2016-07-04,WA,3,A,A1,3.7,14.0 -2016-07-05,WA,3,A,A1,3.72,14.0 -2016-07-06,WA,3,A,A1,3.71,14.0 -2016-07-07,WA,3,A,A1,3.69,15.0 -2016-07-08,WA,3,A,A1,3.69,15.0 -2016-07-09,WA,3,A,A1,3.73,15.0 -2016-07-10,WA,3,A,A1,3.7,14.0 -2016-07-11,WA,3,A,A1,3.74,13.0 -2016-07-12,WA,3,A,A1,3.73,14.0 -2016-07-13,WA,3,A,A1,3.68,14.0 -2016-07-14,WA,3,A,A1,3.67,14.0 -2016-07-15,WA,3,A,A1,3.67,13.0 -2016-07-16,WA,3,A,A1,3.66,13.0 -2016-07-17,WA,3,A,A1,3.64,14.0 -2016-07-18,WA,3,A,A1,3.7,15.0 -2016-07-19,WA,3,A,A1,3.67,14.0 -2016-07-20,WA,3,A,A1,3.69,14.0 -2016-07-21,WA,3,A,A1,3.75,12.0 -2016-07-22,WA,3,A,A1,3.7,14.0 -2016-07-23,WA,3,A,A1,3.67,14.0 -2016-07-24,WA,3,A,A1,3.71,14.0 -2016-07-25,WA,3,A,A1,3.74,13.0 -2016-07-26,WA,3,A,A1,3.67,14.0 -2016-07-27,WA,3,A,A1,3.7,13.0 -2015-01-05,WA,3,A,A2,7.85,13.0 -2015-01-06,WA,3,A,A2,7.72,13.0 -2015-01-07,WA,3,A,A2,7.74,13.0 -2015-01-08,WA,3,A,A2,7.65,13.0 -2015-01-09,WA,3,A,A2,7.75,14.0 -2015-01-10,WA,3,A,A2,7.72,15.0 -2015-01-11,WA,3,A,A2,7.73,13.0 -2015-01-12,WA,3,A,A2,7.84,12.0 -2015-01-13,WA,3,A,A2,7.86,13.0 -2015-01-14,WA,3,A,A2,7.78,13.0 -2015-01-15,WA,3,A,A2,7.73,12.0 -2015-01-16,WA,3,A,A2,7.68,14.0 -2015-01-17,WA,3,A,A2,7.8,13.0 -2015-01-18,WA,3,A,A2,7.79,13.0 -2015-01-19,WA,3,A,A2,7.76,13.0 -2015-01-20,WA,3,A,A2,7.67,13.0 -2015-01-21,WA,3,A,A2,7.6,13.0 -2015-01-22,WA,3,A,A2,7.82,12.0 -2015-01-23,WA,3,A,A2,7.87,13.0 -2015-01-24,WA,3,A,A2,7.72,14.0 -2015-01-25,WA,3,A,A2,7.69,13.0 -2015-01-26,WA,3,A,A2,7.83,12.0 -2015-01-27,WA,3,A,A2,7.8,13.0 -2015-01-28,WA,3,A,A2,8.03,11.0 -2015-01-29,WA,3,A,A2,7.75,13.0 -2015-01-30,WA,3,A,A2,7.75,13.0 -2015-01-31,WA,3,A,A2,7.84,13.0 -2015-02-01,WA,3,A,A2,7.83,13.0 -2015-02-02,WA,3,A,A2,7.73,13.0 -2015-02-03,WA,3,A,A2,7.67,13.0 -2015-02-04,WA,3,A,A2,7.62,13.0 -2015-02-05,WA,3,A,A2,7.9,11.0 -2015-02-06,WA,3,A,A2,7.84,13.0 -2015-02-07,WA,3,A,A2,7.6,16.0 -2015-02-08,WA,3,A,A2,7.83,13.0 -2015-02-09,WA,3,A,A2,7.81,12.0 -2015-02-10,WA,3,A,A2,7.8,13.0 -2015-02-11,WA,3,A,A2,7.74,13.0 -2015-02-12,WA,3,A,A2,7.72,14.0 -2015-02-13,WA,3,A,A2,7.74,13.0 -2015-02-14,WA,3,A,A2,7.78,14.0 -2015-02-15,WA,3,A,A2,7.9,11.0 -2015-02-16,WA,3,A,A2,7.71,13.0 -2015-02-17,WA,3,A,A2,7.78,13.0 -2015-02-18,WA,3,A,A2,7.82,13.0 -2015-02-19,WA,3,A,A2,7.72,13.0 -2015-02-20,WA,3,A,A2,7.69,13.0 -2015-02-21,WA,3,A,A2,7.76,14.0 -2015-02-22,WA,3,A,A2,7.67,13.0 -2015-02-23,WA,3,A,A2,7.8,12.0 -2015-02-24,WA,3,A,A2,7.76,13.0 -2015-02-25,WA,3,A,A2,7.75,14.0 -2015-02-26,WA,3,A,A2,7.81,13.0 -2015-02-27,WA,3,A,A2,7.8,12.0 -2015-02-28,WA,3,A,A2,7.82,13.0 -2015-03-01,WA,3,A,A2,7.73,14.0 -2015-03-02,WA,3,A,A2,7.69,13.0 -2015-03-03,WA,3,A,A2,7.68,13.0 -2015-03-04,WA,3,A,A2,7.76,12.0 -2015-03-05,WA,3,A,A2,7.72,13.0 -2015-03-06,WA,3,A,A2,7.79,13.0 -2015-03-07,WA,3,A,A2,7.65,14.0 -2015-03-08,WA,3,A,A2,7.69,14.0 -2015-03-09,WA,3,A,A2,7.69,13.0 -2015-03-10,WA,3,A,A2,7.73,14.0 -2015-03-11,WA,3,A,A2,7.78,13.0 -2015-03-12,WA,3,A,A2,7.83,12.0 -2015-03-13,WA,3,A,A2,7.78,12.0 -2015-03-14,WA,3,A,A2,7.76,12.0 -2015-03-15,WA,3,A,A2,7.7,14.0 -2015-03-16,WA,3,A,A2,7.7,13.0 -2015-03-17,WA,3,A,A2,7.73,13.0 -2015-03-18,WA,3,A,A2,7.84,11.0 -2015-03-19,WA,3,A,A2,7.74,13.0 -2015-03-20,WA,3,A,A2,7.8,13.0 -2015-03-21,WA,3,A,A2,7.78,14.0 -2015-03-22,WA,3,A,A2,7.67,13.0 -2015-03-23,WA,3,A,A2,7.72,14.0 -2015-03-24,WA,3,A,A2,7.63,14.0 -2015-03-25,WA,3,A,A2,7.74,12.0 -2015-03-26,WA,3,A,A2,7.58,14.0 -2015-03-27,WA,3,A,A2,7.73,14.0 -2015-03-28,WA,3,A,A2,7.78,14.0 -2015-03-29,WA,3,A,A2,7.81,13.0 -2015-03-30,WA,3,A,A2,7.81,13.0 -2015-03-31,WA,3,A,A2,7.82,14.0 -2015-04-01,WA,3,A,A2,7.71,13.0 -2015-04-02,WA,3,A,A2,7.73,14.0 -2015-04-03,WA,3,A,A2,7.8,13.0 -2015-04-04,WA,3,A,A2,7.86,13.0 -2015-04-05,WA,3,A,A2,7.85,14.0 -2015-04-06,WA,3,A,A2,7.75,12.0 -2015-04-07,WA,3,A,A2,7.75,13.0 -2015-04-08,WA,3,A,A2,7.78,13.0 -2015-04-09,WA,3,A,A2,7.71,14.0 -2015-04-10,WA,3,A,A2,7.88,13.0 -2015-04-11,WA,3,A,A2,7.78,14.0 -2015-04-12,WA,3,A,A2,7.63,13.0 -2015-04-13,WA,3,A,A2,7.86,12.0 -2015-04-14,WA,3,A,A2,7.76,12.0 -2015-04-15,WA,3,A,A2,7.81,13.0 -2015-04-16,WA,3,A,A2,7.67,13.0 -2015-04-17,WA,3,A,A2,7.67,14.0 -2015-04-18,WA,3,A,A2,7.61,15.0 -2015-04-19,WA,3,A,A2,7.66,14.0 -2015-04-20,WA,3,A,A2,7.66,13.0 -2015-04-21,WA,3,A,A2,7.65,14.0 -2015-04-22,WA,3,A,A2,7.75,14.0 -2015-04-23,WA,3,A,A2,7.7,13.0 -2015-04-24,WA,3,A,A2,7.72,13.0 -2015-04-25,WA,3,A,A2,7.73,14.0 -2015-04-26,WA,3,A,A2,7.7,14.0 -2015-04-27,WA,3,A,A2,7.63,13.0 -2015-04-28,WA,3,A,A2,7.79,13.0 -2015-04-29,WA,3,A,A2,7.81,13.0 -2015-04-30,WA,3,A,A2,7.7,13.0 -2015-05-01,WA,3,A,A2,7.75,14.0 -2015-05-02,WA,3,A,A2,7.67,14.0 -2015-05-03,WA,3,A,A2,7.81,14.0 -2015-05-04,WA,3,A,A2,7.72,14.0 -2015-05-05,WA,3,A,A2,7.81,14.0 -2015-05-06,WA,3,A,A2,7.71,13.0 -2015-05-07,WA,3,A,A2,7.78,13.0 -2015-05-08,WA,3,A,A2,7.95,13.0 -2015-05-09,WA,3,A,A2,7.8,14.0 -2015-05-10,WA,3,A,A2,7.7,14.0 -2015-05-11,WA,3,A,A2,7.73,12.0 -2015-05-12,WA,3,A,A2,7.64,13.0 -2015-05-13,WA,3,A,A2,7.72,13.0 -2015-05-14,WA,3,A,A2,7.76,13.0 -2015-05-15,WA,3,A,A2,7.7,14.0 -2015-05-16,WA,3,A,A2,7.75,15.0 -2015-05-17,WA,3,A,A2,7.87,13.0 -2015-05-18,WA,3,A,A2,7.73,13.0 -2015-05-19,WA,3,A,A2,7.75,14.0 -2015-05-20,WA,3,A,A2,7.84,13.0 -2015-05-21,WA,3,A,A2,7.7,13.0 -2015-05-22,WA,3,A,A2,7.66,14.0 -2015-05-23,WA,3,A,A2,7.74,13.0 -2015-05-24,WA,3,A,A2,7.89,13.0 -2015-05-25,WA,3,A,A2,7.83,12.0 -2015-05-26,WA,3,A,A2,7.69,13.0 -2015-05-27,WA,3,A,A2,7.62,13.0 -2015-05-28,WA,3,A,A2,7.74,14.0 -2015-05-29,WA,3,A,A2,7.85,12.0 -2015-05-30,WA,3,A,A2,7.85,13.0 -2015-05-31,WA,3,A,A2,7.78,13.0 -2015-06-01,WA,3,A,A2,7.72,12.0 -2015-06-02,WA,3,A,A2,7.78,14.0 -2015-06-03,WA,3,A,A2,7.69,13.0 -2015-06-04,WA,3,A,A2,7.81,12.0 -2015-06-05,WA,3,A,A2,7.77,15.0 -2015-06-06,WA,3,A,A2,7.81,13.0 -2015-06-07,WA,3,A,A2,7.76,13.0 -2015-06-08,WA,3,A,A2,7.71,14.0 -2015-06-09,WA,3,A,A2,7.71,13.0 -2015-06-10,WA,3,A,A2,7.78,13.0 -2015-06-11,WA,3,A,A2,7.85,12.0 -2015-06-12,WA,3,A,A2,7.65,13.0 -2015-06-13,WA,3,A,A2,7.79,13.0 -2015-06-14,WA,3,A,A2,7.62,14.0 -2015-06-15,WA,3,A,A2,7.58,14.0 -2015-06-16,WA,3,A,A2,7.64,14.0 -2015-06-17,WA,3,A,A2,7.74,13.0 -2015-06-18,WA,3,A,A2,7.89,13.0 -2015-06-19,WA,3,A,A2,7.74,13.0 -2015-06-20,WA,3,A,A2,7.69,15.0 -2015-06-21,WA,3,A,A2,7.73,13.0 -2015-06-22,WA,3,A,A2,7.63,14.0 -2015-06-23,WA,3,A,A2,7.72,13.0 -2015-06-24,WA,3,A,A2,7.79,13.0 -2015-06-25,WA,3,A,A2,7.78,13.0 -2015-06-26,WA,3,A,A2,7.81,13.0 -2015-06-27,WA,3,A,A2,7.82,13.0 -2015-06-28,WA,3,A,A2,7.86,12.0 -2015-06-29,WA,3,A,A2,7.83,13.0 -2015-06-30,WA,3,A,A2,7.81,12.0 -2015-07-01,WA,3,A,A2,7.85,12.0 -2015-07-02,WA,3,A,A2,7.77,14.0 -2015-07-03,WA,3,A,A2,7.76,14.0 -2015-07-04,WA,3,A,A2,7.84,14.0 -2015-07-05,WA,3,A,A2,7.72,13.0 -2015-07-06,WA,3,A,A2,7.88,13.0 -2015-07-07,WA,3,A,A2,7.81,13.0 -2015-07-08,WA,3,A,A2,7.62,15.0 -2015-07-09,WA,3,A,A2,7.86,12.0 -2015-07-10,WA,3,A,A2,7.69,13.0 -2015-07-11,WA,3,A,A2,7.72,14.0 -2015-07-12,WA,3,A,A2,7.79,13.0 -2015-07-13,WA,3,A,A2,7.92,12.0 -2015-07-14,WA,3,A,A2,7.78,12.0 -2015-07-15,WA,3,A,A2,7.68,13.0 -2015-07-16,WA,3,A,A2,7.7,13.0 -2015-07-17,WA,3,A,A2,7.82,13.0 -2015-07-18,WA,3,A,A2,7.94,13.0 -2015-07-19,WA,3,A,A2,7.67,14.0 -2015-07-20,WA,3,A,A2,7.81,12.0 -2015-07-21,WA,3,A,A2,7.67,13.0 -2015-07-22,WA,3,A,A2,7.64,14.0 -2015-07-23,WA,3,A,A2,7.8,14.0 -2015-07-24,WA,3,A,A2,7.72,14.0 -2015-07-25,WA,3,A,A2,7.8,13.0 -2015-07-26,WA,3,A,A2,7.8,13.0 -2015-07-27,WA,3,A,A2,7.81,14.0 -2015-07-28,WA,3,A,A2,7.85,13.0 -2015-07-29,WA,3,A,A2,7.76,14.0 -2015-07-30,WA,3,A,A2,7.58,14.0 -2015-07-31,WA,3,A,A2,7.73,12.0 -2015-08-01,WA,3,A,A2,7.77,14.0 -2015-08-02,WA,3,A,A2,7.74,14.0 -2015-08-03,WA,3,A,A2,7.77,13.0 -2015-08-04,WA,3,A,A2,7.77,13.0 -2015-08-05,WA,3,A,A2,7.75,12.0 -2015-08-06,WA,3,A,A2,7.57,14.0 -2015-08-07,WA,3,A,A2,7.78,14.0 -2015-08-08,WA,3,A,A2,7.7,14.0 -2015-08-09,WA,3,A,A2,7.66,15.0 -2015-08-10,WA,3,A,A2,7.66,13.0 -2015-08-11,WA,3,A,A2,7.8,13.0 -2015-08-12,WA,3,A,A2,7.62,14.0 -2015-08-13,WA,3,A,A2,7.7,13.0 -2015-08-14,WA,3,A,A2,7.89,12.0 -2015-08-15,WA,3,A,A2,7.74,15.0 -2015-08-16,WA,3,A,A2,7.71,14.0 -2015-08-17,WA,3,A,A2,7.76,13.0 -2015-08-18,WA,3,A,A2,7.69,13.0 -2015-08-19,WA,3,A,A2,7.61,15.0 -2015-08-20,WA,3,A,A2,7.87,13.0 -2015-08-21,WA,3,A,A2,7.8,13.0 -2015-08-22,WA,3,A,A2,7.77,14.0 -2015-08-23,WA,3,A,A2,7.85,12.0 -2015-08-24,WA,3,A,A2,7.63,13.0 -2015-08-25,WA,3,A,A2,7.73,14.0 -2015-08-26,WA,3,A,A2,7.72,13.0 -2015-08-27,WA,3,A,A2,7.63,14.0 -2015-08-28,WA,3,A,A2,7.7,13.0 -2015-08-29,WA,3,A,A2,7.78,13.0 -2015-08-30,WA,3,A,A2,7.88,12.0 -2015-08-31,WA,3,A,A2,7.72,13.0 -2015-09-01,WA,3,A,A2,7.62,15.0 -2015-09-02,WA,3,A,A2,7.71,13.0 -2015-09-03,WA,3,A,A2,7.86,13.0 -2015-09-04,WA,3,A,A2,7.75,13.0 -2015-09-05,WA,3,A,A2,7.73,14.0 -2015-09-06,WA,3,A,A2,7.86,13.0 -2015-09-07,WA,3,A,A2,7.71,13.0 -2015-09-08,WA,3,A,A2,7.92,12.0 -2015-09-09,WA,3,A,A2,7.73,15.0 -2015-09-10,WA,3,A,A2,7.79,13.0 -2015-09-11,WA,3,A,A2,7.83,14.0 -2015-09-12,WA,3,A,A2,7.79,13.0 -2015-09-13,WA,3,A,A2,7.84,14.0 -2015-09-14,WA,3,A,A2,7.69,13.0 -2015-09-15,WA,3,A,A2,7.68,13.0 -2015-09-16,WA,3,A,A2,7.74,14.0 -2015-09-17,WA,3,A,A2,7.78,12.0 -2015-09-18,WA,3,A,A2,7.69,13.0 -2015-09-19,WA,3,A,A2,7.79,14.0 -2015-09-20,WA,3,A,A2,7.75,13.0 -2015-09-21,WA,3,A,A2,7.73,12.0 -2015-09-22,WA,3,A,A2,7.81,12.0 -2015-09-23,WA,3,A,A2,7.75,13.0 -2015-09-24,WA,3,A,A2,7.75,13.0 -2015-09-25,WA,3,A,A2,7.75,13.0 -2015-09-26,WA,3,A,A2,7.77,14.0 -2015-09-27,WA,3,A,A2,7.66,14.0 -2015-09-28,WA,3,A,A2,7.76,13.0 -2015-09-29,WA,3,A,A2,7.85,13.0 -2015-09-30,WA,3,A,A2,7.65,14.0 -2015-10-01,WA,3,A,A2,7.87,12.0 -2015-10-02,WA,3,A,A2,7.66,13.0 -2015-10-03,WA,3,A,A2,7.94,12.0 -2015-10-04,WA,3,A,A2,7.72,13.0 -2015-10-05,WA,3,A,A2,7.74,13.0 -2015-10-06,WA,3,A,A2,7.82,12.0 -2015-10-07,WA,3,A,A2,7.75,13.0 -2015-10-08,WA,3,A,A2,7.8,12.0 -2015-10-09,WA,3,A,A2,7.76,14.0 -2015-10-10,WA,3,A,A2,7.76,14.0 -2015-10-11,WA,3,A,A2,7.74,13.0 -2015-10-12,WA,3,A,A2,7.82,12.0 -2015-10-13,WA,3,A,A2,7.78,12.0 -2015-10-14,WA,3,A,A2,7.93,12.0 -2015-10-15,WA,3,A,A2,7.66,14.0 -2015-10-16,WA,3,A,A2,7.82,12.0 -2015-10-17,WA,3,A,A2,7.62,15.0 -2015-10-18,WA,3,A,A2,7.83,14.0 -2015-10-19,WA,3,A,A2,7.78,12.0 -2015-10-20,WA,3,A,A2,7.8,13.0 -2015-10-21,WA,3,A,A2,7.69,13.0 -2015-10-22,WA,3,A,A2,7.77,14.0 -2015-10-23,WA,3,A,A2,7.7,14.0 -2015-10-24,WA,3,A,A2,7.79,14.0 -2015-10-25,WA,3,A,A2,7.67,14.0 -2015-10-26,WA,3,A,A2,7.72,13.0 -2015-10-27,WA,3,A,A2,7.79,11.0 -2015-10-28,WA,3,A,A2,7.79,13.0 -2015-10-29,WA,3,A,A2,7.73,14.0 -2015-10-30,WA,3,A,A2,7.6,14.0 -2015-10-31,WA,3,A,A2,7.76,14.0 -2015-11-01,WA,3,A,A2,7.76,14.0 -2015-11-02,WA,3,A,A2,7.68,12.0 -2015-11-03,WA,3,A,A2,7.76,12.0 -2015-11-04,WA,3,A,A2,7.74,13.0 -2015-11-05,WA,3,A,A2,7.74,13.0 -2015-11-06,WA,3,A,A2,7.85,13.0 -2015-11-07,WA,3,A,A2,7.84,13.0 -2015-11-08,WA,3,A,A2,7.69,14.0 -2015-11-09,WA,3,A,A2,7.82,13.0 -2015-11-10,WA,3,A,A2,7.69,13.0 -2015-11-11,WA,3,A,A2,7.69,14.0 -2015-11-12,WA,3,A,A2,7.72,13.0 -2015-11-13,WA,3,A,A2,7.7,13.0 -2015-11-14,WA,3,A,A2,7.74,13.0 -2015-11-15,WA,3,A,A2,7.79,12.0 -2015-11-16,WA,3,A,A2,7.79,13.0 -2015-11-17,WA,3,A,A2,7.66,14.0 -2015-11-18,WA,3,A,A2,7.77,14.0 -2015-11-19,WA,3,A,A2,7.73,14.0 -2015-11-20,WA,3,A,A2,7.66,13.0 -2015-11-21,WA,3,A,A2,7.67,14.0 -2015-11-22,WA,3,A,A2,7.77,14.0 -2015-11-23,WA,3,A,A2,7.58,14.0 -2015-11-24,WA,3,A,A2,7.73,13.0 -2015-11-25,WA,3,A,A2,7.77,13.0 -2015-11-26,WA,3,A,A2,7.72,13.0 -2015-11-27,WA,3,A,A2,7.83,12.0 -2015-11-28,WA,3,A,A2,7.78,14.0 -2015-11-29,WA,3,A,A2,7.73,13.0 -2015-11-30,WA,3,A,A2,7.74,13.0 -2015-12-01,WA,3,A,A2,7.73,14.0 -2015-12-02,WA,3,A,A2,7.81,13.0 -2015-12-03,WA,3,A,A2,7.77,13.0 -2015-12-04,WA,3,A,A2,7.78,13.0 -2015-12-05,WA,3,A,A2,7.89,13.0 -2015-12-06,WA,3,A,A2,7.76,13.0 -2015-12-07,WA,3,A,A2,7.77,12.0 -2015-12-08,WA,3,A,A2,7.8,13.0 -2015-12-09,WA,3,A,A2,7.81,13.0 -2015-12-10,WA,3,A,A2,7.71,13.0 -2015-12-11,WA,3,A,A2,7.82,12.0 -2015-12-12,WA,3,A,A2,7.67,14.0 -2015-12-13,WA,3,A,A2,7.69,14.0 -2015-12-14,WA,3,A,A2,7.8,12.0 -2015-12-15,WA,3,A,A2,7.88,13.0 -2015-12-16,WA,3,A,A2,7.67,13.0 -2015-12-17,WA,3,A,A2,7.71,13.0 -2015-12-18,WA,3,A,A2,7.77,13.0 -2015-12-19,WA,3,A,A2,7.79,14.0 -2015-12-20,WA,3,A,A2,7.8,13.0 -2015-12-21,WA,3,A,A2,7.72,13.0 -2015-12-22,WA,3,A,A2,7.71,13.0 -2015-12-23,WA,3,A,A2,7.75,13.0 -2015-12-24,WA,3,A,A2,7.7,12.0 -2015-12-25,WA,3,A,A2,7.77,13.0 -2015-12-26,WA,3,A,A2,7.63,15.0 -2015-12-27,WA,3,A,A2,7.79,15.0 -2015-12-28,WA,3,A,A2,7.66,13.0 -2015-12-29,WA,3,A,A2,7.77,14.0 -2015-12-30,WA,3,A,A2,7.82,13.0 -2015-12-31,WA,3,A,A2,7.93,12.0 -2016-01-01,WA,3,A,A2,7.61,13.0 -2016-01-02,WA,3,A,A2,7.6,15.0 -2016-01-03,WA,3,A,A2,7.77,13.0 -2016-01-04,WA,3,A,A2,7.8,13.0 -2016-01-05,WA,3,A,A2,7.58,15.0 -2016-01-06,WA,3,A,A2,7.68,13.0 -2016-01-07,WA,3,A,A2,7.72,13.0 -2016-01-08,WA,3,A,A2,7.76,12.0 -2016-01-09,WA,3,A,A2,7.7,15.0 -2016-01-10,WA,3,A,A2,7.94,13.0 -2016-01-11,WA,3,A,A2,7.85,12.0 -2016-01-12,WA,3,A,A2,7.77,13.0 -2016-01-13,WA,3,A,A2,7.71,12.0 -2016-01-14,WA,3,A,A2,7.71,13.0 -2016-01-15,WA,3,A,A2,7.67,14.0 -2016-01-16,WA,3,A,A2,7.89,12.0 -2016-01-17,WA,3,A,A2,7.8,13.0 -2016-01-18,WA,3,A,A2,7.89,13.0 -2016-01-19,WA,3,A,A2,7.69,14.0 -2016-01-20,WA,3,A,A2,7.72,13.0 -2016-01-21,WA,3,A,A2,7.7,13.0 -2016-01-22,WA,3,A,A2,7.76,13.0 -2016-01-23,WA,3,A,A2,7.63,14.0 -2016-01-24,WA,3,A,A2,7.81,13.0 -2016-01-25,WA,3,A,A2,7.99,12.0 -2016-01-26,WA,3,A,A2,7.85,12.0 -2016-01-27,WA,3,A,A2,7.84,12.0 -2016-01-28,WA,3,A,A2,7.83,14.0 -2016-01-29,WA,3,A,A2,7.71,14.0 -2016-01-30,WA,3,A,A2,7.9,14.0 -2016-01-31,WA,3,A,A2,7.85,13.0 -2016-02-01,WA,3,A,A2,7.72,13.0 -2016-02-02,WA,3,A,A2,7.75,12.0 -2016-02-03,WA,3,A,A2,7.81,14.0 -2016-02-04,WA,3,A,A2,7.76,13.0 -2016-02-05,WA,3,A,A2,7.72,13.0 -2016-02-06,WA,3,A,A2,7.7,14.0 -2016-02-07,WA,3,A,A2,7.74,14.0 -2016-02-08,WA,3,A,A2,7.86,12.0 -2016-02-09,WA,3,A,A2,7.78,12.0 -2016-02-10,WA,3,A,A2,7.83,12.0 -2016-02-11,WA,3,A,A2,7.87,12.0 -2016-02-12,WA,3,A,A2,7.75,13.0 -2016-02-13,WA,3,A,A2,7.74,13.0 -2016-02-14,WA,3,A,A2,7.67,14.0 -2016-02-15,WA,3,A,A2,7.73,13.0 -2016-02-16,WA,3,A,A2,7.74,13.0 -2016-02-17,WA,3,A,A2,7.77,14.0 -2016-02-18,WA,3,A,A2,7.88,13.0 -2016-02-19,WA,3,A,A2,7.8,12.0 -2016-02-20,WA,3,A,A2,7.82,14.0 -2016-02-21,WA,3,A,A2,7.86,12.0 -2016-02-22,WA,3,A,A2,7.77,13.0 -2016-02-23,WA,3,A,A2,7.57,13.0 -2016-02-24,WA,3,A,A2,7.7,15.0 -2016-02-25,WA,3,A,A2,7.79,13.0 -2016-02-26,WA,3,A,A2,7.78,13.0 -2016-02-27,WA,3,A,A2,7.67,14.0 -2016-02-28,WA,3,A,A2,7.73,14.0 -2016-02-29,WA,3,A,A2,7.76,13.0 -2016-03-01,WA,3,A,A2,7.98,12.0 -2016-03-02,WA,3,A,A2,7.72,14.0 -2016-03-03,WA,3,A,A2,7.79,14.0 -2016-03-04,WA,3,A,A2,7.63,15.0 -2016-03-05,WA,3,A,A2,7.82,13.0 -2016-03-06,WA,3,A,A2,7.79,14.0 -2016-03-07,WA,3,A,A2,7.81,13.0 -2016-03-08,WA,3,A,A2,7.77,13.0 -2016-03-09,WA,3,A,A2,7.57,14.0 -2016-03-10,WA,3,A,A2,7.7,14.0 -2016-03-11,WA,3,A,A2,7.8,13.0 -2016-03-12,WA,3,A,A2,7.67,14.0 -2016-03-13,WA,3,A,A2,7.76,13.0 -2016-03-14,WA,3,A,A2,7.75,14.0 -2016-03-15,WA,3,A,A2,7.73,13.0 -2016-03-16,WA,3,A,A2,7.69,14.0 -2016-03-17,WA,3,A,A2,7.75,13.0 -2016-03-18,WA,3,A,A2,7.69,14.0 -2016-03-19,WA,3,A,A2,7.67,14.0 -2016-03-20,WA,3,A,A2,7.78,14.0 -2016-03-21,WA,3,A,A2,7.72,13.0 -2016-03-22,WA,3,A,A2,7.76,13.0 -2016-03-23,WA,3,A,A2,7.72,13.0 -2016-03-24,WA,3,A,A2,7.72,13.0 -2016-03-25,WA,3,A,A2,7.64,13.0 -2016-03-26,WA,3,A,A2,7.84,14.0 -2016-03-27,WA,3,A,A2,7.66,13.0 -2016-03-28,WA,3,A,A2,7.75,14.0 -2016-03-29,WA,3,A,A2,7.68,14.0 -2016-03-30,WA,3,A,A2,7.72,13.0 -2016-03-31,WA,3,A,A2,7.73,13.0 -2016-04-01,WA,3,A,A2,7.71,14.0 -2016-04-02,WA,3,A,A2,7.71,14.0 -2016-04-03,WA,3,A,A2,7.85,13.0 -2016-04-04,WA,3,A,A2,7.72,14.0 -2016-04-05,WA,3,A,A2,7.74,13.0 -2016-04-06,WA,3,A,A2,7.72,13.0 -2016-04-07,WA,3,A,A2,7.73,13.0 -2016-04-08,WA,3,A,A2,7.85,13.0 -2016-04-09,WA,3,A,A2,7.82,13.0 -2016-04-10,WA,3,A,A2,7.76,14.0 -2016-04-11,WA,3,A,A2,7.83,13.0 -2016-04-12,WA,3,A,A2,7.71,13.0 -2016-04-13,WA,3,A,A2,7.78,14.0 -2016-04-14,WA,3,A,A2,7.74,13.0 -2016-04-15,WA,3,A,A2,7.77,14.0 -2016-04-16,WA,3,A,A2,7.79,13.0 -2016-04-17,WA,3,A,A2,7.72,14.0 -2016-04-18,WA,3,A,A2,7.69,13.0 -2016-04-19,WA,3,A,A2,7.83,13.0 -2016-04-20,WA,3,A,A2,7.77,14.0 -2016-04-21,WA,3,A,A2,7.71,14.0 -2016-04-22,WA,3,A,A2,7.8,14.0 -2016-04-23,WA,3,A,A2,7.76,14.0 -2016-04-24,WA,3,A,A2,7.66,14.0 -2016-04-25,WA,3,A,A2,7.82,13.0 -2016-04-26,WA,3,A,A2,7.85,13.0 -2016-04-27,WA,3,A,A2,7.87,13.0 -2016-04-28,WA,3,A,A2,7.81,13.0 -2016-04-29,WA,3,A,A2,7.74,13.0 -2016-04-30,WA,3,A,A2,7.86,13.0 -2016-05-01,WA,3,A,A2,7.7,15.0 -2016-05-02,WA,3,A,A2,7.66,12.0 -2016-05-03,WA,3,A,A2,7.79,13.0 -2016-05-04,WA,3,A,A2,7.65,14.0 -2016-05-05,WA,3,A,A2,7.67,13.0 -2016-05-06,WA,3,A,A2,7.78,14.0 -2016-05-07,WA,3,A,A2,7.87,14.0 -2016-05-08,WA,3,A,A2,7.71,13.0 -2016-05-09,WA,3,A,A2,7.79,13.0 -2016-05-10,WA,3,A,A2,7.73,13.0 -2016-05-11,WA,3,A,A2,7.71,14.0 -2016-05-12,WA,3,A,A2,7.76,12.0 -2016-05-13,WA,3,A,A2,7.73,14.0 -2016-05-14,WA,3,A,A2,7.7,14.0 -2016-05-15,WA,3,A,A2,7.73,15.0 -2016-05-16,WA,3,A,A2,7.62,13.0 -2016-05-17,WA,3,A,A2,7.77,12.0 -2016-05-18,WA,3,A,A2,7.67,15.0 -2016-05-19,WA,3,A,A2,7.77,13.0 -2016-05-20,WA,3,A,A2,7.64,15.0 -2016-05-21,WA,3,A,A2,7.81,13.0 -2016-05-22,WA,3,A,A2,7.68,14.0 -2016-05-23,WA,3,A,A2,7.82,13.0 -2016-05-24,WA,3,A,A2,7.71,13.0 -2016-05-25,WA,3,A,A2,7.79,12.0 -2016-05-26,WA,3,A,A2,7.79,12.0 -2016-05-27,WA,3,A,A2,7.69,13.0 -2016-05-28,WA,3,A,A2,7.73,14.0 -2016-05-29,WA,3,A,A2,7.73,12.0 -2016-05-30,WA,3,A,A2,7.79,13.0 -2016-05-31,WA,3,A,A2,7.83,12.0 -2016-06-01,WA,3,A,A2,7.82,13.0 -2016-06-02,WA,3,A,A2,7.69,14.0 -2016-06-03,WA,3,A,A2,7.76,15.0 -2016-06-04,WA,3,A,A2,7.74,13.0 -2016-06-05,WA,3,A,A2,7.76,13.0 -2016-06-06,WA,3,A,A2,7.84,12.0 -2016-06-07,WA,3,A,A2,7.8,14.0 -2016-06-08,WA,3,A,A2,7.62,14.0 -2016-06-09,WA,3,A,A2,7.77,13.0 -2016-06-10,WA,3,A,A2,7.69,14.0 -2016-06-11,WA,3,A,A2,7.81,12.0 -2016-06-12,WA,3,A,A2,7.7,14.0 -2016-06-13,WA,3,A,A2,7.75,13.0 -2016-06-14,WA,3,A,A2,7.84,14.0 -2016-06-15,WA,3,A,A2,7.79,14.0 -2016-06-16,WA,3,A,A2,7.84,12.0 -2016-06-17,WA,3,A,A2,7.71,13.0 -2016-06-18,WA,3,A,A2,7.87,14.0 -2016-06-19,WA,3,A,A2,7.78,14.0 -2016-06-20,WA,3,A,A2,7.8,12.0 -2016-06-21,WA,3,A,A2,7.67,12.0 -2016-06-22,WA,3,A,A2,7.8,14.0 -2016-06-23,WA,3,A,A2,7.81,12.0 -2016-06-24,WA,3,A,A2,7.68,13.0 -2016-06-25,WA,3,A,A2,7.71,13.0 -2016-06-26,WA,3,A,A2,7.8,12.0 -2016-06-27,WA,3,A,A2,7.86,12.0 -2016-06-28,WA,3,A,A2,7.67,13.0 -2016-06-29,WA,3,A,A2,7.7,15.0 -2016-06-30,WA,3,A,A2,7.77,12.0 -2016-07-01,WA,3,A,A2,7.75,13.0 -2016-07-02,WA,3,A,A2,7.92,13.0 -2016-07-03,WA,3,A,A2,7.74,13.0 -2016-07-04,WA,3,A,A2,7.76,13.0 -2016-07-05,WA,3,A,A2,7.65,14.0 -2016-07-06,WA,3,A,A2,7.72,13.0 -2016-07-07,WA,3,A,A2,7.71,14.0 -2016-07-08,WA,3,A,A2,8.0,11.0 -2016-07-09,WA,3,A,A2,7.55,15.0 -2016-07-10,WA,3,A,A2,7.73,13.0 -2016-07-11,WA,3,A,A2,7.78,12.0 -2016-07-12,WA,3,A,A2,7.58,14.0 -2016-07-13,WA,3,A,A2,7.77,14.0 -2016-07-14,WA,3,A,A2,7.77,13.0 -2016-07-15,WA,3,A,A2,7.87,12.0 -2016-07-16,WA,3,A,A2,7.7,14.0 -2016-07-17,WA,3,A,A2,7.73,13.0 -2016-07-18,WA,3,A,A2,7.77,13.0 -2016-07-19,WA,3,A,A2,7.77,13.0 -2016-07-20,WA,3,A,A2,7.79,14.0 -2016-07-21,WA,3,A,A2,7.78,13.0 -2016-07-22,WA,3,A,A2,7.64,14.0 -2016-07-23,WA,3,A,A2,7.9,11.0 -2016-07-24,WA,3,A,A2,7.76,12.0 -2016-07-25,WA,3,A,A2,7.69,13.0 -2016-07-26,WA,3,A,A2,7.83,13.0 -2016-07-27,WA,3,A,A2,7.75,13.0 -2015-01-05,WA,3,A,A3,5.2,4.0 -2015-01-06,WA,3,A,A3,5.15,4.0 -2015-01-07,WA,3,A,A3,5.19,4.0 -2015-01-08,WA,3,A,A3,5.22,4.0 -2015-01-09,WA,3,A,A3,5.17,4.0 -2015-01-10,WA,3,A,A3,5.19,4.0 -2015-01-11,WA,3,A,A3,5.15,4.0 -2015-01-12,WA,3,A,A3,5.11,4.0 -2015-01-13,WA,3,A,A3,5.21,4.0 -2015-01-14,WA,3,A,A3,5.16,3.0 -2015-01-15,WA,3,A,A3,5.25,4.0 -2015-01-16,WA,3,A,A3,5.16,4.0 -2015-01-17,WA,3,A,A3,5.27,4.0 -2015-01-18,WA,3,A,A3,5.13,4.0 -2015-01-19,WA,3,A,A3,5.19,4.0 -2015-01-20,WA,3,A,A3,5.22,4.0 -2015-01-21,WA,3,A,A3,5.24,4.0 -2015-01-22,WA,3,A,A3,5.15,4.0 -2015-01-23,WA,3,A,A3,5.16,4.0 -2015-01-24,WA,3,A,A3,5.21,4.0 -2015-01-25,WA,3,A,A3,5.08,4.0 -2015-01-26,WA,3,A,A3,5.24,3.0 -2015-01-27,WA,3,A,A3,5.19,4.0 -2015-01-28,WA,3,A,A3,5.15,4.0 -2015-01-29,WA,3,A,A3,5.16,4.0 -2015-01-30,WA,3,A,A3,5.23,4.0 -2015-01-31,WA,3,A,A3,5.25,4.0 -2015-02-01,WA,3,A,A3,5.21,4.0 -2015-02-02,WA,3,A,A3,5.1,4.0 -2015-02-03,WA,3,A,A3,5.12,4.0 -2015-02-04,WA,3,A,A3,5.21,4.0 -2015-02-05,WA,3,A,A3,5.2,4.0 -2015-02-06,WA,3,A,A3,5.22,4.0 -2015-02-07,WA,3,A,A3,5.1,4.0 -2015-02-08,WA,3,A,A3,5.19,4.0 -2015-02-09,WA,3,A,A3,5.25,4.0 -2015-02-10,WA,3,A,A3,5.2,4.0 -2015-02-11,WA,3,A,A3,5.14,4.0 -2015-02-12,WA,3,A,A3,5.19,4.0 -2015-02-13,WA,3,A,A3,5.18,4.0 -2015-02-14,WA,3,A,A3,5.19,4.0 -2015-02-15,WA,3,A,A3,5.13,4.0 -2015-02-16,WA,3,A,A3,5.2,4.0 -2015-02-17,WA,3,A,A3,5.1,4.0 -2015-02-18,WA,3,A,A3,5.13,4.0 -2015-02-19,WA,3,A,A3,5.19,4.0 -2015-02-20,WA,3,A,A3,5.17,4.0 -2015-02-21,WA,3,A,A3,5.13,4.0 -2015-02-22,WA,3,A,A3,5.15,4.0 -2015-02-23,WA,3,A,A3,5.14,4.0 -2015-02-24,WA,3,A,A3,5.16,4.0 -2015-02-25,WA,3,A,A3,5.16,4.0 -2015-02-26,WA,3,A,A3,5.17,4.0 -2015-02-27,WA,3,A,A3,5.21,4.0 -2015-02-28,WA,3,A,A3,5.14,4.0 -2015-03-01,WA,3,A,A3,5.12,4.0 -2015-03-02,WA,3,A,A3,5.24,4.0 -2015-03-03,WA,3,A,A3,5.15,4.0 -2015-03-04,WA,3,A,A3,5.23,4.0 -2015-03-05,WA,3,A,A3,5.24,4.0 -2015-03-06,WA,3,A,A3,5.18,4.0 -2015-03-07,WA,3,A,A3,5.2,4.0 -2015-03-08,WA,3,A,A3,5.18,4.0 -2015-03-09,WA,3,A,A3,5.2,4.0 -2015-03-10,WA,3,A,A3,5.12,4.0 -2015-03-11,WA,3,A,A3,5.24,4.0 -2015-03-12,WA,3,A,A3,5.2,4.0 -2015-03-13,WA,3,A,A3,5.31,4.0 -2015-03-14,WA,3,A,A3,5.16,4.0 -2015-03-15,WA,3,A,A3,5.26,4.0 -2015-03-16,WA,3,A,A3,5.22,4.0 -2015-03-17,WA,3,A,A3,5.22,4.0 -2015-03-18,WA,3,A,A3,5.12,4.0 -2015-03-19,WA,3,A,A3,5.19,4.0 -2015-03-20,WA,3,A,A3,5.18,4.0 -2015-03-21,WA,3,A,A3,5.23,4.0 -2015-03-22,WA,3,A,A3,5.18,4.0 -2015-03-23,WA,3,A,A3,5.12,4.0 -2015-03-24,WA,3,A,A3,5.2,4.0 -2015-03-25,WA,3,A,A3,5.27,4.0 -2015-03-26,WA,3,A,A3,5.14,4.0 -2015-03-27,WA,3,A,A3,5.1,4.0 -2015-03-28,WA,3,A,A3,5.26,4.0 -2015-03-29,WA,3,A,A3,5.22,4.0 -2015-03-30,WA,3,A,A3,5.11,4.0 -2015-03-31,WA,3,A,A3,5.12,4.0 -2015-04-01,WA,3,A,A3,5.27,4.0 -2015-04-02,WA,3,A,A3,5.18,4.0 -2015-04-03,WA,3,A,A3,5.13,4.0 -2015-04-04,WA,3,A,A3,5.24,4.0 -2015-04-05,WA,3,A,A3,5.17,4.0 -2015-04-06,WA,3,A,A3,5.14,4.0 -2015-04-07,WA,3,A,A3,5.19,4.0 -2015-04-08,WA,3,A,A3,5.23,4.0 -2015-04-09,WA,3,A,A3,5.15,4.0 -2015-04-10,WA,3,A,A3,5.24,4.0 -2015-04-11,WA,3,A,A3,5.2,4.0 -2015-04-12,WA,3,A,A3,5.22,4.0 -2015-04-13,WA,3,A,A3,5.12,4.0 -2015-04-14,WA,3,A,A3,5.12,4.0 -2015-04-15,WA,3,A,A3,5.14,4.0 -2015-04-16,WA,3,A,A3,5.18,4.0 -2015-04-17,WA,3,A,A3,5.2,4.0 -2015-04-18,WA,3,A,A3,5.22,4.0 -2015-04-19,WA,3,A,A3,5.19,4.0 -2015-04-20,WA,3,A,A3,5.17,4.0 -2015-04-21,WA,3,A,A3,5.19,4.0 -2015-04-22,WA,3,A,A3,5.21,4.0 -2015-04-23,WA,3,A,A3,5.24,4.0 -2015-04-24,WA,3,A,A3,5.28,3.0 -2015-04-25,WA,3,A,A3,5.15,5.0 -2015-04-26,WA,3,A,A3,5.21,4.0 -2015-04-27,WA,3,A,A3,5.23,4.0 -2015-04-28,WA,3,A,A3,5.24,4.0 -2015-04-29,WA,3,A,A3,5.17,4.0 -2015-04-30,WA,3,A,A3,5.18,4.0 -2015-05-01,WA,3,A,A3,5.23,4.0 -2015-05-02,WA,3,A,A3,5.18,4.0 -2015-05-03,WA,3,A,A3,5.15,4.0 -2015-05-04,WA,3,A,A3,5.24,4.0 -2015-05-05,WA,3,A,A3,5.14,4.0 -2015-05-06,WA,3,A,A3,5.23,4.0 -2015-05-07,WA,3,A,A3,5.15,4.0 -2015-05-08,WA,3,A,A3,5.07,4.0 -2015-05-09,WA,3,A,A3,5.24,4.0 -2015-05-10,WA,3,A,A3,5.2,4.0 -2015-05-11,WA,3,A,A3,5.23,4.0 -2015-05-12,WA,3,A,A3,5.17,4.0 -2015-05-13,WA,3,A,A3,5.17,4.0 -2015-05-14,WA,3,A,A3,5.15,4.0 -2015-05-15,WA,3,A,A3,5.2,4.0 -2015-05-16,WA,3,A,A3,5.15,4.0 -2015-05-17,WA,3,A,A3,5.21,4.0 -2015-05-18,WA,3,A,A3,5.21,4.0 -2015-05-19,WA,3,A,A3,5.19,4.0 -2015-05-20,WA,3,A,A3,5.19,4.0 -2015-05-21,WA,3,A,A3,5.23,4.0 -2015-05-22,WA,3,A,A3,5.23,4.0 -2015-05-23,WA,3,A,A3,5.24,4.0 -2015-05-24,WA,3,A,A3,5.27,4.0 -2015-05-25,WA,3,A,A3,5.17,4.0 -2015-05-26,WA,3,A,A3,5.03,4.0 -2015-05-27,WA,3,A,A3,5.22,4.0 -2015-05-28,WA,3,A,A3,5.24,4.0 -2015-05-29,WA,3,A,A3,5.26,4.0 -2015-05-30,WA,3,A,A3,5.12,4.0 -2015-05-31,WA,3,A,A3,5.21,4.0 -2015-06-01,WA,3,A,A3,5.22,4.0 -2015-06-02,WA,3,A,A3,5.19,4.0 -2015-06-03,WA,3,A,A3,5.09,4.0 -2015-06-04,WA,3,A,A3,5.21,4.0 -2015-06-05,WA,3,A,A3,5.27,4.0 -2015-06-06,WA,3,A,A3,5.14,4.0 -2015-06-07,WA,3,A,A3,5.14,4.0 -2015-06-08,WA,3,A,A3,5.22,4.0 -2015-06-09,WA,3,A,A3,5.06,4.0 -2015-06-10,WA,3,A,A3,5.26,4.0 -2015-06-11,WA,3,A,A3,5.2,4.0 -2015-06-12,WA,3,A,A3,5.16,4.0 -2015-06-13,WA,3,A,A3,5.18,4.0 -2015-06-14,WA,3,A,A3,5.17,4.0 -2015-06-15,WA,3,A,A3,5.16,4.0 -2015-06-16,WA,3,A,A3,5.17,4.0 -2015-06-17,WA,3,A,A3,5.22,4.0 -2015-06-18,WA,3,A,A3,5.19,4.0 -2015-06-19,WA,3,A,A3,5.22,4.0 -2015-06-20,WA,3,A,A3,5.21,4.0 -2015-06-21,WA,3,A,A3,5.23,4.0 -2015-06-22,WA,3,A,A3,5.18,4.0 -2015-06-23,WA,3,A,A3,5.24,4.0 -2015-06-24,WA,3,A,A3,5.2,4.0 -2015-06-25,WA,3,A,A3,5.12,4.0 -2015-06-26,WA,3,A,A3,5.28,4.0 -2015-06-27,WA,3,A,A3,5.12,4.0 -2015-06-28,WA,3,A,A3,5.15,5.0 -2015-06-29,WA,3,A,A3,5.14,4.0 -2015-06-30,WA,3,A,A3,5.17,4.0 -2015-07-01,WA,3,A,A3,5.14,4.0 -2015-07-02,WA,3,A,A3,5.22,4.0 -2015-07-03,WA,3,A,A3,5.21,4.0 -2015-07-04,WA,3,A,A3,5.13,4.0 -2015-07-05,WA,3,A,A3,5.16,4.0 -2015-07-06,WA,3,A,A3,5.2,4.0 -2015-07-07,WA,3,A,A3,5.18,4.0 -2015-07-08,WA,3,A,A3,5.17,4.0 -2015-07-09,WA,3,A,A3,5.2,4.0 -2015-07-10,WA,3,A,A3,5.1,4.0 -2015-07-11,WA,3,A,A3,5.22,4.0 -2015-07-12,WA,3,A,A3,5.2,4.0 -2015-07-13,WA,3,A,A3,5.15,4.0 -2015-07-14,WA,3,A,A3,5.23,4.0 -2015-07-15,WA,3,A,A3,5.19,4.0 -2015-07-16,WA,3,A,A3,5.11,4.0 -2015-07-17,WA,3,A,A3,5.17,4.0 -2015-07-18,WA,3,A,A3,5.16,4.0 -2015-07-19,WA,3,A,A3,5.15,4.0 -2015-07-20,WA,3,A,A3,5.15,4.0 -2015-07-21,WA,3,A,A3,5.2,4.0 -2015-07-22,WA,3,A,A3,5.19,4.0 -2015-07-23,WA,3,A,A3,5.22,4.0 -2015-07-24,WA,3,A,A3,5.26,4.0 -2015-07-25,WA,3,A,A3,5.24,4.0 -2015-07-26,WA,3,A,A3,5.19,4.0 -2015-07-27,WA,3,A,A3,5.26,4.0 -2015-07-28,WA,3,A,A3,5.16,4.0 -2015-07-29,WA,3,A,A3,5.11,4.0 -2015-07-30,WA,3,A,A3,5.22,4.0 -2015-07-31,WA,3,A,A3,5.21,4.0 -2015-08-01,WA,3,A,A3,5.16,4.0 -2015-08-02,WA,3,A,A3,5.18,4.0 -2015-08-03,WA,3,A,A3,5.22,4.0 -2015-08-04,WA,3,A,A3,5.13,4.0 -2015-08-05,WA,3,A,A3,5.26,4.0 -2015-08-06,WA,3,A,A3,5.16,4.0 -2015-08-07,WA,3,A,A3,5.17,4.0 -2015-08-08,WA,3,A,A3,5.2,4.0 -2015-08-09,WA,3,A,A3,5.2,4.0 -2015-08-10,WA,3,A,A3,5.24,4.0 -2015-08-11,WA,3,A,A3,5.15,4.0 -2015-08-12,WA,3,A,A3,5.13,4.0 -2015-08-13,WA,3,A,A3,5.22,4.0 -2015-08-14,WA,3,A,A3,5.21,4.0 -2015-08-15,WA,3,A,A3,5.28,4.0 -2015-08-16,WA,3,A,A3,5.27,4.0 -2015-08-17,WA,3,A,A3,5.14,4.0 -2015-08-18,WA,3,A,A3,5.14,4.0 -2015-08-19,WA,3,A,A3,5.18,4.0 -2015-08-20,WA,3,A,A3,5.23,4.0 -2015-08-21,WA,3,A,A3,5.1,4.0 -2015-08-22,WA,3,A,A3,5.15,4.0 -2015-08-23,WA,3,A,A3,5.12,4.0 -2015-08-24,WA,3,A,A3,5.22,4.0 -2015-08-25,WA,3,A,A3,5.11,4.0 -2015-08-26,WA,3,A,A3,5.18,4.0 -2015-08-27,WA,3,A,A3,5.25,3.0 -2015-08-28,WA,3,A,A3,5.27,4.0 -2015-08-29,WA,3,A,A3,5.22,4.0 -2015-08-30,WA,3,A,A3,5.15,4.0 -2015-08-31,WA,3,A,A3,5.15,4.0 -2015-09-01,WA,3,A,A3,5.2,4.0 -2015-09-02,WA,3,A,A3,5.19,4.0 -2015-09-03,WA,3,A,A3,5.16,4.0 -2015-09-04,WA,3,A,A3,5.18,4.0 -2015-09-05,WA,3,A,A3,5.15,4.0 -2015-09-06,WA,3,A,A3,5.16,4.0 -2015-09-07,WA,3,A,A3,5.16,4.0 -2015-09-08,WA,3,A,A3,5.24,4.0 -2015-09-09,WA,3,A,A3,5.14,4.0 -2015-09-10,WA,3,A,A3,5.11,4.0 -2015-09-11,WA,3,A,A3,5.22,4.0 -2015-09-12,WA,3,A,A3,5.16,4.0 -2015-09-13,WA,3,A,A3,5.15,4.0 -2015-09-14,WA,3,A,A3,5.21,4.0 -2015-09-15,WA,3,A,A3,5.13,4.0 -2015-09-16,WA,3,A,A3,5.22,4.0 -2015-09-17,WA,3,A,A3,5.17,4.0 -2015-09-18,WA,3,A,A3,5.1,4.0 -2015-09-19,WA,3,A,A3,5.22,4.0 -2015-09-20,WA,3,A,A3,5.15,4.0 -2015-09-21,WA,3,A,A3,5.2,4.0 -2015-09-22,WA,3,A,A3,5.24,4.0 -2015-09-23,WA,3,A,A3,5.31,4.0 -2015-09-24,WA,3,A,A3,5.22,4.0 -2015-09-25,WA,3,A,A3,5.11,4.0 -2015-09-26,WA,3,A,A3,5.1,4.0 -2015-09-27,WA,3,A,A3,5.15,4.0 -2015-09-28,WA,3,A,A3,5.13,4.0 -2015-09-29,WA,3,A,A3,5.24,4.0 -2015-09-30,WA,3,A,A3,5.18,4.0 -2015-10-01,WA,3,A,A3,5.18,4.0 -2015-10-02,WA,3,A,A3,5.24,4.0 -2015-10-03,WA,3,A,A3,5.12,4.0 -2015-10-04,WA,3,A,A3,5.23,4.0 -2015-10-05,WA,3,A,A3,5.19,4.0 -2015-10-06,WA,3,A,A3,5.14,4.0 -2015-10-07,WA,3,A,A3,5.15,4.0 -2015-10-08,WA,3,A,A3,5.21,4.0 -2015-10-09,WA,3,A,A3,5.23,4.0 -2015-10-10,WA,3,A,A3,5.17,4.0 -2015-10-11,WA,3,A,A3,5.14,4.0 -2015-10-12,WA,3,A,A3,5.24,4.0 -2015-10-13,WA,3,A,A3,5.27,4.0 -2015-10-14,WA,3,A,A3,5.21,4.0 -2015-10-15,WA,3,A,A3,5.2,4.0 -2015-10-16,WA,3,A,A3,5.15,4.0 -2015-10-17,WA,3,A,A3,5.16,4.0 -2015-10-18,WA,3,A,A3,5.19,4.0 -2015-10-19,WA,3,A,A3,5.17,4.0 -2015-10-20,WA,3,A,A3,5.1,4.0 -2015-10-21,WA,3,A,A3,5.17,4.0 -2015-10-22,WA,3,A,A3,5.22,4.0 -2015-10-23,WA,3,A,A3,5.17,4.0 -2015-10-24,WA,3,A,A3,5.13,4.0 -2015-10-25,WA,3,A,A3,5.21,4.0 -2015-10-26,WA,3,A,A3,5.21,4.0 -2015-10-27,WA,3,A,A3,5.2,4.0 -2015-10-28,WA,3,A,A3,5.24,4.0 -2015-10-29,WA,3,A,A3,5.18,4.0 -2015-10-30,WA,3,A,A3,5.21,4.0 -2015-10-31,WA,3,A,A3,5.16,4.0 -2015-11-01,WA,3,A,A3,5.21,4.0 -2015-11-02,WA,3,A,A3,5.16,4.0 -2015-11-03,WA,3,A,A3,5.26,4.0 -2015-11-04,WA,3,A,A3,5.16,4.0 -2015-11-05,WA,3,A,A3,5.17,4.0 -2015-11-06,WA,3,A,A3,5.09,4.0 -2015-11-07,WA,3,A,A3,5.26,4.0 -2015-11-08,WA,3,A,A3,5.19,4.0 -2015-11-09,WA,3,A,A3,5.23,4.0 -2015-11-10,WA,3,A,A3,5.16,4.0 -2015-11-11,WA,3,A,A3,5.19,4.0 -2015-11-12,WA,3,A,A3,5.17,4.0 -2015-11-13,WA,3,A,A3,5.1,4.0 -2015-11-14,WA,3,A,A3,5.21,4.0 -2015-11-15,WA,3,A,A3,5.12,5.0 -2015-11-16,WA,3,A,A3,5.27,4.0 -2015-11-17,WA,3,A,A3,5.17,4.0 -2015-11-18,WA,3,A,A3,5.15,4.0 -2015-11-19,WA,3,A,A3,5.19,4.0 -2015-11-20,WA,3,A,A3,5.25,4.0 -2015-11-21,WA,3,A,A3,5.27,4.0 -2015-11-22,WA,3,A,A3,5.19,4.0 -2015-11-23,WA,3,A,A3,5.12,4.0 -2015-11-24,WA,3,A,A3,5.2,4.0 -2015-11-25,WA,3,A,A3,5.14,4.0 -2015-11-26,WA,3,A,A3,5.1,4.0 -2015-11-27,WA,3,A,A3,5.26,4.0 -2015-11-28,WA,3,A,A3,5.16,4.0 -2015-11-29,WA,3,A,A3,5.05,5.0 -2015-11-30,WA,3,A,A3,5.25,4.0 -2015-12-01,WA,3,A,A3,5.13,4.0 -2015-12-02,WA,3,A,A3,5.27,4.0 -2015-12-03,WA,3,A,A3,5.14,4.0 -2015-12-04,WA,3,A,A3,5.14,4.0 -2015-12-05,WA,3,A,A3,5.15,4.0 -2015-12-06,WA,3,A,A3,5.13,4.0 -2015-12-07,WA,3,A,A3,5.25,4.0 -2015-12-08,WA,3,A,A3,5.19,4.0 -2015-12-09,WA,3,A,A3,5.17,4.0 -2015-12-10,WA,3,A,A3,5.21,4.0 -2015-12-11,WA,3,A,A3,5.11,4.0 -2015-12-12,WA,3,A,A3,5.22,4.0 -2015-12-13,WA,3,A,A3,5.24,4.0 -2015-12-14,WA,3,A,A3,5.12,4.0 -2015-12-15,WA,3,A,A3,5.18,4.0 -2015-12-16,WA,3,A,A3,5.11,4.0 -2015-12-17,WA,3,A,A3,5.15,4.0 -2015-12-18,WA,3,A,A3,5.11,4.0 -2015-12-19,WA,3,A,A3,5.21,4.0 -2015-12-20,WA,3,A,A3,5.13,4.0 -2015-12-21,WA,3,A,A3,5.21,4.0 -2015-12-22,WA,3,A,A3,5.28,4.0 -2015-12-23,WA,3,A,A3,5.16,4.0 -2015-12-24,WA,3,A,A3,5.16,4.0 -2015-12-25,WA,3,A,A3,5.16,4.0 -2015-12-26,WA,3,A,A3,5.15,4.0 -2015-12-27,WA,3,A,A3,5.18,4.0 -2015-12-28,WA,3,A,A3,5.15,4.0 -2015-12-29,WA,3,A,A3,5.24,4.0 -2015-12-30,WA,3,A,A3,5.24,4.0 -2015-12-31,WA,3,A,A3,5.24,4.0 -2016-01-01,WA,3,A,A3,5.2,4.0 -2016-01-02,WA,3,A,A3,5.23,4.0 -2016-01-03,WA,3,A,A3,5.18,4.0 -2016-01-04,WA,3,A,A3,5.16,4.0 -2016-01-05,WA,3,A,A3,5.14,4.0 -2016-01-06,WA,3,A,A3,5.12,4.0 -2016-01-07,WA,3,A,A3,5.2,4.0 -2016-01-08,WA,3,A,A3,5.16,4.0 -2016-01-09,WA,3,A,A3,5.17,4.0 -2016-01-10,WA,3,A,A3,5.21,4.0 -2016-01-11,WA,3,A,A3,5.16,4.0 -2016-01-12,WA,3,A,A3,5.13,4.0 -2016-01-13,WA,3,A,A3,5.21,4.0 -2016-01-14,WA,3,A,A3,5.22,4.0 -2016-01-15,WA,3,A,A3,5.16,4.0 -2016-01-16,WA,3,A,A3,5.21,4.0 -2016-01-17,WA,3,A,A3,5.08,4.0 -2016-01-18,WA,3,A,A3,5.21,4.0 -2016-01-19,WA,3,A,A3,5.2,4.0 -2016-01-20,WA,3,A,A3,5.1,4.0 -2016-01-21,WA,3,A,A3,5.17,4.0 -2016-01-22,WA,3,A,A3,5.14,4.0 -2016-01-23,WA,3,A,A3,5.23,4.0 -2016-01-24,WA,3,A,A3,5.15,4.0 -2016-01-25,WA,3,A,A3,5.2,4.0 -2016-01-26,WA,3,A,A3,5.2,4.0 -2016-01-27,WA,3,A,A3,5.14,4.0 -2016-01-28,WA,3,A,A3,5.13,4.0 -2016-01-29,WA,3,A,A3,5.15,4.0 -2016-01-30,WA,3,A,A3,5.13,4.0 -2016-01-31,WA,3,A,A3,5.24,4.0 -2016-02-01,WA,3,A,A3,5.08,4.0 -2016-02-02,WA,3,A,A3,5.19,4.0 -2016-02-03,WA,3,A,A3,5.19,4.0 -2016-02-04,WA,3,A,A3,5.1,4.0 -2016-02-05,WA,3,A,A3,5.14,4.0 -2016-02-06,WA,3,A,A3,5.24,4.0 -2016-02-07,WA,3,A,A3,5.18,4.0 -2016-02-08,WA,3,A,A3,5.14,4.0 -2016-02-09,WA,3,A,A3,5.18,4.0 -2016-02-10,WA,3,A,A3,5.28,4.0 -2016-02-11,WA,3,A,A3,5.16,4.0 -2016-02-12,WA,3,A,A3,5.07,4.0 -2016-02-13,WA,3,A,A3,5.16,4.0 -2016-02-14,WA,3,A,A3,5.23,4.0 -2016-02-15,WA,3,A,A3,5.23,4.0 -2016-02-16,WA,3,A,A3,5.16,4.0 -2016-02-17,WA,3,A,A3,5.28,4.0 -2016-02-18,WA,3,A,A3,5.25,4.0 -2016-02-19,WA,3,A,A3,5.23,4.0 -2016-02-20,WA,3,A,A3,5.19,4.0 -2016-02-21,WA,3,A,A3,5.24,4.0 -2016-02-22,WA,3,A,A3,5.2,4.0 -2016-02-23,WA,3,A,A3,5.13,4.0 -2016-02-24,WA,3,A,A3,5.24,4.0 -2016-02-25,WA,3,A,A3,5.3,4.0 -2016-02-26,WA,3,A,A3,5.12,4.0 -2016-02-27,WA,3,A,A3,5.17,4.0 -2016-02-28,WA,3,A,A3,5.3,4.0 -2016-02-29,WA,3,A,A3,5.19,4.0 -2016-03-01,WA,3,A,A3,5.12,4.0 -2016-03-02,WA,3,A,A3,5.09,4.0 -2016-03-03,WA,3,A,A3,5.26,4.0 -2016-03-04,WA,3,A,A3,5.25,4.0 -2016-03-05,WA,3,A,A3,5.25,4.0 -2016-03-06,WA,3,A,A3,5.17,4.0 -2016-03-07,WA,3,A,A3,5.22,4.0 -2016-03-08,WA,3,A,A3,5.14,4.0 -2016-03-09,WA,3,A,A3,5.18,4.0 -2016-03-10,WA,3,A,A3,5.23,4.0 -2016-03-11,WA,3,A,A3,5.21,4.0 -2016-03-12,WA,3,A,A3,5.21,4.0 -2016-03-13,WA,3,A,A3,5.18,4.0 -2016-03-14,WA,3,A,A3,5.18,4.0 -2016-03-15,WA,3,A,A3,5.15,4.0 -2016-03-16,WA,3,A,A3,5.29,4.0 -2016-03-17,WA,3,A,A3,5.2,4.0 -2016-03-18,WA,3,A,A3,5.24,4.0 -2016-03-19,WA,3,A,A3,5.14,4.0 -2016-03-20,WA,3,A,A3,5.23,4.0 -2016-03-21,WA,3,A,A3,5.2,4.0 -2016-03-22,WA,3,A,A3,5.23,4.0 -2016-03-23,WA,3,A,A3,5.19,4.0 -2016-03-24,WA,3,A,A3,5.25,4.0 -2016-03-25,WA,3,A,A3,5.17,4.0 -2016-03-26,WA,3,A,A3,5.16,4.0 -2016-03-27,WA,3,A,A3,5.19,4.0 -2016-03-28,WA,3,A,A3,5.13,4.0 -2016-03-29,WA,3,A,A3,5.11,4.0 -2016-03-30,WA,3,A,A3,5.15,4.0 -2016-03-31,WA,3,A,A3,5.29,4.0 -2016-04-01,WA,3,A,A3,5.15,4.0 -2016-04-02,WA,3,A,A3,5.21,4.0 -2016-04-03,WA,3,A,A3,5.19,4.0 -2016-04-04,WA,3,A,A3,5.18,4.0 -2016-04-05,WA,3,A,A3,5.1,4.0 -2016-04-06,WA,3,A,A3,5.25,4.0 -2016-04-07,WA,3,A,A3,5.1,4.0 -2016-04-08,WA,3,A,A3,5.22,4.0 -2016-04-09,WA,3,A,A3,5.28,4.0 -2016-04-10,WA,3,A,A3,5.12,4.0 -2016-04-11,WA,3,A,A3,5.25,4.0 -2016-04-12,WA,3,A,A3,5.11,4.0 -2016-04-13,WA,3,A,A3,5.18,4.0 -2016-04-14,WA,3,A,A3,5.19,4.0 -2016-04-15,WA,3,A,A3,5.12,4.0 -2016-04-16,WA,3,A,A3,5.16,4.0 -2016-04-17,WA,3,A,A3,5.21,4.0 -2016-04-18,WA,3,A,A3,5.24,4.0 -2016-04-19,WA,3,A,A3,5.28,4.0 -2016-04-20,WA,3,A,A3,5.11,4.0 -2016-04-21,WA,3,A,A3,5.12,5.0 -2016-04-22,WA,3,A,A3,5.21,4.0 -2016-04-23,WA,3,A,A3,5.22,4.0 -2016-04-24,WA,3,A,A3,5.21,4.0 -2016-04-25,WA,3,A,A3,5.21,4.0 -2016-04-26,WA,3,A,A3,5.13,4.0 -2016-04-27,WA,3,A,A3,5.19,4.0 -2016-04-28,WA,3,A,A3,5.22,4.0 -2016-04-29,WA,3,A,A3,5.16,5.0 -2016-04-30,WA,3,A,A3,5.28,4.0 -2016-05-01,WA,3,A,A3,5.19,4.0 -2016-05-02,WA,3,A,A3,5.1,4.0 -2016-05-03,WA,3,A,A3,5.23,4.0 -2016-05-04,WA,3,A,A3,5.1,4.0 -2016-05-05,WA,3,A,A3,5.09,4.0 -2016-05-06,WA,3,A,A3,5.13,4.0 -2016-05-07,WA,3,A,A3,5.18,4.0 -2016-05-08,WA,3,A,A3,5.3,4.0 -2016-05-09,WA,3,A,A3,5.15,4.0 -2016-05-10,WA,3,A,A3,5.21,4.0 -2016-05-11,WA,3,A,A3,5.13,4.0 -2016-05-12,WA,3,A,A3,5.2,4.0 -2016-05-13,WA,3,A,A3,5.13,4.0 -2016-05-14,WA,3,A,A3,5.32,4.0 -2016-05-15,WA,3,A,A3,5.2,4.0 -2016-05-16,WA,3,A,A3,5.09,4.0 -2016-05-17,WA,3,A,A3,5.23,4.0 -2016-05-18,WA,3,A,A3,5.17,4.0 -2016-05-19,WA,3,A,A3,5.18,4.0 -2016-05-20,WA,3,A,A3,5.15,4.0 -2016-05-21,WA,3,A,A3,5.15,4.0 -2016-05-22,WA,3,A,A3,5.18,4.0 -2016-05-23,WA,3,A,A3,5.17,4.0 -2016-05-24,WA,3,A,A3,5.29,4.0 -2016-05-25,WA,3,A,A3,5.24,4.0 -2016-05-26,WA,3,A,A3,5.17,4.0 -2016-05-27,WA,3,A,A3,5.15,4.0 -2016-05-28,WA,3,A,A3,5.21,4.0 -2016-05-29,WA,3,A,A3,5.17,4.0 -2016-05-30,WA,3,A,A3,5.21,4.0 -2016-05-31,WA,3,A,A3,5.18,4.0 -2016-06-01,WA,3,A,A3,5.06,4.0 -2016-06-02,WA,3,A,A3,5.21,4.0 -2016-06-03,WA,3,A,A3,5.14,4.0 -2016-06-04,WA,3,A,A3,5.23,4.0 -2016-06-05,WA,3,A,A3,5.11,4.0 -2016-06-06,WA,3,A,A3,5.15,4.0 -2016-06-07,WA,3,A,A3,5.2,4.0 -2016-06-08,WA,3,A,A3,5.18,4.0 -2016-06-09,WA,3,A,A3,5.22,4.0 -2016-06-10,WA,3,A,A3,5.13,4.0 -2016-06-11,WA,3,A,A3,5.12,4.0 -2016-06-12,WA,3,A,A3,5.16,4.0 -2016-06-13,WA,3,A,A3,5.15,4.0 -2016-06-14,WA,3,A,A3,5.25,4.0 -2016-06-15,WA,3,A,A3,5.15,4.0 -2016-06-16,WA,3,A,A3,5.16,4.0 -2016-06-17,WA,3,A,A3,5.22,4.0 -2016-06-18,WA,3,A,A3,5.13,4.0 -2016-06-19,WA,3,A,A3,5.19,4.0 -2016-06-20,WA,3,A,A3,5.17,4.0 -2016-06-21,WA,3,A,A3,5.22,4.0 -2016-06-22,WA,3,A,A3,5.1,4.0 -2016-06-23,WA,3,A,A3,5.13,4.0 -2016-06-24,WA,3,A,A3,5.12,4.0 -2016-06-25,WA,3,A,A3,5.13,4.0 -2016-06-26,WA,3,A,A3,5.27,4.0 -2016-06-27,WA,3,A,A3,5.11,4.0 -2016-06-28,WA,3,A,A3,5.09,4.0 -2016-06-29,WA,3,A,A3,5.14,4.0 -2016-06-30,WA,3,A,A3,5.1,4.0 -2016-07-01,WA,3,A,A3,5.19,4.0 -2016-07-02,WA,3,A,A3,5.26,4.0 -2016-07-03,WA,3,A,A3,5.18,4.0 -2016-07-04,WA,3,A,A3,5.17,4.0 -2016-07-05,WA,3,A,A3,5.17,4.0 -2016-07-06,WA,3,A,A3,5.21,4.0 -2016-07-07,WA,3,A,A3,5.22,4.0 -2016-07-08,WA,3,A,A3,5.18,4.0 -2016-07-09,WA,3,A,A3,5.22,4.0 -2016-07-10,WA,3,A,A3,5.12,4.0 -2016-07-11,WA,3,A,A3,5.21,4.0 -2016-07-12,WA,3,A,A3,5.17,4.0 -2016-07-13,WA,3,A,A3,5.13,4.0 -2016-07-14,WA,3,A,A3,5.23,4.0 -2016-07-15,WA,3,A,A3,5.22,4.0 -2016-07-16,WA,3,A,A3,5.2,4.0 -2016-07-17,WA,3,A,A3,5.16,4.0 -2016-07-18,WA,3,A,A3,5.17,4.0 -2016-07-19,WA,3,A,A3,5.22,4.0 -2016-07-20,WA,3,A,A3,5.19,4.0 -2016-07-21,WA,3,A,A3,5.24,4.0 -2016-07-22,WA,3,A,A3,5.13,4.0 -2016-07-23,WA,3,A,A3,5.31,4.0 -2016-07-24,WA,3,A,A3,5.25,4.0 -2016-07-25,WA,3,A,A3,5.18,4.0 -2016-07-26,WA,3,A,A3,5.23,4.0 -2016-07-27,WA,3,A,A3,5.2,4.0 -2015-01-05,WA,3,B,B1,6.74,620.0 -2015-01-06,WA,3,B,B1,6.64,603.0 -2015-01-07,WA,3,B,B1,6.53,655.0 -2015-01-08,WA,3,B,B1,6.75,638.0 -2015-01-09,WA,3,B,B1,6.6,656.0 -2015-01-10,WA,3,B,B1,6.64,669.0 -2015-01-11,WA,3,B,B1,6.76,626.0 -2015-01-12,WA,3,B,B1,6.7,598.0 -2015-01-13,WA,3,B,B1,6.62,604.0 -2015-01-14,WA,3,B,B1,6.61,686.0 -2015-01-15,WA,3,B,B1,6.63,633.0 -2015-01-16,WA,3,B,B1,6.71,592.0 -2015-01-17,WA,3,B,B1,6.62,626.0 -2015-01-18,WA,3,B,B1,6.63,719.0 -2015-01-19,WA,3,B,B1,6.64,644.0 -2015-01-20,WA,3,B,B1,6.67,600.0 -2015-01-21,WA,3,B,B1,6.68,630.0 -2015-01-22,WA,3,B,B1,6.6,670.0 -2015-01-23,WA,3,B,B1,6.63,684.0 -2015-01-24,WA,3,B,B1,6.63,668.0 -2015-01-25,WA,3,B,B1,6.59,615.0 -2015-01-26,WA,3,B,B1,6.78,617.0 -2015-01-27,WA,3,B,B1,6.68,664.0 -2015-01-28,WA,3,B,B1,6.68,643.0 -2015-01-29,WA,3,B,B1,6.61,656.0 -2015-01-30,WA,3,B,B1,6.67,679.0 -2015-01-31,WA,3,B,B1,6.55,697.0 -2015-02-01,WA,3,B,B1,6.6,659.0 -2015-02-02,WA,3,B,B1,6.84,601.0 -2015-02-03,WA,3,B,B1,6.65,665.0 -2015-02-04,WA,3,B,B1,6.69,611.0 -2015-02-05,WA,3,B,B1,6.57,659.0 -2015-02-06,WA,3,B,B1,6.49,685.0 -2015-02-07,WA,3,B,B1,6.8,625.0 -2015-02-08,WA,3,B,B1,6.66,675.0 -2015-02-09,WA,3,B,B1,6.71,697.0 -2015-02-10,WA,3,B,B1,6.66,627.0 -2015-02-11,WA,3,B,B1,6.64,674.0 -2015-02-12,WA,3,B,B1,6.7,647.0 -2015-02-13,WA,3,B,B1,6.63,672.0 -2015-02-14,WA,3,B,B1,6.71,697.0 -2015-02-15,WA,3,B,B1,6.7,611.0 -2015-02-16,WA,3,B,B1,6.66,671.0 -2015-02-17,WA,3,B,B1,6.59,677.0 -2015-02-18,WA,3,B,B1,6.67,633.0 -2015-02-19,WA,3,B,B1,6.7,668.0 -2015-02-20,WA,3,B,B1,6.58,671.0 -2015-02-21,WA,3,B,B1,6.7,722.0 -2015-02-22,WA,3,B,B1,6.68,573.0 -2015-02-23,WA,3,B,B1,6.55,644.0 -2015-02-24,WA,3,B,B1,6.7,667.0 -2015-02-25,WA,3,B,B1,6.66,642.0 -2015-02-26,WA,3,B,B1,6.53,628.0 -2015-02-27,WA,3,B,B1,6.56,596.0 -2015-02-28,WA,3,B,B1,6.66,672.0 -2015-03-01,WA,3,B,B1,6.65,645.0 -2015-03-02,WA,3,B,B1,6.59,655.0 -2015-03-03,WA,3,B,B1,6.65,666.0 -2015-03-04,WA,3,B,B1,6.78,627.0 -2015-03-05,WA,3,B,B1,6.63,718.0 -2015-03-06,WA,3,B,B1,6.71,645.0 -2015-03-07,WA,3,B,B1,6.72,657.0 -2015-03-08,WA,3,B,B1,6.54,692.0 -2015-03-09,WA,3,B,B1,6.65,680.0 -2015-03-10,WA,3,B,B1,6.7,597.0 -2015-03-11,WA,3,B,B1,6.66,678.0 -2015-03-12,WA,3,B,B1,6.69,623.0 -2015-03-13,WA,3,B,B1,6.64,658.0 -2015-03-14,WA,3,B,B1,6.65,692.0 -2015-03-15,WA,3,B,B1,6.68,716.0 -2015-03-16,WA,3,B,B1,6.64,667.0 -2015-03-17,WA,3,B,B1,6.67,671.0 -2015-03-18,WA,3,B,B1,6.61,653.0 -2015-03-19,WA,3,B,B1,6.71,610.0 -2015-03-20,WA,3,B,B1,6.7,649.0 -2015-03-21,WA,3,B,B1,6.6,771.0 -2015-03-22,WA,3,B,B1,6.69,680.0 -2015-03-23,WA,3,B,B1,6.68,682.0 -2015-03-24,WA,3,B,B1,6.57,689.0 -2015-03-25,WA,3,B,B1,6.75,625.0 -2015-03-26,WA,3,B,B1,6.59,674.0 -2015-03-27,WA,3,B,B1,6.64,649.0 -2015-03-28,WA,3,B,B1,6.71,726.0 -2015-03-29,WA,3,B,B1,6.64,729.0 -2015-03-30,WA,3,B,B1,6.62,644.0 -2015-03-31,WA,3,B,B1,6.63,660.0 -2015-04-01,WA,3,B,B1,6.74,640.0 -2015-04-02,WA,3,B,B1,6.67,621.0 -2015-04-03,WA,3,B,B1,6.63,678.0 -2015-04-04,WA,3,B,B1,6.71,701.0 -2015-04-05,WA,3,B,B1,6.66,660.0 -2015-04-06,WA,3,B,B1,6.71,665.0 -2015-04-07,WA,3,B,B1,6.63,644.0 -2015-04-08,WA,3,B,B1,6.64,691.0 -2015-04-09,WA,3,B,B1,6.61,692.0 -2015-04-10,WA,3,B,B1,6.66,627.0 -2015-04-11,WA,3,B,B1,6.6,704.0 -2015-04-12,WA,3,B,B1,6.58,691.0 -2015-04-13,WA,3,B,B1,6.66,639.0 -2015-04-14,WA,3,B,B1,6.73,613.0 -2015-04-15,WA,3,B,B1,6.7,657.0 -2015-04-16,WA,3,B,B1,6.67,678.0 -2015-04-17,WA,3,B,B1,6.52,680.0 -2015-04-18,WA,3,B,B1,6.65,692.0 -2015-04-19,WA,3,B,B1,6.64,677.0 -2015-04-20,WA,3,B,B1,6.59,641.0 -2015-04-21,WA,3,B,B1,6.65,656.0 -2015-04-22,WA,3,B,B1,6.52,641.0 -2015-04-23,WA,3,B,B1,6.68,675.0 -2015-04-24,WA,3,B,B1,6.7,613.0 -2015-04-25,WA,3,B,B1,6.58,694.0 -2015-04-26,WA,3,B,B1,6.73,651.0 -2015-04-27,WA,3,B,B1,6.61,616.0 -2015-04-28,WA,3,B,B1,6.67,605.0 -2015-04-29,WA,3,B,B1,6.53,676.0 -2015-04-30,WA,3,B,B1,6.62,642.0 -2015-05-01,WA,3,B,B1,6.65,665.0 -2015-05-02,WA,3,B,B1,6.63,694.0 -2015-05-03,WA,3,B,B1,6.62,685.0 -2015-05-04,WA,3,B,B1,6.71,635.0 -2015-05-05,WA,3,B,B1,6.78,597.0 -2015-05-06,WA,3,B,B1,6.53,671.0 -2015-05-07,WA,3,B,B1,6.65,633.0 -2015-05-08,WA,3,B,B1,6.62,709.0 -2015-05-09,WA,3,B,B1,6.65,710.0 -2015-05-10,WA,3,B,B1,6.64,627.0 -2015-05-11,WA,3,B,B1,6.61,594.0 -2015-05-12,WA,3,B,B1,6.74,660.0 -2015-05-13,WA,3,B,B1,6.73,654.0 -2015-05-14,WA,3,B,B1,6.58,702.0 -2015-05-15,WA,3,B,B1,6.62,630.0 -2015-05-16,WA,3,B,B1,6.65,714.0 -2015-05-17,WA,3,B,B1,6.66,657.0 -2015-05-18,WA,3,B,B1,6.64,684.0 -2015-05-19,WA,3,B,B1,6.74,628.0 -2015-05-20,WA,3,B,B1,6.65,639.0 -2015-05-21,WA,3,B,B1,6.62,682.0 -2015-05-22,WA,3,B,B1,6.55,655.0 -2015-05-23,WA,3,B,B1,6.69,654.0 -2015-05-24,WA,3,B,B1,6.74,648.0 -2015-05-25,WA,3,B,B1,6.67,667.0 -2015-05-26,WA,3,B,B1,6.6,634.0 -2015-05-27,WA,3,B,B1,6.61,671.0 -2015-05-28,WA,3,B,B1,6.53,716.0 -2015-05-29,WA,3,B,B1,6.64,654.0 -2015-05-30,WA,3,B,B1,6.64,687.0 -2015-05-31,WA,3,B,B1,6.7,648.0 -2015-06-01,WA,3,B,B1,6.66,638.0 -2015-06-02,WA,3,B,B1,6.73,644.0 -2015-06-03,WA,3,B,B1,6.62,701.0 -2015-06-04,WA,3,B,B1,6.77,627.0 -2015-06-05,WA,3,B,B1,6.62,666.0 -2015-06-06,WA,3,B,B1,6.61,657.0 -2015-06-07,WA,3,B,B1,6.63,710.0 -2015-06-08,WA,3,B,B1,6.63,658.0 -2015-06-09,WA,3,B,B1,6.63,631.0 -2015-06-10,WA,3,B,B1,6.66,659.0 -2015-06-11,WA,3,B,B1,6.7,675.0 -2015-06-12,WA,3,B,B1,6.75,662.0 -2015-06-13,WA,3,B,B1,6.65,659.0 -2015-06-14,WA,3,B,B1,6.56,638.0 -2015-06-15,WA,3,B,B1,6.62,629.0 -2015-06-16,WA,3,B,B1,6.62,655.0 -2015-06-17,WA,3,B,B1,6.63,679.0 -2015-06-18,WA,3,B,B1,6.57,690.0 -2015-06-19,WA,3,B,B1,6.66,686.0 -2015-06-20,WA,3,B,B1,6.55,682.0 -2015-06-21,WA,3,B,B1,6.69,680.0 -2015-06-22,WA,3,B,B1,6.77,658.0 -2015-06-23,WA,3,B,B1,6.68,658.0 -2015-06-24,WA,3,B,B1,6.73,645.0 -2015-06-25,WA,3,B,B1,6.69,653.0 -2015-06-26,WA,3,B,B1,6.6,667.0 -2015-06-27,WA,3,B,B1,6.69,704.0 -2015-06-28,WA,3,B,B1,6.71,639.0 -2015-06-29,WA,3,B,B1,6.7,627.0 -2015-06-30,WA,3,B,B1,6.73,643.0 -2015-07-01,WA,3,B,B1,6.6,709.0 -2015-07-02,WA,3,B,B1,6.74,682.0 -2015-07-03,WA,3,B,B1,6.6,632.0 -2015-07-04,WA,3,B,B1,6.6,694.0 -2015-07-05,WA,3,B,B1,6.69,674.0 -2015-07-06,WA,3,B,B1,6.62,659.0 -2015-07-07,WA,3,B,B1,6.56,689.0 -2015-07-08,WA,3,B,B1,6.68,604.0 -2015-07-09,WA,3,B,B1,6.67,642.0 -2015-07-10,WA,3,B,B1,6.69,641.0 -2015-07-11,WA,3,B,B1,6.76,671.0 -2015-07-12,WA,3,B,B1,6.65,652.0 -2015-07-13,WA,3,B,B1,6.48,661.0 -2015-07-14,WA,3,B,B1,6.69,603.0 -2015-07-15,WA,3,B,B1,6.62,651.0 -2015-07-16,WA,3,B,B1,6.62,703.0 -2015-07-17,WA,3,B,B1,6.62,702.0 -2015-07-18,WA,3,B,B1,6.62,684.0 -2015-07-19,WA,3,B,B1,6.74,670.0 -2015-07-20,WA,3,B,B1,6.67,669.0 -2015-07-21,WA,3,B,B1,6.54,670.0 -2015-07-22,WA,3,B,B1,6.66,714.0 -2015-07-23,WA,3,B,B1,6.64,674.0 -2015-07-24,WA,3,B,B1,6.57,720.0 -2015-07-25,WA,3,B,B1,6.57,729.0 -2015-07-26,WA,3,B,B1,6.64,752.0 -2015-07-27,WA,3,B,B1,6.65,604.0 -2015-07-28,WA,3,B,B1,6.71,661.0 -2015-07-29,WA,3,B,B1,6.67,624.0 -2015-07-30,WA,3,B,B1,6.78,591.0 -2015-07-31,WA,3,B,B1,6.7,669.0 -2015-08-01,WA,3,B,B1,6.57,747.0 -2015-08-02,WA,3,B,B1,6.57,662.0 -2015-08-03,WA,3,B,B1,6.65,657.0 -2015-08-04,WA,3,B,B1,6.63,659.0 -2015-08-05,WA,3,B,B1,6.63,647.0 -2015-08-06,WA,3,B,B1,6.63,648.0 -2015-08-07,WA,3,B,B1,6.7,652.0 -2015-08-08,WA,3,B,B1,6.69,677.0 -2015-08-09,WA,3,B,B1,6.73,653.0 -2015-08-10,WA,3,B,B1,6.6,685.0 -2015-08-11,WA,3,B,B1,6.68,613.0 -2015-08-12,WA,3,B,B1,6.71,656.0 -2015-08-13,WA,3,B,B1,6.69,636.0 -2015-08-14,WA,3,B,B1,6.68,684.0 -2015-08-15,WA,3,B,B1,6.66,692.0 -2015-08-16,WA,3,B,B1,6.71,628.0 -2015-08-17,WA,3,B,B1,6.52,675.0 -2015-08-18,WA,3,B,B1,6.65,685.0 -2015-08-19,WA,3,B,B1,6.54,690.0 -2015-08-20,WA,3,B,B1,6.68,627.0 -2015-08-21,WA,3,B,B1,6.63,687.0 -2015-08-22,WA,3,B,B1,6.53,665.0 -2015-08-23,WA,3,B,B1,6.72,716.0 -2015-08-24,WA,3,B,B1,6.69,670.0 -2015-08-25,WA,3,B,B1,6.64,690.0 -2015-08-26,WA,3,B,B1,6.53,678.0 -2015-08-27,WA,3,B,B1,6.68,665.0 -2015-08-28,WA,3,B,B1,6.64,632.0 -2015-08-29,WA,3,B,B1,6.79,704.0 -2015-08-30,WA,3,B,B1,6.64,750.0 -2015-08-31,WA,3,B,B1,6.67,632.0 -2015-09-01,WA,3,B,B1,6.65,616.0 -2015-09-02,WA,3,B,B1,6.59,696.0 -2015-09-03,WA,3,B,B1,6.57,662.0 -2015-09-04,WA,3,B,B1,6.61,685.0 -2015-09-05,WA,3,B,B1,6.55,717.0 -2015-09-06,WA,3,B,B1,6.69,638.0 -2015-09-07,WA,3,B,B1,6.56,632.0 -2015-09-08,WA,3,B,B1,6.61,668.0 -2015-09-09,WA,3,B,B1,6.6,654.0 -2015-09-10,WA,3,B,B1,6.6,645.0 -2015-09-11,WA,3,B,B1,6.66,638.0 -2015-09-12,WA,3,B,B1,6.65,730.0 -2015-09-13,WA,3,B,B1,6.69,662.0 -2015-09-14,WA,3,B,B1,6.58,663.0 -2015-09-15,WA,3,B,B1,6.76,602.0 -2015-09-16,WA,3,B,B1,6.59,659.0 -2015-09-17,WA,3,B,B1,6.62,686.0 -2015-09-18,WA,3,B,B1,6.68,656.0 -2015-09-19,WA,3,B,B1,6.68,704.0 -2015-09-20,WA,3,B,B1,6.72,701.0 -2015-09-21,WA,3,B,B1,6.66,683.0 -2015-09-22,WA,3,B,B1,6.63,693.0 -2015-09-23,WA,3,B,B1,6.7,654.0 -2015-09-24,WA,3,B,B1,6.68,642.0 -2015-09-25,WA,3,B,B1,6.62,648.0 -2015-09-26,WA,3,B,B1,6.66,684.0 -2015-09-27,WA,3,B,B1,6.6,668.0 -2015-09-28,WA,3,B,B1,6.74,627.0 -2015-09-29,WA,3,B,B1,6.58,681.0 -2015-09-30,WA,3,B,B1,6.67,635.0 -2015-10-01,WA,3,B,B1,6.74,703.0 -2015-10-02,WA,3,B,B1,6.62,659.0 -2015-10-03,WA,3,B,B1,6.6,649.0 -2015-10-04,WA,3,B,B1,6.58,659.0 -2015-10-05,WA,3,B,B1,6.61,660.0 -2015-10-06,WA,3,B,B1,6.72,701.0 -2015-10-07,WA,3,B,B1,6.66,672.0 -2015-10-08,WA,3,B,B1,6.59,700.0 -2015-10-09,WA,3,B,B1,6.61,633.0 -2015-10-10,WA,3,B,B1,6.66,680.0 -2015-10-11,WA,3,B,B1,6.7,635.0 -2015-10-12,WA,3,B,B1,6.83,587.0 -2015-10-13,WA,3,B,B1,6.66,642.0 -2015-10-14,WA,3,B,B1,6.52,674.0 -2015-10-15,WA,3,B,B1,6.56,678.0 -2015-10-16,WA,3,B,B1,6.71,619.0 -2015-10-17,WA,3,B,B1,6.66,697.0 -2015-10-18,WA,3,B,B1,6.67,641.0 -2015-10-19,WA,3,B,B1,6.6,629.0 -2015-10-20,WA,3,B,B1,6.55,667.0 -2015-10-21,WA,3,B,B1,6.64,688.0 -2015-10-22,WA,3,B,B1,6.7,681.0 -2015-10-23,WA,3,B,B1,6.63,688.0 -2015-10-24,WA,3,B,B1,6.61,680.0 -2015-10-25,WA,3,B,B1,6.68,638.0 -2015-10-26,WA,3,B,B1,6.85,605.0 -2015-10-27,WA,3,B,B1,6.69,614.0 -2015-10-28,WA,3,B,B1,6.67,695.0 -2015-10-29,WA,3,B,B1,6.64,679.0 -2015-10-30,WA,3,B,B1,6.69,613.0 -2015-10-31,WA,3,B,B1,6.76,709.0 -2015-11-01,WA,3,B,B1,6.52,668.0 -2015-11-02,WA,3,B,B1,6.6,651.0 -2015-11-03,WA,3,B,B1,6.67,683.0 -2015-11-04,WA,3,B,B1,6.64,586.0 -2015-11-05,WA,3,B,B1,6.68,649.0 -2015-11-06,WA,3,B,B1,6.6,629.0 -2015-11-07,WA,3,B,B1,6.63,708.0 -2015-11-08,WA,3,B,B1,6.7,671.0 -2015-11-09,WA,3,B,B1,6.73,672.0 -2015-11-10,WA,3,B,B1,6.74,623.0 -2015-11-11,WA,3,B,B1,6.7,595.0 -2015-11-12,WA,3,B,B1,6.54,705.0 -2015-11-13,WA,3,B,B1,6.7,643.0 -2015-11-14,WA,3,B,B1,6.61,707.0 -2015-11-15,WA,3,B,B1,6.62,651.0 -2015-11-16,WA,3,B,B1,6.68,635.0 -2015-11-17,WA,3,B,B1,6.63,677.0 -2015-11-18,WA,3,B,B1,6.58,650.0 -2015-11-19,WA,3,B,B1,6.64,680.0 -2015-11-20,WA,3,B,B1,6.66,735.0 -2015-11-21,WA,3,B,B1,6.74,682.0 -2015-11-22,WA,3,B,B1,6.74,671.0 -2015-11-23,WA,3,B,B1,6.74,646.0 -2015-11-24,WA,3,B,B1,6.66,689.0 -2015-11-25,WA,3,B,B1,6.58,686.0 -2015-11-26,WA,3,B,B1,6.59,613.0 -2015-11-27,WA,3,B,B1,6.71,689.0 -2015-11-28,WA,3,B,B1,6.61,689.0 -2015-11-29,WA,3,B,B1,6.69,672.0 -2015-11-30,WA,3,B,B1,6.66,649.0 -2015-12-01,WA,3,B,B1,6.64,704.0 -2015-12-02,WA,3,B,B1,6.64,651.0 -2015-12-03,WA,3,B,B1,6.76,685.0 -2015-12-04,WA,3,B,B1,6.69,689.0 -2015-12-05,WA,3,B,B1,6.63,704.0 -2015-12-06,WA,3,B,B1,6.57,707.0 -2015-12-07,WA,3,B,B1,6.64,628.0 -2015-12-08,WA,3,B,B1,6.61,634.0 -2015-12-09,WA,3,B,B1,6.73,651.0 -2015-12-10,WA,3,B,B1,6.64,681.0 -2015-12-11,WA,3,B,B1,6.74,675.0 -2015-12-12,WA,3,B,B1,6.69,692.0 -2015-12-13,WA,3,B,B1,6.68,665.0 -2015-12-14,WA,3,B,B1,6.53,711.0 -2015-12-15,WA,3,B,B1,6.72,603.0 -2015-12-16,WA,3,B,B1,6.69,681.0 -2015-12-17,WA,3,B,B1,6.61,643.0 -2015-12-18,WA,3,B,B1,6.67,669.0 -2015-12-19,WA,3,B,B1,6.71,703.0 -2015-12-20,WA,3,B,B1,6.64,645.0 -2015-12-21,WA,3,B,B1,6.68,628.0 -2015-12-22,WA,3,B,B1,6.62,659.0 -2015-12-23,WA,3,B,B1,6.73,700.0 -2015-12-24,WA,3,B,B1,6.67,658.0 -2015-12-25,WA,3,B,B1,6.66,646.0 -2015-12-26,WA,3,B,B1,6.81,661.0 -2015-12-27,WA,3,B,B1,6.63,632.0 -2015-12-28,WA,3,B,B1,6.62,681.0 -2015-12-29,WA,3,B,B1,6.56,648.0 -2015-12-30,WA,3,B,B1,6.7,684.0 -2015-12-31,WA,3,B,B1,6.71,664.0 -2016-01-01,WA,3,B,B1,6.64,621.0 -2016-01-02,WA,3,B,B1,6.63,729.0 -2016-01-03,WA,3,B,B1,6.65,694.0 -2016-01-04,WA,3,B,B1,6.67,666.0 -2016-01-05,WA,3,B,B1,6.57,673.0 -2016-01-06,WA,3,B,B1,6.55,656.0 -2016-01-07,WA,3,B,B1,6.71,643.0 -2016-01-08,WA,3,B,B1,6.59,650.0 -2016-01-09,WA,3,B,B1,6.65,645.0 -2016-01-10,WA,3,B,B1,6.61,663.0 -2016-01-11,WA,3,B,B1,6.6,644.0 -2016-01-12,WA,3,B,B1,6.55,636.0 -2016-01-13,WA,3,B,B1,6.72,655.0 -2016-01-14,WA,3,B,B1,6.67,715.0 -2016-01-15,WA,3,B,B1,6.66,672.0 -2016-01-16,WA,3,B,B1,6.79,652.0 -2016-01-17,WA,3,B,B1,6.6,671.0 -2016-01-18,WA,3,B,B1,6.6,631.0 -2016-01-19,WA,3,B,B1,6.68,627.0 -2016-01-20,WA,3,B,B1,6.7,675.0 -2016-01-21,WA,3,B,B1,6.6,675.0 -2016-01-22,WA,3,B,B1,6.65,665.0 -2016-01-23,WA,3,B,B1,6.76,663.0 -2016-01-24,WA,3,B,B1,6.69,714.0 -2016-01-25,WA,3,B,B1,6.62,659.0 -2016-01-26,WA,3,B,B1,6.61,662.0 -2016-01-27,WA,3,B,B1,6.63,673.0 -2016-01-28,WA,3,B,B1,6.64,673.0 -2016-01-29,WA,3,B,B1,6.75,641.0 -2016-01-30,WA,3,B,B1,6.69,715.0 -2016-01-31,WA,3,B,B1,6.67,695.0 -2016-02-01,WA,3,B,B1,6.6,586.0 -2016-02-02,WA,3,B,B1,6.67,674.0 -2016-02-03,WA,3,B,B1,6.57,637.0 -2016-02-04,WA,3,B,B1,6.62,648.0 -2016-02-05,WA,3,B,B1,6.63,698.0 -2016-02-06,WA,3,B,B1,6.73,701.0 -2016-02-07,WA,3,B,B1,6.77,657.0 -2016-02-08,WA,3,B,B1,6.82,631.0 -2016-02-09,WA,3,B,B1,6.59,684.0 -2016-02-10,WA,3,B,B1,6.72,709.0 -2016-02-11,WA,3,B,B1,6.64,620.0 -2016-02-12,WA,3,B,B1,6.66,653.0 -2016-02-13,WA,3,B,B1,6.74,703.0 -2016-02-14,WA,3,B,B1,6.56,657.0 -2016-02-15,WA,3,B,B1,6.71,643.0 -2016-02-16,WA,3,B,B1,6.58,694.0 -2016-02-17,WA,3,B,B1,6.71,733.0 -2016-02-18,WA,3,B,B1,6.62,664.0 -2016-02-19,WA,3,B,B1,6.56,681.0 -2016-02-20,WA,3,B,B1,6.65,700.0 -2016-02-21,WA,3,B,B1,6.57,655.0 -2016-02-22,WA,3,B,B1,6.78,634.0 -2016-02-23,WA,3,B,B1,6.64,651.0 -2016-02-24,WA,3,B,B1,6.77,642.0 -2016-02-25,WA,3,B,B1,6.73,653.0 -2016-02-26,WA,3,B,B1,6.63,643.0 -2016-02-27,WA,3,B,B1,6.59,699.0 -2016-02-28,WA,3,B,B1,6.69,650.0 -2016-02-29,WA,3,B,B1,6.69,653.0 -2016-03-01,WA,3,B,B1,6.73,613.0 -2016-03-02,WA,3,B,B1,6.74,683.0 -2016-03-03,WA,3,B,B1,6.69,671.0 -2016-03-04,WA,3,B,B1,6.58,657.0 -2016-03-05,WA,3,B,B1,6.65,674.0 -2016-03-06,WA,3,B,B1,6.7,697.0 -2016-03-07,WA,3,B,B1,6.65,647.0 -2016-03-08,WA,3,B,B1,6.67,681.0 -2016-03-09,WA,3,B,B1,6.69,611.0 -2016-03-10,WA,3,B,B1,6.69,664.0 -2016-03-11,WA,3,B,B1,6.75,632.0 -2016-03-12,WA,3,B,B1,6.58,709.0 -2016-03-13,WA,3,B,B1,6.61,704.0 -2016-03-14,WA,3,B,B1,6.63,660.0 -2016-03-15,WA,3,B,B1,6.61,681.0 -2016-03-16,WA,3,B,B1,6.67,656.0 -2016-03-17,WA,3,B,B1,6.61,684.0 -2016-03-18,WA,3,B,B1,6.77,664.0 -2016-03-19,WA,3,B,B1,6.67,698.0 -2016-03-20,WA,3,B,B1,6.7,609.0 -2016-03-21,WA,3,B,B1,6.6,632.0 -2016-03-22,WA,3,B,B1,6.66,694.0 -2016-03-23,WA,3,B,B1,6.67,689.0 -2016-03-24,WA,3,B,B1,6.61,645.0 -2016-03-25,WA,3,B,B1,6.67,648.0 -2016-03-26,WA,3,B,B1,6.6,663.0 -2016-03-27,WA,3,B,B1,6.6,720.0 -2016-03-28,WA,3,B,B1,6.68,615.0 -2016-03-29,WA,3,B,B1,6.62,685.0 -2016-03-30,WA,3,B,B1,6.6,678.0 -2016-03-31,WA,3,B,B1,6.51,597.0 -2016-04-01,WA,3,B,B1,6.62,663.0 -2016-04-02,WA,3,B,B1,6.56,693.0 -2016-04-03,WA,3,B,B1,6.62,631.0 -2016-04-04,WA,3,B,B1,6.6,634.0 -2016-04-05,WA,3,B,B1,6.63,699.0 -2016-04-06,WA,3,B,B1,6.67,607.0 -2016-04-07,WA,3,B,B1,6.62,684.0 -2016-04-08,WA,3,B,B1,6.62,704.0 -2016-04-09,WA,3,B,B1,6.64,657.0 -2016-04-10,WA,3,B,B1,6.43,750.0 -2016-04-11,WA,3,B,B1,6.69,654.0 -2016-04-12,WA,3,B,B1,6.59,678.0 -2016-04-13,WA,3,B,B1,6.62,689.0 -2016-04-14,WA,3,B,B1,6.77,690.0 -2016-04-15,WA,3,B,B1,6.78,627.0 -2016-04-16,WA,3,B,B1,6.6,739.0 -2016-04-17,WA,3,B,B1,6.76,657.0 -2016-04-18,WA,3,B,B1,6.59,625.0 -2016-04-19,WA,3,B,B1,6.66,638.0 -2016-04-20,WA,3,B,B1,6.49,620.0 -2016-04-21,WA,3,B,B1,6.66,573.0 -2016-04-22,WA,3,B,B1,6.66,629.0 -2016-04-23,WA,3,B,B1,6.72,657.0 -2016-04-24,WA,3,B,B1,6.72,730.0 -2016-04-25,WA,3,B,B1,6.71,662.0 -2016-04-26,WA,3,B,B1,6.53,629.0 -2016-04-27,WA,3,B,B1,6.64,627.0 -2016-04-28,WA,3,B,B1,6.74,636.0 -2016-04-29,WA,3,B,B1,6.7,689.0 -2016-04-30,WA,3,B,B1,6.7,645.0 -2016-05-01,WA,3,B,B1,6.57,651.0 -2016-05-02,WA,3,B,B1,6.57,649.0 -2016-05-03,WA,3,B,B1,6.67,587.0 -2016-05-04,WA,3,B,B1,6.7,610.0 -2016-05-05,WA,3,B,B1,6.57,686.0 -2016-05-06,WA,3,B,B1,6.74,651.0 -2016-05-07,WA,3,B,B1,6.5,670.0 -2016-05-08,WA,3,B,B1,6.7,650.0 -2016-05-09,WA,3,B,B1,6.65,651.0 -2016-05-10,WA,3,B,B1,6.66,633.0 -2016-05-11,WA,3,B,B1,6.61,705.0 -2016-05-12,WA,3,B,B1,6.65,592.0 -2016-05-13,WA,3,B,B1,6.64,647.0 -2016-05-14,WA,3,B,B1,6.53,676.0 -2016-05-15,WA,3,B,B1,6.58,660.0 -2016-05-16,WA,3,B,B1,6.71,655.0 -2016-05-17,WA,3,B,B1,6.7,659.0 -2016-05-18,WA,3,B,B1,6.67,683.0 -2016-05-19,WA,3,B,B1,6.62,713.0 -2016-05-20,WA,3,B,B1,6.63,623.0 -2016-05-21,WA,3,B,B1,6.7,698.0 -2016-05-22,WA,3,B,B1,6.66,665.0 -2016-05-23,WA,3,B,B1,6.56,662.0 -2016-05-24,WA,3,B,B1,6.71,650.0 -2016-05-25,WA,3,B,B1,6.64,739.0 -2016-05-26,WA,3,B,B1,6.64,636.0 -2016-05-27,WA,3,B,B1,6.57,687.0 -2016-05-28,WA,3,B,B1,6.66,658.0 -2016-05-29,WA,3,B,B1,6.7,636.0 -2016-05-30,WA,3,B,B1,6.69,624.0 -2016-05-31,WA,3,B,B1,6.69,677.0 -2016-06-01,WA,3,B,B1,6.57,633.0 -2016-06-02,WA,3,B,B1,6.73,685.0 -2016-06-03,WA,3,B,B1,6.6,644.0 -2016-06-04,WA,3,B,B1,6.61,704.0 -2016-06-05,WA,3,B,B1,6.66,714.0 -2016-06-06,WA,3,B,B1,6.61,670.0 -2016-06-07,WA,3,B,B1,6.73,608.0 -2016-06-08,WA,3,B,B1,6.67,637.0 -2016-06-09,WA,3,B,B1,6.64,690.0 -2016-06-10,WA,3,B,B1,6.78,652.0 -2016-06-11,WA,3,B,B1,6.64,655.0 -2016-06-12,WA,3,B,B1,6.78,641.0 -2016-06-13,WA,3,B,B1,6.69,685.0 -2016-06-14,WA,3,B,B1,6.55,631.0 -2016-06-15,WA,3,B,B1,6.68,684.0 -2016-06-16,WA,3,B,B1,6.79,604.0 -2016-06-17,WA,3,B,B1,6.58,697.0 -2016-06-18,WA,3,B,B1,6.71,626.0 -2016-06-19,WA,3,B,B1,6.67,645.0 -2016-06-20,WA,3,B,B1,6.7,660.0 -2016-06-21,WA,3,B,B1,6.75,621.0 -2016-06-22,WA,3,B,B1,6.66,692.0 -2016-06-23,WA,3,B,B1,6.57,684.0 -2016-06-24,WA,3,B,B1,6.64,658.0 -2016-06-25,WA,3,B,B1,6.66,665.0 -2016-06-26,WA,3,B,B1,6.73,610.0 -2016-06-27,WA,3,B,B1,6.7,625.0 -2016-06-28,WA,3,B,B1,6.75,631.0 -2016-06-29,WA,3,B,B1,6.77,675.0 -2016-06-30,WA,3,B,B1,6.74,606.0 -2016-07-01,WA,3,B,B1,6.59,713.0 -2016-07-02,WA,3,B,B1,6.62,730.0 -2016-07-03,WA,3,B,B1,6.7,698.0 -2016-07-04,WA,3,B,B1,6.71,647.0 -2016-07-05,WA,3,B,B1,6.58,657.0 -2016-07-06,WA,3,B,B1,6.61,619.0 -2016-07-07,WA,3,B,B1,6.61,667.0 -2016-07-08,WA,3,B,B1,6.57,672.0 -2016-07-09,WA,3,B,B1,6.67,668.0 -2016-07-10,WA,3,B,B1,6.75,674.0 -2016-07-11,WA,3,B,B1,6.62,638.0 -2016-07-12,WA,3,B,B1,6.65,613.0 -2016-07-13,WA,3,B,B1,6.68,655.0 -2016-07-14,WA,3,B,B1,6.69,667.0 -2016-07-15,WA,3,B,B1,6.72,630.0 -2016-07-16,WA,3,B,B1,6.75,633.0 -2016-07-17,WA,3,B,B1,6.55,712.0 -2016-07-18,WA,3,B,B1,6.73,642.0 -2016-07-19,WA,3,B,B1,6.66,649.0 -2016-07-20,WA,3,B,B1,6.64,615.0 -2016-07-21,WA,3,B,B1,6.63,649.0 -2016-07-22,WA,3,B,B1,6.65,675.0 -2016-07-23,WA,3,B,B1,6.58,748.0 -2016-07-24,WA,3,B,B1,6.65,705.0 -2016-07-25,WA,3,B,B1,6.69,681.0 -2016-07-26,WA,3,B,B1,6.58,660.0 -2016-07-27,WA,3,B,B1,6.58,658.0 -2015-01-05,WA,3,B,B2,4.28,23.0 -2015-01-06,WA,3,B,B2,4.22,23.0 -2015-01-07,WA,3,B,B2,4.24,23.0 -2015-01-08,WA,3,B,B2,4.27,23.0 -2015-01-09,WA,3,B,B2,4.21,23.0 -2015-01-10,WA,3,B,B2,4.15,24.0 -2015-01-11,WA,3,B,B2,4.26,23.0 -2015-01-12,WA,3,B,B2,4.24,22.0 -2015-01-13,WA,3,B,B2,4.31,23.0 -2015-01-14,WA,3,B,B2,4.22,24.0 -2015-01-15,WA,3,B,B2,4.32,22.0 -2015-01-16,WA,3,B,B2,4.26,22.0 -2015-01-17,WA,3,B,B2,4.3,24.0 -2015-01-18,WA,3,B,B2,4.24,23.0 -2015-01-19,WA,3,B,B2,4.28,22.0 -2015-01-20,WA,3,B,B2,4.17,25.0 -2015-01-21,WA,3,B,B2,4.26,22.0 -2015-01-22,WA,3,B,B2,4.2,22.0 -2015-01-23,WA,3,B,B2,4.21,26.0 -2015-01-24,WA,3,B,B2,4.23,23.0 -2015-01-25,WA,3,B,B2,4.29,24.0 -2015-01-26,WA,3,B,B2,4.27,20.0 -2015-01-27,WA,3,B,B2,4.25,22.0 -2015-01-28,WA,3,B,B2,4.29,23.0 -2015-01-29,WA,3,B,B2,4.32,22.0 -2015-01-30,WA,3,B,B2,4.24,24.0 -2015-01-31,WA,3,B,B2,4.28,25.0 -2015-02-01,WA,3,B,B2,4.29,23.0 -2015-02-02,WA,3,B,B2,4.22,22.0 -2015-02-03,WA,3,B,B2,4.31,23.0 -2015-02-04,WA,3,B,B2,4.27,22.0 -2015-02-05,WA,3,B,B2,4.24,25.0 -2015-02-06,WA,3,B,B2,4.24,21.0 -2015-02-07,WA,3,B,B2,4.27,25.0 -2015-02-08,WA,3,B,B2,4.25,24.0 -2015-02-09,WA,3,B,B2,4.26,23.0 -2015-02-10,WA,3,B,B2,4.24,24.0 -2015-02-11,WA,3,B,B2,4.31,20.0 -2015-02-12,WA,3,B,B2,4.33,21.0 -2015-02-13,WA,3,B,B2,4.25,24.0 -2015-02-14,WA,3,B,B2,4.23,24.0 -2015-02-15,WA,3,B,B2,4.18,23.0 -2015-02-16,WA,3,B,B2,4.22,23.0 -2015-02-17,WA,3,B,B2,4.26,26.0 -2015-02-18,WA,3,B,B2,4.4,20.0 -2015-02-19,WA,3,B,B2,4.27,24.0 -2015-02-20,WA,3,B,B2,4.32,23.0 -2015-02-21,WA,3,B,B2,4.29,22.0 -2015-02-22,WA,3,B,B2,4.32,23.0 -2015-02-23,WA,3,B,B2,4.26,20.0 -2015-02-24,WA,3,B,B2,4.28,21.0 -2015-02-25,WA,3,B,B2,4.24,23.0 -2015-02-26,WA,3,B,B2,4.25,24.0 -2015-02-27,WA,3,B,B2,4.33,22.0 -2015-02-28,WA,3,B,B2,4.18,25.0 -2015-03-01,WA,3,B,B2,4.32,23.0 -2015-03-02,WA,3,B,B2,4.25,21.0 -2015-03-03,WA,3,B,B2,4.25,23.0 -2015-03-04,WA,3,B,B2,4.25,20.0 -2015-03-05,WA,3,B,B2,4.26,23.0 -2015-03-06,WA,3,B,B2,4.29,21.0 -2015-03-07,WA,3,B,B2,4.26,23.0 -2015-03-08,WA,3,B,B2,4.27,22.0 -2015-03-09,WA,3,B,B2,4.28,22.0 -2015-03-10,WA,3,B,B2,4.24,24.0 -2015-03-11,WA,3,B,B2,4.28,23.0 -2015-03-12,WA,3,B,B2,4.26,24.0 -2015-03-13,WA,3,B,B2,4.3,22.0 -2015-03-14,WA,3,B,B2,4.34,22.0 -2015-03-15,WA,3,B,B2,4.22,23.0 -2015-03-16,WA,3,B,B2,4.36,21.0 -2015-03-17,WA,3,B,B2,4.27,22.0 -2015-03-18,WA,3,B,B2,4.26,23.0 -2015-03-19,WA,3,B,B2,4.26,22.0 -2015-03-20,WA,3,B,B2,4.31,22.0 -2015-03-21,WA,3,B,B2,4.3,23.0 -2015-03-22,WA,3,B,B2,4.27,22.0 -2015-03-23,WA,3,B,B2,4.28,20.0 -2015-03-24,WA,3,B,B2,4.31,24.0 -2015-03-25,WA,3,B,B2,4.24,21.0 -2015-03-26,WA,3,B,B2,4.31,23.0 -2015-03-27,WA,3,B,B2,4.18,24.0 -2015-03-28,WA,3,B,B2,4.19,21.0 -2015-03-29,WA,3,B,B2,4.28,23.0 -2015-03-30,WA,3,B,B2,4.28,23.0 -2015-03-31,WA,3,B,B2,4.32,20.0 -2015-04-01,WA,3,B,B2,4.31,25.0 -2015-04-02,WA,3,B,B2,4.31,21.0 -2015-04-03,WA,3,B,B2,4.29,21.0 -2015-04-04,WA,3,B,B2,4.27,24.0 -2015-04-05,WA,3,B,B2,4.3,21.0 -2015-04-06,WA,3,B,B2,4.27,22.0 -2015-04-07,WA,3,B,B2,4.25,22.0 -2015-04-08,WA,3,B,B2,4.2,24.0 -2015-04-09,WA,3,B,B2,4.23,23.0 -2015-04-10,WA,3,B,B2,4.22,25.0 -2015-04-11,WA,3,B,B2,4.27,24.0 -2015-04-12,WA,3,B,B2,4.29,23.0 -2015-04-13,WA,3,B,B2,4.24,21.0 -2015-04-14,WA,3,B,B2,4.22,22.0 -2015-04-15,WA,3,B,B2,4.24,23.0 -2015-04-16,WA,3,B,B2,4.26,23.0 -2015-04-17,WA,3,B,B2,4.29,23.0 -2015-04-18,WA,3,B,B2,4.29,24.0 -2015-04-19,WA,3,B,B2,4.19,24.0 -2015-04-20,WA,3,B,B2,4.21,21.0 -2015-04-21,WA,3,B,B2,4.24,23.0 -2015-04-22,WA,3,B,B2,4.31,20.0 -2015-04-23,WA,3,B,B2,4.24,23.0 -2015-04-24,WA,3,B,B2,4.29,21.0 -2015-04-25,WA,3,B,B2,4.3,25.0 -2015-04-26,WA,3,B,B2,4.27,21.0 -2015-04-27,WA,3,B,B2,4.32,21.0 -2015-04-28,WA,3,B,B2,4.27,23.0 -2015-04-29,WA,3,B,B2,4.22,23.0 -2015-04-30,WA,3,B,B2,4.24,22.0 -2015-05-01,WA,3,B,B2,4.25,23.0 -2015-05-02,WA,3,B,B2,4.3,24.0 -2015-05-03,WA,3,B,B2,4.3,23.0 -2015-05-04,WA,3,B,B2,4.21,21.0 -2015-05-05,WA,3,B,B2,4.34,21.0 -2015-05-06,WA,3,B,B2,4.22,24.0 -2015-05-07,WA,3,B,B2,4.32,24.0 -2015-05-08,WA,3,B,B2,4.25,24.0 -2015-05-09,WA,3,B,B2,4.26,23.0 -2015-05-10,WA,3,B,B2,4.38,22.0 -2015-05-11,WA,3,B,B2,4.3,20.0 -2015-05-12,WA,3,B,B2,4.38,21.0 -2015-05-13,WA,3,B,B2,4.2,23.0 -2015-05-14,WA,3,B,B2,4.18,23.0 -2015-05-15,WA,3,B,B2,4.21,24.0 -2015-05-16,WA,3,B,B2,4.24,24.0 -2015-05-17,WA,3,B,B2,4.27,22.0 -2015-05-18,WA,3,B,B2,4.29,21.0 -2015-05-19,WA,3,B,B2,4.28,23.0 -2015-05-20,WA,3,B,B2,4.31,21.0 -2015-05-21,WA,3,B,B2,4.3,22.0 -2015-05-22,WA,3,B,B2,4.24,23.0 -2015-05-23,WA,3,B,B2,4.26,26.0 -2015-05-24,WA,3,B,B2,4.19,24.0 -2015-05-25,WA,3,B,B2,4.21,24.0 -2015-05-26,WA,3,B,B2,4.23,21.0 -2015-05-27,WA,3,B,B2,4.24,25.0 -2015-05-28,WA,3,B,B2,4.29,23.0 -2015-05-29,WA,3,B,B2,4.23,24.0 -2015-05-30,WA,3,B,B2,4.27,24.0 -2015-05-31,WA,3,B,B2,4.24,23.0 -2015-06-01,WA,3,B,B2,4.32,22.0 -2015-06-02,WA,3,B,B2,4.3,25.0 -2015-06-03,WA,3,B,B2,4.25,20.0 -2015-06-04,WA,3,B,B2,4.23,23.0 -2015-06-05,WA,3,B,B2,4.32,23.0 -2015-06-06,WA,3,B,B2,4.3,23.0 -2015-06-07,WA,3,B,B2,4.29,24.0 -2015-06-08,WA,3,B,B2,4.28,21.0 -2015-06-09,WA,3,B,B2,4.26,23.0 -2015-06-10,WA,3,B,B2,4.32,21.0 -2015-06-11,WA,3,B,B2,4.26,22.0 -2015-06-12,WA,3,B,B2,4.27,23.0 -2015-06-13,WA,3,B,B2,4.37,23.0 -2015-06-14,WA,3,B,B2,4.27,23.0 -2015-06-15,WA,3,B,B2,4.29,23.0 -2015-06-16,WA,3,B,B2,4.31,25.0 -2015-06-17,WA,3,B,B2,4.26,23.0 -2015-06-18,WA,3,B,B2,4.22,22.0 -2015-06-19,WA,3,B,B2,4.24,22.0 -2015-06-20,WA,3,B,B2,4.21,25.0 -2015-06-21,WA,3,B,B2,4.29,24.0 -2015-06-22,WA,3,B,B2,4.29,20.0 -2015-06-23,WA,3,B,B2,4.26,23.0 -2015-06-24,WA,3,B,B2,4.27,23.0 -2015-06-25,WA,3,B,B2,4.24,23.0 -2015-06-26,WA,3,B,B2,4.29,22.0 -2015-06-27,WA,3,B,B2,4.25,23.0 -2015-06-28,WA,3,B,B2,4.28,23.0 -2015-06-29,WA,3,B,B2,4.23,25.0 -2015-06-30,WA,3,B,B2,4.26,23.0 -2015-07-01,WA,3,B,B2,4.33,21.0 -2015-07-02,WA,3,B,B2,4.35,22.0 -2015-07-03,WA,3,B,B2,4.37,23.0 -2015-07-04,WA,3,B,B2,4.29,23.0 -2015-07-05,WA,3,B,B2,4.39,22.0 -2015-07-06,WA,3,B,B2,4.3,20.0 -2015-07-07,WA,3,B,B2,4.19,23.0 -2015-07-08,WA,3,B,B2,4.28,21.0 -2015-07-09,WA,3,B,B2,4.35,22.0 -2015-07-10,WA,3,B,B2,4.28,23.0 -2015-07-11,WA,3,B,B2,4.26,25.0 -2015-07-12,WA,3,B,B2,4.27,23.0 -2015-07-13,WA,3,B,B2,4.24,22.0 -2015-07-14,WA,3,B,B2,4.29,23.0 -2015-07-15,WA,3,B,B2,4.31,22.0 -2015-07-16,WA,3,B,B2,4.24,23.0 -2015-07-17,WA,3,B,B2,4.18,24.0 -2015-07-18,WA,3,B,B2,4.29,23.0 -2015-07-19,WA,3,B,B2,4.23,26.0 -2015-07-20,WA,3,B,B2,4.23,23.0 -2015-07-21,WA,3,B,B2,4.33,23.0 -2015-07-22,WA,3,B,B2,4.22,22.0 -2015-07-23,WA,3,B,B2,4.2,23.0 -2015-07-24,WA,3,B,B2,4.26,23.0 -2015-07-25,WA,3,B,B2,4.27,23.0 -2015-07-26,WA,3,B,B2,4.22,25.0 -2015-07-27,WA,3,B,B2,4.26,22.0 -2015-07-28,WA,3,B,B2,4.25,24.0 -2015-07-29,WA,3,B,B2,4.3,21.0 -2015-07-30,WA,3,B,B2,4.25,21.0 -2015-07-31,WA,3,B,B2,4.19,25.0 -2015-08-01,WA,3,B,B2,4.32,23.0 -2015-08-02,WA,3,B,B2,4.22,23.0 -2015-08-03,WA,3,B,B2,4.23,21.0 -2015-08-04,WA,3,B,B2,4.27,23.0 -2015-08-05,WA,3,B,B2,4.31,23.0 -2015-08-06,WA,3,B,B2,4.29,22.0 -2015-08-07,WA,3,B,B2,4.31,22.0 -2015-08-08,WA,3,B,B2,4.31,23.0 -2015-08-09,WA,3,B,B2,4.28,22.0 -2015-08-10,WA,3,B,B2,4.2,23.0 -2015-08-11,WA,3,B,B2,4.29,22.0 -2015-08-12,WA,3,B,B2,4.24,22.0 -2015-08-13,WA,3,B,B2,4.28,23.0 -2015-08-14,WA,3,B,B2,4.15,27.0 -2015-08-15,WA,3,B,B2,4.3,26.0 -2015-08-16,WA,3,B,B2,4.27,23.0 -2015-08-17,WA,3,B,B2,4.17,22.0 -2015-08-18,WA,3,B,B2,4.26,23.0 -2015-08-19,WA,3,B,B2,4.21,23.0 -2015-08-20,WA,3,B,B2,4.27,22.0 -2015-08-21,WA,3,B,B2,4.31,22.0 -2015-08-22,WA,3,B,B2,4.2,25.0 -2015-08-23,WA,3,B,B2,4.29,24.0 -2015-08-24,WA,3,B,B2,4.33,22.0 -2015-08-25,WA,3,B,B2,4.29,23.0 -2015-08-26,WA,3,B,B2,4.24,22.0 -2015-08-27,WA,3,B,B2,4.19,24.0 -2015-08-28,WA,3,B,B2,4.27,24.0 -2015-08-29,WA,3,B,B2,4.27,24.0 -2015-08-30,WA,3,B,B2,4.27,24.0 -2015-08-31,WA,3,B,B2,4.19,23.0 -2015-09-01,WA,3,B,B2,4.28,23.0 -2015-09-02,WA,3,B,B2,4.31,22.0 -2015-09-03,WA,3,B,B2,4.32,22.0 -2015-09-04,WA,3,B,B2,4.25,22.0 -2015-09-05,WA,3,B,B2,4.25,25.0 -2015-09-06,WA,3,B,B2,4.24,21.0 -2015-09-07,WA,3,B,B2,4.25,22.0 -2015-09-08,WA,3,B,B2,4.18,23.0 -2015-09-09,WA,3,B,B2,4.26,22.0 -2015-09-10,WA,3,B,B2,4.15,24.0 -2015-09-11,WA,3,B,B2,4.27,22.0 -2015-09-12,WA,3,B,B2,4.31,23.0 -2015-09-13,WA,3,B,B2,4.23,22.0 -2015-09-14,WA,3,B,B2,4.34,21.0 -2015-09-15,WA,3,B,B2,4.27,22.0 -2015-09-16,WA,3,B,B2,4.33,21.0 -2015-09-17,WA,3,B,B2,4.19,22.0 -2015-09-18,WA,3,B,B2,4.26,23.0 -2015-09-19,WA,3,B,B2,4.29,24.0 -2015-09-20,WA,3,B,B2,4.31,23.0 -2015-09-21,WA,3,B,B2,4.31,20.0 -2015-09-22,WA,3,B,B2,4.27,24.0 -2015-09-23,WA,3,B,B2,4.28,23.0 -2015-09-24,WA,3,B,B2,4.23,25.0 -2015-09-25,WA,3,B,B2,4.3,21.0 -2015-09-26,WA,3,B,B2,4.29,24.0 -2015-09-27,WA,3,B,B2,4.28,24.0 -2015-09-28,WA,3,B,B2,4.29,22.0 -2015-09-29,WA,3,B,B2,4.31,22.0 -2015-09-30,WA,3,B,B2,4.25,22.0 -2015-10-01,WA,3,B,B2,4.23,21.0 -2015-10-02,WA,3,B,B2,4.24,24.0 -2015-10-03,WA,3,B,B2,4.31,23.0 -2015-10-04,WA,3,B,B2,4.32,21.0 -2015-10-05,WA,3,B,B2,4.27,22.0 -2015-10-06,WA,3,B,B2,4.28,22.0 -2015-10-07,WA,3,B,B2,4.29,22.0 -2015-10-08,WA,3,B,B2,4.21,23.0 -2015-10-09,WA,3,B,B2,4.19,23.0 -2015-10-10,WA,3,B,B2,4.33,23.0 -2015-10-11,WA,3,B,B2,4.25,22.0 -2015-10-12,WA,3,B,B2,4.25,23.0 -2015-10-13,WA,3,B,B2,4.26,23.0 -2015-10-14,WA,3,B,B2,4.24,23.0 -2015-10-15,WA,3,B,B2,4.26,21.0 -2015-10-16,WA,3,B,B2,4.2,25.0 -2015-10-17,WA,3,B,B2,4.24,24.0 -2015-10-18,WA,3,B,B2,4.25,24.0 -2015-10-19,WA,3,B,B2,4.29,21.0 -2015-10-20,WA,3,B,B2,4.24,24.0 -2015-10-21,WA,3,B,B2,4.26,22.0 -2015-10-22,WA,3,B,B2,4.22,22.0 -2015-10-23,WA,3,B,B2,4.31,22.0 -2015-10-24,WA,3,B,B2,4.29,24.0 -2015-10-25,WA,3,B,B2,4.33,23.0 -2015-10-26,WA,3,B,B2,4.25,23.0 -2015-10-27,WA,3,B,B2,4.24,23.0 -2015-10-28,WA,3,B,B2,4.3,21.0 -2015-10-29,WA,3,B,B2,4.2,24.0 -2015-10-30,WA,3,B,B2,4.25,24.0 -2015-10-31,WA,3,B,B2,4.26,23.0 -2015-11-01,WA,3,B,B2,4.35,22.0 -2015-11-02,WA,3,B,B2,4.24,21.0 -2015-11-03,WA,3,B,B2,4.32,23.0 -2015-11-04,WA,3,B,B2,4.25,22.0 -2015-11-05,WA,3,B,B2,4.34,22.0 -2015-11-06,WA,3,B,B2,4.24,22.0 -2015-11-07,WA,3,B,B2,4.23,23.0 -2015-11-08,WA,3,B,B2,4.25,25.0 -2015-11-09,WA,3,B,B2,4.32,23.0 -2015-11-10,WA,3,B,B2,4.19,22.0 -2015-11-11,WA,3,B,B2,4.27,24.0 -2015-11-12,WA,3,B,B2,4.26,23.0 -2015-11-13,WA,3,B,B2,4.24,23.0 -2015-11-14,WA,3,B,B2,4.27,22.0 -2015-11-15,WA,3,B,B2,4.34,23.0 -2015-11-16,WA,3,B,B2,4.29,21.0 -2015-11-17,WA,3,B,B2,4.32,23.0 -2015-11-18,WA,3,B,B2,4.18,23.0 -2015-11-19,WA,3,B,B2,4.18,24.0 -2015-11-20,WA,3,B,B2,4.35,24.0 -2015-11-21,WA,3,B,B2,4.23,23.0 -2015-11-22,WA,3,B,B2,4.27,25.0 -2015-11-23,WA,3,B,B2,4.27,23.0 -2015-11-24,WA,3,B,B2,4.27,21.0 -2015-11-25,WA,3,B,B2,4.27,24.0 -2015-11-26,WA,3,B,B2,4.28,21.0 -2015-11-27,WA,3,B,B2,4.18,24.0 -2015-11-28,WA,3,B,B2,4.23,25.0 -2015-11-29,WA,3,B,B2,4.16,24.0 -2015-11-30,WA,3,B,B2,4.3,21.0 -2015-12-01,WA,3,B,B2,4.3,23.0 -2015-12-02,WA,3,B,B2,4.21,23.0 -2015-12-03,WA,3,B,B2,4.18,23.0 -2015-12-04,WA,3,B,B2,4.29,22.0 -2015-12-05,WA,3,B,B2,4.29,24.0 -2015-12-06,WA,3,B,B2,4.19,25.0 -2015-12-07,WA,3,B,B2,4.25,22.0 -2015-12-08,WA,3,B,B2,4.27,23.0 -2015-12-09,WA,3,B,B2,4.31,22.0 -2015-12-10,WA,3,B,B2,4.25,24.0 -2015-12-11,WA,3,B,B2,4.21,23.0 -2015-12-12,WA,3,B,B2,4.29,25.0 -2015-12-13,WA,3,B,B2,4.26,25.0 -2015-12-14,WA,3,B,B2,4.31,22.0 -2015-12-15,WA,3,B,B2,4.22,22.0 -2015-12-16,WA,3,B,B2,4.31,23.0 -2015-12-17,WA,3,B,B2,4.23,24.0 -2015-12-18,WA,3,B,B2,4.18,23.0 -2015-12-19,WA,3,B,B2,4.23,24.0 -2015-12-20,WA,3,B,B2,4.28,23.0 -2015-12-21,WA,3,B,B2,4.27,23.0 -2015-12-22,WA,3,B,B2,4.25,23.0 -2015-12-23,WA,3,B,B2,4.28,25.0 -2015-12-24,WA,3,B,B2,4.27,23.0 -2015-12-25,WA,3,B,B2,4.24,23.0 -2015-12-26,WA,3,B,B2,4.32,23.0 -2015-12-27,WA,3,B,B2,4.2,24.0 -2015-12-28,WA,3,B,B2,4.27,22.0 -2015-12-29,WA,3,B,B2,4.24,22.0 -2015-12-30,WA,3,B,B2,4.3,24.0 -2015-12-31,WA,3,B,B2,4.31,23.0 -2016-01-01,WA,3,B,B2,4.26,25.0 -2016-01-02,WA,3,B,B2,4.27,23.0 -2016-01-03,WA,3,B,B2,4.29,22.0 -2016-01-04,WA,3,B,B2,4.26,22.0 -2016-01-05,WA,3,B,B2,4.28,22.0 -2016-01-06,WA,3,B,B2,4.26,23.0 -2016-01-07,WA,3,B,B2,4.32,21.0 -2016-01-08,WA,3,B,B2,4.24,22.0 -2016-01-09,WA,3,B,B2,4.28,24.0 -2016-01-10,WA,3,B,B2,4.25,26.0 -2016-01-11,WA,3,B,B2,4.2,22.0 -2016-01-12,WA,3,B,B2,4.23,24.0 -2016-01-13,WA,3,B,B2,4.29,21.0 -2016-01-14,WA,3,B,B2,4.28,23.0 -2016-01-15,WA,3,B,B2,4.28,22.0 -2016-01-16,WA,3,B,B2,4.2,24.0 -2016-01-17,WA,3,B,B2,4.29,23.0 -2016-01-18,WA,3,B,B2,4.24,23.0 -2016-01-19,WA,3,B,B2,4.26,24.0 -2016-01-20,WA,3,B,B2,4.32,22.0 -2016-01-21,WA,3,B,B2,4.27,23.0 -2016-01-22,WA,3,B,B2,4.22,22.0 -2016-01-23,WA,3,B,B2,4.3,23.0 -2016-01-24,WA,3,B,B2,4.29,24.0 -2016-01-25,WA,3,B,B2,4.29,23.0 -2016-01-26,WA,3,B,B2,4.27,23.0 -2016-01-27,WA,3,B,B2,4.34,21.0 -2016-01-28,WA,3,B,B2,4.27,23.0 -2016-01-29,WA,3,B,B2,4.24,24.0 -2016-01-30,WA,3,B,B2,4.24,23.0 -2016-01-31,WA,3,B,B2,4.25,25.0 -2016-02-01,WA,3,B,B2,4.26,20.0 -2016-02-02,WA,3,B,B2,4.22,24.0 -2016-02-03,WA,3,B,B2,4.25,21.0 -2016-02-04,WA,3,B,B2,4.27,23.0 -2016-02-05,WA,3,B,B2,4.39,22.0 -2016-02-06,WA,3,B,B2,4.3,23.0 -2016-02-07,WA,3,B,B2,4.29,23.0 -2016-02-08,WA,3,B,B2,4.28,22.0 -2016-02-09,WA,3,B,B2,4.25,21.0 -2016-02-10,WA,3,B,B2,4.26,24.0 -2016-02-11,WA,3,B,B2,4.21,22.0 -2016-02-12,WA,3,B,B2,4.23,22.0 -2016-02-13,WA,3,B,B2,4.3,22.0 -2016-02-14,WA,3,B,B2,4.18,23.0 -2016-02-15,WA,3,B,B2,4.31,22.0 -2016-02-16,WA,3,B,B2,4.28,23.0 -2016-02-17,WA,3,B,B2,4.33,22.0 -2016-02-18,WA,3,B,B2,4.32,22.0 -2016-02-19,WA,3,B,B2,4.23,23.0 -2016-02-20,WA,3,B,B2,4.23,23.0 -2016-02-21,WA,3,B,B2,4.29,23.0 -2016-02-22,WA,3,B,B2,4.3,22.0 -2016-02-23,WA,3,B,B2,4.24,21.0 -2016-02-24,WA,3,B,B2,4.28,21.0 -2016-02-25,WA,3,B,B2,4.21,24.0 -2016-02-26,WA,3,B,B2,4.24,26.0 -2016-02-27,WA,3,B,B2,4.24,24.0 -2016-02-28,WA,3,B,B2,4.26,23.0 -2016-02-29,WA,3,B,B2,4.28,24.0 -2016-03-01,WA,3,B,B2,4.26,21.0 -2016-03-02,WA,3,B,B2,4.26,22.0 -2016-03-03,WA,3,B,B2,4.33,23.0 -2016-03-04,WA,3,B,B2,4.31,23.0 -2016-03-05,WA,3,B,B2,4.24,24.0 -2016-03-06,WA,3,B,B2,4.3,23.0 -2016-03-07,WA,3,B,B2,4.26,23.0 -2016-03-08,WA,3,B,B2,4.25,25.0 -2016-03-09,WA,3,B,B2,4.34,20.0 -2016-03-10,WA,3,B,B2,4.29,22.0 -2016-03-11,WA,3,B,B2,4.29,22.0 -2016-03-12,WA,3,B,B2,4.28,24.0 -2016-03-13,WA,3,B,B2,4.27,24.0 -2016-03-14,WA,3,B,B2,4.29,21.0 -2016-03-15,WA,3,B,B2,4.22,23.0 -2016-03-16,WA,3,B,B2,4.25,22.0 -2016-03-17,WA,3,B,B2,4.25,24.0 -2016-03-18,WA,3,B,B2,4.29,21.0 -2016-03-19,WA,3,B,B2,4.3,24.0 -2016-03-20,WA,3,B,B2,4.26,25.0 -2016-03-21,WA,3,B,B2,4.25,21.0 -2016-03-22,WA,3,B,B2,4.29,22.0 -2016-03-23,WA,3,B,B2,4.29,22.0 -2016-03-24,WA,3,B,B2,4.3,23.0 -2016-03-25,WA,3,B,B2,4.21,24.0 -2016-03-26,WA,3,B,B2,4.27,25.0 -2016-03-27,WA,3,B,B2,4.27,26.0 -2016-03-28,WA,3,B,B2,4.25,21.0 -2016-03-29,WA,3,B,B2,4.31,22.0 -2016-03-30,WA,3,B,B2,4.29,22.0 -2016-03-31,WA,3,B,B2,4.27,21.0 -2016-04-01,WA,3,B,B2,4.3,22.0 -2016-04-02,WA,3,B,B2,4.19,25.0 -2016-04-03,WA,3,B,B2,4.3,24.0 -2016-04-04,WA,3,B,B2,4.22,23.0 -2016-04-05,WA,3,B,B2,4.29,23.0 -2016-04-06,WA,3,B,B2,4.32,23.0 -2016-04-07,WA,3,B,B2,4.3,23.0 -2016-04-08,WA,3,B,B2,4.25,23.0 -2016-04-09,WA,3,B,B2,4.31,24.0 -2016-04-10,WA,3,B,B2,4.2,24.0 -2016-04-11,WA,3,B,B2,4.28,21.0 -2016-04-12,WA,3,B,B2,4.23,23.0 -2016-04-13,WA,3,B,B2,4.28,22.0 -2016-04-14,WA,3,B,B2,4.23,26.0 -2016-04-15,WA,3,B,B2,4.21,24.0 -2016-04-16,WA,3,B,B2,4.3,25.0 -2016-04-17,WA,3,B,B2,4.28,22.0 -2016-04-18,WA,3,B,B2,4.29,22.0 -2016-04-19,WA,3,B,B2,4.3,22.0 -2016-04-20,WA,3,B,B2,4.3,23.0 -2016-04-21,WA,3,B,B2,4.22,23.0 -2016-04-22,WA,3,B,B2,4.32,23.0 -2016-04-23,WA,3,B,B2,4.24,24.0 -2016-04-24,WA,3,B,B2,4.3,24.0 -2016-04-25,WA,3,B,B2,4.23,22.0 -2016-04-26,WA,3,B,B2,4.27,23.0 -2016-04-27,WA,3,B,B2,4.27,24.0 -2016-04-28,WA,3,B,B2,4.26,24.0 -2016-04-29,WA,3,B,B2,4.28,23.0 -2016-04-30,WA,3,B,B2,4.22,26.0 -2016-05-01,WA,3,B,B2,4.26,22.0 -2016-05-02,WA,3,B,B2,4.26,22.0 -2016-05-03,WA,3,B,B2,4.22,24.0 -2016-05-04,WA,3,B,B2,4.27,23.0 -2016-05-05,WA,3,B,B2,4.35,22.0 -2016-05-06,WA,3,B,B2,4.29,23.0 -2016-05-07,WA,3,B,B2,4.28,23.0 -2016-05-08,WA,3,B,B2,4.22,24.0 -2016-05-09,WA,3,B,B2,4.33,24.0 -2016-05-10,WA,3,B,B2,4.32,22.0 -2016-05-11,WA,3,B,B2,4.25,24.0 -2016-05-12,WA,3,B,B2,4.16,22.0 -2016-05-13,WA,3,B,B2,4.24,23.0 -2016-05-14,WA,3,B,B2,4.23,25.0 -2016-05-15,WA,3,B,B2,4.33,24.0 -2016-05-16,WA,3,B,B2,4.26,23.0 -2016-05-17,WA,3,B,B2,4.23,23.0 -2016-05-18,WA,3,B,B2,4.29,21.0 -2016-05-19,WA,3,B,B2,4.31,21.0 -2016-05-20,WA,3,B,B2,4.3,22.0 -2016-05-21,WA,3,B,B2,4.27,24.0 -2016-05-22,WA,3,B,B2,4.3,23.0 -2016-05-23,WA,3,B,B2,4.28,22.0 -2016-05-24,WA,3,B,B2,4.31,24.0 -2016-05-25,WA,3,B,B2,4.28,23.0 -2016-05-26,WA,3,B,B2,4.32,24.0 -2016-05-27,WA,3,B,B2,4.31,22.0 -2016-05-28,WA,3,B,B2,4.26,22.0 -2016-05-29,WA,3,B,B2,4.29,23.0 -2016-05-30,WA,3,B,B2,4.28,22.0 -2016-05-31,WA,3,B,B2,4.26,22.0 -2016-06-01,WA,3,B,B2,4.31,23.0 -2016-06-02,WA,3,B,B2,4.31,21.0 -2016-06-03,WA,3,B,B2,4.26,23.0 -2016-06-04,WA,3,B,B2,4.32,26.0 -2016-06-05,WA,3,B,B2,4.3,22.0 -2016-06-06,WA,3,B,B2,4.25,23.0 -2016-06-07,WA,3,B,B2,4.29,22.0 -2016-06-08,WA,3,B,B2,4.31,22.0 -2016-06-09,WA,3,B,B2,4.26,24.0 -2016-06-10,WA,3,B,B2,4.31,22.0 -2016-06-11,WA,3,B,B2,4.23,23.0 -2016-06-12,WA,3,B,B2,4.28,23.0 -2016-06-13,WA,3,B,B2,4.2,23.0 -2016-06-14,WA,3,B,B2,4.27,22.0 -2016-06-15,WA,3,B,B2,4.23,25.0 -2016-06-16,WA,3,B,B2,4.21,24.0 -2016-06-17,WA,3,B,B2,4.24,22.0 -2016-06-18,WA,3,B,B2,4.23,22.0 -2016-06-19,WA,3,B,B2,4.28,25.0 -2016-06-20,WA,3,B,B2,4.32,21.0 -2016-06-21,WA,3,B,B2,4.24,22.0 -2016-06-22,WA,3,B,B2,4.31,24.0 -2016-06-23,WA,3,B,B2,4.26,22.0 -2016-06-24,WA,3,B,B2,4.27,23.0 -2016-06-25,WA,3,B,B2,4.21,26.0 -2016-06-26,WA,3,B,B2,4.3,21.0 -2016-06-27,WA,3,B,B2,4.35,21.0 -2016-06-28,WA,3,B,B2,4.31,24.0 -2016-06-29,WA,3,B,B2,4.3,19.0 -2016-06-30,WA,3,B,B2,4.25,23.0 -2016-07-01,WA,3,B,B2,4.22,23.0 -2016-07-02,WA,3,B,B2,4.27,24.0 -2016-07-03,WA,3,B,B2,4.29,22.0 -2016-07-04,WA,3,B,B2,4.25,21.0 -2016-07-05,WA,3,B,B2,4.31,23.0 -2016-07-06,WA,3,B,B2,4.19,23.0 -2016-07-07,WA,3,B,B2,4.32,23.0 -2016-07-08,WA,3,B,B2,4.24,22.0 -2016-07-09,WA,3,B,B2,4.2,25.0 -2016-07-10,WA,3,B,B2,4.32,23.0 -2016-07-11,WA,3,B,B2,4.29,22.0 -2016-07-12,WA,3,B,B2,4.26,22.0 -2016-07-13,WA,3,B,B2,4.26,23.0 -2016-07-14,WA,3,B,B2,4.24,24.0 -2016-07-15,WA,3,B,B2,4.29,23.0 -2016-07-16,WA,3,B,B2,4.27,23.0 -2016-07-17,WA,3,B,B2,4.27,23.0 -2016-07-18,WA,3,B,B2,4.33,20.0 -2016-07-19,WA,3,B,B2,4.3,23.0 -2016-07-20,WA,3,B,B2,4.29,23.0 -2016-07-21,WA,3,B,B2,4.3,21.0 -2016-07-22,WA,3,B,B2,4.25,21.0 -2016-07-23,WA,3,B,B2,4.29,25.0 -2016-07-24,WA,3,B,B2,4.34,22.0 -2016-07-25,WA,3,B,B2,4.28,22.0 -2016-07-26,WA,3,B,B2,4.23,22.0 -2016-07-27,WA,3,B,B2,4.33,23.0 diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb deleted file mode 100644 index dbd76ecf5..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb +++ /dev/null @@ -1,639 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hierarchical Time Series - Automated ML\n", - "**_Generate hierarchical time series forecasts with Automated Machine Learning_**\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this notebook we are using a synthetic dataset portraying sales data to predict the the quantity of a vartiety of product skus across several states, stores, and product categories.\n", - "\n", - "**NOTE: There are limits on how many runs we can do in parallel per workspace, and we currently recommend to set the parallelism to maximum of 320 runs per experiment per workspace. If users want to have more parallelism and increase this limit they might encounter Too Many Requests errors (HTTP 429).**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prerequisites\n", - "You'll need to create a compute Instance by following the instructions in the [EnvironmentSetup.md](../Setup_Resources/EnvironmentSetup.md)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.0 Set up workspace, datastore, experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613003526897 - } - }, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Datastore\n", - "import pandas as pd\n", - "\n", - "# Set up your workspace\n", - "ws = Workspace.from_config()\n", - "ws.get_details()\n", - "\n", - "# Set up your datastores\n", - "dstore = ws.get_default_datastore()\n", - "\n", - "output = {}\n", - "output[\"SDK version\"] = azureml.core.VERSION\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Default datastore name\"] = dstore.name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Choose an experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613003540729 - } - }, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment = Experiment(ws, \"automl-hts\")\n", - "\n", - "print(\"Experiment name: \" + experiment.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.0 Data\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Upload local csv files to datastore\n", - "You can upload your train and inference csv files to the default datastore in your workspace. \n", - "\n", - "A Datastore is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target.\n", - "Please refer to [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) documentation on how to access data from Datastore." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore_path = \"hts-sample\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore = ws.get_default_datastore()\n", - "datastore" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the TabularDatasets \n", - "\n", - "Datasets in Azure Machine Learning are references to specific data in a Datastore. The data can be retrieved as a [TabularDatasets](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py). We will read in the data as a pandas DataFrame, upload to the data store and register them to your Workspace using ```register_pandas_dataframe``` so they can be called as an input into the training pipeline. We will use the inference dataset as part of the forecasting pipeline. The step need only be completed once." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613007017296 - } - }, - "outputs": [], - "source": [ - "from azureml.data.dataset_factory import TabularDatasetFactory\n", - "\n", - "registered_train = TabularDatasetFactory.register_pandas_dataframe(\n", - " pd.read_csv(\"Data/hts-sample-train.csv\"),\n", - " target=(datastore, \"hts-sample\"),\n", - " name=\"hts-sales-train\",\n", - ")\n", - "registered_inference = TabularDatasetFactory.register_pandas_dataframe(\n", - " pd.read_csv(\"Data/hts-sample-test.csv\"),\n", - " target=(datastore, \"hts-sample\"),\n", - " name=\"hts-sales-test\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.0 Build the training pipeline\n", - "Now that the dataset, WorkSpace, and datastore are set up, we can put together a pipeline for training.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Choose a compute target\n", - "\n", - "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n", - "\n", - "\\*\\*Creation of AmlCompute takes approximately 5 minutes.**\n", - "\n", - "If the AmlCompute with that name is already in your workspace this code will skip the creation process. As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this [article](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613007037308 - } - }, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "\n", - "# Name your cluster\n", - "compute_name = \"hts-compute\"\n", - "\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " print(\"Found compute target: \" + compute_name)\n", - "else:\n", - " print(\"Creating a new compute target...\")\n", - " provisioning_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_D16S_V3\", max_nodes=20\n", - " )\n", - " # Create the compute target\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - "\n", - " # Can poll for a minimum number of nodes and for a specific timeout.\n", - " # If no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(\n", - " show_output=True, min_node_count=None, timeout_in_minutes=20\n", - " )\n", - "\n", - " # For a more detailed view of current cluster status, use the 'status' property\n", - " print(compute_target.status.serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up training parameters\n", - "\n", - "This dictionary defines the AutoML and hierarchy settings. For this forecasting task we need to define several settings inncluding the name of the time column, the maximum forecast horizon, the hierarchy definition, and the level of the hierarchy at which to train.\n", - "\n", - "| Property | Description|\n", - "| :--------------- | :------------------- |\n", - "| **task** | forecasting |\n", - "| **primary_metric** | This is the metric that you want to optimize.
Forecasting supports the following primary metrics
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error |\n", - "| **blocked_models** | Blocked models won't be used by AutoML. |\n", - "| **iteration_timeout_minutes** | Maximum amount of time in minutes that the model can train. This is optional but provides customers with greater control on exit criteria. |\n", - "| **iterations** | Number of models to train. This is optional but provides customers with greater control on exit criteria. |\n", - "| **experiment_timeout_hours** | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n", - "| **label_column_name** | The name of the label column. |\n", - "| **forecast_horizon** | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n", - "| **n_cross_validations** | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n", - "| **enable_early_stopping** | Flag to enable early termination if the score is not improving in the short term. |\n", - "| **time_column_name** | The name of your time column. |\n", - "| **hierarchy_column_names** | The names of columns that define the hierarchical structure of the data from highest level to most granular. |\n", - "| **training_level** | The level of the hierarchy to be used for training models. |\n", - "| **enable_engineered_explanations** | Engineered feature explanations will be downloaded if enable_engineered_explanations flag is set to True. By default it is set to False to save storage space. |\n", - "| **time_series_id_column_name** | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n", - "| **track_child_runs** | Flag to disable tracking of child runs. Only best run is tracked if the flag is set to False (this includes the model and metrics of the run). |\n", - "| **pipeline_fetch_max_batch_size** | Determines how many pipelines (training algorithms) to fetch at a time for training, this helps reduce throttling when training at large scale. |\n", - "| **model_explainability** | Flag to disable explaining the best automated ML model at the end of all training iterations. The default is True and will block non-explainable models which may impact the forecast accuracy. For more information, see [Interpretability: model explanations in automated machine learning](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl). |" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613007061544 - } - }, - "outputs": [], - "source": [ - "from azureml.train.automl.runtime._hts.hts_parameters import HTSTrainParameters\n", - "\n", - "model_explainability = True\n", - "\n", - "engineered_explanations = False\n", - "# Define your hierarchy. Adjust the settings below based on your dataset.\n", - "hierarchy = [\"state\", \"store_id\", \"product_category\", \"SKU\"]\n", - "training_level = \"SKU\"\n", - "\n", - "# Set your forecast parameters. Adjust the settings below based on your dataset.\n", - "time_column_name = \"date\"\n", - "label_column_name = \"quantity\"\n", - "forecast_horizon = 7\n", - "\n", - "\n", - "automl_settings = {\n", - " \"task\": \"forecasting\",\n", - " \"primary_metric\": \"normalized_root_mean_squared_error\",\n", - " \"label_column_name\": label_column_name,\n", - " \"time_column_name\": time_column_name,\n", - " \"forecast_horizon\": forecast_horizon,\n", - " \"hierarchy_column_names\": hierarchy,\n", - " \"hierarchy_training_level\": training_level,\n", - " \"track_child_runs\": False,\n", - " \"pipeline_fetch_max_batch_size\": 15,\n", - " \"model_explainability\": model_explainability,\n", - " # The following settings are specific to this sample and should be adjusted according to your own needs.\n", - " \"iteration_timeout_minutes\": 10,\n", - " \"iterations\": 10,\n", - " \"n_cross_validations\": 2,\n", - "}\n", - "\n", - "hts_parameters = HTSTrainParameters(\n", - " automl_settings=automl_settings,\n", - " hierarchy_column_names=hierarchy,\n", - " training_level=training_level,\n", - " enable_engineered_explanations=engineered_explanations,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up hierarchy training pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parallel run step is leveraged to train the hierarchy. To configure the ParallelRunConfig you will need to determine the appropriate number of workers and nodes for your use case. The `process_count_per_node` is based off the number of cores of the compute VM. The node_count will determine the number of master nodes to use, increasing the node count will speed up the training process.\n", - "\n", - "* **experiment:** The experiment used for training.\n", - "* **train_data:** The tabular dataset to be used as input to the training run.\n", - "* **node_count:** The number of compute nodes to be used for running the user script. We recommend to start with 3 and increase the node_count if the training time is taking too long.\n", - "* **process_count_per_node:** Process count per node, we recommend 2:1 ratio for number of cores: number of processes per node. eg. If node has 16 cores then configure 8 or less process count per node or optimal performance.\n", - "* **train_pipeline_parameters:** The set of configuration parameters defined in the previous section. \n", - "\n", - "Calling this method will create a new aggregated dataset which is generated dynamically on pipeline execution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n", - "\n", - "\n", - "training_pipeline_steps = AutoMLPipelineBuilder.get_many_models_train_steps(\n", - " experiment=experiment,\n", - " train_data=registered_train,\n", - " compute_target=compute_target,\n", - " node_count=2,\n", - " process_count_per_node=8,\n", - " train_pipeline_parameters=hts_parameters,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "\n", - "training_pipeline = Pipeline(ws, steps=training_pipeline_steps)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit the pipeline to run\n", - "Next we submit our pipeline to run. The whole training pipeline takes about 1h using a Standard_D16_V3 VM with our current ParallelRunConfig setting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_run = experiment.submit(training_pipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check the run status, if training_run is in completed state, continue to forecasting. If training_run is in another state, check the portal for failures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### [Optional] Get the explanations\n", - "First we need to download the explanations to the local disk." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if model_explainability:\n", - " expl_output = training_run.get_pipeline_output(\"explanations\")\n", - " expl_output.download(\"training_explanations\")\n", - "else:\n", - " print(\n", - " \"Model explanations are available only if model_explainability is set to True.\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The explanations are downloaded to the \"training_explanations/azureml\" directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "if model_explainability:\n", - " explanations_dirrectory = os.listdir(\n", - " os.path.join(\"training_explanations\", \"azureml\")\n", - " )\n", - " if len(explanations_dirrectory) > 1:\n", - " print(\n", - " \"Warning! The directory contains multiple explanations, only the first one will be displayed.\"\n", - " )\n", - " print(\"The explanations are located at {}.\".format(explanations_dirrectory[0]))\n", - " # Now we will list all the explanations.\n", - " explanation_path = os.path.join(\n", - " \"training_explanations\",\n", - " \"azureml\",\n", - " explanations_dirrectory[0],\n", - " \"training_explanations\",\n", - " )\n", - " print(\"Available explanations\")\n", - " print(\"==============================\")\n", - " print(\"\\n\".join(os.listdir(explanation_path)))\n", - "else:\n", - " print(\n", - " \"Model explanations are available only if model_explainability is set to True.\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "View the explanations on \"state\" level." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import display\n", - "\n", - "explanation_type = \"raw\"\n", - "level = \"state\"\n", - "\n", - "if model_explainability:\n", - " display(\n", - " pd.read_csv(\n", - " os.path.join(explanation_path, \"{}_explanations_{}.csv\").format(\n", - " explanation_type, level\n", - " )\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.0 Forecasting\n", - "For hierarchical forecasting we need to provide the HTSInferenceParameters object.\n", - "#### HTSInferenceParameters arguments\n", - "* **hierarchy_forecast_level:** The default level of the hierarchy to produce prediction/forecast on.\n", - "* **allocation_method:** \\[Optional] The disaggregation method to use if the hierarchy forecast level specified is below the define hierarchy training level.
(average historical proportions) 'average_historical_proportions'
(proportions of the historical averages) 'proportions_of_historical_average'\n", - "\n", - "#### get_many_models_batch_inference_steps arguments\n", - "* **experiment:** The experiment used for inference run.\n", - "* **inference_data:** The data to use for inferencing. It should be the same schema as used for training.\n", - "* **compute_target:** The compute target that runs the inference pipeline.\n", - "* **node_count:** The number of compute nodes to be used for running the user script. We recommend to start with the number of cores per node (varies by compute sku).\n", - "* **process_count_per_node:** The number of processes per node.\n", - "* **train_run_id:** \\[Optional] The run id of the hierarchy training, by default it is the latest successful training hts run in the experiment.\n", - "* **train_experiment_name:** \\[Optional] The train experiment that contains the train pipeline. This one is only needed when the train pipeline is not in the same experiement as the inference pipeline.\n", - "* **process_count_per_node:** \\[Optional] The number of processes per node, by default it's 4." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.automl.runtime._hts.hts_parameters import HTSInferenceParameters\n", - "\n", - "inference_parameters = HTSInferenceParameters(\n", - " hierarchy_forecast_level=\"store_id\", # The setting is specific to this dataset and should be changed based on your dataset.\n", - " allocation_method=\"proportions_of_historical_average\",\n", - ")\n", - "\n", - "steps = AutoMLPipelineBuilder.get_many_models_batch_inference_steps(\n", - " experiment=experiment,\n", - " inference_data=registered_inference,\n", - " compute_target=compute_target,\n", - " inference_pipeline_parameters=inference_parameters,\n", - " node_count=2,\n", - " process_count_per_node=8,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "\n", - "inference_pipeline = Pipeline(ws, steps=steps)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inference_run = experiment.submit(inference_pipeline)\n", - "inference_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrieve results\n", - "\n", - "Forecast results can be retrieved through the following code. The prediction results summary and the actual predictions are downloaded in forecast_results folder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "forecasts = inference_run.get_pipeline_output(\"forecasts\")\n", - "forecasts.download(\"forecast_results\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resbumit the Pipeline\n", - "\n", - "The inference pipeline can be submitted with different configurations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inference_run = experiment.submit(\n", - " inference_pipeline, pipeline_parameters={\"hierarchy_forecast_level\": \"state\"}\n", - ")\n", - "inference_run.wait_for_completion(show_output=False)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jialiu" - } - ], - "categories": [ - "how-to-use-azureml", - "automated-machine-learning" - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.yml b/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.yml deleted file mode 100644 index 8a3162f39..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-hierarchical-timeseries -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb deleted file mode 100644 index a252aec3a..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb +++ /dev/null @@ -1,746 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Many Models - Automated ML\n", - "**_Generate many models time series forecasts with Automated Machine Learning_**\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this notebook we are using a synthetic dataset portraying sales data to predict the the quantity of a vartiety of product skus across several states, stores, and product categories.\n", - "\n", - "**NOTE: There are limits on how many runs we can do in parallel per workspace, and we currently recommend to set the parallelism to maximum of 320 runs per experiment per workspace. If users want to have more parallelism and increase this limit they might encounter Too Many Requests errors (HTTP 429).**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prerequisites\n", - "You'll need to create a compute Instance by following the instructions in the [EnvironmentSetup.md](../Setup_Resources/EnvironmentSetup.md)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.0 Set up workspace, datastore, experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613003526897 - } - }, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Datastore\n", - "import pandas as pd\n", - "\n", - "# Set up your workspace\n", - "ws = Workspace.from_config()\n", - "ws.get_details()\n", - "\n", - "# Set up your datastores\n", - "dstore = ws.get_default_datastore()\n", - "\n", - "output = {}\n", - "output[\"SDK version\"] = azureml.core.VERSION\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Default datastore name\"] = dstore.name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Choose an experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613003540729 - } - }, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment = Experiment(ws, \"automl-many-models\")\n", - "\n", - "print(\"Experiment name: \" + experiment.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.0 Data\n", - "\n", - "This notebook uses simulated orange juice sales data to walk you through the process of training many models on Azure Machine Learning using Automated ML. \n", - "\n", - "The time series data used in this example was simulated based on the University of Chicago's Dominick's Finer Foods dataset which featured two years of sales of 3 different orange juice brands for individual stores. The full simulated dataset includes 3,991 stores with 3 orange juice brands each thus allowing 11,973 models to be trained to showcase the power of the many models pattern.\n", - "\n", - " \n", - "In this notebook, two datasets will be created: one with all 11,973 files and one with only 10 files that can be used to quickly test and debug. For each dataset, you'll be walked through the process of:\n", - "\n", - "1. Registering the blob container as a Datastore to the Workspace\n", - "2. Registering a tabular dataset to the Workspace" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### 2.1 Data Preparation\n", - "The OJ data is available in the public blob container. The data is split to be used for training and for inferencing. For the current dataset, the data was split on time column ('WeekStarting') before and after '1992-5-28' .\n", - "\n", - "The container has\n", - "
    \n", - "
  1. 'oj-data-tabular' and 'oj-inference-tabular' folders that contains training and inference data respectively for the 11,973 models.
    2. \n", - "
  2. It also has 'oj-data-small-tabular' and 'oj-inference-small-tabular' folders that has training and inference data for 10 models.
    3. \n", - "
\n", - "\n", - "To create the [TabularDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset?view=azure-ml-py) needed for the ParallelRunStep, you first need to register the blob container to the workspace." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - " To use your own data, put your own data in a blobstore folder. As shown it can be one file or multiple files. We can then register datastore using that blob as shown below.\n", - " \n", - "

How sample data in blob store looks like

\n", - "\n", - "['oj-data-tabular'](https://ms.portal.azure.com/#blade/Microsoft_Azure_Storage/ContainerMenuBlade/overview/storageAccountId/%2Fsubscriptions%2F102a16c3-37d3-48a8-9237-4c9b1e8e80e0%2FresourceGroups%2FAutoMLSampleNotebooksData%2Fproviders%2FMicrosoft.Storage%2FstorageAccounts%2Fautomlsamplenotebookdata/path/automl-sample-notebook-data/etag/%220x8D84EAA65DE50B7%22/defaultEncryptionScope/%24account-encryption-key/denyEncryptionScopeOverride//defaultId//publicAccessVal/Container)\n", - "![image-4.png](mm-1.png)\n", - "\n", - "['oj-inference-tabular'](https://ms.portal.azure.com/#blade/Microsoft_Azure_Storage/ContainerMenuBlade/overview/storageAccountId/%2Fsubscriptions%2F102a16c3-37d3-48a8-9237-4c9b1e8e80e0%2FresourceGroups%2FAutoMLSampleNotebooksData%2Fproviders%2FMicrosoft.Storage%2FstorageAccounts%2Fautomlsamplenotebookdata/path/automl-sample-notebook-data/etag/%220x8D84EAA65DE50B7%22/defaultEncryptionScope/%24account-encryption-key/denyEncryptionScopeOverride//defaultId//publicAccessVal/Container)\n", - "![image-3.png](mm-2.png)\n", - "\n", - "['oj-data-small-tabular'](https://ms.portal.azure.com/#blade/Microsoft_Azure_Storage/ContainerMenuBlade/overview/storageAccountId/%2Fsubscriptions%2F102a16c3-37d3-48a8-9237-4c9b1e8e80e0%2FresourceGroups%2FAutoMLSampleNotebooksData%2Fproviders%2FMicrosoft.Storage%2FstorageAccounts%2Fautomlsamplenotebookdata/path/automl-sample-notebook-data/etag/%220x8D84EAA65DE50B7%22/defaultEncryptionScope/%24account-encryption-key/denyEncryptionScopeOverride//defaultId//publicAccessVal/Container)\n", - "\n", - "![image-5.png](mm-3.png)\n", - "\n", - "['oj-inference-small-tabular'](https://ms.portal.azure.com/#blade/Microsoft_Azure_Storage/ContainerMenuBlade/overview/storageAccountId/%2Fsubscriptions%2F102a16c3-37d3-48a8-9237-4c9b1e8e80e0%2FresourceGroups%2FAutoMLSampleNotebooksData%2Fproviders%2FMicrosoft.Storage%2FstorageAccounts%2Fautomlsamplenotebookdata/path/automl-sample-notebook-data/etag/%220x8D84EAA65DE50B7%22/defaultEncryptionScope/%24account-encryption-key/denyEncryptionScopeOverride//defaultId//publicAccessVal/Container)\n", - "![image-6.png](mm-4.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.2 Register the blob container as DataStore\n", - "\n", - "A Datastore is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target.\n", - "\n", - "Please refer to [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore(class)?view=azure-ml-py) documentation on how to access data from Datastore.\n", - "\n", - "In this next step, we will be registering blob storage as datastore to the Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Datastore\n", - "\n", - "# Please change the following to point to your own blob container and pass in account_key\n", - "blob_datastore_name = \"automl_many_models\"\n", - "container_name = \"automl-sample-notebook-data\"\n", - "account_name = \"automlsamplenotebookdata\"\n", - "\n", - "oj_datastore = Datastore.register_azure_blob_container(\n", - " workspace=ws,\n", - " datastore_name=blob_datastore_name,\n", - " container_name=container_name,\n", - " account_name=account_name,\n", - " create_if_not_exists=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.3 Using tabular datasets \n", - "\n", - "Now that the datastore is available from the Workspace, [TabularDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset?view=azure-ml-py) can be created. Datasets in Azure Machine Learning are references to specific data in a Datastore. We are using TabularDataset, so that users who have their data which can be in one or many files (*.parquet or *.csv) and have not split up data according to group columns needed for training, can do so using out of box support for 'partiion_by' feature of TabularDataset shown in section 5.0 below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613007017296 - } - }, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "\n", - "ds_name_small = \"oj-data-small-tabular\"\n", - "input_ds_small = Dataset.Tabular.from_delimited_files(\n", - " path=oj_datastore.path(ds_name_small + \"/\"), validate=False\n", - ")\n", - "\n", - "inference_name_small = \"oj-inference-small-tabular\"\n", - "inference_ds_small = Dataset.Tabular.from_delimited_files(\n", - " path=oj_datastore.path(inference_name_small + \"/\"), validate=False\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.0 Build the training pipeline\n", - "Now that the dataset, WorkSpace, and datastore are set up, we can put together a pipeline for training.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Choose a compute target\n", - "\n", - "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n", - "\n", - "\\*\\*Creation of AmlCompute takes approximately 5 minutes.**\n", - "\n", - "If the AmlCompute with that name is already in your workspace this code will skip the creation process. As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this [article](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613007037308 - } - }, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "\n", - "# Name your cluster\n", - "compute_name = \"mm-compute\"\n", - "\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " print(\"Found compute target: \" + compute_name)\n", - "else:\n", - " print(\"Creating a new compute target...\")\n", - " provisioning_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_D16S_V3\", max_nodes=20\n", - " )\n", - " # Create the compute target\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - "\n", - " # Can poll for a minimum number of nodes and for a specific timeout.\n", - " # If no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(\n", - " show_output=True, min_node_count=None, timeout_in_minutes=20\n", - " )\n", - "\n", - " # For a more detailed view of current cluster status, use the 'status' property\n", - " print(compute_target.status.serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up training parameters\n", - "\n", - "This dictionary defines the AutoML and many models settings. For this forecasting task we need to define several settings inncluding the name of the time column, the maximum forecast horizon, and the partition column name definition.\n", - "\n", - "| Property | Description|\n", - "| :--------------- | :------------------- |\n", - "| **task** | forecasting |\n", - "| **primary_metric** | This is the metric that you want to optimize.
Forecasting supports the following primary metrics
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error |\n", - "| **blocked_models** | Blocked models won't be used by AutoML. |\n", - "| **iteration_timeout_minutes** | Maximum amount of time in minutes that the model can train. This is optional but provides customers with greater control on exit criteria. |\n", - "| **iterations** | Number of models to train. This is optional but provides customers with greater control on exit criteria. |\n", - "| **experiment_timeout_hours** | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n", - "| **label_column_name** | The name of the label column. |\n", - "| **forecast_horizon** | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n", - "| **n_cross_validations** | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n", - "| **enable_early_stopping** | Flag to enable early termination if the score is not improving in the short term. |\n", - "| **time_column_name** | The name of your time column. |\n", - "| **enable_engineered_explanations** | Engineered feature explanations will be downloaded if enable_engineered_explanations flag is set to True. By default it is set to False to save storage space. |\n", - "| **time_series_id_column_name** | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n", - "| **track_child_runs** | Flag to disable tracking of child runs. Only best run is tracked if the flag is set to False (this includes the model and metrics of the run). |\n", - "| **pipeline_fetch_max_batch_size** | Determines how many pipelines (training algorithms) to fetch at a time for training, this helps reduce throttling when training at large scale. |\n", - "| **partition_column_names** | The names of columns used to group your models. For timeseries, the groups must not split up individual time-series. That is, each group must contain one or more whole time-series. |" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1613007061544 - } - }, - "outputs": [], - "source": [ - "from azureml.train.automl.runtime._many_models.many_models_parameters import (\n", - " ManyModelsTrainParameters,\n", - ")\n", - "\n", - "partition_column_names = [\"Store\", \"Brand\"]\n", - "automl_settings = {\n", - " \"task\": \"forecasting\",\n", - " \"primary_metric\": \"normalized_root_mean_squared_error\",\n", - " \"iteration_timeout_minutes\": 10, # This needs to be changed based on the dataset. We ask customer to explore how long training is taking before settings this value\n", - " \"iterations\": 15,\n", - " \"experiment_timeout_hours\": 0.25,\n", - " \"label_column_name\": \"Quantity\",\n", - " \"n_cross_validations\": 3,\n", - " \"time_column_name\": \"WeekStarting\",\n", - " \"drop_column_names\": \"Revenue\",\n", - " \"max_horizon\": 6,\n", - " \"grain_column_names\": partition_column_names,\n", - " \"track_child_runs\": False,\n", - "}\n", - "\n", - "mm_paramters = ManyModelsTrainParameters(\n", - " automl_settings=automl_settings, partition_column_names=partition_column_names\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up many models pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parallel run step is leveraged to train multiple models at once. To configure the ParallelRunConfig you will need to determine the appropriate number of workers and nodes for your use case. The process_count_per_node is based off the number of cores of the compute VM. The node_count will determine the number of master nodes to use, increasing the node count will speed up the training process.\n", - "\n", - "| Property | Description|\n", - "| :--------------- | :------------------- |\n", - "| **experiment** | The experiment used for training. |\n", - "| **train_data** | The file dataset to be used as input to the training run. |\n", - "| **node_count** | The number of compute nodes to be used for running the user script. We recommend to start with 3 and increase the node_count if the training time is taking too long. |\n", - "| **process_count_per_node** | Process count per node, we recommend 2:1 ratio for number of cores: number of processes per node. eg. If node has 16 cores then configure 8 or less process count per node or optimal performance. |\n", - "| **train_pipeline_parameters** | The set of configuration parameters defined in the previous section. |\n", - "\n", - "Calling this method will create a new aggregated dataset which is generated dynamically on pipeline execution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n", - "\n", - "\n", - "training_pipeline_steps = AutoMLPipelineBuilder.get_many_models_train_steps(\n", - " experiment=experiment,\n", - " train_data=input_ds_small,\n", - " compute_target=compute_target,\n", - " node_count=2,\n", - " process_count_per_node=8,\n", - " run_invocation_timeout=920,\n", - " train_pipeline_parameters=mm_paramters,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "\n", - "training_pipeline = Pipeline(ws, steps=training_pipeline_steps)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit the pipeline to run\n", - "Next we submit our pipeline to run. The whole training pipeline takes about 40m using a STANDARD_D16S_V3 VM with our current ParallelRunConfig setting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_run = experiment.submit(training_pipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check the run status, if training_run is in completed state, continue to forecasting. If training_run is in another state, check the portal for failures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.0 Publish and schedule the train pipeline (Optional)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.1 Publish the pipeline\n", - "\n", - "Once you have a pipeline you're happy with, you can publish a pipeline so you can call it programmatically later on. See this [tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline#publish-a-pipeline) for additional information on publishing and calling pipelines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# published_pipeline = training_pipeline.publish(name = 'automl_train_many_models',\n", - "# description = 'train many models',\n", - "# version = '1',\n", - "# continue_on_step_failure = False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7.2 Schedule the pipeline\n", - "You can also [schedule the pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines) to run on a time-based or change-based schedule. This could be used to automatically retrain models every month or based on another trigger such as data drift." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from azureml.pipeline.core import Schedule, ScheduleRecurrence\n", - "\n", - "# training_pipeline_id = published_pipeline.id\n", - "\n", - "# recurrence = ScheduleRecurrence(frequency=\"Month\", interval=1, start_time=\"2020-01-01T09:00:00\")\n", - "# recurring_schedule = Schedule.create(ws, name=\"automl_training_recurring_schedule\",\n", - "# description=\"Schedule Training Pipeline to run on the first day of every month\",\n", - "# pipeline_id=training_pipeline_id,\n", - "# experiment_name=experiment.name,\n", - "# recurrence=recurrence)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6.0 Forecasting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up output dataset for inference data\n", - "Output of inference can be represented as [OutputFileDatasetConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.output_dataset_config.outputdatasetconfig?view=azure-ml-py) object and OutputFileDatasetConfig can be registered as a dataset. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.data import OutputFileDatasetConfig\n", - "\n", - "output_inference_data_ds = OutputFileDatasetConfig(\n", - " name=\"many_models_inference_output\", destination=(dstore, \"oj/inference_data/\")\n", - ").register_on_complete(name=\"oj_inference_data_ds\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For many models we need to provide the ManyModelsInferenceParameters object.\n", - "\n", - "#### ManyModelsInferenceParameters arguments\n", - "| Property | Description|\n", - "| :--------------- | :------------------- |\n", - "| **partition_column_names** | List of column names that identifies groups. |\n", - "| **target_column_name** | \\[Optional] Column name only if the inference dataset has the target. |\n", - "| **time_column_name** | \\[Optional] Column name only if it is timeseries. |\n", - "| **many_models_run_id** | \\[Optional] Many models run id where models were trained. |\n", - "\n", - "#### get_many_models_batch_inference_steps arguments\n", - "| Property | Description|\n", - "| :--------------- | :------------------- |\n", - "| **experiment** | The experiment used for inference run. |\n", - "| **inference_data** | The data to use for inferencing. It should be the same schema as used for training.\n", - "| **compute_target** The compute target that runs the inference pipeline.|\n", - "| **node_count** | The number of compute nodes to be used for running the user script. We recommend to start with the number of cores per node (varies by compute sku). |\n", - "| **process_count_per_node** The number of processes per node.\n", - "| **train_run_id** | \\[Optional] The run id of the hierarchy training, by default it is the latest successful training many model run in the experiment. |\n", - "| **train_experiment_name** | \\[Optional] The train experiment that contains the train pipeline. This one is only needed when the train pipeline is not in the same experiement as the inference pipeline. |\n", - "| **process_count_per_node** | \\[Optional] The number of processes per node, by default it's 4. |" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n", - "from azureml.train.automl.runtime._many_models.many_models_parameters import (\n", - " ManyModelsInferenceParameters,\n", - ")\n", - "\n", - "mm_parameters = ManyModelsInferenceParameters(\n", - " partition_column_names=[\"Store\", \"Brand\"],\n", - " time_column_name=\"WeekStarting\",\n", - " target_column_name=\"Quantity\",\n", - ")\n", - "\n", - "inference_steps = AutoMLPipelineBuilder.get_many_models_batch_inference_steps(\n", - " experiment=experiment,\n", - " inference_data=inference_ds_small,\n", - " node_count=2,\n", - " process_count_per_node=8,\n", - " compute_target=compute_target,\n", - " run_invocation_timeout=300,\n", - " output_datastore=output_inference_data_ds,\n", - " train_run_id=training_run.id,\n", - " train_experiment_name=training_run.experiment.name,\n", - " inference_pipeline_parameters=mm_parameters,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "\n", - "inference_pipeline = Pipeline(ws, steps=inference_steps)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inference_run = experiment.submit(inference_pipeline)\n", - "inference_run.wait_for_completion(show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrieve results\n", - "\n", - "The forecasting pipeline forecasts the orange juice quantity for a Store by Brand. The pipeline returns one file with the predictions for each store and outputs the result to the forecasting_output Blob container. The details of the blob container is listed in 'forecasting_output.txt' under Outputs+logs. \n", - "\n", - "The following code snippet:\n", - "1. Downloads the contents of the output folder that is passed in the parallel run step \n", - "2. Reads the parallel_run_step.txt file that has the predictions as pandas dataframe and \n", - "3. Displays the top 10 rows of the predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.automl.pipeline.steps.utilities import get_output_from_mm_pipeline\n", - "\n", - "forecasting_results_name = \"forecasting_results\"\n", - "forecasting_output_name = \"many_models_inference_output\"\n", - "forecast_file = get_output_from_mm_pipeline(\n", - " inference_run, forecasting_results_name, forecasting_output_name\n", - ")\n", - "df = pd.read_csv(forecast_file, delimiter=\" \", header=None)\n", - "df.columns = [\n", - " \"Week Starting\",\n", - " \"Store\",\n", - " \"Brand\",\n", - " \"Quantity\",\n", - " \"Advert\",\n", - " \"Price\",\n", - " \"Revenue\",\n", - " \"Predicted\",\n", - "]\n", - "print(\n", - " \"Prediction has \", df.shape[0], \" rows. Here the first 10 rows are being displayed.\"\n", - ")\n", - "df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7.0 Publish and schedule the inference pipeline (Optional)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7.1 Publish the pipeline\n", - "\n", - "Once you have a pipeline you're happy with, you can publish a pipeline so you can call it programmatically later on. See this [tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline#publish-a-pipeline) for additional information on publishing and calling pipelines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# published_pipeline_inf = inference_pipeline.publish(name = 'automl_forecast_many_models',\n", - "# description = 'forecast many models',\n", - "# version = '1',\n", - "# continue_on_step_failure = False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7.2 Schedule the pipeline\n", - "You can also [schedule the pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines) to run on a time-based or change-based schedule. This could be used to automatically retrain or forecast models every month or based on another trigger such as data drift." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from azureml.pipeline.core import Schedule, ScheduleRecurrence\n", - "\n", - "# forecasting_pipeline_id = published_pipeline.id\n", - "\n", - "# recurrence = ScheduleRecurrence(frequency=\"Month\", interval=1, start_time=\"2020-01-01T09:00:00\")\n", - "# recurring_schedule = Schedule.create(ws, name=\"automl_forecasting_recurring_schedule\",\n", - "# description=\"Schedule Forecasting Pipeline to run on the first day of every week\",\n", - "# pipeline_id=forecasting_pipeline_id,\n", - "# experiment_name=experiment.name,\n", - "# recurrence=recurrence)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jialiu" - } - ], - "categories": [ - "how-to-use-azureml", - "automated-machine-learning" - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.yml b/how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.yml deleted file mode 100644 index a9e06f92a..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-many-models -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-1.png b/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-1.png deleted file mode 100644 index 91c044205..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-1.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-2.png b/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-2.png deleted file mode 100644 index 7da412349..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-2.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-3.png b/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-3.png deleted file mode 100644 index 5773c9b3b..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-3.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-4.png b/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-4.png deleted file mode 100644 index fdd99447b..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/mm-4.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb deleted file mode 100644 index a25344f68..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb +++ /dev/null @@ -1,844 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Orange Juice Sales Forecasting**_\n", - "\n", - "## Contents\n", - "1. [Introduction](#introduction)\n", - "1. [Setup](#setup)\n", - "1. [Compute](#compute)\n", - "1. [Data](#data)\n", - "1. [Train](#train)\n", - "1. [Forecast](#forecast)\n", - "1. [Operationalize](#operationalize)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "In this example, we use AutoML to train, select, and operationalize a time-series forecasting model for multiple time-series.\n", - "\n", - "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n", - "\n", - "The examples in the follow code samples use the University of Chicago's Dominick's Finer Foods dataset to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import logging\n", - "\n", - "import azureml.core\n", - "import pandas as pd\n", - "from azureml.automl.core.featurization import FeaturizationConfig\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.train.automl import AutoMLConfig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is compatible with Azure ML SDK version 1.35.0 or later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As part of the setup you have already created a Workspace. To run AutoML, you also need to create an Experiment. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# choose a name for the run history container in the workspace\n", - "experiment_name = \"automl-ojforecasting\"\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"SKU\"] = ws.sku\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Run History Name\"] = experiment_name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute\n", - "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "#### Creation of AmlCompute takes approximately 5 minutes. \n", - "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "amlcompute_cluster_name = \"oj-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_D12_V2\", max_nodes=6\n", - " )\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n", - "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "time_column_name = \"WeekStarting\"\n", - "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n", - "\n", - "# Drop the columns 'logQuantity' as it is a leaky feature.\n", - "data.drop(\"logQuantity\", axis=1, inplace=True)\n", - "\n", - "data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred. \n", - "\n", - "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we define the **time_series_id_column_names** - the columns whose values determine the boundaries between time-series: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "time_series_id_column_names = [\"Store\", \"Brand\"]\n", - "nseries = data.groupby(time_series_id_column_names).ngroups\n", - "print(\"Data contains {0} individual time-series.\".format(nseries))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For demonstration purposes, we extract sales time-series for just a few of the stores:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "use_stores = [2, 5, 8]\n", - "data_subset = data[data.Store.isin(use_stores)]\n", - "nseries = data_subset.groupby(time_series_id_column_names).ngroups\n", - "print(\"Data subset contains {0} individual time-series.\".format(nseries))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Splitting\n", - "We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the time series identifier columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n_test_periods = 20\n", - "\n", - "\n", - "def split_last_n_by_series_id(df, n):\n", - " \"\"\"Group df by series identifiers and split on last n rows for each group.\"\"\"\n", - " df_grouped = df.sort_values(time_column_name).groupby( # Sort by ascending time\n", - " time_series_id_column_names, group_keys=False\n", - " )\n", - " df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n", - " df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n", - " return df_head, df_tail\n", - "\n", - "\n", - "train, test = split_last_n_by_series_id(data_subset, n_test_periods)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Upload data to datastore\n", - "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace), is paired with the storage account, which contains the default data store. We will use it to upload the train and test data and create [tabular datasets](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training and testing. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.data.dataset_factory import TabularDatasetFactory\n", - "\n", - "datastore = ws.get_default_datastore()\n", - "train_dataset = TabularDatasetFactory.register_pandas_dataframe(\n", - " train, target=(datastore, \"dataset/\"), name=\"dominicks_OJ_train\"\n", - ")\n", - "test_dataset = TabularDatasetFactory.register_pandas_dataframe(\n", - " test, target=(datastore, \"dataset/\"), name=\"dominicks_OJ_test\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create dataset for training" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset.to_pandas_dataframe().tail()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modeling\n", - "\n", - "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n", - "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n", - "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n", - "* Create features based on time series identifiers to enable fixed effects across different series\n", - "* Create time-based features to assist in learning seasonal patterns\n", - "* Encode categorical variables to numeric quantities\n", - "\n", - "In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please see the many-models notebook.\n", - "\n", - "You are almost ready to start an AutoML training job. First, we need to separate the target column from the rest of the DataFrame: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = \"Quantity\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Customization\n", - "\n", - "The featurization customization in forecasting is an advanced feature in AutoML which allows our customers to change the default forecasting featurization behaviors and column types through `FeaturizationConfig`. The supported scenarios include:\n", - "\n", - "1. Column purposes update: Override feature type for the specified column. Currently supports DateTime, Categorical and Numeric. This customization can be used in the scenario that the type of the column cannot correctly reflect its purpose. Some numerical columns, for instance, can be treated as Categorical columns which need to be converted to categorical while some can be treated as epoch timestamp which need to be converted to datetime. To tell our SDK to correctly preprocess these columns, a configuration need to be add with the columns and their desired types.\n", - "2. Transformer parameters update: Currently supports parameter change for Imputer only. User can customize imputation methods. The supported imputing methods for target column are constant and ffill (forward fill). The supported imputing methods for feature columns are mean, median, most frequent, constant and ffill (forward fill). This customization can be used for the scenario that our customers know which imputation methods fit best to the input data. For instance, some datasets use NaN to represent 0 which the correct behavior should impute all the missing value with 0. To achieve this behavior, these columns need to be configured as constant imputation with `fill_value` 0.\n", - "3. Drop columns: Columns to drop from being featurized. These usually are the columns which are leaky or the columns contain no useful data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-featurizationconfig-remarks" - ] - }, - "outputs": [], - "source": [ - "featurization_config = FeaturizationConfig()\n", - "# Force the CPWVOL5 feature to be numeric type.\n", - "featurization_config.add_column_purpose(\"CPWVOL5\", \"Numeric\")\n", - "# Fill missing values in the target column, Quantity, with zeros.\n", - "featurization_config.add_transformer_params(\n", - " \"Imputer\", [\"Quantity\"], {\"strategy\": \"constant\", \"fill_value\": 0}\n", - ")\n", - "# Fill missing values in the INCOME column with median value.\n", - "featurization_config.add_transformer_params(\n", - " \"Imputer\", [\"INCOME\"], {\"strategy\": \"median\"}\n", - ")\n", - "# Fill missing values in the Price column with forward fill (last value carried forward).\n", - "featurization_config.add_transformer_params(\"Imputer\", [\"Price\"], {\"strategy\": \"ffill\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Forecasting Parameters\n", - "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n", - "\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**time_column_name**|The name of your time column.|\n", - "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n", - "|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|\n", - "|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "\n", - "The [AutoMLConfig](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py) object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, the training data, and cross-validation parameters.\n", - "\n", - "For forecasting tasks, there are some additional parameters that can be set in the `ForecastingParameters` class: the name of the column holding the date/time, the timeseries id column names, and the maximum forecast horizon. A time column is required for forecasting, while the time_series_id is optional. If time_series_id columns are not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n", - "\n", - "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up to 20 weeks beyond the latest date in the training data for each series. In this example, we set the forecast horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning application that estimates the next month of sales should set the horizon according to suitable planning time-scales. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n", - "\n", - "We note here that AutoML can sweep over two types of time-series models:\n", - "* Models that are trained for each series such as ARIMA and Facebook's Prophet.\n", - "* Models trained across multiple time-series using a regression approach.\n", - "\n", - "In the first case, AutoML loops over all time-series in your dataset and trains one model (e.g. AutoArima or Prophet, as the case may be) for each series. This can result in long runtimes to train these models if there are a lot of series in the data. One way to mitigate this problem is to fit models for different series in parallel if you have multiple compute cores available. To enable this behavior, set the `max_cores_per_iteration` parameter in your AutoMLConfig as shown in the example in the next cell. \n", - "\n", - "\n", - "Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *validation_data* parameter of AutoMLConfig.\n", - "\n", - "Here is a summary of AutoMLConfig parameters used for training the OJ model:\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|forecasting|\n", - "|**primary_metric**|This is the metric that you want to optimize.
Forecasting supports the following primary metrics
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error\n", - "|**experiment_timeout_hours**|Experimentation timeout in hours.|\n", - "|**enable_early_stopping**|If early stopping is on, training will stop when the primary metric is no longer improving.|\n", - "|**training_data**|Input dataset, containing both features and label column.|\n", - "|**label_column_name**|The name of the label column.|\n", - "|**compute_target**|The remote compute for training.|\n", - "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection|\n", - "|**enable_voting_ensemble**|Allow AutoML to create a Voting ensemble of the best performing models|\n", - "|**enable_stack_ensemble**|Allow AutoML to create a Stack ensemble of the best performing models|\n", - "|**debug_log**|Log file path for writing debugging information|\n", - "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*.|\n", - "|**max_cores_per_iteration**|Maximum number of cores to utilize per iteration. A value of -1 indicates all available cores should be used" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n", - "\n", - "forecasting_parameters = ForecastingParameters(\n", - " time_column_name=time_column_name,\n", - " forecast_horizon=n_test_periods,\n", - " time_series_id_column_names=time_series_id_column_names,\n", - " freq=\"W-THU\", # Set the forecast frequency to be weekly (start on each Thursday)\n", - ")\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"forecasting\",\n", - " debug_log=\"automl_oj_sales_errors.log\",\n", - " primary_metric=\"normalized_mean_absolute_error\",\n", - " experiment_timeout_hours=0.25,\n", - " training_data=train_dataset,\n", - " label_column_name=target_column_name,\n", - " compute_target=compute_target,\n", - " enable_early_stopping=True,\n", - " featurization=featurization_config,\n", - " n_cross_validations=3,\n", - " verbosity=logging.INFO,\n", - " max_cores_per_iteration=-1,\n", - " forecasting_parameters=forecasting_parameters,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can now submit a new training run. Depending on the data and number of iterations this operation may take several minutes.\n", - "Information from each iteration will be printed to the console. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve the Best Run details\n", - "Below we retrieve the best Run object from among all the runs in the experiment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = remote_run.get_best_child()\n", - "model_name = best_run.properties[\"model_name\"]\n", - "best_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Transparency\n", - "\n", - "View updated featurization summary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download the featurization summary JSON file locally\n", - "best_run.download_file(\n", - " \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n", - ")\n", - "\n", - "# Render the JSON as a pandas DataFrame\n", - "with open(\"featurization_summary.json\", \"r\") as f:\n", - " records = json.load(f)\n", - "fs = pd.DataFrame.from_records(records)\n", - "\n", - "# View a summary of the featurization\n", - "fs[\n", - " [\n", - " \"RawFeatureName\",\n", - " \"TypeDetected\",\n", - " \"Dropped\",\n", - " \"EngineeredFeatureCount\",\n", - " \"Transformations\",\n", - " ]\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Forecast\n", - "\n", - "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n", - "\n", - "The inference will run on a remote compute. In this example, it will re-use the training compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_experiment = Experiment(ws, experiment_name + \"_inference\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieving forecasts from the model\n", - "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from run_forecast import run_remote_inference\n", - "\n", - "remote_run_infer = run_remote_inference(\n", - " test_experiment=test_experiment,\n", - " compute_target=compute_target,\n", - " train_run=best_run,\n", - " test_dataset=test_dataset,\n", - " target_column_name=target_column_name,\n", - ")\n", - "remote_run_infer.wait_for_completion(show_output=False)\n", - "\n", - "# download the forecast file to the local machine\n", - "remote_run_infer.download_file(\"outputs/predictions.csv\", \"predictions.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Evaluate\n", - "\n", - "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n", - "\n", - "We'll add predictions and actuals into a single dataframe for convenience in calculating the metrics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# load forecast data frame\n", - "fcst_df = pd.read_csv(\"predictions.csv\", parse_dates=[time_column_name])\n", - "fcst_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.shared import constants\n", - "from azureml.automl.runtime.shared.score import scoring\n", - "from matplotlib import pyplot as plt\n", - "\n", - "# use automl scoring module\n", - "scores = scoring.score_regression(\n", - " y_test=fcst_df[target_column_name],\n", - " y_pred=fcst_df[\"predicted\"],\n", - " metrics=list(constants.Metric.SCALAR_REGRESSION_SET),\n", - ")\n", - "\n", - "print(\"[Test data scores]\\n\")\n", - "for key, value in scores.items():\n", - " print(\"{}: {:.3f}\".format(key, value))\n", - "\n", - "# Plot outputs\n", - "%matplotlib inline\n", - "test_pred = plt.scatter(fcst_df[target_column_name], fcst_df[\"predicted\"], color=\"b\")\n", - "test_test = plt.scatter(\n", - " fcst_df[target_column_name], fcst_df[target_column_name], color=\"g\"\n", - ")\n", - "plt.legend(\n", - " (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Operationalize" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_Operationalization_ means getting the model into the cloud so that other can run it after you close the notebook. We will create a docker running on Azure Container Instances with the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "description = \"AutoML OJ forecaster\"\n", - "tags = None\n", - "model = remote_run.register_model(\n", - " model_name=model_name, description=description, tags=tags\n", - ")\n", - "\n", - "print(remote_run.model_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Develop the scoring script\n", - "\n", - "For the deployment we need a function which will run the forecast on serialized data. It can be obtained from the best_run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "script_file_name = \"score_fcast.py\"\n", - "best_run.download_file(\"outputs/scoring_file_v_1_0_0.py\", script_file_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the model as a Web Service on Azure Container Instance" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.webservice import AciWebservice\n", - "from azureml.core.webservice import Webservice\n", - "from azureml.core.model import Model\n", - "\n", - "inference_config = InferenceConfig(\n", - " environment=best_run.get_environment(), entry_script=script_file_name\n", - ")\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(\n", - " cpu_cores=2,\n", - " memory_gb=4,\n", - " tags={\"type\": \"automl-forecasting\"},\n", - " description=\"Automl forecasting sample service\",\n", - ")\n", - "\n", - "aci_service_name = \"automl-oj-forecast-01\"\n", - "print(aci_service_name)\n", - "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", - "aci_service.wait_for_deployment(True)\n", - "print(aci_service.state)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aci_service.get_logs()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Call the service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "X_query = test.copy()\n", - "X_query.pop(target_column_name)\n", - "# We have to convert datetime to string, because Timestamps cannot be serialized to JSON.\n", - "X_query[time_column_name] = X_query[time_column_name].astype(str)\n", - "# The Service object accept the complex dictionary, which is internally converted to JSON string.\n", - "# The section 'data' contains the data frame in the form of dictionary.\n", - "sample_quantiles = [0.025, 0.975]\n", - "test_sample = json.dumps(\n", - " {\"data\": X_query.to_dict(orient=\"records\"), \"quantiles\": sample_quantiles}\n", - ")\n", - "response = aci_service.run(input_data=test_sample)\n", - "# translate from networkese to datascientese\n", - "try:\n", - " res_dict = json.loads(response)\n", - " y_fcst_all = pd.DataFrame(res_dict[\"index\"])\n", - " y_fcst_all[time_column_name] = pd.to_datetime(\n", - " y_fcst_all[time_column_name], unit=\"ms\"\n", - " )\n", - " y_fcst_all[\"forecast\"] = res_dict[\"forecast\"]\n", - " y_fcst_all[\"prediction_interval\"] = res_dict[\"prediction_interval\"]\n", - "except:\n", - " print(res_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_fcst_all.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Delete the web service if desired" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "serv = Webservice(ws, \"automl-oj-forecast-01\")\n", - "serv.delete() # don't do it accidentally" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jialiu" - } - ], - "category": "tutorial", - "celltoolbar": "Raw Cell Format", - "compute": [ - "Remote" - ], - "datasets": [ - "Orange Juice Sales" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML AutoML" - ], - "friendly_name": "Forecasting orange juice sales with deployment", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "tags": [ - "None" - ], - "task": "Forecasting" - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml deleted file mode 100644 index a6cc3e71a..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-orange-juice-sales -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/dominicks_OJ.csv b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/dominicks_OJ.csv deleted file mode 100644 index 74918cd54..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/dominicks_OJ.csv +++ /dev/null @@ -1,28948 +0,0 @@ -WeekStarting,Store,Brand,Quantity,logQuantity,Advert,Price,Age60,COLLEGE,INCOME,Hincome150,Large HH,Minorities,WorkingWoman,SSTRDIST,SSTRVOL,CPDIST5,CPWVOL5 -6/14/1990,2,dominicks,10560,9.264828557,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/14/1990,2,minute.maid,4480,8.407378325,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/14/1990,2,tropicana,8256,9.018695488,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/14/1990,5,dominicks,1792,7.491087594,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/14/1990,5,minute.maid,4224,8.348537825,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/14/1990,5,tropicana,5888,8.68067166,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/14/1990,8,dominicks,14336,9.570529135,1,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/14/1990,8,minute.maid,6080,8.712759975,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/14/1990,8,tropicana,8896,9.093357017,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/14/1990,9,dominicks,8576,9.056722883,1,1.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/14/1990,9,minute.maid,4672,8.449342525,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/14/1990,9,tropicana,8000,8.987196821,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/14/1990,14,dominicks,7232,8.886270902,1,1.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/14/1990,14,minute.maid,2496,7.82244473,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/14/1990,14,tropicana,6400,8.764053269,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/14/1990,18,dominicks,5056,8.528330936,1,1.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/14/1990,18,minute.maid,4928,8.502688505,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/14/1990,18,tropicana,7232,8.886270902,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/14/1990,21,dominicks,8256,9.018695488,1,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/14/1990,21,minute.maid,3136,8.050703382,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/14/1990,21,tropicana,2496,7.82244473,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/14/1990,28,dominicks,4288,8.363575703,1,1.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/14/1990,28,minute.maid,4160,8.333270353,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/14/1990,28,tropicana,5824,8.66974259,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/14/1990,32,dominicks,6400,8.764053269,1,1.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/14/1990,32,minute.maid,3264,8.090708716,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/14/1990,32,tropicana,10112,9.221478116,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/14/1990,33,dominicks,1920,7.560080465,1,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/14/1990,33,minute.maid,3776,8.236420527,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/14/1990,33,tropicana,8960,9.100525506,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/14/1990,40,dominicks,8576,9.056722883,1,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/14/1990,40,minute.maid,3072,8.030084094,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/14/1990,40,tropicana,6272,8.743850562,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/14/1990,44,dominicks,2432,7.796469243,1,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/14/1990,44,minute.maid,4224,8.348537825,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/14/1990,44,tropicana,6528,8.783855897,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/14/1990,45,dominicks,3520,8.166216269,1,1.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/14/1990,45,minute.maid,3776,8.236420527,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/14/1990,45,tropicana,6464,8.7740036,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/14/1990,47,dominicks,6976,8.850230966,1,1.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/14/1990,47,minute.maid,3584,8.184234774,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/14/1990,47,tropicana,3520,8.166216269,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/14/1990,48,dominicks,8960,9.100525506,1,1.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/14/1990,48,minute.maid,5056,8.528330936,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/14/1990,48,tropicana,4992,8.51559191,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/14/1990,49,dominicks,10304,9.240287448,1,1.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/14/1990,49,minute.maid,1984,7.592870288,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/14/1990,49,tropicana,3712,8.219326094,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/14/1990,50,dominicks,5696,8.647519453,1,1.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/14/1990,50,minute.maid,2560,7.847762538,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/14/1990,50,tropicana,4352,8.378390789,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/14/1990,51,dominicks,7360,8.903815212,1,1.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/14/1990,51,minute.maid,3840,8.253227646,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/14/1990,51,tropicana,3840,8.253227646,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/14/1990,52,dominicks,4864,8.489616424,1,1.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/14/1990,52,minute.maid,5120,8.540909718,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/14/1990,52,tropicana,15552,9.651944527,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/14/1990,53,dominicks,9280,9.135616826,1,1.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/14/1990,53,minute.maid,7168,8.877381955,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/14/1990,53,tropicana,16768,9.727227587,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/14/1990,54,dominicks,1984,7.592870288,1,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/14/1990,54,minute.maid,4032,8.30201781,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/14/1990,54,tropicana,7552,8.929567708,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/14/1990,56,dominicks,5248,8.565602331,1,1.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/14/1990,56,minute.maid,1920,7.560080465,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/14/1990,56,tropicana,6144,8.723231275,0,3.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/14/1990,59,dominicks,2240,7.714231145,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/14/1990,59,minute.maid,2112,7.655390645,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/14/1990,59,tropicana,4864,8.489616424,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/14/1990,62,dominicks,3136,8.050703382,1,1.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/14/1990,62,minute.maid,4672,8.449342525,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/14/1990,62,tropicana,11776,9.373818841,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/14/1990,64,dominicks,8960,9.100525506,1,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/14/1990,64,minute.maid,3520,8.166216269,0,2.62,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/14/1990,64,tropicana,3008,8.009030685,0,3.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/14/1990,67,dominicks,4608,8.435549202,1,1.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/14/1990,67,minute.maid,3200,8.070906089,0,2.62,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/14/1990,67,tropicana,5312,8.577723691,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/14/1990,68,dominicks,7168,8.877381955,1,1.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/14/1990,68,minute.maid,3712,8.219326094,0,3.17,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/14/1990,68,tropicana,7872,8.971067439,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/14/1990,70,dominicks,5376,8.589699882,1,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/14/1990,70,minute.maid,5184,8.553332238,0,2.62,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/14/1990,70,tropicana,8000,8.987196821,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/14/1990,71,dominicks,17856,9.790094865,1,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/14/1990,71,minute.maid,1984,7.592870288,0,3.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/14/1990,71,tropicana,5888,8.68067166,0,3.87,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/14/1990,72,dominicks,9216,9.128696383,1,1.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/14/1990,72,minute.maid,4096,8.317766167,0,3.17,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/14/1990,72,tropicana,11264,9.329367078,0,3.87,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/14/1990,73,dominicks,21312,9.967025573,1,1.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/14/1990,73,minute.maid,6976,8.850230966,0,2.62,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/14/1990,73,tropicana,12992,9.472089062,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/14/1990,75,dominicks,9600,9.169518378,1,1.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/14/1990,75,minute.maid,4096,8.317766167,0,3.17,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/14/1990,75,tropicana,7872,8.971067439,0,3.87,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/14/1990,76,dominicks,13120,9.481893063,1,1.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/14/1990,76,minute.maid,5312,8.577723691,0,2.62,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/14/1990,76,tropicana,8128,9.00307017,0,3.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/14/1990,77,dominicks,8192,9.010913347,1,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/14/1990,77,minute.maid,6272,8.743850562,0,2.62,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/14/1990,77,tropicana,9728,9.182763604,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/14/1990,78,dominicks,7040,8.859363449,1,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/14/1990,78,minute.maid,4800,8.476371197,0,2.62,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/14/1990,78,tropicana,5632,8.636219898,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/14/1990,80,dominicks,16000,9.680344001,1,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/14/1990,80,minute.maid,9984,9.208739091,0,2.62,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/14/1990,80,tropicana,21376,9.970024076,0,3.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/14/1990,81,dominicks,4864,8.489616424,1,1.59,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/14/1990,81,minute.maid,4992,8.51559191,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/14/1990,81,tropicana,6848,8.831711918,0,3.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/14/1990,83,dominicks,22720,10.03100087,1,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/14/1990,83,minute.maid,4416,8.392989588,0,2.62,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/14/1990,83,tropicana,6016,8.702177866,0,3.19,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/14/1990,84,dominicks,1984,7.592870288,1,1.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/14/1990,84,minute.maid,3840,8.253227646,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/14/1990,84,tropicana,5824,8.66974259,0,3.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/14/1990,86,dominicks,14784,9.601300794,1,1.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/14/1990,86,minute.maid,3712,8.219326094,0,2.62,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/14/1990,86,tropicana,7552,8.929567708,0,3.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/14/1990,88,dominicks,5440,8.60153434,1,1.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/14/1990,88,minute.maid,4096,8.317766167,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/14/1990,88,tropicana,4992,8.51559191,0,3.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/14/1990,89,dominicks,10496,9.258749511,1,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/14/1990,89,minute.maid,2752,7.920083199,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/14/1990,89,tropicana,4608,8.435549202,0,3.66,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/14/1990,90,dominicks,10112,9.221478116,1,1.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/14/1990,90,minute.maid,3392,8.129174997,0,2.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/14/1990,90,tropicana,2688,7.896552702,0,3.66,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/14/1990,91,dominicks,3648,8.201934351,1,1.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/14/1990,91,minute.maid,3328,8.110126802,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/14/1990,91,tropicana,4736,8.462948177,0,3.66,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/14/1990,92,dominicks,3840,8.253227646,1,1.59,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/14/1990,92,minute.maid,4160,8.333270353,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/14/1990,92,tropicana,5312,8.577723691,0,3.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/14/1990,93,dominicks,5504,8.61323038,1,1.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/14/1990,93,minute.maid,3584,8.184234774,0,3.17,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/14/1990,93,tropicana,11264,9.329367078,0,3.87,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/14/1990,94,dominicks,17536,9.77201119,1,1.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/14/1990,94,minute.maid,4864,8.489616424,0,2.62,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/14/1990,94,tropicana,5248,8.565602331,0,3.19,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/14/1990,97,dominicks,5632,8.636219898,1,1.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/14/1990,97,minute.maid,2496,7.82244473,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/14/1990,97,tropicana,2688,7.896552702,0,3.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/14/1990,101,dominicks,15360,9.639522007,1,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/14/1990,101,minute.maid,4800,8.476371197,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/14/1990,101,tropicana,7680,8.946374826,0,3.66,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/14/1990,103,dominicks,11264,9.329367078,1,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 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-6/14/1990,109,minute.maid,7168,8.877381955,0,3.17,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/14/1990,109,tropicana,21888,9.99369382,0,3.87,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/14/1990,110,dominicks,2752,7.920083199,1,1.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/14/1990,110,minute.maid,4672,8.449342525,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/14/1990,110,tropicana,4800,8.476371197,0,3.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 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-6/14/1990,114,minute.maid,7616,8.938006577,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/14/1990,114,tropicana,11136,9.317938383,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/14/1990,115,dominicks,2944,7.98752448,1,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/14/1990,115,minute.maid,5248,8.565602331,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/14/1990,115,tropicana,8000,8.987196821,0,3.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 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-6/14/1990,117,tropicana,3904,8.269756948,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/14/1990,118,dominicks,21760,9.987828701,1,1.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/14/1990,118,minute.maid,3776,8.236420527,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/14/1990,118,tropicana,6336,8.754002934,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/14/1990,119,dominicks,7424,8.912473275,1,1.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/14/1990,119,minute.maid,6400,8.764053269,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/14/1990,119,tropicana,9920,9.2023082,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/14/1990,121,dominicks,3072,8.030084094,1,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/14/1990,121,minute.maid,5248,8.565602331,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/14/1990,121,tropicana,12224,9.411156511,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/14/1990,122,dominicks,9088,9.114710141,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/14/1990,122,minute.maid,6208,8.733594062,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/14/1990,122,tropicana,6912,8.841014311,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/14/1990,123,dominicks,6144,8.723231275,1,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/14/1990,123,minute.maid,5312,8.577723691,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/14/1990,123,tropicana,4416,8.392989588,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/14/1990,124,dominicks,2048,7.624618986,1,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/14/1990,124,minute.maid,3904,8.269756948,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/14/1990,124,tropicana,6976,8.850230966,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/14/1990,126,dominicks,1664,7.416979621,1,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/14/1990,126,minute.maid,4352,8.378390789,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/14/1990,126,tropicana,4800,8.476371197,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/14/1990,128,dominicks,9536,9.162829389,1,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/14/1990,128,minute.maid,5696,8.647519453,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/14/1990,128,tropicana,8704,9.071537969,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/14/1990,129,dominicks,1664,7.416979621,1,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/14/1990,129,minute.maid,2944,7.98752448,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/14/1990,129,tropicana,4800,8.476371197,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/14/1990,132,dominicks,9600,9.169518378,1,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/14/1990,132,minute.maid,4032,8.30201781,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/14/1990,132,tropicana,4800,8.476371197,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/21/1990,8,dominicks,6400,8.764053269,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/21/1990,8,minute.maid,51968,10.85838342,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/21/1990,8,tropicana,7296,8.895081532,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/21/1990,9,dominicks,1152,7.049254841,0,2.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/21/1990,9,minute.maid,71168,11.17279856,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/21/1990,9,tropicana,8768,9.078864009,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/21/1990,12,dominicks,1408,7.249925537,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/21/1990,12,minute.maid,69760,11.15281606,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/21/1990,12,tropicana,6784,8.822322178,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/21/1990,14,dominicks,1408,7.249925537,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/21/1990,14,minute.maid,39744,10.59021417,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/21/1990,14,tropicana,6528,8.783855897,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/21/1990,21,dominicks,2816,7.943072717,0,2.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/21/1990,21,minute.maid,29824,10.30306872,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/21/1990,21,tropicana,2624,7.87245515,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/21/1990,28,dominicks,2176,7.685243608,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/21/1990,28,minute.maid,20736,9.939626599,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/21/1990,28,tropicana,3520,8.166216269,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/21/1990,32,dominicks,2880,7.965545573,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/21/1990,32,minute.maid,61632,11.0289365,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/21/1990,32,tropicana,10432,9.252633284,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/21/1990,33,dominicks,1792,7.491087594,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/21/1990,33,minute.maid,31168,10.34714721,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/21/1990,33,tropicana,7808,8.962904128,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/21/1990,40,dominicks,7808,8.962904128,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/21/1990,40,minute.maid,32064,10.37548918,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/21/1990,40,tropicana,5440,8.60153434,0,2.65,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/21/1990,45,dominicks,2176,7.685243608,0,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/21/1990,45,minute.maid,26688,10.19196931,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/21/1990,45,tropicana,4032,8.30201781,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/21/1990,47,dominicks,2624,7.87245515,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/21/1990,47,minute.maid,30464,10.32430094,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/21/1990,47,tropicana,2944,7.98752448,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/21/1990,48,dominicks,1664,7.416979621,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/21/1990,48,minute.maid,24256,10.09641929,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/21/1990,48,tropicana,3456,8.14786713,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/21/1990,50,dominicks,1920,7.560080465,0,2.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/21/1990,50,minute.maid,20736,9.939626599,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/21/1990,50,tropicana,5120,8.540909718,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/21/1990,51,dominicks,1472,7.294377299,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/21/1990,51,minute.maid,44224,10.69702291,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/21/1990,51,tropicana,3264,8.090708716,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/21/1990,52,dominicks,1408,7.249925537,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/21/1990,52,minute.maid,39232,10.57724802,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/21/1990,52,tropicana,9856,9.195835686,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/21/1990,53,dominicks,2432,7.796469243,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/21/1990,53,minute.maid,76736,11.24812624,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/21/1990,53,tropicana,10368,9.246479419,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/21/1990,54,dominicks,1408,7.249925537,0,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/21/1990,54,minute.maid,27328,10.2156671,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/21/1990,54,tropicana,4416,8.392989588,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/21/1990,56,dominicks,1920,7.560080465,0,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/21/1990,56,minute.maid,29312,10.28575227,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/21/1990,56,tropicana,3520,8.166216269,0,3.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/21/1990,62,dominicks,448,6.104793232,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/21/1990,62,minute.maid,42944,10.66765222,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/21/1990,62,tropicana,11712,9.368369236,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/21/1990,64,dominicks,4352,8.378390789,0,2.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/21/1990,64,minute.maid,15424,9.643680017,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/21/1990,64,tropicana,2944,7.98752448,0,3.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/21/1990,67,dominicks,768,6.643789733,0,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/21/1990,67,minute.maid,28544,10.25920204,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/21/1990,67,tropicana,5504,8.61323038,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/21/1990,68,dominicks,2560,7.847762538,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/21/1990,68,minute.maid,35840,10.48681987,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/21/1990,68,tropicana,5952,8.691482577,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/21/1990,70,dominicks,9344,9.142489705,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/21/1990,70,minute.maid,24320,10.09905434,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/21/1990,70,tropicana,9152,9.121727714,0,2.64,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/21/1990,71,dominicks,1344,7.203405521,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/21/1990,71,minute.maid,55808,10.92967251,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/21/1990,71,tropicana,4224,8.348537825,0,3.87,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/21/1990,73,dominicks,8512,9.049232212,0,2.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/21/1990,73,minute.maid,58048,10.96902553,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 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-6/21/1990,109,dominicks,512,6.238324625,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/21/1990,109,minute.maid,85824,11.36005397,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/21/1990,109,tropicana,12800,9.45720045,0,3.87,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/21/1990,110,dominicks,2176,7.685243608,0,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/21/1990,110,minute.maid,31936,10.37148918,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 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-6/21/1990,112,minute.maid,57920,10.96681803,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/21/1990,112,tropicana,15616,9.656051309,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/21/1990,113,dominicks,1984,7.592870288,0,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/21/1990,113,minute.maid,51584,10.85096683,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/21/1990,113,tropicana,8000,8.987196821,0,3.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/21/1990,114,dominicks,5440,8.60153434,0,2.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/21/1990,114,minute.maid,39424,10.58213005,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/21/1990,114,tropicana,6272,8.743850562,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/21/1990,115,dominicks,1344,7.203405521,0,2.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/21/1990,115,minute.maid,44480,10.70279493,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/21/1990,115,tropicana,4736,8.462948177,0,3.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/21/1990,116,dominicks,2944,7.98752448,0,2.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/21/1990,116,minute.maid,26624,10.18956834,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/21/1990,116,tropicana,4608,8.435549202,0,3.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/21/1990,117,dominicks,1728,7.454719949,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/21/1990,117,minute.maid,26752,10.19436452,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/21/1990,117,tropicana,3136,8.050703382,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/21/1990,118,dominicks,1792,7.491087594,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/21/1990,118,minute.maid,66240,11.10103979,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/21/1990,118,tropicana,5632,8.636219898,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/21/1990,119,dominicks,2368,7.769800996,0,2.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/21/1990,119,minute.maid,33152,10.40885833,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/21/1990,119,tropicana,6272,8.743850562,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/21/1990,121,dominicks,2560,7.847762538,0,2.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/21/1990,121,minute.maid,35072,10.46515837,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/21/1990,121,tropicana,10432,9.252633284,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/21/1990,122,dominicks,3904,8.269756948,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/21/1990,122,minute.maid,47552,10.76957913,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/21/1990,122,tropicana,6592,8.793612072,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/21/1990,123,dominicks,2304,7.742402022,0,2.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/21/1990,123,minute.maid,71040,11.17099838,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/21/1990,123,tropicana,5056,8.528330936,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/21/1990,124,dominicks,1600,7.377758908,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/21/1990,124,minute.maid,60160,11.00476296,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/21/1990,124,tropicana,5824,8.66974259,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/21/1990,126,dominicks,896,6.797940413,0,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/21/1990,126,minute.maid,34048,10.43552657,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/21/1990,126,tropicana,3264,8.090708716,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/21/1990,128,dominicks,2432,7.796469243,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/21/1990,128,minute.maid,90688,11.41518032,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/21/1990,128,tropicana,8960,9.100525506,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/21/1990,130,dominicks,5568,8.624791202,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/21/1990,130,minute.maid,62784,11.04745554,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/21/1990,130,tropicana,3200,8.070906089,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/21/1990,132,dominicks,3968,8.286017468,0,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/21/1990,132,minute.maid,48896,10.79745087,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/21/1990,132,tropicana,4800,8.476371197,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/28/1990,5,dominicks,2496,7.82244473,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/28/1990,5,minute.maid,4352,8.378390789,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/28/1990,5,tropicana,6976,8.850230966,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/28/1990,8,dominicks,3968,8.286017468,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/28/1990,8,minute.maid,4928,8.502688505,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/28/1990,8,tropicana,10368,9.246479419,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/28/1990,9,dominicks,896,6.797940413,0,2.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/28/1990,9,minute.maid,3392,8.129174997,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 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-6/28/1990,14,minute.maid,2496,7.82244473,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/28/1990,14,tropicana,10880,9.29468152,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/28/1990,28,dominicks,2368,7.769800996,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/28/1990,28,minute.maid,3328,8.110126802,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/28/1990,28,tropicana,4928,8.502688505,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/28/1990,32,dominicks,2176,7.685243608,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/28/1990,32,minute.maid,3200,8.070906089,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/28/1990,32,tropicana,12288,9.416378455,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/28/1990,33,dominicks,3328,8.110126802,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/28/1990,33,minute.maid,4608,8.435549202,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/28/1990,33,tropicana,11968,9.3899917,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/28/1990,40,dominicks,6336,8.754002934,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/28/1990,40,minute.maid,4928,8.502688505,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/28/1990,40,tropicana,6720,8.812843434,0,2.68,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/28/1990,45,dominicks,1664,7.416979621,0,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/28/1990,45,minute.maid,3776,8.236420527,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/28/1990,45,tropicana,7040,8.859363449,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/28/1990,47,dominicks,3520,8.166216269,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/28/1990,47,minute.maid,3456,8.14786713,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/28/1990,47,tropicana,4032,8.30201781,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/28/1990,48,dominicks,1728,7.454719949,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/28/1990,48,minute.maid,4608,8.435549202,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/28/1990,48,tropicana,5760,8.658692754,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/28/1990,50,dominicks,1984,7.592870288,0,2.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/28/1990,50,minute.maid,2112,7.655390645,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/28/1990,50,tropicana,6464,8.7740036,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/28/1990,51,dominicks,832,6.723832441,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/28/1990,51,minute.maid,3200,8.070906089,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/28/1990,51,tropicana,4992,8.51559191,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/28/1990,52,dominicks,1088,6.992096427,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/28/1990,52,minute.maid,5632,8.636219898,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/28/1990,52,tropicana,12736,9.452187908,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/28/1990,53,dominicks,2560,7.847762538,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/28/1990,53,minute.maid,3200,8.070906089,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/28/1990,53,tropicana,14784,9.601300794,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/28/1990,54,dominicks,3200,8.070906089,0,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/28/1990,54,minute.maid,4352,8.378390789,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/28/1990,54,tropicana,5696,8.647519453,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/28/1990,56,dominicks,3072,8.030084094,0,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/28/1990,56,minute.maid,4480,8.407378325,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/28/1990,56,tropicana,5952,8.691482577,0,3.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/28/1990,62,dominicks,640,6.461468176,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/28/1990,62,minute.maid,6336,8.754002934,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/28/1990,62,tropicana,14336,9.570529135,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/28/1990,64,dominicks,2624,7.87245515,0,2.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/28/1990,64,minute.maid,4352,8.378390789,0,2.62,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/28/1990,64,tropicana,4736,8.462948177,0,3.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/28/1990,67,dominicks,704,6.556778356,0,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/28/1990,67,minute.maid,3648,8.201934351,0,2.62,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/28/1990,67,tropicana,6336,8.754002934,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/28/1990,68,dominicks,2368,7.769800996,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/28/1990,68,minute.maid,3584,8.184234774,0,3.17,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/28/1990,68,tropicana,9408,9.14931567,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/28/1990,70,dominicks,4608,8.435549202,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/28/1990,70,minute.maid,6080,8.712759975,0,2.62,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 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-6/28/1990,73,minute.maid,7488,8.921057018,0,2.62,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/28/1990,73,tropicana,11776,9.373818841,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/28/1990,74,dominicks,1856,7.526178913,0,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/28/1990,74,minute.maid,5760,8.658692754,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/28/1990,74,tropicana,7616,8.938006577,0,3.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 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-6/28/1990,117,minute.maid,2048,7.624618986,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/28/1990,117,tropicana,3136,8.050703382,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/28/1990,118,dominicks,1408,7.249925537,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/28/1990,118,minute.maid,4928,8.502688505,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/28/1990,118,tropicana,7488,8.921057018,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 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-6/28/1990,123,minute.maid,4480,8.407378325,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/28/1990,123,tropicana,7296,8.895081532,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/28/1990,124,dominicks,4032,8.30201781,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/28/1990,124,minute.maid,5056,8.528330936,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/28/1990,124,tropicana,8256,9.018695488,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 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-6/28/1990,130,minute.maid,3264,8.090708716,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/28/1990,130,tropicana,5824,8.66974259,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/28/1990,131,dominicks,3904,8.269756948,0,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/28/1990,131,minute.maid,5888,8.68067166,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/28/1990,131,tropicana,6400,8.764053269,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/28/1990,132,dominicks,5824,8.66974259,0,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/28/1990,132,minute.maid,5248,8.565602331,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/28/1990,132,tropicana,7680,8.946374826,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/5/1990,5,dominicks,2944,7.98752448,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/5/1990,5,minute.maid,4928,8.502688505,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 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-7/5/1990,9,minute.maid,4864,8.489616424,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/5/1990,9,tropicana,9920,9.2023082,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/5/1990,12,dominicks,5376,8.589699882,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/5/1990,12,minute.maid,4736,8.462948177,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/5/1990,12,tropicana,7616,8.938006577,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/5/1990,14,dominicks,3264,8.090708716,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/5/1990,14,minute.maid,2560,7.847762538,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/5/1990,14,tropicana,8128,9.00307017,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/5/1990,18,dominicks,5312,8.577723691,0,2.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/5/1990,18,minute.maid,9536,9.162829389,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/5/1990,18,tropicana,9920,9.2023082,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/5/1990,21,dominicks,3776,8.236420527,0,2.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/5/1990,21,minute.maid,3776,8.236420527,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/5/1990,21,tropicana,3328,8.110126802,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/5/1990,28,dominicks,2816,7.943072717,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/5/1990,28,minute.maid,3776,8.236420527,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/5/1990,28,tropicana,3840,8.253227646,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/5/1990,32,dominicks,8320,9.026417534,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/5/1990,32,minute.maid,5504,8.61323038,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/5/1990,32,tropicana,9344,9.142489705,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/5/1990,33,dominicks,3328,8.110126802,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/5/1990,33,minute.maid,4352,8.378390789,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/5/1990,33,tropicana,8320,9.026417534,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/5/1990,40,dominicks,12736,9.452187908,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/5/1990,40,minute.maid,8896,9.093357017,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/5/1990,40,tropicana,6656,8.803273983,0,2.67,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/5/1990,44,dominicks,2112,7.655390645,0,2.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/5/1990,44,minute.maid,5696,8.647519453,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/5/1990,44,tropicana,6016,8.702177866,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/5/1990,45,dominicks,1856,7.526178913,0,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/5/1990,45,minute.maid,3008,8.009030685,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/5/1990,45,tropicana,5504,8.61323038,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/5/1990,47,dominicks,5184,8.553332238,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/5/1990,47,minute.maid,3200,8.070906089,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/5/1990,47,tropicana,3008,8.009030685,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/5/1990,48,dominicks,3264,8.090708716,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/5/1990,48,minute.maid,6464,8.7740036,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/5/1990,48,tropicana,4608,8.435549202,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/5/1990,49,dominicks,2432,7.796469243,0,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/5/1990,49,minute.maid,4096,8.317766167,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/5/1990,49,tropicana,1728,7.454719949,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/5/1990,50,dominicks,3456,8.14786713,0,2.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/5/1990,50,minute.maid,2240,7.714231145,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/5/1990,50,tropicana,2624,7.87245515,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/5/1990,51,dominicks,4672,8.449342525,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/5/1990,51,minute.maid,4288,8.363575703,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/5/1990,51,tropicana,4928,8.502688505,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/5/1990,52,dominicks,2752,7.920083199,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/5/1990,52,minute.maid,5440,8.60153434,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/5/1990,52,tropicana,11776,9.373818841,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/5/1990,53,dominicks,2944,7.98752448,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/5/1990,53,minute.maid,2944,7.98752448,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/5/1990,53,tropicana,12864,9.462187991,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/5/1990,54,dominicks,1728,7.454719949,0,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/5/1990,54,minute.maid,3520,8.166216269,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/5/1990,54,tropicana,4032,8.30201781,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/5/1990,56,dominicks,2816,7.943072717,0,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/5/1990,56,minute.maid,3456,8.14786713,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/5/1990,56,tropicana,5312,8.577723691,0,3.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/5/1990,59,dominicks,4608,8.435549202,0,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/5/1990,59,minute.maid,3072,8.030084094,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/5/1990,59,tropicana,2944,7.98752448,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/5/1990,62,dominicks,1024,6.931471806,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/5/1990,62,minute.maid,4864,8.489616424,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/5/1990,62,tropicana,12096,9.400630098,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/5/1990,64,dominicks,3520,8.166216269,0,2.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/5/1990,64,minute.maid,4544,8.42156296,0,2.62,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/5/1990,64,tropicana,2880,7.965545573,0,3.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/5/1990,67,dominicks,1664,7.416979621,0,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/5/1990,67,minute.maid,3136,8.050703382,0,2.62,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/5/1990,67,tropicana,4416,8.392989588,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/5/1990,68,dominicks,2752,7.920083199,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/5/1990,68,minute.maid,3136,8.050703382,0,3.17,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/5/1990,68,tropicana,6528,8.783855897,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/5/1990,70,dominicks,9024,9.107642974,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/5/1990,70,minute.maid,6144,8.723231275,0,2.62,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/5/1990,70,tropicana,9152,9.121727714,0,2.66,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/5/1990,71,dominicks,2880,7.965545573,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/5/1990,71,minute.maid,1920,7.560080465,0,3.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/5/1990,71,tropicana,4096,8.317766167,0,3.87,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/5/1990,72,dominicks,2432,7.796469243,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/5/1990,72,minute.maid,4096,8.317766167,0,3.17,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/5/1990,72,tropicana,9600,9.169518378,0,3.87,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/5/1990,73,dominicks,7168,8.877381955,0,2.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/5/1990,73,minute.maid,9536,9.162829389,0,2.62,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 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-7/5/1990,75,minute.maid,4480,8.407378325,0,3.17,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/5/1990,75,tropicana,5568,8.624791202,0,3.87,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/5/1990,76,dominicks,4032,8.30201781,0,2.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/5/1990,76,minute.maid,6272,8.743850562,0,2.62,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/5/1990,76,tropicana,6912,8.841014311,0,3.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 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-7/5/1990,81,minute.maid,7168,8.877381955,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/5/1990,81,tropicana,6912,8.841014311,0,3.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/5/1990,83,dominicks,4160,8.333270353,0,2.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/5/1990,83,minute.maid,5440,8.60153434,0,2.62,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/5/1990,83,tropicana,4928,8.502688505,0,3.19,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 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-7/5/1990,89,minute.maid,4416,8.392989588,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/5/1990,89,tropicana,2944,7.98752448,0,3.66,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/5/1990,90,dominicks,3136,8.050703382,0,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/5/1990,90,minute.maid,3456,8.14786713,0,2.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/5/1990,90,tropicana,2112,7.655390645,0,3.66,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 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-7/5/1990,94,minute.maid,7168,8.877381955,0,2.62,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/5/1990,94,tropicana,5696,8.647519453,0,3.19,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/5/1990,95,dominicks,1280,7.154615357,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/5/1990,95,minute.maid,2432,7.796469243,0,3.17,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/5/1990,95,tropicana,2944,7.98752448,0,3.87,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/5/1990,97,dominicks,1600,7.377758908,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/5/1990,97,minute.maid,2432,7.796469243,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/5/1990,97,tropicana,2048,7.624618986,0,3.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/5/1990,98,dominicks,6528,8.783855897,0,2.29,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/5/1990,98,minute.maid,7168,8.877381955,0,2.62,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/5/1990,98,tropicana,6208,8.733594062,0,3.19,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/5/1990,100,dominicks,3648,8.201934351,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/5/1990,100,minute.maid,4672,8.449342525,0,3.17,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/5/1990,100,tropicana,4992,8.51559191,0,3.87,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/5/1990,101,dominicks,4800,8.476371197,0,2.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/5/1990,101,minute.maid,5952,8.691482577,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/5/1990,101,tropicana,7744,8.954673629,0,3.66,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/5/1990,102,dominicks,7104,8.868413285,0,2.29,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/5/1990,102,minute.maid,7744,8.954673629,0,2.62,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/5/1990,102,tropicana,7808,8.962904128,0,3.19,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/5/1990,103,dominicks,5504,8.61323038,0,2.29,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/5/1990,103,minute.maid,2560,7.847762538,0,2.62,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/5/1990,103,tropicana,2240,7.714231145,0,3.19,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/5/1990,105,dominicks,5184,8.553332238,0,2.29,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/5/1990,105,minute.maid,6720,8.812843434,0,2.62,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/5/1990,105,tropicana,4160,8.333270353,0,3.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/5/1990,106,dominicks,4544,8.42156296,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/5/1990,106,minute.maid,2880,7.965545573,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/5/1990,106,tropicana,1408,7.249925537,0,3.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/5/1990,107,dominicks,6208,8.733594062,0,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/5/1990,107,minute.maid,6336,8.754002934,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/5/1990,107,tropicana,8704,9.071537969,0,3.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/5/1990,109,dominicks,2240,7.714231145,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/5/1990,109,minute.maid,7936,8.979164649,0,3.17,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/5/1990,109,tropicana,16256,9.69621735,0,3.87,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/5/1990,110,dominicks,3264,8.090708716,0,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/5/1990,110,minute.maid,3904,8.269756948,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/5/1990,110,tropicana,3008,8.009030685,0,3.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/5/1990,111,dominicks,4032,8.30201781,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/5/1990,111,minute.maid,3584,8.184234774,0,3.17,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/5/1990,111,tropicana,3200,8.070906089,0,3.87,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/5/1990,112,dominicks,6272,8.743850562,0,2.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/5/1990,112,minute.maid,11008,9.30637756,0,2.62,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/5/1990,112,tropicana,19072,9.85597657,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/5/1990,113,dominicks,5440,8.60153434,0,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/5/1990,113,minute.maid,4928,8.502688505,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/5/1990,113,tropicana,7872,8.971067439,0,3.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/5/1990,114,dominicks,6912,8.841014311,0,2.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/5/1990,114,minute.maid,8000,8.987196821,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/5/1990,114,tropicana,9408,9.14931567,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/5/1990,116,dominicks,3264,8.090708716,0,2.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/5/1990,116,minute.maid,3712,8.219326094,0,2.62,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/5/1990,116,tropicana,5312,8.577723691,0,3.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/5/1990,117,dominicks,2496,7.82244473,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/5/1990,117,minute.maid,3520,8.166216269,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/5/1990,117,tropicana,2816,7.943072717,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/5/1990,118,dominicks,2048,7.624618986,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/5/1990,118,minute.maid,3520,8.166216269,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/5/1990,118,tropicana,6016,8.702177866,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/5/1990,119,dominicks,6208,8.733594062,0,2.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/5/1990,119,minute.maid,5440,8.60153434,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/5/1990,119,tropicana,7552,8.929567708,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/5/1990,121,dominicks,4032,8.30201781,0,2.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/5/1990,121,minute.maid,7296,8.895081532,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/5/1990,121,tropicana,9024,9.107642974,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/5/1990,122,dominicks,5248,8.565602331,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/5/1990,122,minute.maid,8256,9.018695488,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/5/1990,122,tropicana,6656,8.803273983,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/5/1990,123,dominicks,3136,8.050703382,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/5/1990,123,minute.maid,5184,8.553332238,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/5/1990,123,tropicana,4864,8.489616424,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/5/1990,124,dominicks,3840,8.253227646,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/5/1990,124,minute.maid,5248,8.565602331,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/5/1990,124,tropicana,7296,8.895081532,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/5/1990,126,dominicks,2176,7.685243608,0,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/5/1990,126,minute.maid,4544,8.42156296,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/5/1990,126,tropicana,3584,8.184234774,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/5/1990,128,dominicks,4672,8.449342525,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/5/1990,128,minute.maid,7360,8.903815212,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/5/1990,128,tropicana,10496,9.258749511,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/5/1990,130,dominicks,3520,8.166216269,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/5/1990,130,minute.maid,4032,8.30201781,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/5/1990,130,tropicana,3136,8.050703382,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/5/1990,131,dominicks,6912,8.841014311,0,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/5/1990,131,minute.maid,6656,8.803273983,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/5/1990,131,tropicana,4928,8.502688505,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/5/1990,132,dominicks,7808,8.962904128,0,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/5/1990,132,minute.maid,5824,8.66974259,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/5/1990,132,tropicana,3904,8.269756948,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/12/1990,5,dominicks,1024,6.931471806,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/12/1990,5,minute.maid,31168,10.34714721,1,2.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/12/1990,5,tropicana,4928,8.502688505,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/12/1990,8,dominicks,3520,8.166216269,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/12/1990,8,minute.maid,39424,10.58213005,1,2.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/12/1990,8,tropicana,6464,8.7740036,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/12/1990,9,dominicks,1152,7.049254841,0,2.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/12/1990,9,minute.maid,36864,10.51499074,1,2.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/12/1990,9,tropicana,8256,9.018695488,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/12/1990,12,dominicks,1472,7.294377299,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/12/1990,12,minute.maid,46976,10.75739211,1,2.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/12/1990,12,tropicana,7040,8.859363449,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/12/1990,14,dominicks,448,6.104793232,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/12/1990,14,minute.maid,26240,10.17504024,1,2.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/12/1990,14,tropicana,5760,8.658692754,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/12/1990,18,dominicks,4224,8.348537825,0,2.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/12/1990,18,minute.maid,41920,10.64351832,1,2.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/12/1990,18,tropicana,7168,8.877381955,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/12/1990,21,dominicks,2624,7.87245515,0,2.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/12/1990,21,minute.maid,20672,9.936535407,1,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/12/1990,21,tropicana,2560,7.847762538,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/12/1990,28,dominicks,640,6.461468176,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/12/1990,28,minute.maid,17472,9.768354879,1,2.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/12/1990,28,tropicana,3840,8.253227646,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/12/1990,32,dominicks,1024,6.931471806,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/12/1990,32,minute.maid,52544,10.86940619,1,2.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/12/1990,32,tropicana,9600,9.169518378,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/12/1990,33,dominicks,1664,7.416979621,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/12/1990,33,minute.maid,23488,10.06424493,1,2.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/12/1990,33,tropicana,8576,9.056722883,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/12/1990,44,dominicks,896,6.797940413,0,2.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/12/1990,44,minute.maid,36160,10.49570882,1,2.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/12/1990,44,tropicana,6272,8.743850562,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/12/1990,45,dominicks,2112,7.655390645,0,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/12/1990,45,minute.maid,18560,9.828764006,1,2.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/12/1990,45,tropicana,5120,8.540909718,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/12/1990,47,dominicks,1664,7.416979621,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/12/1990,47,minute.maid,24896,10.12246243,1,2.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/12/1990,47,tropicana,2816,7.943072717,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/12/1990,48,dominicks,2240,7.714231145,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/12/1990,48,minute.maid,19584,9.882468185,1,2.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/12/1990,48,tropicana,3328,8.110126802,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/12/1990,49,dominicks,1536,7.336936914,0,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/12/1990,49,minute.maid,15680,9.660141294,1,2.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/12/1990,49,tropicana,2624,7.87245515,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/12/1990,50,dominicks,4800,8.476371197,0,2.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/12/1990,50,minute.maid,12224,9.411156511,1,2.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/12/1990,50,tropicana,2496,7.82244473,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/12/1990,51,dominicks,1536,7.336936914,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/12/1990,51,minute.maid,27968,10.23881628,1,2.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/12/1990,51,tropicana,4352,8.378390789,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/12/1990,52,dominicks,2432,7.796469243,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/12/1990,52,minute.maid,21376,9.970024076,1,2.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/12/1990,52,tropicana,10304,9.240287448,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/12/1990,53,dominicks,1280,7.154615357,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/12/1990,53,minute.maid,39040,10.57234204,1,2.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/12/1990,53,tropicana,8896,9.093357017,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/12/1990,54,dominicks,1088,6.992096427,0,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/12/1990,54,minute.maid,22656,10.02818,1,2.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/12/1990,54,tropicana,2944,7.98752448,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/12/1990,56,dominicks,1280,7.154615357,0,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/12/1990,56,minute.maid,31360,10.35328847,1,2.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/12/1990,56,tropicana,4160,8.333270353,0,3.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/12/1990,62,dominicks,832,6.723832441,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/12/1990,62,minute.maid,30144,10.31374118,1,2.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/12/1990,62,tropicana,11264,9.329367078,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/12/1990,64,dominicks,2368,7.769800996,0,2.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/12/1990,64,minute.maid,13184,9.486759252,1,2.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/12/1990,64,tropicana,1920,7.560080465,0,3.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/12/1990,67,dominicks,512,6.238324625,0,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/12/1990,67,minute.maid,18624,9.832206351,1,2.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/12/1990,67,tropicana,4544,8.42156296,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/12/1990,68,dominicks,1280,7.154615357,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/12/1990,68,minute.maid,30080,10.31161578,1,2.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/12/1990,68,tropicana,5184,8.553332238,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/12/1990,70,dominicks,9088,9.114710141,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/12/1990,70,minute.maid,20544,9.930324207,1,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/12/1990,70,tropicana,7808,8.962904128,0,2.67,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/12/1990,71,dominicks,896,6.797940413,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/12/1990,71,minute.maid,34240,10.44114983,1,2.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/12/1990,71,tropicana,2624,7.87245515,0,3.87,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/12/1990,72,dominicks,384,5.950642553,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/12/1990,72,minute.maid,42752,10.66317126,1,2.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/12/1990,72,tropicana,8320,9.026417534,0,3.87,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/12/1990,73,dominicks,7552,8.929567708,0,2.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/12/1990,73,minute.maid,42304,10.65263692,1,2.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/12/1990,73,tropicana,7424,8.912473275,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/12/1990,74,dominicks,1984,7.592870288,0,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/12/1990,74,minute.maid,47168,10.76147098,1,2.59,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/12/1990,74,tropicana,5824,8.66974259,0,3.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/12/1990,75,dominicks,2240,7.714231145,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/12/1990,75,minute.maid,33472,10.41846455,1,2.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/12/1990,75,tropicana,4800,8.476371197,0,3.87,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/12/1990,76,dominicks,5568,8.624791202,0,2.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/12/1990,76,minute.maid,30144,10.31374118,1,2.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/12/1990,76,tropicana,5120,8.540909718,0,3.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/12/1990,77,dominicks,1792,7.491087594,0,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/12/1990,77,minute.maid,18560,9.828764006,1,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/12/1990,77,tropicana,6272,8.743850562,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/12/1990,78,dominicks,1344,7.203405521,0,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/12/1990,78,minute.maid,32832,10.39915893,1,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/12/1990,78,tropicana,4800,8.476371197,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/12/1990,80,dominicks,4160,8.333270353,0,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/12/1990,80,minute.maid,24192,10.09377728,1,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/12/1990,80,tropicana,12672,9.447150114,0,3.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/12/1990,81,dominicks,1472,7.294377299,0,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/12/1990,81,minute.maid,41664,10.63739273,1,2.59,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/12/1990,81,tropicana,4800,8.476371197,0,3.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/12/1990,83,dominicks,2944,7.98752448,0,2.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/12/1990,83,minute.maid,31168,10.34714721,1,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/12/1990,83,tropicana,5184,8.553332238,0,3.19,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/12/1990,84,dominicks,1408,7.249925537,0,2.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/12/1990,84,minute.maid,32896,10.40110635,1,2.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/12/1990,84,tropicana,2624,7.87245515,0,3.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/12/1990,86,dominicks,1920,7.560080465,0,2.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/12/1990,86,minute.maid,27264,10.21332243,1,2.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/12/1990,86,tropicana,6208,8.733594062,0,3.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/12/1990,88,dominicks,1728,7.454719949,0,2.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/12/1990,88,minute.maid,24960,10.12502982,1,2.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/12/1990,88,tropicana,3264,8.090708716,0,3.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/12/1990,89,dominicks,2432,7.796469243,0,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/12/1990,89,minute.maid,23104,10.04776104,1,2.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/12/1990,89,tropicana,1600,7.377758908,0,3.66,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/12/1990,90,dominicks,576,6.356107661,0,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/12/1990,90,minute.maid,30336,10.32009041,1,2.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/12/1990,90,tropicana,1600,7.377758908,0,3.66,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/12/1990,91,dominicks,1536,7.336936914,0,2.49,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/12/1990,91,minute.maid,22336,10.01395501,1,2.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/12/1990,91,tropicana,2048,7.624618986,0,3.66,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/12/1990,92,dominicks,1536,7.336936914,0,2.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/12/1990,92,minute.maid,29312,10.28575227,1,2.59,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/12/1990,92,tropicana,2496,7.82244473,0,3.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/12/1990,93,dominicks,1344,7.203405521,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/12/1990,93,minute.maid,35072,10.46515837,1,2.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/12/1990,93,tropicana,7680,8.946374826,0,3.87,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/12/1990,94,dominicks,2752,7.920083199,0,2.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/12/1990,94,minute.maid,20672,9.936535407,1,2.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/12/1990,94,tropicana,4928,8.502688505,0,3.19,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/12/1990,95,dominicks,3648,8.201934351,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/12/1990,95,minute.maid,20544,9.930324207,1,2.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/12/1990,95,tropicana,3584,8.184234774,0,3.87,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/12/1990,97,dominicks,704,6.556778356,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/12/1990,97,minute.maid,14784,9.601300794,1,2.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/12/1990,97,tropicana,1088,6.992096427,0,3.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/12/1990,98,dominicks,6016,8.702177866,0,2.29,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/12/1990,98,minute.maid,31872,10.36948316,1,2.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/12/1990,98,tropicana,4864,8.489616424,0,3.19,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/12/1990,100,dominicks,6144,8.723231275,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/12/1990,100,minute.maid,49600,10.81174611,1,2.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/12/1990,100,tropicana,3968,8.286017468,0,3.87,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/12/1990,101,dominicks,2240,7.714231145,0,2.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/12/1990,101,minute.maid,34752,10.4559924,1,2.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/12/1990,101,tropicana,6336,8.754002934,0,3.66,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/12/1990,102,dominicks,4224,8.348537825,0,2.29,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/12/1990,102,minute.maid,47552,10.76957913,1,2.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/12/1990,102,tropicana,6144,8.723231275,0,3.19,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/12/1990,103,dominicks,2176,7.685243608,0,2.29,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/12/1990,103,minute.maid,17920,9.793672686,1,2.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/12/1990,103,tropicana,2624,7.87245515,0,3.19,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/12/1990,104,dominicks,3840,8.253227646,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/12/1990,104,minute.maid,26496,10.18474906,1,2.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/12/1990,104,tropicana,2944,7.98752448,0,3.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/12/1990,105,dominicks,4608,8.435549202,0,2.29,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/12/1990,105,minute.maid,37056,10.52018556,1,2.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/12/1990,105,tropicana,4224,8.348537825,0,3.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/12/1990,106,dominicks,960,6.866933285,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/12/1990,106,minute.maid,15552,9.651944527,1,2.59,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/12/1990,106,tropicana,896,6.797940413,0,3.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/12/1990,107,dominicks,3200,8.070906089,0,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/12/1990,107,minute.maid,29888,10.30521234,1,2.59,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/12/1990,107,tropicana,6016,8.702177866,0,3.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/12/1990,109,dominicks,448,6.104793232,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/12/1990,109,minute.maid,54848,10.912321,1,2.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/12/1990,109,tropicana,11840,9.379238908,0,3.87,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/12/1990,110,dominicks,1792,7.491087594,0,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/12/1990,110,minute.maid,17344,9.761001904,1,2.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/12/1990,110,tropicana,3008,8.009030685,0,3.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/12/1990,111,dominicks,1664,7.416979621,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/12/1990,111,minute.maid,41728,10.63892765,1,2.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/12/1990,111,tropicana,1728,7.454719949,0,3.87,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/12/1990,112,dominicks,2240,7.714231145,0,2.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/12/1990,112,minute.maid,51968,10.85838342,1,2.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/12/1990,112,tropicana,16384,9.704060528,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/12/1990,113,dominicks,832,6.723832441,0,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/12/1990,113,minute.maid,43328,10.67655436,1,2.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/12/1990,113,tropicana,4736,8.462948177,0,3.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/12/1990,114,dominicks,3072,8.030084094,0,2.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/12/1990,114,minute.maid,40256,10.60301434,1,2.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/12/1990,114,tropicana,6336,8.754002934,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/12/1990,116,dominicks,2432,7.796469243,0,2.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/12/1990,116,minute.maid,16896,9.734832187,1,2.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/12/1990,116,tropicana,5120,8.540909718,0,3.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/12/1990,117,dominicks,1216,7.103322063,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/12/1990,117,minute.maid,19392,9.872615889,1,2.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/12/1990,117,tropicana,1472,7.294377299,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/12/1990,118,dominicks,1152,7.049254841,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/12/1990,118,minute.maid,32192,10.37947325,1,2.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/12/1990,118,tropicana,5056,8.528330936,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/12/1990,119,dominicks,2816,7.943072717,0,2.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/12/1990,119,minute.maid,22720,10.03100087,1,2.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/12/1990,119,tropicana,7040,8.859363449,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/12/1990,121,dominicks,3328,8.110126802,0,2.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/12/1990,121,minute.maid,23232,10.05328592,1,2.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/12/1990,121,tropicana,8064,8.99516499,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/12/1990,122,dominicks,2688,7.896552702,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/12/1990,122,minute.maid,26688,10.19196931,1,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/12/1990,122,tropicana,6272,8.743850562,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/12/1990,123,dominicks,2048,7.624618986,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/12/1990,123,minute.maid,46016,10.73674444,1,2.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/12/1990,123,tropicana,3328,8.110126802,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/12/1990,124,dominicks,1472,7.294377299,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/12/1990,124,minute.maid,47680,10.7722673,1,2.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/12/1990,124,tropicana,5056,8.528330936,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/12/1990,126,dominicks,1344,7.203405521,0,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/12/1990,126,minute.maid,23744,10.07508515,1,2.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/12/1990,126,tropicana,3136,8.050703382,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/12/1990,128,dominicks,1728,7.454719949,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/12/1990,128,minute.maid,70016,11.15647907,1,2.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/12/1990,128,tropicana,10112,9.221478116,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/12/1990,129,dominicks,960,6.866933285,0,2.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/12/1990,129,minute.maid,23104,10.04776104,1,2.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/12/1990,129,tropicana,5248,8.565602331,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/12/1990,130,dominicks,3904,8.269756948,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/12/1990,130,minute.maid,40384,10.60618895,1,2.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/12/1990,130,tropicana,3008,8.009030685,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/12/1990,131,dominicks,3200,8.070906089,0,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/12/1990,131,minute.maid,32512,10.38936453,1,2.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/12/1990,131,tropicana,5248,8.565602331,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/12/1990,132,dominicks,4992,8.51559191,0,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/12/1990,132,minute.maid,41088,10.62347139,1,2.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/12/1990,132,tropicana,3456,8.14786713,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/19/1990,8,dominicks,6464,8.7740036,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/19/1990,8,minute.maid,5568,8.624791202,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/19/1990,8,tropicana,8192,9.010913347,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/19/1990,9,dominicks,3328,8.110126802,0,2.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/19/1990,9,minute.maid,3776,8.236420527,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/19/1990,9,tropicana,8704,9.071537969,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/19/1990,12,dominicks,896,6.797940413,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/19/1990,12,minute.maid,4032,8.30201781,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/19/1990,12,tropicana,8576,9.056722883,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/19/1990,14,dominicks,64,4.158883083,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/19/1990,14,minute.maid,3200,8.070906089,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/19/1990,14,tropicana,7488,8.921057018,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/19/1990,18,dominicks,5504,8.61323038,0,2.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/19/1990,18,minute.maid,6272,8.743850562,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/19/1990,18,tropicana,8128,9.00307017,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/19/1990,21,dominicks,4544,8.42156296,0,2.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/19/1990,21,minute.maid,3520,8.166216269,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/19/1990,21,tropicana,3008,8.009030685,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/19/1990,28,dominicks,896,6.797940413,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/19/1990,28,minute.maid,3008,8.009030685,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/19/1990,28,tropicana,5056,8.528330936,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/19/1990,32,dominicks,1728,7.454719949,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/19/1990,32,minute.maid,3136,8.050703382,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/19/1990,32,tropicana,10752,9.282847063,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/19/1990,33,dominicks,1088,6.992096427,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/19/1990,33,minute.maid,3584,8.184234774,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/19/1990,33,tropicana,8832,9.086136769,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/19/1990,44,dominicks,1152,7.049254841,0,2.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/19/1990,44,minute.maid,3200,8.070906089,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/19/1990,44,tropicana,6848,8.831711918,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/19/1990,45,dominicks,2688,7.896552702,0,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/19/1990,45,minute.maid,4800,8.476371197,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/19/1990,45,tropicana,5440,8.60153434,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/19/1990,47,dominicks,2240,7.714231145,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/19/1990,47,minute.maid,2816,7.943072717,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/19/1990,47,tropicana,3456,8.14786713,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/19/1990,48,dominicks,2304,7.742402022,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/19/1990,48,minute.maid,5184,8.553332238,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/19/1990,48,tropicana,3456,8.14786713,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/19/1990,49,dominicks,896,6.797940413,0,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/19/1990,49,minute.maid,2176,7.685243608,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/19/1990,49,tropicana,3328,8.110126802,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/19/1990,50,dominicks,3776,8.236420527,0,2.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/19/1990,50,minute.maid,2496,7.82244473,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/19/1990,50,tropicana,4800,8.476371197,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/19/1990,51,dominicks,1024,6.931471806,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/19/1990,51,minute.maid,3648,8.201934351,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/19/1990,51,tropicana,3840,8.253227646,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/19/1990,52,dominicks,960,6.866933285,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/19/1990,52,minute.maid,5120,8.540909718,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/19/1990,52,tropicana,10304,9.240287448,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/19/1990,53,dominicks,1024,6.931471806,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/19/1990,53,minute.maid,3264,8.090708716,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/19/1990,53,tropicana,10560,9.264828557,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/19/1990,54,dominicks,832,6.723832441,0,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/19/1990,54,minute.maid,3648,8.201934351,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/19/1990,54,tropicana,3520,8.166216269,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/19/1990,59,dominicks,1792,7.491087594,0,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/19/1990,59,minute.maid,3712,8.219326094,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/19/1990,59,tropicana,2816,7.943072717,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/19/1990,62,dominicks,192,5.257495372,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/19/1990,62,minute.maid,5056,8.528330936,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/19/1990,62,tropicana,15232,9.631153757,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/19/1990,64,dominicks,3456,8.14786713,0,2.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/19/1990,64,minute.maid,3712,8.219326094,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/19/1990,64,tropicana,3968,8.286017468,0,3.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/19/1990,67,dominicks,1856,7.526178913,0,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/19/1990,67,minute.maid,3136,8.050703382,0,2.62,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/19/1990,67,tropicana,6016,8.702177866,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/19/1990,68,dominicks,1920,7.560080465,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/19/1990,68,minute.maid,4736,8.462948177,0,3.17,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/19/1990,68,tropicana,6336,8.754002934,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/19/1990,70,dominicks,11840,9.379238908,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/19/1990,70,minute.maid,5440,8.60153434,0,2.62,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/19/1990,70,tropicana,9728,9.182763604,0,2.65,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/19/1990,71,dominicks,640,6.461468176,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/19/1990,71,minute.maid,2496,7.82244473,0,3.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/19/1990,71,tropicana,4672,8.449342525,0,3.87,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/19/1990,72,dominicks,704,6.556778356,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/19/1990,72,minute.maid,13312,9.496421163,0,3.17,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/19/1990,72,tropicana,8192,9.010913347,0,3.87,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/19/1990,73,dominicks,8768,9.078864009,0,2.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/19/1990,73,minute.maid,7680,8.946374826,0,2.62,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/19/1990,73,tropicana,8832,9.086136769,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/19/1990,74,dominicks,1664,7.416979621,0,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/19/1990,74,minute.maid,4288,8.363575703,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/19/1990,74,tropicana,7168,8.877381955,0,3.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/19/1990,75,dominicks,1344,7.203405521,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/19/1990,75,minute.maid,3712,8.219326094,0,3.17,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/19/1990,75,tropicana,7168,8.877381955,0,3.87,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/19/1990,77,dominicks,3264,8.090708716,0,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/19/1990,77,minute.maid,4480,8.407378325,0,2.62,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/19/1990,77,tropicana,7040,8.859363449,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/19/1990,78,dominicks,3776,8.236420527,0,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/19/1990,78,minute.maid,4480,8.407378325,0,2.62,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/19/1990,78,tropicana,6336,8.754002934,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/19/1990,80,dominicks,4224,8.348537825,0,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/19/1990,80,minute.maid,9984,9.208739091,0,2.62,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/19/1990,80,tropicana,13888,9.538780437,0,3.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/19/1990,81,dominicks,2688,7.896552702,0,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/19/1990,81,minute.maid,4992,8.51559191,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/19/1990,81,tropicana,5120,8.540909718,0,3.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/19/1990,83,dominicks,3776,8.236420527,0,2.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/19/1990,83,minute.maid,4224,8.348537825,0,2.62,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/19/1990,83,tropicana,5376,8.589699882,0,3.19,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/19/1990,84,dominicks,1152,7.049254841,0,2.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/19/1990,84,minute.maid,4544,8.42156296,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/19/1990,84,tropicana,4736,8.462948177,0,3.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/19/1990,86,dominicks,3008,8.009030685,0,2.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/19/1990,86,minute.maid,3712,8.219326094,0,2.62,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/19/1990,86,tropicana,6208,8.733594062,0,3.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/19/1990,88,dominicks,896,6.797940413,0,2.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/19/1990,88,minute.maid,3136,8.050703382,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/19/1990,88,tropicana,3328,8.110126802,0,3.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/19/1990,89,dominicks,1472,7.294377299,0,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/19/1990,89,minute.maid,2432,7.796469243,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/19/1990,89,tropicana,2944,7.98752448,0,3.66,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/19/1990,90,dominicks,640,6.461468176,0,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/19/1990,90,minute.maid,2240,7.714231145,0,2.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/19/1990,90,tropicana,2560,7.847762538,0,3.66,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 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-7/19/1990,93,tropicana,6912,8.841014311,0,3.87,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/19/1990,94,dominicks,5184,8.553332238,0,2.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/19/1990,94,minute.maid,4352,8.378390789,0,2.62,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/19/1990,94,tropicana,5312,8.577723691,0,3.19,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/19/1990,95,dominicks,1344,7.203405521,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 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-7/19/1990,105,tropicana,4800,8.476371197,0,3.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/19/1990,106,dominicks,1088,6.992096427,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/19/1990,106,minute.maid,2432,7.796469243,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/19/1990,106,tropicana,1792,7.491087594,0,3.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/19/1990,107,dominicks,1024,6.931471806,0,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/19/1990,107,minute.maid,4416,8.392989588,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/19/1990,107,tropicana,8128,9.00307017,0,3.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/19/1990,109,dominicks,448,6.104793232,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/19/1990,109,minute.maid,6016,8.702177866,0,3.17,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/19/1990,109,tropicana,13952,9.543378146,0,3.87,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 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-7/19/1990,111,tropicana,3008,8.009030685,0,3.87,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/19/1990,112,dominicks,6144,8.723231275,0,2.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/19/1990,112,minute.maid,9344,9.142489705,0,2.62,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/19/1990,112,tropicana,15808,9.66827142,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/19/1990,113,dominicks,1728,7.454719949,0,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/19/1990,113,minute.maid,11008,9.30637756,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/19/1990,113,tropicana,6592,8.793612072,0,3.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/19/1990,114,dominicks,5248,8.565602331,0,2.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/19/1990,114,minute.maid,4736,8.462948177,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/19/1990,114,tropicana,4864,8.489616424,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/19/1990,116,dominicks,3456,8.14786713,0,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/19/1990,116,minute.maid,3008,8.009030685,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/19/1990,116,tropicana,5376,8.589699882,0,3.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/19/1990,117,dominicks,896,6.797940413,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/19/1990,117,minute.maid,3200,8.070906089,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/19/1990,117,tropicana,4672,8.449342525,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/19/1990,118,dominicks,1024,6.931471806,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/19/1990,118,minute.maid,3264,8.090708716,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/19/1990,118,tropicana,6144,8.723231275,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/19/1990,119,dominicks,4544,8.42156296,0,2.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/19/1990,119,minute.maid,3904,8.269756948,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/19/1990,119,tropicana,6720,8.812843434,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/19/1990,121,dominicks,3200,8.070906089,0,2.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/19/1990,121,minute.maid,7680,8.946374826,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/19/1990,121,tropicana,8064,8.99516499,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/19/1990,122,dominicks,4800,8.476371197,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/19/1990,122,minute.maid,8192,9.010913347,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/19/1990,122,tropicana,8768,9.078864009,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/19/1990,123,dominicks,960,6.866933285,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/19/1990,123,minute.maid,3904,8.269756948,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/19/1990,123,tropicana,4672,8.449342525,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/19/1990,124,dominicks,832,6.723832441,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/19/1990,124,minute.maid,2944,7.98752448,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/19/1990,124,tropicana,5632,8.636219898,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/19/1990,126,dominicks,448,6.104793232,0,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/19/1990,126,minute.maid,3392,8.129174997,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/19/1990,126,tropicana,4352,8.378390789,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/19/1990,128,dominicks,1600,7.377758908,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/19/1990,128,minute.maid,3264,8.090708716,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/19/1990,128,tropicana,10688,9.276876896,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/19/1990,129,dominicks,448,6.104793232,0,2.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/19/1990,129,minute.maid,4416,8.392989588,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/19/1990,129,tropicana,7168,8.877381955,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/19/1990,130,dominicks,2560,7.847762538,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/19/1990,130,minute.maid,3776,8.236420527,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/19/1990,130,tropicana,4544,8.42156296,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/19/1990,131,dominicks,2752,7.920083199,0,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/19/1990,131,minute.maid,5888,8.68067166,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/19/1990,131,tropicana,4416,8.392989588,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/19/1990,132,dominicks,6080,8.712759975,0,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/19/1990,132,minute.maid,4992,8.51559191,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/19/1990,132,tropicana,4160,8.333270353,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/26/1990,2,dominicks,8000,8.987196821,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/26/1990,2,minute.maid,4672,8.449342525,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/26/1990,2,tropicana,6144,8.723231275,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/26/1990,5,dominicks,4224,8.348537825,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/26/1990,5,minute.maid,10048,9.215128889,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/26/1990,5,tropicana,5312,8.577723691,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/26/1990,8,dominicks,5952,8.691482577,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/26/1990,8,minute.maid,14592,9.588228712,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/26/1990,8,tropicana,7936,8.979164649,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/26/1990,9,dominicks,4928,8.502688505,0,2.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/26/1990,9,minute.maid,11456,9.346268889,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/26/1990,9,tropicana,9600,9.169518378,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/26/1990,12,dominicks,6912,8.841014311,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/26/1990,12,minute.maid,6400,8.764053269,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/26/1990,12,tropicana,5824,8.66974259,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/26/1990,14,dominicks,896,6.797940413,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/26/1990,14,minute.maid,13376,9.501217335,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/26/1990,14,tropicana,6976,8.850230966,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/26/1990,18,dominicks,8192,9.010913347,0,2.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/26/1990,18,minute.maid,8320,9.026417534,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/26/1990,18,tropicana,9792,9.189321005,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/26/1990,21,dominicks,4160,8.333270353,0,2.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/26/1990,21,minute.maid,6848,8.831711918,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/26/1990,21,tropicana,3072,8.030084094,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/26/1990,28,dominicks,2304,7.742402022,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/26/1990,28,minute.maid,7424,8.912473275,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/26/1990,28,tropicana,3456,8.14786713,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/26/1990,32,dominicks,4736,8.462948177,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/26/1990,32,minute.maid,10432,9.252633284,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/26/1990,32,tropicana,9344,9.142489705,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/26/1990,33,dominicks,4032,8.30201781,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/26/1990,33,minute.maid,7104,8.868413285,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/26/1990,33,tropicana,8768,9.078864009,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/26/1990,44,dominicks,3072,8.030084094,0,2.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/26/1990,44,minute.maid,11904,9.384629757,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/26/1990,44,tropicana,7424,8.912473275,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/26/1990,45,dominicks,3072,8.030084094,0,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/26/1990,45,minute.maid,3904,8.269756948,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/26/1990,45,tropicana,4864,8.489616424,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/26/1990,47,dominicks,4864,8.489616424,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/26/1990,47,minute.maid,6336,8.754002934,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/26/1990,47,tropicana,3648,8.201934351,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/26/1990,48,dominicks,5184,8.553332238,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/26/1990,48,minute.maid,10880,9.29468152,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/26/1990,48,tropicana,5056,8.528330936,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/26/1990,49,dominicks,3456,8.14786713,0,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/26/1990,49,minute.maid,5440,8.60153434,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/26/1990,49,tropicana,3968,8.286017468,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/26/1990,50,dominicks,3392,8.129174997,0,2.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/26/1990,50,minute.maid,7680,8.946374826,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/26/1990,50,tropicana,9984,9.208739091,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/26/1990,51,dominicks,5760,8.658692754,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/26/1990,51,minute.maid,4288,8.363575703,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/26/1990,51,tropicana,4800,8.476371197,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/26/1990,52,dominicks,4736,8.462948177,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/26/1990,52,minute.maid,9664,9.17616292,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/26/1990,52,tropicana,11136,9.317938383,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/26/1990,53,dominicks,3456,8.14786713,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/26/1990,53,minute.maid,8192,9.010913347,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/26/1990,53,tropicana,10240,9.234056899,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/26/1990,54,dominicks,3072,8.030084094,0,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/26/1990,54,minute.maid,8128,9.00307017,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/26/1990,54,tropicana,3200,8.070906089,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/26/1990,59,dominicks,1152,7.049254841,0,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/26/1990,59,minute.maid,3264,8.090708716,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/26/1990,59,tropicana,2560,7.847762538,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/26/1990,62,dominicks,1920,7.560080465,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/26/1990,62,minute.maid,7872,8.971067439,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/26/1990,62,tropicana,10560,9.264828557,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/26/1990,64,dominicks,5120,8.540909718,0,2.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/26/1990,64,minute.maid,3648,8.201934351,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/26/1990,64,tropicana,2816,7.943072717,0,3.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/26/1990,67,dominicks,1792,7.491087594,0,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/26/1990,67,minute.maid,3968,8.286017468,0,2.62,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/26/1990,67,tropicana,5248,8.565602331,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/26/1990,68,dominicks,6144,8.723231275,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/26/1990,68,minute.maid,3968,8.286017468,0,3.17,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/26/1990,68,tropicana,6016,8.702177866,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/26/1990,70,dominicks,7232,8.886270902,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/26/1990,70,minute.maid,11840,9.379238908,0,2.62,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/26/1990,70,tropicana,8512,9.049232212,0,2.66,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/26/1990,71,dominicks,2624,7.87245515,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/26/1990,71,minute.maid,22272,10.01108556,0,3.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/26/1990,71,tropicana,3776,8.236420527,0,3.87,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/26/1990,72,dominicks,3200,8.070906089,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/26/1990,72,minute.maid,6784,8.822322178,0,3.17,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/26/1990,72,tropicana,9664,9.17616292,0,3.87,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/26/1990,73,dominicks,7552,8.929567708,0,2.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/26/1990,73,minute.maid,9152,9.121727714,0,2.62,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/26/1990,73,tropicana,8064,8.99516499,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/26/1990,74,dominicks,6080,8.712759975,0,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/26/1990,74,minute.maid,5952,8.691482577,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/26/1990,74,tropicana,5312,8.577723691,0,3.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/26/1990,75,dominicks,5760,8.658692754,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/26/1990,75,minute.maid,8704,9.071537969,0,3.17,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/26/1990,75,tropicana,5824,8.66974259,0,3.87,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/26/1990,77,dominicks,3200,8.070906089,0,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/26/1990,77,minute.maid,8960,9.100525506,0,2.62,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/26/1990,77,tropicana,6272,8.743850562,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/26/1990,78,dominicks,4352,8.378390789,0,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/26/1990,78,minute.maid,10304,9.240287448,0,2.62,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/26/1990,78,tropicana,4992,8.51559191,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/26/1990,80,dominicks,4800,8.476371197,0,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/26/1990,80,minute.maid,16448,9.707959168,0,2.62,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/26/1990,80,tropicana,12864,9.462187991,0,3.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/26/1990,81,dominicks,4800,8.476371197,0,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/26/1990,81,minute.maid,12608,9.442086812,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/26/1990,81,tropicana,6656,8.803273983,0,3.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 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-7/26/1990,88,minute.maid,7296,8.895081532,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/26/1990,88,tropicana,4032,8.30201781,0,3.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/26/1990,89,dominicks,4160,8.333270353,0,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/26/1990,89,minute.maid,8128,9.00307017,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/26/1990,89,tropicana,2240,7.714231145,0,3.66,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 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-7/26/1990,109,dominicks,3200,8.070906089,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/26/1990,109,minute.maid,14336,9.570529135,0,3.17,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/26/1990,109,tropicana,11584,9.357380115,0,3.87,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/26/1990,110,dominicks,4544,8.42156296,0,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/26/1990,110,minute.maid,8512,9.049232212,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 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-7/26/1990,112,minute.maid,17408,9.76468515,0,2.62,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/26/1990,112,tropicana,15936,9.67633598,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/26/1990,113,dominicks,5824,8.66974259,0,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/26/1990,113,minute.maid,16640,9.719564714,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/26/1990,113,tropicana,6848,8.831711918,0,3.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/26/1990,114,dominicks,4864,8.489616424,0,2.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/26/1990,114,minute.maid,7104,8.868413285,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/26/1990,114,tropicana,4672,8.449342525,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/26/1990,116,dominicks,5888,8.68067166,0,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/26/1990,116,minute.maid,3072,8.030084094,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/26/1990,116,tropicana,4288,8.363575703,0,3.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/26/1990,117,dominicks,2752,7.920083199,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/26/1990,117,minute.maid,3776,8.236420527,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/26/1990,117,tropicana,2880,7.965545573,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/26/1990,118,dominicks,4864,8.489616424,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/26/1990,118,minute.maid,9472,9.156095357,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/26/1990,118,tropicana,6912,8.841014311,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/26/1990,119,dominicks,4032,8.30201781,0,2.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/26/1990,119,minute.maid,11136,9.317938383,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/26/1990,119,tropicana,7104,8.868413285,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/26/1990,121,dominicks,3200,8.070906089,0,2.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/26/1990,121,minute.maid,10560,9.264828557,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/26/1990,121,tropicana,8640,9.064157862,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/26/1990,122,dominicks,5056,8.528330936,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/26/1990,122,minute.maid,19264,9.865993348,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/26/1990,122,tropicana,8064,8.99516499,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/26/1990,123,dominicks,3712,8.219326094,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/26/1990,123,minute.maid,9024,9.107642974,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/26/1990,123,tropicana,4544,8.42156296,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/26/1990,124,dominicks,4224,8.348537825,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/26/1990,124,minute.maid,8000,8.987196821,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/26/1990,124,tropicana,5504,8.61323038,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/26/1990,126,dominicks,3904,8.269756948,0,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/26/1990,126,minute.maid,9664,9.17616292,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/26/1990,126,tropicana,2688,7.896552702,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/26/1990,128,dominicks,6464,8.7740036,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/26/1990,128,minute.maid,13312,9.496421163,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/26/1990,128,tropicana,10560,9.264828557,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/26/1990,129,dominicks,2432,7.796469243,0,2.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/26/1990,129,minute.maid,9920,9.2023082,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/26/1990,129,tropicana,5504,8.61323038,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/26/1990,130,dominicks,6272,8.743850562,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/26/1990,130,minute.maid,4800,8.476371197,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/26/1990,130,tropicana,3648,8.201934351,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/26/1990,131,dominicks,6912,8.841014311,0,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/26/1990,131,minute.maid,10752,9.282847063,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/26/1990,131,tropicana,5184,8.553332238,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/26/1990,132,dominicks,5760,8.658692754,0,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/26/1990,132,minute.maid,9152,9.121727714,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/26/1990,132,tropicana,4480,8.407378325,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/2/1990,2,dominicks,6848,8.831711918,1,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/2/1990,2,minute.maid,20160,9.911455722,1,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/2/1990,2,tropicana,3840,8.253227646,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/2/1990,5,dominicks,4544,8.42156296,1,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/2/1990,5,minute.maid,21760,9.987828701,1,2.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/2/1990,5,tropicana,5120,8.540909718,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/2/1990,8,dominicks,8832,9.086136769,1,2.09,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/2/1990,8,minute.maid,22208,10.00820786,1,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/2/1990,8,tropicana,6656,8.803273983,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/2/1990,9,dominicks,6144,8.723231275,1,2.09,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/2/1990,9,minute.maid,25536,10.1478445,1,2.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/2/1990,9,tropicana,10048,9.215128889,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/2/1990,12,dominicks,6784,8.822322178,1,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/2/1990,12,minute.maid,33984,10.43364511,1,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/2/1990,12,tropicana,7744,8.954673629,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/2/1990,14,dominicks,4992,8.51559191,1,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/2/1990,14,minute.maid,17856,9.790094865,1,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/2/1990,14,tropicana,5376,8.589699882,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/2/1990,18,dominicks,6720,8.812843434,1,2.09,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/2/1990,18,minute.maid,28544,10.25920204,1,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/2/1990,18,tropicana,9344,9.142489705,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/2/1990,21,dominicks,6528,8.783855897,1,2.09,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/2/1990,21,minute.maid,13824,9.534161491,1,2.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/2/1990,21,tropicana,3584,8.184234774,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/2/1990,28,dominicks,4672,8.449342525,1,2.09,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/2/1990,28,minute.maid,12096,9.400630098,1,2.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/2/1990,28,tropicana,4096,8.317766167,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/2/1990,32,dominicks,7296,8.895081532,1,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/2/1990,32,minute.maid,37120,10.52191119,1,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/2/1990,32,tropicana,8832,9.086136769,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/2/1990,33,dominicks,5504,8.61323038,1,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/2/1990,33,minute.maid,18048,9.800790154,1,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/2/1990,33,tropicana,7616,8.938006577,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/2/1990,44,dominicks,4672,8.449342525,1,2.09,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/2/1990,44,minute.maid,21376,9.970024076,1,2.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/2/1990,44,tropicana,6848,8.831711918,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/2/1990,45,dominicks,6528,8.783855897,1,2.09,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/2/1990,45,minute.maid,9856,9.195835686,1,2.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/2/1990,45,tropicana,6016,8.702177866,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/2/1990,47,dominicks,5568,8.624791202,1,2.09,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/2/1990,47,minute.maid,18176,9.807857322,1,2.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/2/1990,47,tropicana,3328,8.110126802,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/2/1990,48,dominicks,4096,8.317766167,1,2.09,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/2/1990,48,minute.maid,13888,9.538780437,1,2.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/2/1990,48,tropicana,3456,8.14786713,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/2/1990,49,dominicks,3584,8.184234774,1,2.09,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/2/1990,49,minute.maid,11264,9.329367078,1,2.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/2/1990,49,tropicana,2176,7.685243608,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/2/1990,50,dominicks,6528,8.783855897,1,2.09,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/2/1990,50,minute.maid,8448,9.041685006,1,2.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/2/1990,50,tropicana,2752,7.920083199,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/2/1990,51,dominicks,5760,8.658692754,1,2.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/2/1990,51,minute.maid,20992,9.951896692,1,2.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/2/1990,51,tropicana,3392,8.129174997,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/2/1990,52,dominicks,5120,8.540909718,1,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/2/1990,52,minute.maid,29440,10.29010957,1,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/2/1990,52,tropicana,10240,9.234056899,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/2/1990,53,dominicks,5760,8.658692754,1,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/2/1990,53,minute.maid,32640,10.39329381,1,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/2/1990,53,tropicana,10816,9.288781798,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/2/1990,54,dominicks,4864,8.489616424,1,2.09,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/2/1990,54,minute.maid,14336,9.570529135,1,2.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/2/1990,54,tropicana,5376,8.589699882,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/2/1990,59,dominicks,5120,8.540909718,1,2.09,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/2/1990,59,minute.maid,12544,9.436997743,1,2.39,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/2/1990,59,tropicana,4224,8.348537825,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/2/1990,64,dominicks,8000,8.987196821,1,2.09,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/2/1990,64,minute.maid,9024,9.107642974,1,2.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/2/1990,64,tropicana,2816,7.943072717,0,3.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/2/1990,67,dominicks,3584,8.184234774,1,2.09,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/2/1990,67,minute.maid,12736,9.452187908,1,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/2/1990,67,tropicana,4096,8.317766167,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/2/1990,68,dominicks,6656,8.803273983,1,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/2/1990,68,minute.maid,21312,9.967025573,1,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/2/1990,68,tropicana,5120,8.540909718,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/2/1990,70,dominicks,12672,9.447150114,1,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/2/1990,70,minute.maid,12672,9.447150114,1,2.39,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/2/1990,70,tropicana,8960,9.100525506,0,2.65,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/2/1990,71,dominicks,4928,8.502688505,1,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/2/1990,71,minute.maid,23680,10.07238609,1,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/2/1990,71,tropicana,3584,8.184234774,0,3.87,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/2/1990,72,dominicks,7040,8.859363449,1,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/2/1990,72,minute.maid,24000,10.08580911,1,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/2/1990,72,tropicana,7744,8.954673629,0,3.87,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/2/1990,73,dominicks,11136,9.317938383,1,2.09,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/2/1990,73,minute.maid,30848,10.3368272,1,2.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/2/1990,73,tropicana,8128,9.00307017,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/2/1990,74,dominicks,9088,9.114710141,1,2.09,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/2/1990,74,minute.maid,27648,10.22730867,1,2.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/2/1990,74,tropicana,6976,8.850230966,0,3.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 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-8/2/1990,102,dominicks,6784,8.822322178,1,2.09,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/2/1990,102,minute.maid,29440,10.29010957,1,2.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/2/1990,102,tropicana,8064,8.99516499,0,3.19,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/2/1990,103,dominicks,5952,8.691482577,1,2.09,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/2/1990,103,minute.maid,8320,9.026417534,1,2.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 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-8/2/1990,105,minute.maid,23040,10.04498712,1,2.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/2/1990,105,tropicana,4352,8.378390789,0,3.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/2/1990,106,dominicks,4096,8.317766167,1,2.09,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/2/1990,106,minute.maid,8192,9.010913347,1,2.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/2/1990,106,tropicana,1920,7.560080465,0,3.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/2/1990,107,dominicks,9024,9.107642974,1,2.09,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/2/1990,107,minute.maid,17600,9.775654181,1,2.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/2/1990,107,tropicana,7616,8.938006577,0,3.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/2/1990,109,dominicks,4864,8.489616424,1,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/2/1990,109,minute.maid,40768,10.61565274,1,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/2/1990,109,tropicana,14464,9.579418083,0,3.87,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/2/1990,110,dominicks,5888,8.68067166,1,2.09,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/2/1990,110,minute.maid,18240,9.811372264,1,2.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/2/1990,110,tropicana,3072,8.030084094,0,3.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/2/1990,111,dominicks,12160,9.405907156,1,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/2/1990,111,minute.maid,34944,10.46150206,1,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/2/1990,111,tropicana,2880,7.965545573,0,3.87,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/2/1990,112,dominicks,9984,9.208739091,1,2.09,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/2/1990,112,minute.maid,26368,10.17990643,1,2.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/2/1990,112,tropicana,14912,9.609921537,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/2/1990,113,dominicks,6400,8.764053269,1,2.09,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/2/1990,113,minute.maid,32640,10.39329381,1,2.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/2/1990,113,tropicana,6336,8.754002934,0,3.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/2/1990,114,dominicks,13248,9.491601877,1,2.09,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/2/1990,114,minute.maid,24640,10.11212642,1,2.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/2/1990,114,tropicana,5440,8.60153434,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/2/1990,116,dominicks,6144,8.723231275,1,2.09,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/2/1990,116,minute.maid,12608,9.442086812,1,2.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/2/1990,116,tropicana,6144,8.723231275,0,3.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/2/1990,117,dominicks,2368,7.769800996,1,2.09,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/2/1990,117,minute.maid,14464,9.579418083,1,2.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/2/1990,117,tropicana,2496,7.82244473,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/2/1990,118,dominicks,5888,8.68067166,1,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/2/1990,118,minute.maid,25920,10.16277015,1,2.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/2/1990,118,tropicana,6016,8.702177866,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/2/1990,119,dominicks,6080,8.712759975,1,2.09,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/2/1990,119,minute.maid,15808,9.66827142,1,2.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/2/1990,119,tropicana,6080,8.712759975,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/2/1990,121,dominicks,6528,8.783855897,1,2.09,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/2/1990,121,minute.maid,22400,10.01681624,1,2.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/2/1990,121,tropicana,8576,9.056722883,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/2/1990,122,dominicks,9728,9.182763604,1,2.09,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/2/1990,122,minute.maid,24640,10.11212642,1,2.39,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/2/1990,122,tropicana,8192,9.010913347,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/2/1990,123,dominicks,6784,8.822322178,1,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/2/1990,123,minute.maid,34688,10.45414909,1,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/2/1990,123,tropicana,5568,8.624791202,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/2/1990,124,dominicks,7552,8.929567708,1,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/2/1990,124,minute.maid,39872,10.5934296,1,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/2/1990,124,tropicana,5696,8.647519453,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/2/1990,126,dominicks,4800,8.476371197,1,2.09,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/2/1990,126,minute.maid,16192,9.692272572,1,2.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/2/1990,126,tropicana,3968,8.286017468,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/2/1990,128,dominicks,9728,9.182763604,1,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/2/1990,128,minute.maid,46016,10.73674444,1,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/2/1990,128,tropicana,11008,9.30637756,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/2/1990,129,dominicks,3520,8.166216269,1,2.09,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/2/1990,129,minute.maid,17536,9.77201119,1,2.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/2/1990,129,tropicana,7680,8.946374826,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/2/1990,131,dominicks,8832,9.086136769,1,2.09,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/2/1990,131,minute.maid,25152,10.1326927,1,2.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/2/1990,131,tropicana,5120,8.540909718,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/2/1990,132,dominicks,11136,9.317938383,1,2.09,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/2/1990,132,minute.maid,20736,9.939626599,1,2.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/2/1990,132,tropicana,4864,8.489616424,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/9/1990,2,dominicks,2880,7.965545573,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/9/1990,2,minute.maid,2688,7.896552702,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/9/1990,2,tropicana,8000,8.987196821,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/9/1990,5,dominicks,1728,7.454719949,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/9/1990,5,minute.maid,4544,8.42156296,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/9/1990,5,tropicana,7936,8.979164649,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/9/1990,8,dominicks,7232,8.886270902,0,2.09,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/9/1990,8,minute.maid,5760,8.658692754,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/9/1990,8,tropicana,8256,9.018695488,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/9/1990,9,dominicks,3712,8.219326094,0,2.09,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/9/1990,9,minute.maid,3968,8.286017468,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/9/1990,9,tropicana,9024,9.107642974,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/9/1990,12,dominicks,3776,8.236420527,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/9/1990,12,minute.maid,4544,8.42156296,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/9/1990,12,tropicana,8320,9.026417534,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/9/1990,14,dominicks,1728,7.454719949,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/9/1990,14,minute.maid,3392,8.129174997,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/9/1990,14,tropicana,8448,9.041685006,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/9/1990,18,dominicks,7040,8.859363449,0,2.09,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/9/1990,18,minute.maid,4672,8.449342525,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/9/1990,18,tropicana,12608,9.442086812,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/9/1990,21,dominicks,2880,7.965545573,0,2.09,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/9/1990,21,minute.maid,3200,8.070906089,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/9/1990,21,tropicana,2816,7.943072717,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/9/1990,28,dominicks,1856,7.526178913,0,2.09,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/9/1990,28,minute.maid,2560,7.847762538,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/9/1990,28,tropicana,5504,8.61323038,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/9/1990,32,dominicks,4160,8.333270353,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/9/1990,32,minute.maid,4416,8.392989588,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/9/1990,32,tropicana,11840,9.379238908,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/9/1990,33,dominicks,2816,7.943072717,0,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/9/1990,33,minute.maid,2944,7.98752448,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/9/1990,33,tropicana,9728,9.182763604,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/9/1990,44,dominicks,1920,7.560080465,0,2.09,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/9/1990,44,minute.maid,3648,8.201934351,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/9/1990,44,tropicana,7488,8.921057018,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/9/1990,45,dominicks,2176,7.685243608,0,2.09,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/9/1990,45,minute.maid,3072,8.030084094,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/9/1990,45,tropicana,8832,9.086136769,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/9/1990,47,dominicks,3648,8.201934351,0,2.09,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/9/1990,47,minute.maid,3712,8.219326094,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/9/1990,47,tropicana,3456,8.14786713,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/9/1990,48,dominicks,3648,8.201934351,0,2.09,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/9/1990,48,minute.maid,5248,8.565602331,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/9/1990,48,tropicana,5504,8.61323038,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/9/1990,50,dominicks,1088,6.992096427,0,2.09,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/9/1990,50,minute.maid,2496,7.82244473,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/9/1990,50,tropicana,2944,7.98752448,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/9/1990,51,dominicks,2432,7.796469243,0,2.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/9/1990,51,minute.maid,3712,8.219326094,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/9/1990,51,tropicana,4288,8.363575703,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/9/1990,52,dominicks,6592,8.793612072,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/9/1990,52,minute.maid,4544,8.42156296,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/9/1990,52,tropicana,12608,9.442086812,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/9/1990,53,dominicks,2304,7.742402022,0,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/9/1990,53,minute.maid,2816,7.943072717,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/9/1990,53,tropicana,10624,9.270870872,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/9/1990,54,dominicks,1984,7.592870288,0,2.09,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/9/1990,54,minute.maid,3584,8.184234774,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/9/1990,54,tropicana,4672,8.449342525,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/9/1990,56,dominicks,1984,7.592870288,0,2.09,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/9/1990,56,minute.maid,2880,7.965545573,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/9/1990,56,tropicana,7936,8.979164649,0,3.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/9/1990,59,dominicks,1024,6.931471806,0,2.09,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/9/1990,59,minute.maid,2304,7.742402022,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/9/1990,59,tropicana,4032,8.30201781,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/9/1990,62,dominicks,1024,6.931471806,0,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/9/1990,62,minute.maid,4352,8.378390789,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/9/1990,62,tropicana,13376,9.501217335,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/9/1990,64,dominicks,3264,8.090708716,0,2.09,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 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-8/9/1990,93,dominicks,1600,7.377758908,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/9/1990,93,minute.maid,3584,8.184234774,0,3.17,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/9/1990,93,tropicana,8384,9.034080407,0,3.87,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/9/1990,94,dominicks,5504,8.61323038,0,2.09,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/9/1990,94,minute.maid,4416,8.392989588,0,2.62,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/9/1990,94,tropicana,6208,8.733594062,0,3.19,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/9/1990,95,dominicks,1344,7.203405521,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/9/1990,95,minute.maid,1856,7.526178913,0,3.17,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/9/1990,95,tropicana,5184,8.553332238,0,3.87,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/9/1990,97,dominicks,2304,7.742402022,0,2.09,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/9/1990,97,minute.maid,2752,7.920083199,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/9/1990,97,tropicana,2112,7.655390645,0,3.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/9/1990,98,dominicks,4544,8.42156296,0,2.09,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/9/1990,98,minute.maid,6912,8.841014311,0,2.62,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/9/1990,98,tropicana,5824,8.66974259,0,3.19,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/9/1990,100,dominicks,4352,8.378390789,0,2.09,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/9/1990,100,minute.maid,2688,7.896552702,0,3.17,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/9/1990,100,tropicana,5824,8.66974259,0,3.87,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/9/1990,101,dominicks,3648,8.201934351,0,2.09,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/9/1990,101,minute.maid,3840,8.253227646,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/9/1990,101,tropicana,9216,9.128696383,0,3.66,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/9/1990,102,dominicks,3328,8.110126802,0,2.09,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/9/1990,102,minute.maid,4544,8.42156296,0,2.62,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/9/1990,102,tropicana,7616,8.938006577,0,3.19,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/9/1990,103,dominicks,1856,7.526178913,0,2.09,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/9/1990,103,minute.maid,2048,7.624618986,0,2.62,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/9/1990,103,tropicana,2816,7.943072717,0,3.19,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/9/1990,104,dominicks,1408,7.249925537,0,2.09,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/9/1990,104,minute.maid,2880,7.965545573,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/9/1990,104,tropicana,3712,8.219326094,0,3.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/9/1990,105,dominicks,5248,8.565602331,0,2.09,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/9/1990,105,minute.maid,4736,8.462948177,0,2.62,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/9/1990,105,tropicana,4672,8.449342525,0,3.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/9/1990,106,dominicks,2304,7.742402022,0,2.09,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/9/1990,106,minute.maid,2176,7.685243608,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/9/1990,106,tropicana,3072,8.030084094,0,3.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/9/1990,107,dominicks,3008,8.009030685,0,2.09,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/9/1990,107,minute.maid,3904,8.269756948,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/9/1990,107,tropicana,9920,9.2023082,0,3.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/9/1990,109,dominicks,2048,7.624618986,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/9/1990,109,minute.maid,6208,8.733594062,0,3.17,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/9/1990,109,tropicana,17344,9.761001904,0,3.87,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/9/1990,110,dominicks,2688,7.896552702,0,2.09,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/9/1990,110,minute.maid,3264,8.090708716,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/9/1990,110,tropicana,4928,8.502688505,0,3.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/9/1990,111,dominicks,4928,8.502688505,0,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/9/1990,111,minute.maid,1216,7.103322063,0,3.17,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/9/1990,111,tropicana,2432,7.796469243,0,3.87,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/9/1990,112,dominicks,6208,8.733594062,0,2.09,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/9/1990,112,minute.maid,9664,9.17616292,0,2.62,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/9/1990,112,tropicana,20800,9.942708266,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/9/1990,113,dominicks,4096,8.317766167,0,2.09,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/9/1990,113,minute.maid,4352,8.378390789,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/9/1990,113,tropicana,7424,8.912473275,0,3.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/9/1990,114,dominicks,4352,8.378390789,0,2.09,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/9/1990,114,minute.maid,4992,8.51559191,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/9/1990,114,tropicana,5888,8.68067166,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/9/1990,116,dominicks,4352,8.378390789,0,2.09,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/9/1990,116,minute.maid,3328,8.110126802,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/9/1990,116,tropicana,5248,8.565602331,0,3.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/9/1990,117,dominicks,960,6.866933285,0,2.09,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/9/1990,117,minute.maid,6848,8.831711918,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/9/1990,117,tropicana,5120,8.540909718,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/9/1990,118,dominicks,5888,8.68067166,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/9/1990,118,minute.maid,5376,8.589699882,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/9/1990,118,tropicana,8064,8.99516499,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/9/1990,119,dominicks,2432,7.796469243,0,2.09,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/9/1990,119,minute.maid,4288,8.363575703,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/9/1990,119,tropicana,7680,8.946374826,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/9/1990,121,dominicks,3328,8.110126802,0,2.09,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/9/1990,121,minute.maid,6720,8.812843434,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/9/1990,121,tropicana,7616,8.938006577,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/9/1990,122,dominicks,5056,8.528330936,0,2.09,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/9/1990,122,minute.maid,5632,8.636219898,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/9/1990,122,tropicana,12224,9.411156511,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/9/1990,123,dominicks,3008,8.009030685,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/9/1990,123,minute.maid,4224,8.348537825,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/9/1990,123,tropicana,5696,8.647519453,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/9/1990,124,dominicks,5888,8.68067166,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/9/1990,124,minute.maid,3648,8.201934351,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/9/1990,124,tropicana,6016,8.702177866,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/9/1990,126,dominicks,1600,7.377758908,0,2.09,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/9/1990,126,minute.maid,3072,8.030084094,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/9/1990,126,tropicana,4352,8.378390789,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/9/1990,128,dominicks,5888,8.68067166,0,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/9/1990,128,minute.maid,4800,8.476371197,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/9/1990,128,tropicana,8064,8.99516499,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/9/1990,129,dominicks,960,6.866933285,0,2.09,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/9/1990,129,minute.maid,3136,8.050703382,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/9/1990,129,tropicana,4608,8.435549202,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/9/1990,131,dominicks,5056,8.528330936,0,2.09,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/9/1990,131,minute.maid,4608,8.435549202,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/9/1990,131,tropicana,6272,8.743850562,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/9/1990,132,dominicks,4160,8.333270353,0,2.09,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/9/1990,132,minute.maid,4160,8.333270353,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/9/1990,132,tropicana,5696,8.647519453,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/16/1990,5,dominicks,1216,7.103322063,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/16/1990,5,minute.maid,52224,10.86329744,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/16/1990,5,tropicana,6080,8.712759975,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/16/1990,8,dominicks,5504,8.61323038,0,2.09,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/16/1990,8,minute.maid,54016,10.89703558,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/16/1990,8,tropicana,5568,8.624791202,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/16/1990,9,dominicks,704,6.556778356,0,2.09,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/16/1990,9,minute.maid,54208,10.90058378,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/16/1990,9,tropicana,8896,9.093357017,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/16/1990,12,dominicks,4096,8.317766167,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/16/1990,12,minute.maid,64960,11.08152698,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/16/1990,12,tropicana,6144,8.723231275,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/16/1990,14,dominicks,1536,7.336936914,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/16/1990,14,minute.maid,36160,10.49570882,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/16/1990,14,tropicana,7232,8.886270902,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/16/1990,18,dominicks,2944,7.98752448,0,2.09,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/16/1990,18,minute.maid,65664,11.09230611,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/16/1990,18,tropicana,8256,9.018695488,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/16/1990,21,dominicks,2176,7.685243608,0,2.09,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/16/1990,21,minute.maid,33856,10.42987152,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/16/1990,21,tropicana,1984,7.592870288,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/16/1990,28,dominicks,960,6.866933285,0,2.09,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/16/1990,28,minute.maid,24192,10.09377728,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/16/1990,28,tropicana,3136,8.050703382,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/16/1990,32,dominicks,2560,7.847762538,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/16/1990,32,minute.maid,60800,11.01534507,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/16/1990,32,tropicana,9216,9.128696383,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/16/1990,33,dominicks,1536,7.336936914,0,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/16/1990,33,minute.maid,26112,10.17015026,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/16/1990,33,tropicana,8832,9.086136769,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/16/1990,44,dominicks,896,6.797940413,0,2.09,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/16/1990,44,minute.maid,46848,10.7546636,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/16/1990,44,tropicana,6336,8.754002934,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/16/1990,45,dominicks,2816,7.943072717,0,2.09,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/16/1990,45,minute.maid,22016,9.999524741,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/16/1990,45,tropicana,4352,8.378390789,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/16/1990,47,dominicks,2496,7.82244473,0,2.09,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/16/1990,47,minute.maid,29120,10.2791805,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/16/1990,47,tropicana,3136,8.050703382,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/16/1990,48,dominicks,1856,7.526178913,0,2.09,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/16/1990,48,minute.maid,26240,10.17504024,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/16/1990,48,tropicana,4736,8.462948177,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/16/1990,49,dominicks,1600,7.377758908,0,2.09,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/16/1990,49,minute.maid,20928,9.948843254,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/16/1990,49,tropicana,2176,7.685243608,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/16/1990,50,dominicks,2624,7.87245515,0,2.09,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/16/1990,50,minute.maid,18624,9.832206351,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/16/1990,50,tropicana,2048,7.624618986,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/16/1990,51,dominicks,1152,7.049254841,0,2.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/16/1990,51,minute.maid,42368,10.65414864,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/16/1990,51,tropicana,3840,8.253227646,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/16/1990,52,dominicks,2688,7.896552702,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/16/1990,52,minute.maid,49408,10.80786763,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/16/1990,52,tropicana,8640,9.064157862,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 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-8/16/1990,84,minute.maid,33920,10.43176009,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/16/1990,84,tropicana,3456,8.14786713,0,3.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/16/1990,86,dominicks,896,6.797940413,0,2.09,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/16/1990,86,minute.maid,40448,10.60777248,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/16/1990,86,tropicana,6592,8.793612072,0,3.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/16/1990,88,dominicks,1920,7.560080465,0,2.09,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/16/1990,88,minute.maid,37184,10.52363384,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/16/1990,88,tropicana,3840,8.253227646,0,3.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/16/1990,89,dominicks,2240,7.714231145,0,2.09,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/16/1990,89,minute.maid,48704,10.79351644,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 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-8/16/1990,114,minute.maid,41344,10.62968259,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/16/1990,114,tropicana,5440,8.60153434,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/16/1990,115,dominicks,1280,7.154615357,0,2.09,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/16/1990,115,minute.maid,44032,10.69267192,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/16/1990,115,tropicana,5376,8.589699882,0,3.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/16/1990,116,dominicks,2176,7.685243608,0,2.09,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/16/1990,116,minute.maid,33280,10.4127119,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/16/1990,116,tropicana,3456,8.14786713,0,3.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/16/1990,117,dominicks,1344,7.203405521,0,2.09,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/16/1990,117,minute.maid,24576,10.10952564,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/16/1990,117,tropicana,2944,7.98752448,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/16/1990,118,dominicks,5632,8.636219898,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/16/1990,118,minute.maid,41024,10.62191254,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/16/1990,118,tropicana,6080,8.712759975,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/16/1990,119,dominicks,3456,8.14786713,0,2.09,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/16/1990,119,minute.maid,29312,10.28575227,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/16/1990,119,tropicana,6848,8.831711918,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/16/1990,121,dominicks,3264,8.090708716,0,2.09,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/16/1990,121,minute.maid,35520,10.4778512,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/16/1990,121,tropicana,8960,9.100525506,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/16/1990,122,dominicks,2496,7.82244473,0,2.09,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/16/1990,122,minute.maid,52480,10.86818742,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/16/1990,122,tropicana,7936,8.979164649,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/16/1990,123,dominicks,3840,8.253227646,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/16/1990,123,minute.maid,60352,11.00794937,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/16/1990,123,tropicana,5312,8.577723691,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/16/1990,124,dominicks,5440,8.60153434,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/16/1990,124,minute.maid,57920,10.96681803,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/16/1990,124,tropicana,4352,8.378390789,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/16/1990,126,dominicks,1664,7.416979621,0,2.09,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/16/1990,126,minute.maid,35008,10.46333189,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/16/1990,126,tropicana,4032,8.30201781,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/16/1990,128,dominicks,7360,8.903815212,0,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/16/1990,128,minute.maid,82368,11.31895229,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/16/1990,128,tropicana,8768,9.078864009,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/16/1990,130,dominicks,7872,8.971067439,0,2.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/16/1990,130,minute.maid,66304,11.10200551,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/16/1990,130,tropicana,3264,8.090708716,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/16/1990,131,dominicks,4608,8.435549202,0,2.09,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/16/1990,131,minute.maid,47104,10.7601132,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/16/1990,131,tropicana,4672,8.449342525,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/16/1990,132,dominicks,3520,8.166216269,0,2.09,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/16/1990,132,minute.maid,54912,10.91348718,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/16/1990,132,tropicana,4288,8.363575703,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/23/1990,2,dominicks,1600,7.377758908,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/23/1990,2,minute.maid,3008,8.009030685,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/23/1990,2,tropicana,8896,9.093357017,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/23/1990,5,dominicks,1152,7.049254841,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/23/1990,5,minute.maid,3584,8.184234774,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/23/1990,5,tropicana,4160,8.333270353,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/23/1990,8,dominicks,4800,8.476371197,0,2.09,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/23/1990,8,minute.maid,5824,8.66974259,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/23/1990,8,tropicana,7488,8.921057018,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/23/1990,9,dominicks,960,6.866933285,0,2.09,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/23/1990,9,minute.maid,3328,8.110126802,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/23/1990,9,tropicana,7360,8.903815212,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/23/1990,12,dominicks,2880,7.965545573,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/23/1990,12,minute.maid,4096,8.317766167,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/23/1990,12,tropicana,6208,8.733594062,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/23/1990,14,dominicks,128,4.852030264,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/23/1990,14,minute.maid,2816,7.943072717,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/23/1990,14,tropicana,5312,8.577723691,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/23/1990,18,dominicks,2304,7.742402022,0,2.09,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/23/1990,18,minute.maid,4544,8.42156296,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/23/1990,18,tropicana,8704,9.071537969,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/23/1990,21,dominicks,2432,7.796469243,0,2.09,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/23/1990,21,minute.maid,3072,8.030084094,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/23/1990,21,tropicana,3968,8.286017468,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/23/1990,28,dominicks,1344,7.203405521,0,2.09,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/23/1990,28,minute.maid,2176,7.685243608,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/23/1990,28,tropicana,7168,8.877381955,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/23/1990,32,dominicks,3648,8.201934351,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/23/1990,32,minute.maid,3776,8.236420527,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/23/1990,32,tropicana,11072,9.312174678,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/23/1990,33,dominicks,1664,7.416979621,0,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/23/1990,33,minute.maid,3520,8.166216269,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/23/1990,33,tropicana,7808,8.962904128,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/23/1990,40,dominicks,10368,9.246479419,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/23/1990,40,minute.maid,3136,8.050703382,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/23/1990,40,tropicana,5760,8.658692754,0,2.66,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/23/1990,44,dominicks,1152,7.049254841,0,2.09,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/23/1990,44,minute.maid,3328,8.110126802,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/23/1990,44,tropicana,6208,8.733594062,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/23/1990,45,dominicks,1856,7.526178913,0,2.09,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/23/1990,45,minute.maid,3136,8.050703382,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/23/1990,45,tropicana,5824,8.66974259,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/23/1990,47,dominicks,3648,8.201934351,0,2.09,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/23/1990,47,minute.maid,3392,8.129174997,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/23/1990,47,tropicana,4160,8.333270353,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/23/1990,48,dominicks,2048,7.624618986,0,2.09,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/23/1990,48,minute.maid,3264,8.090708716,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/23/1990,48,tropicana,4480,8.407378325,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/23/1990,49,dominicks,1856,7.526178913,0,2.09,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/23/1990,49,minute.maid,2432,7.796469243,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/23/1990,49,tropicana,3840,8.253227646,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/23/1990,50,dominicks,1600,7.377758908,0,2.09,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/23/1990,50,minute.maid,2496,7.82244473,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/23/1990,50,tropicana,2432,7.796469243,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/23/1990,51,dominicks,704,6.556778356,0,2.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/23/1990,51,minute.maid,3456,8.14786713,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/23/1990,51,tropicana,4608,8.435549202,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/23/1990,52,dominicks,1344,7.203405521,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/23/1990,52,minute.maid,5312,8.577723691,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/23/1990,52,tropicana,11520,9.351839934,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/23/1990,53,dominicks,2112,7.655390645,0,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/23/1990,53,minute.maid,3776,8.236420527,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/23/1990,53,tropicana,13440,9.505990614,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/23/1990,54,dominicks,960,6.866933285,0,2.09,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/23/1990,54,minute.maid,4288,8.363575703,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/23/1990,54,tropicana,5632,8.636219898,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/23/1990,56,dominicks,1024,6.931471806,0,2.09,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/23/1990,56,minute.maid,2432,7.796469243,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/23/1990,56,tropicana,6848,8.831711918,0,3.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/23/1990,59,dominicks,896,6.797940413,0,2.09,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/23/1990,59,minute.maid,1664,7.416979621,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/23/1990,59,tropicana,2432,7.796469243,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/23/1990,62,dominicks,768,6.643789733,0,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/23/1990,62,minute.maid,9984,9.208739091,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/23/1990,62,tropicana,13504,9.510741217,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/23/1990,64,dominicks,3136,8.050703382,0,2.09,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/23/1990,64,minute.maid,2624,7.87245515,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/23/1990,64,tropicana,3456,8.14786713,0,3.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/23/1990,68,dominicks,2752,7.920083199,0,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/23/1990,68,minute.maid,4288,8.363575703,0,3.17,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/23/1990,68,tropicana,6976,8.850230966,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/23/1990,70,dominicks,8448,9.041685006,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/23/1990,70,minute.maid,5632,8.636219898,0,2.62,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/23/1990,70,tropicana,8768,9.078864009,0,2.66,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/23/1990,71,dominicks,1024,6.931471806,0,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/23/1990,71,minute.maid,1984,7.592870288,0,3.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/23/1990,71,tropicana,4352,8.378390789,0,3.87,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/23/1990,72,dominicks,1856,7.526178913,0,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/23/1990,72,minute.maid,3200,8.070906089,0,3.17,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/23/1990,72,tropicana,8384,9.034080407,0,3.87,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/23/1990,73,dominicks,4352,8.378390789,0,2.09,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/23/1990,73,minute.maid,3648,8.201934351,0,2.62,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/23/1990,73,tropicana,8000,8.987196821,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/23/1990,74,dominicks,2944,7.98752448,0,2.09,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/23/1990,74,minute.maid,4736,8.462948177,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/23/1990,74,tropicana,6336,8.754002934,0,3.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/23/1990,75,dominicks,2432,7.796469243,0,2.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/23/1990,75,minute.maid,3392,8.129174997,0,3.17,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/23/1990,75,tropicana,6912,8.841014311,0,3.87,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/23/1990,76,dominicks,1216,7.103322063,0,2.09,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/23/1990,76,minute.maid,4096,8.317766167,0,2.62,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/23/1990,76,tropicana,5184,8.553332238,0,3.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/23/1990,77,dominicks,1472,7.294377299,0,2.09,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/23/1990,77,minute.maid,4352,8.378390789,0,2.62,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/23/1990,77,tropicana,8832,9.086136769,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/23/1990,78,dominicks,1920,7.560080465,0,2.09,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/23/1990,78,minute.maid,4992,8.51559191,0,2.62,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/23/1990,78,tropicana,6592,8.793612072,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/23/1990,80,dominicks,3072,8.030084094,0,2.09,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/23/1990,80,minute.maid,7936,8.979164649,0,2.62,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/23/1990,80,tropicana,14784,9.601300794,0,3.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/23/1990,81,dominicks,2304,7.742402022,0,2.09,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/23/1990,81,minute.maid,4352,8.378390789,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/23/1990,81,tropicana,5312,8.577723691,0,3.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/23/1990,83,dominicks,1472,7.294377299,0,2.09,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/23/1990,83,minute.maid,3904,8.269756948,0,2.62,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/23/1990,83,tropicana,11072,9.312174678,0,3.19,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/23/1990,84,dominicks,1664,7.416979621,0,2.09,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/23/1990,84,minute.maid,4288,8.363575703,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/23/1990,84,tropicana,5248,8.565602331,0,3.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/23/1990,86,dominicks,1792,7.491087594,0,2.09,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/23/1990,86,minute.maid,3200,8.070906089,0,2.62,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/23/1990,86,tropicana,7296,8.895081532,0,3.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/23/1990,88,dominicks,1024,6.931471806,0,2.09,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/23/1990,88,minute.maid,2688,7.896552702,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/23/1990,88,tropicana,3520,8.166216269,0,3.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/23/1990,89,dominicks,2560,7.847762538,0,2.09,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/23/1990,89,minute.maid,2880,7.965545573,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/23/1990,89,tropicana,4096,8.317766167,0,3.66,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/23/1990,90,dominicks,832,6.723832441,0,2.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/23/1990,90,minute.maid,2496,7.82244473,0,2.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/23/1990,90,tropicana,3328,8.110126802,0,3.66,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/23/1990,91,dominicks,1920,7.560080465,0,2.09,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/23/1990,91,minute.maid,4096,8.317766167,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/23/1990,91,tropicana,4352,8.378390789,0,3.66,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/23/1990,92,dominicks,1536,7.336936914,0,2.09,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/23/1990,92,minute.maid,3456,8.14786713,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/23/1990,92,tropicana,3968,8.286017468,0,3.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/23/1990,93,dominicks,896,6.797940413,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/23/1990,93,minute.maid,2944,7.98752448,0,3.17,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/23/1990,93,tropicana,6592,8.793612072,0,3.87,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/23/1990,94,dominicks,4224,8.348537825,0,2.09,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/23/1990,94,minute.maid,5632,8.636219898,0,2.62,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/23/1990,94,tropicana,6272,8.743850562,0,3.19,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/23/1990,95,dominicks,768,6.643789733,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/23/1990,95,minute.maid,2304,7.742402022,0,3.17,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/23/1990,95,tropicana,3008,8.009030685,0,3.87,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/23/1990,97,dominicks,1536,7.336936914,0,2.09,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/23/1990,97,minute.maid,2752,7.920083199,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/23/1990,97,tropicana,1728,7.454719949,0,3.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/23/1990,98,dominicks,4672,8.449342525,0,2.09,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/23/1990,98,minute.maid,5504,8.61323038,0,2.62,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/23/1990,98,tropicana,6080,8.712759975,0,3.19,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/23/1990,100,dominicks,2112,7.655390645,0,2.09,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/23/1990,100,minute.maid,4288,8.363575703,0,3.17,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/23/1990,100,tropicana,4800,8.476371197,0,3.87,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/23/1990,101,dominicks,2688,7.896552702,0,2.09,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/23/1990,101,minute.maid,3968,8.286017468,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/23/1990,101,tropicana,6848,8.831711918,0,3.66,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/23/1990,102,dominicks,3904,8.269756948,0,2.09,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/23/1990,102,minute.maid,3904,8.269756948,0,2.62,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/23/1990,102,tropicana,6080,8.712759975,0,3.19,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/23/1990,103,dominicks,1536,7.336936914,0,2.09,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/23/1990,103,minute.maid,2368,7.769800996,0,2.62,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/23/1990,103,tropicana,1920,7.560080465,0,3.19,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/23/1990,104,dominicks,1408,7.249925537,0,2.09,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/23/1990,104,minute.maid,2368,7.769800996,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/23/1990,104,tropicana,2688,7.896552702,0,3.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/23/1990,105,dominicks,4224,8.348537825,0,2.09,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/23/1990,105,minute.maid,4672,8.449342525,0,2.62,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/23/1990,105,tropicana,4864,8.489616424,0,3.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/23/1990,106,dominicks,1024,6.931471806,0,2.09,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/23/1990,106,minute.maid,2112,7.655390645,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/23/1990,106,tropicana,1472,7.294377299,0,3.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/23/1990,107,dominicks,1088,6.992096427,0,2.09,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/23/1990,107,minute.maid,3200,8.070906089,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/23/1990,107,tropicana,7424,8.912473275,0,3.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/23/1990,109,dominicks,704,6.556778356,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/23/1990,109,minute.maid,6976,8.850230966,0,3.17,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/23/1990,109,tropicana,17216,9.753594463,0,3.87,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/23/1990,110,dominicks,1408,7.249925537,0,2.09,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/23/1990,110,minute.maid,3264,8.090708716,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/23/1990,110,tropicana,3328,8.110126802,0,3.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/23/1990,111,dominicks,1792,7.491087594,0,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/23/1990,111,minute.maid,2752,7.920083199,0,3.17,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/23/1990,111,tropicana,2752,7.920083199,0,3.87,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/23/1990,112,dominicks,1856,7.526178913,0,2.09,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/23/1990,112,minute.maid,8192,9.010913347,0,2.62,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/23/1990,112,tropicana,13376,9.501217335,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/23/1990,114,dominicks,3456,8.14786713,0,2.09,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/23/1990,114,minute.maid,4288,8.363575703,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/23/1990,114,tropicana,6784,8.822322178,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/23/1990,115,dominicks,1152,7.049254841,0,2.09,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/23/1990,115,minute.maid,4672,8.449342525,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/23/1990,115,tropicana,4480,8.407378325,0,3.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/23/1990,116,dominicks,1792,7.491087594,0,2.09,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/23/1990,116,minute.maid,2368,7.769800996,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/23/1990,116,tropicana,3648,8.201934351,0,3.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/23/1990,117,dominicks,1856,7.526178913,0,2.09,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/23/1990,117,minute.maid,2816,7.943072717,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/23/1990,117,tropicana,3072,8.030084094,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/23/1990,118,dominicks,1856,7.526178913,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/23/1990,118,minute.maid,3776,8.236420527,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/23/1990,118,tropicana,4352,8.378390789,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/23/1990,119,dominicks,3648,8.201934351,0,2.09,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/23/1990,119,minute.maid,4800,8.476371197,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/23/1990,119,tropicana,7552,8.929567708,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/23/1990,121,dominicks,3392,8.129174997,0,2.09,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/23/1990,121,minute.maid,5952,8.691482577,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/23/1990,121,tropicana,9600,9.169518378,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/23/1990,122,dominicks,2496,7.82244473,0,2.09,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/23/1990,122,minute.maid,6208,8.733594062,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/23/1990,122,tropicana,8448,9.041685006,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/23/1990,123,dominicks,2752,7.920083199,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/23/1990,123,minute.maid,3520,8.166216269,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/23/1990,123,tropicana,6656,8.803273983,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/23/1990,124,dominicks,2432,7.796469243,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/23/1990,124,minute.maid,1984,7.592870288,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/23/1990,124,tropicana,7744,8.954673629,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/23/1990,126,dominicks,1152,7.049254841,0,2.09,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/23/1990,126,minute.maid,3392,8.129174997,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/23/1990,126,tropicana,3584,8.184234774,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/23/1990,128,dominicks,2944,7.98752448,0,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/23/1990,128,minute.maid,3392,8.129174997,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/23/1990,128,tropicana,9920,9.2023082,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/23/1990,129,dominicks,576,6.356107661,0,2.09,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/23/1990,129,minute.maid,4288,8.363575703,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/23/1990,129,tropicana,5376,8.589699882,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/23/1990,130,dominicks,2752,7.920083199,0,2.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/23/1990,130,minute.maid,2304,7.742402022,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/23/1990,130,tropicana,3904,8.269756948,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/23/1990,131,dominicks,4480,8.407378325,0,2.09,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/23/1990,131,minute.maid,6080,8.712759975,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/23/1990,131,tropicana,4928,8.502688505,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/23/1990,132,dominicks,1280,7.154615357,0,2.09,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/23/1990,132,minute.maid,3584,8.184234774,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/23/1990,132,tropicana,5760,8.658692754,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/30/1990,2,dominicks,25344,10.1402973,1,1.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/30/1990,2,minute.maid,4672,8.449342525,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/30/1990,2,tropicana,7168,8.877381955,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/30/1990,5,dominicks,30144,10.31374118,1,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/30/1990,5,minute.maid,5120,8.540909718,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/30/1990,5,tropicana,5888,8.68067166,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/30/1990,8,dominicks,52672,10.87183928,1,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/30/1990,8,minute.maid,6528,8.783855897,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/30/1990,8,tropicana,6144,8.723231275,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/30/1990,9,dominicks,39168,10.57561537,1,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/30/1990,9,minute.maid,5568,8.624791202,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/30/1990,9,tropicana,8960,9.100525506,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/30/1990,12,dominicks,35776,10.48503256,1,1.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/30/1990,12,minute.maid,4928,8.502688505,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 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-8/30/1990,18,minute.maid,6336,8.754002934,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/30/1990,18,tropicana,9792,9.189321005,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/30/1990,21,dominicks,26496,10.18474906,1,1.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/30/1990,21,minute.maid,3776,8.236420527,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/30/1990,21,tropicana,3328,8.110126802,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 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-8/30/1990,32,tropicana,9280,9.135616826,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/30/1990,33,dominicks,17536,9.77201119,1,1.89,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/30/1990,33,minute.maid,3776,8.236420527,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/30/1990,33,tropicana,7232,8.886270902,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/30/1990,40,dominicks,29888,10.30521234,1,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/30/1990,40,minute.maid,3904,8.269756948,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/30/1990,40,tropicana,5632,8.636219898,0,2.66,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/30/1990,44,dominicks,26432,10.18233068,1,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/30/1990,44,minute.maid,4096,8.317766167,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/30/1990,44,tropicana,6400,8.764053269,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/30/1990,45,dominicks,20160,9.911455722,1,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/30/1990,45,minute.maid,2880,7.965545573,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/30/1990,45,tropicana,4672,8.449342525,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/30/1990,47,dominicks,25920,10.16277015,1,1.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/30/1990,47,minute.maid,3328,8.110126802,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/30/1990,47,tropicana,3456,8.14786713,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/30/1990,48,dominicks,17088,9.746131742,1,1.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/30/1990,48,minute.maid,6144,8.723231275,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/30/1990,48,tropicana,2752,7.920083199,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/30/1990,49,dominicks,17088,9.746131742,1,1.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/30/1990,49,minute.maid,2560,7.847762538,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/30/1990,49,tropicana,3392,8.129174997,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/30/1990,50,dominicks,17408,9.76468515,1,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/30/1990,50,minute.maid,2688,7.896552702,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/30/1990,50,tropicana,3008,8.009030685,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/30/1990,51,dominicks,32896,10.40110635,1,1.89,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/30/1990,51,minute.maid,3648,8.201934351,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/30/1990,51,tropicana,3392,8.129174997,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/30/1990,52,dominicks,24000,10.08580911,1,1.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/30/1990,52,minute.maid,5888,8.68067166,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/30/1990,52,tropicana,9024,9.107642974,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/30/1990,53,dominicks,55552,10.9250748,1,1.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/30/1990,53,minute.maid,4672,8.449342525,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/30/1990,53,tropicana,8448,9.041685006,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/30/1990,54,dominicks,20288,9.917784857,1,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/30/1990,54,minute.maid,4736,8.462948177,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/30/1990,54,tropicana,2560,7.847762538,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/30/1990,56,dominicks,23168,10.0505273,1,1.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/30/1990,56,minute.maid,3776,8.236420527,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/30/1990,56,tropicana,4288,8.363575703,0,3.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 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-8/30/1990,119,tropicana,7168,8.877381955,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/30/1990,121,dominicks,30720,10.33266919,1,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/30/1990,121,minute.maid,8320,9.026417534,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/30/1990,121,tropicana,9152,9.121727714,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/30/1990,122,dominicks,30464,10.32430094,1,1.89,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/30/1990,122,minute.maid,9088,9.114710141,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/30/1990,122,tropicana,10304,9.240287448,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/30/1990,123,dominicks,57664,10.96238834,1,1.89,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/30/1990,123,minute.maid,5184,8.553332238,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/30/1990,123,tropicana,6400,8.764053269,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/30/1990,124,dominicks,60224,11.00582622,1,1.89,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/30/1990,124,minute.maid,3776,8.236420527,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/30/1990,124,tropicana,4864,8.489616424,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/30/1990,126,dominicks,22336,10.01395501,1,1.89,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/30/1990,126,minute.maid,4096,8.317766167,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/30/1990,126,tropicana,3328,8.110126802,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/30/1990,128,dominicks,52288,10.86452218,1,1.89,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/30/1990,128,minute.maid,7296,8.895081532,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/30/1990,128,tropicana,10304,9.240287448,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/30/1990,129,dominicks,19392,9.872615889,1,1.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/30/1990,129,minute.maid,4288,8.363575703,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/30/1990,129,tropicana,7744,8.954673629,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/30/1990,130,dominicks,64896,11.08054127,1,1.89,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/30/1990,130,minute.maid,3392,8.129174997,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/30/1990,130,tropicana,4288,8.363575703,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/30/1990,131,dominicks,39744,10.59021417,1,1.89,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/30/1990,131,minute.maid,6592,8.793612072,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/30/1990,131,tropicana,5248,8.565602331,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/30/1990,132,dominicks,45184,10.71849832,1,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/30/1990,132,minute.maid,6656,8.803273983,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/30/1990,132,tropicana,4544,8.42156296,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/6/1990,2,dominicks,10752,9.282847063,0,1.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/6/1990,2,minute.maid,2752,7.920083199,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/6/1990,2,tropicana,10880,9.29468152,0,3.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/6/1990,5,dominicks,8960,9.100525506,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/6/1990,5,minute.maid,4416,8.392989588,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/6/1990,5,tropicana,9536,9.162829389,0,3.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/6/1990,8,dominicks,16448,9.707959168,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/6/1990,8,minute.maid,5440,8.60153434,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/6/1990,8,tropicana,11008,9.30637756,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 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-9/6/1990,12,tropicana,13824,9.534161491,0,3.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/6/1990,14,dominicks,8000,8.987196821,0,1.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/6/1990,14,minute.maid,3008,8.009030685,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/6/1990,14,tropicana,11072,9.312174678,0,3.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/6/1990,18,dominicks,16256,9.69621735,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/6/1990,18,minute.maid,8256,9.018695488,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/6/1990,18,tropicana,8000,8.987196821,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/6/1990,21,dominicks,6464,8.7740036,0,1.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/6/1990,21,minute.maid,4224,8.348537825,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/6/1990,21,tropicana,2432,7.796469243,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/6/1990,28,dominicks,6592,8.793612072,0,1.89,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/6/1990,28,minute.maid,3200,8.070906089,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/6/1990,28,tropicana,4992,8.51559191,0,3.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/6/1990,32,dominicks,23232,10.05328592,0,1.89,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/6/1990,32,minute.maid,5376,8.589699882,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/6/1990,32,tropicana,17472,9.768354879,0,3.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/6/1990,33,dominicks,7936,8.979164649,0,1.89,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/6/1990,33,minute.maid,3392,8.129174997,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/6/1990,33,tropicana,14592,9.588228712,0,3.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/6/1990,40,dominicks,15872,9.67231183,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/6/1990,40,minute.maid,3968,8.286017468,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/6/1990,40,tropicana,6400,8.764053269,0,2.65,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/6/1990,44,dominicks,7168,8.877381955,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/6/1990,44,minute.maid,4864,8.489616424,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/6/1990,44,tropicana,13056,9.477003077,0,3.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/6/1990,45,dominicks,10496,9.258749511,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/6/1990,45,minute.maid,3200,8.070906089,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/6/1990,45,tropicana,5312,8.577723691,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/6/1990,47,dominicks,15616,9.656051309,0,1.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/6/1990,47,minute.maid,3136,8.050703382,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/6/1990,47,tropicana,4416,8.392989588,0,3.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/6/1990,48,dominicks,10688,9.276876896,0,1.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/6/1990,48,minute.maid,4288,8.363575703,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/6/1990,48,tropicana,6016,8.702177866,0,3.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/6/1990,49,dominicks,8256,9.018695488,0,1.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/6/1990,49,minute.maid,2880,7.965545573,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/6/1990,49,tropicana,4096,8.317766167,0,3.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/6/1990,50,dominicks,2368,7.769800996,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/6/1990,50,minute.maid,2560,7.847762538,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/6/1990,50,tropicana,2176,7.685243608,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/6/1990,51,dominicks,9216,9.128696383,0,1.89,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/6/1990,51,minute.maid,3264,8.090708716,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/6/1990,51,tropicana,5760,8.658692754,0,3.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/6/1990,53,dominicks,14784,9.601300794,0,1.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/6/1990,53,minute.maid,4032,8.30201781,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/6/1990,53,tropicana,18688,9.835636886,0,3.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/6/1990,54,dominicks,6528,8.783855897,0,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/6/1990,54,minute.maid,3968,8.286017468,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/6/1990,54,tropicana,5504,8.61323038,0,3.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/6/1990,56,dominicks,10112,9.221478116,0,1.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/6/1990,56,minute.maid,2944,7.98752448,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/6/1990,56,tropicana,6080,8.712759975,0,3.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/6/1990,59,dominicks,8192,9.010913347,0,1.89,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/6/1990,59,minute.maid,2624,7.87245515,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/6/1990,59,tropicana,2112,7.655390645,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/6/1990,62,dominicks,9024,9.107642974,0,1.89,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/6/1990,62,minute.maid,5440,8.60153434,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/6/1990,62,tropicana,16576,9.715711145,0,3.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/6/1990,64,dominicks,4736,8.462948177,0,1.89,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/6/1990,64,minute.maid,3712,8.219326094,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/6/1990,64,tropicana,2944,7.98752448,0,3.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/6/1990,67,dominicks,5696,8.647519453,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/6/1990,67,minute.maid,3200,8.070906089,0,2.62,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/6/1990,67,tropicana,7808,8.962904128,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/6/1990,68,dominicks,12864,9.462187991,0,1.89,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/6/1990,68,minute.maid,4864,8.489616424,0,3.17,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/6/1990,68,tropicana,9088,9.114710141,0,3.29,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/6/1990,70,dominicks,16256,9.69621735,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/6/1990,70,minute.maid,5312,8.577723691,0,2.62,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/6/1990,70,tropicana,11008,9.30637756,0,2.66,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/6/1990,71,dominicks,19200,9.862665558,0,1.89,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/6/1990,71,minute.maid,2048,7.624618986,0,3.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/6/1990,71,tropicana,6592,8.793612072,0,3.29,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/6/1990,72,dominicks,12096,9.400630098,0,1.89,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/6/1990,72,minute.maid,3072,8.030084094,0,3.17,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/6/1990,72,tropicana,13952,9.543378146,0,3.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/6/1990,73,dominicks,21824,9.990765561,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/6/1990,73,minute.maid,5888,8.68067166,0,2.62,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/6/1990,73,tropicana,8960,9.100525506,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/6/1990,74,dominicks,20032,9.905086274,0,1.89,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/6/1990,74,minute.maid,5184,8.553332238,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/6/1990,74,tropicana,9728,9.182763604,0,3.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/6/1990,75,dominicks,16128,9.688312171,0,1.89,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/6/1990,75,minute.maid,4032,8.30201781,0,3.17,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/6/1990,75,tropicana,10368,9.246479419,0,3.29,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/6/1990,76,dominicks,10240,9.234056899,0,1.89,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/6/1990,76,minute.maid,4160,8.333270353,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/6/1990,76,tropicana,6784,8.822322178,0,3.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/6/1990,77,dominicks,7360,8.903815212,0,1.89,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/6/1990,77,minute.maid,5696,8.647519453,0,2.62,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/6/1990,77,tropicana,8320,9.026417534,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/6/1990,78,dominicks,11712,9.368369236,0,1.89,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/6/1990,78,minute.maid,4800,8.476371197,0,2.62,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/6/1990,78,tropicana,5376,8.589699882,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/6/1990,80,dominicks,13312,9.496421163,0,1.89,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/6/1990,80,minute.maid,10240,9.234056899,0,2.62,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/6/1990,80,tropicana,10816,9.288781798,0,3.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/6/1990,83,dominicks,17856,9.790094865,0,1.89,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/6/1990,83,minute.maid,4096,8.317766167,0,2.62,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/6/1990,83,tropicana,5120,8.540909718,0,3.19,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/6/1990,84,dominicks,15488,9.64782081,0,1.89,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/6/1990,84,minute.maid,4800,8.476371197,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/6/1990,84,tropicana,5888,8.68067166,0,3.29,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/6/1990,86,dominicks,10624,9.270870872,0,1.89,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/6/1990,86,minute.maid,3584,8.184234774,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/6/1990,86,tropicana,5632,8.636219898,0,3.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/6/1990,88,dominicks,8064,8.99516499,0,1.89,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/6/1990,88,minute.maid,3328,8.110126802,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/6/1990,88,tropicana,11392,9.340666634,0,3.29,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/6/1990,89,dominicks,11968,9.3899917,0,1.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/6/1990,89,minute.maid,4416,8.392989588,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/6/1990,89,tropicana,6272,8.743850562,0,3.29,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/6/1990,90,dominicks,7360,8.903815212,0,1.89,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/6/1990,90,minute.maid,3200,8.070906089,0,2.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/6/1990,90,tropicana,2944,7.98752448,0,3.29,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/6/1990,91,dominicks,8128,9.00307017,0,1.89,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/6/1990,91,minute.maid,2944,7.98752448,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/6/1990,91,tropicana,3840,8.253227646,0,3.29,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/6/1990,92,dominicks,11840,9.379238908,0,1.89,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/6/1990,92,minute.maid,3008,8.009030685,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/6/1990,92,tropicana,4032,8.30201781,0,3.29,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/6/1990,93,dominicks,15680,9.660141294,0,1.89,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/6/1990,93,minute.maid,3136,8.050703382,0,3.17,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/6/1990,93,tropicana,10240,9.234056899,0,3.29,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/6/1990,94,dominicks,11520,9.351839934,0,1.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/6/1990,94,minute.maid,5952,8.691482577,0,2.62,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/6/1990,94,tropicana,6144,8.723231275,0,3.19,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/6/1990,95,dominicks,12352,9.421573272,0,1.89,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/6/1990,95,minute.maid,2560,7.847762538,0,3.17,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/6/1990,95,tropicana,5632,8.636219898,0,3.29,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/6/1990,97,dominicks,8064,8.99516499,0,1.89,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/6/1990,97,minute.maid,2624,7.87245515,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/6/1990,97,tropicana,1536,7.336936914,0,3.29,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/6/1990,98,dominicks,18944,9.849242538,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/6/1990,98,minute.maid,6464,8.7740036,0,2.62,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/6/1990,98,tropicana,6400,8.764053269,0,3.19,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/6/1990,100,dominicks,8192,9.010913347,0,1.89,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/6/1990,100,minute.maid,3520,8.166216269,0,3.17,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/6/1990,100,tropicana,8384,9.034080407,0,3.29,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/6/1990,101,dominicks,9792,9.189321005,0,1.89,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/6/1990,101,minute.maid,5248,8.565602331,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/6/1990,101,tropicana,8960,9.100525506,0,3.29,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/6/1990,102,dominicks,14016,9.547954813,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/6/1990,102,minute.maid,6720,8.812843434,0,2.62,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/6/1990,102,tropicana,7872,8.971067439,0,3.19,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/6/1990,103,dominicks,10432,9.252633284,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/6/1990,103,minute.maid,2752,7.920083199,0,2.62,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/6/1990,103,tropicana,1216,7.103322063,0,3.19,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/6/1990,104,dominicks,6912,8.841014311,0,1.89,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/6/1990,104,minute.maid,3136,8.050703382,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/6/1990,104,tropicana,3712,8.219326094,0,3.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/6/1990,105,dominicks,16448,9.707959168,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/6/1990,105,minute.maid,4800,8.476371197,0,2.62,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/6/1990,105,tropicana,3968,8.286017468,0,3.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/6/1990,106,dominicks,8384,9.034080407,0,1.89,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/6/1990,106,minute.maid,2176,7.685243608,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/6/1990,106,tropicana,1664,7.416979621,0,3.29,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/6/1990,107,dominicks,15168,9.626943225,0,1.89,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/6/1990,107,minute.maid,4800,8.476371197,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/6/1990,107,tropicana,10816,9.288781798,0,3.29,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/6/1990,109,dominicks,13120,9.481893063,0,1.89,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/6/1990,109,minute.maid,6400,8.764053269,0,3.17,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/6/1990,109,tropicana,27968,10.23881628,0,3.29,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/6/1990,110,dominicks,6080,8.712759975,0,1.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/6/1990,110,minute.maid,2624,7.87245515,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/6/1990,110,tropicana,9344,9.142489705,0,3.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/6/1990,111,dominicks,12544,9.436997743,0,1.89,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/6/1990,111,minute.maid,3008,8.009030685,0,3.17,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/6/1990,111,tropicana,3776,8.236420527,0,3.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/6/1990,112,dominicks,13824,9.534161491,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/6/1990,112,minute.maid,10496,9.258749511,0,2.62,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/6/1990,112,tropicana,17728,9.78290059,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/6/1990,113,dominicks,16256,9.69621735,0,1.89,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/6/1990,113,minute.maid,6400,8.764053269,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/6/1990,113,tropicana,7616,8.938006577,0,3.29,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/6/1990,114,dominicks,11200,9.323669057,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/6/1990,114,minute.maid,6528,8.783855897,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/6/1990,114,tropicana,6080,8.712759975,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/6/1990,115,dominicks,5760,8.658692754,0,1.89,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/6/1990,115,minute.maid,5696,8.647519453,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/6/1990,115,tropicana,7616,8.938006577,0,3.29,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/6/1990,116,dominicks,8384,9.034080407,0,1.89,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/6/1990,116,minute.maid,2560,7.847762538,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/6/1990,116,tropicana,5504,8.61323038,0,3.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/6/1990,117,dominicks,6656,8.803273983,0,1.89,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/6/1990,117,minute.maid,6272,8.743850562,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/6/1990,117,tropicana,3776,8.236420527,0,3.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/6/1990,118,dominicks,11648,9.36288977,0,1.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/6/1990,118,minute.maid,4096,8.317766167,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/6/1990,118,tropicana,9792,9.189321005,0,3.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/6/1990,119,dominicks,12224,9.411156511,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/6/1990,119,minute.maid,5632,8.636219898,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/6/1990,119,tropicana,8896,9.093357017,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/6/1990,121,dominicks,17920,9.793672686,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/6/1990,121,minute.maid,7552,8.929567708,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/6/1990,121,tropicana,7424,8.912473275,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/6/1990,122,dominicks,12032,9.395325046,0,1.89,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/6/1990,122,minute.maid,6784,8.822322178,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/6/1990,122,tropicana,7936,8.979164649,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/6/1990,123,dominicks,32384,10.38541975,0,1.89,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/6/1990,123,minute.maid,4864,8.489616424,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/6/1990,123,tropicana,7360,8.903815212,0,3.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/6/1990,124,dominicks,14336,9.570529135,0,1.89,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/6/1990,124,minute.maid,3264,8.090708716,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/6/1990,124,tropicana,8256,9.018695488,0,3.29,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/6/1990,126,dominicks,12224,9.411156511,0,1.89,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/6/1990,126,minute.maid,3520,8.166216269,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/6/1990,126,tropicana,6784,8.822322178,0,3.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/6/1990,128,dominicks,20032,9.905086274,0,1.89,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/6/1990,128,minute.maid,6336,8.754002934,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/6/1990,128,tropicana,19008,9.852615222,0,3.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/6/1990,129,dominicks,7104,8.868413285,0,1.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/6/1990,129,minute.maid,4800,8.476371197,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/6/1990,129,tropicana,7040,8.859363449,0,3.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/6/1990,130,dominicks,22656,10.02818,0,1.89,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/6/1990,130,minute.maid,2816,7.943072717,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/6/1990,130,tropicana,6592,8.793612072,0,3.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/6/1990,131,dominicks,11520,9.351839934,0,1.89,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/6/1990,131,minute.maid,6912,8.841014311,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/6/1990,131,tropicana,7488,8.921057018,0,3.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/6/1990,132,dominicks,9920,9.2023082,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/6/1990,132,minute.maid,5568,8.624791202,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/6/1990,132,tropicana,4480,8.407378325,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/13/1990,2,dominicks,6656,8.803273983,0,1.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/13/1990,2,minute.maid,26176,10.17259824,1,2.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/13/1990,2,tropicana,7744,8.954673629,0,3.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/13/1990,5,dominicks,8192,9.010913347,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/13/1990,5,minute.maid,30208,10.31586207,1,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/13/1990,5,tropicana,8320,9.026417534,0,3.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/13/1990,8,dominicks,19072,9.85597657,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/13/1990,8,minute.maid,36544,10.50627229,1,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/13/1990,8,tropicana,5760,8.658692754,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/13/1990,9,dominicks,8128,9.00307017,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/13/1990,9,minute.maid,35776,10.48503256,1,2.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/13/1990,9,tropicana,9472,9.156095357,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/13/1990,12,dominicks,8256,9.018695488,0,1.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/13/1990,12,minute.maid,45248,10.71991375,1,2.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/13/1990,12,tropicana,12736,9.452187908,0,3.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/13/1990,14,dominicks,1216,7.103322063,0,1.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/13/1990,14,minute.maid,25792,10.15781965,1,2.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/13/1990,14,tropicana,10304,9.240287448,0,3.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/13/1990,18,dominicks,29760,10.30092049,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/13/1990,18,minute.maid,22400,10.01681624,1,2.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/13/1990,18,tropicana,8256,9.018695488,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/13/1990,21,dominicks,9920,9.2023082,0,1.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/13/1990,21,minute.maid,13376,9.501217335,1,2.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/13/1990,21,tropicana,2432,7.796469243,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/13/1990,28,dominicks,8704,9.071537969,0,1.89,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/13/1990,28,minute.maid,14400,9.574983486,1,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/13/1990,28,tropicana,5056,8.528330936,0,3.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/13/1990,32,dominicks,17216,9.753594463,0,1.89,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/13/1990,32,minute.maid,43968,10.69121738,1,2.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/13/1990,32,tropicana,11584,9.357380115,0,3.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/13/1990,33,dominicks,3136,8.050703382,0,1.89,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/13/1990,33,minute.maid,23296,10.05603695,1,2.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/13/1990,33,tropicana,11392,9.340666634,0,3.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/13/1990,40,dominicks,13632,9.520175249,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/13/1990,40,minute.maid,20160,9.911455722,1,2.19,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/13/1990,40,tropicana,5760,8.658692754,0,2.67,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/13/1990,44,dominicks,7680,8.946374826,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/13/1990,44,minute.maid,36288,10.49924239,1,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/13/1990,44,tropicana,8704,9.071537969,0,3.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/13/1990,45,dominicks,13632,9.520175249,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/13/1990,45,minute.maid,9728,9.182763604,1,2.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/13/1990,45,tropicana,5248,8.565602331,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/13/1990,47,dominicks,10560,9.264828557,0,1.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/13/1990,47,minute.maid,14592,9.588228712,1,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/13/1990,47,tropicana,4352,8.378390789,0,3.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/13/1990,48,dominicks,9792,9.189321005,0,1.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/13/1990,48,minute.maid,9664,9.17616292,1,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/13/1990,48,tropicana,5632,8.636219898,0,3.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/13/1990,49,dominicks,6144,8.723231275,0,1.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/13/1990,49,minute.maid,15296,9.635346635,1,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/13/1990,49,tropicana,4544,8.42156296,0,3.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/13/1990,50,dominicks,6144,8.723231275,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/13/1990,50,minute.maid,14080,9.55251063,1,2.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/13/1990,50,tropicana,2688,7.896552702,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/13/1990,51,dominicks,12224,9.411156511,0,1.89,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/13/1990,51,minute.maid,24384,10.10168246,1,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/13/1990,51,tropicana,5184,8.553332238,0,3.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/13/1990,52,dominicks,9472,9.156095357,0,1.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/13/1990,52,minute.maid,36544,10.50627229,1,2.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/13/1990,52,tropicana,15552,9.651944527,0,3.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/13/1990,53,dominicks,13824,9.534161491,0,1.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/13/1990,53,minute.maid,47296,10.764181,1,2.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/13/1990,53,tropicana,15680,9.660141294,0,3.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/13/1990,54,dominicks,8192,9.010913347,0,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/13/1990,54,minute.maid,16448,9.707959168,1,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/13/1990,54,tropicana,5440,8.60153434,0,3.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/13/1990,56,dominicks,5056,8.528330936,0,1.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/13/1990,56,minute.maid,17472,9.768354879,1,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/13/1990,56,tropicana,7680,8.946374826,0,3.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/13/1990,59,dominicks,1280,7.154615357,0,1.89,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/13/1990,59,minute.maid,160960,11.98891117,1,1.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/13/1990,59,tropicana,3264,8.090708716,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/13/1990,62,dominicks,1600,7.377758908,0,1.89,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/13/1990,62,minute.maid,25344,10.1402973,1,2.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/13/1990,62,tropicana,16192,9.692272572,0,3.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/13/1990,64,dominicks,6656,8.803273983,0,1.89,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/13/1990,64,minute.maid,9216,9.128696383,1,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/13/1990,64,tropicana,2112,7.655390645,0,3.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/13/1990,67,dominicks,4736,8.462948177,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/13/1990,67,minute.maid,17920,9.793672686,1,2.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/13/1990,67,tropicana,5888,8.68067166,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/13/1990,70,dominicks,13696,9.524859098,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/13/1990,70,minute.maid,25088,10.13014492,1,2.19,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/13/1990,70,tropicana,10048,9.215128889,0,2.64,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/13/1990,71,dominicks,17152,9.749870064,0,1.89,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/13/1990,71,minute.maid,23360,10.05878044,1,2.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/13/1990,71,tropicana,5184,8.553332238,0,3.29,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/13/1990,72,dominicks,11200,9.323669057,0,1.89,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/13/1990,72,minute.maid,27776,10.23192762,1,2.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/13/1990,72,tropicana,11392,9.340666634,0,3.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/13/1990,73,dominicks,8640,9.064157862,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/13/1990,73,minute.maid,25792,10.15781965,1,2.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 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-9/13/1990,100,tropicana,7680,8.946374826,0,3.29,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/13/1990,101,dominicks,12736,9.452187908,0,1.89,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/13/1990,101,minute.maid,30464,10.32430094,1,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/13/1990,101,tropicana,9408,9.14931567,0,3.29,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/13/1990,102,dominicks,36416,10.50276352,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 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-9/13/1990,104,dominicks,10624,9.270870872,0,1.89,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/13/1990,104,minute.maid,17088,9.746131742,1,2.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/13/1990,104,tropicana,3712,8.219326094,0,3.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/13/1990,105,dominicks,18816,9.842462851,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/13/1990,105,minute.maid,26816,10.196754,1,2.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/13/1990,105,tropicana,5440,8.60153434,0,3.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/13/1990,106,dominicks,6144,8.723231275,0,1.89,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/13/1990,106,minute.maid,8768,9.078864009,1,2.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/13/1990,106,tropicana,2432,7.796469243,0,3.29,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/13/1990,107,dominicks,19008,9.852615222,0,1.89,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/13/1990,107,minute.maid,28416,10.25470765,1,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/13/1990,107,tropicana,9984,9.208739091,0,3.29,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/13/1990,109,dominicks,7680,8.946374826,0,1.89,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/13/1990,109,minute.maid,57152,10.95346966,1,2.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/13/1990,109,tropicana,17920,9.793672686,0,3.29,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/13/1990,110,dominicks,7744,8.954673629,0,1.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/13/1990,110,minute.maid,19904,9.898675996,1,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/13/1990,110,tropicana,4544,8.42156296,0,3.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/13/1990,111,dominicks,20928,9.948843254,0,1.89,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/13/1990,111,minute.maid,48768,10.79482964,1,2.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/13/1990,111,tropicana,3712,8.219326094,0,3.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/13/1990,112,dominicks,14080,9.55251063,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/13/1990,112,minute.maid,40256,10.60301434,1,2.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/13/1990,112,tropicana,16320,9.700146629,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/13/1990,113,dominicks,8384,9.034080407,0,1.89,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/13/1990,113,minute.maid,38464,10.55747802,1,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/13/1990,113,tropicana,6656,8.803273983,0,3.29,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/13/1990,114,dominicks,21824,9.990765561,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/13/1990,114,minute.maid,37248,10.52535353,1,2.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/13/1990,114,tropicana,4992,8.51559191,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/13/1990,115,dominicks,5184,8.553332238,0,1.89,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/13/1990,115,minute.maid,28672,10.26367632,1,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/13/1990,115,tropicana,8768,9.078864009,0,3.29,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/13/1990,116,dominicks,12032,9.395325046,0,1.89,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/13/1990,116,minute.maid,19136,9.859326657,1,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/13/1990,116,tropicana,5568,8.624791202,0,3.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/13/1990,117,dominicks,2880,7.965545573,0,1.89,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/13/1990,117,minute.maid,14720,9.596962392,1,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/13/1990,117,tropicana,3200,8.070906089,0,3.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/13/1990,118,dominicks,5504,8.61323038,0,1.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/13/1990,118,minute.maid,25280,10.13776885,1,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/13/1990,118,tropicana,8320,9.026417534,0,3.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/13/1990,119,dominicks,11136,9.317938383,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/13/1990,119,minute.maid,21312,9.967025573,1,2.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/13/1990,119,tropicana,5824,8.66974259,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/13/1990,121,dominicks,21312,9.967025573,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/13/1990,121,minute.maid,22848,10.03661887,1,2.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/13/1990,121,tropicana,8704,9.071537969,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/13/1990,122,dominicks,12800,9.45720045,0,1.89,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/13/1990,122,minute.maid,33152,10.40885833,1,2.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/13/1990,122,tropicana,8704,9.071537969,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/13/1990,123,dominicks,14976,9.614204199,0,1.89,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/13/1990,123,minute.maid,41984,10.64504387,1,2.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/13/1990,123,tropicana,7232,8.886270902,0,3.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/13/1990,124,dominicks,14528,9.583833101,0,1.89,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/13/1990,124,minute.maid,37440,10.53049493,1,2.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/13/1990,124,tropicana,8640,9.064157862,0,3.29,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/13/1990,126,dominicks,6912,8.841014311,0,1.89,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/13/1990,126,minute.maid,19904,9.898675996,1,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/13/1990,126,tropicana,6464,8.7740036,0,3.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/13/1990,128,dominicks,13184,9.486759252,0,1.89,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/13/1990,128,minute.maid,60416,11.00900925,1,2.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/13/1990,128,tropicana,18112,9.804329981,0,3.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/13/1990,129,dominicks,7744,8.954673629,0,1.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/13/1990,129,minute.maid,19072,9.85597657,1,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/13/1990,129,tropicana,7744,8.954673629,0,3.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/13/1990,130,dominicks,9216,9.128696383,0,1.89,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/13/1990,130,minute.maid,35840,10.48681987,1,2.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/13/1990,130,tropicana,5824,8.66974259,0,3.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/13/1990,131,dominicks,14528,9.583833101,0,1.89,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/13/1990,131,minute.maid,28416,10.25470765,1,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/13/1990,131,tropicana,6336,8.754002934,0,3.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/13/1990,132,dominicks,18176,9.807857322,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/13/1990,132,minute.maid,35264,10.47061789,1,2.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/13/1990,132,tropicana,4480,8.407378325,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/20/1990,2,dominicks,6592,8.793612072,0,1.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/20/1990,2,minute.maid,3712,8.219326094,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/20/1990,2,tropicana,8512,9.049232212,0,3.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/20/1990,5,dominicks,6528,8.783855897,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/20/1990,5,minute.maid,4160,8.333270353,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/20/1990,5,tropicana,8000,8.987196821,0,3.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/20/1990,8,dominicks,13376,9.501217335,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/20/1990,8,minute.maid,3776,8.236420527,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/20/1990,8,tropicana,10112,9.221478116,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/20/1990,9,dominicks,4608,8.435549202,0,1.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/20/1990,9,minute.maid,3776,8.236420527,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/20/1990,9,tropicana,9792,9.189321005,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/20/1990,12,dominicks,8000,8.987196821,0,1.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/20/1990,12,minute.maid,4864,8.489616424,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/20/1990,12,tropicana,10112,9.221478116,0,3.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/20/1990,14,dominicks,5120,8.540909718,0,1.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/20/1990,14,minute.maid,4096,8.317766167,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/20/1990,14,tropicana,9856,9.195835686,0,3.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/20/1990,18,dominicks,19008,9.852615222,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/20/1990,18,minute.maid,5504,8.61323038,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/20/1990,18,tropicana,10880,9.29468152,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/20/1990,21,dominicks,8064,8.99516499,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/20/1990,21,minute.maid,3200,8.070906089,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/20/1990,21,tropicana,2944,7.98752448,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/20/1990,28,dominicks,5888,8.68067166,0,1.79,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/20/1990,28,minute.maid,2368,7.769800996,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/20/1990,28,tropicana,5952,8.691482577,0,3.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/20/1990,32,dominicks,16576,9.715711145,0,1.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/20/1990,32,minute.maid,3712,8.219326094,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/20/1990,32,tropicana,13056,9.477003077,0,3.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/20/1990,33,dominicks,1792,7.491087594,0,1.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/20/1990,33,minute.maid,5312,8.577723691,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/20/1990,33,tropicana,5440,8.60153434,0,3.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/20/1990,40,dominicks,16256,9.69621735,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/20/1990,40,minute.maid,2496,7.82244473,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/20/1990,40,tropicana,6784,8.822322178,0,2.66,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/20/1990,44,dominicks,6912,8.841014311,0,1.79,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/20/1990,44,minute.maid,3072,8.030084094,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/20/1990,44,tropicana,8832,9.086136769,0,3.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/20/1990,45,dominicks,8960,9.100525506,0,1.79,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/20/1990,45,minute.maid,2112,7.655390645,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/20/1990,45,tropicana,7040,8.859363449,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/20/1990,47,dominicks,10048,9.215128889,0,1.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/20/1990,47,minute.maid,3008,8.009030685,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/20/1990,47,tropicana,4288,8.363575703,0,3.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/20/1990,48,dominicks,7808,8.962904128,0,1.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/20/1990,48,minute.maid,4416,8.392989588,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/20/1990,48,tropicana,6080,8.712759975,0,3.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/20/1990,49,dominicks,4672,8.449342525,0,1.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/20/1990,49,minute.maid,2880,7.965545573,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/20/1990,49,tropicana,5504,8.61323038,0,3.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/20/1990,50,dominicks,5632,8.636219898,0,1.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/20/1990,50,minute.maid,2432,7.796469243,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/20/1990,50,tropicana,2176,7.685243608,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/20/1990,51,dominicks,7232,8.886270902,0,1.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/20/1990,51,minute.maid,2816,7.943072717,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/20/1990,51,tropicana,6848,8.831711918,0,3.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/20/1990,52,dominicks,7040,8.859363449,0,1.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/20/1990,52,minute.maid,4736,8.462948177,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/20/1990,52,tropicana,13120,9.481893063,0,3.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/20/1990,53,dominicks,8256,9.018695488,0,1.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/20/1990,53,minute.maid,3584,8.184234774,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/20/1990,53,tropicana,10880,9.29468152,0,3.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/20/1990,54,dominicks,7232,8.886270902,0,1.79,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/20/1990,54,minute.maid,3200,8.070906089,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/20/1990,54,tropicana,6016,8.702177866,0,3.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/20/1990,56,dominicks,9024,9.107642974,0,1.79,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/20/1990,56,minute.maid,2560,7.847762538,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/20/1990,56,tropicana,7424,8.912473275,0,3.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/20/1990,59,dominicks,3392,8.129174997,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/20/1990,59,minute.maid,2624,7.87245515,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/20/1990,59,tropicana,3712,8.219326094,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/20/1990,62,dominicks,3584,8.184234774,0,1.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/20/1990,62,minute.maid,4352,8.378390789,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/20/1990,62,tropicana,15872,9.67231183,0,3.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/20/1990,64,dominicks,5952,8.691482577,0,1.79,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/20/1990,64,minute.maid,2688,7.896552702,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/20/1990,64,tropicana,4608,8.435549202,0,3.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/20/1990,67,dominicks,7104,8.868413285,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/20/1990,67,minute.maid,3584,8.184234774,0,2.62,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/20/1990,67,tropicana,6016,8.702177866,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/20/1990,68,dominicks,14656,9.592605087,0,1.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/20/1990,68,minute.maid,4800,8.476371197,0,3.17,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/20/1990,68,tropicana,7744,8.954673629,0,3.29,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/20/1990,70,dominicks,11200,9.323669057,0,1.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/20/1990,70,minute.maid,5824,8.66974259,0,2.62,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/20/1990,70,tropicana,10112,9.221478116,0,2.68,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/20/1990,71,dominicks,16512,9.711842668,0,1.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/20/1990,71,minute.maid,2240,7.714231145,0,3.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/20/1990,71,tropicana,6272,8.743850562,0,3.29,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/20/1990,72,dominicks,9856,9.195835686,0,1.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/20/1990,72,minute.maid,3136,8.050703382,0,3.17,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/20/1990,72,tropicana,10368,9.246479419,0,3.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/20/1990,73,dominicks,24640,10.11212642,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/20/1990,73,minute.maid,4288,8.363575703,0,2.62,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/20/1990,73,tropicana,7104,8.868413285,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/20/1990,74,dominicks,11584,9.357380115,0,1.79,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/20/1990,74,minute.maid,4544,8.42156296,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/20/1990,74,tropicana,8896,9.093357017,0,3.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/20/1990,75,dominicks,11264,9.329367078,0,1.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/20/1990,75,minute.maid,2752,7.920083199,0,3.17,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/20/1990,75,tropicana,8640,9.064157862,0,3.29,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/20/1990,76,dominicks,8128,9.00307017,0,1.79,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/20/1990,76,minute.maid,4224,8.348537825,0,2.62,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/20/1990,76,tropicana,5376,8.589699882,0,3.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/20/1990,77,dominicks,9600,9.169518378,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/20/1990,77,minute.maid,5632,8.636219898,0,2.62,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/20/1990,77,tropicana,12672,9.447150114,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/20/1990,78,dominicks,9920,9.2023082,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/20/1990,78,minute.maid,4992,8.51559191,0,2.62,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/20/1990,78,tropicana,7872,8.971067439,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/20/1990,80,dominicks,17536,9.77201119,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/20/1990,80,minute.maid,7168,8.877381955,0,2.62,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/20/1990,80,tropicana,17472,9.768354879,0,3.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/20/1990,81,dominicks,8704,9.071537969,0,1.79,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/20/1990,81,minute.maid,3456,8.14786713,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/20/1990,81,tropicana,11072,9.312174678,0,3.29,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/20/1990,83,dominicks,13888,9.538780437,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/20/1990,83,minute.maid,3904,8.269756948,0,2.62,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/20/1990,83,tropicana,7360,8.903815212,0,3.19,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/20/1990,84,dominicks,9856,9.195835686,0,1.79,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/20/1990,84,minute.maid,4224,8.348537825,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/20/1990,84,tropicana,5376,8.589699882,0,3.29,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/20/1990,86,dominicks,11072,9.312174678,0,1.79,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/20/1990,86,minute.maid,4480,8.407378325,0,2.62,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/20/1990,86,tropicana,8320,9.026417534,0,3.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/20/1990,88,dominicks,6464,8.7740036,0,1.79,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/20/1990,88,minute.maid,2880,7.965545573,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/20/1990,88,tropicana,4800,8.476371197,0,3.29,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/20/1990,89,dominicks,15232,9.631153757,0,1.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 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-9/20/1990,114,dominicks,14400,9.574983486,0,1.79,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/20/1990,114,minute.maid,8640,9.064157862,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/20/1990,114,tropicana,8960,9.100525506,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/20/1990,115,dominicks,4992,8.51559191,0,1.79,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/20/1990,115,minute.maid,4032,8.30201781,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/20/1990,115,tropicana,8640,9.064157862,0,3.29,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/20/1990,116,dominicks,8896,9.093357017,0,1.79,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/20/1990,116,minute.maid,2432,7.796469243,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/20/1990,116,tropicana,5248,8.565602331,0,3.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/20/1990,117,dominicks,3776,8.236420527,0,1.79,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/20/1990,117,minute.maid,3072,8.030084094,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/20/1990,117,tropicana,7424,8.912473275,0,3.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/20/1990,118,dominicks,6016,8.702177866,0,1.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/20/1990,118,minute.maid,3328,8.110126802,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/20/1990,118,tropicana,6528,8.783855897,0,3.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/20/1990,119,dominicks,10624,9.270870872,0,1.79,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/20/1990,119,minute.maid,4096,8.317766167,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/20/1990,119,tropicana,8576,9.056722883,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/20/1990,121,dominicks,11072,9.312174678,0,1.79,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/20/1990,121,minute.maid,7488,8.921057018,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/20/1990,121,tropicana,9792,9.189321005,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/20/1990,122,dominicks,8960,9.100525506,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/20/1990,122,minute.maid,6528,8.783855897,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/20/1990,122,tropicana,8896,9.093357017,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/20/1990,123,dominicks,11264,9.329367078,0,1.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/20/1990,123,minute.maid,3328,8.110126802,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/20/1990,123,tropicana,5440,8.60153434,0,3.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/20/1990,124,dominicks,17728,9.78290059,0,1.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/20/1990,124,minute.maid,3072,8.030084094,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/20/1990,124,tropicana,6912,8.841014311,0,3.29,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/20/1990,126,dominicks,7168,8.877381955,0,1.79,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/20/1990,126,minute.maid,2816,7.943072717,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/20/1990,126,tropicana,5056,8.528330936,0,3.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/20/1990,128,dominicks,21056,9.954940834,0,1.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/20/1990,128,minute.maid,5184,8.553332238,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/20/1990,128,tropicana,12608,9.442086812,0,3.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/20/1990,129,dominicks,6976,8.850230966,0,1.79,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/20/1990,129,minute.maid,4032,8.30201781,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/20/1990,129,tropicana,8640,9.064157862,0,3.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/20/1990,130,dominicks,18880,9.84585844,0,1.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/20/1990,130,minute.maid,2368,7.769800996,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/20/1990,130,tropicana,7488,8.921057018,0,3.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/20/1990,131,dominicks,13440,9.505990614,0,1.79,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/20/1990,131,minute.maid,5056,8.528330936,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/20/1990,131,tropicana,7936,8.979164649,0,3.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/20/1990,132,dominicks,16960,9.738612909,0,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/20/1990,132,minute.maid,6272,8.743850562,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/20/1990,132,tropicana,6336,8.754002934,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/27/1990,5,dominicks,34688,10.45414909,1,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/27/1990,5,minute.maid,4992,8.51559191,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/27/1990,5,tropicana,5824,8.66974259,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/27/1990,8,dominicks,61440,11.02581637,1,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/27/1990,8,minute.maid,5504,8.61323038,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/27/1990,8,tropicana,8448,9.041685006,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/27/1990,9,dominicks,45184,10.71849832,1,1.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/27/1990,9,minute.maid,4800,8.476371197,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/27/1990,9,tropicana,9664,9.17616292,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/27/1990,12,dominicks,46912,10.75602879,1,1.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/27/1990,12,minute.maid,6528,8.783855897,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/27/1990,12,tropicana,9088,9.114710141,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/27/1990,14,dominicks,10816,9.288781798,1,1.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/27/1990,14,minute.maid,3392,8.129174997,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/27/1990,14,tropicana,7616,8.938006577,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/27/1990,18,dominicks,56384,10.93994071,1,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/27/1990,18,minute.maid,6720,8.812843434,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/27/1990,18,tropicana,7680,8.946374826,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/27/1990,21,dominicks,29888,10.30521234,1,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/27/1990,21,minute.maid,3456,8.14786713,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/27/1990,21,tropicana,3456,8.14786713,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/27/1990,28,dominicks,17344,9.761001904,1,1.79,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/27/1990,28,minute.maid,3904,8.269756948,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/27/1990,28,tropicana,4672,8.449342525,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/27/1990,32,dominicks,47936,10.77762207,1,1.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/27/1990,32,minute.maid,5248,8.565602331,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/27/1990,32,tropicana,8896,9.093357017,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/27/1990,33,dominicks,11200,9.323669057,1,1.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/27/1990,33,minute.maid,4736,8.462948177,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/27/1990,33,tropicana,9344,9.142489705,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/27/1990,40,dominicks,44352,10.69991308,1,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/27/1990,40,minute.maid,3776,8.236420527,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/27/1990,40,tropicana,7680,8.946374826,0,2.67,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/27/1990,44,dominicks,32704,10.39525267,1,1.79,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/27/1990,44,minute.maid,4864,8.489616424,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/27/1990,44,tropicana,5696,8.647519453,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/27/1990,45,dominicks,20992,9.951896692,1,1.79,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/27/1990,45,minute.maid,3008,8.009030685,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/27/1990,45,tropicana,5760,8.658692754,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/27/1990,47,dominicks,29376,10.28793329,1,1.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/27/1990,47,minute.maid,3840,8.253227646,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/27/1990,47,tropicana,3584,8.184234774,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/27/1990,48,dominicks,16256,9.69621735,1,1.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/27/1990,48,minute.maid,5184,8.553332238,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/27/1990,48,tropicana,4992,8.51559191,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/27/1990,49,dominicks,22784,10.03381381,1,1.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/27/1990,49,minute.maid,2688,7.896552702,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/27/1990,49,tropicana,3328,8.110126802,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/27/1990,50,dominicks,23040,10.04498712,1,1.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/27/1990,50,minute.maid,3648,8.201934351,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/27/1990,50,tropicana,2944,7.98752448,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/27/1990,51,dominicks,35072,10.46515837,1,1.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/27/1990,51,minute.maid,3648,8.201934351,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/27/1990,51,tropicana,5312,8.577723691,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/27/1990,52,dominicks,25984,10.16523624,1,1.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/27/1990,52,minute.maid,5568,8.624791202,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/27/1990,52,tropicana,9280,9.135616826,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/27/1990,53,dominicks,41664,10.63739273,1,1.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/27/1990,53,minute.maid,6208,8.733594062,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/27/1990,53,tropicana,13952,9.543378146,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/27/1990,54,dominicks,20608,9.933434629,1,1.79,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/27/1990,54,minute.maid,3008,8.009030685,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/27/1990,54,tropicana,4096,8.317766167,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/27/1990,56,dominicks,26240,10.17504024,1,1.79,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/27/1990,56,minute.maid,2944,7.98752448,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/27/1990,56,tropicana,5056,8.528330936,0,3.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/27/1990,59,dominicks,141824,11.86234213,1,0.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/27/1990,59,minute.maid,3264,8.090708716,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/27/1990,59,tropicana,3520,8.166216269,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/27/1990,62,dominicks,19328,9.869310101,1,1.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/27/1990,62,minute.maid,5952,8.691482577,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/27/1990,62,tropicana,11712,9.368369236,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/27/1990,64,dominicks,12800,9.45720045,1,1.79,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/27/1990,64,minute.maid,3328,8.110126802,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/27/1990,64,tropicana,3328,8.110126802,0,3.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/27/1990,67,dominicks,28864,10.27035042,1,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/27/1990,67,minute.maid,2688,7.896552702,0,2.62,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/27/1990,67,tropicana,6336,8.754002934,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/27/1990,70,dominicks,35136,10.46698153,1,1.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/27/1990,70,minute.maid,5760,8.658692754,0,2.62,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/27/1990,70,tropicana,10112,9.221478116,0,2.66,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/27/1990,71,dominicks,47680,10.7722673,1,1.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/27/1990,71,minute.maid,2240,7.714231145,0,3.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/27/1990,71,tropicana,5376,8.589699882,0,3.87,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/27/1990,72,dominicks,35520,10.4778512,1,1.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/27/1990,72,minute.maid,3008,8.009030685,0,3.17,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/27/1990,72,tropicana,8000,8.987196821,0,3.87,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/27/1990,73,dominicks,68352,11.1324261,1,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/27/1990,73,minute.maid,4544,8.42156296,0,2.62,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/27/1990,73,tropicana,9024,9.107642974,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/27/1990,74,dominicks,54400,10.90411943,1,1.79,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/27/1990,74,minute.maid,5696,8.647519453,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/27/1990,74,tropicana,7552,8.929567708,0,3.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/27/1990,75,dominicks,35008,10.46333189,1,1.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/27/1990,75,minute.maid,3584,8.184234774,0,3.17,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/27/1990,75,tropicana,5120,8.540909718,0,3.87,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/27/1990,77,dominicks,23680,10.07238609,1,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/27/1990,77,minute.maid,6144,8.723231275,0,2.62,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/27/1990,77,tropicana,7552,8.929567708,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/27/1990,78,dominicks,44160,10.69557468,1,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/27/1990,78,minute.maid,6464,8.7740036,0,2.62,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/27/1990,78,tropicana,6208,8.733594062,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 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-9/27/1990,106,dominicks,18432,9.821843564,1,1.79,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/27/1990,106,minute.maid,1984,7.592870288,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/27/1990,106,tropicana,2240,7.714231145,0,3.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/27/1990,107,dominicks,41920,10.64351832,1,1.79,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/27/1990,107,minute.maid,4480,8.407378325,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 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-9/27/1990,110,minute.maid,3584,8.184234774,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/27/1990,110,tropicana,4480,8.407378325,0,3.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/27/1990,111,dominicks,115200,11.65442503,1,1.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/27/1990,111,minute.maid,3136,8.050703382,0,3.17,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/27/1990,111,tropicana,2880,7.965545573,0,3.87,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/27/1990,112,dominicks,35456,10.47604777,1,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/27/1990,112,minute.maid,11328,9.335032816,0,2.62,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/27/1990,112,tropicana,18560,9.828764006,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/27/1990,113,dominicks,43008,10.66914142,1,1.79,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/27/1990,113,minute.maid,4544,8.42156296,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/27/1990,113,tropicana,7040,8.859363449,0,3.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/27/1990,114,dominicks,48960,10.79875892,1,1.79,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/27/1990,114,minute.maid,6592,8.793612072,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/27/1990,114,tropicana,7744,8.954673629,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/27/1990,115,dominicks,22464,10.01966931,1,1.79,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/27/1990,115,minute.maid,7744,8.954673629,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/27/1990,115,tropicana,7552,8.929567708,0,3.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/27/1990,116,dominicks,26368,10.17990643,1,1.79,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/27/1990,116,minute.maid,2944,7.98752448,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/27/1990,116,tropicana,5056,8.528330936,0,3.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/27/1990,117,dominicks,21184,9.961001459,1,1.79,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/27/1990,117,minute.maid,3648,8.201934351,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/27/1990,117,tropicana,4032,8.30201781,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/27/1990,118,dominicks,25600,10.15034763,1,1.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/27/1990,118,minute.maid,4864,8.489616424,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/27/1990,118,tropicana,5696,8.647519453,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/27/1990,119,dominicks,26432,10.18233068,1,1.79,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/27/1990,119,minute.maid,5632,8.636219898,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/27/1990,119,tropicana,8192,9.010913347,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/27/1990,121,dominicks,37824,10.5406991,1,1.79,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/27/1990,121,minute.maid,7552,8.929567708,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/27/1990,121,tropicana,12608,9.442086812,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/27/1990,122,dominicks,38912,10.56905797,1,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/27/1990,122,minute.maid,9408,9.14931567,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/27/1990,122,tropicana,11200,9.323669057,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/27/1990,123,dominicks,62144,11.03720955,1,1.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/27/1990,123,minute.maid,4288,8.363575703,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/27/1990,123,tropicana,6144,8.723231275,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/27/1990,124,dominicks,76992,11.2514568,1,1.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/27/1990,124,minute.maid,2880,7.965545573,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/27/1990,124,tropicana,7488,8.921057018,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/27/1990,126,dominicks,25536,10.1478445,1,1.79,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/27/1990,126,minute.maid,3328,8.110126802,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/27/1990,126,tropicana,5248,8.565602331,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/27/1990,128,dominicks,54080,10.89821971,1,1.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/27/1990,128,minute.maid,8384,9.034080407,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/27/1990,128,tropicana,11072,9.312174678,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/27/1990,129,dominicks,26816,10.196754,1,1.79,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/27/1990,129,minute.maid,3712,8.219326094,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/27/1990,129,tropicana,7296,8.895081532,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/27/1990,130,dominicks,50944,10.83848227,1,1.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/27/1990,130,minute.maid,3328,8.110126802,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/27/1990,130,tropicana,3264,8.090708716,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/27/1990,131,dominicks,47360,10.76553327,1,1.79,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/27/1990,131,minute.maid,6912,8.841014311,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/27/1990,131,tropicana,5888,8.68067166,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/27/1990,132,dominicks,58752,10.98108047,1,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/27/1990,132,minute.maid,4736,8.462948177,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/27/1990,132,tropicana,6784,8.822322178,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/4/1990,5,dominicks,4672,8.449342525,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/4/1990,5,minute.maid,13952,9.543378146,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/4/1990,5,tropicana,10624,9.270870872,1,3.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/4/1990,8,dominicks,13760,9.529521112,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/4/1990,8,minute.maid,12416,9.426741242,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/4/1990,8,tropicana,8448,9.041685006,1,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/4/1990,9,dominicks,5824,8.66974259,0,1.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/4/1990,9,minute.maid,4288,8.363575703,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/4/1990,9,tropicana,9088,9.114710141,1,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/4/1990,12,dominicks,6464,8.7740036,0,1.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/4/1990,12,minute.maid,9984,9.208739091,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/4/1990,12,tropicana,20992,9.951896692,1,2.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/4/1990,14,dominicks,5632,8.636219898,0,1.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/4/1990,14,minute.maid,10496,9.258749511,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/4/1990,14,tropicana,12160,9.405907156,1,3.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/4/1990,18,dominicks,15168,9.626943225,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/4/1990,18,minute.maid,17408,9.76468515,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/4/1990,18,tropicana,11584,9.357380115,1,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/4/1990,21,dominicks,8064,8.99516499,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/4/1990,21,minute.maid,7232,8.886270902,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/4/1990,21,tropicana,8064,8.99516499,1,2.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/4/1990,28,dominicks,3520,8.166216269,0,1.79,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/4/1990,28,minute.maid,5696,8.647519453,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/4/1990,28,tropicana,6720,8.812843434,1,3.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/4/1990,32,dominicks,10944,9.30054664,0,1.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/4/1990,32,minute.maid,12928,9.467150781,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/4/1990,32,tropicana,19648,9.885730831,1,3.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/4/1990,33,dominicks,2752,7.920083199,0,1.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/4/1990,33,minute.maid,9088,9.114710141,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/4/1990,33,tropicana,13632,9.520175249,1,2.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/4/1990,40,dominicks,13824,9.534161491,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/4/1990,40,minute.maid,6400,8.764053269,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/4/1990,40,tropicana,6016,8.702177866,1,2.67,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/4/1990,44,dominicks,7232,8.886270902,0,1.79,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/4/1990,44,minute.maid,9152,9.121727714,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/4/1990,44,tropicana,14208,9.561560465,1,3.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/4/1990,45,dominicks,9728,9.182763604,0,1.79,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/4/1990,45,minute.maid,4928,8.502688505,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/4/1990,45,tropicana,9088,9.114710141,1,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/4/1990,47,dominicks,8384,9.034080407,0,1.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/4/1990,47,minute.maid,7744,8.954673629,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/4/1990,47,tropicana,8576,9.056722883,1,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/4/1990,48,dominicks,7936,8.979164649,0,1.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/4/1990,48,minute.maid,10112,9.221478116,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/4/1990,48,tropicana,6272,8.743850562,1,3.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/4/1990,49,dominicks,7488,8.921057018,0,1.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/4/1990,49,minute.maid,5888,8.68067166,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/4/1990,49,tropicana,5824,8.66974259,1,3.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/4/1990,50,dominicks,6464,8.7740036,0,1.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/4/1990,50,minute.maid,6016,8.702177866,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/4/1990,50,tropicana,2944,7.98752448,1,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/4/1990,51,dominicks,10304,9.240287448,0,1.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/4/1990,51,minute.maid,8768,9.078864009,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/4/1990,51,tropicana,7040,8.859363449,1,3.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/4/1990,52,dominicks,4736,8.462948177,0,1.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/4/1990,52,minute.maid,17344,9.761001904,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/4/1990,52,tropicana,20864,9.945780465,1,3.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/4/1990,53,dominicks,9664,9.17616292,0,1.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/4/1990,53,minute.maid,14272,9.566054855,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/4/1990,53,tropicana,22144,10.00532186,1,3.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/4/1990,54,dominicks,9280,9.135616826,0,1.79,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/4/1990,54,minute.maid,7744,8.954673629,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/4/1990,54,tropicana,5568,8.624791202,1,3.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/4/1990,56,dominicks,6400,8.764053269,0,1.79,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/4/1990,56,minute.maid,10048,9.215128889,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/4/1990,56,tropicana,8192,9.010913347,1,3.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/4/1990,59,dominicks,2368,7.769800996,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/4/1990,59,minute.maid,5248,8.565602331,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/4/1990,59,tropicana,7104,8.868413285,1,2.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/4/1990,62,dominicks,2304,7.742402022,0,1.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/4/1990,62,minute.maid,9664,9.17616292,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/4/1990,62,tropicana,16896,9.734832187,1,3.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/4/1990,64,dominicks,8832,9.086136769,0,1.79,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/4/1990,64,minute.maid,4608,8.435549202,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/4/1990,64,tropicana,3328,8.110126802,1,3.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/4/1990,67,dominicks,2560,7.847762538,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/4/1990,67,minute.maid,7040,8.859363449,0,2.62,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/4/1990,67,tropicana,5312,8.577723691,1,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/4/1990,68,dominicks,11712,9.368369236,0,1.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/4/1990,68,minute.maid,10880,9.29468152,0,3.17,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/4/1990,68,tropicana,14912,9.609921537,1,2.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 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-10/4/1990,88,minute.maid,8576,9.056722883,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/4/1990,88,tropicana,13248,9.491601877,1,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/4/1990,89,dominicks,18624,9.832206351,0,1.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/4/1990,89,minute.maid,6272,8.743850562,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/4/1990,89,tropicana,11776,9.373818841,1,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 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-10/4/1990,91,tropicana,4800,8.476371197,1,3.29,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/4/1990,92,dominicks,28288,10.25019297,0,1.79,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/4/1990,92,minute.maid,9664,9.17616292,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/4/1990,92,tropicana,5312,8.577723691,1,3.29,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/4/1990,93,dominicks,5248,8.565602331,0,1.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 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-10/4/1990,100,minute.maid,12928,9.467150781,0,3.17,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/4/1990,100,tropicana,15360,9.639522007,1,2.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/4/1990,101,dominicks,7424,8.912473275,0,1.79,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/4/1990,101,minute.maid,11072,9.312174678,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/4/1990,101,tropicana,18496,9.825309772,1,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 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-10/4/1990,111,minute.maid,10432,9.252633284,0,3.17,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/4/1990,111,tropicana,3904,8.269756948,1,3.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/4/1990,112,dominicks,11648,9.36288977,0,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/4/1990,112,minute.maid,15936,9.67633598,0,2.62,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/4/1990,112,tropicana,20800,9.942708266,1,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 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-10/4/1990,116,minute.maid,8256,9.018695488,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/4/1990,116,tropicana,7552,8.929567708,1,3.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/4/1990,117,dominicks,4480,8.407378325,0,1.79,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/4/1990,117,minute.maid,6080,8.712759975,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/4/1990,117,tropicana,3968,8.286017468,1,3.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 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-10/4/1990,119,tropicana,7936,8.979164649,1,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/4/1990,121,dominicks,4864,8.489616424,0,1.79,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/4/1990,121,minute.maid,18304,9.814874894,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/4/1990,121,tropicana,10496,9.258749511,1,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/4/1990,122,dominicks,14912,9.609921537,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/4/1990,122,minute.maid,14592,9.588228712,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/4/1990,122,tropicana,14080,9.55251063,1,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/4/1990,123,dominicks,27264,10.21332243,0,1.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/4/1990,123,minute.maid,10496,9.258749511,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/4/1990,123,tropicana,12864,9.462187991,1,2.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/4/1990,124,dominicks,3840,8.253227646,0,1.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/4/1990,124,minute.maid,11328,9.335032816,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/4/1990,124,tropicana,17920,9.793672686,1,2.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/4/1990,126,dominicks,5376,8.589699882,0,1.79,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/4/1990,126,minute.maid,7488,8.921057018,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/4/1990,126,tropicana,8704,9.071537969,1,3.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/4/1990,128,dominicks,7936,8.979164649,0,1.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/4/1990,128,minute.maid,27648,10.22730867,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/4/1990,128,tropicana,33408,10.41655067,1,2.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/4/1990,129,dominicks,8128,9.00307017,0,1.79,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/4/1990,129,minute.maid,8704,9.071537969,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/4/1990,129,tropicana,7168,8.877381955,1,3.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/4/1990,130,dominicks,11776,9.373818841,0,1.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/4/1990,130,minute.maid,9536,9.162829389,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/4/1990,130,tropicana,6912,8.841014311,1,3.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/4/1990,131,dominicks,12416,9.426741242,0,1.79,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/4/1990,131,minute.maid,13440,9.505990614,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/4/1990,131,tropicana,9152,9.121727714,1,3.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/4/1990,132,dominicks,13568,9.515469358,0,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/4/1990,132,minute.maid,11840,9.379238908,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/4/1990,132,tropicana,8896,9.093357017,1,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/11/1990,2,dominicks,1728,7.454719949,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/11/1990,2,minute.maid,30656,10.33058368,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/11/1990,2,tropicana,5504,8.61323038,0,3.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/11/1990,5,dominicks,1088,6.992096427,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/11/1990,5,minute.maid,47680,10.7722673,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/11/1990,5,tropicana,6656,8.803273983,0,3.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/11/1990,8,dominicks,3136,8.050703382,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/11/1990,8,minute.maid,53696,10.89109379,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/11/1990,8,tropicana,7424,8.912473275,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/11/1990,9,dominicks,576,6.356107661,0,2.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/11/1990,9,minute.maid,50560,10.83091603,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/11/1990,9,tropicana,8640,9.064157862,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/11/1990,12,dominicks,896,6.797940413,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/11/1990,12,minute.maid,64640,11.07658869,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/11/1990,12,tropicana,11648,9.36288977,0,2.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/11/1990,14,dominicks,640,6.461468176,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/11/1990,14,minute.maid,28672,10.26367632,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/11/1990,14,tropicana,7104,8.868413285,0,3.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/11/1990,18,dominicks,5056,8.528330936,0,2.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/11/1990,18,minute.maid,46208,10.74090822,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/11/1990,18,tropicana,9280,9.135616826,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/11/1990,21,dominicks,1792,7.491087594,0,2.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/11/1990,21,minute.maid,28352,10.25245285,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/11/1990,21,tropicana,3072,8.030084094,0,2.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 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-10/11/1990,32,tropicana,12352,9.421573272,0,3.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/11/1990,33,dominicks,1984,7.592870288,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/11/1990,33,minute.maid,29120,10.2791805,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/11/1990,33,tropicana,7616,8.938006577,0,2.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/11/1990,40,dominicks,10880,9.29468152,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/11/1990,40,minute.maid,31296,10.35124557,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/11/1990,40,tropicana,6208,8.733594062,0,2.67,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/11/1990,44,dominicks,1152,7.049254841,0,2.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/11/1990,44,minute.maid,42944,10.66765222,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/11/1990,44,tropicana,7616,8.938006577,0,3.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/11/1990,45,dominicks,2496,7.82244473,0,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/11/1990,45,minute.maid,17984,9.797237753,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/11/1990,45,tropicana,6336,8.754002934,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/11/1990,47,dominicks,2688,7.896552702,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/11/1990,47,minute.maid,24256,10.09641929,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 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-10/11/1990,51,dominicks,896,6.797940413,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/11/1990,51,minute.maid,34880,10.45966888,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/11/1990,51,tropicana,4736,8.462948177,0,3.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/11/1990,52,dominicks,384,5.950642553,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/11/1990,52,minute.maid,42880,10.6661608,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 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-10/11/1990,54,minute.maid,18624,9.832206351,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/11/1990,54,tropicana,3776,8.236420527,0,3.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/11/1990,56,dominicks,1152,7.049254841,0,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/11/1990,56,minute.maid,22848,10.03661887,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/11/1990,56,tropicana,5312,8.577723691,0,3.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/11/1990,59,dominicks,1216,7.103322063,0,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/11/1990,59,minute.maid,31680,10.36344085,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/11/1990,59,tropicana,3840,8.253227646,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/11/1990,62,dominicks,320,5.768320996,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/11/1990,62,minute.maid,28032,10.24110199,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/11/1990,62,tropicana,11456,9.346268889,0,3.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/11/1990,64,dominicks,1280,7.154615357,0,2.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/11/1990,64,minute.maid,20992,9.951896692,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/11/1990,64,tropicana,2048,7.624618986,0,3.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/11/1990,67,dominicks,832,6.723832441,0,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/11/1990,67,minute.maid,29440,10.29010957,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/11/1990,67,tropicana,3904,8.269756948,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/11/1990,68,dominicks,960,6.866933285,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/11/1990,68,minute.maid,41216,10.62658181,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/11/1990,68,tropicana,9344,9.142489705,0,2.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/11/1990,70,dominicks,10688,9.276876896,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/11/1990,70,minute.maid,27968,10.23881628,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/11/1990,70,tropicana,7168,8.877381955,0,2.64,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/11/1990,71,dominicks,1088,6.992096427,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/11/1990,71,minute.maid,41152,10.62502781,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/11/1990,71,tropicana,3904,8.269756948,0,3.29,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/11/1990,72,dominicks,576,6.356107661,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/11/1990,72,minute.maid,38272,10.55247384,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/11/1990,72,tropicana,9472,9.156095357,0,3.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/11/1990,73,dominicks,2624,7.87245515,0,2.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/11/1990,73,minute.maid,78528,11.27121053,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/11/1990,73,tropicana,8512,9.049232212,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/11/1990,74,dominicks,2368,7.769800996,0,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/11/1990,74,minute.maid,66048,11.09813703,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/11/1990,74,tropicana,8064,8.99516499,0,3.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/11/1990,75,dominicks,1344,7.203405521,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/11/1990,75,minute.maid,41984,10.64504387,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/11/1990,75,tropicana,11840,9.379238908,0,2.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/11/1990,76,dominicks,1728,7.454719949,0,2.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/11/1990,76,minute.maid,45376,10.72273861,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/11/1990,76,tropicana,9600,9.169518378,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/11/1990,77,dominicks,1664,7.416979621,0,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/11/1990,77,minute.maid,26560,10.1871616,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/11/1990,77,tropicana,6400,8.764053269,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/11/1990,78,dominicks,1600,7.377758908,0,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/11/1990,78,minute.maid,36864,10.51499074,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/11/1990,78,tropicana,5248,8.565602331,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/11/1990,80,dominicks,2496,7.82244473,0,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/11/1990,80,minute.maid,46720,10.75192762,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/11/1990,80,tropicana,12096,9.400630098,0,3.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 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-10/11/1990,110,minute.maid,29568,10.29444797,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/11/1990,110,tropicana,6208,8.733594062,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/11/1990,111,dominicks,1664,7.416979621,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/11/1990,111,minute.maid,73920,11.21073871,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/11/1990,111,tropicana,3392,8.129174997,0,3.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 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-10/11/1990,114,tropicana,6016,8.702177866,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/11/1990,115,dominicks,576,6.356107661,0,2.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/11/1990,115,minute.maid,31552,10.35939226,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/11/1990,115,tropicana,8128,9.00307017,0,3.29,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/11/1990,116,dominicks,1088,6.992096427,0,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/11/1990,116,minute.maid,24960,10.12502982,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/11/1990,116,tropicana,5120,8.540909718,0,3.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/11/1990,117,dominicks,1920,7.560080465,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/11/1990,117,minute.maid,18112,9.804329981,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/11/1990,117,tropicana,5952,8.691482577,0,3.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/11/1990,118,dominicks,896,6.797940413,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/11/1990,118,minute.maid,39488,10.58375211,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/11/1990,118,tropicana,6080,8.712759975,0,3.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/11/1990,119,dominicks,1856,7.526178913,0,2.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/11/1990,119,minute.maid,31808,10.36747311,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/11/1990,119,tropicana,6208,8.733594062,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/11/1990,121,dominicks,2560,7.847762538,0,2.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/11/1990,121,minute.maid,36416,10.50276352,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/11/1990,121,tropicana,10752,9.282847063,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/11/1990,122,dominicks,4416,8.392989588,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/11/1990,122,minute.maid,43648,10.68391274,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/11/1990,122,tropicana,10240,9.234056899,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/11/1990,123,dominicks,2880,7.965545573,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/11/1990,123,minute.maid,76608,11.24645679,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/11/1990,123,tropicana,4416,8.392989588,0,2.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/11/1990,124,dominicks,2368,7.769800996,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/11/1990,124,minute.maid,62080,11.03617916,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/11/1990,124,tropicana,9152,9.121727714,0,2.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/11/1990,126,dominicks,1152,7.049254841,0,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/11/1990,126,minute.maid,34368,10.44488118,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/11/1990,126,tropicana,5952,8.691482577,0,3.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/11/1990,128,dominicks,2560,7.847762538,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/11/1990,128,minute.maid,76032,11.23890958,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/11/1990,128,tropicana,15424,9.643680017,0,2.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/11/1990,129,dominicks,576,6.356107661,0,2.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/11/1990,129,minute.maid,28672,10.26367632,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/11/1990,129,tropicana,5824,8.66974259,0,3.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/11/1990,130,dominicks,2560,7.847762538,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/11/1990,130,minute.maid,57856,10.96571244,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/11/1990,130,tropicana,4224,8.348537825,0,3.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/11/1990,131,dominicks,2944,7.98752448,0,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/11/1990,131,minute.maid,38976,10.57070135,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/11/1990,131,tropicana,6272,8.743850562,0,3.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/11/1990,132,dominicks,3776,8.236420527,0,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/11/1990,132,minute.maid,50880,10.8372252,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/11/1990,132,tropicana,6912,8.841014311,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/18/1990,2,dominicks,33792,10.42797937,1,1.24,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/18/1990,2,minute.maid,3840,8.253227646,0,2.98,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/18/1990,2,tropicana,5888,8.68067166,0,3.56,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/18/1990,5,dominicks,69440,11.14821835,1,1.24,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/18/1990,5,minute.maid,7616,8.938006577,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/18/1990,5,tropicana,5184,8.553332238,0,3.51,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/18/1990,8,dominicks,186176,12.13444774,1,1.14,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/18/1990,8,minute.maid,5696,8.647519453,0,2.51,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/18/1990,8,tropicana,5824,8.66974259,0,3.04,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/18/1990,9,dominicks,95040,11.46205314,1,1.14,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/18/1990,9,minute.maid,3840,8.253227646,0,2.55,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/18/1990,9,tropicana,9728,9.182763604,0,3.04,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/18/1990,12,dominicks,113088,11.63592156,1,1.24,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/18/1990,12,minute.maid,5312,8.577723691,0,2.88,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/18/1990,12,tropicana,13568,9.515469358,0,2.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/18/1990,14,dominicks,36032,10.49216271,1,1.24,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/18/1990,14,minute.maid,5760,8.658692754,0,2.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/18/1990,14,tropicana,5056,8.528330936,0,3.56,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/18/1990,18,dominicks,94720,11.45868045,1,1.14,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/18/1990,18,minute.maid,8832,9.086136769,0,2.38,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/18/1990,18,tropicana,6720,8.812843434,0,3.04,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/18/1990,21,dominicks,73728,11.20813793,1,1.14,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/18/1990,21,minute.maid,4288,8.363575703,0,2.4,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/18/1990,21,tropicana,3584,8.184234774,0,2.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 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-10/18/1990,32,tropicana,7936,8.979164649,0,3.56,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/18/1990,33,dominicks,39616,10.58698836,1,1.24,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/18/1990,33,minute.maid,6080,8.712759975,0,2.91,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/18/1990,33,tropicana,7040,8.859363449,0,2.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/18/1990,40,dominicks,151872,11.93079334,1,0.92,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/18/1990,40,minute.maid,6208,8.733594062,0,2.3,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/18/1990,40,tropicana,4544,8.42156296,0,2.66,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/18/1990,44,dominicks,65536,11.09035489,1,1.24,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/18/1990,44,minute.maid,4032,8.30201781,0,2.87,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/18/1990,44,tropicana,5632,8.636219898,0,3.51,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/18/1990,45,dominicks,46912,10.75602879,1,1.14,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/18/1990,45,minute.maid,4288,8.363575703,0,2.36,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/18/1990,45,tropicana,6336,8.754002934,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/18/1990,47,dominicks,65024,11.08251171,1,1.24,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/18/1990,47,minute.maid,6976,8.850230966,0,2.64,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/18/1990,47,tropicana,3776,8.236420527,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/18/1990,48,dominicks,38016,10.5457624,1,1.24,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/18/1990,48,minute.maid,4672,8.449342525,0,2.84,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/18/1990,48,tropicana,3840,8.253227646,0,3.51,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/18/1990,49,dominicks,52352,10.86574542,1,1.24,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/18/1990,49,minute.maid,3136,8.050703382,0,2.81,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/18/1990,49,tropicana,3520,8.166216269,0,3.51,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/18/1990,50,dominicks,53312,10.88391673,1,1.14,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/18/1990,50,minute.maid,3072,8.030084094,0,2.47,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/18/1990,50,tropicana,2560,7.847762538,0,3.04,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/18/1990,51,dominicks,84608,11.3457841,1,1.24,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/18/1990,51,minute.maid,4480,8.407378325,0,2.77,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/18/1990,51,tropicana,3328,8.110126802,0,3.51,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/18/1990,52,dominicks,57216,10.95458886,1,1.24,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/18/1990,52,minute.maid,6848,8.831711918,0,2.85,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/18/1990,52,tropicana,6784,8.822322178,0,3.56,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/18/1990,53,dominicks,153984,11.94460398,1,1.24,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/18/1990,53,minute.maid,4416,8.392989588,0,2.83,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/18/1990,53,tropicana,10688,9.276876896,0,3.56,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/18/1990,54,dominicks,57920,10.96681803,1,1.24,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/18/1990,54,minute.maid,3840,8.253227646,0,2.78,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/18/1990,54,tropicana,3008,8.009030685,0,3.51,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/18/1990,56,dominicks,44224,10.69702291,1,1.24,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/18/1990,56,minute.maid,4672,8.449342525,0,2.74,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/18/1990,56,tropicana,3776,8.236420527,0,3.51,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/18/1990,59,dominicks,112320,11.62910722,1,1.14,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/18/1990,59,minute.maid,2496,7.82244473,0,2.48,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/18/1990,59,tropicana,3072,8.030084094,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/18/1990,62,dominicks,28288,10.25019297,1,1.24,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/18/1990,62,minute.maid,7040,8.859363449,0,2.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/18/1990,62,tropicana,10112,9.221478116,0,3.56,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/18/1990,64,dominicks,48000,10.77895629,1,1.24,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/18/1990,64,minute.maid,3968,8.286017468,0,2.76,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/18/1990,64,tropicana,1408,7.249925537,0,3.51,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/18/1990,67,dominicks,99392,11.50682691,1,1.14,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/18/1990,67,minute.maid,3264,8.090708716,0,2.33,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/18/1990,67,tropicana,4736,8.462948177,0,3.04,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/18/1990,68,dominicks,62784,11.04745554,1,1.24,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/18/1990,68,minute.maid,2240,7.714231145,0,3.13,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/18/1990,68,tropicana,8704,9.071537969,0,2.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 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-11/1/1990,33,minute.maid,25152,10.1326927,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/1/1990,33,tropicana,6656,8.803273983,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/1/1990,40,dominicks,41088,10.62347139,1,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/1/1990,40,minute.maid,22336,10.01395501,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/1/1990,40,tropicana,6144,8.723231275,0,2.68,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/1/1990,44,dominicks,23872,10.0804615,1,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/1/1990,44,minute.maid,36800,10.51325312,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/1/1990,44,tropicana,7680,8.946374826,0,3.51,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/1/1990,45,dominicks,16960,9.738612909,1,1.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/1/1990,45,minute.maid,17088,9.746131742,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/1/1990,45,tropicana,5888,8.68067166,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/1/1990,47,dominicks,17152,9.749870064,1,1.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/1/1990,47,minute.maid,18880,9.84585844,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/1/1990,47,tropicana,3520,8.166216269,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/1/1990,48,dominicks,6080,8.712759975,1,1.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/1/1990,48,minute.maid,22464,10.01966931,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/1/1990,48,tropicana,4352,8.378390789,0,3.51,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/1/1990,49,dominicks,19904,9.898675996,1,1.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/1/1990,49,minute.maid,16128,9.688312171,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/1/1990,49,tropicana,3776,8.236420527,0,3.51,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/1/1990,50,dominicks,20480,9.927204079,1,1.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/1/1990,50,minute.maid,11328,9.335032816,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/1/1990,50,tropicana,3328,8.110126802,0,3.04,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/1/1990,51,dominicks,38848,10.56741187,1,1.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/1/1990,51,minute.maid,20672,9.936535407,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/1/1990,51,tropicana,4480,8.407378325,0,3.51,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/1/1990,52,dominicks,15040,9.618468598,1,1.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/1/1990,52,minute.maid,39488,10.58375211,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/1/1990,52,tropicana,10496,9.258749511,0,3.56,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/1/1990,53,dominicks,31552,10.35939226,1,1.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/1/1990,53,minute.maid,47616,10.77092412,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/1/1990,53,tropicana,11520,9.351839934,0,3.56,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/1/1990,54,dominicks,18432,9.821843564,1,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/1/1990,54,minute.maid,17984,9.797237753,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/1/1990,54,tropicana,4032,8.30201781,0,3.51,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/1/1990,56,dominicks,19776,9.89222436,1,1.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/1/1990,56,minute.maid,18880,9.84585844,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/1/1990,56,tropicana,5760,8.658692754,0,3.51,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/1/1990,59,dominicks,19136,9.859326657,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/1/1990,59,minute.maid,25856,10.16029796,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/1/1990,59,tropicana,3264,8.090708716,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/1/1990,62,dominicks,7552,8.929567708,1,1.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/1/1990,62,minute.maid,28288,10.25019297,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/1/1990,62,tropicana,10752,9.282847063,0,3.56,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/1/1990,64,dominicks,11392,9.340666634,1,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/1/1990,64,minute.maid,14848,9.605620455,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/1/1990,64,tropicana,2624,7.87245515,0,3.51,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/1/1990,67,dominicks,5440,8.60153434,1,1.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/1/1990,67,minute.maid,63680,11.06162582,1,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/1/1990,67,tropicana,5504,8.61323038,0,3.04,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/1/1990,68,dominicks,16768,9.727227587,1,1.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/1/1990,68,minute.maid,35072,10.46515837,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/1/1990,68,tropicana,5376,8.589699882,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/1/1990,70,dominicks,21568,9.978966014,1,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/1/1990,70,minute.maid,29632,10.29661014,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/1/1990,70,tropicana,9472,9.156095357,0,2.65,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/1/1990,71,dominicks,44480,10.70279493,1,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/1/1990,71,minute.maid,29376,10.28793329,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/1/1990,71,tropicana,7936,8.979164649,0,3.56,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/1/1990,72,dominicks,18112,9.804329981,1,1.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/1/1990,72,minute.maid,32064,10.37548918,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/1/1990,72,tropicana,8000,8.987196821,0,3.56,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/1/1990,73,dominicks,63104,11.05253944,1,1.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/1/1990,73,minute.maid,51200,10.84349481,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/1/1990,73,tropicana,8512,9.049232212,0,3.04,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/1/1990,75,dominicks,14912,9.609921537,1,1.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/1/1990,75,minute.maid,43520,10.68097588,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/1/1990,75,tropicana,4544,8.42156296,0,3.87,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/1/1990,76,dominicks,47872,10.77628606,1,1.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/1/1990,76,minute.maid,31232,10.34919849,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 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-11/1/1990,86,minute.maid,36928,10.51672535,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/1/1990,86,tropicana,4288,8.363575703,0,3.66,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/1/1990,88,dominicks,9344,9.142489705,1,1.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/1/1990,88,minute.maid,35008,10.46333189,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/1/1990,88,tropicana,3456,8.14786713,0,3.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/1/1990,89,dominicks,36224,10.49747716,1,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/1/1990,89,minute.maid,25088,10.13014492,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/1/1990,89,tropicana,2624,7.87245515,0,3.66,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/1/1990,90,dominicks,38336,10.55414468,1,1.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/1/1990,90,minute.maid,29312,10.28575227,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 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-11/1/1990,102,tropicana,7168,8.877381955,0,3.04,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/1/1990,103,dominicks,24256,10.09641929,1,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/1/1990,103,minute.maid,24064,10.08847223,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/1/1990,103,tropicana,2048,7.624618986,0,3.04,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/1/1990,104,dominicks,13184,9.486759252,1,1.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 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-11/1/1990,107,tropicana,8896,9.093357017,0,3.51,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/1/1990,109,dominicks,11200,9.323669057,1,1.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/1/1990,109,minute.maid,68928,11.14081776,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/1/1990,109,tropicana,14272,9.566054855,0,3.56,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/1/1990,110,dominicks,19840,9.895455381,1,1.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/1/1990,110,minute.maid,30080,10.31161578,1,1.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/1/1990,110,tropicana,3904,8.269756948,0,3.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/1/1990,111,dominicks,57536,10.96016612,1,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/1/1990,111,minute.maid,50816,10.83596655,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/1/1990,111,tropicana,2496,7.82244473,0,3.56,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/1/1990,112,dominicks,31360,10.35328847,1,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/1/1990,112,minute.maid,46592,10.74918413,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/1/1990,112,tropicana,17600,9.775654181,0,3.04,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/1/1990,113,dominicks,14976,9.614204199,1,1.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/1/1990,113,minute.maid,38720,10.56411154,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/1/1990,113,tropicana,5056,8.528330936,0,3.51,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/1/1990,114,dominicks,40704,10.61408165,1,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/1/1990,114,minute.maid,30272,10.31797847,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/1/1990,114,tropicana,5504,8.61323038,0,3.04,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/1/1990,115,dominicks,10176,9.227787286,1,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/1/1990,115,minute.maid,34624,10.45230236,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/1/1990,115,tropicana,5056,8.528330936,0,3.51,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/1/1990,116,dominicks,26176,10.17259824,1,1.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/1/1990,116,minute.maid,15360,9.639522007,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/1/1990,116,tropicana,5632,8.636219898,0,3.51,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/1/1990,117,dominicks,5824,8.66974259,1,1.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/1/1990,117,minute.maid,18112,9.804329981,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/1/1990,117,tropicana,3392,8.129174997,0,3.51,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/1/1990,118,dominicks,27456,10.22034,1,1.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/1/1990,118,minute.maid,30720,10.33266919,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/1/1990,118,tropicana,4672,8.449342525,0,3.51,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/1/1990,119,dominicks,10432,9.252633284,1,1.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/1/1990,119,minute.maid,29504,10.29228113,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/1/1990,119,tropicana,8576,9.056722883,0,3.04,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/1/1990,122,dominicks,18304,9.814874894,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/1/1990,122,minute.maid,41344,10.62968259,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/1/1990,122,tropicana,6656,8.803273983,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/1/1990,123,dominicks,47168,10.76147098,1,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/1/1990,123,minute.maid,50816,10.83596655,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/1/1990,123,tropicana,4096,8.317766167,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/1/1990,124,dominicks,45120,10.71708089,1,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/1/1990,124,minute.maid,44864,10.71139097,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/1/1990,124,tropicana,4224,8.348537825,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/1/1990,126,dominicks,8000,8.987196821,1,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/1/1990,126,minute.maid,36352,10.5010045,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/1/1990,126,tropicana,3136,8.050703382,0,3.51,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/1/1990,128,dominicks,33984,10.43364511,1,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/1/1990,128,minute.maid,68480,11.13429701,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/1/1990,128,tropicana,8320,9.026417534,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/1/1990,129,dominicks,15424,9.643680017,1,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/1/1990,129,minute.maid,23424,10.06151642,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/1/1990,129,tropicana,5312,8.577723691,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/1/1990,130,dominicks,45440,10.72414805,1,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/1/1990,130,minute.maid,50240,10.8245668,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/1/1990,130,tropicana,2560,7.847762538,0,3.56,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/1/1990,131,dominicks,19136,9.859326657,1,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/1/1990,131,minute.maid,64256,11.07063038,1,1.71,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/1/1990,131,tropicana,5952,8.691482577,0,3.51,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/1/1990,132,dominicks,61120,11.02059442,1,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/1/1990,132,minute.maid,31808,10.36747311,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/1/1990,132,tropicana,5568,8.624791202,0,3.04,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/8/1990,2,dominicks,11392,9.340666634,0,1.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/8/1990,2,minute.maid,3392,8.129174997,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/8/1990,2,tropicana,6848,8.831711918,0,3.56,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/8/1990,5,dominicks,13824,9.534161491,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/8/1990,5,minute.maid,5440,8.60153434,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/8/1990,5,tropicana,5312,8.577723691,0,3.51,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/8/1990,8,dominicks,26880,10.1991378,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/8/1990,8,minute.maid,5504,8.61323038,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/8/1990,8,tropicana,6912,8.841014311,0,3.04,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/8/1990,9,dominicks,9408,9.14931567,0,1.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/8/1990,9,minute.maid,2816,7.943072717,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/8/1990,9,tropicana,8832,9.086136769,0,3.04,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/8/1990,12,dominicks,24000,10.08580911,0,1.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/8/1990,12,minute.maid,4800,8.476371197,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 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-11/8/1990,18,minute.maid,5824,8.66974259,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/8/1990,18,tropicana,8512,9.049232212,0,3.04,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/8/1990,21,dominicks,16192,9.692272572,0,1.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/8/1990,21,minute.maid,4032,8.30201781,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/8/1990,21,tropicana,3392,8.129174997,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 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-11/8/1990,32,tropicana,10240,9.234056899,0,3.56,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/8/1990,33,dominicks,21184,9.961001459,0,1.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/8/1990,33,minute.maid,3328,8.110126802,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/8/1990,33,tropicana,6336,8.754002934,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/8/1990,40,dominicks,73344,11.20291598,0,1.29,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/8/1990,40,minute.maid,2496,7.82244473,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/8/1990,40,tropicana,6016,8.702177866,0,2.66,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/8/1990,44,dominicks,43328,10.67655436,0,1.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/8/1990,44,minute.maid,4224,8.348537825,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/8/1990,44,tropicana,6720,8.812843434,0,3.51,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/8/1990,45,dominicks,17984,9.797237753,0,1.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/8/1990,45,minute.maid,2624,7.87245515,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/8/1990,45,tropicana,7616,8.938006577,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/8/1990,47,dominicks,32512,10.38936453,0,1.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/8/1990,47,minute.maid,3200,8.070906089,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/8/1990,47,tropicana,3328,8.110126802,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/8/1990,48,dominicks,10944,9.30054664,0,1.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/8/1990,48,minute.maid,5376,8.589699882,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/8/1990,48,tropicana,3008,8.009030685,0,3.51,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/8/1990,49,dominicks,9472,9.156095357,0,1.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/8/1990,49,minute.maid,2688,7.896552702,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/8/1990,49,tropicana,3264,8.090708716,0,3.51,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/8/1990,50,dominicks,22464,10.01966931,0,1.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/8/1990,50,minute.maid,2624,7.87245515,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/8/1990,50,tropicana,2944,7.98752448,0,3.04,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/8/1990,51,dominicks,21056,9.954940834,0,1.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/8/1990,51,minute.maid,2752,7.920083199,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/8/1990,51,tropicana,5056,8.528330936,0,3.51,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/8/1990,52,dominicks,13056,9.477003077,0,1.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/8/1990,52,minute.maid,4736,8.462948177,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/8/1990,52,tropicana,7744,8.954673629,0,3.56,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/8/1990,53,dominicks,45120,10.71708089,0,1.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/8/1990,53,minute.maid,2880,7.965545573,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/8/1990,53,tropicana,10368,9.246479419,0,3.56,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/8/1990,54,dominicks,22080,10.0024275,0,1.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/8/1990,54,minute.maid,3008,8.009030685,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/8/1990,54,tropicana,5056,8.528330936,0,3.51,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/8/1990,56,dominicks,18560,9.828764006,0,1.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/8/1990,56,minute.maid,2496,7.82244473,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/8/1990,56,tropicana,5632,8.636219898,0,3.51,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 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-11/8/1990,74,minute.maid,4096,8.317766167,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/8/1990,74,tropicana,6464,8.7740036,0,3.51,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/8/1990,75,dominicks,22400,10.01681624,0,1.29,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/8/1990,75,minute.maid,3136,8.050703382,0,3.17,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/8/1990,75,tropicana,6208,8.733594062,0,3.87,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 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-11/8/1990,77,tropicana,6592,8.793612072,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/8/1990,78,dominicks,23488,10.06424493,0,1.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/8/1990,78,minute.maid,3648,8.201934351,0,2.62,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/8/1990,78,tropicana,4288,8.363575703,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/8/1990,80,dominicks,27456,10.22034,0,1.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/8/1990,80,minute.maid,7360,8.903815212,0,2.62,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/8/1990,80,tropicana,11520,9.351839934,0,3.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/8/1990,81,dominicks,15040,9.618468598,0,1.29,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/8/1990,81,minute.maid,3904,8.269756948,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/8/1990,81,tropicana,4928,8.502688505,0,3.51,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 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-11/8/1990,84,tropicana,4416,8.392989588,0,3.51,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/8/1990,86,dominicks,16320,9.700146629,0,1.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/8/1990,86,minute.maid,3072,8.030084094,0,2.73,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/8/1990,86,tropicana,4480,8.407378325,0,3.66,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/8/1990,88,dominicks,11712,9.368369236,0,1.29,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/8/1990,88,minute.maid,3584,8.184234774,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/8/1990,88,tropicana,3392,8.129174997,0,3.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/8/1990,89,dominicks,32960,10.40304998,0,1.29,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/8/1990,89,minute.maid,2304,7.742402022,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/8/1990,89,tropicana,1920,7.560080465,0,3.66,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/8/1990,90,dominicks,56704,10.94560003,0,1.29,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/8/1990,90,minute.maid,2432,7.796469243,0,2.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/8/1990,90,tropicana,1728,7.454719949,0,3.66,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/8/1990,91,dominicks,16896,9.734832187,0,1.29,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/8/1990,91,minute.maid,2112,7.655390645,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/8/1990,91,tropicana,2496,7.82244473,0,3.51,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/8/1990,92,dominicks,39744,10.59021417,0,1.29,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/8/1990,92,minute.maid,2176,7.685243608,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/8/1990,92,tropicana,2944,7.98752448,0,3.51,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/8/1990,93,dominicks,22528,10.02251426,0,1.29,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/8/1990,93,minute.maid,3648,8.201934351,0,3.17,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/8/1990,93,tropicana,7808,8.962904128,0,3.56,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/8/1990,94,dominicks,19776,9.89222436,0,1.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/8/1990,94,minute.maid,4800,8.476371197,0,2.62,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/8/1990,94,tropicana,6016,8.702177866,0,3.04,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 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-11/8/1990,97,tropicana,1408,7.249925537,0,3.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/8/1990,98,dominicks,48512,10.78956647,0,1.29,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/8/1990,98,minute.maid,6208,8.733594062,0,2.62,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/8/1990,98,tropicana,6784,8.822322178,0,3.04,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/8/1990,100,dominicks,64448,11.07361398,0,1.29,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/8/1990,100,minute.maid,3456,8.14786713,0,3.17,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/8/1990,100,tropicana,3840,8.253227646,0,3.87,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/8/1990,101,dominicks,19776,9.89222436,0,1.29,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/8/1990,101,minute.maid,4288,8.363575703,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/8/1990,101,tropicana,6656,8.803273983,0,3.66,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/8/1990,102,dominicks,36160,10.49570882,0,1.29,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/8/1990,102,minute.maid,5440,8.60153434,0,2.62,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/8/1990,102,tropicana,6592,8.793612072,0,3.04,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/8/1990,103,dominicks,25472,10.14533509,0,1.29,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/8/1990,103,minute.maid,3392,8.129174997,0,2.62,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/8/1990,103,tropicana,2368,7.769800996,0,3.04,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/8/1990,104,dominicks,10688,9.276876896,0,1.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/8/1990,104,minute.maid,3328,8.110126802,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/8/1990,104,tropicana,2304,7.742402022,0,3.51,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/8/1990,105,dominicks,28736,10.26590597,0,1.29,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 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-11/8/1990,109,tropicana,13312,9.496421163,0,3.56,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/8/1990,110,dominicks,12672,9.447150114,0,1.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/8/1990,110,minute.maid,3520,8.166216269,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/8/1990,110,tropicana,3712,8.219326094,0,3.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/8/1990,111,dominicks,93504,11.4457595,0,1.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/8/1990,111,minute.maid,1600,7.377758908,0,3.17,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/8/1990,111,tropicana,2816,7.943072717,0,3.56,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/8/1990,112,dominicks,32896,10.40110635,0,1.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/8/1990,112,minute.maid,10048,9.215128889,0,2.62,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/8/1990,112,tropicana,16704,9.723403491,0,3.04,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/8/1990,113,dominicks,11648,9.36288977,0,1.29,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/8/1990,113,minute.maid,4096,8.317766167,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/8/1990,113,tropicana,7040,8.859363449,0,3.51,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/8/1990,114,dominicks,47168,10.76147098,0,1.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/8/1990,114,minute.maid,3968,8.286017468,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/8/1990,114,tropicana,5760,8.658692754,0,3.04,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/8/1990,115,dominicks,8640,9.064157862,0,1.29,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/8/1990,115,minute.maid,4096,8.317766167,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/8/1990,115,tropicana,3136,8.050703382,0,3.51,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/8/1990,116,dominicks,20416,9.924074186,0,1.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/8/1990,116,minute.maid,2496,7.82244473,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/8/1990,116,tropicana,4672,8.449342525,0,3.51,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/8/1990,117,dominicks,23808,10.07777694,0,1.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/8/1990,117,minute.maid,2624,7.87245515,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/8/1990,117,tropicana,1984,7.592870288,0,3.51,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/8/1990,118,dominicks,24832,10.11988842,0,1.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/8/1990,118,minute.maid,3520,8.166216269,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/8/1990,118,tropicana,4608,8.435549202,0,3.51,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/8/1990,119,dominicks,15872,9.67231183,0,1.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/8/1990,119,minute.maid,4864,8.489616424,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/8/1990,119,tropicana,5824,8.66974259,0,3.04,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/8/1990,122,dominicks,19456,9.875910785,0,1.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/8/1990,122,minute.maid,7296,8.895081532,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/8/1990,122,tropicana,13312,9.496421163,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/8/1990,123,dominicks,13952,9.543378146,0,1.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/8/1990,123,minute.maid,3264,8.090708716,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/8/1990,123,tropicana,4544,8.42156296,0,3.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/8/1990,124,dominicks,10688,9.276876896,0,1.29,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/8/1990,124,minute.maid,4480,8.407378325,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/8/1990,124,tropicana,6272,8.743850562,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/8/1990,126,dominicks,11136,9.317938383,0,1.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/8/1990,126,minute.maid,3520,8.166216269,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/8/1990,126,tropicana,3712,8.219326094,0,3.51,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/8/1990,128,dominicks,22592,10.02535114,0,1.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/8/1990,128,minute.maid,4928,8.502688505,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/8/1990,128,tropicana,9152,9.121727714,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/8/1990,129,dominicks,12288,9.416378455,0,1.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/8/1990,129,minute.maid,4096,8.317766167,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/8/1990,129,tropicana,4352,8.378390789,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/8/1990,130,dominicks,70656,11.16557831,0,1.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/8/1990,130,minute.maid,2368,7.769800996,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/8/1990,130,tropicana,2240,7.714231145,0,3.56,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/8/1990,131,dominicks,14144,9.557045785,0,1.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/8/1990,131,minute.maid,5120,8.540909718,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/8/1990,131,tropicana,6400,8.764053269,0,3.51,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/8/1990,132,dominicks,60160,11.00476296,0,1.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/8/1990,132,minute.maid,5120,8.540909718,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/8/1990,132,tropicana,4928,8.502688505,0,3.04,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/15/1990,2,dominicks,28416,10.25470765,0,0.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/15/1990,2,minute.maid,26304,10.1774763,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/15/1990,2,tropicana,9216,9.128696383,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/15/1990,5,dominicks,14208,9.561560465,0,0.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/15/1990,5,minute.maid,52416,10.86696717,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/15/1990,5,tropicana,9984,9.208739091,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/15/1990,8,dominicks,71680,11.17996705,0,0.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/15/1990,8,minute.maid,51008,10.83973776,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/15/1990,8,tropicana,10496,9.258749511,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/15/1990,9,dominicks,25920,10.16277015,0,0.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/15/1990,9,minute.maid,50752,10.83470631,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/15/1990,9,tropicana,13504,9.510741217,0,3.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/15/1990,12,dominicks,67008,11.11256729,0,0.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/15/1990,12,minute.maid,57600,10.96127785,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/15/1990,12,tropicana,5696,8.647519453,0,3.87,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/15/1990,14,dominicks,30272,10.31797847,0,0.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/15/1990,14,minute.maid,29184,10.28137589,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/15/1990,14,tropicana,10496,9.258749511,0,3.87,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/15/1990,18,dominicks,35328,10.47243113,0,0.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/15/1990,18,minute.maid,57472,10.95905315,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/15/1990,18,tropicana,11136,9.317938383,0,3.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/15/1990,21,dominicks,28480,10.25695737,0,0.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/15/1990,21,minute.maid,30592,10.32849382,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/15/1990,21,tropicana,4032,8.30201781,0,3.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/15/1990,28,dominicks,15616,9.656051309,0,0.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/15/1990,28,minute.maid,20544,9.930324207,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/15/1990,28,tropicana,4864,8.489616424,0,3.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/15/1990,32,dominicks,43520,10.68097588,0,0.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/15/1990,32,minute.maid,56896,10.94898032,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/15/1990,32,tropicana,13888,9.538780437,0,3.87,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/15/1990,33,dominicks,22592,10.02535114,0,0.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/15/1990,33,minute.maid,21056,9.954940834,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/15/1990,33,tropicana,9600,9.169518378,0,3.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/15/1990,40,dominicks,38464,10.55747802,0,0.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/15/1990,40,minute.maid,38144,10.54912375,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/15/1990,40,tropicana,7360,8.903815212,0,2.67,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/15/1990,44,dominicks,59648,10.9962159,0,0.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/15/1990,44,minute.maid,25600,10.15034763,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/15/1990,44,tropicana,10432,9.252633284,0,3.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/15/1990,45,dominicks,28160,10.24565781,0,0.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/15/1990,45,minute.maid,13760,9.529521112,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/15/1990,45,tropicana,6528,8.783855897,0,3.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/15/1990,47,dominicks,24960,10.12502982,0,0.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/15/1990,47,minute.maid,25792,10.15781965,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/15/1990,47,tropicana,5760,8.658692754,0,3.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/15/1990,48,dominicks,27264,10.21332243,0,0.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/15/1990,48,minute.maid,21632,9.981928979,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/15/1990,48,tropicana,5760,8.658692754,0,3.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/15/1990,49,dominicks,30208,10.31586207,0,0.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/15/1990,49,minute.maid,22272,10.01108556,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/15/1990,49,tropicana,5056,8.528330936,0,3.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/15/1990,50,dominicks,32640,10.39329381,0,0.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/15/1990,50,minute.maid,12096,9.400630098,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/15/1990,50,tropicana,3456,8.14786713,0,3.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/15/1990,51,dominicks,59520,10.99406767,0,0.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/15/1990,51,minute.maid,27456,10.22034,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/15/1990,51,tropicana,8640,9.064157862,0,3.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/15/1990,52,dominicks,20992,9.951896692,0,0.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/15/1990,52,minute.maid,43840,10.68830192,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/15/1990,52,tropicana,19584,9.882468185,0,3.87,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/15/1990,53,dominicks,57216,10.95458886,0,0.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/15/1990,53,minute.maid,47168,10.76147098,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/15/1990,53,tropicana,14016,9.547954813,0,3.87,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/15/1990,54,dominicks,25280,10.13776885,0,0.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/15/1990,54,minute.maid,22144,10.00532186,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/15/1990,54,tropicana,5632,8.636219898,0,3.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/15/1990,56,dominicks,23616,10.06967973,0,0.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/15/1990,56,minute.maid,23424,10.06151642,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/15/1990,56,tropicana,8448,9.041685006,0,3.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/15/1990,59,dominicks,25984,10.16523624,0,0.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/15/1990,59,minute.maid,36096,10.49393734,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/15/1990,59,tropicana,10624,9.270870872,0,3.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/15/1990,62,dominicks,22912,10.03941607,0,0.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/15/1990,62,minute.maid,32832,10.39915893,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/15/1990,62,tropicana,19968,9.901886271,0,3.87,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/15/1990,64,dominicks,48320,10.78560083,0,0.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/15/1990,64,minute.maid,16192,9.692272572,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/15/1990,64,tropicana,2880,7.965545573,0,3.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/15/1990,67,dominicks,25600,10.15034763,0,0.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/15/1990,67,minute.maid,19904,9.898675996,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/15/1990,67,tropicana,8064,8.99516499,0,3.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/15/1990,68,dominicks,16320,9.700146629,0,0.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/15/1990,68,minute.maid,49600,10.81174611,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/15/1990,68,tropicana,7296,8.895081532,0,3.87,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/15/1990,70,dominicks,75072,11.22620293,0,0.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/15/1990,70,minute.maid,28032,10.24110199,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/15/1990,70,tropicana,12416,9.426741242,0,2.64,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/15/1990,71,dominicks,36096,10.49393734,0,0.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/15/1990,71,minute.maid,47744,10.77360868,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/15/1990,71,tropicana,6528,8.783855897,0,3.87,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/15/1990,72,dominicks,32768,10.39720771,0,0.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/15/1990,72,minute.maid,38656,10.56245728,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/15/1990,72,tropicana,11456,9.346268889,0,3.87,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/15/1990,73,dominicks,43136,10.67211319,0,0.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/15/1990,73,minute.maid,87040,11.37412306,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/15/1990,73,tropicana,11264,9.329367078,0,3.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/15/1990,74,dominicks,62976,11.05050898,0,0.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/15/1990,74,minute.maid,44992,10.71423998,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/15/1990,74,tropicana,12096,9.400630098,0,3.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/15/1990,75,dominicks,8832,9.086136769,0,0.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/15/1990,75,minute.maid,51328,10.84599169,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/15/1990,75,tropicana,8320,9.026417534,0,3.87,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/15/1990,76,dominicks,25472,10.14533509,0,0.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/15/1990,76,minute.maid,58112,10.97012746,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/15/1990,76,tropicana,7936,8.979164649,0,3.66,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/15/1990,77,dominicks,44096,10.69412435,0,0.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/15/1990,77,minute.maid,22592,10.02535114,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/15/1990,77,tropicana,7552,8.929567708,0,3.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/15/1990,78,dominicks,35648,10.48144832,0,0.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/15/1990,78,minute.maid,39616,10.58698836,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/15/1990,78,tropicana,8000,8.987196821,0,3.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/15/1990,80,dominicks,38528,10.55914053,0,0.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/15/1990,80,minute.maid,39680,10.58860256,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/15/1990,80,tropicana,16960,9.738612909,0,3.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/15/1990,81,dominicks,21888,9.99369382,0,0.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/15/1990,81,minute.maid,57408,10.95793895,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/15/1990,81,tropicana,6272,8.743850562,0,3.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 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-11/15/1990,91,tropicana,4416,8.392989588,0,3.66,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/15/1990,92,dominicks,23232,10.05328592,0,0.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/15/1990,92,minute.maid,42496,10.65716523,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/15/1990,92,tropicana,5632,8.636219898,0,3.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/15/1990,93,dominicks,22208,10.00820786,0,0.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 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-11/15/1990,107,dominicks,32000,10.37349118,0,0.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/15/1990,107,minute.maid,30656,10.33058368,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/15/1990,107,tropicana,9728,9.182763604,0,3.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/15/1990,109,dominicks,4672,8.449342525,0,0.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/15/1990,109,minute.maid,86016,11.3622886,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/15/1990,109,tropicana,22976,10.04220547,0,3.87,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/15/1990,110,dominicks,42496,10.65716523,0,0.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/15/1990,110,minute.maid,31488,10.3573618,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/15/1990,110,tropicana,6592,8.793612072,0,3.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/15/1990,111,dominicks,16064,9.684336023,0,0.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/15/1990,111,minute.maid,81216,11.30486755,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/15/1990,111,tropicana,4480,8.407378325,0,3.87,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/15/1990,112,dominicks,43968,10.69121738,0,0.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/15/1990,112,minute.maid,55616,10.92622621,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/15/1990,112,tropicana,23168,10.0505273,0,3.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/15/1990,113,dominicks,2176,7.685243608,0,0.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/15/1990,113,minute.maid,66496,11.10489707,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/15/1990,113,tropicana,10112,9.221478116,0,3.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/15/1990,114,dominicks,256,5.545177445,0,0.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/15/1990,114,minute.maid,53184,10.88151288,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/15/1990,114,tropicana,8832,9.086136769,0,3.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/15/1990,115,dominicks,28032,10.24110199,0,0.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/15/1990,115,minute.maid,26624,10.18956834,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/15/1990,115,tropicana,7296,8.895081532,0,3.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/15/1990,116,dominicks,6720,8.812843434,0,0.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/15/1990,116,minute.maid,30016,10.30948585,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/15/1990,116,tropicana,7808,8.962904128,0,3.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/15/1990,117,dominicks,21312,9.967025573,0,0.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/15/1990,117,minute.maid,24512,10.10691807,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/15/1990,117,tropicana,3072,8.030084094,0,3.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/15/1990,118,dominicks,17024,9.742379392,0,0.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/15/1990,118,minute.maid,37632,10.53561003,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/15/1990,118,tropicana,5632,8.636219898,0,3.66,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/15/1990,119,dominicks,24128,10.09112827,0,0.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/15/1990,119,minute.maid,26560,10.1871616,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/15/1990,119,tropicana,9344,9.142489705,0,3.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/15/1990,121,dominicks,8576,9.056722883,0,0.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/15/1990,121,minute.maid,56192,10.93652968,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/15/1990,121,tropicana,12608,9.442086812,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/15/1990,122,dominicks,65536,11.09035489,0,0.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/15/1990,122,minute.maid,33152,10.40885833,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/15/1990,122,tropicana,19392,9.872615889,0,3.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/15/1990,124,dominicks,16512,9.711842668,0,0.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/15/1990,124,minute.maid,65280,11.08644099,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/15/1990,124,tropicana,7872,8.971067439,0,3.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/15/1990,126,dominicks,25344,10.1402973,0,0.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/15/1990,126,minute.maid,35136,10.46698153,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/15/1990,126,tropicana,7488,8.921057018,0,3.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/15/1990,128,dominicks,69184,11.1445249,0,0.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/15/1990,128,minute.maid,88128,11.38654558,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/15/1990,128,tropicana,15296,9.635346635,0,3.87,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/15/1990,129,dominicks,26816,10.196754,0,0.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/15/1990,129,minute.maid,32704,10.39525267,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/15/1990,129,tropicana,9728,9.182763604,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/15/1990,130,dominicks,82048,11.31505972,0,0.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/15/1990,130,minute.maid,69824,11.15373307,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/15/1990,130,tropicana,4544,8.42156296,0,3.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/15/1990,131,dominicks,62080,11.03617916,0,0.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/15/1990,131,minute.maid,27328,10.2156671,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/15/1990,131,tropicana,7808,8.962904128,0,3.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/15/1990,132,dominicks,35456,10.47604777,0,0.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/15/1990,132,minute.maid,70784,11.16738827,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/15/1990,132,tropicana,7488,8.921057018,0,3.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/15/1990,137,dominicks,47744,10.77360868,0,0.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/15/1990,137,minute.maid,52224,10.86329744,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/15/1990,137,tropicana,21376,9.970024076,0,3.87,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/22/1990,2,dominicks,17152,9.749870064,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/22/1990,2,minute.maid,6336,8.754002934,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/22/1990,2,tropicana,12160,9.405907156,0,2.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/22/1990,5,dominicks,29312,10.28575227,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/22/1990,5,minute.maid,11712,9.368369236,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/22/1990,5,tropicana,8448,9.041685006,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/22/1990,8,dominicks,25088,10.13014492,1,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/22/1990,8,minute.maid,11072,9.312174678,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/22/1990,8,tropicana,11840,9.379238908,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/22/1990,9,dominicks,14016,9.547954813,1,1.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/22/1990,9,minute.maid,9152,9.121727714,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/22/1990,9,tropicana,18944,9.849242538,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/22/1990,12,dominicks,45504,10.72555551,1,1.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/22/1990,12,minute.maid,15488,9.64782081,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/22/1990,12,tropicana,14848,9.605620455,0,2.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/22/1990,14,dominicks,8000,8.987196821,1,1.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/22/1990,14,minute.maid,11392,9.340666634,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/22/1990,14,tropicana,13760,9.529521112,0,2.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/22/1990,18,dominicks,28864,10.27035042,1,1.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/22/1990,18,minute.maid,11904,9.384629757,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/22/1990,18,tropicana,17408,9.76468515,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/22/1990,21,dominicks,14656,9.592605087,1,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/22/1990,21,minute.maid,12672,9.447150114,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/22/1990,21,tropicana,6016,8.702177866,0,2.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/22/1990,28,dominicks,8320,9.026417534,1,1.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/22/1990,28,minute.maid,8384,9.034080407,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/22/1990,28,tropicana,8128,9.00307017,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/22/1990,32,dominicks,18624,9.832206351,1,1.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/22/1990,32,minute.maid,18688,9.835636886,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/22/1990,32,tropicana,25728,10.15533517,0,2.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/22/1990,33,dominicks,12608,9.442086812,1,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/22/1990,33,minute.maid,3072,8.030084094,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/22/1990,33,tropicana,16256,9.69621735,0,2.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/22/1990,40,dominicks,34240,10.44114983,1,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/22/1990,40,minute.maid,9024,9.107642974,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/22/1990,40,tropicana,5888,8.68067166,0,2.67,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/22/1990,44,dominicks,16320,9.700146629,1,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/22/1990,44,minute.maid,10176,9.227787286,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/22/1990,44,tropicana,13824,9.534161491,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/22/1990,45,dominicks,18176,9.807857322,1,1.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/22/1990,45,minute.maid,5184,8.553332238,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/22/1990,45,tropicana,6336,8.754002934,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/22/1990,47,dominicks,11904,9.384629757,1,1.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/22/1990,47,minute.maid,9984,9.208739091,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/22/1990,47,tropicana,5120,8.540909718,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/22/1990,48,dominicks,14848,9.605620455,1,1.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/22/1990,48,minute.maid,9792,9.189321005,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/22/1990,48,tropicana,6400,8.764053269,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/22/1990,49,dominicks,11264,9.329367078,1,1.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/22/1990,49,minute.maid,5824,8.66974259,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/22/1990,49,tropicana,5760,8.658692754,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/22/1990,50,dominicks,5440,8.60153434,1,1.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/22/1990,50,minute.maid,7744,8.954673629,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/22/1990,50,tropicana,7488,8.921057018,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/22/1990,51,dominicks,19392,9.872615889,1,1.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/22/1990,51,minute.maid,10496,9.258749511,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/22/1990,51,tropicana,8128,9.00307017,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/22/1990,52,dominicks,12928,9.467150781,1,1.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/22/1990,52,minute.maid,15808,9.66827142,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/22/1990,52,tropicana,24384,10.10168246,0,2.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/22/1990,53,dominicks,15936,9.67633598,1,1.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/22/1990,53,minute.maid,15552,9.651944527,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/22/1990,53,tropicana,24064,10.08847223,0,2.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/22/1990,54,dominicks,15552,9.651944527,1,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/22/1990,54,minute.maid,8832,9.086136769,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/22/1990,54,tropicana,5760,8.658692754,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/22/1990,56,dominicks,11136,9.317938383,1,1.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/22/1990,56,minute.maid,6400,8.764053269,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/22/1990,56,tropicana,9536,9.162829389,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/22/1990,59,dominicks,9536,9.162829389,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/22/1990,59,minute.maid,11776,9.373818841,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/22/1990,59,tropicana,5248,8.565602331,0,2.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/22/1990,62,dominicks,10368,9.246479419,1,1.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/22/1990,62,minute.maid,7616,8.938006577,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/22/1990,62,tropicana,19200,9.862665558,0,2.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/22/1990,64,dominicks,7616,8.938006577,1,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/22/1990,64,minute.maid,8384,9.034080407,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/22/1990,64,tropicana,3392,8.129174997,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/22/1990,67,dominicks,9600,9.169518378,1,1.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/22/1990,67,minute.maid,14272,9.566054855,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/22/1990,67,tropicana,7040,8.859363449,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/22/1990,68,dominicks,19008,9.852615222,1,1.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/22/1990,68,minute.maid,10240,9.234056899,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/22/1990,68,tropicana,12480,9.431882642,0,2.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/22/1990,70,dominicks,29376,10.28793329,1,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/22/1990,70,minute.maid,8960,9.100525506,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/22/1990,70,tropicana,7936,8.979164649,0,2.65,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/22/1990,71,dominicks,28032,10.24110199,1,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/22/1990,71,minute.maid,7552,8.929567708,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/22/1990,71,tropicana,8960,9.100525506,0,2.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/22/1990,72,dominicks,6208,8.733594062,1,1.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/22/1990,72,minute.maid,16576,9.715711145,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/22/1990,72,tropicana,17216,9.753594463,0,2.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/22/1990,73,dominicks,23296,10.05603695,1,1.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/22/1990,73,minute.maid,34048,10.43552657,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/22/1990,73,tropicana,15296,9.635346635,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/22/1990,74,dominicks,19392,9.872615889,1,1.59,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/22/1990,74,minute.maid,16640,9.719564714,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/22/1990,74,tropicana,14080,9.55251063,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/22/1990,75,dominicks,21120,9.957975738,1,1.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/22/1990,75,minute.maid,16320,9.700146629,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/22/1990,75,tropicana,11648,9.36288977,0,2.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/22/1990,76,dominicks,17408,9.76468515,1,1.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/22/1990,76,minute.maid,6912,8.841014311,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/22/1990,76,tropicana,15040,9.618468598,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/22/1990,77,dominicks,19520,9.87919486,1,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/22/1990,77,minute.maid,8128,9.00307017,0,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/22/1990,77,tropicana,11072,9.312174678,0,2.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/22/1990,78,dominicks,25216,10.13523399,1,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/22/1990,78,minute.maid,8832,9.086136769,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/22/1990,78,tropicana,7360,8.903815212,0,2.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/22/1990,80,dominicks,25536,10.1478445,1,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/22/1990,80,minute.maid,13056,9.477003077,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/22/1990,80,tropicana,15488,9.64782081,0,2.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/22/1990,81,dominicks,23360,10.05878044,1,1.59,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/22/1990,81,minute.maid,14528,9.583833101,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/22/1990,81,tropicana,16896,9.734832187,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/22/1990,83,dominicks,54400,10.90411943,1,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/22/1990,83,minute.maid,6400,8.764053269,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/22/1990,83,tropicana,12096,9.400630098,0,2.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/22/1990,84,dominicks,12032,9.395325046,1,1.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/22/1990,84,minute.maid,11328,9.335032816,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/22/1990,84,tropicana,9344,9.142489705,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/22/1990,86,dominicks,16448,9.707959168,1,1.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/22/1990,86,minute.maid,11392,9.340666634,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/22/1990,86,tropicana,17024,9.742379392,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/22/1990,88,dominicks,18816,9.842462851,1,1.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/22/1990,88,minute.maid,8384,9.034080407,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/22/1990,88,tropicana,9536,9.162829389,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/22/1990,89,dominicks,26944,10.20151592,1,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/22/1990,89,minute.maid,10688,9.276876896,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/22/1990,89,tropicana,5568,8.624791202,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/22/1990,90,dominicks,13696,9.524859098,1,1.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/22/1990,90,minute.maid,6784,8.822322178,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/22/1990,90,tropicana,7808,8.962904128,0,2.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/22/1990,91,dominicks,20096,9.908276069,1,1.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/22/1990,91,minute.maid,8192,9.010913347,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/22/1990,91,tropicana,5632,8.636219898,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/22/1990,92,dominicks,15168,9.626943225,1,1.59,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/22/1990,92,minute.maid,13184,9.486759252,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/22/1990,92,tropicana,6720,8.812843434,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/22/1990,93,dominicks,8896,9.093357017,1,1.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/22/1990,93,minute.maid,5568,8.624791202,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/22/1990,93,tropicana,14464,9.579418083,0,2.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/22/1990,94,dominicks,16256,9.69621735,1,1.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/22/1990,94,minute.maid,14144,9.557045785,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/22/1990,94,tropicana,9664,9.17616292,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/22/1990,95,dominicks,11264,9.329367078,1,1.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/22/1990,95,minute.maid,13568,9.515469358,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/22/1990,95,tropicana,5440,8.60153434,0,2.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/22/1990,97,dominicks,5312,8.577723691,1,1.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/22/1990,97,minute.maid,7424,8.912473275,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/22/1990,97,tropicana,3008,8.009030685,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/22/1990,98,dominicks,39360,10.58050535,1,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/22/1990,98,minute.maid,7872,8.971067439,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/22/1990,98,tropicana,10240,9.234056899,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/22/1990,100,dominicks,13824,9.534161491,1,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/22/1990,100,minute.maid,8768,9.078864009,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/22/1990,100,tropicana,14976,9.614204199,0,2.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/22/1990,101,dominicks,23232,10.05328592,1,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/22/1990,101,minute.maid,10048,9.215128889,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/22/1990,101,tropicana,12800,9.45720045,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/22/1990,102,dominicks,28480,10.25695737,1,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/22/1990,102,minute.maid,17088,9.746131742,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/22/1990,102,tropicana,15168,9.626943225,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/22/1990,103,dominicks,11136,9.317938383,1,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/22/1990,103,minute.maid,9920,9.2023082,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/22/1990,103,tropicana,4160,8.333270353,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/22/1990,104,dominicks,19328,9.869310101,1,1.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/22/1990,104,minute.maid,4736,8.462948177,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/22/1990,104,tropicana,5376,8.589699882,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/22/1990,105,dominicks,33856,10.42987152,1,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/22/1990,105,minute.maid,12800,9.45720045,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/22/1990,105,tropicana,5504,8.61323038,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/22/1990,106,dominicks,18560,9.828764006,1,1.59,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/22/1990,106,minute.maid,3712,8.219326094,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/22/1990,106,tropicana,2688,7.896552702,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/22/1990,107,dominicks,14336,9.570529135,1,1.59,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/22/1990,107,minute.maid,6400,8.764053269,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/22/1990,107,tropicana,10880,9.29468152,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/22/1990,109,dominicks,8896,9.093357017,1,1.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/22/1990,109,minute.maid,15168,9.626943225,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/22/1990,109,tropicana,42176,10.64960662,0,2.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/22/1990,110,dominicks,25216,10.13523399,1,1.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/22/1990,110,minute.maid,9024,9.107642974,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/22/1990,110,tropicana,5760,8.658692754,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/22/1990,111,dominicks,39872,10.5934296,1,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/22/1990,111,minute.maid,20544,9.930324207,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/22/1990,111,tropicana,4864,8.489616424,0,2.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/22/1990,112,dominicks,34688,10.45414909,1,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/22/1990,112,minute.maid,17536,9.77201119,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/22/1990,112,tropicana,25984,10.16523624,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/22/1990,113,dominicks,19904,9.898675996,1,1.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/22/1990,113,minute.maid,12992,9.472089062,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/22/1990,113,tropicana,20416,9.924074186,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/22/1990,114,dominicks,24192,10.09377728,1,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/22/1990,114,minute.maid,13504,9.510741217,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/22/1990,114,tropicana,9216,9.128696383,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/22/1990,115,dominicks,6656,8.803273983,1,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/22/1990,115,minute.maid,10560,9.264828557,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/22/1990,115,tropicana,12992,9.472089062,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/22/1990,116,dominicks,11712,9.368369236,1,1.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/22/1990,116,minute.maid,8512,9.049232212,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/22/1990,116,tropicana,6464,8.7740036,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/22/1990,117,dominicks,12160,9.405907156,1,1.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/22/1990,117,minute.maid,5632,8.636219898,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/22/1990,117,tropicana,4800,8.476371197,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/22/1990,118,dominicks,10240,9.234056899,1,1.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/22/1990,118,minute.maid,14784,9.601300794,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/22/1990,118,tropicana,12032,9.395325046,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/22/1990,119,dominicks,11264,9.329367078,1,1.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/22/1990,119,minute.maid,16064,9.684336023,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/22/1990,119,tropicana,10432,9.252633284,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/22/1990,121,dominicks,7232,8.886270902,1,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/22/1990,121,minute.maid,19840,9.895455381,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/22/1990,121,tropicana,18496,9.825309772,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/22/1990,122,dominicks,18560,9.828764006,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/22/1990,122,minute.maid,14336,9.570529135,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/22/1990,122,tropicana,16448,9.707959168,0,2.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/22/1990,123,dominicks,24768,10.11730778,1,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/22/1990,123,minute.maid,9600,9.169518378,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/22/1990,123,tropicana,20480,9.927204079,0,2.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/22/1990,124,dominicks,19776,9.89222436,1,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/22/1990,124,minute.maid,14016,9.547954813,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/22/1990,124,tropicana,18304,9.814874894,0,2.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/22/1990,126,dominicks,11136,9.317938383,1,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/22/1990,126,minute.maid,8064,8.99516499,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/22/1990,126,tropicana,13888,9.538780437,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/22/1990,128,dominicks,29824,10.30306872,1,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/22/1990,128,minute.maid,20352,9.920934466,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/22/1990,128,tropicana,30464,10.32430094,0,2.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/22/1990,129,dominicks,10176,9.227787286,1,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/22/1990,129,minute.maid,17856,9.790094865,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/22/1990,129,tropicana,10240,9.234056899,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/22/1990,130,dominicks,56448,10.94107514,1,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/22/1990,130,minute.maid,9984,9.208739091,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/22/1990,130,tropicana,8128,9.00307017,0,2.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/22/1990,131,dominicks,14400,9.574983486,1,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/22/1990,131,minute.maid,11136,9.317938383,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/22/1990,131,tropicana,10944,9.30054664,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/22/1990,132,dominicks,35456,10.47604777,1,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/22/1990,132,minute.maid,13568,9.515469358,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/22/1990,132,tropicana,10816,9.288781798,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/22/1990,137,dominicks,32064,10.37548918,1,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/22/1990,137,minute.maid,13824,9.534161491,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/22/1990,137,tropicana,24128,10.09112827,0,2.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/29/1990,2,dominicks,26560,10.1871616,1,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/29/1990,2,minute.maid,9920,9.2023082,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/29/1990,2,tropicana,12672,9.447150114,0,2.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/29/1990,5,dominicks,52992,10.87789624,1,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/29/1990,5,minute.maid,13952,9.543378146,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/29/1990,5,tropicana,10880,9.29468152,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/29/1990,8,dominicks,91456,11.42361326,1,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/29/1990,8,minute.maid,12160,9.405907156,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/29/1990,8,tropicana,9664,9.17616292,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/29/1990,9,dominicks,51456,10.84848235,1,2.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/29/1990,9,minute.maid,11648,9.36288977,0,2.62,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/29/1990,9,tropicana,15040,9.618468598,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/29/1990,12,dominicks,38016,10.5457624,1,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/29/1990,12,minute.maid,18688,9.835636886,0,3.17,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/29/1990,12,tropicana,14592,9.588228712,0,2.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/29/1990,14,dominicks,34304,10.44301724,1,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/29/1990,14,minute.maid,7488,8.921057018,0,3.17,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/29/1990,14,tropicana,11648,9.36288977,0,2.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/29/1990,18,dominicks,89984,11.40738716,1,2.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/29/1990,18,minute.maid,9216,9.128696383,0,2.62,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/29/1990,18,tropicana,14848,9.605620455,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/29/1990,21,dominicks,49792,10.81560961,1,2.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/29/1990,21,minute.maid,5440,8.60153434,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/29/1990,21,tropicana,4416,8.392989588,0,2.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/29/1990,28,dominicks,30080,10.31161578,1,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/29/1990,28,minute.maid,7872,8.971067439,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/29/1990,28,tropicana,6016,8.702177866,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/29/1990,32,dominicks,47808,10.77494827,1,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/29/1990,32,minute.maid,11776,9.373818841,0,3.17,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/29/1990,32,tropicana,18304,9.814874894,0,2.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/29/1990,33,dominicks,11264,9.329367078,1,2.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/29/1990,33,minute.maid,10432,9.252633284,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/29/1990,33,tropicana,11328,9.335032816,0,2.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/29/1990,40,dominicks,84608,11.3457841,1,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/29/1990,40,minute.maid,6272,8.743850562,0,2.62,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/29/1990,40,tropicana,6528,8.783855897,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/29/1990,45,dominicks,33600,10.42228135,1,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/29/1990,45,minute.maid,5952,8.691482577,0,2.62,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/29/1990,45,tropicana,7232,8.886270902,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/29/1990,47,dominicks,38592,10.56080028,1,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/29/1990,47,minute.maid,9920,9.2023082,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/29/1990,47,tropicana,5952,8.691482577,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/29/1990,48,dominicks,25664,10.15284451,1,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/29/1990,48,minute.maid,9792,9.189321005,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/29/1990,48,tropicana,7424,8.912473275,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/29/1990,49,dominicks,24640,10.11212642,1,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/29/1990,49,minute.maid,9216,9.128696383,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/29/1990,49,tropicana,5696,8.647519453,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/29/1990,50,dominicks,29632,10.29661014,1,2.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/29/1990,50,minute.maid,6336,8.754002934,0,2.62,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/29/1990,50,tropicana,4928,8.502688505,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/29/1990,51,dominicks,44992,10.71423998,1,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/29/1990,51,minute.maid,10176,9.227787286,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/29/1990,51,tropicana,8256,9.018695488,0,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/29/1990,52,dominicks,33344,10.41463313,1,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/29/1990,52,minute.maid,12672,9.447150114,0,3.17,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/29/1990,52,tropicana,15232,9.631153757,0,2.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/29/1990,53,dominicks,57088,10.95234922,1,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/29/1990,53,minute.maid,19904,9.898675996,0,3.17,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/29/1990,53,tropicana,13696,9.524859098,0,2.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/29/1990,54,dominicks,32256,10.38145935,1,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/29/1990,54,minute.maid,9152,9.121727714,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/29/1990,54,tropicana,8000,8.987196821,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/29/1990,56,dominicks,27008,10.2038884,1,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/29/1990,56,minute.maid,9792,9.189321005,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/29/1990,56,tropicana,9024,9.107642974,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/29/1990,59,dominicks,53568,10.88870715,1,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/29/1990,59,minute.maid,5696,8.647519453,0,2.62,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/29/1990,59,tropicana,7552,8.929567708,0,2.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/29/1990,62,dominicks,16320,9.700146629,1,2.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/29/1990,62,minute.maid,12416,9.426741242,0,3.17,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/29/1990,62,tropicana,16768,9.727227587,0,2.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/29/1990,64,dominicks,25664,10.15284451,1,2.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/29/1990,64,minute.maid,6464,8.7740036,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/29/1990,64,tropicana,4224,8.348537825,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/29/1990,67,dominicks,30528,10.32639957,1,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/29/1990,67,minute.maid,10944,9.30054664,0,2.62,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/29/1990,67,tropicana,7936,8.979164649,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/29/1990,68,dominicks,31552,10.35939226,1,2.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/29/1990,68,minute.maid,12800,9.45720045,0,3.17,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/29/1990,68,tropicana,10816,9.288781798,0,2.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/29/1990,70,dominicks,59520,10.99406767,1,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/29/1990,70,minute.maid,10688,9.276876896,0,2.62,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/29/1990,70,tropicana,11136,9.317938383,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/29/1990,71,dominicks,42048,10.6465671,1,2.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/29/1990,71,minute.maid,11840,9.379238908,0,3.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/29/1990,71,tropicana,9920,9.2023082,0,2.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/29/1990,72,dominicks,35328,10.47243113,1,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/29/1990,72,minute.maid,13120,9.481893063,0,3.17,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/29/1990,72,tropicana,16064,9.684336023,0,2.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/29/1990,73,dominicks,111104,11.61822198,1,2.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/29/1990,73,minute.maid,19776,9.89222436,0,2.62,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/29/1990,73,tropicana,12096,9.400630098,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/29/1990,74,dominicks,65472,11.08937785,1,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/29/1990,74,minute.maid,13440,9.505990614,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/29/1990,74,tropicana,13568,9.515469358,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/29/1990,75,dominicks,28608,10.26144168,1,2.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/29/1990,75,minute.maid,11648,9.36288977,0,3.17,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/29/1990,75,tropicana,13760,9.529521112,0,2.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/29/1990,76,dominicks,57792,10.96460564,1,2.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/29/1990,76,minute.maid,11072,9.312174678,0,2.74,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/29/1990,76,tropicana,11520,9.351839934,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/29/1990,77,dominicks,36480,10.50451944,1,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/29/1990,77,minute.maid,10176,9.227787286,0,2.62,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/29/1990,77,tropicana,11520,9.351839934,0,2.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/29/1990,78,dominicks,90752,11.41588579,1,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/29/1990,78,minute.maid,9792,9.189321005,0,2.62,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/29/1990,78,tropicana,9088,9.114710141,0,2.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/29/1990,80,dominicks,67584,11.12112655,1,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/29/1990,80,minute.maid,12608,9.442086812,0,2.62,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/29/1990,80,tropicana,15552,9.651944527,0,2.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/29/1990,81,dominicks,57472,10.95905315,1,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/29/1990,81,minute.maid,13888,9.538780437,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/29/1990,81,tropicana,9472,9.156095357,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/29/1990,83,dominicks,82432,11.31972899,1,2.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/29/1990,83,minute.maid,8768,9.078864009,0,2.62,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/29/1990,83,tropicana,10880,9.29468152,0,2.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/29/1990,84,dominicks,48192,10.78294831,1,2.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/29/1990,84,minute.maid,10688,9.276876896,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/29/1990,84,tropicana,8640,9.064157862,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/29/1990,86,dominicks,36672,10.5097688,1,2.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/29/1990,86,minute.maid,14720,9.596962392,0,2.74,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/29/1990,86,tropicana,12736,9.452187908,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/29/1990,88,dominicks,36928,10.51672535,1,2.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/29/1990,88,minute.maid,9152,9.121727714,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/29/1990,88,tropicana,6656,8.803273983,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/29/1990,89,dominicks,56256,10.93766798,1,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/29/1990,89,minute.maid,12480,9.431882642,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 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-11/29/1990,109,minute.maid,14784,9.601300794,0,3.17,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/29/1990,109,tropicana,24448,10.10430369,0,2.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/29/1990,110,dominicks,45120,10.71708089,1,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/29/1990,110,minute.maid,9280,9.135616826,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/29/1990,110,tropicana,7936,8.979164649,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/29/1990,111,dominicks,52800,10.87426647,1,2.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/29/1990,111,minute.maid,16960,9.738612909,0,3.17,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/29/1990,111,tropicana,5888,8.68067166,0,2.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/29/1990,112,dominicks,69888,11.15464924,1,2.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/29/1990,112,minute.maid,20224,9.914625297,0,2.62,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/29/1990,112,tropicana,23936,10.08313888,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/29/1990,113,dominicks,48064,10.78028874,1,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/29/1990,113,minute.maid,12928,9.467150781,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/29/1990,113,tropicana,12032,9.395325046,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/29/1990,114,dominicks,64448,11.07361398,1,2.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/29/1990,114,minute.maid,12480,9.431882642,0,2.62,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/29/1990,114,tropicana,10560,9.264828557,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/29/1990,115,dominicks,42944,10.66765222,1,2.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/29/1990,115,minute.maid,10304,9.240287448,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/29/1990,115,tropicana,11264,9.329367078,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/29/1990,116,dominicks,35584,10.47965138,1,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/29/1990,116,minute.maid,8384,9.034080407,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/29/1990,116,tropicana,9536,9.162829389,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/29/1990,117,dominicks,32000,10.37349118,1,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/29/1990,117,minute.maid,6848,8.831711918,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/29/1990,117,tropicana,3520,8.166216269,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/29/1990,118,dominicks,39360,10.58050535,1,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/29/1990,118,minute.maid,5056,8.528330936,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/29/1990,118,tropicana,11968,9.3899917,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/29/1990,119,dominicks,30144,10.31374118,1,2.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/29/1990,119,minute.maid,9536,9.162829389,0,2.62,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/29/1990,119,tropicana,9216,9.128696383,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/29/1990,121,dominicks,46528,10.74780956,1,2.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/29/1990,121,minute.maid,14912,9.609921537,0,2.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/29/1990,121,tropicana,16384,9.704060528,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/29/1990,122,dominicks,69312,11.14637333,1,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/29/1990,122,minute.maid,12992,9.472089062,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/29/1990,122,tropicana,12224,9.411156511,0,2.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/29/1990,123,dominicks,60160,11.00476296,1,2.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/29/1990,123,minute.maid,11840,9.379238908,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/29/1990,123,tropicana,10304,9.240287448,0,2.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/29/1990,124,dominicks,25984,10.16523624,1,2.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/29/1990,124,minute.maid,14016,9.547954813,0,3.17,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/29/1990,124,tropicana,11200,9.323669057,0,2.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/29/1990,126,dominicks,37696,10.53730927,1,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/29/1990,126,minute.maid,9664,9.17616292,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/29/1990,126,tropicana,9664,9.17616292,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/29/1990,128,dominicks,59840,10.99942961,1,2.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/29/1990,128,minute.maid,21568,9.978966014,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/29/1990,128,tropicana,17088,9.746131742,0,2.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/29/1990,129,dominicks,38912,10.56905797,1,2.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/29/1990,129,minute.maid,6784,8.822322178,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/29/1990,129,tropicana,10752,9.282847063,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/29/1990,130,dominicks,68928,11.14081776,1,2.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/29/1990,130,minute.maid,15552,9.651944527,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/29/1990,130,tropicana,7232,8.886270902,0,2.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/29/1990,131,dominicks,57152,10.95346966,1,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/29/1990,131,minute.maid,10432,9.252633284,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/29/1990,131,tropicana,8768,9.078864009,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/29/1990,132,dominicks,62848,11.04847439,1,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/29/1990,132,minute.maid,14592,9.588228712,0,2.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/29/1990,132,tropicana,8448,9.041685006,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/29/1990,137,dominicks,42560,10.65867012,1,2.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/29/1990,137,minute.maid,14720,9.596962392,0,3.17,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/29/1990,137,tropicana,22912,10.03941607,0,2.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/6/1990,2,dominicks,6336,8.754002934,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/6/1990,2,minute.maid,25280,10.13776885,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/6/1990,2,tropicana,6528,8.783855897,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/6/1990,5,dominicks,15680,9.660141294,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/6/1990,5,minute.maid,36160,10.49570882,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/6/1990,5,tropicana,5696,8.647519453,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/6/1990,8,dominicks,23808,10.07777694,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/6/1990,8,minute.maid,30528,10.32639957,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/6/1990,8,tropicana,6272,8.743850562,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/6/1990,9,dominicks,8192,9.010913347,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/6/1990,9,minute.maid,47424,10.76688371,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/6/1990,9,tropicana,9728,9.182763604,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/6/1990,12,dominicks,23488,10.06424493,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/6/1990,12,minute.maid,67200,11.11542853,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/6/1990,12,tropicana,6656,8.803273983,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/6/1990,14,dominicks,4800,8.476371197,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/6/1990,14,minute.maid,26112,10.17015026,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/6/1990,14,tropicana,6656,8.803273983,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/6/1990,18,dominicks,16704,9.723403491,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/6/1990,18,minute.maid,44864,10.71139097,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/6/1990,18,tropicana,8512,9.049232212,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/6/1990,21,dominicks,24896,10.12246243,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/6/1990,21,minute.maid,16000,9.680344001,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/6/1990,21,tropicana,2944,7.98752448,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/6/1990,28,dominicks,4480,8.407378325,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/6/1990,28,minute.maid,18240,9.811372264,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/6/1990,28,tropicana,4160,8.333270353,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/6/1990,32,dominicks,9472,9.156095357,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/6/1990,32,minute.maid,56320,10.93880499,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/6/1990,32,tropicana,8320,9.026417534,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/6/1990,33,dominicks,7616,8.938006577,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/6/1990,33,minute.maid,28672,10.26367632,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/6/1990,33,tropicana,9344,9.142489705,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/6/1990,40,dominicks,16256,9.69621735,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/6/1990,40,minute.maid,23168,10.0505273,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/6/1990,40,tropicana,6144,8.723231275,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/6/1990,44,dominicks,6080,8.712759975,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/6/1990,44,minute.maid,29312,10.28575227,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/6/1990,44,tropicana,7872,8.971067439,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/6/1990,45,dominicks,10560,9.264828557,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/6/1990,45,minute.maid,15040,9.618468598,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/6/1990,45,tropicana,5440,8.60153434,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/6/1990,47,dominicks,7744,8.954673629,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/6/1990,47,minute.maid,21120,9.957975738,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/6/1990,47,tropicana,3776,8.236420527,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/6/1990,48,dominicks,5056,8.528330936,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/6/1990,48,minute.maid,23808,10.07777694,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/6/1990,48,tropicana,3264,8.090708716,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/6/1990,49,dominicks,4608,8.435549202,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/6/1990,49,minute.maid,15936,9.67633598,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/6/1990,49,tropicana,3648,8.201934351,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/6/1990,50,dominicks,6912,8.841014311,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/6/1990,50,minute.maid,14528,9.583833101,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/6/1990,50,tropicana,3264,8.090708716,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/6/1990,51,dominicks,8640,9.064157862,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/6/1990,51,minute.maid,28672,10.26367632,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/6/1990,51,tropicana,4096,8.317766167,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/6/1990,52,dominicks,5504,8.61323038,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/6/1990,52,minute.maid,33856,10.42987152,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/6/1990,52,tropicana,8832,9.086136769,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/6/1990,53,dominicks,11072,9.312174678,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/6/1990,53,minute.maid,48320,10.78560083,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/6/1990,53,tropicana,7104,8.868413285,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/6/1990,54,dominicks,9344,9.142489705,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/6/1990,54,minute.maid,18112,9.804329981,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/6/1990,54,tropicana,4224,8.348537825,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/6/1990,56,dominicks,7744,8.954673629,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/6/1990,56,minute.maid,18368,9.818365299,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/6/1990,56,tropicana,6912,8.841014311,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/6/1990,59,dominicks,18496,9.825309772,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/6/1990,59,minute.maid,13056,9.477003077,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/6/1990,59,tropicana,3968,8.286017468,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/6/1990,62,dominicks,3072,8.030084094,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/6/1990,62,minute.maid,25856,10.16029796,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/6/1990,62,tropicana,9920,9.2023082,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/6/1990,64,dominicks,8128,9.00307017,0,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/6/1990,64,minute.maid,15680,9.660141294,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/6/1990,64,tropicana,2560,7.847762538,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/6/1990,67,dominicks,13056,9.477003077,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/6/1990,67,minute.maid,24512,10.10691807,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/6/1990,67,tropicana,5504,8.61323038,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/6/1990,68,dominicks,6848,8.831711918,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/6/1990,68,minute.maid,40064,10.59823345,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/6/1990,68,tropicana,6080,8.712759975,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/6/1990,70,dominicks,15104,9.622714888,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 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-12/6/1990,75,minute.maid,40064,10.59823345,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/6/1990,75,tropicana,4800,8.476371197,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/6/1990,76,dominicks,11968,9.3899917,0,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/6/1990,76,minute.maid,49408,10.80786763,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/6/1990,76,tropicana,5376,8.589699882,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 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-12/6/1990,78,tropicana,5760,8.658692754,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/6/1990,80,dominicks,10048,9.215128889,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/6/1990,80,minute.maid,29056,10.27698028,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/6/1990,80,tropicana,13760,9.529521112,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/6/1990,83,dominicks,44864,10.71139097,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/6/1990,83,minute.maid,21504,9.975994243,1,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/6/1990,83,tropicana,6336,8.754002934,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/6/1990,84,dominicks,14592,9.588228712,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/6/1990,84,minute.maid,26752,10.19436452,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/6/1990,84,tropicana,3456,8.14786713,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 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-12/6/1990,88,tropicana,2880,7.965545573,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/6/1990,89,dominicks,12864,9.462187991,0,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/6/1990,89,minute.maid,37120,10.52191119,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/6/1990,89,tropicana,3072,8.030084094,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/6/1990,90,dominicks,18944,9.849242538,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/6/1990,90,minute.maid,29056,10.27698028,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/6/1990,90,tropicana,1920,7.560080465,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/6/1990,91,dominicks,7680,8.946374826,0,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/6/1990,91,minute.maid,28352,10.25245285,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/6/1990,91,tropicana,3008,8.009030685,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/6/1990,92,dominicks,13312,9.496421163,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/6/1990,92,minute.maid,28864,10.27035042,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/6/1990,92,tropicana,2752,7.920083199,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/6/1990,93,dominicks,7488,8.921057018,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/6/1990,93,minute.maid,30528,10.32639957,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/6/1990,93,tropicana,6272,8.743850562,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/6/1990,94,dominicks,12736,9.452187908,0,1.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/6/1990,94,minute.maid,23040,10.04498712,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/6/1990,94,tropicana,6784,8.822322178,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/6/1990,95,dominicks,7424,8.912473275,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/6/1990,95,minute.maid,22272,10.01108556,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/6/1990,95,tropicana,3584,8.184234774,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/6/1990,97,dominicks,4288,8.363575703,0,2.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/6/1990,97,minute.maid,11136,9.317938383,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/6/1990,97,tropicana,1600,7.377758908,0,3.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/6/1990,98,dominicks,19008,9.852615222,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/6/1990,98,minute.maid,41984,10.64504387,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/6/1990,98,tropicana,7360,8.903815212,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/6/1990,100,dominicks,11072,9.312174678,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/6/1990,100,minute.maid,47104,10.7601132,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/6/1990,100,tropicana,3904,8.269756948,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/6/1990,101,dominicks,19264,9.865993348,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/6/1990,101,minute.maid,30784,10.33475035,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/6/1990,101,tropicana,7744,8.954673629,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/6/1990,102,dominicks,33728,10.42608363,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/6/1990,102,minute.maid,53312,10.88391673,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/6/1990,102,tropicana,7040,8.859363449,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/6/1990,103,dominicks,11200,9.323669057,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/6/1990,103,minute.maid,22144,10.00532186,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/6/1990,103,tropicana,1664,7.416979621,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/6/1990,104,dominicks,8704,9.071537969,0,2.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/6/1990,104,minute.maid,24448,10.10430369,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/6/1990,104,tropicana,3392,8.129174997,0,3.39,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/6/1990,105,dominicks,20416,9.924074186,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/6/1990,105,minute.maid,34560,10.45045222,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/6/1990,105,tropicana,4736,8.462948177,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/6/1990,106,dominicks,5312,8.577723691,0,2.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/6/1990,106,minute.maid,11392,9.340666634,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/6/1990,106,tropicana,1472,7.294377299,0,3.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/6/1990,107,dominicks,2304,7.742402022,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/6/1990,107,minute.maid,35200,10.46880136,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/6/1990,107,tropicana,7872,8.971067439,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/6/1990,109,dominicks,5952,8.691482577,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/6/1990,109,minute.maid,52288,10.86452218,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/6/1990,109,tropicana,10560,9.264828557,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/6/1990,110,dominicks,5248,8.565602331,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/6/1990,110,minute.maid,29952,10.30735138,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/6/1990,110,tropicana,3776,8.236420527,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/6/1990,111,dominicks,4032,8.30201781,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/6/1990,111,minute.maid,88704,11.39306026,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/6/1990,111,tropicana,2112,7.655390645,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/6/1990,112,dominicks,14976,9.614204199,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/6/1990,112,minute.maid,43968,10.69121738,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/6/1990,112,tropicana,15616,9.656051309,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/6/1990,113,dominicks,7680,8.946374826,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/6/1990,113,minute.maid,44928,10.71281649,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/6/1990,113,tropicana,6016,8.702177866,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/6/1990,114,dominicks,17408,9.76468515,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/6/1990,114,minute.maid,42432,10.65565807,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/6/1990,114,tropicana,6208,8.733594062,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/6/1990,115,dominicks,8000,8.987196821,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/6/1990,115,minute.maid,30208,10.31586207,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/6/1990,115,tropicana,6016,8.702177866,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/6/1990,116,dominicks,6720,8.812843434,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/6/1990,116,minute.maid,16832,9.731037116,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/6/1990,116,tropicana,4928,8.502688505,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/6/1990,117,dominicks,6400,8.764053269,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/6/1990,117,minute.maid,16384,9.704060528,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/6/1990,117,tropicana,3200,8.070906089,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/6/1990,118,dominicks,5440,8.60153434,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/6/1990,118,minute.maid,34560,10.45045222,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/6/1990,118,tropicana,6464,8.7740036,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/6/1990,119,dominicks,8256,9.018695488,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/6/1990,119,minute.maid,24000,10.08580911,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/6/1990,119,tropicana,8512,9.049232212,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/6/1990,121,dominicks,7424,8.912473275,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/6/1990,121,minute.maid,37632,10.53561003,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/6/1990,121,tropicana,8128,9.00307017,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/6/1990,122,dominicks,14272,9.566054855,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/6/1990,122,minute.maid,25216,10.13523399,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/6/1990,122,tropicana,9408,9.14931567,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/6/1990,123,dominicks,12416,9.426741242,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/6/1990,123,minute.maid,61568,11.02789753,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/6/1990,123,tropicana,4864,8.489616424,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/6/1990,124,dominicks,5632,8.636219898,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/6/1990,124,minute.maid,53760,10.89228498,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/6/1990,124,tropicana,5632,8.636219898,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/6/1990,126,dominicks,5440,8.60153434,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/6/1990,126,minute.maid,33920,10.43176009,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/6/1990,126,tropicana,3904,8.269756948,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/6/1990,128,dominicks,10496,9.258749511,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/6/1990,128,minute.maid,80000,11.28978191,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/6/1990,128,tropicana,9792,9.189321005,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/6/1990,129,dominicks,6656,8.803273983,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/6/1990,129,minute.maid,28480,10.25695737,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/6/1990,129,tropicana,5120,8.540909718,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/6/1990,130,dominicks,6144,8.723231275,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/6/1990,130,minute.maid,55552,10.9250748,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/6/1990,130,tropicana,3136,8.050703382,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/6/1990,131,dominicks,9984,9.208739091,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/6/1990,131,minute.maid,32512,10.38936453,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/6/1990,131,tropicana,4608,8.435549202,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/6/1990,132,dominicks,20288,9.917784857,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/6/1990,132,minute.maid,42240,10.65112292,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/6/1990,132,tropicana,6080,8.712759975,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/6/1990,137,dominicks,9216,9.128696383,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/6/1990,137,minute.maid,41600,10.63585545,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/6/1990,137,tropicana,10624,9.270870872,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/13/1990,2,dominicks,26368,10.17990643,1,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/13/1990,2,minute.maid,14848,9.605620455,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/13/1990,2,tropicana,6144,8.723231275,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/13/1990,5,dominicks,43520,10.68097588,1,1.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/13/1990,5,minute.maid,12864,9.462187991,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/13/1990,5,tropicana,5696,8.647519453,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/13/1990,8,dominicks,89856,11.40596367,1,1.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/13/1990,8,minute.maid,12096,9.400630098,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/13/1990,8,tropicana,7168,8.877381955,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/13/1990,9,dominicks,53760,10.89228498,1,1.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/13/1990,9,minute.maid,12928,9.467150781,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/13/1990,9,tropicana,7680,8.946374826,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/13/1990,12,dominicks,65408,11.08839985,1,1.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/13/1990,12,minute.maid,22656,10.02818,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/13/1990,12,tropicana,8192,9.010913347,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/13/1990,14,dominicks,22848,10.03661887,1,1.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/13/1990,14,minute.maid,13888,9.538780437,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/13/1990,14,tropicana,6784,8.822322178,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/13/1990,18,dominicks,83840,11.3366655,1,1.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/13/1990,18,minute.maid,9536,9.162829389,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/13/1990,18,tropicana,8576,9.056722883,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/13/1990,21,dominicks,53824,10.89347474,1,1.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/13/1990,21,minute.maid,6976,8.850230966,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/13/1990,21,tropicana,2816,7.943072717,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/13/1990,28,dominicks,21824,9.990765561,1,1.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/13/1990,28,minute.maid,8256,9.018695488,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/13/1990,28,tropicana,4032,8.30201781,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/13/1990,32,dominicks,75264,11.22875721,1,1.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/13/1990,32,minute.maid,20608,9.933434629,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/13/1990,32,tropicana,9600,9.169518378,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/13/1990,33,dominicks,36992,10.51845695,1,1.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/13/1990,33,minute.maid,10048,9.215128889,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/13/1990,33,tropicana,8768,9.078864009,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/13/1990,40,dominicks,53632,10.88990118,1,1.39,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/13/1990,40,minute.maid,6592,8.793612072,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/13/1990,40,tropicana,6912,8.841014311,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/13/1990,44,dominicks,32832,10.39915893,1,1.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/13/1990,44,minute.maid,13440,9.505990614,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/13/1990,44,tropicana,8064,8.99516499,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/13/1990,45,dominicks,25024,10.12759064,1,1.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/13/1990,45,minute.maid,7104,8.868413285,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/13/1990,45,tropicana,5760,8.658692754,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 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-12/13/1990,50,minute.maid,7616,8.938006577,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/13/1990,50,tropicana,3456,8.14786713,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/13/1990,51,dominicks,43776,10.686841,1,1.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/13/1990,51,minute.maid,13632,9.520175249,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/13/1990,51,tropicana,4224,8.348537825,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/13/1990,52,dominicks,30144,10.31374118,1,1.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/13/1990,52,minute.maid,19328,9.869310101,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/13/1990,52,tropicana,7872,8.971067439,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/13/1990,53,dominicks,48640,10.79220152,1,1.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/13/1990,53,minute.maid,27072,10.20625526,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/13/1990,53,tropicana,8448,9.041685006,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/13/1990,54,dominicks,27904,10.23652533,1,1.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/13/1990,54,minute.maid,7296,8.895081532,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/13/1990,54,tropicana,4800,8.476371197,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/13/1990,56,dominicks,29248,10.28356647,1,1.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/13/1990,56,minute.maid,8832,9.086136769,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/13/1990,56,tropicana,6272,8.743850562,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/13/1990,59,dominicks,55104,10.91697759,1,1.39,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/13/1990,59,minute.maid,6656,8.803273983,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/13/1990,59,tropicana,4672,8.449342525,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/13/1990,62,dominicks,18048,9.800790154,1,1.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/13/1990,62,minute.maid,13888,9.538780437,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/13/1990,62,tropicana,11072,9.312174678,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/13/1990,64,dominicks,34112,10.43740451,1,1.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/13/1990,64,minute.maid,7808,8.962904128,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/13/1990,64,tropicana,2048,7.624618986,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/13/1990,67,dominicks,48064,10.78028874,1,1.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/13/1990,67,minute.maid,8832,9.086136769,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/13/1990,67,tropicana,5824,8.66974259,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/13/1990,68,dominicks,44032,10.69267192,1,1.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/13/1990,68,minute.maid,14400,9.574983486,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/13/1990,68,tropicana,4800,8.476371197,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/13/1990,70,dominicks,45376,10.72273861,1,1.39,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/13/1990,70,minute.maid,13696,9.524859098,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/13/1990,70,tropicana,8832,9.086136769,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/13/1990,71,dominicks,62976,11.05050898,1,1.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/13/1990,71,minute.maid,16256,9.69621735,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/13/1990,71,tropicana,4288,8.363575703,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/13/1990,72,dominicks,40384,10.60618895,1,1.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/13/1990,72,minute.maid,16448,9.707959168,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/13/1990,72,tropicana,7680,8.946374826,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/13/1990,73,dominicks,124288,11.73035673,1,1.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/13/1990,73,minute.maid,30080,10.31161578,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/13/1990,73,tropicana,8000,8.987196821,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/13/1990,74,dominicks,74432,11.21764124,1,1.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/13/1990,74,minute.maid,13504,9.510741217,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/13/1990,74,tropicana,7104,8.868413285,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/13/1990,75,dominicks,35328,10.47243113,1,1.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/13/1990,75,minute.maid,21888,9.99369382,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/13/1990,75,tropicana,5824,8.66974259,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/13/1990,76,dominicks,77248,11.2547763,1,1.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/13/1990,76,minute.maid,13312,9.496421163,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/13/1990,76,tropicana,6336,8.754002934,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/13/1990,77,dominicks,27136,10.20861654,1,1.39,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/13/1990,77,minute.maid,10304,9.240287448,0,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/13/1990,77,tropicana,8768,9.078864009,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/13/1990,78,dominicks,64832,11.07955459,1,1.39,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/13/1990,78,minute.maid,9216,9.128696383,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/13/1990,78,tropicana,6976,8.850230966,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/13/1990,80,dominicks,31680,10.36344085,1,1.39,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/13/1990,80,minute.maid,12608,9.442086812,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/13/1990,80,tropicana,12544,9.436997743,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/13/1990,81,dominicks,43328,10.67655436,1,1.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/13/1990,81,minute.maid,14272,9.566054855,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/13/1990,81,tropicana,4288,8.363575703,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/13/1990,83,dominicks,108096,11.590775,1,1.39,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/13/1990,83,minute.maid,8640,9.064157862,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/13/1990,83,tropicana,7296,8.895081532,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/13/1990,84,dominicks,51648,10.85220675,1,1.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/13/1990,84,minute.maid,12672,9.447150114,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/13/1990,84,tropicana,3584,8.184234774,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/13/1990,86,dominicks,45056,10.71566144,1,1.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/13/1990,86,minute.maid,21440,9.973013615,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/13/1990,86,tropicana,8512,9.049232212,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/13/1990,88,dominicks,29632,10.29661014,1,1.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/13/1990,88,minute.maid,12288,9.416378455,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/13/1990,88,tropicana,4800,8.476371197,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/13/1990,89,dominicks,72256,11.18797065,1,1.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/13/1990,89,minute.maid,12544,9.436997743,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/13/1990,89,tropicana,3264,8.090708716,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/13/1990,90,dominicks,59008,10.98542831,1,1.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/13/1990,90,minute.maid,9088,9.114710141,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/13/1990,90,tropicana,1856,7.526178913,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/13/1990,91,dominicks,54912,10.91348718,1,1.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/13/1990,91,minute.maid,8832,9.086136769,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/13/1990,91,tropicana,2944,7.98752448,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/13/1990,92,dominicks,58816,10.98216921,1,1.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/13/1990,92,minute.maid,16768,9.727227587,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/13/1990,92,tropicana,2560,7.847762538,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/13/1990,93,dominicks,32256,10.38145935,1,1.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/13/1990,93,minute.maid,11136,9.317938383,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/13/1990,93,tropicana,5120,8.540909718,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/13/1990,94,dominicks,38912,10.56905797,1,1.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/13/1990,94,minute.maid,13312,9.496421163,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/13/1990,94,tropicana,6784,8.822322178,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/13/1990,95,dominicks,31936,10.37148918,1,1.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/13/1990,95,minute.maid,8320,9.026417534,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/13/1990,95,tropicana,3264,8.090708716,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/13/1990,97,dominicks,13440,9.505990614,1,1.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/13/1990,97,minute.maid,6016,8.702177866,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/13/1990,97,tropicana,1088,6.992096427,0,3.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/13/1990,98,dominicks,79808,11.28737903,1,1.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/13/1990,98,minute.maid,13056,9.477003077,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/13/1990,98,tropicana,8256,9.018695488,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/13/1990,100,dominicks,76736,11.24812624,1,1.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/13/1990,100,minute.maid,16064,9.684336023,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/13/1990,100,tropicana,4416,8.392989588,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/13/1990,101,dominicks,55936,10.93196346,1,1.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/13/1990,101,minute.maid,13184,9.486759252,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/13/1990,101,tropicana,5376,8.589699882,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/13/1990,102,dominicks,131328,11.78545329,1,1.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/13/1990,102,minute.maid,16640,9.719564714,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/13/1990,102,tropicana,9088,9.114710141,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/13/1990,103,dominicks,40704,10.61408165,1,1.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/13/1990,103,minute.maid,7232,8.886270902,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/13/1990,103,tropicana,1792,7.491087594,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/13/1990,104,dominicks,35136,10.46698153,1,1.39,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/13/1990,104,minute.maid,8512,9.049232212,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/13/1990,104,tropicana,3200,8.070906089,0,3.39,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/13/1990,105,dominicks,97216,11.48469059,1,1.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/13/1990,105,minute.maid,11392,9.340666634,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/13/1990,105,tropicana,6336,8.754002934,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/13/1990,106,dominicks,25344,10.1402973,1,1.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/13/1990,106,minute.maid,4800,8.476371197,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/13/1990,106,tropicana,1792,7.491087594,0,3.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/13/1990,107,dominicks,48320,10.78560083,1,1.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/13/1990,107,minute.maid,6976,8.850230966,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/13/1990,107,tropicana,9664,9.17616292,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/13/1990,109,dominicks,53760,10.89228498,1,1.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/13/1990,109,minute.maid,21248,9.964018052,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/13/1990,109,tropicana,14336,9.570529135,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/13/1990,110,dominicks,35392,10.47424109,1,1.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/13/1990,110,minute.maid,13440,9.505990614,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/13/1990,110,tropicana,4224,8.348537825,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/13/1990,112,dominicks,44352,10.69991308,1,1.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/13/1990,112,minute.maid,19904,9.898675996,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/13/1990,112,tropicana,14848,9.605620455,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/13/1990,113,dominicks,45376,10.72273861,1,1.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/13/1990,113,minute.maid,16192,9.692272572,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/13/1990,113,tropicana,7296,8.895081532,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/13/1990,114,dominicks,112128,11.62739636,1,1.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/13/1990,114,minute.maid,13632,9.520175249,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/13/1990,114,tropicana,6464,8.7740036,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/13/1990,115,dominicks,37952,10.54407748,1,1.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/13/1990,115,minute.maid,14208,9.561560465,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/13/1990,115,tropicana,4992,8.51559191,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/13/1990,116,dominicks,27072,10.20625526,1,1.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/13/1990,116,minute.maid,8064,8.99516499,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/13/1990,116,tropicana,5056,8.528330936,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/13/1990,117,dominicks,23360,10.05878044,1,1.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/13/1990,117,minute.maid,6080,8.712759975,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/13/1990,117,tropicana,2688,7.896552702,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/13/1990,118,dominicks,51712,10.85344514,1,1.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/13/1990,118,minute.maid,10496,9.258749511,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/13/1990,118,tropicana,5760,8.658692754,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/13/1990,119,dominicks,25344,10.1402973,1,1.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/13/1990,119,minute.maid,10432,9.252633284,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/13/1990,119,tropicana,7168,8.877381955,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/13/1990,121,dominicks,50368,10.82711133,1,1.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/13/1990,121,minute.maid,14784,9.601300794,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/13/1990,121,tropicana,9600,9.169518378,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/13/1990,122,dominicks,45312,10.72132718,1,1.39,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/13/1990,122,minute.maid,12224,9.411156511,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/13/1990,122,tropicana,9088,9.114710141,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/13/1990,123,dominicks,97152,11.48403204,1,1.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/13/1990,123,minute.maid,16384,9.704060528,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/13/1990,123,tropicana,5760,8.658692754,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/13/1990,124,dominicks,79424,11.28255587,1,1.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/13/1990,124,minute.maid,18624,9.832206351,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/13/1990,124,tropicana,5312,8.577723691,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/13/1990,126,dominicks,29952,10.30735138,1,1.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/13/1990,126,minute.maid,10752,9.282847063,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/13/1990,126,tropicana,3776,8.236420527,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/13/1990,128,dominicks,68480,11.13429701,1,1.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/13/1990,128,minute.maid,36800,10.51325312,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/13/1990,128,tropicana,9152,9.121727714,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/13/1990,129,dominicks,29376,10.28793329,1,1.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/13/1990,129,minute.maid,10240,9.234056899,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/13/1990,129,tropicana,6848,8.831711918,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/13/1990,130,dominicks,122752,11.71792134,1,1.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/13/1990,130,minute.maid,7232,8.886270902,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/13/1990,130,tropicana,3520,8.166216269,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/13/1990,131,dominicks,46016,10.73674444,1,1.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/13/1990,131,minute.maid,12288,9.416378455,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/13/1990,131,tropicana,4288,8.363575703,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/13/1990,132,dominicks,68608,11.13616443,1,1.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/13/1990,132,minute.maid,16448,9.707959168,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/13/1990,132,tropicana,7168,8.877381955,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/13/1990,137,dominicks,49344,10.80657146,1,1.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/13/1990,137,minute.maid,16384,9.704060528,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/13/1990,137,tropicana,10752,9.282847063,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/20/1990,2,dominicks,896,6.797940413,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/20/1990,2,minute.maid,12288,9.416378455,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/20/1990,2,tropicana,21120,9.957975738,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/20/1990,5,dominicks,3904,8.269756948,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/20/1990,5,minute.maid,22208,10.00820786,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/20/1990,5,tropicana,32384,10.38541975,0,2.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/20/1990,8,dominicks,12224,9.411156511,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/20/1990,8,minute.maid,16448,9.707959168,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/20/1990,8,tropicana,29504,10.29228113,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/20/1990,9,dominicks,5056,8.528330936,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/20/1990,9,minute.maid,12736,9.452187908,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/20/1990,9,tropicana,41472,10.63277378,0,2.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/20/1990,12,dominicks,2560,7.847762538,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/20/1990,12,minute.maid,23552,10.06696602,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/20/1990,12,tropicana,42816,10.66466714,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/20/1990,14,dominicks,704,6.556778356,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/20/1990,14,minute.maid,16256,9.69621735,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/20/1990,14,tropicana,26496,10.18474906,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/20/1990,18,dominicks,11584,9.357380115,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/20/1990,18,minute.maid,12352,9.421573272,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/20/1990,18,tropicana,32640,10.39329381,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/20/1990,21,dominicks,6144,8.723231275,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/20/1990,21,minute.maid,8896,9.093357017,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/20/1990,21,tropicana,7104,8.868413285,0,2.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/20/1990,28,dominicks,1728,7.454719949,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/20/1990,28,minute.maid,10752,9.282847063,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/20/1990,28,tropicana,16256,9.69621735,0,2.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/20/1990,32,dominicks,2048,7.624618986,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/20/1990,32,minute.maid,22528,10.02251426,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/20/1990,32,tropicana,46592,10.74918413,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/20/1990,40,dominicks,18176,9.807857322,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/20/1990,40,minute.maid,9280,9.135616826,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/20/1990,40,tropicana,24896,10.12246243,0,2.39,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/20/1990,44,dominicks,1856,7.526178913,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/20/1990,44,minute.maid,15296,9.635346635,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/20/1990,44,tropicana,23872,10.0804615,0,2.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/20/1990,45,dominicks,8640,9.064157862,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/20/1990,45,minute.maid,7552,8.929567708,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/20/1990,45,tropicana,11648,9.36288977,0,2.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/20/1990,47,dominicks,9088,9.114710141,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/20/1990,47,minute.maid,12928,9.467150781,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/20/1990,47,tropicana,11392,9.340666634,0,2.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/20/1990,48,dominicks,2176,7.685243608,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/20/1990,48,minute.maid,10944,9.30054664,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/20/1990,48,tropicana,10240,9.234056899,0,2.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/20/1990,49,dominicks,960,6.866933285,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/20/1990,49,minute.maid,9600,9.169518378,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/20/1990,49,tropicana,8640,9.064157862,0,2.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/20/1990,50,dominicks,5568,8.624791202,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/20/1990,50,minute.maid,7488,8.921057018,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/20/1990,50,tropicana,11328,9.335032816,0,2.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/20/1990,51,dominicks,2176,7.685243608,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/20/1990,51,minute.maid,14848,9.605620455,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/20/1990,51,tropicana,29888,10.30521234,0,2.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/20/1990,52,dominicks,1216,7.103322063,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/20/1990,52,minute.maid,21312,9.967025573,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/20/1990,52,tropicana,35200,10.46880136,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/20/1990,53,dominicks,512,6.238324625,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/20/1990,53,minute.maid,23936,10.08313888,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/20/1990,53,tropicana,45312,10.72132718,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/20/1990,54,dominicks,2240,7.714231145,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/20/1990,54,minute.maid,11136,9.317938383,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/20/1990,54,tropicana,17536,9.77201119,0,2.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/20/1990,56,dominicks,2432,7.796469243,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/20/1990,56,minute.maid,11712,9.368369236,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/20/1990,56,tropicana,17344,9.761001904,0,2.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/20/1990,59,dominicks,4928,8.502688505,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/20/1990,59,minute.maid,8000,8.987196821,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/20/1990,59,tropicana,16960,9.738612909,0,2.39,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/20/1990,62,dominicks,320,5.768320996,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/20/1990,62,minute.maid,12800,9.45720045,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/20/1990,62,tropicana,39296,10.57887801,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/20/1990,64,dominicks,2816,7.943072717,0,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/20/1990,64,minute.maid,10752,9.282847063,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/20/1990,64,tropicana,8896,9.093357017,0,2.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/20/1990,67,dominicks,2496,7.82244473,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/20/1990,67,minute.maid,8704,9.071537969,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/20/1990,67,tropicana,14144,9.557045785,0,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/20/1990,68,dominicks,1728,7.454719949,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/20/1990,68,minute.maid,17472,9.768354879,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/20/1990,68,tropicana,20544,9.930324207,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/20/1990,70,dominicks,17984,9.797237753,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/20/1990,70,minute.maid,15488,9.64782081,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/20/1990,70,tropicana,19200,9.862665558,0,2.39,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/20/1990,71,dominicks,2432,7.796469243,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/20/1990,71,minute.maid,21248,9.964018052,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/20/1990,71,tropicana,34112,10.43740451,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/20/1990,72,dominicks,960,6.866933285,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/20/1990,72,minute.maid,17472,9.768354879,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 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-12/20/1990,74,minute.maid,12224,9.411156511,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/20/1990,74,tropicana,30528,10.32639957,0,2.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/20/1990,75,dominicks,3072,8.030084094,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/20/1990,75,minute.maid,18368,9.818365299,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/20/1990,75,tropicana,19072,9.85597657,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 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-12/20/1990,80,minute.maid,16832,9.731037116,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/20/1990,80,tropicana,25984,10.16523624,0,2.39,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/20/1990,81,dominicks,2368,7.769800996,0,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/20/1990,81,minute.maid,18176,9.807857322,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/20/1990,81,tropicana,35904,10.48860399,0,2.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 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-12/20/1990,88,minute.maid,11584,9.357380115,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/20/1990,88,tropicana,16192,9.692272572,0,2.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/20/1990,89,dominicks,1984,7.592870288,0,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/20/1990,89,minute.maid,12736,9.452187908,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/20/1990,89,tropicana,19840,9.895455381,0,2.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 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-12/20/1990,91,tropicana,21440,9.973013615,0,2.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/20/1990,92,dominicks,3008,8.009030685,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/20/1990,92,minute.maid,17280,9.757305042,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/20/1990,92,tropicana,17472,9.768354879,0,2.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/20/1990,93,dominicks,1024,6.931471806,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/20/1990,93,minute.maid,11072,9.312174678,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/20/1990,93,tropicana,26240,10.17504024,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/20/1990,94,dominicks,9984,9.208739091,0,1.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/20/1990,94,minute.maid,19008,9.852615222,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/20/1990,94,tropicana,15104,9.622714888,0,2.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 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-12/20/1990,97,tropicana,6272,8.743850562,0,2.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/20/1990,98,dominicks,12928,9.467150781,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/20/1990,98,minute.maid,13440,9.505990614,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/20/1990,98,tropicana,39296,10.57887801,0,2.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/20/1990,100,dominicks,1856,7.526178913,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/20/1990,100,minute.maid,19008,9.852615222,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/20/1990,100,tropicana,27904,10.23652533,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/20/1990,101,dominicks,6272,8.743850562,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/20/1990,101,minute.maid,13760,9.529521112,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/20/1990,101,tropicana,27904,10.23652533,0,2.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/20/1990,102,dominicks,18368,9.818365299,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/20/1990,102,minute.maid,13440,9.505990614,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/20/1990,102,tropicana,50112,10.82201578,0,2.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/20/1990,103,dominicks,8128,9.00307017,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/20/1990,103,minute.maid,9152,9.121727714,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/20/1990,103,tropicana,11008,9.30637756,0,2.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/20/1990,104,dominicks,2304,7.742402022,0,2.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/20/1990,104,minute.maid,13056,9.477003077,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/20/1990,104,tropicana,12224,9.411156511,0,2.39,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/20/1990,105,dominicks,14208,9.561560465,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/20/1990,105,minute.maid,14080,9.55251063,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/20/1990,105,tropicana,20608,9.933434629,0,2.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/20/1990,106,dominicks,1152,7.049254841,0,2.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/20/1990,106,minute.maid,6784,8.822322178,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/20/1990,106,tropicana,7488,8.921057018,0,2.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/20/1990,107,dominicks,2368,7.769800996,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/20/1990,107,minute.maid,5184,8.553332238,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/20/1990,107,tropicana,36288,10.49924239,0,2.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/20/1990,109,dominicks,256,5.545177445,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/20/1990,109,minute.maid,22400,10.01681624,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/20/1990,109,tropicana,62144,11.03720955,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/20/1990,110,dominicks,2304,7.742402022,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/20/1990,110,minute.maid,15680,9.660141294,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/20/1990,110,tropicana,24256,10.09641929,0,2.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/20/1990,111,dominicks,2176,7.685243608,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/20/1990,111,minute.maid,20800,9.942708266,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/20/1990,111,tropicana,24384,10.10168246,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/20/1990,112,dominicks,11328,9.335032816,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/20/1990,112,minute.maid,19328,9.869310101,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/20/1990,112,tropicana,38208,10.5508002,0,2.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/20/1990,113,dominicks,5184,8.553332238,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/20/1990,113,minute.maid,14464,9.579418083,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/20/1990,113,tropicana,36160,10.49570882,0,2.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/20/1990,114,dominicks,13504,9.510741217,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/20/1990,114,minute.maid,17600,9.775654181,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/20/1990,114,tropicana,18496,9.825309772,0,2.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/20/1990,115,dominicks,4096,8.317766167,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/20/1990,115,minute.maid,12352,9.421573272,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/20/1990,115,tropicana,40384,10.60618895,0,2.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/20/1990,116,dominicks,1728,7.454719949,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/20/1990,116,minute.maid,11328,9.335032816,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/20/1990,116,tropicana,17408,9.76468515,0,2.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/20/1990,117,dominicks,3840,8.253227646,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/20/1990,117,minute.maid,9088,9.114710141,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/20/1990,117,tropicana,15040,9.618468598,0,2.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/20/1990,118,dominicks,3136,8.050703382,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/20/1990,118,minute.maid,3456,8.14786713,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/20/1990,118,tropicana,30592,10.32849382,0,2.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/20/1990,119,dominicks,6528,8.783855897,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/20/1990,119,minute.maid,9472,9.156095357,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/20/1990,119,tropicana,19136,9.859326657,0,2.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/20/1990,121,dominicks,14016,9.547954813,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/20/1990,121,minute.maid,14336,9.570529135,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/20/1990,121,tropicana,27264,10.21332243,0,2.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/20/1990,122,dominicks,9664,9.17616292,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/20/1990,122,minute.maid,12800,9.45720045,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/20/1990,122,tropicana,19456,9.875910785,0,2.39,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/20/1990,123,dominicks,1856,7.526178913,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/20/1990,123,minute.maid,13632,9.520175249,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/20/1990,123,tropicana,41536,10.6343158,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/20/1990,124,dominicks,2240,7.714231145,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/20/1990,124,minute.maid,19968,9.901886271,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/20/1990,124,tropicana,29184,10.28137589,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/20/1990,126,dominicks,1024,6.931471806,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/20/1990,126,minute.maid,15488,9.64782081,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/20/1990,126,tropicana,24192,10.09377728,0,2.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/20/1990,128,dominicks,1984,7.592870288,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/20/1990,128,minute.maid,29888,10.30521234,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/20/1990,128,tropicana,60032,11.00263303,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/20/1990,129,dominicks,1984,7.592870288,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/20/1990,129,minute.maid,12224,9.411156511,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/20/1990,129,tropicana,35072,10.46515837,0,2.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/20/1990,130,dominicks,5312,8.577723691,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/20/1990,130,minute.maid,12096,9.400630098,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/20/1990,130,tropicana,30784,10.33475035,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/20/1990,131,dominicks,3264,8.090708716,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/20/1990,131,minute.maid,13248,9.491601877,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/20/1990,131,tropicana,20416,9.924074186,0,2.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/20/1990,132,dominicks,13568,9.515469358,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/20/1990,132,minute.maid,15296,9.635346635,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/20/1990,132,tropicana,25344,10.1402973,0,2.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/20/1990,134,dominicks,704,6.556778356,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/20/1990,134,minute.maid,7360,8.903815212,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/20/1990,134,tropicana,8704,9.071537969,0,2.39,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/20/1990,137,dominicks,2944,7.98752448,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/20/1990,137,minute.maid,17152,9.749870064,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/20/1990,137,tropicana,58560,10.97780715,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/27/1990,2,dominicks,1472,7.294377299,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/27/1990,2,minute.maid,6272,8.743850562,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/27/1990,2,tropicana,12416,9.426741242,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/27/1990,5,dominicks,896,6.797940413,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/27/1990,5,minute.maid,9984,9.208739091,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/27/1990,5,tropicana,10752,9.282847063,0,2.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/27/1990,8,dominicks,3776,8.236420527,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/27/1990,8,minute.maid,9344,9.142489705,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/27/1990,8,tropicana,8704,9.071537969,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/27/1990,9,dominicks,1856,7.526178913,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/27/1990,9,minute.maid,7104,8.868413285,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/27/1990,9,tropicana,15168,9.626943225,0,2.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/27/1990,12,dominicks,1472,7.294377299,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/27/1990,12,minute.maid,13696,9.524859098,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/27/1990,12,tropicana,11584,9.357380115,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/27/1990,14,dominicks,768,6.643789733,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/27/1990,14,minute.maid,11520,9.351839934,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/27/1990,14,tropicana,17408,9.76468515,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/27/1990,18,dominicks,5120,8.540909718,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/27/1990,18,minute.maid,11776,9.373818841,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/27/1990,18,tropicana,12160,9.405907156,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/27/1990,21,dominicks,12032,9.395325046,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/27/1990,21,minute.maid,6592,8.793612072,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/27/1990,21,tropicana,5888,8.68067166,0,2.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/27/1990,28,dominicks,1024,6.931471806,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/27/1990,28,minute.maid,6400,8.764053269,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/27/1990,28,tropicana,7424,8.912473275,0,2.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/27/1990,32,dominicks,12672,9.447150114,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/27/1990,32,minute.maid,15424,9.643680017,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/27/1990,32,tropicana,17984,9.797237753,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/27/1990,33,dominicks,1408,7.249925537,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/27/1990,33,minute.maid,10752,9.282847063,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/27/1990,33,tropicana,13376,9.501217335,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/27/1990,40,dominicks,13888,9.538780437,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/27/1990,40,minute.maid,6464,8.7740036,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/27/1990,40,tropicana,8512,9.049232212,0,2.39,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/27/1990,44,dominicks,1152,7.049254841,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/27/1990,44,minute.maid,11008,9.30637756,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/27/1990,44,tropicana,13760,9.529521112,0,2.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/27/1990,45,dominicks,3520,8.166216269,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/27/1990,45,minute.maid,4928,8.502688505,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/27/1990,45,tropicana,11008,9.30637756,0,2.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/27/1990,47,dominicks,9408,9.14931567,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/27/1990,47,minute.maid,8448,9.041685006,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/27/1990,47,tropicana,8768,9.078864009,0,2.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/27/1990,48,dominicks,2880,7.965545573,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/27/1990,48,minute.maid,8960,9.100525506,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/27/1990,48,tropicana,7488,8.921057018,0,2.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/27/1990,49,dominicks,896,6.797940413,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/27/1990,49,minute.maid,9152,9.121727714,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/27/1990,49,tropicana,6528,8.783855897,0,2.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/27/1990,50,dominicks,2496,7.82244473,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/27/1990,50,minute.maid,5376,8.589699882,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/27/1990,50,tropicana,7872,8.971067439,0,2.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/27/1990,51,dominicks,1024,6.931471806,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/27/1990,51,minute.maid,8192,9.010913347,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/27/1990,51,tropicana,9024,9.107642974,0,2.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/27/1990,52,dominicks,896,6.797940413,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/27/1990,52,minute.maid,16640,9.719564714,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/27/1990,52,tropicana,17728,9.78290059,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/27/1990,54,dominicks,1664,7.416979621,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/27/1990,54,minute.maid,9216,9.128696383,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/27/1990,54,tropicana,8064,8.99516499,0,2.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/27/1990,56,dominicks,1920,7.560080465,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/27/1990,56,minute.maid,8384,9.034080407,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/27/1990,56,tropicana,8832,9.086136769,0,2.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/27/1990,59,dominicks,2432,7.796469243,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/27/1990,59,minute.maid,4480,8.407378325,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/27/1990,59,tropicana,8576,9.056722883,0,2.39,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/27/1990,62,dominicks,2112,7.655390645,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/27/1990,62,minute.maid,9216,9.128696383,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/27/1990,62,tropicana,22208,10.00820786,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/27/1990,64,dominicks,2496,7.82244473,0,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/27/1990,64,minute.maid,8256,9.018695488,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/27/1990,64,tropicana,4544,8.42156296,0,2.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/27/1990,67,dominicks,1600,7.377758908,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/27/1990,67,minute.maid,5696,8.647519453,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/27/1990,67,tropicana,11072,9.312174678,0,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/27/1990,68,dominicks,1280,7.154615357,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/27/1990,68,minute.maid,11904,9.384629757,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/27/1990,68,tropicana,11456,9.346268889,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/27/1990,70,dominicks,4032,8.30201781,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/27/1990,70,minute.maid,10368,9.246479419,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/27/1990,70,tropicana,11840,9.379238908,0,2.39,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/27/1990,71,dominicks,1216,7.103322063,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/27/1990,71,minute.maid,9664,9.17616292,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/27/1990,71,tropicana,10304,9.240287448,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/27/1990,72,dominicks,1344,7.203405521,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/27/1990,72,minute.maid,10752,9.282847063,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/27/1990,72,tropicana,15616,9.656051309,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/27/1990,73,dominicks,9728,9.182763604,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/27/1990,73,minute.maid,23040,10.04498712,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/27/1990,73,tropicana,35712,10.48324205,0,2.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/27/1990,74,dominicks,1664,7.416979621,0,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/27/1990,74,minute.maid,9856,9.195835686,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/27/1990,74,tropicana,9792,9.189321005,0,2.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/27/1990,75,dominicks,1920,7.560080465,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/27/1990,75,minute.maid,13952,9.543378146,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/27/1990,75,tropicana,13184,9.486759252,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/27/1990,76,dominicks,19520,9.87919486,0,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/27/1990,76,minute.maid,9088,9.114710141,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/27/1990,76,tropicana,11136,9.317938383,0,2.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/27/1990,77,dominicks,2880,7.965545573,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/27/1990,77,minute.maid,8320,9.026417534,0,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/27/1990,77,tropicana,14720,9.596962392,0,2.39,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/27/1990,78,dominicks,2496,7.82244473,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/27/1990,78,minute.maid,7936,8.979164649,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/27/1990,78,tropicana,8960,9.100525506,0,2.39,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/27/1990,80,dominicks,2240,7.714231145,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/27/1990,80,minute.maid,9344,9.142489705,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/27/1990,80,tropicana,16320,9.700146629,0,2.39,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/27/1990,81,dominicks,2432,7.796469243,0,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/27/1990,81,minute.maid,12416,9.426741242,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/27/1990,81,tropicana,9984,9.208739091,0,2.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/27/1990,83,dominicks,6592,8.793612072,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/27/1990,83,minute.maid,11072,9.312174678,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/27/1990,83,tropicana,13504,9.510741217,0,2.39,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/27/1990,84,dominicks,1344,7.203405521,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/27/1990,84,minute.maid,10496,9.258749511,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/27/1990,84,tropicana,9344,9.142489705,0,2.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/27/1990,86,dominicks,1344,7.203405521,0,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/27/1990,86,minute.maid,9920,9.2023082,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/27/1990,86,tropicana,16256,9.69621735,0,2.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/27/1990,88,dominicks,3200,8.070906089,0,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/27/1990,88,minute.maid,9984,9.208739091,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/27/1990,88,tropicana,7616,8.938006577,0,2.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/27/1990,89,dominicks,2496,7.82244473,0,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/27/1990,89,minute.maid,9664,9.17616292,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 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-12/27/1990,91,minute.maid,6528,8.783855897,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/27/1990,91,tropicana,7744,8.954673629,0,2.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/27/1990,92,dominicks,1664,7.416979621,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/27/1990,92,minute.maid,10368,9.246479419,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/27/1990,92,tropicana,11520,9.351839934,0,2.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 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-12/27/1990,95,tropicana,7104,8.868413285,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/27/1990,97,dominicks,768,6.643789733,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/27/1990,97,minute.maid,6656,8.803273983,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/27/1990,97,tropicana,3264,8.090708716,0,2.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/27/1990,98,dominicks,5952,8.691482577,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/27/1990,98,minute.maid,12288,9.416378455,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/27/1990,98,tropicana,12608,9.442086812,0,2.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/27/1990,100,dominicks,2240,7.714231145,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/27/1990,100,minute.maid,15424,9.643680017,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/27/1990,100,tropicana,11712,9.368369236,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/27/1990,101,dominicks,9152,9.121727714,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/27/1990,101,minute.maid,10176,9.227787286,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/27/1990,101,tropicana,11200,9.323669057,0,2.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/27/1990,103,dominicks,2560,7.847762538,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/27/1990,103,minute.maid,4864,8.489616424,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/27/1990,103,tropicana,5952,8.691482577,0,2.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/27/1990,104,dominicks,1088,6.992096427,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/27/1990,104,minute.maid,7232,8.886270902,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/27/1990,104,tropicana,9024,9.107642974,0,2.39,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/27/1990,105,dominicks,4800,8.476371197,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/27/1990,105,minute.maid,8448,9.041685006,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/27/1990,105,tropicana,14016,9.547954813,0,2.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/27/1990,106,dominicks,6592,8.793612072,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/27/1990,106,minute.maid,3584,8.184234774,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/27/1990,106,tropicana,2816,7.943072717,0,2.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 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-12/27/1990,109,tropicana,25920,10.16277015,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/27/1990,110,dominicks,1984,7.592870288,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/27/1990,110,minute.maid,9344,9.142489705,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/27/1990,110,tropicana,7680,8.946374826,0,2.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/27/1990,111,dominicks,2816,7.943072717,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/27/1990,111,minute.maid,27264,10.21332243,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/27/1990,111,tropicana,8128,9.00307017,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/27/1990,113,dominicks,1664,7.416979621,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/27/1990,113,minute.maid,10944,9.30054664,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/27/1990,113,tropicana,8832,9.086136769,0,2.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/27/1990,115,dominicks,1728,7.454719949,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/27/1990,115,minute.maid,7744,8.954673629,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/27/1990,115,tropicana,10048,9.215128889,0,2.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/27/1990,116,dominicks,2304,7.742402022,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/27/1990,116,minute.maid,8512,9.049232212,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/27/1990,116,tropicana,9472,9.156095357,0,2.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/27/1990,117,dominicks,640,6.461468176,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/27/1990,117,minute.maid,5376,8.589699882,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/27/1990,117,tropicana,4736,8.462948177,0,2.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/27/1990,119,dominicks,4928,8.502688505,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/27/1990,119,minute.maid,9536,9.162829389,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/27/1990,119,tropicana,12032,9.395325046,0,2.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/27/1990,121,dominicks,2560,7.847762538,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/27/1990,121,minute.maid,10432,9.252633284,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/27/1990,121,tropicana,14336,9.570529135,0,2.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/27/1990,122,dominicks,3840,8.253227646,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/27/1990,122,minute.maid,10688,9.276876896,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/27/1990,122,tropicana,13632,9.520175249,0,2.39,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/27/1990,123,dominicks,2368,7.769800996,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/27/1990,123,minute.maid,8896,9.093357017,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/27/1990,123,tropicana,9728,9.182763604,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/27/1990,124,dominicks,832,6.723832441,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/27/1990,124,minute.maid,9728,9.182763604,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/27/1990,124,tropicana,15040,9.618468598,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/27/1990,126,dominicks,11712,9.368369236,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/27/1990,126,minute.maid,8896,9.093357017,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/27/1990,126,tropicana,12864,9.462187991,0,2.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/27/1990,129,dominicks,1472,7.294377299,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/27/1990,129,minute.maid,8000,8.987196821,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/27/1990,129,tropicana,13952,9.543378146,0,2.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/27/1990,130,dominicks,5184,8.553332238,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/27/1990,130,minute.maid,7104,8.868413285,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/27/1990,130,tropicana,17344,9.761001904,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/27/1990,131,dominicks,3200,8.070906089,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/27/1990,131,minute.maid,11136,9.317938383,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/27/1990,131,tropicana,9088,9.114710141,0,2.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/27/1990,132,dominicks,4800,8.476371197,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/27/1990,132,minute.maid,8704,9.071537969,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/27/1990,132,tropicana,11968,9.3899917,0,2.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/27/1990,134,dominicks,12864,9.462187991,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/27/1990,134,minute.maid,4096,8.317766167,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/27/1990,134,tropicana,6848,8.831711918,0,2.39,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/27/1990,137,dominicks,1280,7.154615357,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/27/1990,137,minute.maid,18816,9.842462851,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/27/1990,137,tropicana,35328,10.47243113,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/3/1991,2,dominicks,1344,7.203405521,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/3/1991,2,minute.maid,9152,9.121727714,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/3/1991,2,tropicana,9472,9.156095357,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/3/1991,5,dominicks,2240,7.714231145,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/3/1991,5,minute.maid,14016,9.547954813,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/3/1991,5,tropicana,6912,8.841014311,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/3/1991,8,dominicks,13824,9.534161491,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/3/1991,8,minute.maid,16128,9.688312171,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/3/1991,8,tropicana,9280,9.135616826,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/3/1991,9,dominicks,4352,8.378390789,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/3/1991,9,minute.maid,11392,9.340666634,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/3/1991,9,tropicana,12672,9.447150114,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/3/1991,12,dominicks,704,6.556778356,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/3/1991,12,minute.maid,18432,9.821843564,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/3/1991,12,tropicana,10112,9.221478116,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/3/1991,14,dominicks,320,5.768320996,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/3/1991,14,minute.maid,15104,9.622714888,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/3/1991,14,tropicana,11392,9.340666634,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/3/1991,18,dominicks,6848,8.831711918,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/3/1991,18,minute.maid,11520,9.351839934,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/3/1991,18,tropicana,8192,9.010913347,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/3/1991,21,dominicks,18496,9.825309772,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/3/1991,21,minute.maid,7360,8.903815212,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/3/1991,21,tropicana,3904,8.269756948,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/3/1991,28,dominicks,1088,6.992096427,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/3/1991,28,minute.maid,7104,8.868413285,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/3/1991,28,tropicana,5568,8.624791202,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/3/1991,32,dominicks,2112,7.655390645,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/3/1991,32,minute.maid,14464,9.579418083,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/3/1991,32,tropicana,15808,9.66827142,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/3/1991,33,dominicks,2304,7.742402022,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/3/1991,33,minute.maid,9664,9.17616292,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/3/1991,33,tropicana,14272,9.566054855,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/3/1991,40,dominicks,17280,9.757305042,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/3/1991,40,minute.maid,9408,9.14931567,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/3/1991,40,tropicana,6336,8.754002934,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/3/1991,45,dominicks,6976,8.850230966,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/3/1991,45,minute.maid,6720,8.812843434,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/3/1991,45,tropicana,8512,9.049232212,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/3/1991,47,dominicks,3968,8.286017468,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/3/1991,47,minute.maid,7424,8.912473275,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/3/1991,47,tropicana,6720,8.812843434,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/3/1991,48,dominicks,3136,8.050703382,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/3/1991,48,minute.maid,11072,9.312174678,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/3/1991,48,tropicana,7360,8.903815212,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/3/1991,49,dominicks,1600,7.377758908,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/3/1991,49,minute.maid,9536,9.162829389,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/3/1991,49,tropicana,5824,8.66974259,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/3/1991,50,dominicks,6208,8.733594062,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/3/1991,50,minute.maid,7360,8.903815212,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/3/1991,50,tropicana,5952,8.691482577,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/3/1991,51,dominicks,1920,7.560080465,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/3/1991,51,minute.maid,11520,9.351839934,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/3/1991,51,tropicana,4928,8.502688505,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/3/1991,52,dominicks,704,6.556778356,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/3/1991,52,minute.maid,15744,9.664214619,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/3/1991,52,tropicana,18560,9.828764006,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/3/1991,53,dominicks,2432,7.796469243,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/3/1991,53,minute.maid,21440,9.973013615,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/3/1991,53,tropicana,14080,9.55251063,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/3/1991,54,dominicks,3520,8.166216269,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/3/1991,54,minute.maid,8576,9.056722883,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/3/1991,54,tropicana,7296,8.895081532,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/3/1991,56,dominicks,3520,8.166216269,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/3/1991,56,minute.maid,10624,9.270870872,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/3/1991,56,tropicana,8768,9.078864009,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/3/1991,59,dominicks,6592,8.793612072,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/3/1991,59,minute.maid,7168,8.877381955,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/3/1991,59,tropicana,5184,8.553332238,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/3/1991,62,dominicks,384,5.950642553,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/3/1991,62,minute.maid,10432,9.252633284,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/3/1991,62,tropicana,14912,9.609921537,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/3/1991,64,dominicks,6912,8.841014311,0,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/3/1991,64,minute.maid,9024,9.107642974,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/3/1991,64,tropicana,4032,8.30201781,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/3/1991,67,dominicks,1856,7.526178913,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/3/1991,67,minute.maid,7360,8.903815212,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/3/1991,67,tropicana,6656,8.803273983,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/3/1991,68,dominicks,1856,7.526178913,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -1/3/1991,68,minute.maid,13056,9.477003077,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -1/3/1991,68,tropicana,9088,9.114710141,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -1/3/1991,70,dominicks,19520,9.87919486,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -1/3/1991,70,minute.maid,10752,9.282847063,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -1/3/1991,70,tropicana,7360,8.903815212,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -1/3/1991,71,dominicks,1088,6.992096427,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/3/1991,71,minute.maid,12800,9.45720045,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/3/1991,71,tropicana,8128,9.00307017,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/3/1991,72,dominicks,832,6.723832441,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/3/1991,72,minute.maid,16384,9.704060528,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/3/1991,72,tropicana,10688,9.276876896,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/3/1991,73,dominicks,15936,9.67633598,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/3/1991,73,minute.maid,14400,9.574983486,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/3/1991,73,tropicana,17216,9.753594463,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/3/1991,74,dominicks,4352,8.378390789,0,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/3/1991,74,minute.maid,14912,9.609921537,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/3/1991,74,tropicana,10240,9.234056899,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/3/1991,75,dominicks,1472,7.294377299,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/3/1991,75,minute.maid,17408,9.76468515,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/3/1991,75,tropicana,9920,9.2023082,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/3/1991,76,dominicks,2432,7.796469243,0,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/3/1991,76,minute.maid,14016,9.547954813,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/3/1991,76,tropicana,7232,8.886270902,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/3/1991,77,dominicks,5696,8.647519453,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/3/1991,77,minute.maid,10688,9.276876896,0,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/3/1991,77,tropicana,11840,9.379238908,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/3/1991,78,dominicks,7232,8.886270902,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/3/1991,78,minute.maid,11712,9.368369236,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/3/1991,78,tropicana,8768,9.078864009,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/3/1991,80,dominicks,12160,9.405907156,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -1/3/1991,80,minute.maid,12864,9.462187991,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -1/3/1991,80,tropicana,13376,9.501217335,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -1/3/1991,81,dominicks,2624,7.87245515,0,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -1/3/1991,81,minute.maid,13952,9.543378146,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -1/3/1991,81,tropicana,8320,9.026417534,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -1/3/1991,83,dominicks,31488,10.3573618,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -1/3/1991,83,minute.maid,9024,9.107642974,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -1/3/1991,83,tropicana,10560,9.264828557,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -1/3/1991,84,dominicks,2304,7.742402022,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -1/3/1991,84,minute.maid,11648,9.36288977,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -1/3/1991,84,tropicana,5952,8.691482577,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -1/3/1991,86,dominicks,5824,8.66974259,0,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/3/1991,86,minute.maid,11904,9.384629757,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/3/1991,86,tropicana,8448,9.041685006,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/3/1991,88,dominicks,3264,8.090708716,0,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -1/3/1991,88,minute.maid,6976,8.850230966,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -1/3/1991,88,tropicana,6400,8.764053269,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -1/3/1991,89,dominicks,41024,10.62191254,0,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/3/1991,89,minute.maid,11392,9.340666634,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/3/1991,89,tropicana,4224,8.348537825,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/3/1991,90,dominicks,3200,8.070906089,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -1/3/1991,90,minute.maid,9664,9.17616292,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -1/3/1991,90,tropicana,4480,8.407378325,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -1/3/1991,91,dominicks,1664,7.416979621,0,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -1/3/1991,91,minute.maid,8640,9.064157862,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -1/3/1991,91,tropicana,4224,8.348537825,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 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-1/3/1991,94,tropicana,9024,9.107642974,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -1/3/1991,95,dominicks,832,6.723832441,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/3/1991,95,minute.maid,12032,9.395325046,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/3/1991,95,tropicana,5568,8.624791202,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/3/1991,97,dominicks,640,6.461468176,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/3/1991,97,minute.maid,8512,9.049232212,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/3/1991,97,tropicana,2240,7.714231145,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/3/1991,98,dominicks,11456,9.346268889,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -1/3/1991,98,minute.maid,11264,9.329367078,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -1/3/1991,98,tropicana,7616,8.938006577,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -1/3/1991,100,dominicks,3840,8.253227646,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/3/1991,100,minute.maid,14720,9.596962392,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/3/1991,100,tropicana,8896,9.093357017,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/3/1991,101,dominicks,3136,8.050703382,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -1/3/1991,101,minute.maid,13632,9.520175249,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -1/3/1991,101,tropicana,10752,9.282847063,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -1/3/1991,103,dominicks,6336,8.754002934,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -1/3/1991,103,minute.maid,6592,8.793612072,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -1/3/1991,103,tropicana,4672,8.449342525,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -1/3/1991,104,dominicks,1920,7.560080465,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -1/3/1991,104,minute.maid,7872,8.971067439,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -1/3/1991,104,tropicana,5824,8.66974259,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -1/3/1991,105,dominicks,15616,9.656051309,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/3/1991,105,minute.maid,15296,9.635346635,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/3/1991,105,tropicana,7296,8.895081532,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/3/1991,106,dominicks,4992,8.51559191,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/3/1991,106,minute.maid,5632,8.636219898,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/3/1991,106,tropicana,3456,8.14786713,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/3/1991,107,dominicks,4992,8.51559191,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/3/1991,107,minute.maid,4096,8.317766167,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/3/1991,107,tropicana,11520,9.351839934,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/3/1991,109,dominicks,448,6.104793232,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/3/1991,109,minute.maid,19520,9.87919486,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/3/1991,109,tropicana,20032,9.905086274,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/3/1991,110,dominicks,3904,8.269756948,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/3/1991,110,minute.maid,11264,9.329367078,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/3/1991,110,tropicana,5056,8.528330936,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/3/1991,111,dominicks,2752,7.920083199,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/3/1991,111,minute.maid,14912,9.609921537,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/3/1991,111,tropicana,6208,8.733594062,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/3/1991,112,dominicks,8512,9.049232212,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/3/1991,112,minute.maid,18688,9.835636886,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/3/1991,112,tropicana,20096,9.908276069,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/3/1991,113,dominicks,3712,8.219326094,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/3/1991,113,minute.maid,10496,9.258749511,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/3/1991,113,tropicana,7680,8.946374826,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/3/1991,114,dominicks,10240,9.234056899,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/3/1991,114,minute.maid,15552,9.651944527,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/3/1991,114,tropicana,7296,8.895081532,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/3/1991,115,dominicks,1536,7.336936914,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/3/1991,115,minute.maid,10496,9.258749511,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/3/1991,115,tropicana,7168,8.877381955,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/3/1991,116,dominicks,1600,7.377758908,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/3/1991,116,minute.maid,11648,9.36288977,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/3/1991,116,tropicana,6080,8.712759975,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/3/1991,117,dominicks,1600,7.377758908,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/3/1991,117,minute.maid,8064,8.99516499,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/3/1991,117,tropicana,4544,8.42156296,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/3/1991,118,dominicks,3584,8.184234774,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/3/1991,118,minute.maid,4736,8.462948177,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/3/1991,118,tropicana,10112,9.221478116,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/3/1991,119,dominicks,7104,8.868413285,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/3/1991,119,minute.maid,10752,9.282847063,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/3/1991,119,tropicana,9920,9.2023082,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/3/1991,121,dominicks,6016,8.702177866,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/3/1991,121,minute.maid,9728,9.182763604,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/3/1991,121,tropicana,12672,9.447150114,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/3/1991,122,dominicks,12608,9.442086812,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/3/1991,122,minute.maid,11328,9.335032816,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/3/1991,122,tropicana,11776,9.373818841,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/3/1991,123,dominicks,23808,10.07777694,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/3/1991,123,minute.maid,9280,9.135616826,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/3/1991,123,tropicana,5696,8.647519453,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/3/1991,124,dominicks,3584,8.184234774,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/3/1991,124,minute.maid,17792,9.786504197,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/3/1991,124,tropicana,10944,9.30054664,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/3/1991,126,dominicks,1728,7.454719949,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/3/1991,126,minute.maid,12352,9.421573272,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/3/1991,126,tropicana,5888,8.68067166,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/3/1991,129,dominicks,1344,7.203405521,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/3/1991,129,minute.maid,7424,8.912473275,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/3/1991,129,tropicana,12928,9.467150781,0,2.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/3/1991,130,dominicks,3392,8.129174997,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/3/1991,130,minute.maid,3904,8.269756948,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/3/1991,130,tropicana,8000,8.987196821,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/3/1991,131,dominicks,4672,8.449342525,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/3/1991,131,minute.maid,11264,9.329367078,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/3/1991,131,tropicana,9152,9.121727714,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/3/1991,132,dominicks,13184,9.486759252,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/3/1991,132,minute.maid,13056,9.477003077,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/3/1991,132,tropicana,8960,9.100525506,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/3/1991,134,dominicks,12224,9.411156511,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/3/1991,134,minute.maid,3840,8.253227646,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/3/1991,134,tropicana,6528,8.783855897,0,2.39,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/3/1991,137,dominicks,2304,7.742402022,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/3/1991,137,minute.maid,19136,9.859326657,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/3/1991,137,tropicana,19840,9.895455381,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/10/1991,2,dominicks,111680,11.62339292,1,0.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/10/1991,2,minute.maid,4160,8.333270353,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/10/1991,2,tropicana,17920,9.793672686,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/10/1991,5,dominicks,125760,11.74213061,1,0.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/10/1991,5,minute.maid,6080,8.712759975,0,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/10/1991,5,tropicana,13440,9.505990614,0,2.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/10/1991,8,dominicks,251072,12.43349503,1,0.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/10/1991,8,minute.maid,5376,8.589699882,0,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/10/1991,8,tropicana,12224,9.411156511,0,2.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/10/1991,9,dominicks,130560,11.77958817,1,0.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/10/1991,9,minute.maid,6336,8.754002934,0,2.17,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/10/1991,9,tropicana,17344,9.761001904,0,2.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/10/1991,12,dominicks,209088,12.2505105,1,0.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/10/1991,12,minute.maid,5888,8.68067166,0,2.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/10/1991,12,tropicana,24704,10.11472045,0,2.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/10/1991,14,dominicks,70720,11.1664837,1,0.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/10/1991,14,minute.maid,4864,8.489616424,0,2.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/10/1991,14,tropicana,17920,9.793672686,0,2.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/10/1991,18,dominicks,175360,12.07459628,1,0.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/10/1991,18,minute.maid,7552,8.929567708,0,2.17,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/10/1991,18,tropicana,16896,9.734832187,0,2.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/10/1991,21,dominicks,95680,11.46876457,1,0.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/10/1991,21,minute.maid,5120,8.540909718,0,1.98,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/10/1991,21,tropicana,3968,8.286017468,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/10/1991,28,dominicks,69888,11.15464924,1,0.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/10/1991,28,minute.maid,3968,8.286017468,0,2.46,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/10/1991,28,tropicana,7296,8.895081532,0,2.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/10/1991,32,dominicks,166400,12.02214981,1,0.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/10/1991,32,minute.maid,7744,8.954673629,0,2.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/10/1991,32,tropicana,26432,10.18233068,0,2.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/10/1991,33,dominicks,77888,11.26302718,1,0.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/10/1991,33,minute.maid,6208,8.733594062,0,2.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/10/1991,33,tropicana,26688,10.19196931,0,2.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/10/1991,40,dominicks,167168,12.02675457,1,0.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/10/1991,40,minute.maid,5312,8.577723691,0,1.98,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/10/1991,40,tropicana,5440,8.60153434,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/10/1991,45,dominicks,56768,10.94672807,1,0.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/10/1991,45,minute.maid,4288,8.363575703,0,2.17,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/10/1991,45,tropicana,9856,9.195835686,0,2.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/10/1991,47,dominicks,87808,11.38290789,1,0.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/10/1991,47,minute.maid,3456,8.14786713,0,2.46,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/10/1991,47,tropicana,8960,9.100525506,0,2.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/10/1991,48,dominicks,68736,11.13802836,1,0.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/10/1991,48,minute.maid,4736,8.462948177,0,2.46,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/10/1991,48,tropicana,11200,9.323669057,0,2.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/10/1991,49,dominicks,71744,11.18085951,1,0.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/10/1991,49,minute.maid,3776,8.236420527,0,2.46,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/10/1991,49,tropicana,7872,8.971067439,0,2.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/10/1991,50,dominicks,84288,11.34199479,1,0.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/10/1991,50,minute.maid,3008,8.009030685,0,2.17,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/10/1991,50,tropicana,5056,8.528330936,0,2.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/10/1991,51,dominicks,140288,11.85145273,1,0.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/10/1991,51,minute.maid,3776,8.236420527,0,2.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/10/1991,51,tropicana,12736,9.452187908,0,2.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/10/1991,52,dominicks,90112,11.40880862,1,0.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/10/1991,52,minute.maid,7232,8.886270902,0,2.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/10/1991,52,tropicana,36608,10.50802208,0,2.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/10/1991,53,dominicks,208192,12.24621601,1,0.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/10/1991,53,minute.maid,4544,8.42156296,0,2.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/10/1991,53,tropicana,29504,10.29228113,0,2.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/10/1991,54,dominicks,90688,11.41518032,1,0.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/10/1991,54,minute.maid,3520,8.166216269,0,2.46,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/10/1991,54,tropicana,9728,9.182763604,0,2.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/10/1991,56,dominicks,85504,11.35631844,1,0.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/10/1991,56,minute.maid,3648,8.201934351,0,2.46,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/10/1991,56,tropicana,11392,9.340666634,0,2.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/10/1991,59,dominicks,141888,11.86279329,1,0.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/10/1991,59,minute.maid,4224,8.348537825,0,1.98,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/10/1991,59,tropicana,8576,9.056722883,0,2.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/10/1991,62,dominicks,55872,10.93081864,1,0.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/10/1991,62,minute.maid,7360,8.903815212,0,2.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/10/1991,62,tropicana,30272,10.31797847,0,2.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/10/1991,64,dominicks,92416,11.4340554,1,0.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/10/1991,64,minute.maid,2496,7.82244473,0,2.46,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/10/1991,64,tropicana,5632,8.636219898,0,2.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/10/1991,67,dominicks,125760,11.74213061,1,0.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/10/1991,67,minute.maid,4288,8.363575703,0,2.42,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/10/1991,67,tropicana,11200,9.323669057,0,2.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 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-1/10/1991,72,minute.maid,4672,8.449342525,0,2.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/10/1991,72,tropicana,25088,10.13014492,0,2.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/10/1991,73,dominicks,324992,12.69155585,1,0.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/10/1991,73,minute.maid,6528,8.783855897,0,2.17,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/10/1991,73,tropicana,23168,10.0505273,0,2.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/10/1991,74,dominicks,224768,12.32282404,1,0.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/10/1991,74,minute.maid,5248,8.565602331,0,2.46,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/10/1991,74,tropicana,19392,9.872615889,0,2.59,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/10/1991,75,dominicks,127040,11.75225728,1,0.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/10/1991,75,minute.maid,3968,8.286017468,0,2.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/10/1991,75,tropicana,22144,10.00532186,0,2.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/10/1991,76,dominicks,224320,12.32082888,1,0.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/10/1991,76,minute.maid,3072,8.030084094,0,2.74,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/10/1991,76,tropicana,18752,9.839055692,0,2.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/10/1991,77,dominicks,93824,11.44917597,1,0.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/10/1991,77,minute.maid,6016,8.702177866,0,1.98,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/10/1991,77,tropicana,12352,9.421573272,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/10/1991,78,dominicks,162944,12.00116186,1,0.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/10/1991,78,minute.maid,4736,8.462948177,0,1.98,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/10/1991,78,tropicana,11584,9.357380115,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 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-1/10/1991,81,tropicana,16960,9.738612909,0,2.59,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -1/10/1991,83,dominicks,276288,12.52919908,1,0.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -1/10/1991,83,minute.maid,5312,8.577723691,0,1.98,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -1/10/1991,83,tropicana,11456,9.346268889,0,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -1/10/1991,84,dominicks,138240,11.83674658,1,0.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -1/10/1991,84,minute.maid,5248,8.565602331,0,2.46,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -1/10/1991,84,tropicana,13056,9.477003077,0,2.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -1/10/1991,86,dominicks,175680,12.07641944,1,0.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/10/1991,86,minute.maid,3264,8.090708716,0,2.74,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/10/1991,86,tropicana,16064,9.684336023,0,2.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/10/1991,88,dominicks,108544,11.5949109,1,0.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -1/10/1991,88,minute.maid,4864,8.489616424,0,2.46,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -1/10/1991,88,tropicana,10304,9.240287448,0,2.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -1/10/1991,89,dominicks,265152,12.48805853,1,0.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/10/1991,89,minute.maid,3456,8.14786713,0,2.46,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/10/1991,89,tropicana,9472,9.156095357,0,2.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/10/1991,90,dominicks,197312,12.19254151,1,0.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -1/10/1991,90,minute.maid,2944,7.98752448,0,2.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -1/10/1991,90,tropicana,7488,8.921057018,0,2.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -1/10/1991,91,dominicks,147712,11.90301971,1,0.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -1/10/1991,91,minute.maid,2112,7.655390645,0,2.46,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -1/10/1991,91,tropicana,10944,9.30054664,0,2.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -1/10/1991,92,dominicks,203200,12.221946,1,0.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -1/10/1991,92,minute.maid,2880,7.965545573,0,2.46,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -1/10/1991,92,tropicana,9536,9.162829389,0,2.59,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -1/10/1991,93,dominicks,109760,11.60605144,1,0.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -1/10/1991,93,minute.maid,3712,8.219326094,0,2.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -1/10/1991,93,tropicana,22976,10.04220547,0,2.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -1/10/1991,94,dominicks,96832,11.4807328,1,0.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -1/10/1991,94,minute.maid,6592,8.793612072,0,2.17,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -1/10/1991,94,tropicana,13760,9.529521112,0,2.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -1/10/1991,95,dominicks,103936,11.5515306,1,0.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/10/1991,95,minute.maid,2432,7.796469243,0,2.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/10/1991,95,tropicana,11520,9.351839934,0,2.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/10/1991,97,dominicks,55808,10.92967251,1,0.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/10/1991,97,minute.maid,4160,8.333270353,0,2.46,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/10/1991,97,tropicana,6336,8.754002934,0,2.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/10/1991,98,dominicks,250624,12.43170909,1,0.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -1/10/1991,98,minute.maid,6080,8.712759975,0,2.17,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -1/10/1991,98,tropicana,13632,9.520175249,0,2.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -1/10/1991,100,dominicks,301312,12.61590155,1,0.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/10/1991,100,minute.maid,4800,8.476371197,0,2.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/10/1991,100,tropicana,19008,9.852615222,0,2.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/10/1991,101,dominicks,132480,11.79418697,1,0.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -1/10/1991,101,minute.maid,5568,8.624791202,0,2.46,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 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-1/10/1991,103,minute.maid,2816,7.943072717,0,2.17,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -1/10/1991,103,tropicana,4096,8.317766167,0,2.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -1/10/1991,104,dominicks,103360,11.54597332,1,0.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -1/10/1991,104,minute.maid,3648,8.201934351,0,2.46,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -1/10/1991,104,tropicana,9856,9.195835686,0,2.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -1/10/1991,105,dominicks,247936,12.42092593,1,0.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/10/1991,105,minute.maid,5056,8.528330936,0,2.17,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/10/1991,105,tropicana,9216,9.128696383,0,2.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/10/1991,106,dominicks,78528,11.27121053,1,0.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/10/1991,106,minute.maid,2240,7.714231145,0,2.46,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/10/1991,106,tropicana,4928,8.502688505,0,2.59,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/10/1991,107,dominicks,128896,11.76676116,1,0.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/10/1991,107,minute.maid,5248,8.565602331,0,2.46,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/10/1991,107,tropicana,25216,10.13523399,0,2.59,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/10/1991,109,dominicks,112576,11.63138383,1,0.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/10/1991,109,minute.maid,9472,9.156095357,0,2.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/10/1991,109,tropicana,39232,10.57724802,0,2.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/10/1991,110,dominicks,105472,11.56620079,1,0.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/10/1991,110,minute.maid,4800,8.476371197,0,2.46,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/10/1991,110,tropicana,11008,9.30637756,0,2.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/10/1991,111,dominicks,553600,13.22419768,1,0.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/10/1991,111,minute.maid,3136,8.050703382,0,2.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/10/1991,111,tropicana,9536,9.162829389,0,2.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/10/1991,112,dominicks,102272,11.53539121,1,0.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/10/1991,112,minute.maid,12608,9.442086812,0,2.17,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/10/1991,112,tropicana,36160,10.49570882,0,2.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/10/1991,113,dominicks,139200,11.84366703,1,0.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/10/1991,113,minute.maid,4416,8.392989588,0,2.46,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/10/1991,113,tropicana,20544,9.930324207,0,2.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/10/1991,114,dominicks,248320,12.42247352,1,0.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/10/1991,114,minute.maid,6784,8.822322178,0,2.17,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/10/1991,114,tropicana,13312,9.496421163,0,2.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/10/1991,115,dominicks,104000,11.55214618,1,0.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/10/1991,115,minute.maid,5568,8.624791202,0,2.46,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/10/1991,115,tropicana,12096,9.400630098,0,2.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/10/1991,116,dominicks,100864,11.52152835,1,0.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/10/1991,116,minute.maid,3456,8.14786713,0,2.46,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/10/1991,116,tropicana,11264,9.329367078,0,2.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/10/1991,117,dominicks,67072,11.11352195,1,0.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/10/1991,117,minute.maid,2816,7.943072717,0,2.46,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/10/1991,117,tropicana,11712,9.368369236,0,2.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/10/1991,118,dominicks,112960,11.63478905,1,0.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/10/1991,118,minute.maid,3584,8.184234774,0,2.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/10/1991,118,tropicana,13376,9.501217335,0,2.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/10/1991,119,dominicks,63232,11.05456578,1,0.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/10/1991,119,minute.maid,6720,8.812843434,0,2.17,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/10/1991,119,tropicana,11840,9.379238908,0,2.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/10/1991,121,dominicks,111936,11.62568256,1,0.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/10/1991,121,minute.maid,8512,9.049232212,0,2.17,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/10/1991,121,tropicana,16576,9.715711145,0,2.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/10/1991,122,dominicks,122496,11.71583366,1,0.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/10/1991,122,minute.maid,8448,9.041685006,0,1.98,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/10/1991,122,tropicana,15360,9.639522007,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/10/1991,123,dominicks,185664,12.13169387,1,0.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/10/1991,123,minute.maid,5696,8.647519453,0,2.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/10/1991,123,tropicana,12352,9.421573272,0,2.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/10/1991,124,dominicks,279936,12.54231629,1,0.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/10/1991,124,minute.maid,4992,8.51559191,0,2.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/10/1991,124,tropicana,21632,9.981928979,0,2.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/10/1991,126,dominicks,106304,11.57405819,1,0.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/10/1991,126,minute.maid,6144,8.723231275,0,2.46,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/10/1991,126,tropicana,18368,9.818365299,0,2.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/10/1991,128,dominicks,205248,12.23197428,1,0.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/10/1991,128,minute.maid,7040,8.859363449,0,2.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/10/1991,128,tropicana,25984,10.16523624,0,2.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 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-1/10/1991,137,minute.maid,6400,8.764053269,0,2.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/10/1991,137,tropicana,42752,10.66317126,0,2.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/17/1991,2,dominicks,1856,7.526178913,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/17/1991,2,minute.maid,10176,9.227787286,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/17/1991,2,tropicana,9408,9.14931567,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 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-1/17/1991,12,minute.maid,7104,8.868413285,0,2.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/17/1991,12,tropicana,9024,9.107642974,0,2.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/17/1991,14,dominicks,3456,8.14786713,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/17/1991,14,minute.maid,4352,8.378390789,0,2.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/17/1991,14,tropicana,12672,9.447150114,0,2.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 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-1/17/1991,21,tropicana,3520,8.166216269,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/17/1991,28,dominicks,1088,6.992096427,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/17/1991,28,minute.maid,4096,8.317766167,0,2.46,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/17/1991,28,tropicana,4864,8.489616424,0,2.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/17/1991,32,dominicks,2048,7.624618986,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/17/1991,32,minute.maid,8256,9.018695488,0,2.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/17/1991,32,tropicana,15488,9.64782081,0,2.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/17/1991,33,dominicks,704,6.556778356,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/17/1991,33,minute.maid,7232,8.886270902,0,2.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/17/1991,33,tropicana,15424,9.643680017,0,2.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/17/1991,40,dominicks,14720,9.596962392,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/17/1991,40,minute.maid,4224,8.348537825,0,1.98,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/17/1991,40,tropicana,6080,8.712759975,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/17/1991,44,dominicks,1792,7.491087594,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/17/1991,44,minute.maid,14976,9.614204199,0,2.46,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/17/1991,44,tropicana,10368,9.246479419,0,2.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/17/1991,45,dominicks,3584,8.184234774,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/17/1991,45,minute.maid,3776,8.236420527,0,2.17,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/17/1991,45,tropicana,7872,8.971067439,0,2.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/17/1991,47,dominicks,5056,8.528330936,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/17/1991,47,minute.maid,6016,8.702177866,0,2.46,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/17/1991,47,tropicana,5376,8.589699882,0,2.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/17/1991,48,dominicks,5440,8.60153434,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/17/1991,48,minute.maid,6848,8.831711918,0,2.46,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/17/1991,48,tropicana,6464,8.7740036,0,2.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 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-1/17/1991,76,minute.maid,5760,8.658692754,0,2.74,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/17/1991,76,tropicana,9280,9.135616826,0,2.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/17/1991,77,dominicks,27712,10.22962081,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/17/1991,77,minute.maid,5824,8.66974259,0,1.98,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/17/1991,77,tropicana,9920,9.2023082,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/17/1991,78,dominicks,3840,8.253227646,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/17/1991,78,minute.maid,7168,8.877381955,0,1.98,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/17/1991,78,tropicana,7424,8.912473275,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/17/1991,80,dominicks,21632,9.981928979,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -1/17/1991,80,minute.maid,7936,8.979164649,0,1.98,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 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-1/17/1991,109,minute.maid,10624,9.270870872,0,2.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/17/1991,109,tropicana,21440,9.973013615,0,2.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/17/1991,110,dominicks,1728,7.454719949,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/17/1991,110,minute.maid,5952,8.691482577,0,2.46,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/17/1991,110,tropicana,6208,8.733594062,0,2.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 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-1/17/1991,112,tropicana,20352,9.920934466,0,2.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/17/1991,113,dominicks,896,6.797940413,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/17/1991,113,minute.maid,6272,8.743850562,0,2.46,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/17/1991,113,tropicana,6336,8.754002934,0,2.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/17/1991,114,dominicks,8576,9.056722883,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/17/1991,114,minute.maid,7168,8.877381955,0,2.17,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/17/1991,114,tropicana,8384,9.034080407,0,2.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/17/1991,115,dominicks,2880,7.965545573,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/17/1991,115,minute.maid,9536,9.162829389,0,2.46,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/17/1991,115,tropicana,6976,8.850230966,0,2.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/17/1991,116,dominicks,2880,7.965545573,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/17/1991,116,minute.maid,5248,8.565602331,0,2.46,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/17/1991,116,tropicana,6848,8.831711918,0,2.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/17/1991,117,dominicks,6016,8.702177866,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/17/1991,117,minute.maid,4864,8.489616424,0,2.46,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/17/1991,117,tropicana,5632,8.636219898,0,2.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/17/1991,118,dominicks,2240,7.714231145,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/17/1991,118,minute.maid,6080,8.712759975,0,2.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/17/1991,118,tropicana,4992,8.51559191,0,2.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/17/1991,119,dominicks,3712,8.219326094,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/17/1991,119,minute.maid,6208,8.733594062,0,2.17,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/17/1991,119,tropicana,12032,9.395325046,0,2.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/17/1991,121,dominicks,4288,8.363575703,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/17/1991,121,minute.maid,8064,8.99516499,0,2.17,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/17/1991,121,tropicana,13632,9.520175249,0,2.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/17/1991,122,dominicks,17920,9.793672686,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/17/1991,122,minute.maid,10368,9.246479419,0,1.98,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/17/1991,122,tropicana,11072,9.312174678,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/17/1991,123,dominicks,3008,8.009030685,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/17/1991,123,minute.maid,6720,8.812843434,0,2.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/17/1991,123,tropicana,6848,8.831711918,0,2.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/17/1991,124,dominicks,3840,8.253227646,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/17/1991,124,minute.maid,9472,9.156095357,0,2.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/17/1991,124,tropicana,15680,9.660141294,0,2.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/17/1991,126,dominicks,1984,7.592870288,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/17/1991,126,minute.maid,6272,8.743850562,0,2.46,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/17/1991,126,tropicana,8256,9.018695488,0,2.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/17/1991,128,dominicks,1216,7.103322063,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/17/1991,128,minute.maid,8576,9.056722883,0,2.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/17/1991,128,tropicana,17280,9.757305042,0,2.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 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-1/17/1991,130,tropicana,6464,8.7740036,0,2.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/17/1991,131,dominicks,4160,8.333270353,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/17/1991,131,minute.maid,6400,8.764053269,0,2.7,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/17/1991,131,tropicana,9280,9.135616826,0,2.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/17/1991,132,dominicks,35392,10.47424109,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/17/1991,132,minute.maid,9088,9.114710141,0,2.17,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/17/1991,132,tropicana,9984,9.208739091,0,2.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/17/1991,134,dominicks,1920,7.560080465,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/17/1991,134,minute.maid,10816,9.288781798,0,2.46,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/17/1991,134,tropicana,7872,8.971067439,0,2.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/17/1991,137,dominicks,2880,7.965545573,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/17/1991,137,minute.maid,9536,9.162829389,0,2.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/17/1991,137,tropicana,21504,9.975994243,0,2.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/24/1991,2,dominicks,5568,8.624791202,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/24/1991,2,minute.maid,29056,10.27698028,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/24/1991,2,tropicana,6272,8.743850562,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/24/1991,5,dominicks,7232,8.886270902,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/24/1991,5,minute.maid,40896,10.61878754,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/24/1991,5,tropicana,5248,8.565602331,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/24/1991,8,dominicks,10176,9.227787286,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/24/1991,8,minute.maid,59712,10.99728828,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/24/1991,8,tropicana,8128,9.00307017,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/24/1991,9,dominicks,5760,8.658692754,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/24/1991,9,minute.maid,55808,10.92967251,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/24/1991,9,tropicana,9728,9.182763604,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/24/1991,12,dominicks,7424,8.912473275,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/24/1991,12,minute.maid,70208,11.15921754,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/24/1991,12,tropicana,5632,8.636219898,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/24/1991,14,dominicks,9344,9.142489705,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/24/1991,14,minute.maid,28800,10.26813067,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/24/1991,14,tropicana,8256,9.018695488,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/24/1991,18,dominicks,7808,8.962904128,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/24/1991,18,minute.maid,55616,10.92622621,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/24/1991,18,tropicana,7296,8.895081532,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/24/1991,21,dominicks,12160,9.405907156,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/24/1991,21,minute.maid,24128,10.09112827,1,1.98,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/24/1991,21,tropicana,3008,8.009030685,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/24/1991,28,dominicks,4544,8.42156296,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/24/1991,28,minute.maid,19968,9.901886271,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/24/1991,28,tropicana,5248,8.565602331,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/24/1991,32,dominicks,10112,9.221478116,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/24/1991,32,minute.maid,58048,10.96902553,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/24/1991,32,tropicana,8512,9.049232212,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/24/1991,33,dominicks,6976,8.850230966,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/24/1991,33,minute.maid,31232,10.34919849,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/24/1991,33,tropicana,7040,8.859363449,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/24/1991,40,dominicks,14592,9.588228712,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/24/1991,40,minute.maid,34560,10.45045222,1,1.98,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/24/1991,40,tropicana,5248,8.565602331,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/24/1991,44,dominicks,6336,8.754002934,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/24/1991,44,minute.maid,37312,10.52707027,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/24/1991,44,tropicana,4608,8.435549202,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/24/1991,45,dominicks,8832,9.086136769,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/24/1991,45,minute.maid,17088,9.746131742,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/24/1991,45,tropicana,4608,8.435549202,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/24/1991,47,dominicks,4800,8.476371197,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/24/1991,47,minute.maid,31808,10.36747311,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/24/1991,47,tropicana,3392,8.129174997,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/24/1991,48,dominicks,4992,8.51559191,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/24/1991,48,minute.maid,23744,10.07508515,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/24/1991,48,tropicana,4800,8.476371197,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/24/1991,49,dominicks,4736,8.462948177,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/24/1991,49,minute.maid,20800,9.942708266,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/24/1991,49,tropicana,2816,7.943072717,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/24/1991,50,dominicks,7744,8.954673629,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/24/1991,50,minute.maid,18304,9.814874894,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/24/1991,50,tropicana,4480,8.407378325,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/24/1991,51,dominicks,4864,8.489616424,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/24/1991,51,minute.maid,38720,10.56411154,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/24/1991,51,tropicana,3648,8.201934351,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/24/1991,52,dominicks,3776,8.236420527,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/24/1991,52,minute.maid,47104,10.7601132,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/24/1991,52,tropicana,6848,8.831711918,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/24/1991,53,dominicks,3328,8.110126802,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/24/1991,53,minute.maid,56896,10.94898032,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/24/1991,53,tropicana,8064,8.99516499,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/24/1991,54,dominicks,5568,8.624791202,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/24/1991,54,minute.maid,24064,10.08847223,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/24/1991,54,tropicana,3712,8.219326094,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/24/1991,56,dominicks,7040,8.859363449,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/24/1991,56,minute.maid,27072,10.20625526,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/24/1991,56,tropicana,7552,8.929567708,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/24/1991,59,dominicks,7616,8.938006577,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/24/1991,59,minute.maid,29248,10.28356647,1,1.98,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/24/1991,59,tropicana,3264,8.090708716,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/24/1991,62,dominicks,3136,8.050703382,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/24/1991,62,minute.maid,28416,10.25470765,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/24/1991,62,tropicana,11648,9.36288977,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/24/1991,64,dominicks,7168,8.877381955,0,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/24/1991,64,minute.maid,17152,9.749870064,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/24/1991,64,tropicana,2112,7.655390645,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/24/1991,67,dominicks,12480,9.431882642,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/24/1991,67,minute.maid,25920,10.16277015,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/24/1991,67,tropicana,5184,8.553332238,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/24/1991,68,dominicks,7232,8.886270902,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 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-1/24/1991,94,minute.maid,30784,10.33475035,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -1/24/1991,94,tropicana,5568,8.624791202,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -1/24/1991,95,dominicks,6144,8.723231275,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/24/1991,95,minute.maid,33728,10.42608363,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/24/1991,95,tropicana,2880,7.965545573,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/24/1991,97,dominicks,2752,7.920083199,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/24/1991,97,minute.maid,17664,9.779283949,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/24/1991,97,tropicana,1216,7.103322063,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/24/1991,98,dominicks,16448,9.707959168,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -1/24/1991,98,minute.maid,42176,10.64960662,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -1/24/1991,98,tropicana,7296,8.895081532,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -1/24/1991,100,dominicks,11840,9.379238908,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/24/1991,100,minute.maid,58176,10.97122818,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/24/1991,100,tropicana,6016,8.702177866,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/24/1991,101,dominicks,16384,9.704060528,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -1/24/1991,101,minute.maid,40448,10.60777248,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -1/24/1991,101,tropicana,6144,8.723231275,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -1/24/1991,102,dominicks,21696,9.984883191,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -1/24/1991,102,minute.maid,64832,11.07955459,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -1/24/1991,102,tropicana,6208,8.733594062,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -1/24/1991,103,dominicks,15424,9.643680017,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -1/24/1991,103,minute.maid,29504,10.29228113,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -1/24/1991,103,tropicana,2432,7.796469243,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -1/24/1991,105,dominicks,13568,9.515469358,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/24/1991,105,minute.maid,46144,10.73952222,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/24/1991,105,tropicana,4928,8.502688505,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/24/1991,106,dominicks,4160,8.333270353,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/24/1991,106,minute.maid,20096,9.908276069,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/24/1991,106,tropicana,1408,7.249925537,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/24/1991,107,dominicks,2816,7.943072717,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/24/1991,107,minute.maid,57280,10.9557068,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/24/1991,107,tropicana,7488,8.921057018,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/24/1991,109,dominicks,6272,8.743850562,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/24/1991,109,minute.maid,57856,10.96571244,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/24/1991,109,tropicana,9984,9.208739091,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/24/1991,110,dominicks,9024,9.107642974,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/24/1991,110,minute.maid,39296,10.57887801,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/24/1991,110,tropicana,2944,7.98752448,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/24/1991,111,dominicks,16448,9.707959168,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/24/1991,111,minute.maid,90048,11.40809814,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/24/1991,111,tropicana,2752,7.920083199,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/24/1991,112,dominicks,8896,9.093357017,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/24/1991,112,minute.maid,48256,10.78427545,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/24/1991,112,tropicana,12224,9.411156511,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/24/1991,113,dominicks,6208,8.733594062,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/24/1991,113,minute.maid,53696,10.89109379,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/24/1991,113,tropicana,4800,8.476371197,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/24/1991,114,dominicks,11968,9.3899917,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/24/1991,114,minute.maid,47488,10.76823233,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/24/1991,114,tropicana,6080,8.712759975,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/24/1991,115,dominicks,4864,8.489616424,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/24/1991,115,minute.maid,39872,10.5934296,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/24/1991,115,tropicana,4224,8.348537825,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/24/1991,116,dominicks,10624,9.270870872,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/24/1991,116,minute.maid,26624,10.18956834,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/24/1991,116,tropicana,3840,8.253227646,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/24/1991,117,dominicks,3968,8.286017468,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/24/1991,117,minute.maid,22336,10.01395501,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/24/1991,117,tropicana,2240,7.714231145,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/24/1991,118,dominicks,1536,7.336936914,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/24/1991,118,minute.maid,41984,10.64504387,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/24/1991,118,tropicana,5120,8.540909718,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/24/1991,119,dominicks,5312,8.577723691,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/24/1991,119,minute.maid,25856,10.16029796,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/24/1991,119,tropicana,6336,8.754002934,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/24/1991,121,dominicks,6528,8.783855897,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/24/1991,121,minute.maid,43392,10.67803037,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/24/1991,121,tropicana,7424,8.912473275,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/24/1991,122,dominicks,16960,9.738612909,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/24/1991,122,minute.maid,37696,10.53730927,1,1.98,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/24/1991,122,tropicana,8896,9.093357017,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/24/1991,123,dominicks,10304,9.240287448,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/24/1991,123,minute.maid,65344,11.0874209,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/24/1991,123,tropicana,5824,8.66974259,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/24/1991,124,dominicks,12608,9.442086812,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/24/1991,124,minute.maid,54720,10.90998455,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/24/1991,124,tropicana,4800,8.476371197,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/24/1991,126,dominicks,6656,8.803273983,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/24/1991,126,minute.maid,41664,10.63739273,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/24/1991,126,tropicana,3648,8.201934351,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/24/1991,128,dominicks,12160,9.405907156,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/24/1991,128,minute.maid,68800,11.13895902,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/24/1991,128,tropicana,8256,9.018695488,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/24/1991,129,dominicks,4928,8.502688505,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/24/1991,129,minute.maid,30336,10.32009041,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/24/1991,129,tropicana,3904,8.269756948,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/24/1991,130,dominicks,5632,8.636219898,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/24/1991,130,minute.maid,79872,11.28818063,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/24/1991,130,tropicana,2880,7.965545573,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/24/1991,131,dominicks,7424,8.912473275,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/24/1991,131,minute.maid,43008,10.66914142,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/24/1991,131,tropicana,5056,8.528330936,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/24/1991,132,dominicks,15104,9.622714888,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/24/1991,132,minute.maid,51712,10.85344514,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/24/1991,132,tropicana,7488,8.921057018,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/24/1991,134,dominicks,10880,9.29468152,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/24/1991,134,minute.maid,31872,10.36948316,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/24/1991,134,tropicana,2624,7.87245515,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/24/1991,137,dominicks,6976,8.850230966,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/24/1991,137,minute.maid,60288,11.00688836,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/24/1991,137,tropicana,14784,9.601300794,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/31/1991,2,dominicks,32064,10.37548918,1,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/31/1991,2,minute.maid,7104,8.868413285,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/31/1991,2,tropicana,6912,8.841014311,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/31/1991,5,dominicks,41216,10.62658181,1,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/31/1991,5,minute.maid,6272,8.743850562,0,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/31/1991,5,tropicana,6208,8.733594062,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/31/1991,8,dominicks,105344,11.56498647,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/31/1991,8,minute.maid,9856,9.195835686,0,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/31/1991,8,tropicana,5952,8.691482577,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/31/1991,9,dominicks,57216,10.95458886,1,1.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/31/1991,9,minute.maid,7168,8.877381955,0,2.17,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/31/1991,9,tropicana,10048,9.215128889,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/31/1991,12,dominicks,91328,11.4222127,1,1.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/31/1991,12,minute.maid,9856,9.195835686,0,2.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/31/1991,12,tropicana,6336,8.754002934,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/31/1991,14,dominicks,25024,10.12759064,1,1.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/31/1991,14,minute.maid,5184,8.553332238,0,2.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/31/1991,14,tropicana,8448,9.041685006,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/31/1991,18,dominicks,77440,11.25725872,1,1.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/31/1991,18,minute.maid,8576,9.056722883,0,2.17,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/31/1991,18,tropicana,8960,9.100525506,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/31/1991,21,dominicks,44672,10.70710219,1,1.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/31/1991,21,minute.maid,6784,8.822322178,0,1.98,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/31/1991,21,tropicana,3264,8.090708716,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/31/1991,28,dominicks,22080,10.0024275,1,1.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/31/1991,28,minute.maid,3392,8.129174997,0,2.46,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/31/1991,28,tropicana,5184,8.553332238,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/31/1991,32,dominicks,79040,11.27770933,1,1.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/31/1991,32,minute.maid,9984,9.208739091,0,2.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/31/1991,32,tropicana,10944,9.30054664,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/31/1991,33,dominicks,36800,10.51325312,1,1.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/31/1991,33,minute.maid,5888,8.68067166,0,2.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/31/1991,33,tropicana,9088,9.114710141,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/31/1991,40,dominicks,69056,11.14267305,1,1.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/31/1991,40,minute.maid,9792,9.189321005,0,1.98,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/31/1991,40,tropicana,6784,8.822322178,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/31/1991,44,dominicks,36288,10.49924239,1,1.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/31/1991,44,minute.maid,6528,8.783855897,0,2.46,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/31/1991,44,tropicana,7104,8.868413285,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/31/1991,45,dominicks,25088,10.13014492,1,1.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/31/1991,45,minute.maid,5184,8.553332238,0,2.17,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/31/1991,45,tropicana,7296,8.895081532,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/31/1991,47,dominicks,35328,10.47243113,1,1.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/31/1991,47,minute.maid,5952,8.691482577,0,2.46,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/31/1991,47,tropicana,3648,8.201934351,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/31/1991,48,dominicks,23232,10.05328592,1,1.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/31/1991,48,minute.maid,5056,8.528330936,0,2.46,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/31/1991,48,tropicana,5376,8.589699882,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/31/1991,49,dominicks,24896,10.12246243,1,1.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/31/1991,49,minute.maid,2752,7.920083199,0,2.46,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/31/1991,49,tropicana,3200,8.070906089,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/31/1991,50,dominicks,22976,10.04220547,1,1.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/31/1991,50,minute.maid,5632,8.636219898,0,2.17,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/31/1991,50,tropicana,3648,8.201934351,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/31/1991,51,dominicks,49472,10.80916213,1,1.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/31/1991,51,minute.maid,3520,8.166216269,0,2.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/31/1991,51,tropicana,4544,8.42156296,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/31/1991,52,dominicks,22592,10.02535114,1,1.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/31/1991,52,minute.maid,10304,9.240287448,0,2.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/31/1991,52,tropicana,8448,9.041685006,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/31/1991,53,dominicks,58368,10.97452307,1,1.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/31/1991,53,minute.maid,7936,8.979164649,0,2.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/31/1991,53,tropicana,10752,9.282847063,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/31/1991,54,dominicks,34752,10.4559924,1,1.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/31/1991,54,minute.maid,4800,8.476371197,0,2.46,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/31/1991,54,tropicana,3648,8.201934351,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/31/1991,56,dominicks,39360,10.58050535,1,1.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/31/1991,56,minute.maid,3264,8.090708716,0,2.46,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/31/1991,56,tropicana,6016,8.702177866,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/31/1991,59,dominicks,47104,10.7601132,1,1.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/31/1991,59,minute.maid,8960,9.100525506,0,1.98,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/31/1991,59,tropicana,4032,8.30201781,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/31/1991,62,dominicks,12480,9.431882642,1,1.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/31/1991,62,minute.maid,16896,9.734832187,0,2.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 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-1/31/1991,67,minute.maid,4544,8.42156296,0,2.17,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/31/1991,67,tropicana,4672,8.449342525,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/31/1991,68,dominicks,42048,10.6465671,1,1.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -1/31/1991,68,minute.maid,7424,8.912473275,0,2.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -1/31/1991,68,tropicana,5312,8.577723691,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 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-1/31/1991,71,tropicana,4608,8.435549202,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/31/1991,72,dominicks,39680,10.58860256,1,1.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/31/1991,72,minute.maid,6656,8.803273983,0,2.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/31/1991,72,tropicana,7104,8.868413285,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/31/1991,73,dominicks,140224,11.85099642,1,1.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/31/1991,73,minute.maid,10432,9.252633284,0,2.17,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/31/1991,73,tropicana,11968,9.3899917,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/31/1991,74,dominicks,93760,11.44849361,1,1.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/31/1991,74,minute.maid,7808,8.962904128,0,2.46,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/31/1991,74,tropicana,7744,8.954673629,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/31/1991,75,dominicks,49344,10.80657146,1,1.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/31/1991,75,minute.maid,7936,8.979164649,0,2.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/31/1991,75,tropicana,6912,8.841014311,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/31/1991,76,dominicks,111552,11.62224613,1,1.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/31/1991,76,minute.maid,5952,8.691482577,0,2.46,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/31/1991,76,tropicana,5184,8.553332238,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/31/1991,77,dominicks,23360,10.05878044,1,1.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/31/1991,77,minute.maid,9088,9.114710141,0,1.98,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/31/1991,77,tropicana,7744,8.954673629,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/31/1991,78,dominicks,50048,10.82073782,1,1.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/31/1991,78,minute.maid,11072,9.312174678,0,1.98,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/31/1991,78,tropicana,7744,8.954673629,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/31/1991,80,dominicks,28672,10.26367632,1,1.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -1/31/1991,80,minute.maid,12096,9.400630098,0,1.98,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -1/31/1991,80,tropicana,10816,9.288781798,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -1/31/1991,81,dominicks,41088,10.62347139,1,1.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -1/31/1991,81,minute.maid,8960,9.100525506,0,2.46,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -1/31/1991,81,tropicana,5632,8.636219898,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -1/31/1991,83,dominicks,113984,11.64381337,1,1.49,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -1/31/1991,83,minute.maid,10752,9.282847063,0,1.98,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 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-1/31/1991,86,minute.maid,5376,8.589699882,0,2.46,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/31/1991,86,tropicana,6272,8.743850562,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/31/1991,89,dominicks,88384,11.38944624,1,1.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/31/1991,89,minute.maid,5824,8.66974259,0,2.46,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/31/1991,89,tropicana,2112,7.655390645,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 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-1/31/1991,93,minute.maid,6272,8.743850562,0,2.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -1/31/1991,93,tropicana,5760,8.658692754,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -1/31/1991,94,dominicks,24512,10.10691807,1,1.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -1/31/1991,94,minute.maid,7744,8.954673629,0,2.17,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -1/31/1991,94,tropicana,6592,8.793612072,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 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-1/31/1991,100,minute.maid,8832,9.086136769,0,2.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/31/1991,100,tropicana,6208,8.733594062,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -1/31/1991,101,dominicks,56640,10.94447073,1,1.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -1/31/1991,101,minute.maid,6592,8.793612072,0,2.46,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -1/31/1991,101,tropicana,4160,8.333270353,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 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-1/31/1991,105,minute.maid,6400,8.764053269,0,2.17,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/31/1991,105,tropicana,7488,8.921057018,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/31/1991,106,dominicks,32128,10.3774832,1,1.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/31/1991,106,minute.maid,3776,8.236420527,0,2.46,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/31/1991,106,tropicana,1344,7.203405521,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 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-1/31/1991,109,tropicana,15616,9.656051309,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/31/1991,110,dominicks,45248,10.71991375,1,1.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/31/1991,110,minute.maid,5056,8.528330936,0,2.46,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/31/1991,110,tropicana,3456,8.14786713,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/31/1991,111,dominicks,176192,12.07932959,1,1.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/31/1991,111,minute.maid,6528,8.783855897,0,2.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/31/1991,111,tropicana,3840,8.253227646,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/31/1991,112,dominicks,48576,10.79088486,1,1.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/31/1991,112,minute.maid,13376,9.501217335,0,2.17,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/31/1991,112,tropicana,16128,9.688312171,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 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-1/31/1991,116,minute.maid,3200,8.070906089,0,2.46,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/31/1991,116,tropicana,6336,8.754002934,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/31/1991,117,dominicks,25344,10.1402973,1,1.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/31/1991,117,minute.maid,3200,8.070906089,0,2.46,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/31/1991,117,tropicana,2432,7.796469243,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 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-1/31/1991,122,minute.maid,11904,9.384629757,0,1.98,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/31/1991,122,tropicana,9344,9.142489705,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/31/1991,123,dominicks,115456,11.65664478,1,1.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/31/1991,123,minute.maid,6656,8.803273983,0,2.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/31/1991,123,tropicana,7680,8.946374826,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 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-1/31/1991,126,tropicana,3008,8.009030685,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/31/1991,128,dominicks,96960,11.4820538,1,1.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/31/1991,128,minute.maid,9856,9.195835686,0,2.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/31/1991,128,tropicana,12480,9.431882642,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/31/1991,129,dominicks,33152,10.40885833,1,1.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/31/1991,129,minute.maid,4928,8.502688505,0,2.46,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/31/1991,129,tropicana,5440,8.60153434,0,3.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/31/1991,130,dominicks,131008,11.78301367,1,1.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/31/1991,130,minute.maid,6144,8.723231275,0,2.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/31/1991,130,tropicana,4160,8.333270353,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 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-1/31/1991,132,tropicana,8832,9.086136769,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/31/1991,134,dominicks,35520,10.4778512,1,1.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/31/1991,134,minute.maid,2752,7.920083199,0,2.46,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/31/1991,134,tropicana,2624,7.87245515,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/31/1991,137,dominicks,68288,11.13148934,1,1.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/31/1991,137,minute.maid,9088,9.114710141,0,2.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/31/1991,137,tropicana,16768,9.727227587,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/7/1991,2,dominicks,4352,8.378390789,0,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/7/1991,2,minute.maid,7488,8.921057018,0,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/7/1991,2,tropicana,16768,9.727227587,0,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/7/1991,5,dominicks,9024,9.107642974,0,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/7/1991,5,minute.maid,7872,8.971067439,0,2.41,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/7/1991,5,tropicana,21440,9.973013615,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/7/1991,8,dominicks,33600,10.42228135,0,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/7/1991,8,minute.maid,6720,8.812843434,0,2.12,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/7/1991,8,tropicana,21696,9.984883191,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/7/1991,9,dominicks,11456,9.346268889,0,1.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/7/1991,9,minute.maid,9600,9.169518378,0,2.12,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/7/1991,9,tropicana,29376,10.28793329,0,2.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/7/1991,12,dominicks,7552,8.929567708,0,1.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/7/1991,12,minute.maid,9344,9.142489705,0,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/7/1991,12,tropicana,40192,10.60142325,0,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/7/1991,14,dominicks,9472,9.156095357,0,1.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/7/1991,14,minute.maid,5440,8.60153434,0,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/7/1991,14,tropicana,23808,10.07777694,0,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/7/1991,18,dominicks,11712,9.368369236,0,1.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/7/1991,18,minute.maid,13376,9.501217335,0,2.12,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/7/1991,18,tropicana,25920,10.16277015,0,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/7/1991,21,dominicks,15936,9.67633598,0,1.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/7/1991,21,minute.maid,8192,9.010913347,0,1.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/7/1991,21,tropicana,5504,8.61323038,0,2.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/7/1991,28,dominicks,4544,8.42156296,0,1.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/7/1991,28,minute.maid,4096,8.317766167,0,2.41,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/7/1991,28,tropicana,7552,8.929567708,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/7/1991,32,dominicks,16128,9.688312171,0,1.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/7/1991,32,minute.maid,9216,9.128696383,0,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/7/1991,32,tropicana,42624,10.66017275,0,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/7/1991,33,dominicks,10368,9.246479419,0,1.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/7/1991,33,minute.maid,8128,9.00307017,0,2.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/7/1991,33,tropicana,31808,10.36747311,0,2.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/7/1991,40,dominicks,34048,10.43552657,0,1.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/7/1991,40,minute.maid,9344,9.142489705,0,1.89,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/7/1991,40,tropicana,9408,9.14931567,0,2.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/7/1991,44,dominicks,6080,8.712759975,0,1.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/7/1991,44,minute.maid,6208,8.733594062,0,2.41,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/7/1991,44,tropicana,34432,10.44674164,0,2.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/7/1991,45,dominicks,11456,9.346268889,0,1.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/7/1991,45,minute.maid,4736,8.462948177,0,2.12,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/7/1991,45,tropicana,10368,9.246479419,0,2.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/7/1991,47,dominicks,13312,9.496421163,0,1.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/7/1991,47,minute.maid,5504,8.61323038,0,2.41,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/7/1991,47,tropicana,11840,9.379238908,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/7/1991,48,dominicks,8192,9.010913347,0,1.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/7/1991,48,minute.maid,6016,8.702177866,0,2.41,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/7/1991,48,tropicana,10432,9.252633284,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/7/1991,49,dominicks,6592,8.793612072,0,1.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/7/1991,49,minute.maid,3264,8.090708716,0,2.41,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/7/1991,49,tropicana,9280,9.135616826,0,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/7/1991,50,dominicks,7488,8.921057018,0,1.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/7/1991,50,minute.maid,4352,8.378390789,0,2.12,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/7/1991,50,tropicana,7616,8.938006577,0,2.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/7/1991,51,dominicks,8704,9.071537969,0,1.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/7/1991,51,minute.maid,3456,8.14786713,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/7/1991,51,tropicana,18752,9.839055692,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/7/1991,52,dominicks,8000,8.987196821,0,1.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/7/1991,52,minute.maid,8768,9.078864009,0,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/7/1991,52,tropicana,32960,10.40304998,0,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/7/1991,53,dominicks,22720,10.03100087,0,1.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/7/1991,53,minute.maid,8256,9.018695488,0,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/7/1991,53,tropicana,35328,10.47243113,0,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/7/1991,54,dominicks,11968,9.3899917,0,1.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/7/1991,54,minute.maid,3968,8.286017468,0,2.41,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/7/1991,54,tropicana,10944,9.30054664,0,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/7/1991,56,dominicks,10240,9.234056899,0,1.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/7/1991,56,minute.maid,3712,8.219326094,0,2.41,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/7/1991,56,tropicana,13504,9.510741217,0,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/7/1991,59,dominicks,6848,8.831711918,0,1.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/7/1991,59,minute.maid,12032,9.395325046,0,1.89,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/7/1991,59,tropicana,11328,9.335032816,0,2.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/7/1991,62,dominicks,5056,8.528330936,0,1.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/7/1991,62,minute.maid,8128,9.00307017,0,2.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/7/1991,62,tropicana,29952,10.30735138,0,2.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/7/1991,64,dominicks,13696,9.524859098,0,1.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/7/1991,64,minute.maid,2688,7.896552702,0,2.41,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/7/1991,64,tropicana,5632,8.636219898,0,2.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/7/1991,67,dominicks,8960,9.100525506,0,1.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/7/1991,67,minute.maid,5952,8.691482577,0,2.12,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/7/1991,67,tropicana,7808,8.962904128,0,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/7/1991,68,dominicks,10752,9.282847063,0,1.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/7/1991,68,minute.maid,5440,8.60153434,0,2.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/7/1991,68,tropicana,21568,9.978966014,0,2.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/7/1991,70,dominicks,17152,9.749870064,0,1.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/7/1991,70,minute.maid,15104,9.622714888,0,1.89,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/7/1991,70,tropicana,13632,9.520175249,0,2.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/7/1991,71,dominicks,29632,10.29661014,0,1.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/7/1991,71,minute.maid,4224,8.348537825,0,2.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/7/1991,71,tropicana,23552,10.06696602,0,2.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/7/1991,72,dominicks,8064,8.99516499,0,1.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/7/1991,72,minute.maid,5760,8.658692754,0,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/7/1991,72,tropicana,31552,10.35939226,0,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/7/1991,73,dominicks,31936,10.37148918,0,1.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/7/1991,73,minute.maid,10240,9.234056899,0,2.12,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/7/1991,73,tropicana,23872,10.0804615,0,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/7/1991,74,dominicks,21568,9.978966014,0,1.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/7/1991,74,minute.maid,8512,9.049232212,0,2.41,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/7/1991,74,tropicana,28096,10.2433825,0,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/7/1991,75,dominicks,14336,9.570529135,0,1.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/7/1991,75,minute.maid,6464,8.7740036,0,2.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/7/1991,75,tropicana,23680,10.07238609,0,2.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/7/1991,76,dominicks,10112,9.221478116,0,1.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/7/1991,76,minute.maid,6400,8.764053269,0,2.41,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/7/1991,76,tropicana,30720,10.33266919,0,2.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/7/1991,77,dominicks,10176,9.227787286,0,1.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/7/1991,77,minute.maid,9344,9.142489705,0,1.89,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/7/1991,77,tropicana,12544,9.436997743,0,2.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/7/1991,78,dominicks,9600,9.169518378,0,1.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/7/1991,78,minute.maid,11008,9.30637756,0,1.89,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/7/1991,78,tropicana,14464,9.579418083,0,2.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/7/1991,80,dominicks,11776,9.373818841,0,1.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/7/1991,80,minute.maid,14272,9.566054855,0,1.89,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/7/1991,80,tropicana,22976,10.04220547,0,2.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/7/1991,81,dominicks,10176,9.227787286,0,1.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/7/1991,81,minute.maid,8320,9.026417534,0,2.41,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/7/1991,81,tropicana,23936,10.08313888,0,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/7/1991,83,dominicks,52992,10.87789624,0,1.49,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/7/1991,83,minute.maid,10240,9.234056899,0,1.89,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/7/1991,83,tropicana,17088,9.746131742,0,2.49,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/7/1991,84,dominicks,13248,9.491601877,0,1.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/7/1991,84,minute.maid,6144,8.723231275,0,2.41,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/7/1991,84,tropicana,18176,9.807857322,0,2.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/7/1991,86,dominicks,16320,9.700146629,0,1.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/7/1991,86,minute.maid,4032,8.30201781,0,2.41,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/7/1991,86,tropicana,24256,10.09641929,0,2.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/7/1991,88,dominicks,9344,9.142489705,0,1.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/7/1991,88,minute.maid,5952,8.691482577,0,2.41,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/7/1991,88,tropicana,19520,9.87919486,0,2.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/7/1991,89,dominicks,16768,9.727227587,0,1.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/7/1991,89,minute.maid,4352,8.378390789,0,2.41,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/7/1991,89,tropicana,16640,9.719564714,0,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/7/1991,90,dominicks,13568,9.515469358,0,1.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/7/1991,90,minute.maid,4288,8.363575703,0,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/7/1991,90,tropicana,12544,9.436997743,0,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/7/1991,91,dominicks,11776,9.373818841,0,1.49,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/7/1991,91,minute.maid,3648,8.201934351,0,2.41,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/7/1991,91,tropicana,16192,9.692272572,0,2.49,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/7/1991,92,dominicks,26240,10.17504024,0,1.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/7/1991,92,minute.maid,5312,8.577723691,0,2.41,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/7/1991,92,tropicana,13248,9.491601877,0,2.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/7/1991,93,dominicks,17216,9.753594463,0,1.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/7/1991,93,minute.maid,6080,8.712759975,0,2.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/7/1991,93,tropicana,21376,9.970024076,0,2.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/7/1991,94,dominicks,14144,9.557045785,0,1.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/7/1991,94,minute.maid,6400,8.764053269,0,2.12,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/7/1991,94,tropicana,11008,9.30637756,0,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/7/1991,95,dominicks,14080,9.55251063,0,1.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/7/1991,95,minute.maid,3072,8.030084094,0,2.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/7/1991,95,tropicana,13888,9.538780437,0,2.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/7/1991,97,dominicks,4864,8.489616424,0,1.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/7/1991,97,minute.maid,3136,8.050703382,0,2.41,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/7/1991,97,tropicana,6528,8.783855897,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/7/1991,98,dominicks,39232,10.57724802,0,1.49,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/7/1991,98,minute.maid,6976,8.850230966,0,2.12,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/7/1991,98,tropicana,21248,9.964018052,0,2.49,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/7/1991,100,dominicks,19648,9.885730831,0,1.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/7/1991,100,minute.maid,7616,8.938006577,0,2.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/7/1991,100,tropicana,32192,10.37947325,0,2.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/7/1991,101,dominicks,20416,9.924074186,0,1.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/7/1991,101,minute.maid,6080,8.712759975,0,2.41,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/7/1991,101,tropicana,18944,9.849242538,0,2.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/7/1991,102,dominicks,38656,10.56245728,0,1.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/7/1991,102,minute.maid,11328,9.335032816,0,2.12,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/7/1991,102,tropicana,24832,10.11988842,0,2.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/7/1991,103,dominicks,17152,9.749870064,0,1.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/7/1991,103,minute.maid,5120,8.540909718,0,2.12,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/7/1991,103,tropicana,4992,8.51559191,0,2.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/7/1991,104,dominicks,7808,8.962904128,0,1.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/7/1991,104,minute.maid,5248,8.565602331,0,2.41,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/7/1991,104,tropicana,13760,9.529521112,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/7/1991,105,dominicks,25792,10.15781965,0,1.49,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/7/1991,105,minute.maid,7872,8.971067439,0,2.12,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/7/1991,105,tropicana,14016,9.547954813,0,2.49,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/7/1991,106,dominicks,10368,9.246479419,0,1.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/7/1991,106,minute.maid,3072,8.030084094,0,2.41,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/7/1991,106,tropicana,5760,8.658692754,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/7/1991,107,dominicks,13056,9.477003077,0,1.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/7/1991,107,minute.maid,5632,8.636219898,0,2.41,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/7/1991,107,tropicana,27264,10.21332243,0,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/7/1991,109,dominicks,11968,9.3899917,0,1.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/7/1991,109,minute.maid,11008,9.30637756,0,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/7/1991,109,tropicana,54016,10.89703558,0,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/7/1991,110,dominicks,15168,9.626943225,0,1.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/7/1991,110,minute.maid,5120,8.540909718,0,2.41,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/7/1991,110,tropicana,18560,9.828764006,0,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/7/1991,111,dominicks,46976,10.75739211,0,1.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/7/1991,111,minute.maid,5504,8.61323038,0,2.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/7/1991,111,tropicana,27840,10.23422911,0,2.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/7/1991,112,dominicks,24576,10.10952564,0,1.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/7/1991,112,minute.maid,14336,9.570529135,0,2.12,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/7/1991,112,tropicana,33728,10.42608363,0,2.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/7/1991,113,dominicks,18368,9.818365299,0,1.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/7/1991,113,minute.maid,6528,8.783855897,0,2.41,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/7/1991,113,tropicana,34048,10.43552657,0,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/7/1991,114,dominicks,31552,10.35939226,0,1.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/7/1991,114,minute.maid,8576,9.056722883,0,2.12,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/7/1991,114,tropicana,13120,9.481893063,0,2.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/7/1991,115,dominicks,7296,8.895081532,0,1.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/7/1991,115,minute.maid,6848,8.831711918,0,2.41,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/7/1991,115,tropicana,23680,10.07238609,0,2.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/7/1991,116,dominicks,15936,9.67633598,0,1.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/7/1991,116,minute.maid,4736,8.462948177,0,2.41,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/7/1991,116,tropicana,12608,9.442086812,0,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/7/1991,117,dominicks,6080,8.712759975,0,1.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/7/1991,117,minute.maid,4224,8.348537825,0,2.41,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/7/1991,117,tropicana,9152,9.121727714,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/7/1991,118,dominicks,16448,9.707959168,0,1.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/7/1991,118,minute.maid,3520,8.166216269,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/7/1991,118,tropicana,25984,10.16523624,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/7/1991,119,dominicks,12544,9.436997743,0,1.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/7/1991,119,minute.maid,6720,8.812843434,0,2.12,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/7/1991,119,tropicana,16000,9.680344001,0,2.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/7/1991,121,dominicks,14016,9.547954813,0,1.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/7/1991,121,minute.maid,10368,9.246479419,0,2.12,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/7/1991,121,tropicana,21312,9.967025573,0,2.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/7/1991,122,dominicks,14976,9.614204199,0,1.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/7/1991,122,minute.maid,15744,9.664214619,0,1.89,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/7/1991,122,tropicana,19840,9.895455381,0,2.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/7/1991,123,dominicks,12032,9.395325046,0,1.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/7/1991,123,minute.maid,6400,8.764053269,0,2.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/7/1991,123,tropicana,32064,10.37548918,0,2.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/7/1991,124,dominicks,18880,9.84585844,0,1.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/7/1991,124,minute.maid,7552,8.929567708,0,2.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/7/1991,124,tropicana,32320,10.38344151,0,2.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/7/1991,126,dominicks,7104,8.868413285,0,1.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/7/1991,126,minute.maid,6592,8.793612072,0,2.41,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/7/1991,126,tropicana,16000,9.680344001,0,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/7/1991,128,dominicks,37376,10.52878407,0,1.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/7/1991,128,minute.maid,9088,9.114710141,0,2.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/7/1991,128,tropicana,54016,10.89703558,0,2.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/7/1991,129,dominicks,10624,9.270870872,0,1.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/7/1991,129,minute.maid,5248,8.565602331,0,2.41,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/7/1991,129,tropicana,20480,9.927204079,0,2.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/7/1991,130,dominicks,3200,8.070906089,0,1.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/7/1991,130,minute.maid,9024,9.107642974,0,2.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/7/1991,130,tropicana,28544,10.25920204,0,2.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/7/1991,131,dominicks,15680,9.660141294,0,1.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/7/1991,131,minute.maid,7872,8.971067439,0,2.41,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/7/1991,131,tropicana,16320,9.700146629,0,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/7/1991,132,dominicks,25216,10.13523399,0,1.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/7/1991,132,minute.maid,7936,8.979164649,0,2.12,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/7/1991,132,tropicana,24640,10.11212642,0,2.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/7/1991,134,dominicks,16896,9.734832187,0,1.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/7/1991,134,minute.maid,2496,7.82244473,0,2.41,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/7/1991,134,tropicana,7616,8.938006577,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/7/1991,137,dominicks,24000,10.08580911,0,1.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/7/1991,137,minute.maid,10304,9.240287448,0,2.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/7/1991,137,tropicana,48128,10.78161941,0,2.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/14/1991,2,dominicks,704,6.556778356,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/14/1991,2,minute.maid,4224,8.348537825,0,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/14/1991,2,tropicana,6272,8.743850562,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/14/1991,5,dominicks,1600,7.377758908,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/14/1991,5,minute.maid,6144,8.723231275,0,2.41,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/14/1991,5,tropicana,7360,8.903815212,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/14/1991,8,dominicks,4736,8.462948177,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/14/1991,8,minute.maid,4224,8.348537825,0,2.12,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/14/1991,8,tropicana,7808,8.962904128,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/14/1991,9,dominicks,3712,8.219326094,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/14/1991,9,minute.maid,6336,8.754002934,0,2.12,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/14/1991,9,tropicana,11200,9.323669057,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/14/1991,12,dominicks,1856,7.526178913,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/14/1991,12,minute.maid,7680,8.946374826,0,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/14/1991,12,tropicana,11200,9.323669057,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/14/1991,14,dominicks,1024,6.931471806,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/14/1991,14,minute.maid,5888,8.68067166,0,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/14/1991,14,tropicana,10048,9.215128889,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/14/1991,18,dominicks,1536,7.336936914,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/14/1991,18,minute.maid,7168,8.877381955,0,2.12,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/14/1991,18,tropicana,8512,9.049232212,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/14/1991,21,dominicks,2048,7.624618986,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/14/1991,21,minute.maid,5568,8.624791202,0,1.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/14/1991,21,tropicana,2432,7.796469243,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/14/1991,28,dominicks,1024,6.931471806,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/14/1991,28,minute.maid,3200,8.070906089,0,2.41,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/14/1991,28,tropicana,6144,8.723231275,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/14/1991,32,dominicks,1664,7.416979621,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/14/1991,32,minute.maid,6208,8.733594062,0,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/14/1991,32,tropicana,10752,9.282847063,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/14/1991,33,dominicks,1216,7.103322063,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/14/1991,33,minute.maid,8640,9.064157862,0,2.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/14/1991,33,tropicana,9728,9.182763604,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/14/1991,40,dominicks,3392,8.129174997,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/14/1991,40,minute.maid,4672,8.449342525,0,1.89,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/14/1991,40,tropicana,5376,8.589699882,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/14/1991,44,dominicks,1728,7.454719949,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/14/1991,44,minute.maid,4416,8.392989588,0,2.41,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/14/1991,44,tropicana,6592,8.793612072,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/14/1991,45,dominicks,4224,8.348537825,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/14/1991,45,minute.maid,3520,8.166216269,0,2.12,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/14/1991,45,tropicana,6592,8.793612072,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/14/1991,47,dominicks,5696,8.647519453,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/14/1991,47,minute.maid,4416,8.392989588,0,2.41,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/14/1991,47,tropicana,5120,8.540909718,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/14/1991,48,dominicks,3328,8.110126802,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/14/1991,48,minute.maid,6208,8.733594062,0,2.41,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/14/1991,48,tropicana,4992,8.51559191,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/14/1991,49,dominicks,1344,7.203405521,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/14/1991,49,minute.maid,3904,8.269756948,0,2.41,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/14/1991,49,tropicana,4096,8.317766167,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/14/1991,50,dominicks,2496,7.82244473,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/14/1991,50,minute.maid,3520,8.166216269,0,2.12,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/14/1991,50,tropicana,3200,8.070906089,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/14/1991,51,dominicks,2048,7.624618986,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/14/1991,51,minute.maid,4928,8.502688505,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/14/1991,51,tropicana,6592,8.793612072,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/14/1991,52,dominicks,448,6.104793232,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/14/1991,52,minute.maid,9984,9.208739091,0,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/14/1991,52,tropicana,7552,8.929567708,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/14/1991,53,dominicks,832,6.723832441,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/14/1991,53,minute.maid,4992,8.51559191,0,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/14/1991,53,tropicana,7104,8.868413285,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/14/1991,54,dominicks,1152,7.049254841,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/14/1991,54,minute.maid,3648,8.201934351,0,2.41,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/14/1991,54,tropicana,3968,8.286017468,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/14/1991,56,dominicks,1984,7.592870288,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/14/1991,56,minute.maid,3648,8.201934351,0,2.41,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/14/1991,56,tropicana,8064,8.99516499,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/14/1991,59,dominicks,704,6.556778356,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/14/1991,59,minute.maid,4864,8.489616424,0,1.89,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/14/1991,59,tropicana,4096,8.317766167,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/14/1991,62,dominicks,256,5.545177445,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/14/1991,62,minute.maid,6080,8.712759975,0,2.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/14/1991,62,tropicana,16960,9.738612909,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/14/1991,64,dominicks,3072,8.030084094,0,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/14/1991,64,minute.maid,2944,7.98752448,0,2.41,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/14/1991,64,tropicana,5120,8.540909718,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/14/1991,67,dominicks,1728,7.454719949,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/14/1991,67,minute.maid,3968,8.286017468,0,2.12,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/14/1991,67,tropicana,6272,8.743850562,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/14/1991,68,dominicks,1728,7.454719949,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/14/1991,68,minute.maid,7168,8.877381955,0,2.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/14/1991,68,tropicana,6720,8.812843434,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/14/1991,70,dominicks,4160,8.333270353,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/14/1991,70,minute.maid,11008,9.30637756,0,1.89,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/14/1991,70,tropicana,10880,9.29468152,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/14/1991,71,dominicks,11136,9.317938383,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/14/1991,71,minute.maid,2816,7.943072717,0,2.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/14/1991,71,tropicana,7232,8.886270902,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/14/1991,72,dominicks,448,6.104793232,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/14/1991,72,minute.maid,4608,8.435549202,0,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/14/1991,72,tropicana,8768,9.078864009,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/14/1991,73,dominicks,11200,9.323669057,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/14/1991,73,minute.maid,7744,8.954673629,0,2.12,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/14/1991,73,tropicana,11008,9.30637756,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/14/1991,74,dominicks,5504,8.61323038,0,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/14/1991,74,minute.maid,6272,8.743850562,0,2.41,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/14/1991,74,tropicana,6208,8.733594062,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/14/1991,75,dominicks,1600,7.377758908,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/14/1991,75,minute.maid,16960,9.738612909,0,2.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/14/1991,75,tropicana,7808,8.962904128,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/14/1991,76,dominicks,4160,8.333270353,0,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/14/1991,76,minute.maid,6080,8.712759975,0,2.41,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/14/1991,76,tropicana,6400,8.764053269,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/14/1991,77,dominicks,1792,7.491087594,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/14/1991,77,minute.maid,6272,8.743850562,0,1.89,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/14/1991,77,tropicana,8896,9.093357017,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/14/1991,78,dominicks,6080,8.712759975,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/14/1991,78,minute.maid,10816,9.288781798,0,1.89,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/14/1991,78,tropicana,9216,9.128696383,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/14/1991,80,dominicks,2048,7.624618986,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/14/1991,80,minute.maid,10560,9.264828557,0,1.89,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/14/1991,80,tropicana,13824,9.534161491,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/14/1991,81,dominicks,4544,8.42156296,0,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/14/1991,81,minute.maid,6848,8.831711918,0,2.41,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/14/1991,81,tropicana,7488,8.921057018,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/14/1991,83,dominicks,2816,7.943072717,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/14/1991,83,minute.maid,7040,8.859363449,0,1.89,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/14/1991,83,tropicana,7360,8.903815212,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/14/1991,84,dominicks,1152,7.049254841,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/14/1991,84,minute.maid,5248,8.565602331,0,2.41,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/14/1991,84,tropicana,4736,8.462948177,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/14/1991,86,dominicks,5440,8.60153434,0,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/14/1991,86,minute.maid,4928,8.502688505,0,2.41,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/14/1991,86,tropicana,11712,9.368369236,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/14/1991,88,dominicks,3136,8.050703382,0,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/14/1991,88,minute.maid,4544,8.42156296,0,2.41,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/14/1991,88,tropicana,3712,8.219326094,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/14/1991,89,dominicks,2432,7.796469243,0,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/14/1991,89,minute.maid,3584,8.184234774,0,2.41,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/14/1991,89,tropicana,3008,8.009030685,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/14/1991,90,dominicks,1280,7.154615357,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/14/1991,90,minute.maid,4352,8.378390789,0,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/14/1991,90,tropicana,5888,8.68067166,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/14/1991,91,dominicks,1152,7.049254841,0,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/14/1991,91,minute.maid,2880,7.965545573,0,2.41,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/14/1991,91,tropicana,4672,8.449342525,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/14/1991,92,dominicks,1472,7.294377299,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/14/1991,92,minute.maid,2816,7.943072717,0,2.41,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/14/1991,92,tropicana,4736,8.462948177,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/14/1991,93,dominicks,1920,7.560080465,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/14/1991,93,minute.maid,6272,8.743850562,0,2.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/14/1991,93,tropicana,7040,8.859363449,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/14/1991,95,dominicks,3840,8.253227646,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/14/1991,95,minute.maid,2432,7.796469243,0,2.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/14/1991,95,tropicana,4992,8.51559191,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/14/1991,97,dominicks,64,4.158883083,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/14/1991,97,minute.maid,3328,8.110126802,0,2.41,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/14/1991,97,tropicana,2816,7.943072717,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/14/1991,98,dominicks,3328,8.110126802,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/14/1991,98,minute.maid,5248,8.565602331,0,2.12,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/14/1991,98,tropicana,8128,9.00307017,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/14/1991,100,dominicks,896,6.797940413,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/14/1991,100,minute.maid,5888,8.68067166,0,2.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/14/1991,100,tropicana,5696,8.647519453,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/14/1991,101,dominicks,1984,7.592870288,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/14/1991,101,minute.maid,5056,8.528330936,0,2.41,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/14/1991,101,tropicana,6080,8.712759975,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/14/1991,102,dominicks,4864,8.489616424,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/14/1991,102,minute.maid,6400,8.764053269,0,2.12,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/14/1991,102,tropicana,6272,8.743850562,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/14/1991,104,dominicks,2688,7.896552702,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/14/1991,104,minute.maid,4288,8.363575703,0,2.41,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/14/1991,104,tropicana,5056,8.528330936,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/14/1991,105,dominicks,4416,8.392989588,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/14/1991,105,minute.maid,4992,8.51559191,0,2.12,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/14/1991,105,tropicana,8448,9.041685006,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/14/1991,106,dominicks,832,6.723832441,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/14/1991,106,minute.maid,2880,7.965545573,0,2.41,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/14/1991,106,tropicana,1536,7.336936914,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/14/1991,107,dominicks,2112,7.655390645,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/14/1991,107,minute.maid,4992,8.51559191,0,2.41,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/14/1991,107,tropicana,9472,9.156095357,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/14/1991,109,dominicks,960,6.866933285,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/14/1991,109,minute.maid,9856,9.195835686,0,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/14/1991,109,tropicana,13888,9.538780437,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/14/1991,110,dominicks,2368,7.769800996,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/14/1991,110,minute.maid,5184,8.553332238,0,2.41,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/14/1991,110,tropicana,8192,9.010913347,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/14/1991,111,dominicks,1152,7.049254841,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/14/1991,111,minute.maid,4032,8.30201781,0,2.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/14/1991,111,tropicana,5504,8.61323038,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/14/1991,112,dominicks,3840,8.253227646,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/14/1991,112,minute.maid,11328,9.335032816,0,2.12,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/14/1991,112,tropicana,15936,9.67633598,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/14/1991,113,dominicks,1792,7.491087594,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/14/1991,113,minute.maid,5440,8.60153434,0,2.41,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/14/1991,113,tropicana,5824,8.66974259,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/14/1991,114,dominicks,3968,8.286017468,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/14/1991,114,minute.maid,5440,8.60153434,0,2.12,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/14/1991,114,tropicana,3776,8.236420527,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/14/1991,115,dominicks,1856,7.526178913,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/14/1991,115,minute.maid,5440,8.60153434,0,2.41,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/14/1991,115,tropicana,7872,8.971067439,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/14/1991,116,dominicks,3136,8.050703382,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/14/1991,116,minute.maid,3584,8.184234774,0,2.41,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/14/1991,116,tropicana,5056,8.528330936,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/14/1991,117,dominicks,832,6.723832441,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/14/1991,117,minute.maid,2304,7.742402022,0,2.41,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/14/1991,117,tropicana,4608,8.435549202,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/14/1991,118,dominicks,1664,7.416979621,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/14/1991,118,minute.maid,2688,7.896552702,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/14/1991,118,tropicana,4800,8.476371197,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/14/1991,119,dominicks,4096,8.317766167,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/14/1991,119,minute.maid,6208,8.733594062,0,2.12,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/14/1991,119,tropicana,9984,9.208739091,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/14/1991,121,dominicks,1664,7.416979621,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/14/1991,121,minute.maid,6144,8.723231275,0,2.12,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/14/1991,121,tropicana,10496,9.258749511,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/14/1991,122,dominicks,5376,8.589699882,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/14/1991,122,minute.maid,10496,9.258749511,0,1.89,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/14/1991,122,tropicana,10240,9.234056899,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/14/1991,123,dominicks,1344,7.203405521,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/14/1991,123,minute.maid,5568,8.624791202,0,2.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/14/1991,123,tropicana,7936,8.979164649,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/14/1991,124,dominicks,2368,7.769800996,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/14/1991,124,minute.maid,9216,9.128696383,0,2.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/14/1991,124,tropicana,9984,9.208739091,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/14/1991,126,dominicks,1664,7.416979621,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/14/1991,126,minute.maid,5120,8.540909718,0,2.41,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/14/1991,126,tropicana,5376,8.589699882,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/14/1991,128,dominicks,4544,8.42156296,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/14/1991,128,minute.maid,10048,9.215128889,0,2.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/14/1991,128,tropicana,10048,9.215128889,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/14/1991,129,dominicks,1024,6.931471806,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/14/1991,129,minute.maid,4032,8.30201781,0,2.41,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/14/1991,129,tropicana,6848,8.831711918,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/14/1991,130,dominicks,2752,7.920083199,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/14/1991,130,minute.maid,4352,8.378390789,0,2.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/14/1991,130,tropicana,75648,11.23384628,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/14/1991,131,dominicks,2624,7.87245515,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/14/1991,131,minute.maid,5568,8.624791202,0,2.41,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/14/1991,131,tropicana,5248,8.565602331,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/14/1991,132,dominicks,5568,8.624791202,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/14/1991,132,minute.maid,6592,8.793612072,0,2.12,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/14/1991,132,tropicana,12480,9.431882642,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/14/1991,134,dominicks,512,6.238324625,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/14/1991,134,minute.maid,1792,7.491087594,0,2.41,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/14/1991,134,tropicana,2816,7.943072717,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/14/1991,137,dominicks,1536,7.336936914,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/14/1991,137,minute.maid,7552,8.929567708,0,2.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/14/1991,137,tropicana,12928,9.467150781,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/21/1991,2,dominicks,13760,9.529521112,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/21/1991,2,minute.maid,8960,9.100525506,0,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/21/1991,2,tropicana,7936,8.979164649,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/21/1991,5,dominicks,2496,7.82244473,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/21/1991,5,minute.maid,8448,9.041685006,0,2.41,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/21/1991,5,tropicana,6720,8.812843434,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/21/1991,8,dominicks,10304,9.240287448,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/21/1991,8,minute.maid,9728,9.182763604,0,2.12,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/21/1991,8,tropicana,8128,9.00307017,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/21/1991,9,dominicks,4416,8.392989588,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/21/1991,9,minute.maid,8576,9.056722883,0,2.12,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/21/1991,9,tropicana,7104,8.868413285,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/21/1991,12,dominicks,2560,7.847762538,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/21/1991,12,minute.maid,17344,9.761001904,0,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/21/1991,12,tropicana,16512,9.711842668,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/21/1991,14,dominicks,1344,7.203405521,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/21/1991,14,minute.maid,9856,9.195835686,0,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/21/1991,14,tropicana,7424,8.912473275,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/21/1991,18,dominicks,3392,8.129174997,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/21/1991,18,minute.maid,11840,9.379238908,0,2.12,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/21/1991,18,tropicana,9024,9.107642974,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/21/1991,21,dominicks,3904,8.269756948,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/21/1991,21,minute.maid,5184,8.553332238,0,1.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/21/1991,21,tropicana,3648,8.201934351,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/21/1991,28,dominicks,2432,7.796469243,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/21/1991,28,minute.maid,4992,8.51559191,0,2.41,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/21/1991,28,tropicana,3264,8.090708716,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/21/1991,32,dominicks,5248,8.565602331,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/21/1991,32,minute.maid,13312,9.496421163,0,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/21/1991,32,tropicana,6592,8.793612072,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/21/1991,33,dominicks,2176,7.685243608,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/21/1991,33,minute.maid,9088,9.114710141,0,2.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/21/1991,33,tropicana,8512,9.049232212,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/21/1991,44,dominicks,2880,7.965545573,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/21/1991,44,minute.maid,9216,9.128696383,0,2.41,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/21/1991,44,tropicana,7296,8.895081532,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/21/1991,45,dominicks,6400,8.764053269,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/21/1991,45,minute.maid,4672,8.449342525,0,2.12,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/21/1991,45,tropicana,6528,8.783855897,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/21/1991,47,dominicks,4800,8.476371197,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/21/1991,47,minute.maid,7488,8.921057018,0,2.41,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/21/1991,47,tropicana,4672,8.449342525,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/21/1991,48,dominicks,3328,8.110126802,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/21/1991,48,minute.maid,7040,8.859363449,0,2.41,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/21/1991,48,tropicana,4032,8.30201781,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/21/1991,49,dominicks,2048,7.624618986,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/21/1991,49,minute.maid,5760,8.658692754,0,2.41,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/21/1991,49,tropicana,3648,8.201934351,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/21/1991,50,dominicks,5760,8.658692754,0,1.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/21/1991,50,minute.maid,5760,8.658692754,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/21/1991,50,tropicana,3264,8.090708716,0,2.73,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/21/1991,52,dominicks,2560,7.847762538,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/21/1991,52,minute.maid,15616,9.656051309,0,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/21/1991,52,tropicana,7744,8.954673629,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/21/1991,53,dominicks,4096,8.317766167,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/21/1991,53,minute.maid,17024,9.742379392,0,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/21/1991,53,tropicana,5824,8.66974259,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/21/1991,54,dominicks,1728,7.454719949,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/21/1991,54,minute.maid,5568,8.624791202,0,2.41,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/21/1991,54,tropicana,4160,8.333270353,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/21/1991,56,dominicks,3968,8.286017468,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/21/1991,56,minute.maid,5696,8.647519453,0,2.41,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/21/1991,56,tropicana,5952,8.691482577,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/21/1991,59,dominicks,4288,8.363575703,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/21/1991,59,minute.maid,5312,8.577723691,0,1.89,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/21/1991,59,tropicana,3200,8.070906089,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/21/1991,62,dominicks,1344,7.203405521,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/21/1991,62,minute.maid,14336,9.570529135,0,2.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/21/1991,62,tropicana,10112,9.221478116,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/21/1991,64,dominicks,3392,8.129174997,0,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/21/1991,64,minute.maid,5184,8.553332238,0,2.41,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/21/1991,64,tropicana,2624,7.87245515,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/21/1991,67,dominicks,2048,7.624618986,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/21/1991,67,minute.maid,5696,8.647519453,0,2.12,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/21/1991,67,tropicana,5056,8.528330936,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/21/1991,68,dominicks,4096,8.317766167,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/21/1991,68,minute.maid,14720,9.596962392,0,2.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/21/1991,68,tropicana,4672,8.449342525,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/21/1991,70,dominicks,10944,9.30054664,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/21/1991,70,minute.maid,14656,9.592605087,0,1.89,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/21/1991,70,tropicana,9472,9.156095357,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/21/1991,71,dominicks,3008,8.009030685,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/21/1991,71,minute.maid,8320,9.026417534,0,2.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/21/1991,71,tropicana,5120,8.540909718,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/21/1991,72,dominicks,1856,7.526178913,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/21/1991,72,minute.maid,7424,8.912473275,0,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/21/1991,72,tropicana,7488,8.921057018,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/21/1991,73,dominicks,10048,9.215128889,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/21/1991,73,minute.maid,9664,9.17616292,0,2.12,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/21/1991,73,tropicana,7488,8.921057018,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/21/1991,74,dominicks,7168,8.877381955,0,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/21/1991,74,minute.maid,11968,9.3899917,0,2.41,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/21/1991,74,tropicana,6784,8.822322178,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/21/1991,75,dominicks,4416,8.392989588,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/21/1991,75,minute.maid,12288,9.416378455,0,2.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/21/1991,75,tropicana,7552,8.929567708,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/21/1991,76,dominicks,2816,7.943072717,0,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/21/1991,76,minute.maid,8128,9.00307017,0,2.41,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/21/1991,76,tropicana,5824,8.66974259,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/21/1991,77,dominicks,4864,8.489616424,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/21/1991,77,minute.maid,9152,9.121727714,0,1.89,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/21/1991,77,tropicana,9280,9.135616826,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/21/1991,78,dominicks,5952,8.691482577,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/21/1991,78,minute.maid,8896,9.093357017,0,1.89,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/21/1991,78,tropicana,8128,9.00307017,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/21/1991,80,dominicks,3712,8.219326094,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/21/1991,80,minute.maid,12224,9.411156511,0,1.89,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/21/1991,80,tropicana,13376,9.501217335,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/21/1991,81,dominicks,4288,8.363575703,0,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/21/1991,81,minute.maid,10624,9.270870872,0,2.41,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/21/1991,81,tropicana,6080,8.712759975,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/21/1991,83,dominicks,2688,7.896552702,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/21/1991,83,minute.maid,7168,8.877381955,0,1.89,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/21/1991,83,tropicana,6912,8.841014311,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/21/1991,84,dominicks,2304,7.742402022,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/21/1991,84,minute.maid,9344,9.142489705,0,2.41,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/21/1991,84,tropicana,6464,8.7740036,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/21/1991,86,dominicks,2816,7.943072717,0,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/21/1991,86,minute.maid,6208,8.733594062,0,2.41,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/21/1991,86,tropicana,6336,8.754002934,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/21/1991,88,dominicks,3456,8.14786713,0,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/21/1991,88,minute.maid,7680,8.946374826,0,2.41,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/21/1991,88,tropicana,4032,8.30201781,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/21/1991,89,dominicks,4160,8.333270353,0,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/21/1991,89,minute.maid,6400,8.764053269,0,2.41,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/21/1991,89,tropicana,2624,7.87245515,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/21/1991,90,dominicks,2752,7.920083199,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/21/1991,90,minute.maid,5632,8.636219898,0,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/21/1991,90,tropicana,3648,8.201934351,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/21/1991,91,dominicks,2368,7.769800996,0,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/21/1991,91,minute.maid,5056,8.528330936,0,2.41,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/21/1991,91,tropicana,4672,8.449342525,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/21/1991,92,dominicks,1984,7.592870288,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/21/1991,92,minute.maid,5312,8.577723691,0,2.41,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/21/1991,92,tropicana,3968,8.286017468,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/21/1991,93,dominicks,2816,7.943072717,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/21/1991,93,minute.maid,9344,9.142489705,0,2.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/21/1991,93,tropicana,7104,8.868413285,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/21/1991,94,dominicks,5056,8.528330936,0,1.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/21/1991,94,minute.maid,8128,9.00307017,0,2.12,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/21/1991,94,tropicana,5376,8.589699882,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/21/1991,95,dominicks,2368,7.769800996,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/21/1991,95,minute.maid,6080,8.712759975,0,2.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/21/1991,95,tropicana,3840,8.253227646,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/21/1991,97,dominicks,2176,7.685243608,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/21/1991,97,minute.maid,6784,8.822322178,0,2.41,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/21/1991,97,tropicana,1792,7.491087594,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/21/1991,98,dominicks,5440,8.60153434,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/21/1991,98,minute.maid,6912,8.841014311,0,2.12,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/21/1991,98,tropicana,6848,8.831711918,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/21/1991,100,dominicks,5824,8.66974259,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/21/1991,100,minute.maid,11328,9.335032816,0,2.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/21/1991,100,tropicana,4992,8.51559191,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/21/1991,101,dominicks,4864,8.489616424,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/21/1991,101,minute.maid,8384,9.034080407,0,2.41,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/21/1991,101,tropicana,6720,8.812843434,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/21/1991,102,dominicks,14528,9.583833101,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/21/1991,102,minute.maid,30848,10.3368272,0,2.12,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/21/1991,102,tropicana,6848,8.831711918,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/21/1991,103,dominicks,6272,8.743850562,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/21/1991,103,minute.maid,5504,8.61323038,0,2.12,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/21/1991,103,tropicana,2560,7.847762538,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/21/1991,104,dominicks,2560,7.847762538,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/21/1991,104,minute.maid,5440,8.60153434,0,2.41,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/21/1991,104,tropicana,3136,8.050703382,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/21/1991,105,dominicks,7488,8.921057018,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/21/1991,105,minute.maid,7680,8.946374826,0,2.12,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/21/1991,105,tropicana,8320,9.026417534,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/21/1991,106,dominicks,2176,7.685243608,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/21/1991,106,minute.maid,2880,7.965545573,0,2.41,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/21/1991,106,tropicana,2368,7.769800996,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/21/1991,107,dominicks,3136,8.050703382,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/21/1991,107,minute.maid,8448,9.041685006,0,2.41,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/21/1991,107,tropicana,7616,8.938006577,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/21/1991,109,dominicks,1472,7.294377299,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/21/1991,109,minute.maid,14784,9.601300794,0,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/21/1991,109,tropicana,14016,9.547954813,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/21/1991,110,dominicks,5632,8.636219898,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/21/1991,110,minute.maid,9728,9.182763604,0,2.41,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/21/1991,110,tropicana,5120,8.540909718,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/21/1991,111,dominicks,5248,8.565602331,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/21/1991,111,minute.maid,14208,9.561560465,0,2.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/21/1991,111,tropicana,6080,8.712759975,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/21/1991,112,dominicks,6080,8.712759975,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/21/1991,112,minute.maid,15616,9.656051309,0,2.12,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/21/1991,112,tropicana,13696,9.524859098,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/21/1991,113,dominicks,6464,8.7740036,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/21/1991,113,minute.maid,9408,9.14931567,0,2.41,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/21/1991,113,tropicana,7744,8.954673629,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/21/1991,114,dominicks,8832,9.086136769,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/21/1991,114,minute.maid,10944,9.30054664,0,2.12,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/21/1991,114,tropicana,9600,9.169518378,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/21/1991,115,dominicks,2176,7.685243608,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/21/1991,115,minute.maid,6784,8.822322178,0,2.41,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/21/1991,115,tropicana,7232,8.886270902,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/21/1991,116,dominicks,2944,7.98752448,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/21/1991,116,minute.maid,7232,8.886270902,0,2.41,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/21/1991,116,tropicana,5248,8.565602331,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/21/1991,117,dominicks,1856,7.526178913,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/21/1991,117,minute.maid,5248,8.565602331,0,2.41,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/21/1991,117,tropicana,3136,8.050703382,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/21/1991,118,dominicks,6144,8.723231275,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/21/1991,118,minute.maid,4672,8.449342525,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/21/1991,118,tropicana,5248,8.565602331,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/21/1991,119,dominicks,6144,8.723231275,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/21/1991,119,minute.maid,8512,9.049232212,0,2.12,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/21/1991,119,tropicana,7232,8.886270902,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/21/1991,121,dominicks,4480,8.407378325,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/21/1991,121,minute.maid,10176,9.227787286,0,2.12,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/21/1991,121,tropicana,10304,9.240287448,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/21/1991,122,dominicks,7744,8.954673629,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/21/1991,122,minute.maid,13376,9.501217335,0,1.89,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/21/1991,122,tropicana,10688,9.276876896,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/21/1991,123,dominicks,4608,8.435549202,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/21/1991,123,minute.maid,10048,9.215128889,0,2.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/21/1991,123,tropicana,5632,8.636219898,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/21/1991,124,dominicks,3520,8.166216269,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/21/1991,124,minute.maid,12288,9.416378455,0,2.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/21/1991,124,tropicana,8256,9.018695488,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/21/1991,126,dominicks,2432,7.796469243,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/21/1991,126,minute.maid,7360,8.903815212,0,2.41,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/21/1991,126,tropicana,5824,8.66974259,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/21/1991,128,dominicks,2560,7.847762538,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/21/1991,128,minute.maid,18368,9.818365299,0,2.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/21/1991,128,tropicana,11200,9.323669057,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/21/1991,129,dominicks,1792,7.491087594,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/21/1991,129,minute.maid,6272,8.743850562,0,2.41,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/21/1991,129,tropicana,9856,9.195835686,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/21/1991,130,dominicks,5440,8.60153434,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/21/1991,130,minute.maid,11968,9.3899917,0,2.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/21/1991,130,tropicana,3328,8.110126802,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/21/1991,131,dominicks,4032,8.30201781,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/21/1991,131,minute.maid,13120,9.481893063,0,2.41,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/21/1991,131,tropicana,5824,8.66974259,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/21/1991,132,dominicks,11264,9.329367078,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/21/1991,132,minute.maid,9344,9.142489705,0,2.12,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/21/1991,132,tropicana,11904,9.384629757,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/21/1991,134,dominicks,2624,7.87245515,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/21/1991,134,minute.maid,3840,8.253227646,0,2.41,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/21/1991,134,tropicana,3328,8.110126802,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/21/1991,137,dominicks,2432,7.796469243,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/21/1991,137,minute.maid,11648,9.36288977,0,2.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/21/1991,137,tropicana,17344,9.761001904,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/28/1991,2,dominicks,43328,10.67655436,1,1.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/28/1991,2,minute.maid,22464,10.01966931,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/28/1991,2,tropicana,6144,8.723231275,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/28/1991,5,dominicks,6336,8.754002934,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/28/1991,5,minute.maid,18688,9.835636886,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/28/1991,5,tropicana,6656,8.803273983,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/28/1991,8,dominicks,5056,8.528330936,1,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/28/1991,8,minute.maid,40320,10.6046029,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/28/1991,8,tropicana,7424,8.912473275,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/28/1991,9,dominicks,3776,8.236420527,1,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/28/1991,9,minute.maid,38912,10.56905797,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/28/1991,9,tropicana,8640,9.064157862,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/28/1991,12,dominicks,5056,8.528330936,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/28/1991,12,minute.maid,69184,11.1445249,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/28/1991,12,tropicana,5376,8.589699882,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/28/1991,14,dominicks,2624,7.87245515,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/28/1991,14,minute.maid,23488,10.06424493,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/28/1991,14,tropicana,6912,8.841014311,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/28/1991,18,dominicks,4288,8.363575703,1,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/28/1991,18,minute.maid,32256,10.38145935,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/28/1991,18,tropicana,6912,8.841014311,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/28/1991,21,dominicks,11392,9.340666634,1,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/28/1991,21,minute.maid,9344,9.142489705,1,1.98,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/28/1991,21,tropicana,3072,8.030084094,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/28/1991,28,dominicks,7360,8.903815212,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/28/1991,28,minute.maid,12800,9.45720045,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/28/1991,28,tropicana,3008,8.009030685,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/28/1991,32,dominicks,21248,9.964018052,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/28/1991,32,minute.maid,30912,10.33889974,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/28/1991,32,tropicana,9664,9.17616292,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/28/1991,33,dominicks,7232,8.886270902,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/28/1991,33,minute.maid,24832,10.11988842,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/28/1991,33,tropicana,7168,8.877381955,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/28/1991,44,dominicks,4544,8.42156296,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/28/1991,44,minute.maid,25664,10.15284451,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/28/1991,44,tropicana,7552,8.929567708,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/28/1991,45,dominicks,10624,9.270870872,1,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/28/1991,45,minute.maid,12096,9.400630098,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/28/1991,45,tropicana,5312,8.577723691,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/28/1991,47,dominicks,6016,8.702177866,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/28/1991,47,minute.maid,19712,9.888982866,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/28/1991,47,tropicana,2304,7.742402022,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/28/1991,48,dominicks,3520,8.166216269,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/28/1991,48,minute.maid,16896,9.734832187,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/28/1991,48,tropicana,4160,8.333270353,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/28/1991,49,dominicks,3200,8.070906089,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/28/1991,49,minute.maid,14848,9.605620455,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/28/1991,49,tropicana,2624,7.87245515,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/28/1991,50,dominicks,4160,8.333270353,1,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/28/1991,50,minute.maid,11392,9.340666634,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/28/1991,50,tropicana,3264,8.090708716,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/28/1991,52,dominicks,5952,8.691482577,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/28/1991,52,minute.maid,31616,10.3614186,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/28/1991,52,tropicana,8256,9.018695488,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/28/1991,53,dominicks,7296,8.895081532,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/28/1991,53,minute.maid,33792,10.42797937,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/28/1991,53,tropicana,8128,9.00307017,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/28/1991,54,dominicks,4800,8.476371197,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/28/1991,54,minute.maid,15168,9.626943225,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/28/1991,54,tropicana,5120,8.540909718,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/28/1991,56,dominicks,4992,8.51559191,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/28/1991,56,minute.maid,16640,9.719564714,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/28/1991,56,tropicana,4032,8.30201781,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/28/1991,59,dominicks,5184,8.553332238,1,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/28/1991,59,minute.maid,7040,8.859363449,1,1.98,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/28/1991,59,tropicana,4288,8.363575703,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/28/1991,62,dominicks,5888,8.68067166,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/28/1991,62,minute.maid,21056,9.954940834,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/28/1991,62,tropicana,9024,9.107642974,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/28/1991,64,dominicks,5760,8.658692754,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/28/1991,64,minute.maid,16896,9.734832187,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/28/1991,64,tropicana,2112,7.655390645,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/28/1991,67,dominicks,2880,7.965545573,1,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/28/1991,67,minute.maid,16896,9.734832187,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/28/1991,67,tropicana,6208,8.733594062,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/28/1991,68,dominicks,11456,9.346268889,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/28/1991,68,minute.maid,28416,10.25470765,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/28/1991,68,tropicana,5184,8.553332238,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/28/1991,70,dominicks,7424,8.912473275,1,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/28/1991,70,minute.maid,12160,9.405907156,1,1.98,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/28/1991,70,tropicana,9920,9.2023082,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/28/1991,71,dominicks,5888,8.68067166,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/28/1991,71,minute.maid,34048,10.43552657,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/28/1991,71,tropicana,3584,8.184234774,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/28/1991,72,dominicks,7040,8.859363449,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/28/1991,72,minute.maid,31040,10.34303197,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/28/1991,72,tropicana,6528,8.783855897,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/28/1991,73,dominicks,9984,9.208739091,1,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/28/1991,73,minute.maid,52096,10.86084345,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/28/1991,73,tropicana,10112,9.221478116,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/28/1991,74,dominicks,11712,9.368369236,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/28/1991,74,minute.maid,30848,10.3368272,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/28/1991,74,tropicana,7232,8.886270902,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/28/1991,75,dominicks,7360,8.903815212,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/28/1991,75,minute.maid,36480,10.50451944,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/28/1991,75,tropicana,6080,8.712759975,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/28/1991,76,dominicks,3136,8.050703382,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/28/1991,76,minute.maid,38016,10.5457624,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/28/1991,76,tropicana,5504,8.61323038,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/28/1991,77,dominicks,2176,7.685243608,1,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/28/1991,77,minute.maid,12096,9.400630098,1,1.98,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/28/1991,77,tropicana,7552,8.929567708,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/28/1991,78,dominicks,5568,8.624791202,1,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/28/1991,78,minute.maid,21056,9.954940834,1,1.98,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/28/1991,78,tropicana,7360,8.903815212,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/28/1991,80,dominicks,12224,9.411156511,1,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/28/1991,80,minute.maid,12096,9.400630098,1,1.98,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/28/1991,80,tropicana,12160,9.405907156,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/28/1991,81,dominicks,6400,8.764053269,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/28/1991,81,minute.maid,26048,10.16769627,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/28/1991,81,tropicana,5248,8.565602331,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/28/1991,83,dominicks,10752,9.282847063,1,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/28/1991,83,minute.maid,7552,8.929567708,1,1.98,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/28/1991,83,tropicana,10112,9.221478116,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/28/1991,84,dominicks,4416,8.392989588,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/28/1991,84,minute.maid,16320,9.700146629,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/28/1991,84,tropicana,3904,8.269756948,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/28/1991,86,dominicks,6976,8.850230966,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/28/1991,86,minute.maid,28544,10.25920204,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/28/1991,86,tropicana,5568,8.624791202,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/28/1991,88,dominicks,5568,8.624791202,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/28/1991,88,minute.maid,21120,9.957975738,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/28/1991,88,tropicana,2880,7.965545573,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/28/1991,89,dominicks,5696,8.647519453,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/28/1991,89,minute.maid,32000,10.37349118,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/28/1991,89,tropicana,2240,7.714231145,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/28/1991,90,dominicks,4352,8.378390789,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/28/1991,90,minute.maid,31168,10.34714721,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/28/1991,90,tropicana,2496,7.82244473,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/28/1991,91,dominicks,3072,8.030084094,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/28/1991,91,minute.maid,18880,9.84585844,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/28/1991,91,tropicana,2176,7.685243608,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/28/1991,92,dominicks,13248,9.491601877,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/28/1991,92,minute.maid,25152,10.1326927,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/28/1991,92,tropicana,2816,7.943072717,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/28/1991,93,dominicks,10304,9.240287448,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/28/1991,93,minute.maid,19456,9.875910785,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/28/1991,93,tropicana,5696,8.647519453,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/28/1991,94,dominicks,6464,8.7740036,1,1.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/28/1991,94,minute.maid,20864,9.945780465,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/28/1991,94,tropicana,7232,8.886270902,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/28/1991,95,dominicks,14144,9.557045785,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/28/1991,95,minute.maid,22144,10.00532186,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/28/1991,95,tropicana,2880,7.965545573,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/28/1991,97,dominicks,5120,8.540909718,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/28/1991,97,minute.maid,10496,9.258749511,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/28/1991,97,tropicana,1984,7.592870288,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/28/1991,98,dominicks,6144,8.723231275,1,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/28/1991,98,minute.maid,28032,10.24110199,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/28/1991,98,tropicana,8640,9.064157862,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/28/1991,100,dominicks,12288,9.416378455,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/28/1991,100,minute.maid,45376,10.72273861,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/28/1991,100,tropicana,3584,8.184234774,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/28/1991,101,dominicks,15808,9.66827142,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/28/1991,101,minute.maid,19392,9.872615889,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/28/1991,101,tropicana,6784,8.822322178,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/28/1991,102,dominicks,14016,9.547954813,1,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/28/1991,102,minute.maid,52608,10.87062348,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/28/1991,102,tropicana,7360,8.903815212,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/28/1991,103,dominicks,13632,9.520175249,1,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/28/1991,103,minute.maid,16576,9.715711145,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/28/1991,103,tropicana,2368,7.769800996,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/28/1991,104,dominicks,6592,8.793612072,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/28/1991,104,minute.maid,13824,9.534161491,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/28/1991,104,tropicana,2880,7.965545573,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/28/1991,105,dominicks,8320,9.026417534,1,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/28/1991,105,minute.maid,36992,10.51845695,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/28/1991,105,tropicana,6656,8.803273983,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/28/1991,106,dominicks,4928,8.502688505,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/28/1991,106,minute.maid,10496,9.258749511,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/28/1991,106,tropicana,2240,7.714231145,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/28/1991,107,dominicks,2944,7.98752448,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/28/1991,107,minute.maid,28352,10.25245285,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/28/1991,107,tropicana,7232,8.886270902,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/28/1991,109,dominicks,6784,8.822322178,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/28/1991,109,minute.maid,43968,10.69121738,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/28/1991,109,tropicana,13056,9.477003077,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/28/1991,110,dominicks,6656,8.803273983,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/28/1991,110,minute.maid,30080,10.31161578,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/28/1991,110,tropicana,3072,8.030084094,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/28/1991,111,dominicks,14016,9.547954813,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/28/1991,111,minute.maid,65984,11.09716757,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/28/1991,111,tropicana,3200,8.070906089,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/28/1991,112,dominicks,6464,8.7740036,1,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/28/1991,112,minute.maid,49344,10.80657146,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/28/1991,112,tropicana,17536,9.77201119,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/28/1991,113,dominicks,10816,9.288781798,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/28/1991,113,minute.maid,40704,10.61408165,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/28/1991,113,tropicana,6272,8.743850562,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/28/1991,114,dominicks,15680,9.660141294,1,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/28/1991,114,minute.maid,22592,10.02535114,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/28/1991,114,tropicana,8768,9.078864009,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/28/1991,115,dominicks,3968,8.286017468,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/28/1991,115,minute.maid,33792,10.42797937,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/28/1991,115,tropicana,4160,8.333270353,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/28/1991,116,dominicks,7040,8.859363449,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/28/1991,116,minute.maid,17216,9.753594463,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/28/1991,116,tropicana,4672,8.449342525,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/28/1991,117,dominicks,2624,7.87245515,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/28/1991,117,minute.maid,19584,9.882468185,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/28/1991,117,tropicana,2432,7.796469243,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/28/1991,118,dominicks,6016,8.702177866,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/28/1991,118,minute.maid,28160,10.24565781,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/28/1991,118,tropicana,6272,8.743850562,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/28/1991,119,dominicks,8512,9.049232212,1,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/28/1991,119,minute.maid,12672,9.447150114,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/28/1991,119,tropicana,6912,8.841014311,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/28/1991,121,dominicks,18944,9.849242538,1,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/28/1991,121,minute.maid,15488,9.64782081,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/28/1991,121,tropicana,10304,9.240287448,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/28/1991,122,dominicks,7488,8.921057018,1,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/28/1991,122,minute.maid,17856,9.790094865,1,1.98,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/28/1991,122,tropicana,10624,9.270870872,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/28/1991,123,dominicks,9408,9.14931567,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/28/1991,123,minute.maid,61056,11.01954676,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/28/1991,123,tropicana,5568,8.624791202,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/28/1991,124,dominicks,21312,9.967025573,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/28/1991,124,minute.maid,33024,10.40498985,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/28/1991,124,tropicana,4864,8.489616424,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/28/1991,126,dominicks,3520,8.166216269,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/28/1991,126,minute.maid,32512,10.38936453,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/28/1991,126,tropicana,4608,8.435549202,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/28/1991,128,dominicks,5632,8.636219898,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/28/1991,128,minute.maid,61696,11.02997438,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/28/1991,128,tropicana,9344,9.142489705,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/28/1991,129,dominicks,6208,8.733594062,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/28/1991,129,minute.maid,16000,9.680344001,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/28/1991,129,tropicana,8128,9.00307017,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/28/1991,130,dominicks,4864,8.489616424,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/28/1991,130,minute.maid,32384,10.38541975,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/28/1991,130,tropicana,4416,8.392989588,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/28/1991,131,dominicks,5120,8.540909718,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/28/1991,131,minute.maid,25600,10.15034763,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/28/1991,131,tropicana,5312,8.577723691,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/28/1991,132,dominicks,18368,9.818365299,1,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/28/1991,132,minute.maid,20352,9.920934466,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/28/1991,132,tropicana,12224,9.411156511,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/28/1991,134,dominicks,15936,9.67633598,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/28/1991,134,minute.maid,16192,9.692272572,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/28/1991,134,tropicana,3904,8.269756948,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/28/1991,137,dominicks,13248,9.491601877,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/28/1991,137,minute.maid,41984,10.64504387,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/28/1991,137,tropicana,10944,9.30054664,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/7/1991,2,dominicks,57600,10.96127785,1,1.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/7/1991,2,minute.maid,3840,8.253227646,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/7/1991,2,tropicana,7936,8.979164649,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/7/1991,5,dominicks,56384,10.93994071,1,1.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/7/1991,5,minute.maid,6272,8.743850562,0,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/7/1991,5,tropicana,6016,8.702177866,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/7/1991,8,dominicks,179968,12.10053434,1,0.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/7/1991,8,minute.maid,5120,8.540909718,0,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/7/1991,8,tropicana,5952,8.691482577,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/7/1991,9,dominicks,99520,11.50811391,1,0.94,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/7/1991,9,minute.maid,6080,8.712759975,0,2.17,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/7/1991,9,tropicana,10112,9.221478116,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/7/1991,12,dominicks,140928,11.8560044,1,1.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/7/1991,12,minute.maid,7104,8.868413285,0,2.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/7/1991,12,tropicana,6592,8.793612072,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/7/1991,14,dominicks,35328,10.47243113,1,1.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/7/1991,14,minute.maid,4096,8.317766167,0,2.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/7/1991,14,tropicana,6336,8.754002934,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/7/1991,18,dominicks,129984,11.77516665,1,0.94,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/7/1991,18,minute.maid,5504,8.61323038,0,2.17,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/7/1991,18,tropicana,7168,8.877381955,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/7/1991,21,dominicks,93248,11.44301789,1,0.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/7/1991,21,minute.maid,5056,8.528330936,0,1.98,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/7/1991,21,tropicana,2496,7.82244473,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/7/1991,28,dominicks,35456,10.47604777,1,1.09,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/7/1991,28,minute.maid,2752,7.920083199,0,2.46,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/7/1991,28,tropicana,3648,8.201934351,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/7/1991,32,dominicks,89728,11.40453815,1,1.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/7/1991,32,minute.maid,7744,8.954673629,0,2.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/7/1991,32,tropicana,7296,8.895081532,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/7/1991,33,dominicks,33024,10.40498985,1,1.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/7/1991,33,minute.maid,4736,8.462948177,0,2.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/7/1991,33,tropicana,8832,9.086136769,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/7/1991,40,dominicks,103488,11.54721094,1,0.92,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/7/1991,40,minute.maid,4992,8.51559191,0,1.98,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/7/1991,40,tropicana,5440,8.60153434,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/7/1991,44,dominicks,67904,11.12585022,1,1.09,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/7/1991,44,minute.maid,5184,8.553332238,0,2.46,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/7/1991,44,tropicana,6976,8.850230966,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/7/1991,45,dominicks,39808,10.59182318,1,0.94,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/7/1991,45,minute.maid,4032,8.30201781,0,2.17,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/7/1991,45,tropicana,5248,8.565602331,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/7/1991,47,dominicks,52800,10.87426647,1,1.09,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/7/1991,47,minute.maid,3328,8.110126802,0,2.46,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/7/1991,47,tropicana,3200,8.070906089,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/7/1991,48,dominicks,29568,10.29444797,1,1.09,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/7/1991,48,minute.maid,4608,8.435549202,0,2.46,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/7/1991,48,tropicana,4096,8.317766167,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/7/1991,49,dominicks,37888,10.54238972,1,1.09,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/7/1991,49,minute.maid,2304,7.742402022,0,2.46,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/7/1991,49,tropicana,2944,7.98752448,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/7/1991,50,dominicks,39744,10.59021417,1,0.94,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/7/1991,50,minute.maid,3968,8.286017468,0,2.17,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/7/1991,50,tropicana,3008,8.009030685,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/7/1991,51,dominicks,70848,11.16829202,1,1.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/7/1991,51,minute.maid,3776,8.236420527,0,2.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/7/1991,51,tropicana,3520,8.166216269,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/7/1991,52,dominicks,49472,10.80916213,1,1.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/7/1991,52,minute.maid,7168,8.877381955,0,2.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/7/1991,52,tropicana,8768,9.078864009,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/7/1991,53,dominicks,98944,11.50230931,1,1.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/7/1991,53,minute.maid,4544,8.42156296,0,2.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/7/1991,53,tropicana,7680,8.946374826,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/7/1991,54,dominicks,41216,10.62658181,1,1.09,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/7/1991,54,minute.maid,3008,8.009030685,0,2.46,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/7/1991,54,tropicana,3456,8.14786713,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/7/1991,56,dominicks,34112,10.43740451,1,1.09,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/7/1991,56,minute.maid,4608,8.435549202,0,2.46,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/7/1991,56,tropicana,4480,8.407378325,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/7/1991,59,dominicks,107584,11.58602722,1,0.89,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/7/1991,59,minute.maid,3904,8.269756948,0,1.98,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/7/1991,59,tropicana,5248,8.565602331,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/7/1991,62,dominicks,28864,10.27035042,1,1.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/7/1991,62,minute.maid,5632,8.636219898,0,2.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/7/1991,62,tropicana,9024,9.107642974,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/7/1991,64,dominicks,59904,11.00049856,1,1.09,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/7/1991,64,minute.maid,2752,7.920083199,0,2.46,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/7/1991,64,tropicana,1600,7.377758908,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/7/1991,67,dominicks,103040,11.54287254,1,0.94,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/7/1991,67,minute.maid,4416,8.392989588,0,2.17,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/7/1991,67,tropicana,5440,8.60153434,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/7/1991,68,dominicks,78528,11.27121053,1,1.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/7/1991,68,minute.maid,5056,8.528330936,0,2.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/7/1991,68,tropicana,6336,8.754002934,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/7/1991,70,dominicks,98240,11.49516874,1,0.92,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/7/1991,70,minute.maid,7552,8.929567708,0,1.98,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/7/1991,70,tropicana,7296,8.895081532,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/7/1991,71,dominicks,104192,11.55399063,1,1.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/7/1991,71,minute.maid,2240,7.714231145,0,2.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/7/1991,71,tropicana,4096,8.317766167,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/7/1991,72,dominicks,66304,11.10200551,1,1.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/7/1991,72,minute.maid,3264,8.090708716,0,2.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/7/1991,72,tropicana,7168,8.877381955,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/7/1991,73,dominicks,267520,12.49694961,1,0.94,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/7/1991,73,minute.maid,5632,8.636219898,0,2.17,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 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-3/7/1991,75,minute.maid,3200,8.070906089,0,2.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/7/1991,75,tropicana,5824,8.66974259,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/7/1991,76,dominicks,121600,11.70849225,1,1.09,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/7/1991,76,minute.maid,4736,8.462948177,0,2.46,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/7/1991,76,tropicana,4288,8.363575703,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 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-3/7/1991,81,minute.maid,5120,8.540909718,0,2.46,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/7/1991,81,tropicana,5248,8.565602331,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/7/1991,83,dominicks,237312,12.37713101,1,0.89,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/7/1991,83,minute.maid,5376,8.589699882,0,1.98,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/7/1991,83,tropicana,6272,8.743850562,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 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-3/7/1991,86,tropicana,5568,8.624791202,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/7/1991,88,dominicks,50880,10.8372252,1,1.09,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/7/1991,88,minute.maid,3776,8.236420527,0,2.46,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/7/1991,88,tropicana,3328,8.110126802,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/7/1991,89,dominicks,113792,11.6421275,1,1.09,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/7/1991,89,minute.maid,3200,8.070906089,0,2.46,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/7/1991,89,tropicana,1856,7.526178913,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/7/1991,90,dominicks,86720,11.37043982,1,1.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/7/1991,90,minute.maid,3136,8.050703382,0,2.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/7/1991,90,tropicana,1600,7.377758908,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/7/1991,91,dominicks,79104,11.27851872,1,1.09,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/7/1991,91,minute.maid,2432,7.796469243,0,2.46,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/7/1991,91,tropicana,2816,7.943072717,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/7/1991,92,dominicks,70464,11.16285722,1,1.09,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/7/1991,92,minute.maid,3648,8.201934351,0,2.46,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/7/1991,92,tropicana,2304,7.742402022,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/7/1991,93,dominicks,51776,10.854682,1,1.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/7/1991,93,minute.maid,4800,8.476371197,0,2.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/7/1991,93,tropicana,4992,8.51559191,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/7/1991,94,dominicks,58432,10.97561896,1,0.94,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/7/1991,94,minute.maid,4800,8.476371197,0,2.17,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/7/1991,94,tropicana,5376,8.589699882,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/7/1991,95,dominicks,70464,11.16285722,1,1.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/7/1991,95,minute.maid,2688,7.896552702,0,2.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/7/1991,95,tropicana,3904,8.269756948,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/7/1991,97,dominicks,31040,10.34303197,1,1.09,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/7/1991,97,minute.maid,2112,7.655390645,0,2.46,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/7/1991,97,tropicana,1216,7.103322063,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/7/1991,98,dominicks,160128,11.98372877,1,0.94,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/7/1991,98,minute.maid,4864,8.489616424,0,2.17,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/7/1991,98,tropicana,7424,8.912473275,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/7/1991,100,dominicks,130752,11.78105768,1,1.09,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/7/1991,100,minute.maid,5184,8.553332238,0,2.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/7/1991,100,tropicana,4288,8.363575703,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/7/1991,101,dominicks,93760,11.44849361,1,1.09,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/7/1991,101,minute.maid,5376,8.589699882,0,2.46,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/7/1991,101,tropicana,5056,8.528330936,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/7/1991,102,dominicks,212032,12.26449249,1,0.94,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/7/1991,102,minute.maid,6528,8.783855897,0,2.17,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/7/1991,102,tropicana,6080,8.712759975,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/7/1991,103,dominicks,113920,11.64325173,1,0.94,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/7/1991,103,minute.maid,2624,7.87245515,0,2.17,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/7/1991,103,tropicana,2112,7.655390645,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/7/1991,104,dominicks,34112,10.43740451,1,1.09,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/7/1991,104,minute.maid,3776,8.236420527,0,2.46,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/7/1991,104,tropicana,2944,7.98752448,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/7/1991,105,dominicks,158912,11.97610587,1,0.94,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/7/1991,105,minute.maid,3648,8.201934351,0,2.17,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/7/1991,105,tropicana,4800,8.476371197,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/7/1991,106,dominicks,34624,10.45230236,1,1.09,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/7/1991,106,minute.maid,1344,7.203405521,0,2.46,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/7/1991,106,tropicana,1536,7.336936914,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/7/1991,107,dominicks,54656,10.90881428,1,1.09,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/7/1991,107,minute.maid,4992,8.51559191,0,2.46,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/7/1991,107,tropicana,7232,8.886270902,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/7/1991,109,dominicks,74304,11.21592007,1,1.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/7/1991,109,minute.maid,8000,8.987196821,0,2.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/7/1991,109,tropicana,12096,9.400630098,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/7/1991,110,dominicks,64256,11.07063038,1,1.09,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/7/1991,110,minute.maid,3008,8.009030685,0,2.46,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/7/1991,110,tropicana,2944,7.98752448,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/7/1991,111,dominicks,250240,12.43017574,1,1.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/7/1991,111,minute.maid,3008,8.009030685,0,2.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/7/1991,111,tropicana,3328,8.110126802,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/7/1991,112,dominicks,57984,10.96792239,1,0.94,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/7/1991,112,minute.maid,12672,9.447150114,0,2.17,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/7/1991,112,tropicana,16384,9.704060528,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/7/1991,113,dominicks,82944,11.32592096,1,1.09,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/7/1991,113,minute.maid,4096,8.317766167,0,2.46,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/7/1991,113,tropicana,5824,8.66974259,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/7/1991,114,dominicks,212480,12.26660315,1,0.94,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/7/1991,114,minute.maid,4480,8.407378325,0,2.17,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/7/1991,114,tropicana,5376,8.589699882,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/7/1991,116,dominicks,54144,10.89940244,1,1.09,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/7/1991,116,minute.maid,3712,8.219326094,0,2.46,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/7/1991,116,tropicana,3840,8.253227646,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/7/1991,117,dominicks,35776,10.48503256,1,1.09,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/7/1991,117,minute.maid,2432,7.796469243,0,2.46,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/7/1991,117,tropicana,2496,7.82244473,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/7/1991,118,dominicks,61312,11.02373086,1,1.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/7/1991,118,minute.maid,3456,8.14786713,0,2.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/7/1991,118,tropicana,5696,8.647519453,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/7/1991,119,dominicks,52736,10.87305361,1,0.94,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/7/1991,119,minute.maid,5440,8.60153434,0,2.17,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/7/1991,119,tropicana,6784,8.822322178,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/7/1991,121,dominicks,82240,11.31739708,1,0.94,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/7/1991,121,minute.maid,5504,8.61323038,0,2.17,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/7/1991,121,tropicana,10688,9.276876896,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/7/1991,122,dominicks,75520,11.2321528,1,0.89,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/7/1991,122,minute.maid,9664,9.17616292,0,1.98,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/7/1991,122,tropicana,9664,9.17616292,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/7/1991,123,dominicks,135488,11.81663835,1,1.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/7/1991,123,minute.maid,6016,8.702177866,0,2.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/7/1991,123,tropicana,4288,8.363575703,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/7/1991,124,dominicks,122880,11.71896355,1,1.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/7/1991,124,minute.maid,5952,8.691482577,0,2.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/7/1991,124,tropicana,4672,8.449342525,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/7/1991,126,dominicks,62016,11.0351477,1,1.09,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/7/1991,126,minute.maid,4736,8.462948177,0,2.46,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/7/1991,126,tropicana,4032,8.30201781,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/7/1991,128,dominicks,152832,11.93709456,1,1.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/7/1991,128,minute.maid,6144,8.723231275,0,2.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/7/1991,128,tropicana,10496,9.258749511,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/7/1991,129,dominicks,50176,10.8232921,1,1.09,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/7/1991,129,minute.maid,4736,8.462948177,0,2.46,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/7/1991,129,tropicana,8768,9.078864009,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/7/1991,130,dominicks,211328,12.26116671,1,1.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/7/1991,130,minute.maid,3328,8.110126802,0,2.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/7/1991,130,tropicana,3712,8.219326094,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/7/1991,131,dominicks,62720,11.04643566,1,1.09,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/7/1991,131,minute.maid,4224,8.348537825,0,2.46,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/7/1991,131,tropicana,4864,8.489616424,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/7/1991,132,dominicks,148224,11.90647992,1,0.94,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/7/1991,132,minute.maid,5312,8.577723691,0,2.17,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/7/1991,132,tropicana,5952,8.691482577,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/7/1991,134,dominicks,41216,10.62658181,1,1.09,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/7/1991,134,minute.maid,2368,7.769800996,0,2.46,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/7/1991,134,tropicana,2304,7.742402022,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/7/1991,137,dominicks,63872,11.06463636,1,1.09,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/7/1991,137,minute.maid,8960,9.100525506,0,2.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/7/1991,137,tropicana,12672,9.447150114,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/14/1991,2,dominicks,704,6.556778356,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/14/1991,2,minute.maid,12992,9.472089062,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/14/1991,2,tropicana,7808,8.962904128,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/14/1991,5,dominicks,1600,7.377758908,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/14/1991,5,minute.maid,12096,9.400630098,0,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/14/1991,5,tropicana,6144,8.723231275,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/14/1991,8,dominicks,4992,8.51559191,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/14/1991,8,minute.maid,19264,9.865993348,0,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/14/1991,8,tropicana,7616,8.938006577,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/14/1991,9,dominicks,1344,7.203405521,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/14/1991,9,minute.maid,13056,9.477003077,0,2.17,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/14/1991,9,tropicana,9984,9.208739091,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/14/1991,12,dominicks,2496,7.82244473,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/14/1991,12,minute.maid,19968,9.901886271,0,2.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/14/1991,12,tropicana,6080,8.712759975,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/14/1991,14,dominicks,832,6.723832441,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/14/1991,14,minute.maid,12928,9.467150781,0,2.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/14/1991,14,tropicana,6976,8.850230966,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/14/1991,18,dominicks,3712,8.219326094,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/14/1991,18,minute.maid,22848,10.03661887,0,2.17,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/14/1991,18,tropicana,7232,8.886270902,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/14/1991,21,dominicks,3072,8.030084094,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/14/1991,21,minute.maid,8640,9.064157862,0,1.98,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/14/1991,21,tropicana,2880,7.965545573,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/14/1991,28,dominicks,512,6.238324625,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/14/1991,28,minute.maid,7680,8.946374826,0,2.46,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/14/1991,28,tropicana,4096,8.317766167,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/14/1991,32,dominicks,1472,7.294377299,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/14/1991,32,minute.maid,23488,10.06424493,0,2.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/14/1991,32,tropicana,9536,9.162829389,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/14/1991,33,dominicks,1024,6.931471806,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/14/1991,33,minute.maid,3712,8.219326094,0,2.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/14/1991,33,tropicana,8576,9.056722883,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/14/1991,40,dominicks,2880,7.965545573,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/14/1991,40,minute.maid,14464,9.579418083,0,1.98,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/14/1991,40,tropicana,6208,8.733594062,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/14/1991,44,dominicks,2048,7.624618986,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/14/1991,44,minute.maid,15232,9.631153757,0,2.46,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/14/1991,44,tropicana,6720,8.812843434,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/14/1991,45,dominicks,3136,8.050703382,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/14/1991,45,minute.maid,8384,9.034080407,0,2.17,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/14/1991,45,tropicana,6144,8.723231275,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/14/1991,47,dominicks,2048,7.624618986,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/14/1991,47,minute.maid,10496,9.258749511,0,2.46,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/14/1991,47,tropicana,3520,8.166216269,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/14/1991,48,dominicks,1536,7.336936914,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/14/1991,48,minute.maid,8000,8.987196821,0,2.46,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/14/1991,48,tropicana,4928,8.502688505,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/14/1991,49,dominicks,2624,7.87245515,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/14/1991,49,minute.maid,11776,9.373818841,0,2.46,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/14/1991,49,tropicana,2752,7.920083199,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/14/1991,50,dominicks,1600,7.377758908,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/14/1991,50,minute.maid,7680,8.946374826,0,2.17,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/14/1991,50,tropicana,3776,8.236420527,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/14/1991,51,dominicks,1856,7.526178913,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/14/1991,51,minute.maid,14656,9.592605087,0,2.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/14/1991,51,tropicana,3200,8.070906089,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/14/1991,52,dominicks,704,6.556778356,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/14/1991,52,minute.maid,14144,9.557045785,0,2.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/14/1991,52,tropicana,8576,9.056722883,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/14/1991,53,dominicks,21184,9.961001459,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/14/1991,53,minute.maid,20096,9.908276069,0,2.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/14/1991,53,tropicana,7040,8.859363449,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/14/1991,54,dominicks,1216,7.103322063,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/14/1991,54,minute.maid,5696,8.647519453,0,2.46,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/14/1991,54,tropicana,4352,8.378390789,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/14/1991,56,dominicks,1408,7.249925537,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/14/1991,56,minute.maid,9088,9.114710141,0,2.46,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/14/1991,56,tropicana,4672,8.449342525,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/14/1991,59,dominicks,3584,8.184234774,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/14/1991,59,minute.maid,14656,9.592605087,0,1.98,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/14/1991,59,tropicana,4672,8.449342525,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/14/1991,62,dominicks,384,5.950642553,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/14/1991,62,minute.maid,11584,9.357380115,0,2.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/14/1991,62,tropicana,11136,9.317938383,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/14/1991,67,dominicks,896,6.797940413,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/14/1991,67,minute.maid,13952,9.543378146,0,2.17,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/14/1991,67,tropicana,4928,8.502688505,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/14/1991,68,dominicks,832,6.723832441,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/14/1991,68,minute.maid,19136,9.859326657,0,2.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/14/1991,68,tropicana,6400,8.764053269,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/14/1991,70,dominicks,3840,8.253227646,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/14/1991,70,minute.maid,22208,10.00820786,0,1.98,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/14/1991,70,tropicana,9088,9.114710141,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/14/1991,71,dominicks,1920,7.560080465,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/14/1991,71,minute.maid,21568,9.978966014,0,2.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/14/1991,71,tropicana,6400,8.764053269,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/14/1991,72,dominicks,448,6.104793232,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/14/1991,72,minute.maid,11328,9.335032816,0,2.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/14/1991,72,tropicana,7296,8.895081532,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/14/1991,73,dominicks,8512,9.049232212,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/14/1991,73,minute.maid,29312,10.28575227,0,2.17,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/14/1991,73,tropicana,8768,9.078864009,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/14/1991,74,dominicks,3712,8.219326094,0,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/14/1991,74,minute.maid,21632,9.981928979,0,2.46,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/14/1991,74,tropicana,4928,8.502688505,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/14/1991,75,dominicks,3264,8.090708716,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/14/1991,75,minute.maid,22336,10.01395501,0,2.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/14/1991,75,tropicana,6016,8.702177866,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/14/1991,76,dominicks,1664,7.416979621,0,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/14/1991,76,minute.maid,9024,9.107642974,0,2.46,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/14/1991,76,tropicana,4480,8.407378325,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/14/1991,77,dominicks,2240,7.714231145,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/14/1991,77,minute.maid,11264,9.329367078,0,1.98,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/14/1991,77,tropicana,7616,8.938006577,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/14/1991,78,dominicks,2176,7.685243608,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/14/1991,78,minute.maid,13888,9.538780437,0,1.98,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/14/1991,78,tropicana,6656,8.803273983,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/14/1991,80,dominicks,1344,7.203405521,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/14/1991,80,minute.maid,15040,9.618468598,0,1.98,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/14/1991,80,tropicana,13248,9.491601877,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/14/1991,81,dominicks,1600,7.377758908,0,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/14/1991,81,minute.maid,13184,9.486759252,0,2.46,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/14/1991,81,tropicana,5696,8.647519453,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/14/1991,83,dominicks,3648,8.201934351,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/14/1991,83,minute.maid,18304,9.814874894,0,1.98,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/14/1991,83,tropicana,6464,8.7740036,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/14/1991,84,dominicks,2304,7.742402022,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/14/1991,84,minute.maid,12736,9.452187908,0,2.46,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/14/1991,84,tropicana,4928,8.502688505,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/14/1991,86,dominicks,832,6.723832441,0,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/14/1991,86,minute.maid,5888,8.68067166,0,2.46,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/14/1991,86,tropicana,5760,8.658692754,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/14/1991,88,dominicks,1216,7.103322063,0,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/14/1991,88,minute.maid,12544,9.436997743,0,2.46,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/14/1991,88,tropicana,3200,8.070906089,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/14/1991,90,dominicks,960,6.866933285,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/14/1991,90,minute.maid,13248,9.491601877,0,2.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/14/1991,90,tropicana,3072,8.030084094,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/14/1991,91,dominicks,704,6.556778356,0,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/14/1991,91,minute.maid,10432,9.252633284,0,2.46,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/14/1991,91,tropicana,2624,7.87245515,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/14/1991,92,dominicks,1856,7.526178913,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/14/1991,92,minute.maid,11712,9.368369236,0,2.46,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/14/1991,92,tropicana,4544,8.42156296,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/14/1991,93,dominicks,1152,7.049254841,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/14/1991,93,minute.maid,12032,9.395325046,0,2.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/14/1991,93,tropicana,5504,8.61323038,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/14/1991,95,dominicks,768,6.643789733,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/14/1991,95,minute.maid,13568,9.515469358,0,2.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/14/1991,95,tropicana,4160,8.333270353,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/14/1991,97,dominicks,768,6.643789733,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/14/1991,97,minute.maid,11968,9.3899917,0,2.46,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/14/1991,97,tropicana,1280,7.154615357,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/14/1991,100,dominicks,1536,7.336936914,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/14/1991,100,minute.maid,20032,9.905086274,0,2.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/14/1991,100,tropicana,7296,8.895081532,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/14/1991,101,dominicks,4288,8.363575703,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/14/1991,101,minute.maid,15680,9.660141294,0,2.46,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/14/1991,101,tropicana,4864,8.489616424,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/14/1991,102,dominicks,5056,8.528330936,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/14/1991,102,minute.maid,26496,10.18474906,0,2.17,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/14/1991,102,tropicana,7104,8.868413285,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/14/1991,103,dominicks,2048,7.624618986,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/14/1991,103,minute.maid,12096,9.400630098,0,2.17,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/14/1991,103,tropicana,2240,7.714231145,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/14/1991,104,dominicks,576,6.356107661,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/14/1991,104,minute.maid,15040,9.618468598,0,2.46,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/14/1991,104,tropicana,3200,8.070906089,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/14/1991,106,dominicks,2112,7.655390645,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/14/1991,106,minute.maid,9728,9.182763604,0,2.46,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/14/1991,106,tropicana,1536,7.336936914,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/14/1991,107,dominicks,1152,7.049254841,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/14/1991,107,minute.maid,18176,9.807857322,0,2.46,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/14/1991,107,tropicana,7360,8.903815212,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/14/1991,109,dominicks,640,6.461468176,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/14/1991,109,minute.maid,16064,9.684336023,0,2.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/14/1991,109,tropicana,11520,9.351839934,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/14/1991,110,dominicks,1984,7.592870288,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/14/1991,110,minute.maid,14080,9.55251063,0,2.46,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/14/1991,110,tropicana,4032,8.30201781,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/14/1991,111,dominicks,3264,8.090708716,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/14/1991,111,minute.maid,40448,10.60777248,0,2.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/14/1991,111,tropicana,3200,8.070906089,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/14/1991,112,dominicks,3328,8.110126802,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/14/1991,112,minute.maid,21696,9.984883191,0,2.17,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/14/1991,112,tropicana,15168,9.626943225,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/14/1991,113,dominicks,1728,7.454719949,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/14/1991,113,minute.maid,17792,9.786504197,0,2.46,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/14/1991,113,tropicana,4544,8.42156296,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/14/1991,114,dominicks,4800,8.476371197,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/14/1991,114,minute.maid,19904,9.898675996,0,2.17,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/14/1991,114,tropicana,24000,10.08580911,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/14/1991,115,dominicks,960,6.866933285,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/14/1991,115,minute.maid,10752,9.282847063,0,2.46,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/14/1991,115,tropicana,5248,8.565602331,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/14/1991,116,dominicks,1344,7.203405521,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/14/1991,116,minute.maid,13248,9.491601877,0,2.46,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/14/1991,116,tropicana,5056,8.528330936,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/14/1991,117,dominicks,896,6.797940413,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/14/1991,117,minute.maid,6656,8.803273983,0,2.46,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/14/1991,117,tropicana,3072,8.030084094,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/14/1991,118,dominicks,1536,7.336936914,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/14/1991,118,minute.maid,10880,9.29468152,0,2.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/14/1991,118,tropicana,4800,8.476371197,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/14/1991,119,dominicks,2880,7.965545573,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/14/1991,119,minute.maid,10560,9.264828557,0,2.17,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/14/1991,119,tropicana,6784,8.822322178,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/14/1991,121,dominicks,1536,7.336936914,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/14/1991,121,minute.maid,19072,9.85597657,0,2.17,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/14/1991,121,tropicana,10240,9.234056899,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/14/1991,122,dominicks,4288,8.363575703,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/14/1991,122,minute.maid,15680,9.660141294,0,1.98,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/14/1991,122,tropicana,9024,9.107642974,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/14/1991,123,dominicks,1280,7.154615357,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/14/1991,123,minute.maid,21376,9.970024076,0,2.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/14/1991,123,tropicana,5568,8.624791202,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/14/1991,124,dominicks,2112,7.655390645,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/14/1991,124,minute.maid,18752,9.839055692,0,2.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/14/1991,124,tropicana,4992,8.51559191,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/14/1991,128,dominicks,1472,7.294377299,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/14/1991,128,minute.maid,19328,9.869310101,0,2.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/14/1991,128,tropicana,9152,9.121727714,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/14/1991,129,dominicks,1344,7.203405521,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/14/1991,129,minute.maid,8704,9.071537969,0,2.46,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/14/1991,129,tropicana,7104,8.868413285,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/14/1991,130,dominicks,3968,8.286017468,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/14/1991,130,minute.maid,21440,9.973013615,0,2.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/14/1991,130,tropicana,4224,8.348537825,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/14/1991,131,dominicks,4608,8.435549202,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/14/1991,131,minute.maid,12736,9.452187908,0,2.46,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/14/1991,131,tropicana,5056,8.528330936,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/14/1991,132,dominicks,3904,8.269756948,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/14/1991,132,minute.maid,19584,9.882468185,0,2.17,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/14/1991,132,tropicana,7424,8.912473275,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/14/1991,134,dominicks,192,5.257495372,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/14/1991,134,minute.maid,9216,9.128696383,0,2.46,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/14/1991,134,tropicana,3648,8.201934351,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/14/1991,137,dominicks,1472,7.294377299,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/14/1991,137,minute.maid,13888,9.538780437,0,2.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/14/1991,137,tropicana,12352,9.421573272,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/21/1991,2,dominicks,6016,8.702177866,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/21/1991,2,minute.maid,70144,11.15830555,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/21/1991,2,tropicana,6080,8.712759975,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/21/1991,5,dominicks,2944,7.98752448,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/21/1991,5,minute.maid,73216,11.20116926,1,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/21/1991,5,tropicana,4928,8.502688505,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/21/1991,8,dominicks,6400,8.764053269,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/21/1991,8,minute.maid,170432,12.04609167,1,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/21/1991,8,tropicana,5312,8.577723691,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/21/1991,9,dominicks,3008,8.009030685,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/21/1991,9,minute.maid,93504,11.4457595,1,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/21/1991,9,tropicana,8128,9.00307017,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/21/1991,12,dominicks,4352,8.378390789,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/21/1991,12,minute.maid,132096,11.79128421,1,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/21/1991,12,tropicana,5696,8.647519453,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/21/1991,18,dominicks,10112,9.221478116,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/21/1991,18,minute.maid,128576,11.76427545,1,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/21/1991,18,tropicana,6912,8.841014311,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/21/1991,21,dominicks,7168,8.877381955,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/21/1991,21,minute.maid,64896,11.08054127,1,1.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/21/1991,21,tropicana,2816,7.943072717,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/21/1991,28,dominicks,2432,7.796469243,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/21/1991,28,minute.maid,40192,10.60142325,1,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/21/1991,28,tropicana,3072,8.030084094,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/21/1991,32,dominicks,4160,8.333270353,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/21/1991,32,minute.maid,114368,11.6471766,1,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/21/1991,32,tropicana,7488,8.921057018,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/21/1991,33,dominicks,2688,7.896552702,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/21/1991,33,minute.maid,47808,10.77494827,1,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/21/1991,33,tropicana,24448,10.10430369,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/21/1991,40,dominicks,5760,8.658692754,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/21/1991,40,minute.maid,119296,11.68936308,1,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/21/1991,40,tropicana,5120,8.540909718,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/21/1991,44,dominicks,2688,7.896552702,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/21/1991,44,minute.maid,86912,11.37265139,1,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/21/1991,44,tropicana,5824,8.66974259,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/21/1991,45,dominicks,3200,8.070906089,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/21/1991,45,minute.maid,41344,10.62968259,1,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/21/1991,45,tropicana,4928,8.502688505,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/21/1991,47,dominicks,3520,8.166216269,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/21/1991,47,minute.maid,60352,11.00794937,1,1.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/21/1991,47,tropicana,2048,7.624618986,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/21/1991,48,dominicks,3008,8.009030685,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/21/1991,48,minute.maid,44672,10.70710219,1,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/21/1991,48,tropicana,4160,8.333270353,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/21/1991,49,dominicks,2432,7.796469243,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/21/1991,49,minute.maid,53312,10.88391673,1,1.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/21/1991,49,tropicana,3008,8.009030685,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/21/1991,50,dominicks,2432,7.796469243,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/21/1991,50,minute.maid,60672,11.01323759,1,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/21/1991,50,tropicana,2048,7.624618986,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/21/1991,52,dominicks,3008,8.009030685,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/21/1991,52,minute.maid,78016,11.26466921,1,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/21/1991,52,tropicana,9344,9.142489705,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/21/1991,53,dominicks,5824,8.66974259,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/21/1991,53,minute.maid,116672,11.66712186,1,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/21/1991,53,tropicana,7552,8.929567708,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/21/1991,54,dominicks,3520,8.166216269,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/21/1991,54,minute.maid,44736,10.70853383,1,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/21/1991,54,tropicana,3904,8.269756948,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/21/1991,56,dominicks,5824,8.66974259,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/21/1991,56,minute.maid,50688,10.83344448,1,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/21/1991,56,tropicana,3456,8.14786713,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/21/1991,59,dominicks,8896,9.093357017,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/21/1991,59,minute.maid,60864,11.01639715,1,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/21/1991,59,tropicana,3264,8.090708716,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/21/1991,62,dominicks,2176,7.685243608,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/21/1991,62,minute.maid,58240,10.97232768,1,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/21/1991,62,tropicana,9792,9.189321005,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/21/1991,67,dominicks,2560,7.847762538,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/21/1991,67,minute.maid,84800,11.34805082,1,1.94,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/21/1991,67,tropicana,4800,8.476371197,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/21/1991,68,dominicks,6016,8.702177866,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/21/1991,68,minute.maid,92928,11.43958028,1,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/21/1991,68,tropicana,3840,8.253227646,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/21/1991,70,dominicks,4800,8.476371197,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/21/1991,70,minute.maid,76032,11.23890958,1,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/21/1991,70,tropicana,8192,9.010913347,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/21/1991,72,dominicks,2176,7.685243608,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/21/1991,72,minute.maid,90752,11.41588579,1,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/21/1991,72,tropicana,5888,8.68067166,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/21/1991,73,dominicks,31104,10.34509171,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/21/1991,73,minute.maid,183744,12.12129876,1,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/21/1991,73,tropicana,24768,10.11730778,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/21/1991,74,dominicks,9728,9.182763604,0,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/21/1991,74,minute.maid,130752,11.78105768,1,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/21/1991,74,tropicana,4608,8.435549202,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/21/1991,75,dominicks,4800,8.476371197,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/21/1991,75,minute.maid,85440,11.35556965,1,1.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/21/1991,75,tropicana,5440,8.60153434,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/21/1991,76,dominicks,4096,8.317766167,0,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/21/1991,76,minute.maid,129536,11.77171411,1,2.46,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/21/1991,76,tropicana,3648,8.201934351,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/21/1991,77,dominicks,2816,7.943072717,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/21/1991,77,minute.maid,59136,10.98759516,1,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/21/1991,77,tropicana,7232,8.886270902,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/21/1991,78,dominicks,3840,8.253227646,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/21/1991,78,minute.maid,109952,11.60779919,1,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/21/1991,78,tropicana,4480,8.407378325,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/21/1991,80,dominicks,3968,8.286017468,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/21/1991,80,minute.maid,78272,11.26794522,1,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/21/1991,80,tropicana,8704,9.071537969,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/21/1991,81,dominicks,5120,8.540909718,0,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/21/1991,81,minute.maid,92160,11.43128148,1,2.17,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/21/1991,81,tropicana,4224,8.348537825,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/21/1991,83,dominicks,9024,9.107642974,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/21/1991,83,minute.maid,129728,11.77319523,1,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/21/1991,83,tropicana,5824,8.66974259,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/21/1991,84,dominicks,4992,8.51559191,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/21/1991,84,minute.maid,70144,11.15830555,1,1.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/21/1991,84,tropicana,2880,7.965545573,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/21/1991,86,dominicks,7552,8.929567708,0,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/21/1991,86,minute.maid,69184,11.1445249,1,2.46,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/21/1991,86,tropicana,4928,8.502688505,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/21/1991,88,dominicks,3392,8.129174997,0,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/21/1991,88,minute.maid,75136,11.22705508,1,1.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/21/1991,88,tropicana,2816,7.943072717,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/21/1991,90,dominicks,6912,8.841014311,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/21/1991,90,minute.maid,92032,11.42989162,1,1.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/21/1991,90,tropicana,1472,7.294377299,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/21/1991,91,dominicks,4224,8.348537825,0,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/21/1991,91,minute.maid,98496,11.49777122,1,1.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/21/1991,91,tropicana,2304,7.742402022,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/21/1991,92,dominicks,3840,8.253227646,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/21/1991,92,minute.maid,64576,11.0755981,1,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/21/1991,92,tropicana,2176,7.685243608,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/21/1991,93,dominicks,4480,8.407378325,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/21/1991,93,minute.maid,65600,11.09133098,1,1.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/21/1991,93,tropicana,5696,8.647519453,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/21/1991,95,dominicks,1792,7.491087594,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/21/1991,95,minute.maid,110208,11.61012477,1,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/21/1991,95,tropicana,1664,7.416979621,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/21/1991,97,dominicks,1792,7.491087594,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/21/1991,97,minute.maid,39488,10.58375211,1,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/21/1991,97,tropicana,832,6.723832441,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/21/1991,98,dominicks,6720,8.812843434,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/21/1991,98,minute.maid,146112,11.89212873,1,1.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/21/1991,98,tropicana,6976,8.850230966,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/21/1991,100,dominicks,8128,9.00307017,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/21/1991,100,minute.maid,114496,11.64829517,1,1.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/21/1991,100,tropicana,4992,8.51559191,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/21/1991,101,dominicks,10560,9.264828557,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/21/1991,101,minute.maid,88128,11.38654558,1,1.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/21/1991,101,tropicana,3520,8.166216269,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/21/1991,102,dominicks,13440,9.505990614,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/21/1991,102,minute.maid,172672,12.05914912,1,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/21/1991,102,tropicana,6336,8.754002934,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/21/1991,103,dominicks,9536,9.162829389,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/21/1991,103,minute.maid,59584,10.99514236,1,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/21/1991,103,tropicana,1728,7.454719949,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/21/1991,104,dominicks,4608,8.435549202,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/21/1991,104,minute.maid,51072,10.84099168,1,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/21/1991,104,tropicana,3200,8.070906089,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/21/1991,105,dominicks,11456,9.346268889,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/21/1991,105,minute.maid,114048,11.64437469,1,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/21/1991,105,tropicana,4672,8.449342525,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/21/1991,106,dominicks,2432,7.796469243,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/21/1991,106,minute.maid,40704,10.61408165,1,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/21/1991,106,tropicana,1280,7.154615357,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/21/1991,107,dominicks,5696,8.647519453,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/21/1991,107,minute.maid,116160,11.66272383,1,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/21/1991,107,tropicana,6528,8.783855897,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/21/1991,109,dominicks,2176,7.685243608,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/21/1991,109,minute.maid,93056,11.44095674,1,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/21/1991,109,tropicana,9728,9.182763604,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/21/1991,110,dominicks,3200,8.070906089,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/21/1991,110,minute.maid,81920,11.31349844,1,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/21/1991,110,tropicana,2560,7.847762538,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/21/1991,111,dominicks,17984,9.797237753,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/21/1991,111,minute.maid,189184,12.15047537,1,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/21/1991,111,tropicana,3136,8.050703382,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/21/1991,112,dominicks,6336,8.754002934,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/21/1991,112,minute.maid,77056,11.25228771,1,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/21/1991,112,tropicana,14080,9.55251063,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/21/1991,113,dominicks,3392,8.129174997,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/21/1991,113,minute.maid,102848,11.54100745,1,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/21/1991,113,tropicana,5824,8.66974259,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/21/1991,114,dominicks,9792,9.189321005,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/21/1991,114,minute.maid,139072,11.84274706,1,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/21/1991,114,tropicana,8512,9.049232212,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/21/1991,115,dominicks,1920,7.560080465,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/21/1991,115,minute.maid,73664,11.20726949,1,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/21/1991,115,tropicana,3712,8.219326094,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/21/1991,116,dominicks,4416,8.392989588,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/21/1991,116,minute.maid,66432,11.10393415,1,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/21/1991,116,tropicana,3392,8.129174997,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/21/1991,117,dominicks,3328,8.110126802,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/21/1991,117,minute.maid,58048,10.96902553,1,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/21/1991,117,tropicana,1856,7.526178913,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/21/1991,118,dominicks,3904,8.269756948,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/21/1991,118,minute.maid,83840,11.3366655,1,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/21/1991,118,tropicana,3840,8.253227646,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/21/1991,119,dominicks,4672,8.449342525,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/21/1991,119,minute.maid,48384,10.78692446,1,1.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/21/1991,119,tropicana,7168,8.877381955,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/21/1991,121,dominicks,3840,8.253227646,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/21/1991,121,minute.maid,83520,11.3328414,1,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/21/1991,121,tropicana,8704,9.071537969,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/21/1991,122,dominicks,5568,8.624791202,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/21/1991,122,minute.maid,84224,11.3412352,1,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/21/1991,122,tropicana,9216,9.128696383,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/21/1991,123,dominicks,6848,8.831711918,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/21/1991,123,minute.maid,162560,11.99880244,1,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/21/1991,123,tropicana,3904,8.269756948,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/21/1991,124,dominicks,6528,8.783855897,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/21/1991,124,minute.maid,118272,11.68074234,1,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/21/1991,124,tropicana,3520,8.166216269,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/21/1991,128,dominicks,10560,9.264828557,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/21/1991,128,minute.maid,147328,11.90041667,1,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/21/1991,128,tropicana,9216,9.128696383,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/21/1991,129,dominicks,3136,8.050703382,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/21/1991,129,minute.maid,61952,11.03411517,1,1.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/21/1991,129,tropicana,8448,9.041685006,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/21/1991,130,dominicks,5440,8.60153434,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/21/1991,130,minute.maid,185984,12.13341593,1,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/21/1991,130,tropicana,2752,7.920083199,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/21/1991,131,dominicks,4288,8.363575703,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/21/1991,131,minute.maid,81216,11.30486755,1,2.08,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/21/1991,131,tropicana,5184,8.553332238,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/21/1991,132,dominicks,10112,9.221478116,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/21/1991,132,minute.maid,85568,11.35706666,1,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/21/1991,132,tropicana,5184,8.553332238,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/21/1991,134,dominicks,1600,7.377758908,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/21/1991,134,minute.maid,66752,11.10873954,1,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/21/1991,134,tropicana,3072,8.030084094,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/21/1991,137,dominicks,4480,8.407378325,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/21/1991,137,minute.maid,96256,11.47476659,1,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/21/1991,137,tropicana,10688,9.276876896,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/28/1991,2,dominicks,10368,9.246479419,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/28/1991,2,minute.maid,21248,9.964018052,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/28/1991,2,tropicana,42176,10.64960662,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/28/1991,5,dominicks,13504,9.510741217,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/28/1991,5,minute.maid,18944,9.849242538,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/28/1991,5,tropicana,67712,11.1230187,1,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/28/1991,8,dominicks,14912,9.609921537,1,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/28/1991,8,minute.maid,39680,10.58860256,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/28/1991,8,tropicana,161792,11.99406684,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/28/1991,9,dominicks,12288,9.416378455,1,1.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/28/1991,9,minute.maid,18944,9.849242538,0,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/28/1991,9,tropicana,124096,11.72881074,1,1.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/28/1991,12,dominicks,21056,9.954940834,1,1.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/28/1991,12,minute.maid,29056,10.27698028,0,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/28/1991,12,tropicana,133376,11.80092749,1,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/28/1991,14,dominicks,5760,8.658692754,1,1.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/28/1991,14,minute.maid,19520,9.87919486,0,1.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/28/1991,14,tropicana,62784,11.04745554,1,1.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/28/1991,18,dominicks,27456,10.22034,1,1.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/28/1991,18,minute.maid,33792,10.42797937,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/28/1991,18,tropicana,75328,11.22960719,1,1.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/28/1991,21,dominicks,7040,8.859363449,1,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/28/1991,21,minute.maid,14784,9.601300794,0,1.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/28/1991,21,tropicana,96832,11.4807328,1,1.36,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/28/1991,28,dominicks,6784,8.822322178,1,1.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/28/1991,28,minute.maid,13440,9.505990614,0,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/28/1991,28,tropicana,31744,10.36545901,1,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/28/1991,32,dominicks,36096,10.49393734,1,1.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/28/1991,32,minute.maid,24320,10.09905434,0,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/28/1991,32,tropicana,67520,11.12017913,1,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/28/1991,33,dominicks,12352,9.421573272,1,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/28/1991,33,minute.maid,12992,9.472089062,0,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/28/1991,33,tropicana,68096,11.12867375,1,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/28/1991,40,dominicks,6144,8.723231275,1,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/28/1991,40,minute.maid,33984,10.43364511,0,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/28/1991,40,tropicana,143104,11.87132692,1,1.36,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/28/1991,44,dominicks,8896,9.093357017,1,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/28/1991,44,minute.maid,19520,9.87919486,0,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/28/1991,44,tropicana,96064,11.47276992,1,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/28/1991,45,dominicks,6400,8.764053269,1,1.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/28/1991,45,minute.maid,8960,9.100525506,0,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/28/1991,45,tropicana,53696,10.89109379,1,1.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/28/1991,48,dominicks,8768,9.078864009,1,1.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/28/1991,48,minute.maid,17536,9.77201119,0,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/28/1991,48,tropicana,23232,10.05328592,1,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/28/1991,49,dominicks,12096,9.400630098,1,1.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/28/1991,49,minute.maid,16128,9.688312171,0,1.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/28/1991,49,tropicana,30848,10.3368272,1,1.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/28/1991,50,dominicks,7232,8.886270902,1,1.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/28/1991,50,minute.maid,16704,9.723403491,0,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/28/1991,50,tropicana,35072,10.46515837,1,1.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/28/1991,52,dominicks,13696,9.524859098,1,1.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/28/1991,52,minute.maid,24192,10.09377728,0,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/28/1991,52,tropicana,84160,11.34047503,1,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/28/1991,53,dominicks,14784,9.601300794,1,1.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/28/1991,53,minute.maid,28672,10.26367632,0,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/28/1991,53,tropicana,126656,11.74923003,1,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/28/1991,54,dominicks,9536,9.162829389,1,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/28/1991,54,minute.maid,16000,9.680344001,0,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/28/1991,54,tropicana,60032,11.00263303,1,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/28/1991,56,dominicks,16832,9.731037116,1,1.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/28/1991,56,minute.maid,17600,9.775654181,0,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/28/1991,56,tropicana,39040,10.57234204,1,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/28/1991,59,dominicks,11584,9.357380115,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/28/1991,59,minute.maid,16128,9.688312171,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/28/1991,59,tropicana,69632,11.15097951,1,1.36,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/28/1991,62,dominicks,4992,8.51559191,1,1.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/28/1991,62,minute.maid,16512,9.711842668,0,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/28/1991,62,tropicana,53760,10.89228498,1,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/28/1991,64,dominicks,17792,9.786504197,1,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/28/1991,64,minute.maid,9792,9.189321005,0,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/28/1991,64,tropicana,25536,10.1478445,1,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/28/1991,67,dominicks,2496,7.82244473,1,1.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/28/1991,67,minute.maid,19392,9.872615889,0,1.96,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/28/1991,67,tropicana,107648,11.58662192,1,1.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/28/1991,68,dominicks,16384,9.704060528,1,1.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/28/1991,68,minute.maid,27584,10.22499117,0,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/28/1991,68,tropicana,81984,11.31427939,1,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/28/1991,70,dominicks,10048,9.215128889,1,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/28/1991,70,minute.maid,23808,10.07777694,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/28/1991,70,tropicana,107392,11.58424097,1,1.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/28/1991,71,dominicks,35136,10.46698153,1,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/28/1991,71,minute.maid,29952,10.30735138,0,1.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/28/1991,71,tropicana,84480,11.3442701,1,1.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/28/1991,72,dominicks,11072,9.312174678,1,1.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/28/1991,72,minute.maid,19008,9.852615222,0,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/28/1991,72,tropicana,122048,11.71216969,1,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/28/1991,73,dominicks,30016,10.30948585,1,1.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/28/1991,73,minute.maid,29056,10.27698028,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/28/1991,73,tropicana,142016,11.86369501,1,1.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/28/1991,74,dominicks,27904,10.23652533,1,1.59,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/28/1991,74,minute.maid,41664,10.63739273,0,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/28/1991,74,tropicana,38272,10.55247384,1,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/28/1991,75,dominicks,14656,9.592605087,1,1.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/28/1991,75,minute.maid,25856,10.16029796,0,1.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/28/1991,75,tropicana,95360,11.46541448,1,1.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/28/1991,76,dominicks,36160,10.49570882,1,1.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/28/1991,76,minute.maid,11072,9.312174678,0,2.46,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/28/1991,76,tropicana,72960,11.19766663,1,1.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/28/1991,77,dominicks,7872,8.971067439,1,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/28/1991,77,minute.maid,16128,9.688312171,0,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/28/1991,77,tropicana,67776,11.12396343,1,1.36,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/28/1991,78,dominicks,6208,8.733594062,1,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/28/1991,78,minute.maid,18816,9.842462851,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/28/1991,78,tropicana,180736,12.10479268,1,1.36,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/28/1991,80,dominicks,3392,8.129174997,1,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/28/1991,80,minute.maid,23552,10.06696602,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/28/1991,80,tropicana,126208,11.74568662,1,1.36,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/28/1991,81,dominicks,17728,9.78290059,1,1.59,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/28/1991,81,minute.maid,23616,10.06967973,0,2.02,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/28/1991,81,tropicana,47680,10.7722673,1,1.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/28/1991,83,dominicks,52416,10.86696717,1,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/28/1991,83,minute.maid,17984,9.797237753,0,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/28/1991,83,tropicana,57792,10.96460564,1,1.36,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/28/1991,84,dominicks,7168,8.877381955,1,1.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/28/1991,84,minute.maid,19712,9.888982866,0,1.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/28/1991,84,tropicana,59200,10.98867682,1,1.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/28/1991,86,dominicks,15616,9.656051309,1,1.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/28/1991,86,minute.maid,18368,9.818365299,0,2.46,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/28/1991,86,tropicana,106752,11.57826367,1,1.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/28/1991,88,dominicks,9984,9.208739091,1,1.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/28/1991,88,minute.maid,16000,9.680344001,0,1.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/28/1991,88,tropicana,56704,10.94560003,1,1.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/28/1991,89,dominicks,30400,10.32219789,1,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/28/1991,89,minute.maid,45632,10.7283645,0,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/28/1991,89,tropicana,47040,10.75875358,1,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/28/1991,90,dominicks,27136,10.20861654,1,1.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/28/1991,90,minute.maid,21888,9.99369382,0,1.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/28/1991,90,tropicana,58240,10.97232768,1,1.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/28/1991,91,dominicks,26880,10.1991378,1,1.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/28/1991,91,minute.maid,17472,9.768354879,0,1.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/28/1991,91,tropicana,58496,10.97671366,1,1.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/28/1991,92,dominicks,33152,10.40885833,1,1.59,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/28/1991,92,minute.maid,18560,9.828764006,0,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/28/1991,92,tropicana,58048,10.96902553,1,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/28/1991,93,dominicks,18304,9.814874894,1,1.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/28/1991,93,minute.maid,15616,9.656051309,0,1.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/28/1991,93,tropicana,52736,10.87305361,1,1.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/28/1991,94,dominicks,10688,9.276876896,1,1.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/28/1991,94,minute.maid,20544,9.930324207,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/28/1991,94,tropicana,49920,10.818177,1,1.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/28/1991,95,dominicks,12800,9.45720045,1,1.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/28/1991,95,minute.maid,32512,10.38936453,0,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/28/1991,95,tropicana,62912,11.0494922,1,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/28/1991,97,dominicks,5632,8.636219898,1,1.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/28/1991,97,minute.maid,15424,9.643680017,0,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/28/1991,97,tropicana,30272,10.31797847,1,1.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/28/1991,98,dominicks,14912,9.609921537,1,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/28/1991,98,minute.maid,30080,10.31161578,0,1.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/28/1991,98,tropicana,163200,12.00273172,1,1.49,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/28/1991,100,dominicks,50880,10.8372252,1,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/28/1991,100,minute.maid,35008,10.46333189,0,1.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/28/1991,100,tropicana,52096,10.86084345,1,1.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/28/1991,101,dominicks,28672,10.26367632,1,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/28/1991,101,minute.maid,27264,10.21332243,0,1.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/28/1991,101,tropicana,47488,10.76823233,1,1.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/28/1991,102,dominicks,71424,11.17638923,1,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/28/1991,102,minute.maid,51264,10.84474403,0,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/28/1991,102,tropicana,65280,11.08644099,1,1.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/28/1991,103,dominicks,31040,10.34303197,1,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/28/1991,103,minute.maid,15744,9.664214619,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/28/1991,103,tropicana,32832,10.39915893,1,1.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/28/1991,104,dominicks,10432,9.252633284,1,1.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/28/1991,104,minute.maid,18496,9.825309772,0,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/28/1991,104,tropicana,40320,10.6046029,1,1.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/28/1991,105,dominicks,13888,9.538780437,1,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/28/1991,105,minute.maid,26304,10.1774763,0,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/28/1991,105,tropicana,120128,11.69631312,1,1.49,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/28/1991,106,dominicks,6784,8.822322178,1,1.59,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/28/1991,106,minute.maid,12480,9.431882642,0,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/28/1991,106,tropicana,28224,10.24792796,1,1.59,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/28/1991,107,dominicks,10304,9.240287448,1,1.59,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/28/1991,107,minute.maid,39040,10.57234204,0,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/28/1991,107,tropicana,86528,11.36822334,1,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/28/1991,109,dominicks,42304,10.65263692,1,1.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/28/1991,109,minute.maid,19776,9.89222436,0,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/28/1991,109,tropicana,87936,11.38436456,1,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/28/1991,110,dominicks,28992,10.27477521,1,1.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/28/1991,110,minute.maid,30592,10.32849382,0,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/28/1991,110,tropicana,30336,10.32009041,1,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/28/1991,111,dominicks,29056,10.27698028,1,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/28/1991,111,minute.maid,39616,10.58698836,0,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/28/1991,111,tropicana,217216,12.28864753,1,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/28/1991,112,dominicks,9728,9.182763604,1,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/28/1991,112,minute.maid,20416,9.924074186,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/28/1991,112,tropicana,125056,11.73651692,1,1.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/28/1991,113,dominicks,9024,9.107642974,1,1.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/28/1991,113,minute.maid,24768,10.11730778,0,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/28/1991,113,tropicana,120896,11.70268595,1,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/28/1991,114,dominicks,14912,9.609921537,1,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/28/1991,114,minute.maid,30848,10.3368272,0,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/28/1991,114,tropicana,164608,12.01132217,1,1.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/28/1991,115,dominicks,20224,9.914625297,1,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/28/1991,115,minute.maid,13568,9.515469358,0,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/28/1991,115,tropicana,74048,11.21246881,1,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/28/1991,116,dominicks,13696,9.524859098,1,1.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/28/1991,116,minute.maid,22464,10.01966931,0,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/28/1991,116,tropicana,48704,10.79351644,1,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/28/1991,117,dominicks,7936,8.979164649,1,1.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/28/1991,117,minute.maid,13056,9.477003077,0,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/28/1991,117,tropicana,38720,10.56411154,1,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/28/1991,118,dominicks,13376,9.501217335,1,1.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/28/1991,118,minute.maid,22080,10.0024275,0,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/28/1991,118,tropicana,79296,11.28094297,1,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/28/1991,119,dominicks,8320,9.026417534,1,1.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/28/1991,119,minute.maid,11328,9.335032816,0,1.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/28/1991,119,tropicana,54208,10.90058378,1,1.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/28/1991,121,dominicks,21248,9.964018052,1,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/28/1991,121,minute.maid,21696,9.984883191,0,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/28/1991,121,tropicana,81472,11.30801468,1,1.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/28/1991,122,dominicks,10496,9.258749511,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/28/1991,122,minute.maid,21760,9.987828701,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/28/1991,122,tropicana,104256,11.55460469,1,1.36,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/28/1991,123,dominicks,11072,9.312174678,1,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/28/1991,123,minute.maid,21504,9.975994243,0,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/28/1991,123,tropicana,150016,11.91849723,1,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/28/1991,124,dominicks,21120,9.957975738,1,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/28/1991,124,minute.maid,21312,9.967025573,0,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/28/1991,124,tropicana,120448,11.6989734,1,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/28/1991,126,dominicks,12928,9.467150781,1,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/28/1991,126,minute.maid,37504,10.53220287,0,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/28/1991,126,tropicana,73856,11.20987253,1,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/28/1991,128,dominicks,34240,10.44114983,1,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/28/1991,128,minute.maid,14784,9.601300794,0,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/28/1991,128,tropicana,161280,11.99089726,1,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/28/1991,129,dominicks,9408,9.14931567,1,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/28/1991,129,minute.maid,15552,9.651944527,0,1.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/28/1991,129,tropicana,76096,11.23975098,1,1.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/28/1991,130,dominicks,66688,11.10778031,1,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/28/1991,130,minute.maid,50304,10.82583988,0,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/28/1991,130,tropicana,60736,11.01429188,1,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/28/1991,131,dominicks,26304,10.1774763,1,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/28/1991,131,minute.maid,26432,10.18233068,0,2.23,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/28/1991,131,tropicana,32384,10.38541975,1,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/28/1991,132,dominicks,9344,9.142489705,1,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/28/1991,132,minute.maid,21504,9.975994243,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/28/1991,132,tropicana,139200,11.84366703,1,1.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/28/1991,134,dominicks,7872,8.971067439,1,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/28/1991,134,minute.maid,20160,9.911455722,0,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/28/1991,134,tropicana,57216,10.95458886,1,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/28/1991,137,dominicks,14464,9.579418083,1,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/28/1991,137,minute.maid,16768,9.727227587,0,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/28/1991,137,tropicana,155008,11.95123201,1,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/4/1991,2,dominicks,12608,9.442086812,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/4/1991,2,minute.maid,5696,8.647519453,1,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/4/1991,2,tropicana,4928,8.502688505,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/4/1991,5,dominicks,5376,8.589699882,0,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/4/1991,5,minute.maid,6400,8.764053269,1,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/4/1991,5,tropicana,8640,9.064157862,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/4/1991,8,dominicks,34624,10.45230236,0,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/4/1991,8,minute.maid,8128,9.00307017,1,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/4/1991,8,tropicana,17280,9.757305042,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/4/1991,9,dominicks,6208,8.733594062,0,1.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/4/1991,9,minute.maid,4672,8.449342525,1,2.17,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/4/1991,9,tropicana,10560,9.264828557,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/4/1991,12,dominicks,25152,10.1326927,0,1.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/4/1991,12,minute.maid,7552,8.929567708,1,2.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/4/1991,12,tropicana,26240,10.17504024,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/4/1991,14,dominicks,7808,8.962904128,0,1.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/4/1991,14,minute.maid,5184,8.553332238,1,2.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/4/1991,14,tropicana,10304,9.240287448,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/4/1991,18,dominicks,18880,9.84585844,0,1.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/4/1991,18,minute.maid,8128,9.00307017,1,2.17,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/4/1991,18,tropicana,11904,9.384629757,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/4/1991,21,dominicks,6720,8.812843434,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/4/1991,21,minute.maid,5952,8.691482577,1,1.98,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/4/1991,21,tropicana,3456,8.14786713,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/4/1991,28,dominicks,7488,8.921057018,0,1.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/4/1991,28,minute.maid,3712,8.219326094,1,2.46,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/4/1991,28,tropicana,5824,8.66974259,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/4/1991,32,dominicks,14720,9.596962392,0,1.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/4/1991,32,minute.maid,9856,9.195835686,1,2.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/4/1991,32,tropicana,14592,9.588228712,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/4/1991,33,dominicks,13888,9.538780437,0,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/4/1991,33,minute.maid,4800,8.476371197,1,2.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/4/1991,33,tropicana,6784,8.822322178,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/4/1991,40,dominicks,6912,8.841014311,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/4/1991,40,minute.maid,6080,8.712759975,1,1.98,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/4/1991,40,tropicana,34496,10.44859865,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/4/1991,44,dominicks,9408,9.14931567,0,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/4/1991,44,minute.maid,5440,8.60153434,1,2.46,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/4/1991,44,tropicana,11072,9.312174678,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/4/1991,45,dominicks,8320,9.026417534,0,1.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/4/1991,45,minute.maid,3904,8.269756948,1,2.17,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/4/1991,45,tropicana,6144,8.723231275,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/4/1991,47,dominicks,12416,9.426741242,0,1.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/4/1991,47,minute.maid,4992,8.51559191,1,2.46,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/4/1991,47,tropicana,5632,8.636219898,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/4/1991,48,dominicks,8832,9.086136769,0,1.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/4/1991,48,minute.maid,5248,8.565602331,1,2.46,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/4/1991,48,tropicana,7616,8.938006577,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/4/1991,49,dominicks,10432,9.252633284,0,1.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/4/1991,49,minute.maid,3648,8.201934351,1,2.46,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/4/1991,49,tropicana,3456,8.14786713,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/4/1991,50,dominicks,5184,8.553332238,0,1.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/4/1991,50,minute.maid,4096,8.317766167,1,2.17,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/4/1991,50,tropicana,6016,8.702177866,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/4/1991,52,dominicks,11072,9.312174678,0,1.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/4/1991,52,minute.maid,6208,8.733594062,1,2.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/4/1991,52,tropicana,20096,9.908276069,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/4/1991,53,dominicks,10496,9.258749511,0,1.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/4/1991,53,minute.maid,16768,9.727227587,1,2.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/4/1991,53,tropicana,24448,10.10430369,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/4/1991,54,dominicks,7616,8.938006577,0,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/4/1991,54,minute.maid,4416,8.392989588,1,2.46,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/4/1991,54,tropicana,8256,9.018695488,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/4/1991,56,dominicks,11136,9.317938383,0,1.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/4/1991,56,minute.maid,3520,8.166216269,1,2.46,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/4/1991,56,tropicana,9920,9.2023082,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/4/1991,59,dominicks,6720,8.812843434,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/4/1991,59,minute.maid,3904,8.269756948,1,1.98,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/4/1991,59,tropicana,4928,8.502688505,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/4/1991,62,dominicks,5504,8.61323038,0,1.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/4/1991,62,minute.maid,7168,8.877381955,1,2.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/4/1991,62,tropicana,14720,9.596962392,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/4/1991,64,dominicks,12864,9.462187991,0,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/4/1991,64,minute.maid,2880,7.965545573,1,2.46,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/4/1991,64,tropicana,3648,8.201934351,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/4/1991,67,dominicks,8064,8.99516499,0,1.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/4/1991,67,minute.maid,4992,8.51559191,1,2.17,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/4/1991,67,tropicana,6592,8.793612072,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/4/1991,68,dominicks,17344,9.761001904,0,1.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/4/1991,68,minute.maid,6464,8.7740036,1,2.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/4/1991,68,tropicana,14400,9.574983486,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/4/1991,70,dominicks,7488,8.921057018,0,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/4/1991,70,minute.maid,9216,9.128696383,1,1.98,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/4/1991,70,tropicana,7296,8.895081532,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/4/1991,72,dominicks,5632,8.636219898,0,1.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/4/1991,72,minute.maid,5184,8.553332238,1,2.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/4/1991,72,tropicana,19072,9.85597657,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/4/1991,74,dominicks,23744,10.07508515,0,1.59,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/4/1991,74,minute.maid,8192,9.010913347,1,2.46,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/4/1991,74,tropicana,11200,9.323669057,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/4/1991,75,dominicks,12608,9.442086812,0,1.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/4/1991,75,minute.maid,5312,8.577723691,1,2.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/4/1991,75,tropicana,11520,9.351839934,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/4/1991,76,dominicks,10240,9.234056899,0,1.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/4/1991,76,minute.maid,4096,8.317766167,1,2.46,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/4/1991,76,tropicana,9408,9.14931567,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/4/1991,77,dominicks,4352,8.378390789,0,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/4/1991,77,minute.maid,7040,8.859363449,1,1.98,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/4/1991,77,tropicana,8960,9.100525506,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/4/1991,80,dominicks,5888,8.68067166,0,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/4/1991,80,minute.maid,8960,9.100525506,1,1.98,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/4/1991,80,tropicana,14976,9.614204199,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/4/1991,81,dominicks,9216,9.128696383,0,1.59,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/4/1991,81,minute.maid,6528,8.783855897,1,2.46,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/4/1991,81,tropicana,6656,8.803273983,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/4/1991,83,dominicks,41920,10.64351832,0,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/4/1991,83,minute.maid,7360,8.903815212,1,1.98,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/4/1991,83,tropicana,8000,8.987196821,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/4/1991,84,dominicks,11520,9.351839934,0,1.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/4/1991,84,minute.maid,5184,8.553332238,1,2.46,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/4/1991,84,tropicana,11456,9.346268889,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/4/1991,86,dominicks,12416,9.426741242,0,1.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/4/1991,86,minute.maid,6976,8.850230966,1,2.46,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/4/1991,86,tropicana,9600,9.169518378,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/4/1991,88,dominicks,19328,9.869310101,0,1.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/4/1991,88,minute.maid,5312,8.577723691,1,2.46,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/4/1991,88,tropicana,13312,9.496421163,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/4/1991,89,dominicks,18368,9.818365299,0,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/4/1991,89,minute.maid,4416,8.392989588,1,2.46,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/4/1991,89,tropicana,7424,8.912473275,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/4/1991,90,dominicks,21952,9.996613531,0,1.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/4/1991,90,minute.maid,5184,8.553332238,1,2.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/4/1991,90,tropicana,4096,8.317766167,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/4/1991,91,dominicks,9472,9.156095357,0,1.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/4/1991,91,minute.maid,4224,8.348537825,1,2.46,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/4/1991,91,tropicana,9024,9.107642974,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/4/1991,92,dominicks,35776,10.48503256,0,1.59,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/4/1991,92,minute.maid,4800,8.476371197,1,2.46,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/4/1991,92,tropicana,5504,8.61323038,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/4/1991,93,dominicks,16128,9.688312171,0,1.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/4/1991,93,minute.maid,6464,8.7740036,1,2.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/4/1991,93,tropicana,7040,8.859363449,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/4/1991,94,dominicks,7744,8.954673629,0,1.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/4/1991,94,minute.maid,17088,9.746131742,1,2.17,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/4/1991,94,tropicana,7872,8.971067439,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/4/1991,95,dominicks,17472,9.768354879,0,1.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/4/1991,95,minute.maid,3840,8.253227646,1,2.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/4/1991,95,tropicana,9920,9.2023082,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/4/1991,97,dominicks,5952,8.691482577,0,1.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/4/1991,97,minute.maid,2752,7.920083199,1,2.46,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/4/1991,97,tropicana,1152,7.049254841,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/4/1991,98,dominicks,31808,10.36747311,0,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/4/1991,98,minute.maid,7296,8.895081532,1,2.17,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/4/1991,98,tropicana,12416,9.426741242,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/4/1991,100,dominicks,22016,9.999524741,0,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/4/1991,100,minute.maid,7040,8.859363449,1,2.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/4/1991,100,tropicana,9088,9.114710141,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/4/1991,101,dominicks,27520,10.22266829,0,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/4/1991,101,minute.maid,6528,8.783855897,1,2.46,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/4/1991,101,tropicana,8320,9.026417534,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/4/1991,102,dominicks,23872,10.0804615,0,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/4/1991,102,minute.maid,10176,9.227787286,1,2.17,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/4/1991,102,tropicana,12864,9.462187991,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/4/1991,103,dominicks,16576,9.715711145,0,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/4/1991,103,minute.maid,4032,8.30201781,1,2.17,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/4/1991,103,tropicana,6208,8.733594062,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/4/1991,104,dominicks,8640,9.064157862,0,1.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/4/1991,104,minute.maid,4416,8.392989588,1,2.46,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/4/1991,104,tropicana,10560,9.264828557,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/4/1991,105,dominicks,22144,10.00532186,0,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/4/1991,105,minute.maid,6976,8.850230966,1,2.17,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/4/1991,105,tropicana,6912,8.841014311,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/4/1991,106,dominicks,11776,9.373818841,0,1.59,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/4/1991,106,minute.maid,2752,7.920083199,1,2.46,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/4/1991,106,tropicana,4160,8.333270353,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/4/1991,107,dominicks,16704,9.723403491,0,1.59,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/4/1991,107,minute.maid,7808,8.962904128,1,2.46,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/4/1991,107,tropicana,6336,8.754002934,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/4/1991,109,dominicks,8320,9.026417534,0,1.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/4/1991,109,minute.maid,9728,9.182763604,1,2.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/4/1991,109,tropicana,16448,9.707959168,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/4/1991,110,dominicks,11072,9.312174678,0,1.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/4/1991,110,minute.maid,5056,8.528330936,1,2.46,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/4/1991,110,tropicana,5184,8.553332238,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/4/1991,111,dominicks,33536,10.42037477,0,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/4/1991,111,minute.maid,6464,8.7740036,1,2.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/4/1991,111,tropicana,14720,9.596962392,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/4/1991,112,dominicks,10688,9.276876896,0,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/4/1991,112,minute.maid,11392,9.340666634,1,2.17,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/4/1991,112,tropicana,18816,9.842462851,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/4/1991,113,dominicks,7552,8.929567708,0,1.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/4/1991,113,minute.maid,22272,10.01108556,1,2.46,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/4/1991,113,tropicana,15744,9.664214619,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/4/1991,114,dominicks,56000,10.93310697,0,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/4/1991,114,minute.maid,6400,8.764053269,1,2.17,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/4/1991,114,tropicana,11136,9.317938383,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/4/1991,115,dominicks,8832,9.086136769,0,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/4/1991,115,minute.maid,6656,8.803273983,1,2.46,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/4/1991,115,tropicana,12544,9.436997743,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/4/1991,116,dominicks,10432,9.252633284,0,1.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/4/1991,116,minute.maid,4032,8.30201781,1,2.46,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/4/1991,116,tropicana,9792,9.189321005,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/4/1991,117,dominicks,8640,9.064157862,0,1.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/4/1991,117,minute.maid,3264,8.090708716,1,2.46,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/4/1991,117,tropicana,5248,8.565602331,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/4/1991,118,dominicks,5376,8.589699882,0,1.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/4/1991,118,minute.maid,7936,8.979164649,1,2.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/4/1991,118,tropicana,5056,8.528330936,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/4/1991,119,dominicks,9536,9.162829389,0,1.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/4/1991,119,minute.maid,6912,8.841014311,1,2.17,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/4/1991,119,tropicana,9344,9.142489705,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/4/1991,121,dominicks,11840,9.379238908,0,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/4/1991,121,minute.maid,8960,9.100525506,1,2.17,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/4/1991,121,tropicana,10944,9.30054664,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/4/1991,122,dominicks,23296,10.05603695,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/4/1991,122,minute.maid,10240,9.234056899,1,1.98,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/4/1991,122,tropicana,12416,9.426741242,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/4/1991,123,dominicks,14016,9.547954813,0,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/4/1991,123,minute.maid,6464,8.7740036,1,2.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/4/1991,123,tropicana,11392,9.340666634,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/4/1991,124,dominicks,31296,10.35124557,0,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/4/1991,124,minute.maid,6208,8.733594062,1,2.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/4/1991,124,tropicana,13312,9.496421163,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/4/1991,126,dominicks,7552,8.929567708,0,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/4/1991,126,minute.maid,5120,8.540909718,1,2.46,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/4/1991,126,tropicana,9408,9.14931567,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/4/1991,128,dominicks,12352,9.421573272,0,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/4/1991,128,minute.maid,8192,9.010913347,1,2.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/4/1991,128,tropicana,35584,10.47965138,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/4/1991,129,dominicks,6144,8.723231275,0,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/4/1991,129,minute.maid,5696,8.647519453,1,2.46,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/4/1991,129,tropicana,7680,8.946374826,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/4/1991,131,dominicks,11840,9.379238908,0,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/4/1991,131,minute.maid,7104,8.868413285,1,2.46,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/4/1991,131,tropicana,8640,9.064157862,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/4/1991,132,dominicks,16064,9.684336023,0,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/4/1991,132,minute.maid,6720,8.812843434,1,2.17,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/4/1991,132,tropicana,10944,9.30054664,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/4/1991,134,dominicks,5184,8.553332238,0,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/4/1991,134,minute.maid,2688,7.896552702,1,2.46,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/4/1991,134,tropicana,4096,8.317766167,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/4/1991,137,dominicks,26048,10.16769627,0,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/4/1991,137,minute.maid,9024,9.107642974,1,2.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/4/1991,137,tropicana,32896,10.40110635,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/11/1991,2,dominicks,6336,8.754002934,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/11/1991,2,minute.maid,7680,8.946374826,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/11/1991,2,tropicana,29504,10.29228113,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/11/1991,5,dominicks,6656,8.803273983,0,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/11/1991,5,minute.maid,8640,9.064157862,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/11/1991,5,tropicana,35520,10.4778512,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/11/1991,8,dominicks,10368,9.246479419,0,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/11/1991,8,minute.maid,9088,9.114710141,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/11/1991,8,tropicana,47040,10.75875358,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/11/1991,9,dominicks,5696,8.647519453,0,1.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/11/1991,9,minute.maid,5888,8.68067166,0,2.09,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/11/1991,9,tropicana,68480,11.13429701,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/11/1991,12,dominicks,7872,8.971067439,0,1.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/11/1991,12,minute.maid,8768,9.078864009,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/11/1991,12,tropicana,72256,11.18797065,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/11/1991,14,dominicks,4928,8.502688505,0,1.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/11/1991,14,minute.maid,8640,9.064157862,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/11/1991,14,tropicana,32576,10.3913311,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/11/1991,18,dominicks,6016,8.702177866,0,1.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/11/1991,18,minute.maid,11840,9.379238908,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/11/1991,18,tropicana,61632,11.0289365,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/11/1991,21,dominicks,6464,8.7740036,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/11/1991,21,minute.maid,5888,8.68067166,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/11/1991,21,tropicana,22656,10.02818,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/11/1991,28,dominicks,4288,8.363575703,0,1.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/11/1991,28,minute.maid,3648,8.201934351,0,2.09,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/11/1991,28,tropicana,22592,10.02535114,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/11/1991,32,dominicks,2944,7.98752448,0,1.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/11/1991,32,minute.maid,13632,9.520175249,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/11/1991,32,tropicana,81792,11.31193472,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/11/1991,33,dominicks,12160,9.405907156,0,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/11/1991,33,minute.maid,7616,8.938006577,0,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/11/1991,33,tropicana,46080,10.7381343,1,1.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/11/1991,40,dominicks,5824,8.66974259,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/11/1991,40,minute.maid,4864,8.489616424,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/11/1991,40,tropicana,22784,10.03381381,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/11/1991,44,dominicks,15680,9.660141294,0,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/11/1991,44,minute.maid,8320,9.026417534,0,2.09,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/11/1991,44,tropicana,55808,10.92967251,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/11/1991,45,dominicks,7424,8.912473275,0,1.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/11/1991,45,minute.maid,4416,8.392989588,0,2.09,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/11/1991,45,tropicana,21760,9.987828701,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/11/1991,47,dominicks,6848,8.831711918,0,1.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/11/1991,47,minute.maid,6272,8.743850562,0,2.09,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/11/1991,47,tropicana,24128,10.09112827,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/11/1991,48,dominicks,5824,8.66974259,0,1.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/11/1991,48,minute.maid,6848,8.831711918,0,2.09,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/11/1991,48,tropicana,18752,9.839055692,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/11/1991,49,dominicks,4992,8.51559191,0,1.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/11/1991,49,minute.maid,5568,8.624791202,0,2.09,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/11/1991,49,tropicana,20544,9.930324207,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/11/1991,50,dominicks,11904,9.384629757,0,1.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/11/1991,50,minute.maid,2880,7.965545573,0,2.09,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/11/1991,50,tropicana,18880,9.84585844,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/11/1991,52,dominicks,5568,8.624791202,0,1.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/11/1991,52,minute.maid,12480,9.431882642,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/11/1991,52,tropicana,59712,10.99728828,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/11/1991,53,dominicks,6720,8.812843434,0,1.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/11/1991,53,minute.maid,12032,9.395325046,0,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/11/1991,53,tropicana,76928,11.2506252,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/11/1991,54,dominicks,4672,8.449342525,0,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/11/1991,54,minute.maid,6272,8.743850562,0,2.09,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/11/1991,54,tropicana,28416,10.25470765,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/11/1991,56,dominicks,7168,8.877381955,0,1.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/11/1991,56,minute.maid,4736,8.462948177,0,2.09,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/11/1991,56,tropicana,29184,10.28137589,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/11/1991,59,dominicks,2176,7.685243608,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/11/1991,59,minute.maid,4736,8.462948177,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/11/1991,59,tropicana,24704,10.11472045,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/11/1991,62,dominicks,1792,7.491087594,0,1.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/11/1991,62,minute.maid,9408,9.14931567,0,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/11/1991,62,tropicana,47360,10.76553327,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/11/1991,64,dominicks,19968,9.901886271,0,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/11/1991,64,minute.maid,4096,8.317766167,0,2.09,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/11/1991,64,tropicana,14080,9.55251063,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/11/1991,67,dominicks,3520,8.166216269,0,1.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/11/1991,67,minute.maid,5568,8.624791202,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/11/1991,67,tropicana,16576,9.715711145,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/11/1991,68,dominicks,10816,9.288781798,0,1.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/11/1991,68,minute.maid,9856,9.195835686,0,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/11/1991,68,tropicana,47744,10.77360868,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/11/1991,70,dominicks,5952,8.691482577,0,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/11/1991,70,minute.maid,14016,9.547954813,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/11/1991,70,tropicana,26048,10.16769627,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/11/1991,71,dominicks,7936,8.979164649,0,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/11/1991,71,minute.maid,5696,8.647519453,0,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/11/1991,71,tropicana,41856,10.64199044,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/11/1991,72,dominicks,3136,8.050703382,0,1.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/11/1991,72,minute.maid,5696,8.647519453,0,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/11/1991,72,tropicana,63296,11.05557742,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/11/1991,74,dominicks,11264,9.329367078,0,1.59,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/11/1991,74,minute.maid,10816,9.288781798,0,2.09,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/11/1991,74,tropicana,52480,10.86818742,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/11/1991,75,dominicks,5504,8.61323038,0,1.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/11/1991,75,minute.maid,8832,9.086136769,0,2.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/11/1991,75,tropicana,44672,10.70710219,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/11/1991,76,dominicks,2880,7.965545573,0,1.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/11/1991,76,minute.maid,5888,8.68067166,0,2.09,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/11/1991,76,tropicana,24832,10.11988842,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/11/1991,77,dominicks,5184,8.553332238,0,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/11/1991,77,minute.maid,7232,8.886270902,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/11/1991,77,tropicana,23552,10.06696602,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/11/1991,78,dominicks,8128,9.00307017,0,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/11/1991,78,minute.maid,7104,8.868413285,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/11/1991,78,tropicana,33088,10.40692596,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/11/1991,80,dominicks,4224,8.348537825,0,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/11/1991,80,minute.maid,12800,9.45720045,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/11/1991,80,tropicana,43712,10.68537794,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/11/1991,81,dominicks,8960,9.100525506,0,1.59,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/11/1991,81,minute.maid,7808,8.962904128,0,2.09,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/11/1991,81,tropicana,51648,10.85220675,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/11/1991,83,dominicks,6144,8.723231275,0,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/11/1991,83,minute.maid,8960,9.100525506,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/11/1991,83,tropicana,40320,10.6046029,1,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/11/1991,84,dominicks,5504,8.61323038,0,1.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/11/1991,84,minute.maid,6784,8.822322178,0,2.09,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/11/1991,84,tropicana,36864,10.51499074,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/11/1991,86,dominicks,11584,9.357380115,0,1.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/11/1991,86,minute.maid,6400,8.764053269,0,2.09,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/11/1991,86,tropicana,55616,10.92622621,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/11/1991,88,dominicks,4928,8.502688505,0,1.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/11/1991,88,minute.maid,8192,9.010913347,0,2.09,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/11/1991,88,tropicana,34560,10.45045222,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/11/1991,89,dominicks,10880,9.29468152,0,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/11/1991,89,minute.maid,4800,8.476371197,0,2.09,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/11/1991,89,tropicana,44032,10.69267192,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/11/1991,90,dominicks,7680,8.946374826,0,1.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/11/1991,90,minute.maid,6464,8.7740036,0,2.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/11/1991,90,tropicana,35712,10.48324205,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/11/1991,91,dominicks,5632,8.636219898,0,1.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/11/1991,91,minute.maid,4224,8.348537825,0,2.09,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/11/1991,91,tropicana,30400,10.32219789,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/11/1991,92,dominicks,9152,9.121727714,0,1.59,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/11/1991,92,minute.maid,3968,8.286017468,0,2.09,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/11/1991,92,tropicana,34176,10.43927892,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/11/1991,93,dominicks,5248,8.565602331,0,1.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/11/1991,93,minute.maid,7424,8.912473275,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/11/1991,93,tropicana,48576,10.79088486,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/11/1991,94,dominicks,6976,8.850230966,0,1.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/11/1991,94,minute.maid,17536,9.77201119,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/11/1991,94,tropicana,20736,9.939626599,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/11/1991,95,dominicks,4864,8.489616424,0,1.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/11/1991,95,minute.maid,5184,8.553332238,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/11/1991,95,tropicana,32512,10.38936453,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/11/1991,97,dominicks,2880,7.965545573,0,1.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/11/1991,97,minute.maid,3968,8.286017468,0,2.09,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/11/1991,97,tropicana,17216,9.753594463,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/11/1991,98,dominicks,44096,10.69412435,0,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/11/1991,98,minute.maid,8448,9.041685006,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/11/1991,98,tropicana,36672,10.5097688,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/11/1991,100,dominicks,12416,9.426741242,0,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/11/1991,100,minute.maid,7872,8.971067439,0,2.09,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/11/1991,100,tropicana,55936,10.93196346,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/11/1991,101,dominicks,7808,8.962904128,0,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/11/1991,101,minute.maid,7424,8.912473275,0,2.09,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/11/1991,101,tropicana,43968,10.69121738,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/11/1991,102,dominicks,9024,9.107642974,0,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/11/1991,102,minute.maid,9856,9.195835686,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/11/1991,102,tropicana,58944,10.98434312,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/11/1991,103,dominicks,9344,9.142489705,0,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/11/1991,103,minute.maid,4032,8.30201781,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/11/1991,103,tropicana,20928,9.948843254,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/11/1991,104,dominicks,4480,8.407378325,0,1.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/11/1991,104,minute.maid,5952,8.691482577,0,2.09,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/11/1991,104,tropicana,31872,10.36948316,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/11/1991,105,dominicks,11584,9.357380115,0,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/11/1991,105,minute.maid,8128,9.00307017,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/11/1991,105,tropicana,36480,10.50451944,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/11/1991,106,dominicks,4736,8.462948177,0,1.59,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/11/1991,106,minute.maid,2880,7.965545573,0,2.09,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/11/1991,106,tropicana,15168,9.626943225,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/11/1991,107,dominicks,6208,8.733594062,0,1.59,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/11/1991,107,minute.maid,10176,9.227787286,0,2.09,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/11/1991,107,tropicana,49984,10.81945823,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/11/1991,109,dominicks,3392,8.129174997,0,1.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/11/1991,109,minute.maid,11328,9.335032816,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/11/1991,109,tropicana,94144,11.4525808,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/11/1991,110,dominicks,7360,8.903815212,0,1.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/11/1991,110,minute.maid,7040,8.859363449,0,2.09,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/11/1991,110,tropicana,33600,10.42228135,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/11/1991,111,dominicks,20608,9.933434629,0,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/11/1991,111,minute.maid,8320,9.026417534,0,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/11/1991,111,tropicana,80704,11.29854342,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/11/1991,112,dominicks,11328,9.335032816,0,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/11/1991,112,minute.maid,12928,9.467150781,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/11/1991,112,tropicana,69376,11.14729627,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/11/1991,113,dominicks,5056,8.528330936,0,1.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/11/1991,113,minute.maid,6656,8.803273983,0,2.09,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/11/1991,113,tropicana,50304,10.82583988,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/11/1991,114,dominicks,10816,9.288781798,0,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/11/1991,114,minute.maid,18048,9.800790154,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/11/1991,114,tropicana,29248,10.28356647,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/11/1991,115,dominicks,2688,7.896552702,0,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/11/1991,115,minute.maid,6144,8.723231275,0,2.09,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/11/1991,115,tropicana,47232,10.76282691,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/11/1991,116,dominicks,6208,8.733594062,0,1.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/11/1991,116,minute.maid,6272,8.743850562,0,2.09,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/11/1991,116,tropicana,30336,10.32009041,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/11/1991,117,dominicks,4800,8.476371197,0,1.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/11/1991,117,minute.maid,4992,8.51559191,0,2.09,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/11/1991,117,tropicana,21504,9.975994243,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/11/1991,118,dominicks,1024,6.931471806,0,1.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/11/1991,118,minute.maid,9536,9.162829389,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/11/1991,118,tropicana,42368,10.65414864,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/11/1991,119,dominicks,6208,8.733594062,0,1.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/11/1991,119,minute.maid,5888,8.68067166,0,2.09,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/11/1991,119,tropicana,32576,10.3913311,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/11/1991,121,dominicks,3136,8.050703382,0,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/11/1991,121,minute.maid,7872,8.971067439,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/11/1991,121,tropicana,41920,10.64351832,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/11/1991,122,dominicks,11776,9.373818841,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/11/1991,122,minute.maid,11840,9.379238908,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/11/1991,122,tropicana,41536,10.6343158,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/11/1991,123,dominicks,4608,8.435549202,0,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/11/1991,123,minute.maid,10752,9.282847063,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/11/1991,123,tropicana,45376,10.72273861,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/11/1991,124,dominicks,15680,9.660141294,0,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/11/1991,124,minute.maid,8256,9.018695488,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/11/1991,124,tropicana,77056,11.25228771,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/11/1991,126,dominicks,5312,8.577723691,0,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/11/1991,126,minute.maid,9088,9.114710141,0,2.09,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/11/1991,126,tropicana,52096,10.86084345,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/11/1991,128,dominicks,7296,8.895081532,0,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/11/1991,128,minute.maid,8320,9.026417534,0,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/11/1991,128,tropicana,108736,11.59667821,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/11/1991,129,dominicks,8064,8.99516499,0,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/11/1991,129,minute.maid,6080,8.712759975,0,2.09,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/11/1991,129,tropicana,40832,10.61722137,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/11/1991,131,dominicks,8000,8.987196821,0,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/11/1991,131,minute.maid,8384,9.034080407,0,2.09,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/11/1991,131,tropicana,40064,10.59823345,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/11/1991,132,dominicks,10880,9.29468152,0,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/11/1991,132,minute.maid,7168,8.877381955,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/11/1991,132,tropicana,45120,10.71708089,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/11/1991,134,dominicks,4992,8.51559191,0,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/11/1991,134,minute.maid,2752,7.920083199,0,2.09,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/11/1991,134,tropicana,28096,10.2433825,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/11/1991,137,dominicks,16384,9.704060528,0,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/11/1991,137,minute.maid,9664,9.17616292,0,2.09,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/11/1991,137,tropicana,91904,11.42849983,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/18/1991,2,dominicks,140736,11.85464107,1,0.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/18/1991,2,minute.maid,6336,8.754002934,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/18/1991,2,tropicana,9984,9.208739091,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/18/1991,5,dominicks,95680,11.46876457,1,0.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/18/1991,5,minute.maid,7296,8.895081532,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/18/1991,5,tropicana,9664,9.17616292,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/18/1991,8,dominicks,194880,12.18013926,1,0.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/18/1991,8,minute.maid,6720,8.812843434,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/18/1991,8,tropicana,14464,9.579418083,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/18/1991,9,dominicks,106496,11.57586271,1,0.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/18/1991,9,minute.maid,7488,8.921057018,0,2.09,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/18/1991,9,tropicana,15872,9.67231183,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/18/1991,12,dominicks,224576,12.32196946,1,0.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/18/1991,12,minute.maid,11264,9.329367078,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/18/1991,12,tropicana,26304,10.1774763,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/18/1991,14,dominicks,78400,11.26957921,1,0.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/18/1991,14,minute.maid,5888,8.68067166,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/18/1991,14,tropicana,12032,9.395325046,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/18/1991,18,dominicks,182016,12.11184987,1,0.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/18/1991,18,minute.maid,8768,9.078864009,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/18/1991,18,tropicana,11584,9.357380115,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/18/1991,21,dominicks,129728,11.77319523,1,0.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/18/1991,21,minute.maid,6080,8.712759975,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/18/1991,21,tropicana,5120,8.540909718,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/18/1991,28,dominicks,74112,11.21333274,1,0.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/18/1991,28,minute.maid,4224,8.348537825,0,2.09,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/18/1991,28,tropicana,4864,8.489616424,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/18/1991,32,dominicks,141376,11.85917829,1,0.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/18/1991,32,minute.maid,15616,9.656051309,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/18/1991,32,tropicana,21376,9.970024076,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/18/1991,33,dominicks,71360,11.17549277,1,0.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/18/1991,33,minute.maid,12992,9.472089062,0,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/18/1991,33,tropicana,17408,9.76468515,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/18/1991,40,dominicks,118912,11.686139,1,0.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/18/1991,40,minute.maid,4480,8.407378325,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/18/1991,40,tropicana,6784,8.822322178,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/18/1991,44,dominicks,83008,11.32669227,1,0.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/18/1991,44,minute.maid,7424,8.912473275,0,2.09,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/18/1991,44,tropicana,12928,9.467150781,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/18/1991,45,dominicks,59712,10.99728828,1,0.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/18/1991,45,minute.maid,3520,8.166216269,0,2.09,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/18/1991,45,tropicana,6848,8.831711918,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/18/1991,47,dominicks,123072,11.72052483,1,0.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/18/1991,47,minute.maid,5248,8.565602331,0,2.09,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/18/1991,47,tropicana,5056,8.528330936,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/18/1991,48,dominicks,54464,10.90529521,1,0.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/18/1991,48,minute.maid,8512,9.049232212,0,2.09,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/18/1991,48,tropicana,7552,8.929567708,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/18/1991,49,dominicks,75456,11.23130498,1,0.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/18/1991,49,minute.maid,6592,8.793612072,0,2.09,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/18/1991,49,tropicana,7424,8.912473275,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/18/1991,50,dominicks,59328,10.99083665,1,0.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/18/1991,50,minute.maid,3264,8.090708716,0,2.09,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/18/1991,50,tropicana,4992,8.51559191,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/18/1991,52,dominicks,82752,11.32360346,1,0.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/18/1991,52,minute.maid,11648,9.36288977,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/18/1991,52,tropicana,18368,9.818365299,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/18/1991,53,dominicks,207360,12.24221169,1,0.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/18/1991,53,minute.maid,8256,9.018695488,0,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/18/1991,53,tropicana,17728,9.78290059,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/18/1991,54,dominicks,83840,11.3366655,1,0.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/18/1991,54,minute.maid,4864,8.489616424,0,2.09,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/18/1991,54,tropicana,6528,8.783855897,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/18/1991,56,dominicks,80256,11.29297681,1,0.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/18/1991,56,minute.maid,4800,8.476371197,0,2.09,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/18/1991,56,tropicana,7360,8.903815212,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/18/1991,59,dominicks,84352,11.3427538,1,0.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/18/1991,59,minute.maid,3200,8.070906089,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/18/1991,59,tropicana,8960,9.100525506,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/18/1991,62,dominicks,47360,10.76553327,1,0.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/18/1991,62,minute.maid,11136,9.317938383,0,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/18/1991,62,tropicana,15680,9.660141294,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/18/1991,64,dominicks,89024,11.39666128,1,0.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/18/1991,64,minute.maid,2752,7.920083199,0,2.09,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/18/1991,64,tropicana,4160,8.333270353,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/18/1991,67,dominicks,138560,11.83905872,1,0.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/18/1991,67,minute.maid,5120,8.540909718,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/18/1991,67,tropicana,6784,8.822322178,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/18/1991,68,dominicks,126208,11.74568662,1,0.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/18/1991,68,minute.maid,11584,9.357380115,0,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/18/1991,68,tropicana,15744,9.664214619,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/18/1991,70,dominicks,169856,12.0427063,1,0.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/18/1991,70,minute.maid,7488,8.921057018,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/18/1991,70,tropicana,8640,9.064157862,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/18/1991,71,dominicks,246400,12.41471151,1,0.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/18/1991,71,minute.maid,4480,8.407378325,0,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/18/1991,71,tropicana,8000,8.987196821,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/18/1991,72,dominicks,128256,11.76178355,1,0.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/18/1991,72,minute.maid,6080,8.712759975,0,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/18/1991,72,tropicana,17984,9.797237753,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/18/1991,74,dominicks,235456,12.36927934,1,0.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/18/1991,74,minute.maid,7616,8.938006577,0,2.09,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/18/1991,74,tropicana,7744,8.954673629,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/18/1991,75,dominicks,136384,11.82322972,1,0.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/18/1991,75,minute.maid,8576,9.056722883,0,2.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/18/1991,75,tropicana,28096,10.2433825,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/18/1991,76,dominicks,194624,12.17882477,1,0.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/18/1991,76,minute.maid,5632,8.636219898,0,2.09,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/18/1991,76,tropicana,15744,9.664214619,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/18/1991,77,dominicks,82560,11.32128058,1,0.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/18/1991,77,minute.maid,6912,8.841014311,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/18/1991,77,tropicana,9600,9.169518378,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/18/1991,78,dominicks,160640,11.98692112,1,0.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/18/1991,78,minute.maid,7040,8.859363449,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/18/1991,78,tropicana,9600,9.169518378,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/18/1991,80,dominicks,95360,11.46541448,1,0.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/18/1991,80,minute.maid,7488,8.921057018,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/18/1991,80,tropicana,11648,9.36288977,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/18/1991,81,dominicks,145920,11.89081381,1,0.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/18/1991,81,minute.maid,7360,8.903815212,0,2.09,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/18/1991,81,tropicana,10304,9.240287448,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/18/1991,83,dominicks,242112,12.39715571,1,0.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/18/1991,83,minute.maid,7232,8.886270902,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/18/1991,83,tropicana,6272,8.743850562,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/18/1991,84,dominicks,129536,11.77171411,1,0.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/18/1991,84,minute.maid,4864,8.489616424,0,2.09,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/18/1991,84,tropicana,6016,8.702177866,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/18/1991,86,dominicks,162688,11.99958954,1,0.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/18/1991,86,minute.maid,10816,9.288781798,0,2.09,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/18/1991,86,tropicana,10944,9.30054664,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/18/1991,88,dominicks,105408,11.56559381,1,0.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/18/1991,88,minute.maid,5504,8.61323038,0,2.09,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/18/1991,88,tropicana,7232,8.886270902,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/18/1991,89,dominicks,250048,12.42940818,1,0.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/18/1991,89,minute.maid,5632,8.636219898,0,2.09,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/18/1991,89,tropicana,5888,8.68067166,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/18/1991,90,dominicks,205696,12.23415463,1,0.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/18/1991,90,minute.maid,5312,8.577723691,0,2.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/18/1991,90,tropicana,5888,8.68067166,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/18/1991,91,dominicks,107584,11.58602722,1,0.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/18/1991,91,minute.maid,3840,8.253227646,0,2.09,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/18/1991,91,tropicana,8000,8.987196821,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/18/1991,92,dominicks,122176,11.71321791,1,0.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/18/1991,92,minute.maid,5568,8.624791202,0,2.09,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/18/1991,92,tropicana,6400,8.764053269,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/18/1991,93,dominicks,117440,11.67368284,1,0.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/18/1991,93,minute.maid,7424,8.912473275,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/18/1991,93,tropicana,13632,9.520175249,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/18/1991,94,dominicks,63872,11.06463636,1,0.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/18/1991,94,minute.maid,7808,8.962904128,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/18/1991,94,tropicana,9600,9.169518378,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/18/1991,95,dominicks,139968,11.8491691,1,0.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/18/1991,95,minute.maid,5504,8.61323038,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/18/1991,95,tropicana,7424,8.912473275,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/18/1991,97,dominicks,59712,10.99728828,1,0.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/18/1991,97,minute.maid,4288,8.363575703,0,2.09,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/18/1991,97,tropicana,4992,8.51559191,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/18/1991,98,dominicks,184576,12.12581658,1,0.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/18/1991,98,minute.maid,6464,8.7740036,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/18/1991,98,tropicana,11712,9.368369236,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/18/1991,100,dominicks,261824,12.4754278,1,0.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/18/1991,100,minute.maid,12352,9.421573272,0,2.09,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/18/1991,100,tropicana,17984,9.797237753,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/18/1991,101,dominicks,149952,11.91807052,1,0.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/18/1991,101,minute.maid,7360,8.903815212,0,2.09,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/18/1991,101,tropicana,8512,9.049232212,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/18/1991,102,dominicks,302400,12.61950592,1,0.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/18/1991,102,minute.maid,9664,9.17616292,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/18/1991,102,tropicana,10432,9.252633284,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/18/1991,103,dominicks,151296,11.92699346,1,0.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/18/1991,103,minute.maid,3776,8.236420527,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/18/1991,103,tropicana,9152,9.121727714,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/18/1991,104,dominicks,98816,11.50101481,1,0.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/18/1991,104,minute.maid,5184,8.553332238,0,2.09,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/18/1991,104,tropicana,6656,8.803273983,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/18/1991,105,dominicks,235904,12.37118022,1,0.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/18/1991,105,minute.maid,5760,8.658692754,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/18/1991,105,tropicana,8384,9.034080407,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/18/1991,106,dominicks,79616,11.28497036,1,0.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/18/1991,106,minute.maid,3776,8.236420527,0,2.09,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/18/1991,106,tropicana,3392,8.129174997,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/18/1991,107,dominicks,146048,11.89169061,1,0.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/18/1991,107,minute.maid,9152,9.121727714,0,2.09,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/18/1991,107,tropicana,5312,8.577723691,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/18/1991,109,dominicks,105920,11.57043937,1,0.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/18/1991,109,minute.maid,14784,9.601300794,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/18/1991,109,tropicana,17472,9.768354879,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/18/1991,110,dominicks,106560,11.57646349,1,0.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/18/1991,110,minute.maid,5696,8.647519453,0,2.09,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/18/1991,110,tropicana,6528,8.783855897,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/18/1991,111,dominicks,716416,13.48201628,1,0.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/18/1991,111,minute.maid,8640,9.064157862,0,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/18/1991,111,tropicana,5440,8.60153434,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/18/1991,112,dominicks,95488,11.46675586,1,0.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/18/1991,112,minute.maid,12416,9.426741242,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/18/1991,112,tropicana,20736,9.939626599,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/18/1991,113,dominicks,147904,11.90431869,1,0.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/18/1991,113,minute.maid,22272,10.01108556,0,2.09,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/18/1991,113,tropicana,9600,9.169518378,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/18/1991,114,dominicks,307072,12.63483753,1,0.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/18/1991,114,minute.maid,7232,8.886270902,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/18/1991,114,tropicana,10880,9.29468152,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/18/1991,115,dominicks,61376,11.02477416,1,0.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/18/1991,115,minute.maid,8384,9.034080407,0,2.09,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/18/1991,115,tropicana,8320,9.026417534,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/18/1991,116,dominicks,91968,11.42919597,1,0.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/18/1991,116,minute.maid,4480,8.407378325,0,2.09,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/18/1991,116,tropicana,6208,8.733594062,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/18/1991,117,dominicks,75392,11.23045645,1,0.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/18/1991,117,minute.maid,3840,8.253227646,0,2.09,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/18/1991,117,tropicana,5888,8.68067166,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/18/1991,118,dominicks,104704,11.5588926,1,0.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/18/1991,118,minute.maid,10176,9.227787286,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/18/1991,118,tropicana,4032,8.30201781,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/18/1991,119,dominicks,61760,11.03101119,1,0.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/18/1991,119,minute.maid,5952,8.691482577,0,2.09,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/18/1991,119,tropicana,10048,9.215128889,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/18/1991,121,dominicks,121984,11.71164517,1,0.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/18/1991,121,minute.maid,7744,8.954673629,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/18/1991,121,tropicana,17920,9.793672686,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/18/1991,122,dominicks,98304,11.49582,1,0.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/18/1991,122,minute.maid,11136,9.317938383,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/18/1991,122,tropicana,17408,9.76468515,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/18/1991,123,dominicks,266048,12.49143202,1,0.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/18/1991,123,minute.maid,8704,9.071537969,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/18/1991,123,tropicana,9664,9.17616292,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/18/1991,124,dominicks,406080,12.91430546,1,0.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/18/1991,124,minute.maid,7744,8.954673629,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/18/1991,124,tropicana,6656,8.803273983,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/18/1991,126,dominicks,96320,11.47543126,1,0.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/18/1991,126,minute.maid,10176,9.227787286,0,2.09,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/18/1991,126,tropicana,10432,9.252633284,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/18/1991,128,dominicks,228800,12.34060354,1,0.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/18/1991,128,minute.maid,14080,9.55251063,0,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/18/1991,128,tropicana,25728,10.15533517,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/18/1991,129,dominicks,77504,11.25808483,1,0.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/18/1991,129,minute.maid,5696,8.647519453,0,2.09,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/18/1991,129,tropicana,10752,9.282847063,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/18/1991,130,dominicks,484608,13.0910956,1,0.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/18/1991,130,minute.maid,8832,9.086136769,0,2.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/18/1991,130,tropicana,12608,9.442086812,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/18/1991,131,dominicks,109504,11.60371636,1,0.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/18/1991,131,minute.maid,7296,8.895081532,0,2.09,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/18/1991,131,tropicana,9600,9.169518378,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/18/1991,132,dominicks,186624,12.13685118,1,0.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/18/1991,132,minute.maid,9152,9.121727714,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/18/1991,132,tropicana,13376,9.501217335,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/18/1991,134,dominicks,67776,11.12396343,1,0.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/18/1991,134,minute.maid,3008,8.009030685,0,2.09,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/18/1991,134,tropicana,4480,8.407378325,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/18/1991,137,dominicks,155584,11.95494106,1,0.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/18/1991,137,minute.maid,10880,9.29468152,0,2.09,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/18/1991,137,tropicana,21696,9.984883191,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/25/1991,2,dominicks,960,6.866933285,1,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/25/1991,2,minute.maid,8576,9.056722883,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/25/1991,2,tropicana,35200,10.46880136,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/25/1991,5,dominicks,896,6.797940413,1,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/25/1991,5,minute.maid,12480,9.431882642,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/25/1991,5,tropicana,49088,10.80136989,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/25/1991,8,dominicks,5696,8.647519453,1,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/25/1991,8,minute.maid,7552,8.929567708,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/25/1991,8,tropicana,52928,10.87668778,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/25/1991,9,dominicks,2944,7.98752448,1,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/25/1991,9,minute.maid,5888,8.68067166,0,2.09,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/25/1991,9,tropicana,70144,11.15830555,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/25/1991,12,dominicks,1152,7.049254841,1,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/25/1991,12,minute.maid,8384,9.034080407,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/25/1991,12,tropicana,94464,11.45597409,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/25/1991,14,dominicks,384,5.950642553,1,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/25/1991,14,minute.maid,7296,8.895081532,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/25/1991,14,tropicana,38784,10.56576307,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/25/1991,18,dominicks,4480,8.407378325,1,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/25/1991,18,minute.maid,11904,9.384629757,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/25/1991,18,tropicana,59520,10.99406767,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/25/1991,21,dominicks,5056,8.528330936,1,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/25/1991,21,minute.maid,6464,8.7740036,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/25/1991,21,tropicana,25344,10.1402973,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/25/1991,28,dominicks,640,6.461468176,1,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/25/1991,28,minute.maid,4672,8.449342525,0,2.09,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/25/1991,28,tropicana,29376,10.28793329,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/25/1991,32,dominicks,1024,6.931471806,1,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/25/1991,32,minute.maid,11456,9.346268889,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/25/1991,32,tropicana,86912,11.37265139,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/25/1991,33,dominicks,640,6.461468176,1,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/25/1991,33,minute.maid,9984,9.208739091,0,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/25/1991,33,tropicana,55680,10.9273763,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/25/1991,40,dominicks,5568,8.624791202,1,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/25/1991,40,minute.maid,6336,8.754002934,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/25/1991,40,tropicana,27648,10.22730867,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/25/1991,44,dominicks,1536,7.336936914,1,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/25/1991,44,minute.maid,6784,8.822322178,0,2.09,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/25/1991,44,tropicana,68032,11.12773346,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/25/1991,45,dominicks,3584,8.184234774,1,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/25/1991,45,minute.maid,3072,8.030084094,0,2.09,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/25/1991,45,tropicana,24128,10.09112827,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/25/1991,47,dominicks,1792,7.491087594,1,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/25/1991,47,minute.maid,6016,8.702177866,0,2.09,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/25/1991,47,tropicana,28864,10.27035042,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/25/1991,48,dominicks,1216,7.103322063,1,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/25/1991,48,minute.maid,6336,8.754002934,0,2.09,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/25/1991,48,tropicana,24832,10.11988842,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/25/1991,49,dominicks,896,6.797940413,1,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/25/1991,49,minute.maid,7232,8.886270902,0,2.09,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/25/1991,49,tropicana,22528,10.02251426,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/25/1991,50,dominicks,3264,8.090708716,1,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/25/1991,50,minute.maid,3200,8.070906089,0,2.09,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/25/1991,50,tropicana,21312,9.967025573,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/25/1991,51,dominicks,832,6.723832441,1,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/25/1991,51,minute.maid,4672,8.449342525,0,2.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/25/1991,51,tropicana,31936,10.37148918,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/25/1991,52,dominicks,896,6.797940413,1,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/25/1991,52,minute.maid,10816,9.288781798,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/25/1991,52,tropicana,75200,11.22790651,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/25/1991,53,dominicks,1664,7.416979621,1,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/25/1991,53,minute.maid,10432,9.252633284,0,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/25/1991,53,tropicana,82752,11.32360346,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/25/1991,54,dominicks,704,6.556778356,1,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/25/1991,54,minute.maid,6016,8.702177866,0,2.09,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/25/1991,54,tropicana,37248,10.52535353,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/25/1991,56,dominicks,1536,7.336936914,1,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/25/1991,56,minute.maid,5504,8.61323038,0,2.09,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/25/1991,56,tropicana,34624,10.45230236,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/25/1991,59,dominicks,5888,8.68067166,1,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/25/1991,59,minute.maid,5312,8.577723691,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/25/1991,59,tropicana,28352,10.25245285,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/25/1991,62,dominicks,64,4.158883083,1,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/25/1991,62,minute.maid,9792,9.189321005,0,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/25/1991,62,tropicana,53696,10.89109379,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/25/1991,64,dominicks,384,5.950642553,1,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/25/1991,64,minute.maid,3328,8.110126802,0,2.09,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/25/1991,64,tropicana,17600,9.775654181,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/25/1991,67,dominicks,1920,7.560080465,1,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/25/1991,67,minute.maid,4416,8.392989588,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/25/1991,67,tropicana,42304,10.65263692,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/25/1991,68,dominicks,1344,7.203405521,1,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/25/1991,68,minute.maid,10112,9.221478116,0,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/25/1991,68,tropicana,54016,10.89703558,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/25/1991,70,dominicks,4864,8.489616424,1,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/25/1991,70,minute.maid,11136,9.317938383,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/25/1991,70,tropicana,40704,10.61408165,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/25/1991,71,dominicks,640,6.461468176,1,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/25/1991,71,minute.maid,6464,8.7740036,0,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/25/1991,71,tropicana,58496,10.97671366,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/25/1991,72,dominicks,512,6.238324625,1,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/25/1991,72,minute.maid,6080,8.712759975,0,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/25/1991,72,tropicana,80064,11.29058159,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/25/1991,74,dominicks,2112,7.655390645,1,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/25/1991,74,minute.maid,9152,9.121727714,0,2.09,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/25/1991,74,tropicana,51840,10.85591733,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/25/1991,75,dominicks,256,5.545177445,1,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/25/1991,75,minute.maid,7168,8.877381955,0,2.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/25/1991,75,tropicana,64832,11.07955459,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/25/1991,76,dominicks,1856,7.526178913,1,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/25/1991,76,minute.maid,6720,8.812843434,0,2.09,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/25/1991,76,tropicana,73472,11.20465966,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/25/1991,77,dominicks,3648,8.201934351,1,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/25/1991,77,minute.maid,7040,8.859363449,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/25/1991,77,tropicana,30208,10.31586207,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/25/1991,78,dominicks,5120,8.540909718,1,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/25/1991,78,minute.maid,7680,8.946374826,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/25/1991,78,tropicana,51520,10.84972536,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/25/1991,80,dominicks,4608,8.435549202,1,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/25/1991,80,minute.maid,9472,9.156095357,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/25/1991,80,tropicana,46656,10.75055682,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/25/1991,81,dominicks,1472,7.294377299,1,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/25/1991,81,minute.maid,7104,8.868413285,0,2.09,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/25/1991,81,tropicana,49664,10.8130356,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/25/1991,83,dominicks,5184,8.553332238,1,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/25/1991,83,minute.maid,7040,8.859363449,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/25/1991,83,tropicana,48448,10.78824634,1,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/25/1991,84,dominicks,1088,6.992096427,1,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/25/1991,84,minute.maid,5760,8.658692754,0,2.09,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/25/1991,84,tropicana,42880,10.6661608,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/25/1991,86,dominicks,1472,7.294377299,1,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/25/1991,86,minute.maid,4416,8.392989588,0,2.09,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/25/1991,86,tropicana,56640,10.94447073,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/25/1991,88,dominicks,960,6.866933285,1,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/25/1991,88,minute.maid,5696,8.647519453,0,2.09,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/25/1991,88,tropicana,38528,10.55914053,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/25/1991,89,dominicks,1728,7.454719949,1,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/25/1991,89,minute.maid,4736,8.462948177,0,2.09,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/25/1991,89,tropicana,53504,10.8875117,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/25/1991,90,dominicks,1856,7.526178913,1,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/25/1991,90,minute.maid,5632,8.636219898,0,2.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/25/1991,90,tropicana,35328,10.47243113,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/25/1991,91,dominicks,1088,6.992096427,1,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/25/1991,91,minute.maid,4224,8.348537825,0,2.09,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/25/1991,91,tropicana,33152,10.40885833,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/25/1991,92,dominicks,2560,7.847762538,1,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/25/1991,92,minute.maid,6272,8.743850562,0,2.09,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/25/1991,92,tropicana,42816,10.66466714,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/25/1991,93,dominicks,704,6.556778356,1,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/25/1991,93,minute.maid,7936,8.979164649,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/25/1991,93,tropicana,50752,10.83470631,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/25/1991,94,dominicks,5888,8.68067166,1,1.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/25/1991,94,minute.maid,8064,8.99516499,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/25/1991,94,tropicana,22080,10.0024275,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/25/1991,95,dominicks,704,6.556778356,1,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/25/1991,95,minute.maid,15424,9.643680017,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/25/1991,95,tropicana,37248,10.52535353,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/25/1991,97,dominicks,768,6.643789733,1,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/25/1991,97,minute.maid,4032,8.30201781,0,2.09,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/25/1991,97,tropicana,16384,9.704060528,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/25/1991,98,dominicks,11136,9.317938383,1,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/25/1991,98,minute.maid,8320,9.026417534,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/25/1991,98,tropicana,47168,10.76147098,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/25/1991,100,dominicks,2176,7.685243608,1,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/25/1991,100,minute.maid,10176,9.227787286,0,2.09,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/25/1991,100,tropicana,54592,10.90764263,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/25/1991,101,dominicks,1536,7.336936914,1,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/25/1991,101,minute.maid,6400,8.764053269,0,2.09,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/25/1991,101,tropicana,46336,10.74367448,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/25/1991,102,dominicks,18112,9.804329981,1,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/25/1991,102,minute.maid,7872,8.971067439,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/25/1991,102,tropicana,56128,10.93539008,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/25/1991,103,dominicks,3776,8.236420527,1,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/25/1991,103,minute.maid,3776,8.236420527,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/25/1991,103,tropicana,22912,10.03941607,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/25/1991,104,dominicks,640,6.461468176,1,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/25/1991,104,minute.maid,4800,8.476371197,0,2.09,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/25/1991,104,tropicana,38272,10.55247384,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/25/1991,105,dominicks,9920,9.2023082,1,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/25/1991,105,minute.maid,7424,8.912473275,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/25/1991,105,tropicana,42880,10.6661608,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/25/1991,106,dominicks,704,6.556778356,1,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/25/1991,106,minute.maid,5184,8.553332238,0,2.09,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/25/1991,106,tropicana,16192,9.692272572,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/25/1991,107,dominicks,1664,7.416979621,1,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/25/1991,107,minute.maid,9024,9.107642974,0,2.09,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/25/1991,107,tropicana,64640,11.07658869,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/25/1991,109,dominicks,1664,7.416979621,1,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/25/1991,109,minute.maid,14016,9.547954813,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/25/1991,109,tropicana,90624,11.41447436,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/25/1991,110,dominicks,1600,7.377758908,1,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/25/1991,110,minute.maid,7360,8.903815212,0,2.09,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/25/1991,110,tropicana,40256,10.60301434,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/25/1991,111,dominicks,448,6.104793232,1,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/25/1991,111,minute.maid,8576,9.056722883,0,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/25/1991,111,tropicana,78336,11.26876255,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/25/1991,112,dominicks,5632,8.636219898,1,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/25/1991,112,minute.maid,12096,9.400630098,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/25/1991,112,tropicana,61312,11.02373086,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/25/1991,113,dominicks,2496,7.82244473,1,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/25/1991,113,minute.maid,7296,8.895081532,0,2.09,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/25/1991,113,tropicana,67072,11.11352195,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/25/1991,114,dominicks,9024,9.107642974,1,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/25/1991,114,minute.maid,8512,9.049232212,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/25/1991,114,tropicana,53376,10.88511649,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/25/1991,115,dominicks,704,6.556778356,1,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/25/1991,115,minute.maid,6272,8.743850562,0,2.09,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/25/1991,115,tropicana,49920,10.818177,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/25/1991,116,dominicks,1664,7.416979621,1,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/25/1991,116,minute.maid,6976,8.850230966,0,2.09,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/25/1991,116,tropicana,28032,10.24110199,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/25/1991,117,dominicks,1152,7.049254841,1,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/25/1991,117,minute.maid,3072,8.030084094,0,2.09,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/25/1991,117,tropicana,30272,10.31797847,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/25/1991,118,dominicks,1472,7.294377299,1,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/25/1991,118,minute.maid,8064,8.99516499,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/25/1991,118,tropicana,53120,10.88030878,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/25/1991,119,dominicks,4800,8.476371197,1,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/25/1991,119,minute.maid,4736,8.462948177,0,2.09,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/25/1991,119,tropicana,28992,10.27477521,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/25/1991,121,dominicks,3584,8.184234774,1,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/25/1991,121,minute.maid,7872,8.971067439,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/25/1991,121,tropicana,54208,10.90058378,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/25/1991,122,dominicks,6528,8.783855897,1,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/25/1991,122,minute.maid,9088,9.114710141,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/25/1991,122,tropicana,39168,10.57561537,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/25/1991,123,dominicks,576,6.356107661,1,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/25/1991,123,minute.maid,8448,9.041685006,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/25/1991,123,tropicana,88256,11.38799696,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/25/1991,124,dominicks,1600,7.377758908,1,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/25/1991,124,minute.maid,6592,8.793612072,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/25/1991,124,tropicana,71360,11.17549277,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/25/1991,126,dominicks,1472,7.294377299,1,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/25/1991,126,minute.maid,8896,9.093357017,0,2.09,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/25/1991,126,tropicana,53760,10.89228498,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/25/1991,128,dominicks,1536,7.336936914,1,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/25/1991,128,minute.maid,9920,9.2023082,0,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/25/1991,128,tropicana,131008,11.78301367,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/25/1991,129,dominicks,384,5.950642553,1,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/25/1991,129,minute.maid,6144,8.723231275,0,2.09,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/25/1991,129,tropicana,47872,10.77628606,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/25/1991,130,dominicks,2432,7.796469243,1,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/25/1991,130,minute.maid,8320,9.026417534,0,2.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/25/1991,130,tropicana,77312,11.25560446,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/25/1991,131,dominicks,1216,7.103322063,1,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/25/1991,131,minute.maid,9920,9.2023082,0,2.09,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/25/1991,131,tropicana,38208,10.5508002,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/25/1991,132,dominicks,5632,8.636219898,1,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/25/1991,132,minute.maid,7296,8.895081532,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/25/1991,132,tropicana,57344,10.9568235,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/25/1991,134,dominicks,320,5.768320996,1,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/25/1991,134,minute.maid,2752,7.920083199,0,2.09,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/25/1991,134,tropicana,27776,10.23192762,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/25/1991,137,dominicks,1728,7.454719949,1,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/25/1991,137,minute.maid,9728,9.182763604,0,2.09,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/25/1991,137,tropicana,119104,11.68775234,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/2/1991,2,dominicks,1216,7.103322063,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/2/1991,2,minute.maid,15104,9.622714888,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/2/1991,2,tropicana,23936,10.08313888,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/2/1991,5,dominicks,1728,7.454719949,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/2/1991,5,minute.maid,14144,9.557045785,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/2/1991,5,tropicana,14912,9.609921537,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/2/1991,8,dominicks,7168,8.877381955,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/2/1991,8,minute.maid,24768,10.11730778,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/2/1991,8,tropicana,21184,9.961001459,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/2/1991,9,dominicks,2944,7.98752448,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/2/1991,9,minute.maid,28288,10.25019297,0,2.09,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/2/1991,9,tropicana,25408,10.14281936,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/2/1991,14,dominicks,384,5.950642553,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/2/1991,14,minute.maid,23808,10.07777694,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/2/1991,14,tropicana,19136,9.859326657,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/2/1991,18,dominicks,5440,8.60153434,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/2/1991,18,minute.maid,27648,10.22730867,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/2/1991,18,tropicana,16000,9.680344001,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/2/1991,21,dominicks,5504,8.61323038,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/2/1991,21,minute.maid,15296,9.635346635,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/2/1991,21,tropicana,5504,8.61323038,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/2/1991,28,dominicks,1216,7.103322063,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/2/1991,28,minute.maid,15296,9.635346635,0,2.09,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/2/1991,28,tropicana,14336,9.570529135,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/2/1991,32,dominicks,1856,7.526178913,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/2/1991,32,minute.maid,28992,10.27477521,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/2/1991,32,tropicana,26240,10.17504024,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/2/1991,33,dominicks,192,5.257495372,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/2/1991,33,minute.maid,17088,9.746131742,0,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/2/1991,33,tropicana,26368,10.17990643,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/2/1991,40,dominicks,9408,9.14931567,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/2/1991,40,minute.maid,12608,9.442086812,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/2/1991,40,tropicana,10944,9.30054664,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/2/1991,44,dominicks,1600,7.377758908,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/2/1991,44,minute.maid,21440,9.973013615,0,2.09,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/2/1991,44,tropicana,26112,10.17015026,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/2/1991,45,dominicks,5888,8.68067166,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/2/1991,45,minute.maid,9536,9.162829389,0,2.09,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/2/1991,45,tropicana,12480,9.431882642,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/2/1991,47,dominicks,1920,7.560080465,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/2/1991,47,minute.maid,17216,9.753594463,0,2.09,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/2/1991,47,tropicana,14848,9.605620455,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/2/1991,48,dominicks,2432,7.796469243,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/2/1991,48,minute.maid,14720,9.596962392,0,2.09,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/2/1991,48,tropicana,6208,8.733594062,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/2/1991,49,dominicks,1152,7.049254841,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/2/1991,49,minute.maid,18944,9.849242538,0,2.09,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/2/1991,49,tropicana,6144,8.723231275,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/2/1991,50,dominicks,4992,8.51559191,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/2/1991,50,minute.maid,9856,9.195835686,0,2.09,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/2/1991,50,tropicana,10752,9.282847063,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/2/1991,51,dominicks,1728,7.454719949,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/2/1991,51,minute.maid,14528,9.583833101,0,2.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/2/1991,51,tropicana,11136,9.317938383,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/2/1991,52,dominicks,960,6.866933285,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/2/1991,52,minute.maid,25344,10.1402973,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/2/1991,52,tropicana,30528,10.32639957,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/2/1991,53,dominicks,1856,7.526178913,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/2/1991,53,minute.maid,37248,10.52535353,0,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/2/1991,53,tropicana,33408,10.41655067,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/2/1991,54,dominicks,1344,7.203405521,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/2/1991,54,minute.maid,16576,9.715711145,0,2.09,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/2/1991,54,tropicana,10624,9.270870872,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/2/1991,56,dominicks,3136,8.050703382,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/2/1991,56,minute.maid,12480,9.431882642,0,2.09,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/2/1991,56,tropicana,12672,9.447150114,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/2/1991,59,dominicks,3840,8.253227646,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/2/1991,59,minute.maid,17856,9.790094865,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/2/1991,59,tropicana,15872,9.67231183,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/2/1991,62,dominicks,64,4.158883083,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/2/1991,62,minute.maid,22336,10.01395501,0,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/2/1991,62,tropicana,27328,10.2156671,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/2/1991,64,dominicks,1344,7.203405521,0,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/2/1991,64,minute.maid,11328,9.335032816,0,2.09,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/2/1991,64,tropicana,27712,10.22962081,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/2/1991,67,dominicks,1856,7.526178913,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/2/1991,67,minute.maid,23424,10.06151642,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/2/1991,67,tropicana,11392,9.340666634,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/2/1991,68,dominicks,1536,7.336936914,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/2/1991,68,minute.maid,20416,9.924074186,0,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/2/1991,68,tropicana,32576,10.3913311,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/2/1991,70,dominicks,7104,8.868413285,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/2/1991,70,minute.maid,25664,10.15284451,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/2/1991,70,tropicana,15168,9.626943225,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/2/1991,71,dominicks,768,6.643789733,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/2/1991,71,minute.maid,31872,10.36948316,0,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/2/1991,71,tropicana,25408,10.14281936,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/2/1991,72,dominicks,1152,7.049254841,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/2/1991,72,minute.maid,22016,9.999524741,0,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/2/1991,72,tropicana,28608,10.26144168,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/2/1991,73,dominicks,14016,9.547954813,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/2/1991,73,minute.maid,26304,10.1774763,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/2/1991,73,tropicana,17984,9.797237753,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/2/1991,74,dominicks,3712,8.219326094,0,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/2/1991,74,minute.maid,27264,10.21332243,0,2.09,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/2/1991,74,tropicana,26944,10.20151592,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/2/1991,75,dominicks,1216,7.103322063,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/2/1991,75,minute.maid,36544,10.50627229,0,2.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/2/1991,75,tropicana,29056,10.27698028,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/2/1991,76,dominicks,3392,8.129174997,0,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/2/1991,76,minute.maid,23488,10.06424493,0,2.09,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/2/1991,76,tropicana,14080,9.55251063,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/2/1991,78,dominicks,5696,8.647519453,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/2/1991,78,minute.maid,22784,10.03381381,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/2/1991,78,tropicana,22848,10.03661887,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/2/1991,80,dominicks,3072,8.030084094,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/2/1991,80,minute.maid,21376,9.970024076,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/2/1991,80,tropicana,20352,9.920934466,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/2/1991,81,dominicks,2048,7.624618986,0,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/2/1991,81,minute.maid,17728,9.78290059,0,2.09,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/2/1991,81,tropicana,15680,9.660141294,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/2/1991,83,dominicks,7040,8.859363449,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/2/1991,83,minute.maid,21312,9.967025573,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/2/1991,83,tropicana,5888,8.68067166,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/2/1991,84,dominicks,2048,7.624618986,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/2/1991,84,minute.maid,16064,9.684336023,0,2.09,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/2/1991,84,tropicana,11584,9.357380115,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/2/1991,86,dominicks,1856,7.526178913,0,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/2/1991,86,minute.maid,18496,9.825309772,0,2.09,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/2/1991,86,tropicana,26560,10.1871616,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/2/1991,88,dominicks,2304,7.742402022,0,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/2/1991,88,minute.maid,17088,9.746131742,0,2.09,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/2/1991,88,tropicana,4032,8.30201781,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/2/1991,89,dominicks,2944,7.98752448,0,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/2/1991,89,minute.maid,22272,10.01108556,0,2.09,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/2/1991,89,tropicana,18048,9.800790154,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/2/1991,90,dominicks,18304,9.814874894,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/2/1991,90,minute.maid,22016,9.999524741,0,2.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/2/1991,90,tropicana,10496,9.258749511,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/2/1991,91,dominicks,3008,8.009030685,0,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/2/1991,91,minute.maid,15104,9.622714888,0,2.09,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/2/1991,91,tropicana,17024,9.742379392,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/2/1991,92,dominicks,4608,8.435549202,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/2/1991,92,minute.maid,16384,9.704060528,0,2.09,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/2/1991,92,tropicana,12352,9.421573272,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/2/1991,93,dominicks,576,6.356107661,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/2/1991,93,minute.maid,14976,9.614204199,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/2/1991,93,tropicana,25792,10.15781965,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/2/1991,94,dominicks,6080,8.712759975,0,1.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/2/1991,94,minute.maid,17728,9.78290059,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/2/1991,94,tropicana,12288,9.416378455,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/2/1991,95,dominicks,1280,7.154615357,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/2/1991,95,minute.maid,24448,10.10430369,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/2/1991,95,tropicana,18752,9.839055692,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/2/1991,97,dominicks,832,6.723832441,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/2/1991,97,minute.maid,6720,8.812843434,0,2.09,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/2/1991,97,tropicana,10944,9.30054664,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/2/1991,98,dominicks,10496,9.258749511,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/2/1991,98,minute.maid,14848,9.605620455,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/2/1991,98,tropicana,24640,10.11212642,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/2/1991,100,dominicks,1472,7.294377299,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/2/1991,100,minute.maid,35328,10.47243113,0,2.09,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/2/1991,100,tropicana,35968,10.49038493,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/2/1991,101,dominicks,2560,7.847762538,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/2/1991,101,minute.maid,28800,10.26813067,0,2.09,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/2/1991,101,tropicana,20992,9.951896692,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/2/1991,102,dominicks,9984,9.208739091,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/2/1991,102,minute.maid,28032,10.24110199,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/2/1991,102,tropicana,38080,10.54744449,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/2/1991,103,dominicks,4544,8.42156296,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/2/1991,103,minute.maid,17152,9.749870064,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/2/1991,103,tropicana,7872,8.971067439,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/2/1991,104,dominicks,768,6.643789733,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/2/1991,104,minute.maid,18048,9.800790154,0,2.09,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/2/1991,104,tropicana,14016,9.547954813,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/2/1991,105,dominicks,7808,8.962904128,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/2/1991,105,minute.maid,25728,10.15533517,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/2/1991,105,tropicana,13888,9.538780437,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/2/1991,106,dominicks,256,5.545177445,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/2/1991,106,minute.maid,12544,9.436997743,0,2.09,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/2/1991,106,tropicana,6592,8.793612072,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/2/1991,107,dominicks,2880,7.965545573,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/2/1991,107,minute.maid,17216,9.753594463,0,2.09,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/2/1991,107,tropicana,29632,10.29661014,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/2/1991,109,dominicks,256,5.545177445,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/2/1991,109,minute.maid,26560,10.1871616,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/2/1991,109,tropicana,30720,10.33266919,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/2/1991,111,dominicks,1152,7.049254841,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/2/1991,111,minute.maid,33856,10.42987152,0,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/2/1991,111,tropicana,31552,10.35939226,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/2/1991,112,dominicks,5760,8.658692754,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/2/1991,112,minute.maid,24768,10.11730778,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/2/1991,112,tropicana,29888,10.30521234,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/2/1991,113,dominicks,2880,7.965545573,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/2/1991,113,minute.maid,24832,10.11988842,0,2.09,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/2/1991,113,tropicana,32320,10.38344151,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/2/1991,114,dominicks,7424,8.912473275,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/2/1991,114,minute.maid,48832,10.79614112,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/2/1991,114,tropicana,17856,9.790094865,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/2/1991,115,dominicks,1792,7.491087594,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/2/1991,115,minute.maid,16320,9.700146629,0,2.09,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/2/1991,115,tropicana,9024,9.107642974,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/2/1991,116,dominicks,1856,7.526178913,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/2/1991,116,minute.maid,18688,9.835636886,0,2.09,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/2/1991,116,tropicana,12608,9.442086812,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/2/1991,117,dominicks,1024,6.931471806,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/2/1991,117,minute.maid,9536,9.162829389,0,2.09,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/2/1991,117,tropicana,13184,9.486759252,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/2/1991,118,dominicks,3968,8.286017468,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/2/1991,118,minute.maid,17344,9.761001904,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/2/1991,118,tropicana,15232,9.631153757,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/2/1991,119,dominicks,3392,8.129174997,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/2/1991,119,minute.maid,13696,9.524859098,0,2.09,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/2/1991,119,tropicana,14144,9.557045785,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/2/1991,121,dominicks,2880,7.965545573,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/2/1991,121,minute.maid,22208,10.00820786,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/2/1991,121,tropicana,19968,9.901886271,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/2/1991,122,dominicks,7168,8.877381955,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/2/1991,122,minute.maid,23360,10.05878044,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/2/1991,122,tropicana,14720,9.596962392,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/2/1991,123,dominicks,2048,7.624618986,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/2/1991,123,minute.maid,30208,10.31586207,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/2/1991,123,tropicana,12544,9.436997743,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/2/1991,124,dominicks,2624,7.87245515,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/2/1991,124,minute.maid,24704,10.11472045,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/2/1991,124,tropicana,20864,9.945780465,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/2/1991,126,dominicks,1408,7.249925537,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/2/1991,126,minute.maid,21632,9.981928979,0,2.09,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/2/1991,126,tropicana,25344,10.1402973,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/2/1991,128,dominicks,1664,7.416979621,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/2/1991,128,minute.maid,22720,10.03100087,0,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/2/1991,128,tropicana,76928,11.2506252,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/2/1991,129,dominicks,960,6.866933285,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/2/1991,129,minute.maid,15360,9.639522007,0,2.09,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/2/1991,129,tropicana,17792,9.786504197,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/2/1991,130,dominicks,4352,8.378390789,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/2/1991,130,minute.maid,24704,10.11472045,0,2.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/2/1991,130,tropicana,41152,10.62502781,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/2/1991,131,dominicks,2304,7.742402022,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/2/1991,131,minute.maid,22208,10.00820786,0,2.09,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/2/1991,131,tropicana,12608,9.442086812,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/2/1991,132,dominicks,9984,9.208739091,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/2/1991,132,minute.maid,18560,9.828764006,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/2/1991,132,tropicana,22144,10.00532186,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/2/1991,134,dominicks,1024,6.931471806,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/2/1991,134,minute.maid,10048,9.215128889,0,2.09,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/2/1991,134,tropicana,8320,9.026417534,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/2/1991,137,dominicks,2368,7.769800996,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/2/1991,137,minute.maid,27648,10.22730867,0,2.09,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/2/1991,137,tropicana,72576,11.19238957,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/9/1991,2,dominicks,1664,7.416979621,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/9/1991,2,minute.maid,76480,11.24478455,1,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/9/1991,2,tropicana,7104,8.868413285,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/9/1991,5,dominicks,1280,7.154615357,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/9/1991,5,minute.maid,88256,11.38799696,1,1.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/9/1991,5,tropicana,6464,8.7740036,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/9/1991,8,dominicks,2880,7.965545573,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/9/1991,8,minute.maid,183296,12.11885761,1,1.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/9/1991,8,tropicana,7360,8.903815212,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/9/1991,12,dominicks,1024,6.931471806,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/9/1991,12,minute.maid,205056,12.23103839,1,1.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/9/1991,12,tropicana,6656,8.803273983,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/9/1991,18,dominicks,2688,7.896552702,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/9/1991,18,minute.maid,146944,11.89780684,1,1.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/9/1991,18,tropicana,7424,8.912473275,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/9/1991,21,dominicks,2240,7.714231145,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/9/1991,21,minute.maid,89152,11.39809806,1,1.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/9/1991,21,tropicana,2688,7.896552702,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/9/1991,28,dominicks,1152,7.049254841,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/9/1991,28,minute.maid,51968,10.85838342,1,1.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/9/1991,28,tropicana,2368,7.769800996,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/9/1991,32,dominicks,1472,7.294377299,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/9/1991,32,minute.maid,166784,12.02445484,1,1.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/9/1991,32,tropicana,9344,9.142489705,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/9/1991,33,dominicks,640,6.461468176,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/9/1991,33,minute.maid,93568,11.44644372,1,1.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/9/1991,33,tropicana,8896,9.093357017,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/9/1991,40,dominicks,2752,7.920083199,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/9/1991,40,minute.maid,107840,11.58840393,1,1.39,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/9/1991,40,tropicana,4160,8.333270353,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/9/1991,44,dominicks,1344,7.203405521,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/9/1991,44,minute.maid,132224,11.79225273,1,1.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/9/1991,44,tropicana,11776,9.373818841,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/9/1991,45,dominicks,2688,7.896552702,0,2.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/9/1991,45,minute.maid,44352,10.69991308,1,1.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/9/1991,45,tropicana,3584,8.184234774,0,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/9/1991,48,dominicks,1792,7.491087594,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/9/1991,48,minute.maid,58688,10.97999056,1,1.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/9/1991,48,tropicana,4416,8.392989588,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/9/1991,50,dominicks,1664,7.416979621,0,2.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/9/1991,50,minute.maid,67904,11.12585022,1,1.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/9/1991,50,tropicana,3072,8.030084094,0,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/9/1991,51,dominicks,768,6.643789733,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/9/1991,51,minute.maid,93760,11.44849361,1,1.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/9/1991,51,tropicana,4352,8.378390789,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/9/1991,52,dominicks,1152,7.049254841,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/9/1991,52,minute.maid,118144,11.6796595,1,1.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/9/1991,52,tropicana,10368,9.246479419,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/9/1991,53,dominicks,768,6.643789733,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/9/1991,53,minute.maid,196416,12.18799014,1,1.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/9/1991,53,tropicana,9344,9.142489705,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/9/1991,54,dominicks,1152,7.049254841,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/9/1991,54,minute.maid,72320,11.188856,1,1.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/9/1991,54,tropicana,2752,7.920083199,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/9/1991,56,dominicks,2432,7.796469243,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/9/1991,56,minute.maid,53248,10.88271552,1,1.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/9/1991,56,tropicana,4672,8.449342525,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/9/1991,59,dominicks,3328,8.110126802,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/9/1991,59,minute.maid,100224,11.51516296,1,1.39,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/9/1991,59,tropicana,3200,8.070906089,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/9/1991,62,dominicks,576,6.356107661,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/9/1991,62,minute.maid,67392,11.1182816,1,1.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/9/1991,62,tropicana,10752,9.282847063,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/9/1991,64,dominicks,1472,7.294377299,0,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/9/1991,64,minute.maid,58048,10.96902553,1,1.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/9/1991,64,tropicana,2240,7.714231145,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/9/1991,67,dominicks,1472,7.294377299,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/9/1991,67,minute.maid,97024,11.48271365,1,1.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/9/1991,67,tropicana,5312,8.577723691,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/9/1991,68,dominicks,2176,7.685243608,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/9/1991,68,minute.maid,95552,11.46742588,1,1.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/9/1991,68,tropicana,9344,9.142489705,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/9/1991,70,dominicks,2944,7.98752448,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/9/1991,70,minute.maid,99136,11.50424792,1,1.39,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/9/1991,70,tropicana,7936,8.979164649,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/9/1991,71,dominicks,832,6.723832441,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/9/1991,71,minute.maid,167936,12.03133823,1,1.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/9/1991,71,tropicana,3840,8.253227646,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/9/1991,72,dominicks,256,5.545177445,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/9/1991,72,minute.maid,161024,11.9893087,1,1.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/9/1991,72,tropicana,8512,9.049232212,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/9/1991,73,dominicks,6080,8.712759975,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/9/1991,73,minute.maid,214016,12.27380606,1,1.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/9/1991,73,tropicana,8512,9.049232212,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/9/1991,74,dominicks,3328,8.110126802,0,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/9/1991,74,minute.maid,174080,12.06727024,1,1.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/9/1991,74,tropicana,5952,8.691482577,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/9/1991,75,dominicks,1536,7.336936914,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/9/1991,75,minute.maid,148672,11.90949782,1,1.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/9/1991,75,tropicana,5696,8.647519453,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/9/1991,76,dominicks,576,6.356107661,0,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/9/1991,76,minute.maid,161856,11.99446233,1,1.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/9/1991,76,tropicana,4160,8.333270353,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/9/1991,77,dominicks,1920,7.560080465,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/9/1991,77,minute.maid,73216,11.20116926,1,1.39,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/9/1991,77,tropicana,7168,8.877381955,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/9/1991,78,dominicks,1280,7.154615357,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/9/1991,78,minute.maid,153792,11.94335632,1,1.39,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/9/1991,78,tropicana,5632,8.636219898,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/9/1991,80,dominicks,1664,7.416979621,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/9/1991,80,minute.maid,107264,11.58304836,1,1.39,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/9/1991,80,tropicana,8896,9.093357017,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/9/1991,81,dominicks,1856,7.526178913,0,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/9/1991,81,minute.maid,129408,11.77072548,1,1.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/9/1991,81,tropicana,5632,8.636219898,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/9/1991,83,dominicks,2944,7.98752448,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/9/1991,83,minute.maid,152512,11.93499856,1,1.39,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/9/1991,83,tropicana,5952,8.691482577,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/9/1991,84,dominicks,1280,7.154615357,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/9/1991,84,minute.maid,122752,11.71792134,1,1.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/9/1991,84,tropicana,4224,8.348537825,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/9/1991,86,dominicks,1344,7.203405521,0,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/9/1991,86,minute.maid,147520,11.90171904,1,1.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/9/1991,86,tropicana,5888,8.68067166,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/9/1991,88,dominicks,1216,7.103322063,0,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/9/1991,88,minute.maid,87552,11.37998818,1,1.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/9/1991,88,tropicana,3200,8.070906089,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/9/1991,89,dominicks,1344,7.203405521,0,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/9/1991,89,minute.maid,156352,11.95986516,1,1.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/9/1991,89,tropicana,2496,7.82244473,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/9/1991,90,dominicks,896,6.797940413,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/9/1991,90,minute.maid,113664,11.64100201,1,1.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/9/1991,90,tropicana,2240,7.714231145,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/9/1991,91,dominicks,1216,7.103322063,0,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/9/1991,91,minute.maid,115584,11.65775282,1,1.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/9/1991,91,tropicana,3264,8.090708716,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/9/1991,92,dominicks,2944,7.98752448,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/9/1991,92,minute.maid,103680,11.54906451,1,1.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/9/1991,92,tropicana,2688,7.896552702,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/9/1991,93,dominicks,448,6.104793232,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/9/1991,93,minute.maid,98304,11.49582,1,1.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/9/1991,93,tropicana,6656,8.803273983,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/9/1991,94,dominicks,2880,7.965545573,0,1.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/9/1991,94,minute.maid,77120,11.25311793,1,1.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/9/1991,94,tropicana,7104,8.868413285,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/9/1991,95,dominicks,1536,7.336936914,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/9/1991,95,minute.maid,121152,11.70480124,1,1.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/9/1991,95,tropicana,3200,8.070906089,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/9/1991,97,dominicks,704,6.556778356,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/9/1991,97,minute.maid,40576,10.61093204,1,1.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/9/1991,97,tropicana,1344,7.203405521,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/9/1991,98,dominicks,6336,8.754002934,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/9/1991,98,minute.maid,148416,11.90777442,1,1.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/9/1991,98,tropicana,6912,8.841014311,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/9/1991,100,dominicks,1216,7.103322063,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/9/1991,100,minute.maid,165248,12.01520266,1,1.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/9/1991,100,tropicana,5760,8.658692754,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/9/1991,101,dominicks,1344,7.203405521,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/9/1991,101,minute.maid,115200,11.65442503,1,1.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/9/1991,101,tropicana,5184,8.553332238,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/9/1991,102,dominicks,5056,8.528330936,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/9/1991,102,minute.maid,190656,12.15822604,1,1.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/9/1991,102,tropicana,6720,8.812843434,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/9/1991,103,dominicks,2112,7.655390645,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/9/1991,103,minute.maid,90624,11.41447436,1,1.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/9/1991,103,tropicana,2304,7.742402022,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/9/1991,104,dominicks,512,6.238324625,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/9/1991,104,minute.maid,74112,11.21333274,1,1.39,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/9/1991,104,tropicana,4544,8.42156296,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/9/1991,105,dominicks,2880,7.965545573,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/9/1991,105,minute.maid,122176,11.71321791,1,1.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/9/1991,105,tropicana,4288,8.363575703,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/9/1991,106,dominicks,448,6.104793232,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/9/1991,106,minute.maid,50496,10.8296494,1,1.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/9/1991,106,tropicana,2112,7.655390645,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/9/1991,107,dominicks,1280,7.154615357,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/9/1991,107,minute.maid,147072,11.89867754,1,1.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/9/1991,107,tropicana,6592,8.793612072,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/9/1991,109,dominicks,704,6.556778356,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/9/1991,109,minute.maid,147520,11.90171904,1,1.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/9/1991,109,tropicana,11328,9.335032816,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/9/1991,111,dominicks,1472,7.294377299,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/9/1991,111,minute.maid,267456,12.49671035,1,1.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/9/1991,111,tropicana,4160,8.333270353,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/9/1991,112,dominicks,2880,7.965545573,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/9/1991,112,minute.maid,100416,11.51707684,1,1.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/9/1991,112,tropicana,17152,9.749870064,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/9/1991,113,dominicks,1664,7.416979621,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/9/1991,113,minute.maid,151232,11.92657036,1,1.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/9/1991,113,tropicana,6144,8.723231275,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/9/1991,114,dominicks,1984,7.592870288,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/9/1991,114,minute.maid,141312,11.85872549,1,1.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/9/1991,114,tropicana,5504,8.61323038,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/9/1991,116,dominicks,2368,7.769800996,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/9/1991,116,minute.maid,90816,11.41659076,1,1.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/9/1991,116,tropicana,3520,8.166216269,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/9/1991,117,dominicks,1664,7.416979621,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/9/1991,117,minute.maid,58368,10.97452307,1,1.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/9/1991,117,tropicana,2304,7.742402022,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/9/1991,118,dominicks,1792,7.491087594,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/9/1991,118,minute.maid,118400,11.681824,1,1.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/9/1991,118,tropicana,4480,8.407378325,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/9/1991,119,dominicks,896,6.797940413,0,2.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/9/1991,119,minute.maid,60352,11.00794937,1,1.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/9/1991,119,tropicana,6656,8.803273983,0,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/9/1991,121,dominicks,768,6.643789733,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/9/1991,121,minute.maid,116416,11.66492526,1,1.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/9/1991,121,tropicana,14976,9.614204199,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/9/1991,122,dominicks,4416,8.392989588,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/9/1991,122,minute.maid,96000,11.47210347,1,1.39,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/9/1991,122,tropicana,9088,9.114710141,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/9/1991,123,dominicks,1216,7.103322063,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/9/1991,123,minute.maid,214976,12.27828167,1,1.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/9/1991,123,tropicana,6784,8.822322178,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/9/1991,124,dominicks,960,6.866933285,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/9/1991,124,minute.maid,146112,11.89212873,1,1.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/9/1991,124,tropicana,4224,8.348537825,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/9/1991,126,dominicks,1152,7.049254841,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/9/1991,126,minute.maid,110208,11.61012477,1,1.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/9/1991,126,tropicana,4992,8.51559191,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/9/1991,128,dominicks,768,6.643789733,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/9/1991,128,minute.maid,236992,12.37578166,1,1.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/9/1991,128,tropicana,8768,9.078864009,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/9/1991,129,dominicks,896,6.797940413,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/9/1991,129,minute.maid,78784,11.27446521,1,1.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/9/1991,129,tropicana,7872,8.971067439,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/9/1991,130,dominicks,2432,7.796469243,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/9/1991,130,minute.maid,211072,12.25995459,1,1.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/9/1991,130,tropicana,3904,8.269756948,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/9/1991,131,dominicks,1472,7.294377299,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/9/1991,131,minute.maid,106368,11.57466006,1,1.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/9/1991,131,tropicana,5568,8.624791202,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/9/1991,132,dominicks,4416,8.392989588,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/9/1991,132,minute.maid,120896,11.70268595,1,1.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/9/1991,132,tropicana,6016,8.702177866,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/9/1991,134,dominicks,448,6.104793232,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/9/1991,134,minute.maid,69696,11.15189821,1,1.39,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/9/1991,134,tropicana,1728,7.454719949,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/9/1991,137,dominicks,1600,7.377758908,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/9/1991,137,minute.maid,158336,11.97247464,1,1.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/9/1991,137,tropicana,20864,9.945780465,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/16/1991,2,dominicks,4992,8.51559191,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/16/1991,2,minute.maid,5056,8.528330936,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/16/1991,2,tropicana,24512,10.10691807,1,2.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/16/1991,5,dominicks,5696,8.647519453,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/16/1991,5,minute.maid,6848,8.831711918,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/16/1991,5,tropicana,25024,10.12759064,1,2.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/16/1991,8,dominicks,12288,9.416378455,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/16/1991,8,minute.maid,8896,9.093357017,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/16/1991,8,tropicana,15744,9.664214619,1,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/16/1991,12,dominicks,5312,8.577723691,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/16/1991,12,minute.maid,47232,10.76282691,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/16/1991,12,tropicana,31744,10.36545901,1,2.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/16/1991,14,dominicks,4928,8.502688505,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/16/1991,14,minute.maid,4928,8.502688505,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/16/1991,14,tropicana,27520,10.22266829,1,2.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/16/1991,18,dominicks,12032,9.395325046,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/16/1991,18,minute.maid,22400,10.01681624,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/16/1991,18,tropicana,16192,9.692272572,1,2.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/16/1991,21,dominicks,4928,8.502688505,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/16/1991,21,minute.maid,5632,8.636219898,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/16/1991,21,tropicana,5760,8.658692754,1,2.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/16/1991,28,dominicks,3520,8.166216269,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/16/1991,28,minute.maid,3328,8.110126802,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/16/1991,28,tropicana,13760,9.529521112,1,2.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/16/1991,32,dominicks,6144,8.723231275,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/16/1991,32,minute.maid,8256,9.018695488,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/16/1991,32,tropicana,42880,10.6661608,1,2.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/16/1991,33,dominicks,4480,8.407378325,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/16/1991,33,minute.maid,7424,8.912473275,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/16/1991,33,tropicana,25792,10.15781965,1,2.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/16/1991,40,dominicks,7104,8.868413285,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/16/1991,40,minute.maid,4160,8.333270353,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/16/1991,40,tropicana,49664,10.8130356,1,2.29,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/16/1991,44,dominicks,3456,8.14786713,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/16/1991,44,minute.maid,5120,8.540909718,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/16/1991,44,tropicana,34048,10.43552657,1,2.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/16/1991,45,dominicks,5376,8.589699882,0,2.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/16/1991,45,minute.maid,3904,8.269756948,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/16/1991,45,tropicana,11968,9.3899917,1,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/16/1991,48,dominicks,4608,8.435549202,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/16/1991,48,minute.maid,6144,8.723231275,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/16/1991,48,tropicana,13120,9.481893063,1,2.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/16/1991,49,dominicks,4160,8.333270353,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/16/1991,49,minute.maid,3456,8.14786713,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/16/1991,49,tropicana,9216,9.128696383,1,2.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/16/1991,50,dominicks,2944,7.98752448,0,2.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/16/1991,50,minute.maid,3072,8.030084094,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/16/1991,50,tropicana,9088,9.114710141,1,2.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/16/1991,51,dominicks,3776,8.236420527,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/16/1991,51,minute.maid,3264,8.090708716,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/16/1991,51,tropicana,15424,9.643680017,1,2.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/16/1991,52,dominicks,3520,8.166216269,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/16/1991,52,minute.maid,7616,8.938006577,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/16/1991,52,tropicana,38784,10.56576307,1,2.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/16/1991,53,dominicks,8512,9.049232212,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/16/1991,53,minute.maid,8576,9.056722883,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/16/1991,53,tropicana,50048,10.82073782,1,2.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/16/1991,54,dominicks,5184,8.553332238,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/16/1991,54,minute.maid,5504,8.61323038,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/16/1991,54,tropicana,14976,9.614204199,1,2.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/16/1991,56,dominicks,7616,8.938006577,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/16/1991,56,minute.maid,3456,8.14786713,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/16/1991,56,tropicana,16640,9.719564714,1,2.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/16/1991,59,dominicks,8000,8.987196821,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/16/1991,59,minute.maid,5312,8.577723691,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/16/1991,59,tropicana,13760,9.529521112,1,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/16/1991,62,dominicks,1984,7.592870288,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/16/1991,62,minute.maid,6720,8.812843434,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/16/1991,62,tropicana,35328,10.47243113,1,2.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/16/1991,64,dominicks,9664,9.17616292,0,2.19,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/16/1991,64,minute.maid,3648,8.201934351,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/16/1991,64,tropicana,8512,9.049232212,1,2.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/16/1991,67,dominicks,3648,8.201934351,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/16/1991,67,minute.maid,5696,8.647519453,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/16/1991,67,tropicana,7552,8.929567708,1,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/16/1991,68,dominicks,20672,9.936535407,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/16/1991,68,minute.maid,5696,8.647519453,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/16/1991,68,tropicana,26304,10.1774763,1,2.29,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/16/1991,70,dominicks,11392,9.340666634,0,1.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/16/1991,70,minute.maid,11776,9.373818841,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/16/1991,70,tropicana,16768,9.727227587,1,2.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/16/1991,71,dominicks,4480,8.407378325,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/16/1991,71,minute.maid,27968,10.23881628,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/16/1991,71,tropicana,14976,9.614204199,1,2.29,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/16/1991,72,dominicks,4480,8.407378325,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/16/1991,72,minute.maid,4608,8.435549202,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/16/1991,72,tropicana,32256,10.38145935,1,2.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/16/1991,73,dominicks,18048,9.800790154,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/16/1991,73,minute.maid,13760,9.529521112,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/16/1991,73,tropicana,25920,10.16277015,1,2.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/16/1991,74,dominicks,13696,9.524859098,0,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/16/1991,74,minute.maid,7360,8.903815212,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/16/1991,74,tropicana,23360,10.05878044,1,2.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/16/1991,75,dominicks,5056,8.528330936,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/16/1991,75,minute.maid,6208,8.733594062,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/16/1991,75,tropicana,29568,10.29444797,1,2.29,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/16/1991,76,dominicks,4224,8.348537825,0,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/16/1991,76,minute.maid,4352,8.378390789,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/16/1991,76,tropicana,16832,9.731037116,1,2.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/16/1991,77,dominicks,5376,8.589699882,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/16/1991,77,minute.maid,9216,9.128696383,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/16/1991,77,tropicana,17600,9.775654181,1,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/16/1991,78,dominicks,7936,8.979164649,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/16/1991,78,minute.maid,7616,8.938006577,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/16/1991,78,tropicana,16512,9.711842668,1,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/16/1991,80,dominicks,5376,8.589699882,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/16/1991,80,minute.maid,9408,9.14931567,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/16/1991,80,tropicana,18688,9.835636886,1,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/16/1991,81,dominicks,8832,9.086136769,0,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/16/1991,81,minute.maid,7936,8.979164649,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/16/1991,81,tropicana,17920,9.793672686,1,2.29,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/16/1991,83,dominicks,7296,8.895081532,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/16/1991,83,minute.maid,5824,8.66974259,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/16/1991,83,tropicana,12032,9.395325046,1,2.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/16/1991,84,dominicks,4288,8.363575703,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/16/1991,84,minute.maid,5376,8.589699882,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/16/1991,84,tropicana,18624,9.832206351,1,2.29,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/16/1991,86,dominicks,6272,8.743850562,0,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/16/1991,86,minute.maid,3648,8.201934351,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/16/1991,86,tropicana,34048,10.43552657,1,2.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/16/1991,88,dominicks,4928,8.502688505,0,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/16/1991,88,minute.maid,4992,8.51559191,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/16/1991,88,tropicana,19072,9.85597657,1,2.29,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/16/1991,89,dominicks,8192,9.010913347,0,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/16/1991,89,minute.maid,3904,8.269756948,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/16/1991,89,tropicana,19136,9.859326657,1,2.29,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/16/1991,90,dominicks,10688,9.276876896,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/16/1991,90,minute.maid,4928,8.502688505,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/16/1991,90,tropicana,8512,9.049232212,1,2.29,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/16/1991,91,dominicks,4992,8.51559191,0,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/16/1991,91,minute.maid,3648,8.201934351,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/16/1991,91,tropicana,15936,9.67633598,1,2.29,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/16/1991,92,dominicks,6400,8.764053269,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/16/1991,92,minute.maid,4992,8.51559191,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/16/1991,92,tropicana,15104,9.622714888,1,2.29,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/16/1991,93,dominicks,2816,7.943072717,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/16/1991,93,minute.maid,5632,8.636219898,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/16/1991,93,tropicana,23424,10.06151642,1,2.29,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/16/1991,94,dominicks,7168,8.877381955,0,1.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/16/1991,94,minute.maid,10176,9.227787286,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/16/1991,94,tropicana,11200,9.323669057,1,2.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/16/1991,95,dominicks,4992,8.51559191,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/16/1991,95,minute.maid,3328,8.110126802,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/16/1991,95,tropicana,14848,9.605620455,1,2.29,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/16/1991,97,dominicks,3200,8.070906089,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/16/1991,97,minute.maid,2752,7.920083199,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/16/1991,97,tropicana,5376,8.589699882,1,2.29,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/16/1991,98,dominicks,16192,9.692272572,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/16/1991,98,minute.maid,12224,9.411156511,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/16/1991,98,tropicana,7424,8.912473275,1,2.29,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/16/1991,100,dominicks,11520,9.351839934,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/16/1991,100,minute.maid,4800,8.476371197,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/16/1991,100,tropicana,38592,10.56080028,1,2.29,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/16/1991,101,dominicks,6784,8.822322178,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/16/1991,101,minute.maid,6208,8.733594062,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/16/1991,101,tropicana,16384,9.704060528,1,2.29,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/16/1991,102,dominicks,18304,9.814874894,0,1.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/16/1991,102,minute.maid,11456,9.346268889,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/16/1991,102,tropicana,29760,10.30092049,1,2.29,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/16/1991,103,dominicks,7168,8.877381955,0,1.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/16/1991,103,minute.maid,5376,8.589699882,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/16/1991,103,tropicana,7936,8.979164649,1,2.29,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/16/1991,104,dominicks,3200,8.070906089,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/16/1991,104,minute.maid,4736,8.462948177,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/16/1991,104,tropicana,15360,9.639522007,1,2.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/16/1991,105,dominicks,8960,9.100525506,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/16/1991,105,minute.maid,6528,8.783855897,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/16/1991,105,tropicana,10368,9.246479419,1,2.29,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/16/1991,106,dominicks,5248,8.565602331,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/16/1991,106,minute.maid,2880,7.965545573,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/16/1991,106,tropicana,7168,8.877381955,1,2.29,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/16/1991,107,dominicks,5888,8.68067166,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/16/1991,107,minute.maid,6080,8.712759975,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/16/1991,107,tropicana,19072,9.85597657,1,2.29,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/16/1991,109,dominicks,1984,7.592870288,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/16/1991,109,minute.maid,8896,9.093357017,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/16/1991,109,tropicana,64448,11.07361398,1,2.29,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/16/1991,110,dominicks,6272,8.743850562,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/16/1991,110,minute.maid,9792,9.189321005,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/16/1991,110,tropicana,19072,9.85597657,1,2.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/16/1991,111,dominicks,12416,9.426741242,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/16/1991,111,minute.maid,4928,8.502688505,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/16/1991,111,tropicana,24320,10.09905434,1,2.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/16/1991,112,dominicks,11712,9.368369236,0,1.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/16/1991,112,minute.maid,15488,9.64782081,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/16/1991,112,tropicana,34752,10.4559924,1,2.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/16/1991,113,dominicks,6272,8.743850562,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/16/1991,113,minute.maid,4096,8.317766167,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/16/1991,113,tropicana,19904,9.898675996,1,2.29,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/16/1991,114,dominicks,8896,9.093357017,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/16/1991,114,minute.maid,7232,8.886270902,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/16/1991,114,tropicana,18240,9.811372264,1,2.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/16/1991,115,dominicks,4416,8.392989588,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/16/1991,115,minute.maid,5376,8.589699882,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/16/1991,115,tropicana,24384,10.10168246,1,2.29,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/16/1991,116,dominicks,6720,8.812843434,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/16/1991,116,minute.maid,3584,8.184234774,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/16/1991,116,tropicana,10368,9.246479419,1,2.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/16/1991,117,dominicks,2368,7.769800996,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/16/1991,117,minute.maid,2880,7.965545573,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/16/1991,117,tropicana,11520,9.351839934,1,2.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/16/1991,118,dominicks,5056,8.528330936,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/16/1991,118,minute.maid,7616,8.938006577,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/16/1991,118,tropicana,21952,9.996613531,1,2.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/16/1991,119,dominicks,5120,8.540909718,0,2.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/16/1991,119,minute.maid,5312,8.577723691,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/16/1991,119,tropicana,20544,9.930324207,1,2.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/16/1991,121,dominicks,4480,8.407378325,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/16/1991,121,minute.maid,10368,9.246479419,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/16/1991,121,tropicana,34304,10.44301724,1,2.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/16/1991,122,dominicks,10112,9.221478116,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/16/1991,122,minute.maid,10304,9.240287448,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/16/1991,122,tropicana,26752,10.19436452,1,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/16/1991,123,dominicks,7488,8.921057018,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/16/1991,123,minute.maid,4800,8.476371197,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/16/1991,123,tropicana,34560,10.45045222,1,2.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/16/1991,124,dominicks,8448,9.041685006,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/16/1991,124,minute.maid,4096,8.317766167,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/16/1991,124,tropicana,36928,10.51672535,1,2.29,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/16/1991,126,dominicks,8064,8.99516499,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/16/1991,126,minute.maid,4928,8.502688505,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/16/1991,126,tropicana,23936,10.08313888,1,2.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/16/1991,128,dominicks,4672,8.449342525,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/16/1991,128,minute.maid,28544,10.25920204,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/16/1991,128,tropicana,38080,10.54744449,1,2.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/16/1991,129,dominicks,4480,8.407378325,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/16/1991,129,minute.maid,4864,8.489616424,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/16/1991,129,tropicana,24768,10.11730778,1,2.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/16/1991,130,dominicks,7616,8.938006577,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/16/1991,130,minute.maid,55488,10.92392206,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/16/1991,130,tropicana,24704,10.11472045,1,2.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/16/1991,131,dominicks,7552,8.929567708,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/16/1991,131,minute.maid,11392,9.340666634,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/16/1991,131,tropicana,14400,9.574983486,1,2.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/16/1991,132,dominicks,10944,9.30054664,0,1.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/16/1991,132,minute.maid,9920,9.2023082,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/16/1991,132,tropicana,25216,10.13523399,1,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/16/1991,134,dominicks,2880,7.965545573,0,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/16/1991,134,minute.maid,2176,7.685243608,0,2.26,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/16/1991,134,tropicana,7488,8.921057018,1,2.29,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/16/1991,137,dominicks,6144,8.723231275,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/16/1991,137,minute.maid,9920,9.2023082,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/16/1991,137,tropicana,55616,10.92622621,1,2.29,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/23/1991,2,dominicks,27968,10.23881628,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/23/1991,2,minute.maid,4736,8.462948177,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/23/1991,2,tropicana,6336,8.754002934,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/23/1991,5,dominicks,28288,10.25019297,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/23/1991,5,minute.maid,7808,8.962904128,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/23/1991,5,tropicana,6272,8.743850562,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/23/1991,9,dominicks,46080,10.7381343,1,1.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/23/1991,9,minute.maid,5696,8.647519453,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/23/1991,9,tropicana,5632,8.636219898,0,3.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/23/1991,14,dominicks,19136,9.859326657,1,1.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/23/1991,14,minute.maid,5120,8.540909718,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/23/1991,14,tropicana,7232,8.886270902,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/23/1991,18,dominicks,34688,10.45414909,1,1.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/23/1991,18,minute.maid,12288,9.416378455,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/23/1991,18,tropicana,6400,8.764053269,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/23/1991,21,dominicks,28416,10.25470765,1,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/23/1991,21,minute.maid,10560,9.264828557,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/23/1991,21,tropicana,3456,8.14786713,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/23/1991,28,dominicks,22528,10.02251426,1,1.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/23/1991,28,minute.maid,3264,8.090708716,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/23/1991,28,tropicana,3200,8.070906089,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/23/1991,32,dominicks,72576,11.19238957,1,1.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/23/1991,32,minute.maid,8000,8.987196821,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/23/1991,32,tropicana,9088,9.114710141,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/23/1991,33,dominicks,19008,9.852615222,1,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/23/1991,33,minute.maid,8320,9.026417534,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/23/1991,33,tropicana,7040,8.859363449,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/23/1991,40,dominicks,34752,10.4559924,1,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/23/1991,40,minute.maid,5632,8.636219898,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/23/1991,40,tropicana,4288,8.363575703,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/23/1991,44,dominicks,30976,10.34096799,1,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/23/1991,44,minute.maid,5696,8.647519453,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/23/1991,44,tropicana,14848,9.605620455,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/23/1991,45,dominicks,23040,10.04498712,1,1.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/23/1991,45,minute.maid,3648,8.201934351,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/23/1991,45,tropicana,3392,8.129174997,0,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/23/1991,48,dominicks,19008,9.852615222,1,1.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/23/1991,48,minute.maid,5248,8.565602331,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/23/1991,48,tropicana,3712,8.219326094,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/23/1991,50,dominicks,21952,9.996613531,1,1.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/23/1991,50,minute.maid,4096,8.317766167,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/23/1991,50,tropicana,2368,7.769800996,0,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/23/1991,51,dominicks,27840,10.23422911,1,1.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/23/1991,51,minute.maid,3584,8.184234774,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/23/1991,51,tropicana,3456,8.14786713,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/23/1991,52,dominicks,30848,10.3368272,1,1.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/23/1991,52,minute.maid,9024,9.107642974,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/23/1991,52,tropicana,8768,9.078864009,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/23/1991,53,dominicks,60800,11.01534507,1,1.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/23/1991,53,minute.maid,10048,9.215128889,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/23/1991,53,tropicana,9536,9.162829389,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/23/1991,54,dominicks,18176,9.807857322,1,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/23/1991,54,minute.maid,3584,8.184234774,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/23/1991,54,tropicana,4032,8.30201781,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/23/1991,56,dominicks,27840,10.23422911,1,1.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/23/1991,56,minute.maid,3456,8.14786713,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/23/1991,56,tropicana,5440,8.60153434,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/23/1991,59,dominicks,34752,10.4559924,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/23/1991,59,minute.maid,4928,8.502688505,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/23/1991,59,tropicana,7104,8.868413285,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/23/1991,62,dominicks,18048,9.800790154,1,1.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/23/1991,62,minute.maid,8256,9.018695488,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/23/1991,62,tropicana,9280,9.135616826,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/23/1991,64,dominicks,18944,9.849242538,1,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/23/1991,64,minute.maid,7936,8.979164649,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/23/1991,64,tropicana,1856,7.526178913,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/23/1991,67,dominicks,30848,10.3368272,1,1.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/23/1991,67,minute.maid,10880,9.29468152,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/23/1991,67,tropicana,4800,8.476371197,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/23/1991,68,dominicks,37056,10.52018556,1,1.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/23/1991,68,minute.maid,7232,8.886270902,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/23/1991,68,tropicana,5056,8.528330936,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/23/1991,70,dominicks,36672,10.5097688,1,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/23/1991,70,minute.maid,8192,9.010913347,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/23/1991,70,tropicana,7680,8.946374826,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/23/1991,71,dominicks,47680,10.7722673,1,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/23/1991,71,minute.maid,10368,9.246479419,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/23/1991,71,tropicana,6080,8.712759975,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/23/1991,72,dominicks,35264,10.47061789,1,1.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/23/1991,72,minute.maid,6528,8.783855897,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/23/1991,72,tropicana,7104,8.868413285,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/23/1991,73,dominicks,109504,11.60371636,1,1.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/23/1991,73,minute.maid,12608,9.442086812,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/23/1991,73,tropicana,8448,9.041685006,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/23/1991,74,dominicks,59456,10.99299182,1,1.59,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/23/1991,74,minute.maid,6336,8.754002934,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/23/1991,74,tropicana,4672,8.449342525,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/23/1991,76,dominicks,43008,10.66914142,1,1.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/23/1991,76,minute.maid,6336,8.754002934,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/23/1991,76,tropicana,5056,8.528330936,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/23/1991,77,dominicks,23232,10.05328592,1,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/23/1991,77,minute.maid,7104,8.868413285,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/23/1991,77,tropicana,7104,8.868413285,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/23/1991,78,dominicks,46016,10.73674444,1,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/23/1991,78,minute.maid,7424,8.912473275,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/23/1991,78,tropicana,5440,8.60153434,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/23/1991,80,dominicks,25856,10.16029796,1,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/23/1991,80,minute.maid,9600,9.169518378,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/23/1991,80,tropicana,11328,9.335032816,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/23/1991,83,dominicks,52992,10.87789624,1,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/23/1991,83,minute.maid,6656,8.803273983,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/23/1991,83,tropicana,6912,8.841014311,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/23/1991,84,dominicks,31296,10.35124557,1,1.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/23/1991,84,minute.maid,4736,8.462948177,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/23/1991,84,tropicana,3072,8.030084094,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/23/1991,86,dominicks,43776,10.686841,1,1.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/23/1991,86,minute.maid,4032,8.30201781,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/23/1991,86,tropicana,6336,8.754002934,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/23/1991,88,dominicks,35584,10.47965138,1,1.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/23/1991,88,minute.maid,4224,8.348537825,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/23/1991,88,tropicana,3008,8.009030685,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/23/1991,89,dominicks,67648,11.12207307,1,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/23/1991,89,minute.maid,3584,8.184234774,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/23/1991,89,tropicana,1984,7.592870288,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/23/1991,90,dominicks,36416,10.50276352,1,1.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/23/1991,90,minute.maid,5248,8.565602331,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/23/1991,90,tropicana,1728,7.454719949,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/23/1991,91,dominicks,35904,10.48860399,1,1.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/23/1991,91,minute.maid,2624,7.87245515,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/23/1991,91,tropicana,2752,7.920083199,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/23/1991,92,dominicks,39104,10.57398004,1,1.59,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/23/1991,92,minute.maid,4096,8.317766167,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/23/1991,92,tropicana,1664,7.416979621,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/23/1991,93,dominicks,26560,10.1871616,1,1.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/23/1991,93,minute.maid,5440,8.60153434,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/23/1991,93,tropicana,6976,8.850230966,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/23/1991,94,dominicks,31424,10.35532721,1,1.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/23/1991,94,minute.maid,8064,8.99516499,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/23/1991,94,tropicana,5760,8.658692754,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/23/1991,95,dominicks,40000,10.59663473,1,1.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/23/1991,95,minute.maid,3392,8.129174997,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/23/1991,95,tropicana,4032,8.30201781,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/23/1991,97,dominicks,18304,9.814874894,1,1.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/23/1991,97,minute.maid,2112,7.655390645,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/23/1991,97,tropicana,1280,7.154615357,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/23/1991,98,dominicks,65856,11.09522582,1,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/23/1991,98,minute.maid,8832,9.086136769,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/23/1991,98,tropicana,6336,8.754002934,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/23/1991,100,dominicks,64256,11.07063038,1,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/23/1991,100,minute.maid,5952,8.691482577,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/23/1991,100,tropicana,8896,9.093357017,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/23/1991,101,dominicks,41152,10.62502781,1,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/23/1991,101,minute.maid,6016,8.702177866,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/23/1991,101,tropicana,5888,8.68067166,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/23/1991,102,dominicks,93440,11.4450748,1,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/23/1991,102,minute.maid,9664,9.17616292,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/23/1991,102,tropicana,5184,8.553332238,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/23/1991,103,dominicks,25152,10.1326927,1,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/23/1991,103,minute.maid,4992,8.51559191,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/23/1991,103,tropicana,1984,7.592870288,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/23/1991,104,dominicks,25920,10.16277015,1,1.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/23/1991,104,minute.maid,3840,8.253227646,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/23/1991,104,tropicana,1984,7.592870288,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/23/1991,105,dominicks,51520,10.84972536,1,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/23/1991,105,minute.maid,6336,8.754002934,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/23/1991,105,tropicana,4416,8.392989588,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/23/1991,106,dominicks,19200,9.862665558,1,1.59,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/23/1991,106,minute.maid,2304,7.742402022,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/23/1991,106,tropicana,1728,7.454719949,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/23/1991,107,dominicks,36992,10.51845695,1,1.59,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/23/1991,107,minute.maid,6784,8.822322178,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/23/1991,107,tropicana,6976,8.850230966,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/23/1991,110,dominicks,31488,10.3573618,1,1.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/23/1991,110,minute.maid,16064,9.684336023,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/23/1991,110,tropicana,2432,7.796469243,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/23/1991,111,dominicks,108160,11.59136689,1,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/23/1991,111,minute.maid,5056,8.528330936,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/23/1991,111,tropicana,3072,8.030084094,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/23/1991,112,dominicks,41024,10.62191254,1,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/23/1991,112,minute.maid,14528,9.583833101,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/23/1991,112,tropicana,14720,9.596962392,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/23/1991,113,dominicks,42112,10.64808802,1,1.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/23/1991,113,minute.maid,6336,8.754002934,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/23/1991,113,tropicana,6208,8.733594062,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/23/1991,114,dominicks,60800,11.01534507,1,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/23/1991,114,minute.maid,10496,9.258749511,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/23/1991,114,tropicana,5248,8.565602331,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/23/1991,115,dominicks,33920,10.43176009,1,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/23/1991,115,minute.maid,15680,9.660141294,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/23/1991,115,tropicana,6784,8.822322178,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/23/1991,117,dominicks,22528,10.02251426,1,1.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/23/1991,117,minute.maid,2688,7.896552702,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/23/1991,117,tropicana,2176,7.685243608,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/23/1991,118,dominicks,31168,10.34714721,1,1.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/23/1991,118,minute.maid,5888,8.68067166,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/23/1991,118,tropicana,4416,8.392989588,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/23/1991,119,dominicks,23168,10.0505273,1,1.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/23/1991,119,minute.maid,6144,8.723231275,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/23/1991,119,tropicana,4800,8.476371197,0,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/23/1991,121,dominicks,38144,10.54912375,1,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/23/1991,121,minute.maid,10304,9.240287448,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/23/1991,121,tropicana,9472,9.156095357,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/23/1991,122,dominicks,38784,10.56576307,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/23/1991,122,minute.maid,10048,9.215128889,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/23/1991,122,tropicana,10048,9.215128889,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/23/1991,123,dominicks,76928,11.2506252,1,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/23/1991,123,minute.maid,5824,8.66974259,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/23/1991,123,tropicana,5696,8.647519453,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/23/1991,124,dominicks,59904,11.00049856,1,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/23/1991,124,minute.maid,4224,8.348537825,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/23/1991,124,tropicana,3904,8.269756948,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/23/1991,126,dominicks,35264,10.47061789,1,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/23/1991,126,minute.maid,5248,8.565602331,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/23/1991,126,tropicana,3200,8.070906089,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/23/1991,128,dominicks,77184,11.25394746,1,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/23/1991,128,minute.maid,18304,9.814874894,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/23/1991,128,tropicana,10752,9.282847063,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/23/1991,129,dominicks,28352,10.25245285,1,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/23/1991,129,minute.maid,5632,8.636219898,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/23/1991,129,tropicana,7744,8.954673629,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/23/1991,130,dominicks,103168,11.54411401,1,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/23/1991,130,minute.maid,4800,8.476371197,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/23/1991,130,tropicana,3136,8.050703382,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/23/1991,131,dominicks,36992,10.51845695,1,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/23/1991,131,minute.maid,7488,8.921057018,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/23/1991,131,tropicana,5504,8.61323038,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/23/1991,132,dominicks,53824,10.89347474,1,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/23/1991,132,minute.maid,7808,8.962904128,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/23/1991,132,tropicana,5120,8.540909718,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/23/1991,134,dominicks,26496,10.18474906,1,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/23/1991,134,minute.maid,2112,7.655390645,0,2.26,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/23/1991,134,tropicana,2368,7.769800996,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/23/1991,137,dominicks,51520,10.84972536,1,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/23/1991,137,minute.maid,8896,9.093357017,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/23/1991,137,tropicana,20480,9.927204079,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/30/1991,2,dominicks,12160,9.405907156,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/30/1991,2,minute.maid,4480,8.407378325,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/30/1991,2,tropicana,6080,8.712759975,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/30/1991,5,dominicks,4864,8.489616424,0,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/30/1991,5,minute.maid,6272,8.743850562,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/30/1991,5,tropicana,5056,8.528330936,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/30/1991,9,dominicks,3264,8.090708716,0,1.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/30/1991,9,minute.maid,5760,8.658692754,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/30/1991,9,tropicana,5696,8.647519453,0,3.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/30/1991,12,dominicks,21824,9.990765561,0,1.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/30/1991,12,minute.maid,28992,10.27477521,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/30/1991,12,tropicana,6464,8.7740036,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/30/1991,14,dominicks,5824,8.66974259,0,1.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/30/1991,14,minute.maid,5184,8.553332238,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/30/1991,14,tropicana,8064,8.99516499,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/30/1991,18,dominicks,6208,8.733594062,0,1.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/30/1991,18,minute.maid,8128,9.00307017,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/30/1991,18,tropicana,7488,8.921057018,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/30/1991,21,dominicks,2880,7.965545573,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/30/1991,21,minute.maid,4928,8.502688505,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/30/1991,21,tropicana,3264,8.090708716,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/30/1991,28,dominicks,4800,8.476371197,0,1.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/30/1991,28,minute.maid,3008,8.009030685,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/30/1991,28,tropicana,3072,8.030084094,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/30/1991,32,dominicks,9856,9.195835686,0,1.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/30/1991,32,minute.maid,10240,9.234056899,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/30/1991,32,tropicana,8832,9.086136769,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/30/1991,33,dominicks,8128,9.00307017,0,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/30/1991,33,minute.maid,9472,9.156095357,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/30/1991,33,tropicana,8256,9.018695488,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/30/1991,40,dominicks,6144,8.723231275,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/30/1991,40,minute.maid,5248,8.565602331,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/30/1991,40,tropicana,5184,8.553332238,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/30/1991,44,dominicks,4416,8.392989588,0,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/30/1991,44,minute.maid,5440,8.60153434,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/30/1991,44,tropicana,15424,9.643680017,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/30/1991,45,dominicks,5504,8.61323038,0,1.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/30/1991,45,minute.maid,4416,8.392989588,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/30/1991,45,tropicana,4096,8.317766167,0,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/30/1991,47,dominicks,7232,8.886270902,0,1.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/30/1991,47,minute.maid,3392,8.129174997,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/30/1991,47,tropicana,2240,7.714231145,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/30/1991,48,dominicks,7808,8.962904128,0,1.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/30/1991,48,minute.maid,5056,8.528330936,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/30/1991,48,tropicana,2944,7.98752448,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/30/1991,49,dominicks,4672,8.449342525,0,1.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/30/1991,49,minute.maid,4480,8.407378325,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/30/1991,49,tropicana,3072,8.030084094,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/30/1991,50,dominicks,2560,7.847762538,0,1.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/30/1991,50,minute.maid,3712,8.219326094,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/30/1991,50,tropicana,2304,7.742402022,0,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/30/1991,51,dominicks,11648,9.36288977,0,1.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/30/1991,51,minute.maid,3392,8.129174997,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/30/1991,51,tropicana,3328,8.110126802,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/30/1991,52,dominicks,9728,9.182763604,0,1.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/30/1991,52,minute.maid,9152,9.121727714,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/30/1991,52,tropicana,8832,9.086136769,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/30/1991,53,dominicks,10240,9.234056899,0,1.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/30/1991,53,minute.maid,8256,9.018695488,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/30/1991,53,tropicana,8256,9.018695488,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/30/1991,54,dominicks,4096,8.317766167,0,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/30/1991,54,minute.maid,4544,8.42156296,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/30/1991,54,tropicana,3456,8.14786713,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/30/1991,56,dominicks,9536,9.162829389,0,1.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/30/1991,56,minute.maid,4416,8.392989588,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/30/1991,56,tropicana,4480,8.407378325,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/30/1991,59,dominicks,11136,9.317938383,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/30/1991,59,minute.maid,4608,8.435549202,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/30/1991,59,tropicana,4096,8.317766167,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/30/1991,62,dominicks,1600,7.377758908,0,1.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/30/1991,62,minute.maid,7040,8.859363449,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/30/1991,62,tropicana,10048,9.215128889,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/30/1991,64,dominicks,4352,8.378390789,0,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/30/1991,64,minute.maid,24128,10.09112827,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/30/1991,64,tropicana,1984,7.592870288,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/30/1991,67,dominicks,2624,7.87245515,0,1.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/30/1991,67,minute.maid,10048,9.215128889,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/30/1991,67,tropicana,5824,8.66974259,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/30/1991,70,dominicks,10880,9.29468152,0,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/30/1991,70,minute.maid,29120,10.2791805,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/30/1991,70,tropicana,9728,9.182763604,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/30/1991,71,dominicks,43072,10.67062841,0,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/30/1991,71,minute.maid,2816,7.943072717,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/30/1991,71,tropicana,9216,9.128696383,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/30/1991,72,dominicks,11712,9.368369236,0,1.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/30/1991,72,minute.maid,4544,8.42156296,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/30/1991,72,tropicana,7040,8.859363449,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/30/1991,73,dominicks,37696,10.53730927,0,1.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/30/1991,73,minute.maid,12416,9.426741242,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/30/1991,73,tropicana,12224,9.411156511,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/30/1991,74,dominicks,15936,9.67633598,0,1.59,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/30/1991,74,minute.maid,7936,8.979164649,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/30/1991,74,tropicana,5312,8.577723691,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/30/1991,76,dominicks,3264,8.090708716,0,1.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/30/1991,76,minute.maid,2752,7.920083199,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/30/1991,76,tropicana,3392,8.129174997,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/30/1991,77,dominicks,9344,9.142489705,0,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/30/1991,77,minute.maid,6784,8.822322178,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/30/1991,77,tropicana,7232,8.886270902,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/30/1991,78,dominicks,4480,8.407378325,0,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/30/1991,78,minute.maid,5824,8.66974259,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/30/1991,78,tropicana,5184,8.553332238,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/30/1991,80,dominicks,6656,8.803273983,0,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/30/1991,80,minute.maid,8512,9.049232212,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/30/1991,80,tropicana,9024,9.107642974,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/30/1991,83,dominicks,4160,8.333270353,0,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/30/1991,83,minute.maid,4288,8.363575703,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/30/1991,83,tropicana,6272,8.743850562,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/30/1991,84,dominicks,10112,9.221478116,0,1.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/30/1991,84,minute.maid,5760,8.658692754,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/30/1991,84,tropicana,4288,8.363575703,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/30/1991,86,dominicks,11328,9.335032816,0,1.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/30/1991,86,minute.maid,5056,8.528330936,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/30/1991,86,tropicana,4800,8.476371197,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/30/1991,88,dominicks,27840,10.23422911,0,1.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/30/1991,88,minute.maid,4288,8.363575703,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/30/1991,88,tropicana,3008,8.009030685,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/30/1991,89,dominicks,7808,8.962904128,0,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/30/1991,89,minute.maid,24576,10.10952564,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/30/1991,89,tropicana,2560,7.847762538,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/30/1991,90,dominicks,1920,7.560080465,0,1.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/30/1991,90,minute.maid,24960,10.12502982,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/30/1991,90,tropicana,1472,7.294377299,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/30/1991,91,dominicks,8256,9.018695488,0,1.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/30/1991,91,minute.maid,2880,7.965545573,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/30/1991,91,tropicana,3328,8.110126802,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/30/1991,92,dominicks,7296,8.895081532,0,1.59,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/30/1991,92,minute.maid,4288,8.363575703,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/30/1991,92,tropicana,2304,7.742402022,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/30/1991,93,dominicks,15168,9.626943225,0,1.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/30/1991,93,minute.maid,5952,8.691482577,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/30/1991,93,tropicana,6912,8.841014311,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/30/1991,94,dominicks,8320,9.026417534,0,1.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/30/1991,94,minute.maid,9344,9.142489705,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/30/1991,94,tropicana,7232,8.886270902,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/30/1991,95,dominicks,25088,10.13014492,0,1.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/30/1991,95,minute.maid,4352,8.378390789,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/30/1991,95,tropicana,3392,8.129174997,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/30/1991,97,dominicks,1472,7.294377299,0,1.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/30/1991,97,minute.maid,2880,7.965545573,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/30/1991,97,tropicana,1344,7.203405521,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/30/1991,98,dominicks,9792,9.189321005,0,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/30/1991,98,minute.maid,7936,8.979164649,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/30/1991,98,tropicana,7424,8.912473275,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/30/1991,100,dominicks,15808,9.66827142,0,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/30/1991,100,minute.maid,7872,8.971067439,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/30/1991,100,tropicana,8640,9.064157862,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/30/1991,101,dominicks,9536,9.162829389,0,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/30/1991,101,minute.maid,4992,8.51559191,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/30/1991,101,tropicana,5888,8.68067166,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/30/1991,102,dominicks,114432,11.64773604,0,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/30/1991,102,minute.maid,14016,9.547954813,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/30/1991,102,tropicana,8384,9.034080407,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/30/1991,103,dominicks,6336,8.754002934,0,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/30/1991,103,minute.maid,3904,8.269756948,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/30/1991,103,tropicana,4416,8.392989588,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/30/1991,104,dominicks,6848,8.831711918,0,1.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/30/1991,104,minute.maid,4160,8.333270353,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/30/1991,104,tropicana,3008,8.009030685,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/30/1991,105,dominicks,11520,9.351839934,0,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/30/1991,105,minute.maid,6400,8.764053269,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/30/1991,105,tropicana,4032,8.30201781,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/30/1991,106,dominicks,3392,8.129174997,0,1.59,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/30/1991,106,minute.maid,9792,9.189321005,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/30/1991,106,tropicana,1664,7.416979621,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/30/1991,107,dominicks,11712,9.368369236,0,1.59,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/30/1991,107,minute.maid,5632,8.636219898,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/30/1991,107,tropicana,8704,9.071537969,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/30/1991,109,dominicks,3264,8.090708716,0,1.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/30/1991,109,minute.maid,10560,9.264828557,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/30/1991,109,tropicana,12416,9.426741242,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/30/1991,110,dominicks,7872,8.971067439,0,1.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/30/1991,110,minute.maid,8384,9.034080407,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/30/1991,110,tropicana,3840,8.253227646,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/30/1991,111,dominicks,39360,10.58050535,0,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/30/1991,111,minute.maid,4864,8.489616424,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/30/1991,111,tropicana,3008,8.009030685,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/30/1991,112,dominicks,12992,9.472089062,0,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/30/1991,112,minute.maid,13696,9.524859098,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/30/1991,112,tropicana,15296,9.635346635,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/30/1991,113,dominicks,10368,9.246479419,0,1.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/30/1991,113,minute.maid,6592,8.793612072,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/30/1991,113,tropicana,5952,8.691482577,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/30/1991,114,dominicks,7168,8.877381955,0,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/30/1991,114,minute.maid,7424,8.912473275,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/30/1991,114,tropicana,4608,8.435549202,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/30/1991,115,dominicks,2816,7.943072717,0,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/30/1991,115,minute.maid,54208,10.90058378,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/30/1991,115,tropicana,4480,8.407378325,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/30/1991,116,dominicks,6784,8.822322178,0,1.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/30/1991,116,minute.maid,3200,8.070906089,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/30/1991,116,tropicana,4288,8.363575703,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/30/1991,117,dominicks,3392,8.129174997,0,1.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/30/1991,117,minute.maid,3712,8.219326094,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/30/1991,117,tropicana,1792,7.491087594,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/30/1991,118,dominicks,9600,9.169518378,0,1.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/30/1991,118,minute.maid,6784,8.822322178,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/30/1991,118,tropicana,4096,8.317766167,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/30/1991,119,dominicks,6720,8.812843434,0,1.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/30/1991,119,minute.maid,7232,8.886270902,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/30/1991,119,tropicana,6144,8.723231275,0,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/30/1991,121,dominicks,8768,9.078864009,0,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/30/1991,121,minute.maid,9152,9.121727714,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/30/1991,121,tropicana,13824,9.534161491,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/30/1991,122,dominicks,12096,9.400630098,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/30/1991,122,minute.maid,10496,9.258749511,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/30/1991,122,tropicana,10112,9.221478116,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/30/1991,123,dominicks,5312,8.577723691,0,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/30/1991,123,minute.maid,5888,8.68067166,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/30/1991,123,tropicana,6464,8.7740036,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/30/1991,124,dominicks,24832,10.11988842,0,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/30/1991,124,minute.maid,5056,8.528330936,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/30/1991,124,tropicana,6144,8.723231275,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/30/1991,126,dominicks,7488,8.921057018,0,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/30/1991,126,minute.maid,6144,8.723231275,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/30/1991,126,tropicana,4480,8.407378325,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/30/1991,128,dominicks,3392,8.129174997,0,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/30/1991,128,minute.maid,9728,9.182763604,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/30/1991,128,tropicana,8960,9.100525506,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/30/1991,129,dominicks,10944,9.30054664,0,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/30/1991,129,minute.maid,5632,8.636219898,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/30/1991,129,tropicana,9216,9.128696383,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/30/1991,130,dominicks,37184,10.52363384,0,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/30/1991,130,minute.maid,6592,8.793612072,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/30/1991,130,tropicana,3712,8.219326094,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/30/1991,132,dominicks,16640,9.719564714,0,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/30/1991,132,minute.maid,8832,9.086136769,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/30/1991,132,tropicana,7040,8.859363449,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/30/1991,134,dominicks,11008,9.30637756,0,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/30/1991,134,minute.maid,1856,7.526178913,0,2.26,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/30/1991,134,tropicana,2432,7.796469243,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/30/1991,137,dominicks,16704,9.723403491,0,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/30/1991,137,minute.maid,10432,9.252633284,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/30/1991,137,tropicana,15104,9.622714888,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/6/1991,2,dominicks,2240,7.714231145,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/6/1991,2,minute.maid,4032,8.30201781,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/6/1991,2,tropicana,33536,10.42037477,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/6/1991,5,dominicks,2880,7.965545573,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/6/1991,5,minute.maid,6144,8.723231275,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/6/1991,5,tropicana,47616,10.77092412,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/6/1991,8,dominicks,9280,9.135616826,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/6/1991,8,minute.maid,6656,8.803273983,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/6/1991,8,tropicana,46912,10.75602879,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/6/1991,9,dominicks,2112,7.655390645,0,2.09,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/6/1991,9,minute.maid,3648,8.201934351,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/6/1991,9,tropicana,76480,11.24478455,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/6/1991,12,dominicks,1728,7.454719949,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/6/1991,12,minute.maid,5568,8.624791202,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/6/1991,12,tropicana,84096,11.33971428,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/6/1991,14,dominicks,704,6.556778356,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/6/1991,14,minute.maid,2880,7.965545573,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/6/1991,14,tropicana,49216,10.80397405,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/6/1991,18,dominicks,6144,8.723231275,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/6/1991,18,minute.maid,9280,9.135616826,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/6/1991,18,tropicana,60800,11.01534507,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/6/1991,21,dominicks,6336,8.754002934,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/6/1991,21,minute.maid,3584,8.184234774,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/6/1991,21,tropicana,26048,10.16769627,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/6/1991,28,dominicks,1408,7.249925537,0,2.09,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/6/1991,28,minute.maid,2112,7.655390645,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/6/1991,28,tropicana,29120,10.2791805,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/6/1991,32,dominicks,2560,7.847762538,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/6/1991,32,minute.maid,6336,8.754002934,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/6/1991,32,tropicana,92672,11.43682166,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/6/1991,33,dominicks,832,6.723832441,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/6/1991,33,minute.maid,5120,8.540909718,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/6/1991,33,tropicana,56960,10.95010455,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/6/1991,40,dominicks,11072,9.312174678,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/6/1991,40,minute.maid,5760,8.658692754,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/6/1991,40,tropicana,27968,10.23881628,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/6/1991,44,dominicks,2688,7.896552702,0,2.09,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/6/1991,44,minute.maid,3520,8.166216269,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/6/1991,44,tropicana,64000,11.06663836,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/6/1991,45,dominicks,2368,7.769800996,0,2.09,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/6/1991,45,minute.maid,4096,8.317766167,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/6/1991,45,tropicana,27520,10.22266829,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/6/1991,47,dominicks,2624,7.87245515,0,2.09,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/6/1991,47,minute.maid,3008,8.009030685,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/6/1991,47,tropicana,30016,10.30948585,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/6/1991,48,dominicks,2688,7.896552702,0,2.09,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/6/1991,48,minute.maid,4736,8.462948177,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/6/1991,48,tropicana,25088,10.13014492,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/6/1991,49,dominicks,2176,7.685243608,0,2.09,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/6/1991,49,minute.maid,2496,7.82244473,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/6/1991,49,tropicana,28224,10.24792796,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/6/1991,50,dominicks,2304,7.742402022,0,2.09,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/6/1991,50,minute.maid,1984,7.592870288,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/6/1991,50,tropicana,23808,10.07777694,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/6/1991,51,dominicks,2816,7.943072717,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/6/1991,51,minute.maid,2752,7.920083199,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/6/1991,51,tropicana,37760,10.53900562,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/6/1991,52,dominicks,448,6.104793232,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/6/1991,52,minute.maid,6592,8.793612072,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/6/1991,52,tropicana,77632,11.25973499,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/6/1991,53,dominicks,2624,7.87245515,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/6/1991,53,minute.maid,6784,8.822322178,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/6/1991,53,tropicana,85056,11.35106514,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/6/1991,54,dominicks,2048,7.624618986,0,2.09,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/6/1991,54,minute.maid,2816,7.943072717,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/6/1991,54,tropicana,34816,10.45783233,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/6/1991,56,dominicks,3136,8.050703382,0,2.09,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/6/1991,56,minute.maid,2112,7.655390645,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/6/1991,56,tropicana,35200,10.46880136,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/6/1991,59,dominicks,8576,9.056722883,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/6/1991,59,minute.maid,4736,8.462948177,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/6/1991,59,tropicana,26944,10.20151592,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/6/1991,62,dominicks,448,6.104793232,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/6/1991,62,minute.maid,5248,8.565602331,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/6/1991,62,tropicana,63488,11.05860619,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/6/1991,67,dominicks,3712,8.219326094,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/6/1991,67,minute.maid,33024,10.40498985,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/6/1991,67,tropicana,17536,9.77201119,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/6/1991,68,dominicks,1344,7.203405521,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/6/1991,68,minute.maid,4480,8.407378325,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/6/1991,68,tropicana,67200,11.11542853,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/6/1991,70,dominicks,10176,9.227787286,0,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/6/1991,70,minute.maid,11136,9.317938383,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/6/1991,70,tropicana,30464,10.32430094,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/6/1991,72,dominicks,1024,6.931471806,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/6/1991,72,minute.maid,3584,8.184234774,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/6/1991,72,tropicana,75712,11.23469195,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/6/1991,73,dominicks,13696,9.524859098,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/6/1991,73,minute.maid,12864,9.462187991,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/6/1991,73,tropicana,72832,11.1959107,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/6/1991,74,dominicks,7360,8.903815212,0,2.09,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/6/1991,74,minute.maid,5568,8.624791202,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/6/1991,74,tropicana,57600,10.96127785,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/6/1991,76,dominicks,1408,7.249925537,0,2.09,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/6/1991,76,minute.maid,2752,7.920083199,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/6/1991,76,tropicana,68864,11.13988882,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/6/1991,77,dominicks,9024,9.107642974,0,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/6/1991,77,minute.maid,7040,8.859363449,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/6/1991,77,tropicana,31616,10.3614186,0,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/6/1991,78,dominicks,7232,8.886270902,0,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/6/1991,78,minute.maid,6848,8.831711918,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/6/1991,78,tropicana,42944,10.66765222,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/6/1991,80,dominicks,6400,8.764053269,0,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/6/1991,80,minute.maid,7616,8.938006577,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/6/1991,80,tropicana,49344,10.80657146,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/6/1991,81,dominicks,4096,8.317766167,0,2.09,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/6/1991,81,minute.maid,9088,9.114710141,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/6/1991,81,tropicana,59840,10.99942961,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/6/1991,83,dominicks,12224,9.411156511,0,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/6/1991,83,minute.maid,5760,8.658692754,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/6/1991,83,tropicana,53568,10.88870715,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/6/1991,84,dominicks,2688,7.896552702,0,2.09,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/6/1991,84,minute.maid,3776,8.236420527,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/6/1991,84,tropicana,38720,10.56411154,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/6/1991,86,dominicks,2112,7.655390645,0,2.09,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/6/1991,86,minute.maid,10752,9.282847063,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/6/1991,86,tropicana,51776,10.854682,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/6/1991,88,dominicks,2560,7.847762538,0,2.09,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/6/1991,88,minute.maid,3392,8.129174997,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/6/1991,88,tropicana,33984,10.43364511,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/6/1991,89,dominicks,3072,8.030084094,0,2.09,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/6/1991,89,minute.maid,3200,8.070906089,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/6/1991,89,tropicana,56832,10.94785483,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/6/1991,90,dominicks,2176,7.685243608,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/6/1991,90,minute.maid,3264,8.090708716,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/6/1991,90,tropicana,42560,10.65867012,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/6/1991,91,dominicks,1536,7.336936914,0,2.09,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/6/1991,91,minute.maid,2304,7.742402022,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/6/1991,91,tropicana,37056,10.52018556,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/6/1991,92,dominicks,3776,8.236420527,0,2.09,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/6/1991,92,minute.maid,3264,8.090708716,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/6/1991,92,tropicana,48128,10.78161941,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/6/1991,94,dominicks,6528,8.783855897,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/6/1991,94,minute.maid,8000,8.987196821,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/6/1991,94,tropicana,25472,10.14533509,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/6/1991,95,dominicks,14080,9.55251063,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/6/1991,95,minute.maid,2752,7.920083199,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/6/1991,95,tropicana,40128,10.59982962,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/6/1991,97,dominicks,1280,7.154615357,0,2.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/6/1991,97,minute.maid,1984,7.592870288,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/6/1991,97,tropicana,12544,9.436997743,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/6/1991,100,dominicks,2688,7.896552702,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/6/1991,100,minute.maid,4928,8.502688505,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/6/1991,100,tropicana,78976,11.27689929,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/6/1991,101,dominicks,3712,8.219326094,0,2.09,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/6/1991,101,minute.maid,4288,8.363575703,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/6/1991,101,tropicana,51200,10.84349481,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/6/1991,102,dominicks,6464,8.7740036,0,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/6/1991,102,minute.maid,9536,9.162829389,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/6/1991,102,tropicana,67136,11.11447569,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/6/1991,104,dominicks,1344,7.203405521,0,2.39,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/6/1991,104,minute.maid,3648,8.201934351,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/6/1991,104,tropicana,41856,10.64199044,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/6/1991,105,dominicks,13952,9.543378146,0,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/6/1991,105,minute.maid,5632,8.636219898,0,2.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/6/1991,105,tropicana,37568,10.5339079,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/6/1991,106,dominicks,384,5.950642553,0,2.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/6/1991,106,minute.maid,2304,7.742402022,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/6/1991,106,tropicana,17728,9.78290059,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/6/1991,107,dominicks,4800,8.476371197,0,2.09,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/6/1991,107,minute.maid,5248,8.565602331,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/6/1991,107,tropicana,57920,10.96681803,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/6/1991,109,dominicks,1344,7.203405521,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/6/1991,109,minute.maid,9024,9.107642974,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/6/1991,109,tropicana,106240,11.57345597,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/6/1991,110,dominicks,2112,7.655390645,0,2.09,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/6/1991,110,minute.maid,4992,8.51559191,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/6/1991,110,tropicana,42112,10.64808802,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/6/1991,111,dominicks,4288,8.363575703,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/6/1991,111,minute.maid,4032,8.30201781,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/6/1991,111,tropicana,78336,11.26876255,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/6/1991,112,dominicks,9344,9.142489705,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/6/1991,112,minute.maid,12032,9.395325046,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/6/1991,112,tropicana,68928,11.14081776,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/6/1991,113,dominicks,4288,8.363575703,0,2.09,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/6/1991,113,minute.maid,4224,8.348537825,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/6/1991,113,tropicana,56000,10.93310697,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/6/1991,115,dominicks,1216,7.103322063,0,2.09,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/6/1991,115,minute.maid,3072,8.030084094,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/6/1991,115,tropicana,53888,10.8946631,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/6/1991,116,dominicks,2816,7.943072717,0,2.09,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/6/1991,116,minute.maid,2688,7.896552702,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/6/1991,116,tropicana,34880,10.45966888,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/6/1991,118,dominicks,2048,7.624618986,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/6/1991,118,minute.maid,5184,8.553332238,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/6/1991,118,tropicana,53952,10.89585004,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/6/1991,119,dominicks,1984,7.592870288,0,2.09,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/6/1991,119,minute.maid,6272,8.743850562,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/6/1991,119,tropicana,39104,10.57398004,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/6/1991,121,dominicks,4224,8.348537825,0,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/6/1991,121,minute.maid,8128,9.00307017,0,2.26,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/6/1991,121,tropicana,54720,10.90998455,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/6/1991,122,dominicks,10304,9.240287448,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/6/1991,122,minute.maid,8704,9.071537969,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/6/1991,122,tropicana,40704,10.61408165,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/6/1991,123,dominicks,2112,7.655390645,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/6/1991,123,minute.maid,4608,8.435549202,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/6/1991,123,tropicana,84352,11.3427538,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/6/1991,124,dominicks,2944,7.98752448,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/6/1991,124,minute.maid,4224,8.348537825,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/6/1991,124,tropicana,71040,11.17099838,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/6/1991,126,dominicks,2304,7.742402022,0,2.09,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/6/1991,126,minute.maid,4480,8.407378325,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/6/1991,126,tropicana,56512,10.94220828,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/6/1991,128,dominicks,1600,7.377758908,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/6/1991,128,minute.maid,6144,8.723231275,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/6/1991,128,tropicana,138688,11.83998209,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/6/1991,129,dominicks,2240,7.714231145,0,2.09,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/6/1991,129,minute.maid,4416,8.392989588,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/6/1991,129,tropicana,46208,10.74090822,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/6/1991,130,dominicks,2496,7.82244473,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/6/1991,130,minute.maid,3840,8.253227646,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/6/1991,130,tropicana,80192,11.29217904,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/6/1991,131,dominicks,3968,8.286017468,0,2.09,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/6/1991,131,minute.maid,9408,9.14931567,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/6/1991,131,tropicana,29504,10.29228113,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/6/1991,132,dominicks,11648,9.36288977,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/6/1991,132,minute.maid,8384,9.034080407,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/6/1991,132,tropicana,44288,10.69846904,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/6/1991,134,dominicks,5632,8.636219898,0,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/6/1991,134,minute.maid,2304,7.742402022,0,2.26,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/6/1991,134,tropicana,20672,9.936535407,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/6/1991,137,dominicks,2496,7.82244473,0,2.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/6/1991,137,minute.maid,6208,8.733594062,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/6/1991,137,tropicana,132352,11.79322032,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/13/1991,2,dominicks,5504,8.61323038,1,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/13/1991,2,minute.maid,14784,9.601300794,1,1.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/13/1991,2,tropicana,13248,9.491601877,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/13/1991,5,dominicks,5760,8.658692754,1,1.41,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/13/1991,5,minute.maid,27776,10.23192762,1,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/13/1991,5,tropicana,13888,9.538780437,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/13/1991,8,dominicks,25856,10.16029796,1,1.26,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/13/1991,8,minute.maid,35456,10.47604777,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/13/1991,8,tropicana,18240,9.811372264,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/13/1991,9,dominicks,7616,8.938006577,1,1.41,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/13/1991,9,minute.maid,41216,10.62658181,1,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/13/1991,9,tropicana,7744,8.954673629,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/13/1991,12,dominicks,15808,9.66827142,1,1.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/13/1991,12,minute.maid,46144,10.73952222,1,1.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/13/1991,12,tropicana,20480,9.927204079,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/13/1991,14,dominicks,8704,9.071537969,1,1.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/13/1991,14,minute.maid,14272,9.566054855,1,1.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/13/1991,14,tropicana,19456,9.875910785,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/13/1991,18,dominicks,6336,8.754002934,1,1.26,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/13/1991,18,minute.maid,54528,10.90646961,1,1.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/13/1991,18,tropicana,17600,9.775654181,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/13/1991,21,dominicks,7936,8.979164649,1,1.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/13/1991,21,minute.maid,13824,9.534161491,1,1.38,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/13/1991,21,tropicana,20480,9.927204079,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/13/1991,28,dominicks,4928,8.502688505,1,1.41,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/13/1991,28,minute.maid,10624,9.270870872,1,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/13/1991,28,tropicana,6848,8.831711918,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/13/1991,32,dominicks,11328,9.335032816,1,1.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/13/1991,32,minute.maid,27392,10.21800628,1,1.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/13/1991,32,tropicana,15168,9.626943225,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/13/1991,33,dominicks,9856,9.195835686,1,1.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/13/1991,33,minute.maid,20096,9.908276069,1,1.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/13/1991,33,tropicana,14144,9.557045785,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/13/1991,40,dominicks,43520,10.68097588,1,1.19,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/13/1991,40,minute.maid,10432,9.252633284,1,1.38,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/13/1991,40,tropicana,12544,9.436997743,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/13/1991,44,dominicks,13376,9.501217335,1,1.41,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/13/1991,44,minute.maid,19584,9.882468185,1,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/13/1991,44,tropicana,14528,9.583833101,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/13/1991,45,dominicks,9600,9.169518378,1,1.41,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/13/1991,45,minute.maid,9728,9.182763604,1,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/13/1991,45,tropicana,13504,9.510741217,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/13/1991,47,dominicks,14528,9.583833101,1,1.41,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/13/1991,47,minute.maid,15296,9.635346635,1,1.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/13/1991,47,tropicana,6912,8.841014311,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/13/1991,48,dominicks,6016,8.702177866,1,1.41,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/13/1991,48,minute.maid,16064,9.684336023,1,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/13/1991,48,tropicana,5760,8.658692754,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/13/1991,49,dominicks,6080,8.712759975,1,1.41,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/13/1991,49,minute.maid,10880,9.29468152,1,1.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/13/1991,49,tropicana,8192,9.010913347,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/13/1991,50,dominicks,4032,8.30201781,1,1.41,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/13/1991,50,minute.maid,10112,9.221478116,1,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/13/1991,50,tropicana,7616,8.938006577,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/13/1991,51,dominicks,14080,9.55251063,1,1.41,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/13/1991,51,minute.maid,27264,10.21332243,1,1.69,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/13/1991,51,tropicana,9344,9.142489705,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/13/1991,52,dominicks,14592,9.588228712,1,1.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/13/1991,52,minute.maid,21504,9.975994243,1,1.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/13/1991,52,tropicana,18496,9.825309772,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/13/1991,53,dominicks,15232,9.631153757,1,1.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/13/1991,53,minute.maid,20096,9.908276069,1,1.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/13/1991,53,tropicana,25856,10.16029796,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/13/1991,54,dominicks,7872,8.971067439,1,1.41,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/13/1991,54,minute.maid,13504,9.510741217,1,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/13/1991,54,tropicana,10752,9.282847063,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/13/1991,56,dominicks,10432,9.252633284,1,1.41,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/13/1991,56,minute.maid,10048,9.215128889,1,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/13/1991,56,tropicana,10944,9.30054664,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/13/1991,59,dominicks,21504,9.975994243,1,1.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/13/1991,59,minute.maid,15168,9.626943225,1,1.38,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/13/1991,59,tropicana,11584,9.357380115,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/13/1991,62,dominicks,6144,8.723231275,1,1.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/13/1991,62,minute.maid,16704,9.723403491,1,1.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/13/1991,62,tropicana,24512,10.10691807,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/13/1991,64,dominicks,8000,8.987196821,1,1.41,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/13/1991,64,minute.maid,6208,8.733594062,1,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/13/1991,64,tropicana,4160,8.333270353,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/13/1991,67,dominicks,20096,9.908276069,1,1.26,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/13/1991,67,minute.maid,14464,9.579418083,1,1.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/13/1991,67,tropicana,9728,9.182763604,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/13/1991,68,dominicks,13696,9.524859098,1,1.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/13/1991,68,minute.maid,20736,9.939626599,1,1.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/13/1991,68,tropicana,18752,9.839055692,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/13/1991,70,dominicks,17984,9.797237753,1,1.19,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/13/1991,70,minute.maid,24512,10.10691807,1,1.38,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/13/1991,70,tropicana,16768,9.727227587,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/13/1991,71,dominicks,19904,9.898675996,1,1.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/13/1991,71,minute.maid,14080,9.55251063,1,1.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/13/1991,71,tropicana,14272,9.566054855,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/13/1991,72,dominicks,8320,9.026417534,1,1.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/13/1991,72,minute.maid,20864,9.945780465,1,1.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/13/1991,72,tropicana,25728,10.15533517,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/13/1991,73,dominicks,28672,10.26367632,1,1.26,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/13/1991,73,minute.maid,66048,11.09813703,1,1.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/13/1991,73,tropicana,18368,9.818365299,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/13/1991,74,dominicks,21824,9.990765561,1,1.41,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/13/1991,74,minute.maid,26368,10.17990643,1,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/13/1991,74,tropicana,13824,9.534161491,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/13/1991,75,dominicks,14720,9.596962392,1,1.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/13/1991,75,minute.maid,21184,9.961001459,1,1.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/13/1991,75,tropicana,18624,9.832206351,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/13/1991,76,dominicks,8512,9.049232212,1,1.41,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/13/1991,76,minute.maid,52928,10.87668778,1,1.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/13/1991,76,tropicana,28352,10.25245285,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/13/1991,77,dominicks,18624,9.832206351,1,1.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/13/1991,77,minute.maid,23360,10.05878044,1,1.38,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/13/1991,77,tropicana,13184,9.486759252,0,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/13/1991,78,dominicks,29376,10.28793329,1,1.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/13/1991,78,minute.maid,13248,9.491601877,1,1.38,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/13/1991,78,tropicana,18368,9.818365299,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/13/1991,80,dominicks,9344,9.142489705,1,1.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/13/1991,80,minute.maid,32512,10.38936453,1,1.38,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/13/1991,80,tropicana,19520,9.87919486,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/13/1991,81,dominicks,10368,9.246479419,1,1.41,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/13/1991,81,minute.maid,23936,10.08313888,1,1.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/13/1991,81,tropicana,8512,9.049232212,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/13/1991,83,dominicks,60352,11.00794937,1,1.19,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/13/1991,83,minute.maid,8128,9.00307017,1,1.38,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/13/1991,83,tropicana,8640,9.064157862,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/13/1991,84,dominicks,10624,9.270870872,1,1.41,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/13/1991,84,minute.maid,15872,9.67231183,1,1.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/13/1991,84,tropicana,5376,8.589699882,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/13/1991,86,dominicks,15296,9.635346635,1,1.41,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/13/1991,86,minute.maid,20864,9.945780465,1,1.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/13/1991,86,tropicana,13056,9.477003077,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/13/1991,88,dominicks,5696,8.647519453,1,1.41,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/13/1991,88,minute.maid,19776,9.89222436,1,1.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/13/1991,88,tropicana,4864,8.489616424,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/13/1991,89,dominicks,16128,9.688312171,1,1.41,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/13/1991,89,minute.maid,29568,10.29444797,1,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/13/1991,89,tropicana,8448,9.041685006,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/13/1991,90,dominicks,11008,9.30637756,1,1.41,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/13/1991,90,minute.maid,26432,10.18233068,1,1.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/13/1991,90,tropicana,5568,8.624791202,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/13/1991,91,dominicks,4864,8.489616424,1,1.41,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/13/1991,91,minute.maid,24384,10.10168246,1,1.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/13/1991,91,tropicana,7616,8.938006577,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/13/1991,92,dominicks,16384,9.704060528,1,1.41,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/13/1991,92,minute.maid,29248,10.28356647,1,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/13/1991,92,tropicana,6528,8.783855897,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/13/1991,93,dominicks,12096,9.400630098,1,1.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/13/1991,93,minute.maid,13440,9.505990614,1,1.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/13/1991,93,tropicana,21184,9.961001459,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/13/1991,94,dominicks,10944,9.30054664,1,1.26,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/13/1991,94,minute.maid,21568,9.978966014,1,1.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/13/1991,94,tropicana,12864,9.462187991,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/13/1991,95,dominicks,15232,9.631153757,1,1.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/13/1991,95,minute.maid,11136,9.317938383,1,1.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/13/1991,95,tropicana,16960,9.738612909,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/13/1991,97,dominicks,3712,8.219326094,1,1.41,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/13/1991,97,minute.maid,11840,9.379238908,1,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/13/1991,97,tropicana,4288,8.363575703,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/13/1991,98,dominicks,25408,10.14281936,1,1.26,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/13/1991,98,minute.maid,45760,10.73116563,1,1.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/13/1991,98,tropicana,12032,9.395325046,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/13/1991,100,dominicks,17152,9.749870064,1,1.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/13/1991,100,minute.maid,53632,10.88990118,1,1.79,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/13/1991,100,tropicana,21056,9.954940834,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/13/1991,101,dominicks,13824,9.534161491,1,1.41,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/13/1991,101,minute.maid,24960,10.12502982,1,1.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/13/1991,101,tropicana,5056,8.528330936,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/13/1991,102,dominicks,34304,10.44301724,1,1.26,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/13/1991,102,minute.maid,36864,10.51499074,1,1.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/13/1991,102,tropicana,19200,9.862665558,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/13/1991,103,dominicks,14528,9.583833101,1,1.26,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/13/1991,103,minute.maid,30656,10.33058368,1,1.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/13/1991,103,tropicana,5632,8.636219898,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/13/1991,104,dominicks,9856,9.195835686,1,1.41,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/13/1991,104,minute.maid,22016,9.999524741,1,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/13/1991,104,tropicana,7872,8.971067439,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/13/1991,105,dominicks,26048,10.16769627,1,1.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/13/1991,105,minute.maid,19648,9.885730831,1,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/13/1991,105,tropicana,9728,9.182763604,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/13/1991,106,dominicks,4352,8.378390789,1,1.41,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/13/1991,106,minute.maid,9536,9.162829389,1,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/13/1991,106,tropicana,4544,8.42156296,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/13/1991,107,dominicks,12736,9.452187908,1,1.41,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/13/1991,107,minute.maid,18816,9.842462851,1,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/13/1991,107,tropicana,18496,9.825309772,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/13/1991,109,dominicks,12480,9.431882642,1,1.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/13/1991,109,minute.maid,37184,10.52363384,1,1.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/13/1991,109,tropicana,48576,10.79088486,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/13/1991,110,dominicks,12160,9.405907156,1,1.41,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/13/1991,110,minute.maid,22080,10.0024275,1,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/13/1991,110,tropicana,10304,9.240287448,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/13/1991,111,dominicks,101376,11.52659166,1,1.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/13/1991,111,minute.maid,29696,10.29876764,1,1.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/13/1991,111,tropicana,6976,8.850230966,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/13/1991,112,dominicks,22144,10.00532186,1,1.26,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/13/1991,112,minute.maid,47744,10.77360868,1,1.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/13/1991,112,tropicana,25024,10.12759064,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/13/1991,113,dominicks,18816,9.842462851,1,1.41,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/13/1991,113,minute.maid,25792,10.15781965,1,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/13/1991,113,tropicana,13952,9.543378146,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/13/1991,114,dominicks,32896,10.40110635,1,1.26,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/13/1991,114,minute.maid,43584,10.68244539,1,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/13/1991,114,tropicana,14016,9.547954813,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/13/1991,115,dominicks,6080,8.712759975,1,1.41,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/13/1991,115,minute.maid,13952,9.543378146,1,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/13/1991,115,tropicana,19264,9.865993348,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/13/1991,116,dominicks,16640,9.719564714,1,1.41,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/13/1991,116,minute.maid,7488,8.921057018,1,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/13/1991,116,tropicana,10624,9.270870872,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/13/1991,117,dominicks,6080,8.712759975,1,1.41,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/13/1991,117,minute.maid,16768,9.727227587,1,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/13/1991,117,tropicana,5376,8.589699882,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/13/1991,118,dominicks,5952,8.691482577,1,1.41,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/13/1991,118,minute.maid,22016,9.999524741,1,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/13/1991,118,tropicana,12736,9.452187908,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/13/1991,119,dominicks,7424,8.912473275,1,1.41,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/13/1991,119,minute.maid,9728,9.182763604,1,1.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/13/1991,119,tropicana,17536,9.77201119,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/13/1991,121,dominicks,9536,9.162829389,1,1.26,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/13/1991,121,minute.maid,37696,10.53730927,1,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/13/1991,121,tropicana,11072,9.312174678,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/13/1991,122,dominicks,14080,9.55251063,1,1.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/13/1991,122,minute.maid,36608,10.50802208,1,1.38,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/13/1991,122,tropicana,13184,9.486759252,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/13/1991,123,dominicks,17664,9.779283949,1,1.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/13/1991,123,minute.maid,16640,9.719564714,1,1.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/13/1991,123,tropicana,46592,10.74918413,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/13/1991,124,dominicks,32576,10.3913311,1,1.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/13/1991,124,minute.maid,21184,9.961001459,1,1.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/13/1991,124,tropicana,16448,9.707959168,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/13/1991,126,dominicks,17920,9.793672686,1,1.41,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/13/1991,126,minute.maid,16320,9.700146629,1,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/13/1991,126,tropicana,12800,9.45720045,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/13/1991,128,dominicks,18368,9.818365299,1,1.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/13/1991,128,minute.maid,53440,10.88631481,1,1.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/13/1991,128,tropicana,29632,10.29661014,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/13/1991,129,dominicks,15296,9.635346635,1,1.41,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/13/1991,129,minute.maid,12288,9.416378455,1,1.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/13/1991,129,tropicana,19392,9.872615889,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/13/1991,130,dominicks,60800,11.01534507,1,1.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/13/1991,130,minute.maid,28224,10.24792796,1,1.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/13/1991,130,tropicana,10752,9.282847063,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/13/1991,132,dominicks,16832,9.731037116,1,1.26,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/13/1991,132,minute.maid,53952,10.89585004,1,1.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/13/1991,132,tropicana,12096,9.400630098,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/13/1991,134,dominicks,10432,9.252633284,1,1.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/13/1991,134,minute.maid,6272,8.743850562,1,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/13/1991,134,tropicana,6528,8.783855897,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/13/1991,137,dominicks,40960,10.62035126,1,1.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/13/1991,137,minute.maid,26816,10.196754,1,1.79,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/13/1991,137,tropicana,51712,10.85344514,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/20/1991,2,dominicks,8832,9.086136769,0,1.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/20/1991,2,minute.maid,12096,9.400630098,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/20/1991,2,tropicana,6208,8.733594062,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/20/1991,5,dominicks,15040,9.618468598,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/20/1991,5,minute.maid,20800,9.942708266,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/20/1991,5,tropicana,6144,8.723231275,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/20/1991,8,dominicks,19264,9.865993348,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/20/1991,8,minute.maid,17408,9.76468515,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/20/1991,8,tropicana,6464,8.7740036,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/20/1991,9,dominicks,4096,8.317766167,0,1.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/20/1991,9,minute.maid,12352,9.421573272,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/20/1991,9,tropicana,6976,8.850230966,0,3.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/20/1991,12,dominicks,12096,9.400630098,0,1.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/20/1991,12,minute.maid,24064,10.08847223,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/20/1991,12,tropicana,7104,8.868413285,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/20/1991,14,dominicks,5760,8.658692754,0,1.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/20/1991,14,minute.maid,12416,9.426741242,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/20/1991,14,tropicana,6720,8.812843434,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/20/1991,18,dominicks,5440,8.60153434,0,1.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/20/1991,18,minute.maid,22784,10.03381381,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/20/1991,18,tropicana,9152,9.121727714,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/20/1991,21,dominicks,16896,9.734832187,0,1.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/20/1991,21,minute.maid,9536,9.162829389,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/20/1991,21,tropicana,3520,8.166216269,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/20/1991,28,dominicks,6400,8.764053269,0,1.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/20/1991,28,minute.maid,6720,8.812843434,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/20/1991,28,tropicana,3392,8.129174997,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/20/1991,32,dominicks,10816,9.288781798,0,1.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/20/1991,32,minute.maid,23424,10.06151642,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/20/1991,32,tropicana,9024,9.107642974,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/20/1991,33,dominicks,4608,8.435549202,0,1.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/20/1991,33,minute.maid,19008,9.852615222,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/20/1991,33,tropicana,6528,8.783855897,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/20/1991,40,dominicks,28416,10.25470765,0,1.29,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/20/1991,40,minute.maid,10752,9.282847063,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/20/1991,40,tropicana,4160,8.333270353,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/20/1991,44,dominicks,17856,9.790094865,0,1.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/20/1991,44,minute.maid,7040,8.859363449,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/20/1991,44,tropicana,7040,8.859363449,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/20/1991,45,dominicks,6784,8.822322178,0,1.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/20/1991,45,minute.maid,15296,9.635346635,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/20/1991,45,tropicana,4224,8.348537825,0,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/20/1991,47,dominicks,16256,9.69621735,0,1.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/20/1991,47,minute.maid,10304,9.240287448,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/20/1991,47,tropicana,2880,7.965545573,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/20/1991,48,dominicks,6336,8.754002934,0,1.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/20/1991,48,minute.maid,10112,9.221478116,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/20/1991,48,tropicana,4544,8.42156296,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/20/1991,49,dominicks,6016,8.702177866,0,1.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/20/1991,49,minute.maid,7744,8.954673629,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/20/1991,49,tropicana,2880,7.965545573,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/20/1991,50,dominicks,11520,9.351839934,0,1.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/20/1991,50,minute.maid,6464,8.7740036,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/20/1991,50,tropicana,2368,7.769800996,0,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/20/1991,51,dominicks,9536,9.162829389,0,1.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/20/1991,51,minute.maid,9920,9.2023082,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/20/1991,51,tropicana,3264,8.090708716,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/20/1991,52,dominicks,3648,8.201934351,0,1.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/20/1991,52,minute.maid,18816,9.842462851,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/20/1991,52,tropicana,9728,9.182763604,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/20/1991,53,dominicks,13824,9.534161491,0,1.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/20/1991,53,minute.maid,35968,10.49038493,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/20/1991,53,tropicana,8512,9.049232212,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/20/1991,54,dominicks,15744,9.664214619,0,1.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/20/1991,54,minute.maid,10240,9.234056899,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/20/1991,54,tropicana,3584,8.184234774,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/20/1991,56,dominicks,9728,9.182763604,0,1.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/20/1991,56,minute.maid,11840,9.379238908,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/20/1991,56,tropicana,5504,8.61323038,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/20/1991,59,dominicks,14528,9.583833101,0,1.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/20/1991,59,minute.maid,9664,9.17616292,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/20/1991,59,tropicana,5184,8.553332238,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/20/1991,62,dominicks,7552,8.929567708,0,1.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/20/1991,62,minute.maid,13952,9.543378146,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/20/1991,62,tropicana,10624,9.270870872,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/20/1991,64,dominicks,10816,9.288781798,0,1.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/20/1991,64,minute.maid,6336,8.754002934,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/20/1991,64,tropicana,192,5.257495372,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/20/1991,68,dominicks,16320,9.700146629,0,1.29,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/20/1991,68,minute.maid,15936,9.67633598,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/20/1991,68,tropicana,6976,8.850230966,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/20/1991,70,dominicks,22272,10.01108556,0,1.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/20/1991,70,minute.maid,15168,9.626943225,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/20/1991,70,tropicana,7552,8.929567708,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/20/1991,71,dominicks,17280,9.757305042,0,1.29,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/20/1991,71,minute.maid,11776,9.373818841,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/20/1991,71,tropicana,3840,8.253227646,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/20/1991,72,dominicks,11392,9.340666634,0,1.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/20/1991,72,minute.maid,14656,9.592605087,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/20/1991,72,tropicana,7488,8.921057018,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/20/1991,73,dominicks,19712,9.888982866,0,1.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/20/1991,73,minute.maid,26624,10.18956834,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/20/1991,73,tropicana,8512,9.049232212,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/20/1991,74,dominicks,15552,9.651944527,0,1.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/20/1991,74,minute.maid,13632,9.520175249,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/20/1991,74,tropicana,6144,8.723231275,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/20/1991,75,dominicks,4288,8.363575703,0,1.29,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/20/1991,75,minute.maid,21568,9.978966014,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/20/1991,75,tropicana,7168,8.877381955,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/20/1991,76,dominicks,14848,9.605620455,0,1.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/20/1991,76,minute.maid,22784,10.03381381,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/20/1991,76,tropicana,4288,8.363575703,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/20/1991,77,dominicks,10304,9.240287448,0,1.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/20/1991,77,minute.maid,12224,9.411156511,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/20/1991,77,tropicana,8000,8.987196821,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/20/1991,78,dominicks,16256,9.69621735,0,1.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/20/1991,78,minute.maid,10944,9.30054664,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/20/1991,78,tropicana,7040,8.859363449,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/20/1991,80,dominicks,20736,9.939626599,0,1.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/20/1991,80,minute.maid,11584,9.357380115,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/20/1991,80,tropicana,12608,9.442086812,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/20/1991,81,dominicks,20416,9.924074186,0,1.29,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/20/1991,81,minute.maid,16064,9.684336023,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/20/1991,81,tropicana,4992,8.51559191,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/20/1991,83,dominicks,18112,9.804329981,0,1.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/20/1991,83,minute.maid,13376,9.501217335,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/20/1991,83,tropicana,6848,8.831711918,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/20/1991,84,dominicks,10624,9.270870872,0,1.29,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/20/1991,84,minute.maid,9984,9.208739091,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/20/1991,84,tropicana,3264,8.090708716,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/20/1991,86,dominicks,10624,9.270870872,0,1.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/20/1991,86,minute.maid,11072,9.312174678,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/20/1991,86,tropicana,7040,8.859363449,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/20/1991,88,dominicks,19968,9.901886271,0,1.29,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/20/1991,88,minute.maid,9216,9.128696383,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/20/1991,88,tropicana,3392,8.129174997,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/20/1991,89,dominicks,18240,9.811372264,0,1.29,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/20/1991,89,minute.maid,21568,9.978966014,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/20/1991,89,tropicana,3008,8.009030685,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/20/1991,90,dominicks,2368,7.769800996,0,1.29,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/20/1991,90,minute.maid,24512,10.10691807,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/20/1991,90,tropicana,1472,7.294377299,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/20/1991,91,dominicks,13696,9.524859098,0,1.29,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/20/1991,91,minute.maid,15040,9.618468598,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/20/1991,91,tropicana,3136,8.050703382,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/20/1991,92,dominicks,21504,9.975994243,0,1.29,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/20/1991,92,minute.maid,10240,9.234056899,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/20/1991,92,tropicana,1984,7.592870288,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/20/1991,93,dominicks,11584,9.357380115,0,1.29,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/20/1991,93,minute.maid,8000,8.987196821,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/20/1991,93,tropicana,6400,8.764053269,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/20/1991,94,dominicks,14464,9.579418083,0,1.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/20/1991,94,minute.maid,11968,9.3899917,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/20/1991,94,tropicana,5696,8.647519453,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/20/1991,95,dominicks,16000,9.680344001,0,1.29,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/20/1991,95,minute.maid,8448,9.041685006,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/20/1991,95,tropicana,4160,8.333270353,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/20/1991,97,dominicks,8768,9.078864009,0,1.29,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/20/1991,97,minute.maid,6208,8.733594062,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/20/1991,97,tropicana,1664,7.416979621,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/20/1991,98,dominicks,35904,10.48860399,0,1.22,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/20/1991,98,minute.maid,15488,9.64782081,0,1.64,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/20/1991,98,tropicana,5056,8.528330936,0,3.03,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/20/1991,100,dominicks,11776,9.373818841,0,1.29,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/20/1991,100,minute.maid,23744,10.07508515,0,1.91,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/20/1991,100,tropicana,4736,8.462948177,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/20/1991,101,dominicks,14400,9.574983486,0,1.12,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/20/1991,101,minute.maid,13440,9.505990614,0,1.9,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/20/1991,101,tropicana,5696,8.647519453,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/20/1991,102,dominicks,20672,9.936535407,0,1.29,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/20/1991,102,minute.maid,23488,10.06424493,0,1.8,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/20/1991,102,tropicana,8640,9.064157862,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/20/1991,103,dominicks,16640,9.719564714,0,1.1,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/20/1991,103,minute.maid,8448,9.041685006,0,1.83,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/20/1991,103,tropicana,2560,7.847762538,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/20/1991,104,dominicks,25408,10.14281936,0,1.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/20/1991,104,minute.maid,12224,9.411156511,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/20/1991,104,tropicana,3072,8.030084094,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/20/1991,105,dominicks,37056,10.52018556,0,1.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/20/1991,105,minute.maid,13504,9.510741217,0,1.92,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/20/1991,105,tropicana,4352,8.378390789,0,3.05,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/20/1991,106,dominicks,14208,9.561560465,0,1.29,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/20/1991,106,minute.maid,4224,8.348537825,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/20/1991,106,tropicana,1088,6.992096427,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/20/1991,107,dominicks,22656,10.02818,0,1.29,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/20/1991,107,minute.maid,16768,9.727227587,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/20/1991,107,tropicana,7872,8.971067439,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/20/1991,109,dominicks,9216,9.128696383,0,1.29,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/20/1991,109,minute.maid,19328,9.869310101,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/20/1991,109,tropicana,11264,9.329367078,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/20/1991,110,dominicks,4928,8.502688505,0,1.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/20/1991,110,minute.maid,12352,9.421573272,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/20/1991,110,tropicana,3712,8.219326094,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/20/1991,111,dominicks,20288,9.917784857,0,1.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/20/1991,111,minute.maid,72704,11.19415168,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/20/1991,111,tropicana,3008,8.009030685,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/20/1991,112,dominicks,28992,10.27477521,0,1.13,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/20/1991,112,minute.maid,22720,10.03100087,0,1.82,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/20/1991,112,tropicana,15168,9.626943225,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/20/1991,113,dominicks,17600,9.775654181,0,1.29,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/20/1991,113,minute.maid,7680,8.946374826,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/20/1991,113,tropicana,10048,9.215128889,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/20/1991,114,dominicks,41664,10.63739273,0,1.17,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/20/1991,114,minute.maid,14144,9.557045785,0,1.87,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/20/1991,114,tropicana,8512,9.049232212,0,3.11,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/20/1991,115,dominicks,14080,9.55251063,0,0.88,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/20/1991,115,minute.maid,24448,10.10430369,0,1.13,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/20/1991,115,tropicana,6144,8.723231275,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/20/1991,116,dominicks,10112,9.221478116,0,1.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/20/1991,116,minute.maid,8128,9.00307017,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/20/1991,116,tropicana,3776,8.236420527,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/20/1991,117,dominicks,9152,9.121727714,0,1.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/20/1991,117,minute.maid,6656,8.803273983,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/20/1991,117,tropicana,1856,7.526178913,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/20/1991,118,dominicks,6976,8.850230966,0,1.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/20/1991,118,minute.maid,13248,9.491601877,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/20/1991,118,tropicana,5376,8.589699882,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/20/1991,119,dominicks,9984,9.208739091,0,1.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/20/1991,119,minute.maid,9280,9.135616826,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/20/1991,119,tropicana,5696,8.647519453,0,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/20/1991,121,dominicks,16256,9.69621735,0,1.07,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/20/1991,121,minute.maid,17600,9.775654181,0,1.83,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/20/1991,121,tropicana,7616,8.938006577,0,3.13,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/20/1991,122,dominicks,18880,9.84585844,0,1,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/20/1991,122,minute.maid,12864,9.462187991,0,1.77,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/20/1991,122,tropicana,8320,9.026417534,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/20/1991,123,dominicks,19520,9.87919486,0,1.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/20/1991,123,minute.maid,13568,9.515469358,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/20/1991,123,tropicana,7296,8.895081532,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/20/1991,124,dominicks,43392,10.67803037,0,1.29,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/20/1991,124,minute.maid,12928,9.467150781,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/20/1991,124,tropicana,5568,8.624791202,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/20/1991,126,dominicks,11648,9.36288977,0,1.06,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/20/1991,126,minute.maid,7424,8.912473275,0,1.98,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/20/1991,126,tropicana,4224,8.348537825,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/20/1991,128,dominicks,4928,8.502688505,0,1.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/20/1991,128,minute.maid,24192,10.09377728,0,1.92,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/20/1991,128,tropicana,7488,8.921057018,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/20/1991,129,dominicks,7552,8.929567708,0,1.1,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/20/1991,129,minute.maid,11968,9.3899917,0,1.93,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/20/1991,129,tropicana,6208,8.733594062,0,3.12,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/20/1991,131,dominicks,15872,9.67231183,0,1.16,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/20/1991,131,minute.maid,14976,9.614204199,0,1.9,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/20/1991,131,tropicana,5440,8.60153434,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/20/1991,132,dominicks,13248,9.491601877,0,1.04,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/20/1991,132,minute.maid,17856,9.790094865,0,1.82,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/20/1991,132,tropicana,6080,8.712759975,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/20/1991,134,dominicks,12992,9.472089062,0,1.05,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/20/1991,134,minute.maid,2944,7.98752448,0,1.95,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/20/1991,134,tropicana,2944,7.98752448,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/20/1991,137,dominicks,26240,10.17504024,0,1.29,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/20/1991,137,minute.maid,27008,10.2038884,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/20/1991,137,tropicana,13056,9.477003077,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/27/1991,2,dominicks,2624,7.87245515,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/27/1991,2,minute.maid,41792,10.64046021,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/27/1991,2,tropicana,10624,9.270870872,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/27/1991,5,dominicks,5120,8.540909718,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/27/1991,5,minute.maid,45696,10.72976605,1,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/27/1991,5,tropicana,9344,9.142489705,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/27/1991,8,dominicks,6848,8.831711918,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/27/1991,8,minute.maid,75520,11.2321528,1,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/27/1991,8,tropicana,8512,9.049232212,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/27/1991,9,dominicks,704,6.556778356,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/27/1991,9,minute.maid,65152,11.08447828,1,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/27/1991,9,tropicana,14528,9.583833101,0,3.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/27/1991,12,dominicks,2880,7.965545573,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/27/1991,12,minute.maid,91328,11.4222127,1,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/27/1991,12,tropicana,19712,9.888982866,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/27/1991,14,dominicks,1664,7.416979621,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/27/1991,14,minute.maid,38848,10.56741187,1,1.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/27/1991,14,tropicana,12032,9.395325046,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/27/1991,18,dominicks,3712,8.219326094,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/27/1991,18,minute.maid,66368,11.10297029,1,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/27/1991,18,tropicana,17024,9.742379392,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/27/1991,21,dominicks,7552,8.929567708,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/27/1991,21,minute.maid,30656,10.33058368,1,1.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/27/1991,21,tropicana,4928,8.502688505,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/27/1991,28,dominicks,2816,7.943072717,0,1.89,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/27/1991,28,minute.maid,27456,10.22034,1,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/27/1991,28,tropicana,5120,8.540909718,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/27/1991,32,dominicks,2688,7.896552702,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/27/1991,32,minute.maid,91776,11.42710611,1,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/27/1991,32,tropicana,32960,10.40304998,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/27/1991,33,dominicks,1472,7.294377299,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/27/1991,33,minute.maid,51072,10.84099168,1,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/27/1991,33,tropicana,15936,9.67633598,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/27/1991,40,dominicks,8832,9.086136769,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/27/1991,40,minute.maid,48064,10.78028874,1,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/27/1991,40,tropicana,9600,9.169518378,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/27/1991,44,dominicks,4096,8.317766167,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/27/1991,44,minute.maid,39296,10.57887801,1,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/27/1991,44,tropicana,15168,9.626943225,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/27/1991,45,dominicks,1088,6.992096427,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/27/1991,45,minute.maid,29440,10.29010957,1,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/27/1991,45,tropicana,6208,8.733594062,0,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/27/1991,47,dominicks,4928,8.502688505,0,1.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/27/1991,47,minute.maid,31104,10.34509171,1,1.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/27/1991,47,tropicana,5056,8.528330936,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/27/1991,48,dominicks,2368,7.769800996,0,1.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/27/1991,48,minute.maid,23936,10.08313888,1,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/27/1991,48,tropicana,6272,8.743850562,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/27/1991,49,dominicks,1216,7.103322063,0,1.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/27/1991,49,minute.maid,30976,10.34096799,1,1.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/27/1991,49,tropicana,6912,8.841014311,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/27/1991,50,dominicks,3456,8.14786713,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/27/1991,50,minute.maid,22080,10.0024275,1,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/27/1991,50,tropicana,8640,9.064157862,0,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/27/1991,51,dominicks,2304,7.742402022,0,1.89,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/27/1991,51,minute.maid,37568,10.5339079,1,1.69,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/27/1991,51,tropicana,8000,8.987196821,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/27/1991,52,dominicks,1664,7.416979621,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/27/1991,52,minute.maid,45568,10.726961,1,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/27/1991,52,tropicana,21376,9.970024076,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/27/1991,53,dominicks,3520,8.166216269,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/27/1991,53,minute.maid,88960,11.39594211,1,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/27/1991,53,tropicana,20032,9.905086274,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/27/1991,54,dominicks,1856,7.526178913,0,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/27/1991,54,minute.maid,26240,10.17504024,1,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/27/1991,54,tropicana,7488,8.921057018,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/27/1991,56,dominicks,6464,8.7740036,0,1.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/27/1991,56,minute.maid,25408,10.14281936,1,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/27/1991,56,tropicana,11200,9.323669057,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/27/1991,59,dominicks,4224,8.348537825,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/27/1991,59,minute.maid,41792,10.64046021,1,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/27/1991,59,tropicana,8000,8.987196821,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/27/1991,62,dominicks,3072,8.030084094,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/27/1991,62,minute.maid,37120,10.52191119,1,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/27/1991,62,tropicana,23936,10.08313888,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/27/1991,64,dominicks,2560,7.847762538,0,1.89,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/27/1991,64,minute.maid,19328,9.869310101,1,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/27/1991,64,tropicana,2176,7.685243608,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/27/1991,67,dominicks,1984,7.592870288,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/27/1991,67,minute.maid,39744,10.59021417,1,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/27/1991,67,tropicana,7040,8.859363449,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/27/1991,68,dominicks,4288,8.363575703,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/27/1991,68,minute.maid,73856,11.20987253,1,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/27/1991,68,tropicana,23744,10.07508515,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/27/1991,70,dominicks,9856,9.195835686,0,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/27/1991,70,minute.maid,37504,10.53220287,1,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/27/1991,70,tropicana,13184,9.486759252,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/27/1991,71,dominicks,1920,7.560080465,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/27/1991,71,minute.maid,64192,11.06963387,1,1.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/27/1991,71,tropicana,9984,9.208739091,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/27/1991,72,dominicks,1856,7.526178913,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/27/1991,72,minute.maid,57664,10.96238834,1,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/27/1991,72,tropicana,13952,9.543378146,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/27/1991,73,dominicks,6208,8.733594062,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/27/1991,73,minute.maid,112384,11.62967686,1,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/27/1991,73,tropicana,15168,9.626943225,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/27/1991,74,dominicks,5440,8.60153434,0,1.89,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/27/1991,74,minute.maid,44928,10.71281649,1,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/27/1991,74,tropicana,46528,10.74780956,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/27/1991,75,dominicks,2560,7.847762538,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/27/1991,75,minute.maid,53504,10.8875117,1,1.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/27/1991,75,tropicana,16768,9.727227587,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/27/1991,77,dominicks,5632,8.636219898,0,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/27/1991,77,minute.maid,35520,10.4778512,1,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/27/1991,77,tropicana,11648,9.36288977,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/27/1991,78,dominicks,8640,9.064157862,0,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/27/1991,78,minute.maid,41344,10.62968259,1,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/27/1991,78,tropicana,11712,9.368369236,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/27/1991,80,dominicks,5376,8.589699882,0,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/27/1991,80,minute.maid,39936,10.59503345,1,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/27/1991,80,tropicana,18368,9.818365299,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/27/1991,81,dominicks,3200,8.070906089,0,1.89,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/27/1991,81,minute.maid,61504,11.02685749,1,1.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/27/1991,81,tropicana,12864,9.462187991,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/27/1991,83,dominicks,7744,8.954673629,0,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/27/1991,83,minute.maid,75136,11.22705508,1,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/27/1991,83,tropicana,7744,8.954673629,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/27/1991,84,dominicks,1984,7.592870288,0,1.89,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/27/1991,84,minute.maid,35712,10.48324205,1,1.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/27/1991,84,tropicana,6528,8.783855897,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/27/1991,86,dominicks,4544,8.42156296,0,1.89,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/27/1991,86,minute.maid,44928,10.71281649,1,1.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/27/1991,86,tropicana,9664,9.17616292,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/27/1991,88,dominicks,1152,7.049254841,0,1.89,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/27/1991,88,minute.maid,35008,10.46333189,1,1.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/27/1991,88,tropicana,6528,8.783855897,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/27/1991,89,dominicks,3648,8.201934351,0,1.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/27/1991,89,minute.maid,70208,11.15921754,1,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/27/1991,89,tropicana,6144,8.723231275,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/27/1991,90,dominicks,2368,7.769800996,0,1.89,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/27/1991,90,minute.maid,46976,10.75739211,1,1.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/27/1991,90,tropicana,3136,8.050703382,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/27/1991,91,dominicks,2048,7.624618986,0,1.89,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/27/1991,91,minute.maid,55104,10.91697759,1,1.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/27/1991,91,tropicana,6528,8.783855897,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/27/1991,92,dominicks,2624,7.87245515,0,1.89,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/27/1991,92,minute.maid,47296,10.764181,1,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/27/1991,92,tropicana,6016,8.702177866,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/27/1991,93,dominicks,4160,8.333270353,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/27/1991,93,minute.maid,37120,10.52191119,1,1.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/27/1991,93,tropicana,8320,9.026417534,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/27/1991,94,dominicks,3968,8.286017468,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/27/1991,94,minute.maid,33984,10.43364511,1,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/27/1991,94,tropicana,16704,9.723403491,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/27/1991,95,dominicks,1600,7.377758908,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/27/1991,95,minute.maid,48704,10.79351644,1,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/27/1991,95,tropicana,9472,9.156095357,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/27/1991,97,dominicks,1024,6.931471806,0,1.89,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/27/1991,97,minute.maid,22464,10.01966931,1,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/27/1991,97,tropicana,3712,8.219326094,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/27/1991,98,dominicks,11200,9.323669057,0,1.34,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/27/1991,98,minute.maid,72512,11.19150734,1,1.57,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/27/1991,98,tropicana,16128,9.688312171,0,2.3,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/27/1991,100,dominicks,3968,8.286017468,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/27/1991,100,minute.maid,100224,11.51516296,1,1.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/27/1991,100,tropicana,12864,9.462187991,0,2.4,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/27/1991,101,dominicks,3520,8.166216269,0,1.89,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/27/1991,101,minute.maid,46016,10.73674444,1,1.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/27/1991,101,tropicana,10752,9.282847063,0,2.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/27/1991,102,dominicks,35456,10.47604777,0,1.46,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/27/1991,102,minute.maid,84480,11.3442701,1,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/27/1991,102,tropicana,16128,9.688312171,0,2.17,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/27/1991,104,dominicks,1536,7.336936914,0,1.89,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/27/1991,104,minute.maid,38592,10.56080028,1,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/27/1991,104,tropicana,8832,9.086136769,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/27/1991,105,dominicks,14784,9.601300794,0,0.52,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/27/1991,105,minute.maid,56832,10.94785483,1,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/27/1991,105,tropicana,8448,9.041685006,0,2.42,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/27/1991,106,dominicks,8384,9.034080407,0,1.89,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/27/1991,106,minute.maid,17728,9.78290059,1,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/27/1991,106,tropicana,4992,8.51559191,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/27/1991,107,dominicks,3008,8.009030685,0,1.89,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/27/1991,107,minute.maid,67008,11.11256729,1,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/27/1991,107,tropicana,9984,9.208739091,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/27/1991,109,dominicks,640,6.461468176,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/27/1991,109,minute.maid,73088,11.19941947,1,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/27/1991,109,tropicana,23552,10.06696602,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/27/1991,110,dominicks,2432,7.796469243,0,1.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/27/1991,110,minute.maid,43584,10.68244539,1,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/27/1991,110,tropicana,9152,9.121727714,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/27/1991,111,dominicks,1984,7.592870288,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/27/1991,111,minute.maid,120384,11.69844191,1,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/27/1991,111,tropicana,8768,9.078864009,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/27/1991,112,dominicks,7936,8.979164649,0,0.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/27/1991,112,minute.maid,56192,10.93652968,1,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/27/1991,112,tropicana,30144,10.31374118,0,2.36,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/27/1991,113,dominicks,2880,7.965545573,0,1.89,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/27/1991,113,minute.maid,61696,11.02997438,1,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/27/1991,113,tropicana,18688,9.835636886,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/27/1991,114,dominicks,7552,8.929567708,0,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/27/1991,114,minute.maid,81280,11.30565526,1,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/27/1991,114,tropicana,10048,9.215128889,0,2.28,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/27/1991,115,dominicks,1280,7.154615357,0,1.89,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/27/1991,115,minute.maid,35968,10.49038493,1,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/27/1991,115,tropicana,8512,9.049232212,0,2.3,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/27/1991,117,dominicks,4288,8.363575703,0,1.89,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/27/1991,117,minute.maid,27968,10.23881628,1,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/27/1991,117,tropicana,6592,8.793612072,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/27/1991,118,dominicks,4864,8.489616424,0,1.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/27/1991,118,minute.maid,43456,10.67950421,1,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/27/1991,118,tropicana,10752,9.282847063,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/27/1991,119,dominicks,3008,8.009030685,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/27/1991,119,minute.maid,31424,10.35532721,1,1.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/27/1991,119,tropicana,9280,9.135616826,0,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/27/1991,121,dominicks,3008,8.009030685,0,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/27/1991,121,minute.maid,53504,10.8875117,1,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/27/1991,121,tropicana,13056,9.477003077,0,2.43,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/27/1991,122,dominicks,5248,8.565602331,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/27/1991,122,minute.maid,53248,10.88271552,1,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/27/1991,122,tropicana,12672,9.447150114,0,2.32,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/27/1991,123,dominicks,2176,7.685243608,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/27/1991,123,minute.maid,107584,11.58602722,1,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/27/1991,123,tropicana,15040,9.618468598,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/27/1991,124,dominicks,4480,8.407378325,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/27/1991,124,minute.maid,75904,11.23722466,1,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/27/1991,124,tropicana,17216,9.753594463,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/27/1991,126,dominicks,9024,9.107642974,0,1.6,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/27/1991,126,minute.maid,43072,10.67062841,1,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/27/1991,126,tropicana,12288,9.416378455,0,2.31,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/27/1991,128,dominicks,5504,8.61323038,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/27/1991,128,minute.maid,84480,11.3442701,1,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/27/1991,128,tropicana,34944,10.46150206,0,2.28,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/27/1991,129,dominicks,2048,7.624618986,0,1.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/27/1991,129,minute.maid,47040,10.75875358,1,1.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/27/1991,129,tropicana,8768,9.078864009,0,2.63,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/27/1991,130,dominicks,28032,10.24110199,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/27/1991,130,minute.maid,115584,11.65775282,1,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/27/1991,130,tropicana,25024,10.12759064,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/27/1991,132,dominicks,4928,8.502688505,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/27/1991,132,minute.maid,64704,11.0775783,1,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/27/1991,132,tropicana,8384,9.034080407,0,2.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/27/1991,134,dominicks,4160,8.333270353,0,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/27/1991,134,minute.maid,18688,9.835636886,1,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/27/1991,134,tropicana,5632,8.636219898,0,2.18,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/27/1991,137,dominicks,3264,8.090708716,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/27/1991,137,minute.maid,75648,11.23384628,1,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/27/1991,137,tropicana,32000,10.37349118,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/4/1991,2,dominicks,10432,9.252633284,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/4/1991,2,minute.maid,10560,9.264828557,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/4/1991,2,tropicana,44672,10.70710219,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/4/1991,5,dominicks,3264,8.090708716,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/4/1991,5,minute.maid,14336,9.570529135,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/4/1991,5,tropicana,32896,10.40110635,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/4/1991,8,dominicks,12928,9.467150781,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/4/1991,8,minute.maid,21632,9.981928979,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/4/1991,8,tropicana,28416,10.25470765,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/4/1991,9,dominicks,3136,8.050703382,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/4/1991,9,minute.maid,13056,9.477003077,0,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/4/1991,9,tropicana,79616,11.28497036,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/4/1991,12,dominicks,6912,8.841014311,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/4/1991,12,minute.maid,19328,9.869310101,0,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/4/1991,12,tropicana,89216,11.39881568,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/4/1991,14,dominicks,2368,7.769800996,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/4/1991,14,minute.maid,11200,9.323669057,0,1.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/4/1991,14,tropicana,37056,10.52018556,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/4/1991,18,dominicks,4544,8.42156296,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/4/1991,18,minute.maid,17472,9.768354879,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/4/1991,18,tropicana,53056,10.87910324,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/4/1991,21,dominicks,8000,8.987196821,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/4/1991,21,minute.maid,14464,9.579418083,0,1.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/4/1991,21,tropicana,16384,9.704060528,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/4/1991,28,dominicks,2240,7.714231145,0,1.89,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/4/1991,28,minute.maid,6592,8.793612072,0,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/4/1991,28,tropicana,15040,9.618468598,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/4/1991,32,dominicks,6720,8.812843434,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/4/1991,32,minute.maid,21888,9.99369382,0,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/4/1991,32,tropicana,56384,10.93994071,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/4/1991,33,dominicks,4416,8.392989588,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/4/1991,33,minute.maid,19584,9.882468185,0,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/4/1991,33,tropicana,57984,10.96792239,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/4/1991,40,dominicks,12288,9.416378455,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/4/1991,40,minute.maid,19456,9.875910785,0,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/4/1991,40,tropicana,18176,9.807857322,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/4/1991,44,dominicks,2496,7.82244473,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/4/1991,44,minute.maid,11264,9.329367078,0,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/4/1991,44,tropicana,53056,10.87910324,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/4/1991,45,dominicks,4416,8.392989588,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/4/1991,45,minute.maid,7040,8.859363449,0,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/4/1991,45,tropicana,20160,9.911455722,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/4/1991,47,dominicks,4800,8.476371197,0,1.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/4/1991,47,minute.maid,12160,9.405907156,0,1.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/4/1991,47,tropicana,17792,9.786504197,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/4/1991,48,dominicks,4224,8.348537825,0,1.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/4/1991,48,minute.maid,10560,9.264828557,0,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/4/1991,48,tropicana,22144,10.00532186,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/4/1991,49,dominicks,2816,7.943072717,0,1.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/4/1991,49,minute.maid,3328,8.110126802,0,1.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/4/1991,49,tropicana,15872,9.67231183,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/4/1991,50,dominicks,4416,8.392989588,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/4/1991,50,minute.maid,8832,9.086136769,0,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/4/1991,50,tropicana,14144,9.557045785,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/4/1991,51,dominicks,3136,8.050703382,0,1.89,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/4/1991,51,minute.maid,7680,8.946374826,0,1.69,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/4/1991,51,tropicana,32896,10.40110635,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/4/1991,52,dominicks,2944,7.98752448,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/4/1991,52,minute.maid,22400,10.01681624,0,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/4/1991,52,tropicana,65856,11.09522582,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/4/1991,53,dominicks,5632,8.636219898,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/4/1991,53,minute.maid,29248,10.28356647,0,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/4/1991,53,tropicana,87296,11.37705992,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/4/1991,54,dominicks,4736,8.462948177,0,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/4/1991,54,minute.maid,7104,8.868413285,0,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/4/1991,54,tropicana,23872,10.0804615,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/4/1991,56,dominicks,5632,8.636219898,0,1.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/4/1991,56,minute.maid,5952,8.691482577,0,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/4/1991,56,tropicana,19648,9.885730831,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/4/1991,59,dominicks,3456,8.14786713,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/4/1991,59,minute.maid,15936,9.67633598,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/4/1991,59,tropicana,19584,9.882468185,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/4/1991,62,dominicks,2752,7.920083199,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/4/1991,62,minute.maid,13696,9.524859098,0,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/4/1991,62,tropicana,55360,10.92161259,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/4/1991,64,dominicks,4544,8.42156296,0,1.89,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/4/1991,64,minute.maid,7552,8.929567708,0,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/4/1991,64,tropicana,13952,9.543378146,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/4/1991,68,dominicks,7808,8.962904128,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/4/1991,68,minute.maid,17344,9.761001904,0,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/4/1991,68,tropicana,44160,10.69557468,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/4/1991,70,dominicks,9024,9.107642974,0,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/4/1991,70,minute.maid,14528,9.583833101,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/4/1991,70,tropicana,27456,10.22034,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/4/1991,71,dominicks,6336,8.754002934,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/4/1991,71,minute.maid,20224,9.914625297,0,1.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/4/1991,71,tropicana,42880,10.6661608,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/4/1991,72,dominicks,2880,7.965545573,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/4/1991,72,minute.maid,17472,9.768354879,0,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/4/1991,72,tropicana,70976,11.17009707,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/4/1991,73,dominicks,12736,9.452187908,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/4/1991,73,minute.maid,29376,10.28793329,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/4/1991,73,tropicana,59648,10.9962159,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/4/1991,74,dominicks,9728,9.182763604,0,1.89,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/4/1991,74,minute.maid,15360,9.639522007,0,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/4/1991,74,tropicana,62848,11.04847439,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/4/1991,75,dominicks,4800,8.476371197,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/4/1991,75,minute.maid,25280,10.13776885,0,1.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/4/1991,75,tropicana,50240,10.8245668,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/4/1991,76,dominicks,5184,8.553332238,0,1.89,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/4/1991,76,minute.maid,12672,9.447150114,0,1.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/4/1991,76,tropicana,62336,11.04029439,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/4/1991,78,dominicks,5696,8.647519453,0,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/4/1991,78,minute.maid,15552,9.651944527,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/4/1991,78,tropicana,27520,10.22266829,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/4/1991,80,dominicks,2304,7.742402022,0,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/4/1991,80,minute.maid,15296,9.635346635,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/4/1991,80,tropicana,41600,10.63585545,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/4/1991,81,dominicks,7040,8.859363449,0,1.89,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/4/1991,81,minute.maid,13440,9.505990614,0,1.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/4/1991,81,tropicana,38528,10.55914053,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/4/1991,83,dominicks,8064,8.99516499,0,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/4/1991,83,minute.maid,5376,8.589699882,0,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/4/1991,83,tropicana,40512,10.60935351,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/4/1991,84,dominicks,5376,8.589699882,0,1.89,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/4/1991,84,minute.maid,14400,9.574983486,0,1.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/4/1991,84,tropicana,24448,10.10430369,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/4/1991,86,dominicks,6272,8.743850562,0,1.89,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/4/1991,86,minute.maid,11968,9.3899917,0,1.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/4/1991,86,tropicana,43456,10.67950421,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/4/1991,88,dominicks,4032,8.30201781,0,1.89,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/4/1991,88,minute.maid,9728,9.182763604,0,1.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/4/1991,88,tropicana,25536,10.1478445,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/4/1991,89,dominicks,7168,8.877381955,0,1.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/4/1991,89,minute.maid,21312,9.967025573,0,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/4/1991,89,tropicana,46912,10.75602879,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/4/1991,90,dominicks,3904,8.269756948,0,1.89,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/4/1991,90,minute.maid,13504,9.510741217,0,1.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/4/1991,90,tropicana,17152,9.749870064,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/4/1991,91,dominicks,4736,8.462948177,0,1.89,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/4/1991,91,minute.maid,19584,9.882468185,0,1.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/4/1991,91,tropicana,19968,9.901886271,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/4/1991,92,dominicks,5568,8.624791202,0,1.89,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/4/1991,92,minute.maid,17088,9.746131742,0,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/4/1991,92,tropicana,22336,10.01395501,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/4/1991,93,dominicks,1728,7.454719949,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/4/1991,93,minute.maid,10368,9.246479419,0,1.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/4/1991,93,tropicana,36160,10.49570882,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/4/1991,94,dominicks,4992,8.51559191,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/4/1991,94,minute.maid,13696,9.524859098,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/4/1991,94,tropicana,27136,10.20861654,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/4/1991,95,dominicks,3648,8.201934351,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/4/1991,95,minute.maid,17600,9.775654181,0,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/4/1991,95,tropicana,32320,10.38344151,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/4/1991,97,dominicks,2112,7.655390645,0,1.89,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/4/1991,97,minute.maid,8704,9.071537969,0,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/4/1991,97,tropicana,10112,9.221478116,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/4/1991,98,dominicks,11264,9.329367078,0,1.62,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/4/1991,98,minute.maid,16192,9.692272572,0,1.73,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/4/1991,98,tropicana,58112,10.97012746,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/4/1991,100,dominicks,8320,9.026417534,0,1.72,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/4/1991,100,minute.maid,25344,10.1402973,0,1.74,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/4/1991,100,tropicana,58304,10.97342598,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/4/1991,101,dominicks,6592,8.793612072,0,1.68,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/4/1991,101,minute.maid,17216,9.753594463,0,1.75,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/4/1991,101,tropicana,25472,10.14533509,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/4/1991,102,dominicks,23232,10.05328592,0,1.44,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/4/1991,102,minute.maid,8512,9.049232212,0,1.77,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/4/1991,102,tropicana,49280,10.8052736,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/4/1991,104,dominicks,4480,8.407378325,0,1.89,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/4/1991,104,minute.maid,9344,9.142489705,0,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/4/1991,104,tropicana,30144,10.31374118,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/4/1991,105,dominicks,12352,9.421573272,0,1.63,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/4/1991,105,minute.maid,15936,9.67633598,0,1.74,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/4/1991,105,tropicana,32192,10.37947325,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/4/1991,106,dominicks,3520,8.166216269,0,1.89,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/4/1991,106,minute.maid,5568,8.624791202,0,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/4/1991,106,tropicana,12224,9.411156511,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/4/1991,107,dominicks,3904,8.269756948,0,1.89,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/4/1991,107,minute.maid,13184,9.486759252,0,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/4/1991,107,tropicana,33216,10.41078697,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/4/1991,109,dominicks,2304,7.742402022,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/4/1991,109,minute.maid,14528,9.583833101,0,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/4/1991,109,tropicana,88768,11.3937815,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/4/1991,110,dominicks,4800,8.476371197,0,1.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/4/1991,110,minute.maid,14208,9.561560465,0,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/4/1991,110,tropicana,22784,10.03381381,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/4/1991,111,dominicks,8000,8.987196821,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/4/1991,111,minute.maid,18624,9.832206351,0,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/4/1991,111,tropicana,80384,11.29457043,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/4/1991,112,dominicks,8512,9.049232212,0,1.27,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/4/1991,112,minute.maid,17216,9.753594463,0,1.75,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/4/1991,112,tropicana,63552,11.05961375,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/4/1991,113,dominicks,6208,8.733594062,0,1.89,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/4/1991,113,minute.maid,12544,9.436997743,0,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/4/1991,113,tropicana,54720,10.90998455,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/4/1991,114,dominicks,13376,9.501217335,0,1.63,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/4/1991,114,minute.maid,21248,9.964018052,0,1.72,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/4/1991,114,tropicana,45504,10.72555551,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/4/1991,115,dominicks,3072,8.030084094,0,1.63,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/4/1991,115,minute.maid,13504,9.510741217,0,1.74,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/4/1991,115,tropicana,40896,10.61878754,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/4/1991,116,dominicks,3840,8.253227646,0,1.89,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/4/1991,116,minute.maid,10688,9.276876896,0,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/4/1991,116,tropicana,29056,10.27698028,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/4/1991,118,dominicks,4160,8.333270353,0,1.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/4/1991,118,minute.maid,11584,9.357380115,0,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/4/1991,118,tropicana,51840,10.85591733,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/4/1991,119,dominicks,4544,8.42156296,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/4/1991,119,minute.maid,15232,9.631153757,0,1.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/4/1991,119,tropicana,24320,10.09905434,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/4/1991,121,dominicks,5440,8.60153434,0,1.63,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/4/1991,121,minute.maid,11904,9.384629757,0,1.79,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/4/1991,121,tropicana,41088,10.62347139,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/4/1991,122,dominicks,9792,9.189321005,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/4/1991,122,minute.maid,16448,9.707959168,0,1.72,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/4/1991,122,tropicana,35392,10.47424109,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/4/1991,123,dominicks,5760,8.658692754,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/4/1991,123,minute.maid,16192,9.692272572,0,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/4/1991,123,tropicana,74368,11.21678102,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/4/1991,124,dominicks,10880,9.29468152,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/4/1991,124,minute.maid,17152,9.749870064,0,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/4/1991,124,tropicana,58432,10.97561896,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/4/1991,126,dominicks,7808,8.962904128,0,1.73,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/4/1991,126,minute.maid,10624,9.270870872,0,1.8,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/4/1991,126,tropicana,45504,10.72555551,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/4/1991,128,dominicks,6016,8.702177866,0,1.68,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/4/1991,128,minute.maid,16576,9.715711145,0,1.75,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/4/1991,128,tropicana,126080,11.74467191,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/4/1991,129,dominicks,2432,7.796469243,0,1.76,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/4/1991,129,minute.maid,14592,9.588228712,0,1.76,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/4/1991,129,tropicana,30272,10.31797847,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/4/1991,130,dominicks,11264,9.329367078,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/4/1991,130,minute.maid,9856,9.195835686,0,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/4/1991,130,tropicana,75904,11.23722466,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/4/1991,131,dominicks,4800,8.476371197,0,1.37,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/4/1991,131,minute.maid,11008,9.30637756,0,1.75,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/4/1991,131,tropicana,28992,10.27477521,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/4/1991,132,dominicks,10624,9.270870872,0,1.62,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/4/1991,132,minute.maid,13696,9.524859098,0,1.72,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/4/1991,132,tropicana,44032,10.69267192,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/11/1991,5,dominicks,9536,9.162829389,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/11/1991,5,minute.maid,4928,8.502688505,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/11/1991,5,tropicana,21056,9.954940834,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/11/1991,8,dominicks,44032,10.69267192,1,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/11/1991,8,minute.maid,8384,9.034080407,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/11/1991,8,tropicana,16960,9.738612909,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/11/1991,9,dominicks,33024,10.40498985,1,1.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/11/1991,9,minute.maid,4800,8.476371197,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/11/1991,9,tropicana,24640,10.11212642,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/11/1991,12,dominicks,47872,10.77628606,1,1.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/11/1991,12,minute.maid,5248,8.565602331,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/11/1991,12,tropicana,44160,10.69557468,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/11/1991,14,dominicks,12864,9.462187991,1,1.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/11/1991,14,minute.maid,2688,7.896552702,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/11/1991,14,tropicana,20288,9.917784857,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/11/1991,18,dominicks,37760,10.53900562,1,1.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/11/1991,18,minute.maid,7424,8.912473275,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/11/1991,18,tropicana,20608,9.933434629,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/11/1991,21,dominicks,15680,9.660141294,1,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/11/1991,21,minute.maid,17216,9.753594463,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/11/1991,21,tropicana,6400,8.764053269,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/11/1991,28,dominicks,11648,9.36288977,1,1.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/11/1991,28,minute.maid,2048,7.624618986,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/11/1991,28,tropicana,17216,9.753594463,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/11/1991,32,dominicks,41280,10.6281334,1,1.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/11/1991,32,minute.maid,4992,8.51559191,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/11/1991,32,tropicana,36160,10.49570882,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/11/1991,33,dominicks,23168,10.0505273,1,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/11/1991,33,minute.maid,5248,8.565602331,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/11/1991,33,tropicana,32512,10.38936453,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/11/1991,40,dominicks,31936,10.37148918,1,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/11/1991,40,minute.maid,6272,8.743850562,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/11/1991,40,tropicana,11456,9.346268889,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/11/1991,44,dominicks,14976,9.614204199,1,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/11/1991,44,minute.maid,4864,8.489616424,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/11/1991,44,tropicana,9536,9.162829389,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/11/1991,45,dominicks,13696,9.524859098,1,1.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/11/1991,45,minute.maid,2816,7.943072717,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/11/1991,45,tropicana,15360,9.639522007,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/11/1991,47,dominicks,16896,9.734832187,1,1.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/11/1991,47,minute.maid,3712,8.219326094,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/11/1991,47,tropicana,10816,9.288781798,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/11/1991,48,dominicks,10112,9.221478116,1,1.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/11/1991,48,minute.maid,3968,8.286017468,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/11/1991,48,tropicana,11520,9.351839934,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/11/1991,49,dominicks,11392,9.340666634,1,1.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/11/1991,49,minute.maid,2240,7.714231145,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/11/1991,49,tropicana,7680,8.946374826,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/11/1991,50,dominicks,13184,9.486759252,1,1.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/11/1991,50,minute.maid,2688,7.896552702,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/11/1991,50,tropicana,8512,9.049232212,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/11/1991,51,dominicks,21248,9.964018052,1,1.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/11/1991,51,minute.maid,3136,8.050703382,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/11/1991,51,tropicana,13824,9.534161491,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/11/1991,52,dominicks,15424,9.643680017,1,1.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/11/1991,52,minute.maid,15616,9.656051309,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/11/1991,52,tropicana,21440,9.973013615,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/11/1991,53,dominicks,30592,10.32849382,1,1.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/11/1991,53,minute.maid,3520,8.166216269,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/11/1991,53,tropicana,44416,10.70135504,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/11/1991,54,dominicks,20032,9.905086274,1,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/11/1991,54,minute.maid,2368,7.769800996,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/11/1991,54,tropicana,12096,9.400630098,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/11/1991,56,dominicks,17984,9.797237753,1,1.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/11/1991,56,minute.maid,4416,8.392989588,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/11/1991,56,tropicana,9408,9.14931567,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/11/1991,59,dominicks,22912,10.03941607,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/11/1991,59,minute.maid,4800,8.476371197,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/11/1991,59,tropicana,11584,9.357380115,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/11/1991,62,dominicks,8832,9.086136769,1,1.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/11/1991,62,minute.maid,4992,8.51559191,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/11/1991,62,tropicana,31296,10.35124557,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/11/1991,68,dominicks,23424,10.06151642,1,1.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/11/1991,68,minute.maid,4992,8.51559191,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/11/1991,68,tropicana,38464,10.55747802,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/11/1991,70,dominicks,24512,10.10691807,1,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/11/1991,70,minute.maid,8192,9.010913347,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/11/1991,70,tropicana,13440,9.505990614,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/11/1991,71,dominicks,32576,10.3913311,1,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/11/1991,71,minute.maid,2176,7.685243608,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/11/1991,71,tropicana,25408,10.14281936,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/11/1991,72,dominicks,14272,9.566054855,1,1.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/11/1991,72,minute.maid,3072,8.030084094,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/11/1991,72,tropicana,25088,10.13014492,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/11/1991,73,dominicks,62464,11.04234567,1,1.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/11/1991,73,minute.maid,11200,9.323669057,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/11/1991,73,tropicana,28608,10.26144168,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/11/1991,74,dominicks,41472,10.63277378,1,1.59,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/11/1991,74,minute.maid,4480,8.407378325,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/11/1991,74,tropicana,27456,10.22034,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/11/1991,75,dominicks,18624,9.832206351,1,1.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/11/1991,75,minute.maid,5248,8.565602331,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/11/1991,75,tropicana,33984,10.43364511,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/11/1991,76,dominicks,47616,10.77092412,1,1.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/11/1991,76,minute.maid,3008,8.009030685,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/11/1991,76,tropicana,20800,9.942708266,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/11/1991,77,dominicks,13056,9.477003077,1,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/11/1991,77,minute.maid,6208,8.733594062,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/11/1991,77,tropicana,13376,9.501217335,0,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/11/1991,78,dominicks,27328,10.2156671,1,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/11/1991,78,minute.maid,8704,9.071537969,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/11/1991,78,tropicana,14720,9.596962392,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/11/1991,80,dominicks,11904,9.384629757,1,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/11/1991,80,minute.maid,7808,8.962904128,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/11/1991,80,tropicana,22912,10.03941607,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/11/1991,81,dominicks,24192,10.09377728,1,1.59,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/11/1991,81,minute.maid,4160,8.333270353,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/11/1991,81,tropicana,16448,9.707959168,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/11/1991,83,dominicks,47488,10.76823233,1,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/11/1991,83,minute.maid,4992,8.51559191,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/11/1991,83,tropicana,14464,9.579418083,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/11/1991,84,dominicks,27008,10.2038884,1,1.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/11/1991,84,minute.maid,3392,8.129174997,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/11/1991,84,tropicana,9536,9.162829389,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/11/1991,86,dominicks,30016,10.30948585,1,1.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/11/1991,86,minute.maid,2944,7.98752448,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/11/1991,86,tropicana,17088,9.746131742,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/11/1991,88,dominicks,21120,9.957975738,1,1.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/11/1991,88,minute.maid,3776,8.236420527,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/11/1991,88,tropicana,9408,9.14931567,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/11/1991,89,dominicks,40384,10.60618895,1,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/11/1991,89,minute.maid,3136,8.050703382,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/11/1991,89,tropicana,16640,9.719564714,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/11/1991,90,dominicks,33664,10.4241843,1,1.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/11/1991,90,minute.maid,3200,8.070906089,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/11/1991,90,tropicana,8320,9.026417534,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/11/1991,91,dominicks,29824,10.30306872,1,1.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/11/1991,91,minute.maid,2624,7.87245515,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/11/1991,91,tropicana,6976,8.850230966,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/11/1991,93,dominicks,20672,9.936535407,1,1.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/11/1991,93,minute.maid,3392,8.129174997,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/11/1991,93,tropicana,20672,9.936535407,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/11/1991,94,dominicks,17216,9.753594463,1,1.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/11/1991,94,minute.maid,6656,8.803273983,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/11/1991,94,tropicana,14720,9.596962392,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/11/1991,95,dominicks,24192,10.09377728,1,1.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/11/1991,95,minute.maid,2176,7.685243608,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/11/1991,95,tropicana,24192,10.09377728,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/11/1991,97,dominicks,15616,9.656051309,1,1.59,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/11/1991,97,minute.maid,1920,7.560080465,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/11/1991,97,tropicana,6592,8.793612072,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/11/1991,98,dominicks,41216,10.62658181,1,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/11/1991,98,minute.maid,6464,8.7740036,0,2.26,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/11/1991,98,tropicana,17088,9.746131742,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/11/1991,100,dominicks,59456,10.99299182,1,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/11/1991,100,minute.maid,3968,8.286017468,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/11/1991,100,tropicana,26624,10.18956834,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/11/1991,101,dominicks,27200,10.21097225,1,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/11/1991,101,minute.maid,3584,8.184234774,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/11/1991,101,tropicana,15872,9.67231183,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/11/1991,103,dominicks,29376,10.28793329,1,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/11/1991,103,minute.maid,4032,8.30201781,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/11/1991,103,tropicana,7808,8.962904128,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/11/1991,104,dominicks,15872,9.67231183,1,1.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/11/1991,104,minute.maid,3264,8.090708716,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/11/1991,104,tropicana,7872,8.971067439,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/11/1991,105,dominicks,40640,10.61250808,1,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/11/1991,105,minute.maid,4736,8.462948177,0,2.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/11/1991,105,tropicana,12160,9.405907156,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/11/1991,106,dominicks,16064,9.684336023,1,1.59,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/11/1991,106,minute.maid,1536,7.336936914,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/11/1991,106,tropicana,7104,8.868413285,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/11/1991,107,dominicks,19136,9.859326657,1,1.59,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/11/1991,107,minute.maid,4608,8.435549202,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/11/1991,107,tropicana,27648,10.22730867,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/11/1991,109,dominicks,33280,10.4127119,1,1.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/11/1991,109,minute.maid,8064,8.99516499,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/11/1991,109,tropicana,33984,10.43364511,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/11/1991,110,dominicks,25984,10.16523624,1,1.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/11/1991,110,minute.maid,3776,8.236420527,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/11/1991,110,tropicana,10432,9.252633284,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/11/1991,111,dominicks,132416,11.79370376,1,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/11/1991,111,minute.maid,3648,8.201934351,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/11/1991,111,tropicana,19392,9.872615889,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/11/1991,112,dominicks,20480,9.927204079,1,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/11/1991,112,minute.maid,11520,9.351839934,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/11/1991,112,tropicana,32384,10.38541975,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/11/1991,113,dominicks,30336,10.32009041,1,1.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/11/1991,113,minute.maid,3904,8.269756948,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/11/1991,113,tropicana,28800,10.26813067,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/11/1991,114,dominicks,61952,11.03411517,1,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/11/1991,114,minute.maid,4672,8.449342525,0,2.26,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/11/1991,114,tropicana,19648,9.885730831,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/11/1991,115,dominicks,22848,10.03661887,1,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/11/1991,115,minute.maid,3840,8.253227646,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/11/1991,115,tropicana,17600,9.775654181,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/11/1991,116,dominicks,15744,9.664214619,1,1.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/11/1991,116,minute.maid,2048,7.624618986,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/11/1991,116,tropicana,12352,9.421573272,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/11/1991,117,dominicks,8192,9.010913347,1,1.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/11/1991,117,minute.maid,2368,7.769800996,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/11/1991,117,tropicana,12800,9.45720045,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/11/1991,118,dominicks,22720,10.03100087,1,1.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/11/1991,118,minute.maid,3200,8.070906089,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/11/1991,118,tropicana,18240,9.811372264,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/11/1991,119,dominicks,18688,9.835636886,1,1.59,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/11/1991,119,minute.maid,3072,8.030084094,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/11/1991,119,tropicana,15680,9.660141294,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/11/1991,121,dominicks,32320,10.38344151,1,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/11/1991,121,minute.maid,5184,8.553332238,0,2.26,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/11/1991,121,tropicana,17600,9.775654181,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/11/1991,122,dominicks,27904,10.23652533,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/11/1991,122,minute.maid,7424,8.912473275,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/11/1991,122,tropicana,17088,9.746131742,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/11/1991,123,dominicks,48064,10.78028874,1,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/11/1991,123,minute.maid,5120,8.540909718,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/11/1991,123,tropicana,17536,9.77201119,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/11/1991,124,dominicks,67200,11.11542853,1,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/11/1991,124,minute.maid,3840,8.253227646,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/11/1991,124,tropicana,23744,10.07508515,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/11/1991,126,dominicks,19520,9.87919486,1,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/11/1991,126,minute.maid,3456,8.14786713,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/11/1991,126,tropicana,22016,9.999524741,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/11/1991,128,dominicks,54720,10.90998455,1,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/11/1991,128,minute.maid,5248,8.565602331,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/11/1991,128,tropicana,67776,11.12396343,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/11/1991,129,dominicks,17600,9.775654181,1,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/11/1991,129,minute.maid,4480,8.407378325,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/11/1991,129,tropicana,12288,9.416378455,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/11/1991,130,dominicks,72320,11.188856,1,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/11/1991,130,minute.maid,3392,8.129174997,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/11/1991,130,tropicana,46080,10.7381343,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/11/1991,131,dominicks,21632,9.981928979,1,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/11/1991,131,minute.maid,4992,8.51559191,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/11/1991,131,tropicana,17472,9.768354879,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/11/1991,132,dominicks,42688,10.66167313,1,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/11/1991,132,minute.maid,7232,8.886270902,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/11/1991,132,tropicana,12864,9.462187991,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/11/1991,134,dominicks,11776,9.373818841,1,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/11/1991,134,minute.maid,1664,7.416979621,0,2.26,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/11/1991,134,tropicana,7808,8.962904128,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/11/1991,137,dominicks,31168,10.34714721,1,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/11/1991,137,minute.maid,7680,8.946374826,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/11/1991,137,tropicana,77760,11.26138244,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/18/1991,2,dominicks,8320,9.026417534,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/18/1991,2,minute.maid,4224,8.348537825,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/18/1991,2,tropicana,20096,9.908276069,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/18/1991,5,dominicks,6208,8.733594062,0,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/18/1991,5,minute.maid,4608,8.435549202,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/18/1991,5,tropicana,15360,9.639522007,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/18/1991,8,dominicks,25408,10.14281936,0,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/18/1991,8,minute.maid,9920,9.2023082,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/18/1991,8,tropicana,8320,9.026417534,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/18/1991,9,dominicks,22592,10.02535114,0,1.59,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/18/1991,9,minute.maid,4928,8.502688505,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/18/1991,9,tropicana,7744,8.954673629,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/18/1991,12,dominicks,15936,9.67633598,0,1.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/18/1991,12,minute.maid,6336,8.754002934,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/18/1991,12,tropicana,22528,10.02251426,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/18/1991,14,dominicks,3200,8.070906089,0,1.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/18/1991,14,minute.maid,4096,8.317766167,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/18/1991,14,tropicana,17472,9.768354879,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/18/1991,18,dominicks,11648,9.36288977,0,1.59,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/18/1991,18,minute.maid,9088,9.114710141,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/18/1991,18,tropicana,8832,9.086136769,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/18/1991,21,dominicks,12608,9.442086812,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/18/1991,21,minute.maid,9280,9.135616826,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/18/1991,21,tropicana,4864,8.489616424,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/18/1991,28,dominicks,9536,9.162829389,0,1.59,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/18/1991,28,minute.maid,6464,8.7740036,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/18/1991,28,tropicana,8768,9.078864009,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/18/1991,32,dominicks,13504,9.510741217,0,1.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/18/1991,32,minute.maid,6080,8.712759975,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/18/1991,32,tropicana,29504,10.29228113,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/18/1991,33,dominicks,15552,9.651944527,0,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/18/1991,33,minute.maid,7168,8.877381955,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/18/1991,33,tropicana,25536,10.1478445,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/18/1991,40,dominicks,12672,9.447150114,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/18/1991,40,minute.maid,5888,8.68067166,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/18/1991,40,tropicana,9856,9.195835686,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/18/1991,44,dominicks,6464,8.7740036,0,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/18/1991,44,minute.maid,5184,8.553332238,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/18/1991,44,tropicana,11904,9.384629757,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/18/1991,45,dominicks,7744,8.954673629,0,1.59,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/18/1991,45,minute.maid,2560,7.847762538,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/18/1991,45,tropicana,10752,9.282847063,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/18/1991,47,dominicks,9216,9.128696383,0,1.59,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/18/1991,47,minute.maid,3392,8.129174997,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/18/1991,47,tropicana,8832,9.086136769,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/18/1991,48,dominicks,5952,8.691482577,0,1.59,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/18/1991,48,minute.maid,4160,8.333270353,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/18/1991,48,tropicana,9472,9.156095357,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/18/1991,49,dominicks,7296,8.895081532,0,1.59,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/18/1991,49,minute.maid,2752,7.920083199,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/18/1991,49,tropicana,7808,8.962904128,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/18/1991,50,dominicks,6080,8.712759975,0,1.59,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/18/1991,50,minute.maid,2688,7.896552702,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/18/1991,50,tropicana,6528,8.783855897,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/18/1991,51,dominicks,3200,8.070906089,0,1.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/18/1991,51,minute.maid,2432,7.796469243,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/18/1991,51,tropicana,8640,9.064157862,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/18/1991,53,dominicks,14912,9.609921537,0,1.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/18/1991,53,minute.maid,5696,8.647519453,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/18/1991,53,tropicana,18752,9.839055692,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/18/1991,54,dominicks,4736,8.462948177,0,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/18/1991,54,minute.maid,2432,7.796469243,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/18/1991,54,tropicana,9792,9.189321005,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/18/1991,56,dominicks,8320,9.026417534,0,1.59,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/18/1991,56,minute.maid,4096,8.317766167,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/18/1991,56,tropicana,9536,9.162829389,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/18/1991,59,dominicks,3840,8.253227646,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/18/1991,59,minute.maid,5120,8.540909718,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/18/1991,59,tropicana,7936,8.979164649,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/18/1991,64,dominicks,7488,8.921057018,0,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/18/1991,64,minute.maid,2368,7.769800996,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/18/1991,64,tropicana,4992,8.51559191,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/18/1991,67,dominicks,3776,8.236420527,0,1.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/18/1991,67,minute.maid,5440,8.60153434,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/18/1991,67,tropicana,7488,8.921057018,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/18/1991,68,dominicks,10816,9.288781798,0,1.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/18/1991,68,minute.maid,5632,8.636219898,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/18/1991,68,tropicana,19392,9.872615889,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/18/1991,70,dominicks,11840,9.379238908,0,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/18/1991,70,minute.maid,9984,9.208739091,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/18/1991,70,tropicana,7872,8.971067439,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/18/1991,71,dominicks,12736,9.452187908,0,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/18/1991,71,minute.maid,6336,8.754002934,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/18/1991,71,tropicana,11840,9.379238908,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/18/1991,72,dominicks,7168,8.877381955,0,1.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/18/1991,72,minute.maid,4352,8.378390789,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/18/1991,72,tropicana,9664,9.17616292,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/18/1991,73,dominicks,31104,10.34509171,0,1.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/18/1991,73,minute.maid,16320,9.700146629,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/18/1991,73,tropicana,8384,9.034080407,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/18/1991,74,dominicks,20480,9.927204079,0,1.59,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/18/1991,74,minute.maid,5120,8.540909718,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/18/1991,74,tropicana,20096,9.908276069,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/18/1991,75,dominicks,8192,9.010913347,0,1.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/18/1991,75,minute.maid,4992,8.51559191,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/18/1991,75,tropicana,17344,9.761001904,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/18/1991,76,dominicks,8320,9.026417534,0,1.59,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/18/1991,76,minute.maid,3648,8.201934351,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/18/1991,76,tropicana,11136,9.317938383,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/18/1991,77,dominicks,5312,8.577723691,0,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/18/1991,77,minute.maid,8320,9.026417534,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/18/1991,77,tropicana,8576,9.056722883,0,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/18/1991,78,dominicks,8896,9.093357017,0,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/18/1991,78,minute.maid,9344,9.142489705,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/18/1991,78,tropicana,8512,9.049232212,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/18/1991,80,dominicks,3648,8.201934351,0,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/18/1991,80,minute.maid,8768,9.078864009,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/18/1991,80,tropicana,8576,9.056722883,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/18/1991,81,dominicks,7808,8.962904128,0,1.59,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/18/1991,81,minute.maid,4352,8.378390789,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/18/1991,81,tropicana,8640,9.064157862,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/18/1991,83,dominicks,26496,10.18474906,0,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/18/1991,83,minute.maid,6784,8.822322178,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/18/1991,83,tropicana,9600,9.169518378,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/18/1991,84,dominicks,4416,8.392989588,0,1.59,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/18/1991,84,minute.maid,4544,8.42156296,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/18/1991,84,tropicana,7360,8.903815212,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/18/1991,86,dominicks,9856,9.195835686,0,1.59,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/18/1991,86,minute.maid,3456,8.14786713,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/18/1991,86,tropicana,11648,9.36288977,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/18/1991,88,dominicks,7488,8.921057018,0,1.59,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/18/1991,88,minute.maid,3712,8.219326094,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/18/1991,88,tropicana,4544,8.42156296,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/18/1991,89,dominicks,11200,9.323669057,0,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/18/1991,89,minute.maid,3840,8.253227646,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/18/1991,89,tropicana,10112,9.221478116,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/18/1991,90,dominicks,6144,8.723231275,0,1.59,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/18/1991,90,minute.maid,4352,8.378390789,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/18/1991,90,tropicana,7424,8.912473275,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/18/1991,91,dominicks,16832,9.731037116,0,1.59,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/18/1991,91,minute.maid,3008,8.009030685,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/18/1991,91,tropicana,8768,9.078864009,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/18/1991,92,dominicks,12544,9.436997743,0,1.59,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/18/1991,92,minute.maid,2496,7.82244473,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/18/1991,92,tropicana,5120,8.540909718,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/18/1991,94,dominicks,11776,9.373818841,0,1.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/18/1991,94,minute.maid,8128,9.00307017,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/18/1991,94,tropicana,3456,8.14786713,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/18/1991,95,dominicks,11072,9.312174678,0,1.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/18/1991,95,minute.maid,3584,8.184234774,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/18/1991,95,tropicana,7808,8.962904128,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/18/1991,98,dominicks,16064,9.684336023,0,1.61,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/18/1991,98,minute.maid,4736,8.462948177,0,2.26,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/18/1991,98,tropicana,7040,8.859363449,0,2.27,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/18/1991,100,dominicks,14336,9.570529135,0,1.64,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/18/1991,100,minute.maid,6144,8.723231275,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/18/1991,100,tropicana,3456,8.14786713,0,2.38,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/18/1991,101,dominicks,13824,9.534161491,0,1.61,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/18/1991,101,minute.maid,5248,8.565602331,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/18/1991,101,tropicana,5888,8.68067166,0,2.36,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/18/1991,102,dominicks,39168,10.57561537,0,1.61,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/18/1991,102,minute.maid,11072,9.312174678,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/18/1991,102,tropicana,9728,9.182763604,0,2.26,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/18/1991,103,dominicks,9728,9.182763604,0,1.62,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/18/1991,103,minute.maid,3776,8.236420527,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/18/1991,103,tropicana,5824,8.66974259,0,2.11,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/18/1991,104,dominicks,8064,8.99516499,0,1.59,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/18/1991,104,minute.maid,3392,8.129174997,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/18/1991,104,tropicana,7232,8.886270902,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/18/1991,105,dominicks,20800,9.942708266,0,1.61,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/18/1991,105,minute.maid,5504,8.61323038,0,2.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/18/1991,105,tropicana,5376,8.589699882,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/18/1991,107,dominicks,13824,9.534161491,0,1.59,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/18/1991,107,minute.maid,5184,8.553332238,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/18/1991,107,tropicana,16000,9.680344001,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/18/1991,109,dominicks,13888,9.538780437,0,1.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/18/1991,109,minute.maid,8064,8.99516499,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/18/1991,109,tropicana,13376,9.501217335,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/18/1991,110,dominicks,9408,9.14931567,0,1.59,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/18/1991,110,minute.maid,3584,8.184234774,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/18/1991,110,tropicana,8512,9.049232212,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/18/1991,111,dominicks,82560,11.32128058,0,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/18/1991,111,minute.maid,2048,7.624618986,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/18/1991,111,tropicana,11520,9.351839934,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/18/1991,112,dominicks,11264,9.329367078,0,1.63,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/18/1991,112,minute.maid,12672,9.447150114,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/18/1991,112,tropicana,11904,9.384629757,0,2.4,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/18/1991,113,dominicks,3264,8.090708716,0,1.59,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/18/1991,113,minute.maid,4800,8.476371197,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/18/1991,113,tropicana,11520,9.351839934,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/18/1991,115,dominicks,14912,9.609921537,0,1.63,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/18/1991,115,minute.maid,4224,8.348537825,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/18/1991,115,tropicana,12544,9.436997743,0,2.17,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/18/1991,116,dominicks,6272,8.743850562,0,1.59,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/18/1991,116,minute.maid,1984,7.592870288,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/18/1991,116,tropicana,8192,9.010913347,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/18/1991,117,dominicks,5824,8.66974259,0,1.59,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/18/1991,117,minute.maid,2944,7.98752448,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/18/1991,117,tropicana,7936,8.979164649,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/18/1991,118,dominicks,8640,9.064157862,0,1.59,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/18/1991,118,minute.maid,3840,8.253227646,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/18/1991,118,tropicana,6528,8.783855897,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/18/1991,121,dominicks,16896,9.734832187,0,1.61,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/18/1991,121,minute.maid,4736,8.462948177,0,2.26,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/18/1991,121,tropicana,10048,9.215128889,0,2.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/18/1991,122,dominicks,10496,9.258749511,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/18/1991,122,minute.maid,9536,9.162829389,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/18/1991,122,tropicana,16000,9.680344001,0,2.12,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/18/1991,123,dominicks,6976,8.850230966,0,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/18/1991,123,minute.maid,4864,8.489616424,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/18/1991,123,tropicana,7552,8.929567708,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/18/1991,124,dominicks,15552,9.651944527,0,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/18/1991,124,minute.maid,7680,8.946374826,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/18/1991,124,tropicana,5056,8.528330936,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/18/1991,126,dominicks,6464,8.7740036,0,1.64,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/18/1991,126,minute.maid,4352,8.378390789,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/18/1991,126,tropicana,9536,9.162829389,0,2.24,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/18/1991,128,dominicks,16128,9.688312171,0,1.63,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/18/1991,128,minute.maid,8128,9.00307017,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/18/1991,128,tropicana,7808,8.962904128,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/18/1991,129,dominicks,5056,8.528330936,0,1.63,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/18/1991,129,minute.maid,5504,8.61323038,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/18/1991,129,tropicana,7040,8.859363449,0,2.15,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/18/1991,130,dominicks,11840,9.379238908,0,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/18/1991,130,minute.maid,5312,8.577723691,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/18/1991,130,tropicana,12224,9.411156511,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/18/1991,131,dominicks,11392,9.340666634,0,1.63,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/18/1991,131,minute.maid,4672,8.449342525,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/18/1991,131,tropicana,4224,8.348537825,0,2.36,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/18/1991,132,dominicks,18048,9.800790154,0,1.61,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/18/1991,132,minute.maid,6400,8.764053269,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/18/1991,132,tropicana,6656,8.803273983,0,2.22,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/18/1991,134,dominicks,4032,8.30201781,0,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/18/1991,134,minute.maid,2240,7.714231145,0,2.26,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/18/1991,134,tropicana,6400,8.764053269,0,2.18,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/18/1991,137,dominicks,17920,9.793672686,0,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/18/1991,137,minute.maid,6848,8.831711918,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/18/1991,137,tropicana,55232,10.91929777,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/25/1991,2,dominicks,6784,8.822322178,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/25/1991,2,minute.maid,2880,7.965545573,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/25/1991,2,tropicana,9152,9.121727714,1,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/25/1991,5,dominicks,6592,8.793612072,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/25/1991,5,minute.maid,5248,8.565602331,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/25/1991,5,tropicana,8000,8.987196821,1,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/25/1991,8,dominicks,38336,10.55414468,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/25/1991,8,minute.maid,6592,8.793612072,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/25/1991,8,tropicana,11136,9.317938383,1,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/25/1991,9,dominicks,3904,8.269756948,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/25/1991,9,minute.maid,3072,8.030084094,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/25/1991,9,tropicana,7680,8.946374826,1,3.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/25/1991,12,dominicks,8320,9.026417534,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/25/1991,12,minute.maid,4480,8.407378325,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/25/1991,12,tropicana,10496,9.258749511,1,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/25/1991,14,dominicks,3520,8.166216269,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/25/1991,14,minute.maid,3136,8.050703382,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/25/1991,14,tropicana,14080,9.55251063,1,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/25/1991,18,dominicks,18048,9.800790154,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/25/1991,18,minute.maid,6656,8.803273983,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/25/1991,18,tropicana,12928,9.467150781,1,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/25/1991,21,dominicks,19584,9.882468185,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/25/1991,21,minute.maid,6336,8.754002934,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/25/1991,21,tropicana,6720,8.812843434,1,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/25/1991,28,dominicks,3712,8.219326094,0,1.89,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/25/1991,28,minute.maid,2112,7.655390645,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/25/1991,28,tropicana,4992,8.51559191,1,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/25/1991,32,dominicks,15104,9.622714888,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/25/1991,32,minute.maid,4800,8.476371197,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/25/1991,32,tropicana,16704,9.723403491,1,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/25/1991,33,dominicks,2304,7.742402022,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/25/1991,33,minute.maid,4416,8.392989588,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/25/1991,33,tropicana,9792,9.189321005,1,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/25/1991,40,dominicks,12288,9.416378455,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/25/1991,40,minute.maid,5056,8.528330936,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/25/1991,40,tropicana,5504,8.61323038,1,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/25/1991,44,dominicks,3648,8.201934351,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/25/1991,44,minute.maid,3392,8.129174997,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/25/1991,44,tropicana,11136,9.317938383,1,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/25/1991,45,dominicks,5824,8.66974259,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/25/1991,45,minute.maid,3136,8.050703382,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/25/1991,45,tropicana,7296,8.895081532,1,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/25/1991,47,dominicks,11520,9.351839934,0,1.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/25/1991,47,minute.maid,4480,8.407378325,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/25/1991,47,tropicana,5696,8.647519453,1,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/25/1991,48,dominicks,4992,8.51559191,0,1.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/25/1991,48,minute.maid,5056,8.528330936,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/25/1991,48,tropicana,8512,9.049232212,1,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/25/1991,49,dominicks,4352,8.378390789,0,1.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/25/1991,49,minute.maid,2688,7.896552702,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/25/1991,49,tropicana,7424,8.912473275,1,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/25/1991,50,dominicks,4416,8.392989588,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/25/1991,50,minute.maid,1920,7.560080465,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/25/1991,50,tropicana,5056,8.528330936,1,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/25/1991,51,dominicks,4480,8.407378325,0,1.89,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/25/1991,51,minute.maid,2368,7.769800996,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/25/1991,51,tropicana,5248,8.565602331,1,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/25/1991,53,dominicks,15168,9.626943225,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/25/1991,53,minute.maid,4352,8.378390789,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/25/1991,53,tropicana,21632,9.981928979,1,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/25/1991,54,dominicks,7552,8.929567708,0,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/25/1991,54,minute.maid,3008,8.009030685,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/25/1991,54,tropicana,7552,8.929567708,1,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/25/1991,56,dominicks,5696,8.647519453,0,1.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/25/1991,56,minute.maid,3264,8.090708716,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/25/1991,56,tropicana,12288,9.416378455,1,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/25/1991,59,dominicks,7552,8.929567708,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/25/1991,59,minute.maid,5312,8.577723691,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/25/1991,59,tropicana,6784,8.822322178,1,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/25/1991,62,dominicks,2752,7.920083199,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/25/1991,62,minute.maid,5312,8.577723691,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/25/1991,62,tropicana,20096,9.908276069,1,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/25/1991,67,dominicks,9472,9.156095357,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/25/1991,67,minute.maid,5824,8.66974259,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/25/1991,67,tropicana,9856,9.195835686,1,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/25/1991,68,dominicks,10624,9.270870872,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/25/1991,68,minute.maid,3456,8.14786713,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/25/1991,68,tropicana,12992,9.472089062,1,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/25/1991,70,dominicks,12992,9.472089062,0,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/25/1991,70,minute.maid,9216,9.128696383,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/25/1991,70,tropicana,10816,9.288781798,1,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/25/1991,71,dominicks,6528,8.783855897,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/25/1991,71,minute.maid,6848,8.831711918,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/25/1991,71,tropicana,9216,9.128696383,1,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/25/1991,72,dominicks,7360,8.903815212,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/25/1991,72,minute.maid,4288,8.363575703,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/25/1991,72,tropicana,16000,9.680344001,1,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/25/1991,73,dominicks,24512,10.10691807,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/25/1991,73,minute.maid,11264,9.329367078,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/25/1991,73,tropicana,22272,10.01108556,1,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/25/1991,74,dominicks,15936,9.67633598,0,1.89,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/25/1991,74,minute.maid,4224,8.348537825,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/25/1991,74,tropicana,10432,9.252633284,1,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/25/1991,75,dominicks,4992,8.51559191,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/25/1991,75,minute.maid,2688,7.896552702,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/25/1991,75,tropicana,9856,9.195835686,1,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/25/1991,76,dominicks,7232,8.886270902,0,1.89,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/25/1991,76,minute.maid,3136,8.050703382,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/25/1991,76,tropicana,6784,8.822322178,1,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/25/1991,77,dominicks,10496,9.258749511,0,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/25/1991,77,minute.maid,8704,9.071537969,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/25/1991,77,tropicana,10880,9.29468152,1,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/25/1991,78,dominicks,14464,9.579418083,0,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/25/1991,78,minute.maid,5888,8.68067166,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/25/1991,78,tropicana,11264,9.329367078,1,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/25/1991,80,dominicks,5376,8.589699882,0,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/25/1991,80,minute.maid,9216,9.128696383,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/25/1991,80,tropicana,19328,9.869310101,1,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/25/1991,81,dominicks,8832,9.086136769,0,1.89,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/25/1991,81,minute.maid,3648,8.201934351,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/25/1991,81,tropicana,9088,9.114710141,1,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/25/1991,83,dominicks,25216,10.13523399,0,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/25/1991,83,minute.maid,5696,8.647519453,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/25/1991,83,tropicana,6976,8.850230966,1,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/25/1991,84,dominicks,7680,8.946374826,0,1.89,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/25/1991,84,minute.maid,3840,8.253227646,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/25/1991,84,tropicana,7296,8.895081532,1,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/25/1991,86,dominicks,10880,9.29468152,0,1.89,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/25/1991,86,minute.maid,2752,7.920083199,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/25/1991,86,tropicana,8640,9.064157862,1,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/25/1991,88,dominicks,6528,8.783855897,0,1.89,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/25/1991,88,minute.maid,3200,8.070906089,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/25/1991,88,tropicana,4928,8.502688505,1,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/25/1991,89,dominicks,15744,9.664214619,0,1.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/25/1991,89,minute.maid,1152,7.049254841,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/25/1991,89,tropicana,4032,8.30201781,1,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/25/1991,90,dominicks,3072,8.030084094,0,1.89,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/25/1991,90,minute.maid,5888,8.68067166,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/25/1991,90,tropicana,7040,8.859363449,1,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/25/1991,92,dominicks,16768,9.727227587,0,1.89,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/25/1991,92,minute.maid,2496,7.82244473,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/25/1991,92,tropicana,5760,8.658692754,1,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/25/1991,93,dominicks,1664,7.416979621,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/25/1991,93,minute.maid,3072,8.030084094,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/25/1991,93,tropicana,6720,8.812843434,1,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/25/1991,94,dominicks,14400,9.574983486,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/25/1991,94,minute.maid,5888,8.68067166,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/25/1991,94,tropicana,11200,9.323669057,1,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/25/1991,95,dominicks,6272,8.743850562,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/25/1991,95,minute.maid,2048,7.624618986,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/25/1991,95,tropicana,7040,8.859363449,1,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/25/1991,97,dominicks,8320,9.026417534,0,1.89,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/25/1991,97,minute.maid,1856,7.526178913,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/25/1991,97,tropicana,5056,8.528330936,1,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/25/1991,98,dominicks,12864,9.462187991,0,1.47,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/25/1991,98,minute.maid,3776,8.236420527,0,2.26,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/25/1991,98,tropicana,13312,9.496421163,1,2.63,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/25/1991,100,dominicks,9408,9.14931567,0,1.09,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/25/1991,100,minute.maid,4864,8.489616424,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/25/1991,100,tropicana,13120,9.481893063,1,2.61,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/25/1991,101,dominicks,7040,8.859363449,0,1.34,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/25/1991,101,minute.maid,2816,7.943072717,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/25/1991,101,tropicana,11264,9.329367078,1,2.73,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/25/1991,102,dominicks,20480,9.927204079,0,1.46,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/25/1991,102,minute.maid,7360,8.903815212,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/25/1991,102,tropicana,13312,9.496421163,1,2.54,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/25/1991,103,dominicks,6784,8.822322178,0,1.42,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/25/1991,103,minute.maid,4416,8.392989588,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/25/1991,103,tropicana,4672,8.449342525,1,2.54,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/25/1991,104,dominicks,8896,9.093357017,0,1.89,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/25/1991,104,minute.maid,2624,7.87245515,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/25/1991,104,tropicana,5632,8.636219898,1,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/25/1991,105,dominicks,22144,10.00532186,0,1.32,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/25/1991,105,minute.maid,4352,8.378390789,0,2.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/25/1991,105,tropicana,7616,8.938006577,1,2.75,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/25/1991,106,dominicks,5440,8.60153434,0,1.89,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/25/1991,106,minute.maid,1920,7.560080465,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/25/1991,106,tropicana,4160,8.333270353,1,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/25/1991,107,dominicks,15296,9.635346635,0,1.89,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/25/1991,107,minute.maid,4544,8.42156296,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/25/1991,107,tropicana,14976,9.614204199,1,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/25/1991,109,dominicks,7680,8.946374826,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/25/1991,109,minute.maid,7872,8.971067439,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/25/1991,109,tropicana,18880,9.84585844,1,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/25/1991,110,dominicks,5248,8.565602331,0,1.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/25/1991,110,minute.maid,3328,8.110126802,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/25/1991,110,tropicana,9536,9.162829389,1,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/25/1991,111,dominicks,17024,9.742379392,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/25/1991,111,minute.maid,3392,8.129174997,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/25/1991,111,tropicana,8256,9.018695488,1,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/25/1991,112,dominicks,14400,9.574983486,0,1.2,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/25/1991,112,minute.maid,10816,9.288781798,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/25/1991,112,tropicana,19968,9.901886271,1,2.57,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/25/1991,113,dominicks,8128,9.00307017,0,1.89,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/25/1991,113,minute.maid,3456,8.14786713,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/25/1991,113,tropicana,9024,9.107642974,1,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/25/1991,114,dominicks,16384,9.704060528,0,1.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/25/1991,114,minute.maid,3968,8.286017468,0,2.26,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/25/1991,114,tropicana,7296,8.895081532,1,2.64,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/25/1991,115,dominicks,9280,9.135616826,0,1.1,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/25/1991,115,minute.maid,3392,8.129174997,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/25/1991,115,tropicana,11648,9.36288977,1,2.61,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/25/1991,116,dominicks,6016,8.702177866,0,1.89,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/25/1991,116,minute.maid,2688,7.896552702,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/25/1991,116,tropicana,7104,8.868413285,1,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/25/1991,117,dominicks,7680,8.946374826,0,1.89,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/25/1991,117,minute.maid,3328,8.110126802,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/25/1991,117,tropicana,11456,9.346268889,1,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/25/1991,118,dominicks,7616,8.938006577,0,1.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/25/1991,118,minute.maid,3072,8.030084094,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/25/1991,118,tropicana,13696,9.524859098,1,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/25/1991,119,dominicks,4544,8.42156296,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/25/1991,119,minute.maid,3392,8.129174997,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/25/1991,119,tropicana,11264,9.329367078,1,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/25/1991,121,dominicks,8512,9.049232212,0,1.17,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/25/1991,121,minute.maid,4032,8.30201781,0,2.26,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/25/1991,121,tropicana,13312,9.496421163,1,2.75,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/25/1991,122,dominicks,14464,9.579418083,0,1.27,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/25/1991,122,minute.maid,10752,9.282847063,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/25/1991,122,tropicana,15488,9.64782081,1,2.52,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/25/1991,123,dominicks,6080,8.712759975,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/25/1991,123,minute.maid,3072,8.030084094,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/25/1991,123,tropicana,10112,9.221478116,1,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/25/1991,124,dominicks,19904,9.898675996,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/25/1991,124,minute.maid,2560,7.847762538,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/25/1991,124,tropicana,8192,9.010913347,1,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/25/1991,126,dominicks,8384,9.034080407,0,1.2,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/25/1991,126,minute.maid,3776,8.236420527,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/25/1991,126,tropicana,10688,9.276876896,1,2.74,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/25/1991,128,dominicks,7040,8.859363449,0,1.28,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/25/1991,128,minute.maid,6272,8.743850562,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/25/1991,128,tropicana,14016,9.547954813,1,2.86,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/25/1991,129,dominicks,5056,8.528330936,0,1.22,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/25/1991,129,minute.maid,4288,8.363575703,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/25/1991,129,tropicana,9280,9.135616826,1,2.56,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/25/1991,130,dominicks,21504,9.975994243,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/25/1991,130,minute.maid,2496,7.82244473,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/25/1991,130,tropicana,6272,8.743850562,1,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/25/1991,131,dominicks,2880,7.965545573,0,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/25/1991,131,minute.maid,4096,8.317766167,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/25/1991,131,tropicana,11776,9.373818841,1,2.77,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/25/1991,132,dominicks,14016,9.547954813,0,1.35,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/25/1991,132,minute.maid,6528,8.783855897,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/25/1991,132,tropicana,11392,9.340666634,1,2.64,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/25/1991,137,dominicks,20160,9.911455722,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/25/1991,137,minute.maid,5760,8.658692754,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/25/1991,137,tropicana,24256,10.09641929,1,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/1/1991,2,dominicks,60544,11.01112565,1,0.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/1/1991,2,minute.maid,3968,8.286017468,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/1/1991,2,tropicana,21952,9.996613531,0,2.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/1/1991,5,dominicks,63552,11.05961375,1,0.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/1/1991,5,minute.maid,4224,8.348537825,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/1/1991,5,tropicana,21120,9.957975738,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/1/1991,8,dominicks,152384,11.93415893,1,0.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/1/1991,8,minute.maid,7168,8.877381955,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/1/1991,8,tropicana,27712,10.22962081,0,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/1/1991,9,dominicks,95296,11.46474312,1,0.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/1/1991,9,minute.maid,3712,8.219326094,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/1/1991,9,tropicana,30912,10.33889974,0,2.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/1/1991,12,dominicks,173888,12.06616669,1,0.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/1/1991,12,minute.maid,4864,8.489616424,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/1/1991,12,tropicana,23808,10.07777694,0,2.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/1/1991,14,dominicks,31360,10.35328847,1,0.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/1/1991,14,minute.maid,2688,7.896552702,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/1/1991,14,tropicana,31552,10.35939226,0,2.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/1/1991,18,dominicks,104576,11.55766936,1,0.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/1/1991,18,minute.maid,8256,9.018695488,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/1/1991,18,tropicana,21376,9.970024076,0,2.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/1/1991,21,dominicks,67968,11.12679229,1,0.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/1/1991,21,minute.maid,4864,8.489616424,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/1/1991,21,tropicana,7936,8.979164649,0,2.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/1/1991,28,dominicks,32896,10.40110635,1,0.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/1/1991,28,minute.maid,2304,7.742402022,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/1/1991,28,tropicana,13312,9.496421163,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/1/1991,32,dominicks,111296,11.6199486,1,0.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/1/1991,32,minute.maid,4224,8.348537825,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/1/1991,32,tropicana,42304,10.65263692,0,2.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/1/1991,33,dominicks,61568,11.02789753,1,0.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/1/1991,33,minute.maid,4864,8.489616424,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/1/1991,33,tropicana,39424,10.58213005,0,2.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/1/1991,40,dominicks,49600,10.81174611,1,0.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/1/1991,40,minute.maid,3200,8.070906089,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/1/1991,40,tropicana,10880,9.29468152,0,2.19,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/1/1991,44,dominicks,52928,10.87668778,1,0.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/1/1991,44,minute.maid,3712,8.219326094,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/1/1991,44,tropicana,37568,10.5339079,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/1/1991,48,dominicks,22720,10.03100087,1,0.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/1/1991,48,minute.maid,4608,8.435549202,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/1/1991,48,tropicana,17088,9.746131742,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/1/1991,49,dominicks,44096,10.69412435,1,0.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/1/1991,49,minute.maid,1984,7.592870288,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/1/1991,49,tropicana,8640,9.064157862,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/1/1991,50,dominicks,36160,10.49570882,1,0.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/1/1991,50,minute.maid,2432,7.796469243,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/1/1991,50,tropicana,13376,9.501217335,0,2.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/1/1991,51,dominicks,67392,11.1182816,1,0.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/1/1991,51,minute.maid,2496,7.82244473,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/1/1991,51,tropicana,16832,9.731037116,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/1/1991,52,dominicks,37312,10.52707027,1,0.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/1/1991,52,minute.maid,5824,8.66974259,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/1/1991,52,tropicana,54976,10.91465201,0,2.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/1/1991,53,dominicks,116224,11.66327464,1,0.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/1/1991,53,minute.maid,2944,7.98752448,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/1/1991,53,tropicana,52992,10.87789624,0,2.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/1/1991,54,dominicks,47680,10.7722673,1,0.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/1/1991,54,minute.maid,2624,7.87245515,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/1/1991,54,tropicana,15296,9.635346635,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/1/1991,56,dominicks,44736,10.70853383,1,0.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/1/1991,56,minute.maid,3648,8.201934351,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/1/1991,56,tropicana,17984,9.797237753,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/1/1991,59,dominicks,62080,11.03617916,1,0.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/1/1991,59,minute.maid,4864,8.489616424,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/1/1991,59,tropicana,13184,9.486759252,0,2.19,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/1/1991,62,dominicks,28032,10.24110199,1,0.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/1/1991,62,minute.maid,4672,8.449342525,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/1/1991,62,tropicana,38336,10.55414468,0,2.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/1/1991,67,dominicks,81088,11.30329026,1,0.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/1/1991,67,minute.maid,5696,8.647519453,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/1/1991,67,tropicana,12352,9.421573272,0,2.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/1/1991,68,dominicks,52672,10.87183928,1,0.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/1/1991,68,minute.maid,3456,8.14786713,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/1/1991,68,tropicana,37888,10.54238972,0,2.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/1/1991,70,dominicks,102400,11.53664199,1,0.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/1/1991,70,minute.maid,7424,8.912473275,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/1/1991,70,tropicana,15744,9.664214619,0,2.19,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/1/1991,71,dominicks,114368,11.6471766,1,0.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/1/1991,71,minute.maid,1472,7.294377299,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/1/1991,71,tropicana,34816,10.45783233,0,2.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/1/1991,72,dominicks,79104,11.27851872,1,0.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/1/1991,72,minute.maid,3840,8.253227646,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/1/1991,72,tropicana,41536,10.6343158,0,2.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/1/1991,73,dominicks,241088,12.39291729,1,0.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/1/1991,73,minute.maid,10048,9.215128889,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/1/1991,73,tropicana,32960,10.40304998,0,2.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/1/1991,74,dominicks,115584,11.65775282,1,0.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/1/1991,74,minute.maid,4352,8.378390789,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/1/1991,74,tropicana,28032,10.24110199,0,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/1/1991,75,dominicks,76224,11.24143165,1,0.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/1/1991,75,minute.maid,2432,7.796469243,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/1/1991,75,tropicana,35648,10.48144832,0,2.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/1/1991,76,dominicks,173504,12.06395593,1,0.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/1/1991,76,minute.maid,2944,7.98752448,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 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-8/1/1991,78,minute.maid,4864,8.489616424,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/1/1991,78,tropicana,23488,10.06424493,0,2.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/1/1991,80,dominicks,67136,11.11447569,1,0.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/1/1991,80,minute.maid,8576,9.056722883,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/1/1991,80,tropicana,26496,10.18474906,0,2.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 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-8/1/1991,97,dominicks,40896,10.61878754,1,0.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/1/1991,97,minute.maid,2368,7.769800996,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/1/1991,97,tropicana,6336,8.754002934,0,2.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/1/1991,98,dominicks,163648,12.00547306,1,0.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/1/1991,98,minute.maid,3712,8.219326094,0,2.26,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/1/1991,98,tropicana,26112,10.17015026,0,2.19,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/1/1991,100,dominicks,187520,12.14164079,1,0.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/1/1991,100,minute.maid,4864,8.489616424,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/1/1991,100,tropicana,21952,9.996613531,0,2.19,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/1/1991,101,dominicks,77440,11.25725872,1,0.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/1/1991,101,minute.maid,3136,8.050703382,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/1/1991,101,tropicana,22912,10.03941607,0,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/1/1991,102,dominicks,204352,12.22759928,1,0.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/1/1991,102,minute.maid,9216,9.128696383,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/1/1991,102,tropicana,26880,10.1991378,0,2.19,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/1/1991,103,dominicks,69376,11.14729627,1,0.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/1/1991,103,minute.maid,4288,8.363575703,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/1/1991,103,tropicana,7168,8.877381955,0,2.19,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/1/1991,104,dominicks,59008,10.98542831,1,0.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/1/1991,104,minute.maid,3008,8.009030685,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/1/1991,104,tropicana,12928,9.467150781,0,2.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/1/1991,105,dominicks,116032,11.66162129,1,0.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/1/1991,105,minute.maid,4800,8.476371197,0,2.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/1/1991,105,tropicana,14976,9.614204199,0,2.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/1/1991,106,dominicks,40384,10.60618895,1,0.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/1/1991,106,minute.maid,1920,7.560080465,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/1/1991,106,tropicana,8000,8.987196821,0,2.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/1/1991,107,dominicks,62400,11.04132055,1,0.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/1/1991,107,minute.maid,5440,8.60153434,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/1/1991,107,tropicana,43904,10.68976071,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/1/1991,109,dominicks,77184,11.25394746,1,0.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/1/1991,109,minute.maid,8960,9.100525506,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/1/1991,109,tropicana,49344,10.80657146,0,2.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/1/1991,110,dominicks,52992,10.87789624,1,0.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/1/1991,110,minute.maid,3264,8.090708716,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/1/1991,110,tropicana,16832,9.731037116,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/1/1991,111,dominicks,446208,13.00854049,1,0.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/1/1991,111,minute.maid,2624,7.87245515,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/1/1991,111,tropicana,22528,10.02251426,0,2.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/1/1991,112,dominicks,70848,11.16829202,1,0.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/1/1991,112,minute.maid,11712,9.368369236,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/1/1991,112,tropicana,26496,10.18474906,0,2.19,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/1/1991,113,dominicks,105408,11.56559381,1,0.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/1/1991,113,minute.maid,3968,8.286017468,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/1/1991,113,tropicana,30080,10.31161578,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/1/1991,114,dominicks,199296,12.20254644,1,0.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/1/1991,114,minute.maid,3776,8.236420527,0,2.26,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/1/1991,114,tropicana,18944,9.849242538,0,2.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/1/1991,115,dominicks,56448,10.94107514,1,0.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/1/1991,115,minute.maid,2880,7.965545573,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/1/1991,115,tropicana,24448,10.10430369,0,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/1/1991,116,dominicks,51456,10.84848235,1,0.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/1/1991,116,minute.maid,2560,7.847762538,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/1/1991,116,tropicana,15104,9.622714888,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/1/1991,117,dominicks,31232,10.34919849,1,0.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/1/1991,117,minute.maid,1856,7.526178913,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/1/1991,117,tropicana,15424,9.643680017,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/1/1991,118,dominicks,63104,11.05253944,1,0.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/1/1991,118,minute.maid,3648,8.201934351,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/1/1991,118,tropicana,34368,10.44488118,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/1/1991,119,dominicks,39552,10.58537154,1,0.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/1/1991,119,minute.maid,2944,7.98752448,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/1/1991,119,tropicana,14464,9.579418083,0,2.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/1/1991,121,dominicks,51776,10.854682,1,0.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/1/1991,121,minute.maid,5312,8.577723691,0,2.26,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/1/1991,121,tropicana,30336,10.32009041,0,2.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/1/1991,122,dominicks,38016,10.5457624,1,0.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/1/1991,122,minute.maid,8768,9.078864009,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/1/1991,122,tropicana,20352,9.920934466,0,2.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/1/1991,123,dominicks,146368,11.89387928,1,0.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/1/1991,123,minute.maid,3072,8.030084094,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/1/1991,123,tropicana,36800,10.51325312,0,2.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/1/1991,124,dominicks,215360,12.28006633,1,0.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/1/1991,124,minute.maid,3072,8.030084094,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/1/1991,124,tropicana,23040,10.04498712,0,2.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/1/1991,126,dominicks,67136,11.11447569,1,0.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/1/1991,126,minute.maid,3264,8.090708716,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/1/1991,126,tropicana,35008,10.46333189,0,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/1/1991,128,dominicks,148928,11.91121825,1,0.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/1/1991,128,minute.maid,8000,8.987196821,0,1.96,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/1/1991,128,tropicana,70848,11.16829202,0,2.18,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/1/1991,129,dominicks,51456,10.84848235,1,0.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/1/1991,129,minute.maid,4608,8.435549202,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/1/1991,129,tropicana,15168,9.626943225,0,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/1/1991,130,dominicks,228480,12.33920396,1,0.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/1/1991,130,minute.maid,3456,8.14786713,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/1/1991,130,tropicana,21184,9.961001459,0,2.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/1/1991,131,dominicks,56896,10.94898032,1,0.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/1/1991,131,minute.maid,3520,8.166216269,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/1/1991,131,tropicana,21952,9.996613531,0,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/1/1991,132,dominicks,127488,11.75577752,1,0.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/1/1991,132,minute.maid,6528,8.783855897,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/1/1991,132,tropicana,17408,9.76468515,0,2.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/1/1991,137,dominicks,102464,11.5372668,1,0.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/1/1991,137,minute.maid,5568,8.624791202,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/1/1991,137,tropicana,77824,11.26220515,0,2.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/8/1991,2,dominicks,20608,9.933434629,0,0.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/8/1991,2,minute.maid,3712,8.219326094,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/8/1991,2,tropicana,13568,9.515469358,0,2.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/8/1991,5,dominicks,27968,10.23881628,0,0.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/8/1991,5,minute.maid,4288,8.363575703,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/8/1991,5,tropicana,11904,9.384629757,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/8/1991,8,dominicks,54464,10.90529521,0,0.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/8/1991,8,minute.maid,6208,8.733594062,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/8/1991,8,tropicana,7744,8.954673629,0,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/8/1991,9,dominicks,15744,9.664214619,0,0.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/8/1991,9,minute.maid,4288,8.363575703,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/8/1991,9,tropicana,6400,8.764053269,0,2.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/8/1991,12,dominicks,59904,11.00049856,0,0.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/8/1991,12,minute.maid,4928,8.502688505,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/8/1991,12,tropicana,12096,9.400630098,0,2.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/8/1991,14,dominicks,12544,9.436997743,0,0.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/8/1991,14,minute.maid,2688,7.896552702,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/8/1991,14,tropicana,15552,9.651944527,0,2.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/8/1991,18,dominicks,34304,10.44301724,0,0.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/8/1991,18,minute.maid,7936,8.979164649,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/8/1991,18,tropicana,12032,9.395325046,0,2.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/8/1991,21,dominicks,16960,9.738612909,0,0.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/8/1991,21,minute.maid,5888,8.68067166,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/8/1991,21,tropicana,4032,8.30201781,0,2.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/8/1991,28,dominicks,8576,9.056722883,0,0.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/8/1991,28,minute.maid,2496,7.82244473,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/8/1991,28,tropicana,6720,8.812843434,0,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/8/1991,32,dominicks,29952,10.30735138,0,0.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/8/1991,32,minute.maid,6592,8.793612072,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/8/1991,32,tropicana,22976,10.04220547,0,2.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/8/1991,33,dominicks,15424,9.643680017,0,0.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/8/1991,33,minute.maid,6080,8.712759975,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/8/1991,33,tropicana,16960,9.738612909,0,2.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/8/1991,40,dominicks,20864,9.945780465,0,0.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/8/1991,40,minute.maid,5632,8.636219898,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/8/1991,40,tropicana,5760,8.658692754,0,2.19,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/8/1991,44,dominicks,24448,10.10430369,0,0.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/8/1991,44,minute.maid,3904,8.269756948,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/8/1991,44,tropicana,13376,9.501217335,0,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/8/1991,45,dominicks,9536,9.162829389,0,0.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/8/1991,45,minute.maid,3264,8.090708716,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/8/1991,45,tropicana,8192,9.010913347,0,2.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/8/1991,47,dominicks,21248,9.964018052,0,0.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/8/1991,47,minute.maid,4032,8.30201781,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/8/1991,47,tropicana,5120,8.540909718,0,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/8/1991,48,dominicks,4736,8.462948177,0,0.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/8/1991,48,minute.maid,5248,8.565602331,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/8/1991,48,tropicana,6976,8.850230966,0,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/8/1991,49,dominicks,23552,10.06696602,0,0.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/8/1991,49,minute.maid,1984,7.592870288,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/8/1991,49,tropicana,3776,8.236420527,0,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/8/1991,50,dominicks,15872,9.67231183,0,0.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/8/1991,50,minute.maid,2880,7.965545573,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/8/1991,50,tropicana,3264,8.090708716,0,2.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/8/1991,51,dominicks,32576,10.3913311,0,0.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/8/1991,51,minute.maid,2496,7.82244473,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/8/1991,51,tropicana,7616,8.938006577,0,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/8/1991,52,dominicks,9408,9.14931567,0,0.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/8/1991,52,minute.maid,6528,8.783855897,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/8/1991,52,tropicana,18048,9.800790154,0,2.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/8/1991,53,dominicks,48704,10.79351644,0,0.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/8/1991,53,minute.maid,5120,8.540909718,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/8/1991,53,tropicana,28160,10.24565781,0,2.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/8/1991,54,dominicks,22720,10.03100087,0,0.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/8/1991,54,minute.maid,3584,8.184234774,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/8/1991,54,tropicana,5824,8.66974259,0,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/8/1991,56,dominicks,17472,9.768354879,0,0.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/8/1991,56,minute.maid,2624,7.87245515,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/8/1991,56,tropicana,6848,8.831711918,0,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 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-8/8/1991,67,minute.maid,6208,8.733594062,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/8/1991,67,tropicana,8704,9.071537969,0,2.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/8/1991,68,dominicks,11008,9.30637756,0,0.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/8/1991,68,minute.maid,4416,8.392989588,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/8/1991,68,tropicana,17920,9.793672686,0,2.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 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-8/8/1991,78,minute.maid,4992,8.51559191,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/8/1991,78,tropicana,8256,9.018695488,0,2.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/8/1991,80,dominicks,11456,9.346268889,0,0.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/8/1991,80,minute.maid,8192,9.010913347,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/8/1991,80,tropicana,14528,9.583833101,0,2.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 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-8/8/1991,83,tropicana,6592,8.793612072,0,2.19,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/8/1991,84,dominicks,13056,9.477003077,0,0.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/8/1991,84,minute.maid,3200,8.070906089,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/8/1991,84,tropicana,5952,8.691482577,0,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/8/1991,86,dominicks,39040,10.57234204,0,0.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/8/1991,86,minute.maid,2688,7.896552702,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/8/1991,86,tropicana,9984,9.208739091,0,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/8/1991,88,dominicks,13440,9.505990614,0,0.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/8/1991,88,minute.maid,3008,8.009030685,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/8/1991,88,tropicana,7040,8.859363449,0,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/8/1991,89,dominicks,26240,10.17504024,0,0.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/8/1991,89,minute.maid,2368,7.769800996,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/8/1991,89,tropicana,3072,8.030084094,0,2.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/8/1991,90,dominicks,14720,9.596962392,0,0.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/8/1991,90,minute.maid,2240,7.714231145,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/8/1991,90,tropicana,4480,8.407378325,0,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/8/1991,91,dominicks,12416,9.426741242,0,0.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/8/1991,91,minute.maid,2432,7.796469243,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/8/1991,91,tropicana,7360,8.903815212,0,2.19,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/8/1991,92,dominicks,40128,10.59982962,0,0.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/8/1991,92,minute.maid,2560,7.847762538,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/8/1991,92,tropicana,5952,8.691482577,0,2.19,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/8/1991,93,dominicks,22976,10.04220547,0,0.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/8/1991,93,minute.maid,2880,7.965545573,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/8/1991,93,tropicana,13248,9.491601877,0,2.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/8/1991,94,dominicks,4160,8.333270353,0,0.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/8/1991,94,minute.maid,7872,8.971067439,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/8/1991,94,tropicana,8896,9.093357017,0,2.19,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/8/1991,95,dominicks,20928,9.948843254,0,0.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/8/1991,95,minute.maid,3008,8.009030685,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/8/1991,95,tropicana,6336,8.754002934,0,2.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/8/1991,97,dominicks,14080,9.55251063,0,0.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/8/1991,97,minute.maid,2496,7.82244473,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/8/1991,97,tropicana,4288,8.363575703,0,2.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/8/1991,98,dominicks,50688,10.83344448,0,1.06,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/8/1991,98,minute.maid,4928,8.502688505,0,2.26,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/8/1991,98,tropicana,13248,9.491601877,0,2.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/8/1991,100,dominicks,14016,9.547954813,0,1.32,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/8/1991,100,minute.maid,4992,8.51559191,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/8/1991,100,tropicana,7488,8.921057018,0,2.5,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/8/1991,101,dominicks,9600,9.169518378,0,1.27,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/8/1991,101,minute.maid,4480,8.407378325,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/8/1991,101,tropicana,9984,9.208739091,0,2.37,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/8/1991,102,dominicks,16832,9.731037116,0,1.21,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/8/1991,102,minute.maid,6336,8.754002934,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/8/1991,102,tropicana,6656,8.803273983,0,2.44,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/8/1991,103,dominicks,41792,10.64046021,0,1.08,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/8/1991,103,minute.maid,2624,7.87245515,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/8/1991,103,tropicana,3456,8.14786713,0,2.41,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/8/1991,104,dominicks,14464,9.579418083,0,0.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/8/1991,104,minute.maid,2688,7.896552702,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/8/1991,104,tropicana,5760,8.658692754,0,2.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/8/1991,105,dominicks,21376,9.970024076,0,1.2,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/8/1991,105,minute.maid,3904,8.269756948,0,2.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/8/1991,105,tropicana,5888,8.68067166,0,2.37,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/8/1991,106,dominicks,4096,8.317766167,0,0.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/8/1991,106,minute.maid,1664,7.416979621,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/8/1991,106,tropicana,3264,8.090708716,0,2.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/8/1991,107,dominicks,8576,9.056722883,0,0.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/8/1991,107,minute.maid,5632,8.636219898,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/8/1991,107,tropicana,14464,9.579418083,0,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/8/1991,109,dominicks,15936,9.67633598,0,0.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/8/1991,109,minute.maid,8064,8.99516499,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/8/1991,109,tropicana,21632,9.981928979,0,2.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/8/1991,110,dominicks,15872,9.67231183,0,0.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/8/1991,110,minute.maid,3392,8.129174997,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/8/1991,110,tropicana,9792,9.189321005,0,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/8/1991,111,dominicks,189312,12.15115173,0,0.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/8/1991,111,minute.maid,3328,8.110126802,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/8/1991,111,tropicana,6144,8.723231275,0,2.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/8/1991,112,dominicks,33600,10.42228135,0,1.12,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/8/1991,112,minute.maid,10368,9.246479419,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/8/1991,112,tropicana,24448,10.10430369,0,2.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/8/1991,113,dominicks,9856,9.195835686,0,0.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/8/1991,113,minute.maid,4160,8.333270353,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/8/1991,113,tropicana,17728,9.78290059,0,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/8/1991,114,dominicks,92992,11.44026875,0,1.08,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/8/1991,114,minute.maid,4672,8.449342525,0,2.26,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/8/1991,114,tropicana,8896,9.093357017,0,2.37,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/8/1991,115,dominicks,12928,9.467150781,0,1.17,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/8/1991,115,minute.maid,4160,8.333270353,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/8/1991,115,tropicana,20992,9.951896692,0,2.37,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/8/1991,116,dominicks,14144,9.557045785,0,0.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/8/1991,116,minute.maid,2816,7.943072717,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/8/1991,116,tropicana,8704,9.071537969,0,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/8/1991,117,dominicks,7808,8.962904128,0,0.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/8/1991,117,minute.maid,2560,7.847762538,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/8/1991,117,tropicana,7936,8.979164649,0,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/8/1991,118,dominicks,26304,10.1774763,0,0.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/8/1991,118,minute.maid,3456,8.14786713,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/8/1991,118,tropicana,10752,9.282847063,0,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/8/1991,119,dominicks,9216,9.128696383,0,0.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/8/1991,119,minute.maid,4864,8.489616424,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/8/1991,119,tropicana,8832,9.086136769,0,2.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/8/1991,121,dominicks,14592,9.588228712,0,1.07,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/8/1991,121,minute.maid,5440,8.60153434,0,2.26,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/8/1991,121,tropicana,10496,9.258749511,0,2.34,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/8/1991,122,dominicks,13760,9.529521112,0,1.25,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/8/1991,122,minute.maid,7872,8.971067439,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/8/1991,122,tropicana,11712,9.368369236,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/8/1991,123,dominicks,18112,9.804329981,0,0.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/8/1991,123,minute.maid,4288,8.363575703,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/8/1991,123,tropicana,10624,9.270870872,0,2.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/8/1991,124,dominicks,48640,10.79220152,0,0.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/8/1991,124,minute.maid,2944,7.98752448,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/8/1991,124,tropicana,10368,9.246479419,0,2.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/8/1991,126,dominicks,40896,10.61878754,0,1.06,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/8/1991,126,minute.maid,5184,8.553332238,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/8/1991,126,tropicana,9216,9.128696383,0,2.31,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/8/1991,128,dominicks,23296,10.05603695,0,1.43,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/8/1991,128,minute.maid,6720,8.812843434,0,2.18,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/8/1991,128,tropicana,37056,10.52018556,0,2.35,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/8/1991,129,dominicks,10944,9.30054664,0,1.12,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/8/1991,129,minute.maid,5312,8.577723691,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/8/1991,129,tropicana,9920,9.2023082,0,2.38,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/8/1991,130,dominicks,38720,10.56411154,0,0.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/8/1991,130,minute.maid,2752,7.920083199,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/8/1991,130,tropicana,11968,9.3899917,0,2.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/8/1991,131,dominicks,9984,9.208739091,0,1.16,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/8/1991,131,minute.maid,3840,8.253227646,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/8/1991,131,tropicana,11776,9.373818841,0,2.4,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/8/1991,132,dominicks,24064,10.08847223,0,1.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/8/1991,132,minute.maid,7040,8.859363449,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/8/1991,132,tropicana,7168,8.877381955,0,2.4,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/8/1991,134,dominicks,23872,10.0804615,0,1.05,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/8/1991,134,minute.maid,1536,7.336936914,0,2.26,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/8/1991,134,tropicana,3264,8.090708716,0,2.4,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/8/1991,137,dominicks,56640,10.94447073,0,0.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/8/1991,137,minute.maid,5888,8.68067166,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/8/1991,137,tropicana,35328,10.47243113,0,2.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/15/1991,5,dominicks,21760,9.987828701,1,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/15/1991,5,minute.maid,16896,9.734832187,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/15/1991,5,tropicana,5056,8.528330936,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/15/1991,8,dominicks,47680,10.7722673,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/15/1991,8,minute.maid,30528,10.32639957,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/15/1991,8,tropicana,5184,8.553332238,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/15/1991,9,dominicks,33984,10.43364511,1,1.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/15/1991,9,minute.maid,17984,9.797237753,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/15/1991,9,tropicana,5440,8.60153434,0,3.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/15/1991,12,dominicks,55424,10.92276799,1,1.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/15/1991,12,minute.maid,21888,9.99369382,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/15/1991,12,tropicana,6592,8.793612072,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/15/1991,14,dominicks,13760,9.529521112,1,1.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/15/1991,14,minute.maid,15552,9.651944527,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/15/1991,14,tropicana,9024,9.107642974,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/15/1991,18,dominicks,41216,10.62658181,1,1.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/15/1991,18,minute.maid,22464,10.01966931,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/15/1991,18,tropicana,7232,8.886270902,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/15/1991,21,dominicks,24896,10.12246243,1,1.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/15/1991,21,minute.maid,13056,9.477003077,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/15/1991,21,tropicana,2624,7.87245515,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/15/1991,28,dominicks,15360,9.639522007,1,1.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/15/1991,28,minute.maid,3712,8.219326094,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/15/1991,28,tropicana,3008,8.009030685,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/15/1991,32,dominicks,51392,10.8472378,1,1.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/15/1991,32,minute.maid,14976,9.614204199,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/15/1991,32,tropicana,8192,9.010913347,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/15/1991,33,dominicks,30144,10.31374118,1,1.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/15/1991,33,minute.maid,8576,9.056722883,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/15/1991,33,tropicana,7040,8.859363449,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/15/1991,40,dominicks,34304,10.44301724,1,1.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/15/1991,40,minute.maid,12672,9.447150114,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/15/1991,40,tropicana,4352,8.378390789,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/15/1991,44,dominicks,20800,9.942708266,1,1.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/15/1991,44,minute.maid,8960,9.100525506,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/15/1991,44,tropicana,6144,8.723231275,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/15/1991,45,dominicks,13696,9.524859098,1,1.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/15/1991,45,minute.maid,8128,9.00307017,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/15/1991,45,tropicana,4224,8.348537825,0,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/15/1991,47,dominicks,17600,9.775654181,1,1.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/15/1991,47,minute.maid,12096,9.400630098,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/15/1991,47,tropicana,2752,7.920083199,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/15/1991,48,dominicks,14400,9.574983486,1,1.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/15/1991,48,minute.maid,7360,8.903815212,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/15/1991,48,tropicana,4736,8.462948177,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/15/1991,49,dominicks,13312,9.496421163,1,1.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/15/1991,49,minute.maid,8448,9.041685006,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/15/1991,49,tropicana,3904,8.269756948,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/15/1991,50,dominicks,15744,9.664214619,1,1.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/15/1991,50,minute.maid,6080,8.712759975,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/15/1991,50,tropicana,1728,7.454719949,0,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/15/1991,51,dominicks,22080,10.0024275,1,1.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/15/1991,51,minute.maid,10816,9.288781798,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/15/1991,51,tropicana,2944,7.98752448,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/15/1991,52,dominicks,26944,10.20151592,1,1.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/15/1991,52,minute.maid,17792,9.786504197,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/15/1991,52,tropicana,9792,9.189321005,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/15/1991,53,dominicks,44224,10.69702291,1,1.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/15/1991,53,minute.maid,21120,9.957975738,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/15/1991,53,tropicana,12224,9.411156511,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/15/1991,54,dominicks,16960,9.738612909,1,1.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/15/1991,54,minute.maid,12224,9.411156511,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/15/1991,54,tropicana,4864,8.489616424,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/15/1991,56,dominicks,19968,9.901886271,1,1.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/15/1991,56,minute.maid,9536,9.162829389,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/15/1991,56,tropicana,4416,8.392989588,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/15/1991,62,dominicks,13568,9.515469358,1,1.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/15/1991,62,minute.maid,17216,9.753594463,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/15/1991,62,tropicana,12096,9.400630098,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/15/1991,67,dominicks,22208,10.00820786,1,1.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/15/1991,67,minute.maid,11392,9.340666634,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/15/1991,67,tropicana,6208,8.733594062,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/15/1991,68,dominicks,37696,10.53730927,1,1.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/15/1991,68,minute.maid,31808,10.36747311,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/15/1991,68,tropicana,7040,8.859363449,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/15/1991,70,dominicks,27072,10.20625526,1,1.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/15/1991,70,minute.maid,17728,9.78290059,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/15/1991,70,tropicana,6144,8.723231275,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/15/1991,72,dominicks,27904,10.23652533,1,1.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/15/1991,72,minute.maid,11840,9.379238908,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/15/1991,72,tropicana,7168,8.877381955,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/15/1991,73,dominicks,62720,11.04643566,1,1.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/15/1991,73,minute.maid,50368,10.82711133,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/15/1991,73,tropicana,7936,8.979164649,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/15/1991,74,dominicks,49600,10.81174611,1,1.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/15/1991,74,minute.maid,8256,9.018695488,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/15/1991,74,tropicana,4480,8.407378325,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/15/1991,75,dominicks,22912,10.03941607,1,1.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/15/1991,75,minute.maid,19520,9.87919486,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/15/1991,75,tropicana,6848,8.831711918,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/15/1991,76,dominicks,52736,10.87305361,1,1.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/15/1991,76,minute.maid,16448,9.707959168,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/15/1991,76,tropicana,4352,8.378390789,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/15/1991,77,dominicks,17984,9.797237753,1,1.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/15/1991,77,minute.maid,15680,9.660141294,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/15/1991,77,tropicana,6784,8.822322178,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/15/1991,78,dominicks,24000,10.08580911,1,1.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/15/1991,78,minute.maid,16256,9.69621735,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/15/1991,78,tropicana,5824,8.66974259,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/15/1991,80,dominicks,22400,10.01681624,1,1.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/15/1991,80,minute.maid,17792,9.786504197,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/15/1991,80,tropicana,9600,9.169518378,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/15/1991,81,dominicks,33664,10.4241843,1,1.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/15/1991,81,minute.maid,12096,9.400630098,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/15/1991,81,tropicana,3456,8.14786713,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/15/1991,83,dominicks,46912,10.75602879,1,1.49,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/15/1991,83,minute.maid,14400,9.574983486,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/15/1991,83,tropicana,5376,8.589699882,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/15/1991,84,dominicks,24640,10.11212642,1,1.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/15/1991,84,minute.maid,9792,9.189321005,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/15/1991,84,tropicana,3072,8.030084094,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/15/1991,86,dominicks,34880,10.45966888,1,1.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/15/1991,86,minute.maid,9600,9.169518378,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/15/1991,86,tropicana,3712,8.219326094,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/15/1991,88,dominicks,20416,9.924074186,1,1.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/15/1991,88,minute.maid,10816,9.288781798,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/15/1991,88,tropicana,3072,8.030084094,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/15/1991,89,dominicks,48576,10.79088486,1,1.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/15/1991,89,minute.maid,20224,9.914625297,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/15/1991,89,tropicana,2048,7.624618986,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/15/1991,90,dominicks,28224,10.24792796,1,1.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/15/1991,90,minute.maid,11968,9.3899917,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/15/1991,90,tropicana,2176,7.685243608,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/15/1991,91,dominicks,28288,10.25019297,1,1.49,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/15/1991,91,minute.maid,10496,9.258749511,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/15/1991,91,tropicana,3584,8.184234774,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/15/1991,92,dominicks,31488,10.3573618,1,1.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/15/1991,92,minute.maid,9920,9.2023082,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/15/1991,92,tropicana,1792,7.491087594,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/15/1991,93,dominicks,22720,10.03100087,1,1.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/15/1991,93,minute.maid,6016,8.702177866,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/15/1991,93,tropicana,5056,8.528330936,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/15/1991,94,dominicks,17664,9.779283949,1,1.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/15/1991,94,minute.maid,17280,9.757305042,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/15/1991,94,tropicana,5568,8.624791202,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/15/1991,95,dominicks,30976,10.34096799,1,1.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/15/1991,95,minute.maid,14592,9.588228712,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/15/1991,95,tropicana,3520,8.166216269,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/15/1991,97,dominicks,12608,9.442086812,1,1.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/15/1991,97,minute.maid,7872,8.971067439,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/15/1991,97,tropicana,1408,7.249925537,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/15/1991,98,dominicks,36224,10.49747716,1,1.49,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/15/1991,98,minute.maid,14976,9.614204199,0,1.51,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/15/1991,98,tropicana,5952,8.691482577,0,3.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/15/1991,100,dominicks,53568,10.88870715,1,1.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/15/1991,100,minute.maid,13184,9.486759252,0,1.66,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/15/1991,100,tropicana,5376,8.589699882,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/15/1991,101,dominicks,38208,10.5508002,1,1.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/15/1991,101,minute.maid,14080,9.55251063,0,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/15/1991,101,tropicana,4288,8.363575703,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/15/1991,102,dominicks,67072,11.11352195,1,1.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/15/1991,102,minute.maid,23616,10.06967973,0,1.44,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/15/1991,102,tropicana,7168,8.877381955,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/15/1991,103,dominicks,28800,10.26813067,1,1.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/15/1991,103,minute.maid,9600,9.169518378,0,1.5,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/15/1991,103,tropicana,2240,7.714231145,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/15/1991,104,dominicks,19456,9.875910785,1,1.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/15/1991,104,minute.maid,7744,8.954673629,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/15/1991,104,tropicana,2496,7.82244473,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/15/1991,105,dominicks,42688,10.66167313,1,1.49,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/15/1991,105,minute.maid,18240,9.811372264,0,1.46,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/15/1991,105,tropicana,3008,8.009030685,0,3.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/15/1991,106,dominicks,15232,9.631153757,1,1.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/15/1991,106,minute.maid,4928,8.502688505,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/15/1991,106,tropicana,1664,7.416979621,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/15/1991,107,dominicks,24448,10.10430369,1,1.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/15/1991,107,minute.maid,17792,9.786504197,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/15/1991,107,tropicana,8192,9.010913347,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/15/1991,109,dominicks,34048,10.43552657,1,1.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/15/1991,109,minute.maid,12416,9.426741242,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/15/1991,109,tropicana,12096,9.400630098,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/15/1991,110,dominicks,19456,9.875910785,1,1.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/15/1991,110,minute.maid,13376,9.501217335,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/15/1991,110,tropicana,3776,8.236420527,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/15/1991,111,dominicks,94848,11.46003089,1,1.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/15/1991,111,minute.maid,22080,10.0024275,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/15/1991,111,tropicana,3008,8.009030685,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/15/1991,112,dominicks,27520,10.22266829,1,1.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/15/1991,112,minute.maid,27584,10.22499117,0,1.51,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/15/1991,112,tropicana,14592,9.588228712,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/15/1991,113,dominicks,30912,10.33889974,1,1.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/15/1991,113,minute.maid,17216,9.753594463,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/15/1991,113,tropicana,5632,8.636219898,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/15/1991,114,dominicks,53888,10.8946631,1,1.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/15/1991,114,minute.maid,22656,10.02818,0,1.46,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/15/1991,114,tropicana,4992,8.51559191,0,3.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/15/1991,115,dominicks,25216,10.13523399,1,1.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/15/1991,115,minute.maid,11840,9.379238908,0,1.57,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/15/1991,115,tropicana,6912,8.841014311,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/15/1991,116,dominicks,20160,9.911455722,1,1.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/15/1991,116,minute.maid,9920,9.2023082,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/15/1991,116,tropicana,4096,8.317766167,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/15/1991,117,dominicks,17408,9.76468515,1,1.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/15/1991,117,minute.maid,8704,9.071537969,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/15/1991,117,tropicana,3136,8.050703382,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/15/1991,118,dominicks,20544,9.930324207,1,1.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/15/1991,118,minute.maid,21504,9.975994243,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/15/1991,118,tropicana,4544,8.42156296,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/15/1991,119,dominicks,8512,9.049232212,1,1.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/15/1991,119,minute.maid,13440,9.505990614,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/15/1991,119,tropicana,5824,8.66974259,0,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/15/1991,121,dominicks,27648,10.22730867,1,1.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/15/1991,121,minute.maid,17472,9.768354879,0,1.47,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/15/1991,121,tropicana,5760,8.658692754,0,3.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/15/1991,122,dominicks,31680,10.36344085,1,1.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/15/1991,122,minute.maid,17664,9.779283949,0,1.5,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/15/1991,122,tropicana,7552,8.929567708,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/15/1991,123,dominicks,46976,10.75739211,1,1.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/15/1991,123,minute.maid,19584,9.882468185,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/15/1991,123,tropicana,5632,8.636219898,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/15/1991,124,dominicks,62912,11.0494922,1,1.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/15/1991,124,minute.maid,22016,9.999524741,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/15/1991,124,tropicana,4736,8.462948177,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/15/1991,126,dominicks,35648,10.48144832,1,1.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/15/1991,126,minute.maid,21184,9.961001459,0,1.64,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/15/1991,126,tropicana,5504,8.61323038,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/15/1991,128,dominicks,90752,11.41588579,1,1.23,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/15/1991,128,minute.maid,14784,9.601300794,0,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/15/1991,128,tropicana,8000,8.987196821,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/15/1991,129,dominicks,23872,10.0804615,1,1.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/15/1991,129,minute.maid,7680,8.946374826,0,1.67,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/15/1991,129,tropicana,5184,8.553332238,0,3.13,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/15/1991,130,dominicks,69312,11.14637333,1,1.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/15/1991,130,minute.maid,26368,10.17990643,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/15/1991,130,tropicana,3328,8.110126802,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/15/1991,131,dominicks,28160,10.24565781,1,1.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/15/1991,131,minute.maid,9408,9.14931567,0,1.63,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/15/1991,131,tropicana,6464,8.7740036,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/15/1991,132,dominicks,44480,10.70279493,1,1.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/15/1991,132,minute.maid,16960,9.738612909,0,1.5,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/15/1991,132,tropicana,5440,8.60153434,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/15/1991,134,dominicks,15488,9.64782081,1,1.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/15/1991,134,minute.maid,7360,8.903815212,0,1.45,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/15/1991,134,tropicana,2048,7.624618986,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/15/1991,137,dominicks,42240,10.65112292,1,1.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/15/1991,137,minute.maid,26304,10.1774763,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/15/1991,137,tropicana,16640,9.719564714,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/22/1991,5,dominicks,2688,7.896552702,0,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/22/1991,5,minute.maid,77184,11.25394746,1,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/22/1991,5,tropicana,4608,8.435549202,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/22/1991,8,dominicks,14720,9.596962392,0,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/22/1991,8,minute.maid,155840,11.95658512,1,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/22/1991,8,tropicana,6272,8.743850562,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/22/1991,9,dominicks,11904,9.384629757,0,1.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/22/1991,9,minute.maid,94272,11.4539395,1,1.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/22/1991,9,tropicana,6912,8.841014311,0,3.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/22/1991,12,dominicks,6080,8.712759975,0,1.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/22/1991,12,minute.maid,151744,11.92995017,1,1.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/22/1991,12,tropicana,5696,8.647519453,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/22/1991,14,dominicks,9088,9.114710141,0,1.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/22/1991,14,minute.maid,119616,11.69204189,1,0.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/22/1991,14,tropicana,10624,9.270870872,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/22/1991,18,dominicks,7232,8.886270902,0,1.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/22/1991,18,minute.maid,130688,11.78056808,1,1.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/22/1991,18,tropicana,6912,8.841014311,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/22/1991,21,dominicks,3776,8.236420527,0,1.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/22/1991,21,minute.maid,83456,11.33207483,1,1.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/22/1991,21,tropicana,3328,8.110126802,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/22/1991,28,dominicks,4096,8.317766167,0,1.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/22/1991,28,minute.maid,43136,10.67211319,1,1.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/22/1991,28,tropicana,2944,7.98752448,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/22/1991,32,dominicks,10880,9.29468152,0,1.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/22/1991,32,minute.maid,121984,11.71164517,1,1.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/22/1991,32,tropicana,9920,9.2023082,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/22/1991,33,dominicks,11648,9.36288977,0,1.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/22/1991,33,minute.maid,55488,10.92392206,1,1.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/22/1991,33,tropicana,7296,8.895081532,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/22/1991,40,dominicks,7744,8.954673629,0,1.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/22/1991,40,minute.maid,92992,11.44026875,1,1.29,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/22/1991,40,tropicana,4672,8.449342525,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/22/1991,44,dominicks,5312,8.577723691,0,1.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/22/1991,44,minute.maid,83072,11.32746298,1,1.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/22/1991,44,tropicana,7104,8.868413285,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/22/1991,45,dominicks,5760,8.658692754,0,1.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/22/1991,45,minute.maid,36800,10.51325312,1,1.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/22/1991,45,tropicana,5184,8.553332238,0,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/22/1991,47,dominicks,7680,8.946374826,0,1.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/22/1991,47,minute.maid,67392,11.1182816,1,1.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/22/1991,47,tropicana,3712,8.219326094,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/22/1991,48,dominicks,5376,8.589699882,0,1.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/22/1991,48,minute.maid,34176,10.43927892,1,1.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/22/1991,48,tropicana,5440,8.60153434,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/22/1991,49,dominicks,4480,8.407378325,0,1.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/22/1991,49,minute.maid,51456,10.84848235,1,1.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/22/1991,49,tropicana,3200,8.070906089,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/22/1991,50,dominicks,2560,7.847762538,0,1.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/22/1991,50,minute.maid,54784,10.91115346,1,1.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/22/1991,50,tropicana,2432,7.796469243,0,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/22/1991,51,dominicks,3456,8.14786713,0,1.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/22/1991,51,minute.maid,76224,11.24143165,1,1.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/22/1991,51,tropicana,4096,8.317766167,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/22/1991,52,dominicks,10176,9.227787286,0,1.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/22/1991,52,minute.maid,72576,11.19238957,1,1.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/22/1991,52,tropicana,9152,9.121727714,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/22/1991,53,dominicks,12736,9.452187908,0,1.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/22/1991,53,minute.maid,149312,11.91379336,1,1.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/22/1991,53,tropicana,8768,9.078864009,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/22/1991,54,dominicks,3392,8.129174997,0,1.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/22/1991,54,minute.maid,60992,11.01849799,1,1.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/22/1991,54,tropicana,3904,8.269756948,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/22/1991,56,dominicks,8320,9.026417534,0,1.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/22/1991,56,minute.maid,52608,10.87062348,1,1.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/22/1991,56,tropicana,5568,8.624791202,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/22/1991,59,dominicks,2496,7.82244473,0,1.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/22/1991,59,minute.maid,82368,11.31895229,1,1.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/22/1991,59,tropicana,4800,8.476371197,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/22/1991,62,dominicks,3648,8.201934351,0,1.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/22/1991,62,minute.maid,52096,10.86084345,1,1.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/22/1991,62,tropicana,13120,9.481893063,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/22/1991,64,dominicks,2880,7.965545573,0,1.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/22/1991,64,minute.maid,40384,10.60618895,1,1.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/22/1991,64,tropicana,1280,7.154615357,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/22/1991,67,dominicks,1664,7.416979621,0,1.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/22/1991,67,minute.maid,91008,11.41870269,1,1.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/22/1991,67,tropicana,5184,8.553332238,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/22/1991,68,dominicks,9920,9.2023082,0,1.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/22/1991,68,minute.maid,76096,11.23975098,1,1.29,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/22/1991,68,tropicana,5696,8.647519453,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/22/1991,70,dominicks,6400,8.764053269,0,1.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/22/1991,70,minute.maid,119296,11.68936308,1,1.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/22/1991,70,tropicana,8128,9.00307017,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/22/1991,71,dominicks,4928,8.502688505,0,1.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/22/1991,71,minute.maid,138112,11.83582023,1,1.29,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/22/1991,71,tropicana,2688,7.896552702,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/22/1991,72,dominicks,2880,7.965545573,0,1.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/22/1991,72,minute.maid,111552,11.62224613,1,1.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/22/1991,72,tropicana,8960,9.100525506,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/22/1991,73,dominicks,12928,9.467150781,0,1.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/22/1991,73,minute.maid,155392,11.95370624,1,1.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/22/1991,73,tropicana,9024,9.107642974,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/22/1991,74,dominicks,10240,9.234056899,0,1.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/22/1991,74,minute.maid,134592,11.81000326,1,1.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/22/1991,74,tropicana,5440,8.60153434,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/22/1991,75,dominicks,8320,9.026417534,0,1.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/22/1991,75,minute.maid,89664,11.40382463,1,1.29,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/22/1991,75,tropicana,4608,8.435549202,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/22/1991,76,dominicks,10624,9.270870872,0,1.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/22/1991,76,minute.maid,155776,11.95617436,1,1.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/22/1991,76,tropicana,4800,8.476371197,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/22/1991,77,dominicks,9152,9.121727714,0,1.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/22/1991,77,minute.maid,48256,10.78427545,1,1.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/22/1991,77,tropicana,7808,8.962904128,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/22/1991,78,dominicks,12416,9.426741242,0,1.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/22/1991,78,minute.maid,89920,11.40667567,1,1.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/22/1991,78,tropicana,7552,8.929567708,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/22/1991,80,dominicks,5440,8.60153434,0,1.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/22/1991,80,minute.maid,98048,11.49321243,1,1.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/22/1991,80,tropicana,9280,9.135616826,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/22/1991,81,dominicks,8768,9.078864009,0,1.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/22/1991,81,minute.maid,90112,11.40880862,1,1.29,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/22/1991,81,tropicana,6144,8.723231275,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/22/1991,83,dominicks,3392,8.129174997,0,1.49,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/22/1991,83,minute.maid,150464,11.92147913,1,1.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/22/1991,83,tropicana,4864,8.489616424,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/22/1991,84,dominicks,4032,8.30201781,0,1.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/22/1991,84,minute.maid,80832,11.30012821,1,1.29,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/22/1991,84,tropicana,3840,8.253227646,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/22/1991,86,dominicks,14592,9.588228712,0,1.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/22/1991,86,minute.maid,68608,11.13616443,1,1.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/22/1991,86,tropicana,4480,8.407378325,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/22/1991,88,dominicks,3456,8.14786713,0,1.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/22/1991,88,minute.maid,59200,10.98867682,1,1.29,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/22/1991,88,tropicana,2944,7.98752448,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/22/1991,89,dominicks,6592,8.793612072,0,1.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/22/1991,89,minute.maid,136448,11.82369887,1,1.29,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/22/1991,89,tropicana,3008,8.009030685,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/22/1991,90,dominicks,6848,8.831711918,0,1.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/22/1991,90,minute.maid,100672,11.51962299,1,1.29,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/22/1991,90,tropicana,2048,7.624618986,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/22/1991,91,dominicks,7296,8.895081532,0,1.49,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/22/1991,91,minute.maid,92288,11.4326694,1,1.29,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/22/1991,91,tropicana,2624,7.87245515,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/22/1991,92,dominicks,5504,8.61323038,0,1.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/22/1991,92,minute.maid,95360,11.46541448,1,1.29,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/22/1991,92,tropicana,1984,7.592870288,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/22/1991,93,dominicks,10368,9.246479419,0,1.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/22/1991,93,minute.maid,52544,10.86940619,1,1.29,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/22/1991,93,tropicana,6080,8.712759975,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/22/1991,94,dominicks,4416,8.392989588,0,1.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/22/1991,94,minute.maid,49280,10.8052736,1,1.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/22/1991,94,tropicana,5696,8.647519453,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/22/1991,95,dominicks,5184,8.553332238,0,1.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/22/1991,95,minute.maid,104384,11.55583169,1,1.29,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/22/1991,95,tropicana,3840,8.253227646,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/22/1991,97,dominicks,1984,7.592870288,0,1.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/22/1991,97,minute.maid,47488,10.76823233,1,1.29,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/22/1991,97,tropicana,1024,6.931471806,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/22/1991,98,dominicks,18688,9.835636886,0,1.42,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/22/1991,98,minute.maid,122368,11.71478818,1,1.29,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/22/1991,98,tropicana,4032,8.30201781,0,3.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/22/1991,100,dominicks,15488,9.64782081,0,1.44,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/22/1991,100,minute.maid,160384,11.98532622,1,1.29,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/22/1991,100,tropicana,4736,8.462948177,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/22/1991,101,dominicks,5824,8.66974259,0,1.46,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/22/1991,101,minute.maid,97536,11.48797682,1,1.29,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/22/1991,101,tropicana,5440,8.60153434,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/22/1991,102,dominicks,35200,10.46880136,0,1.41,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/22/1991,102,minute.maid,115008,11.65275697,1,1.29,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/22/1991,102,tropicana,8064,8.99516499,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/22/1991,103,dominicks,12992,9.472089062,0,1.44,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/22/1991,103,minute.maid,84800,11.34805082,1,1.29,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/22/1991,103,tropicana,1856,7.526178913,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/22/1991,104,dominicks,2560,7.847762538,0,1.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/22/1991,104,minute.maid,63424,11.05759762,1,1.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/22/1991,104,tropicana,3776,8.236420527,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/22/1991,105,dominicks,9152,9.121727714,0,1.44,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/22/1991,105,minute.maid,107840,11.58840393,1,1.29,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/22/1991,105,tropicana,3456,8.14786713,0,3.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/22/1991,106,dominicks,3392,8.129174997,0,1.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/22/1991,106,minute.maid,48256,10.78427545,1,1.29,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/22/1991,106,tropicana,1664,7.416979621,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/22/1991,107,dominicks,3968,8.286017468,0,1.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/22/1991,107,minute.maid,112832,11.63365527,1,1.29,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/22/1991,107,tropicana,9792,9.189321005,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/22/1991,109,dominicks,9024,9.107642974,0,1.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/22/1991,109,minute.maid,86912,11.37265139,1,1.29,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/22/1991,109,tropicana,14976,9.614204199,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/22/1991,110,dominicks,3072,8.030084094,0,1.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/22/1991,110,minute.maid,71424,11.17638923,1,1.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/22/1991,110,tropicana,2944,7.98752448,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/22/1991,111,dominicks,7936,8.979164649,0,1.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/22/1991,111,minute.maid,304320,12.62583506,1,1.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/22/1991,111,tropicana,2752,7.920083199,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/22/1991,112,dominicks,5184,8.553332238,0,1.44,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/22/1991,112,minute.maid,95552,11.46742588,1,1.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/22/1991,112,tropicana,15168,9.626943225,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/22/1991,113,dominicks,10816,9.288781798,0,1.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/22/1991,113,minute.maid,109248,11.60137581,1,1.29,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/22/1991,113,tropicana,4352,8.378390789,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/22/1991,114,dominicks,9408,9.14931567,0,1.43,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/22/1991,114,minute.maid,144320,11.87978834,1,1.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/22/1991,114,tropicana,3264,8.090708716,0,3.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/22/1991,115,dominicks,2432,7.796469243,0,1.44,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/22/1991,115,minute.maid,89664,11.40382463,1,1.29,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/22/1991,115,tropicana,5056,8.528330936,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/22/1991,116,dominicks,4864,8.489616424,0,1.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/22/1991,116,minute.maid,66560,11.10585908,1,1.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/22/1991,116,tropicana,4224,8.348537825,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/22/1991,117,dominicks,5888,8.68067166,0,1.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/22/1991,117,minute.maid,38144,10.54912375,1,1.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/22/1991,117,tropicana,2752,7.920083199,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/22/1991,118,dominicks,6976,8.850230966,0,1.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/22/1991,118,minute.maid,80512,11.29616152,1,1.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/22/1991,118,tropicana,5056,8.528330936,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/22/1991,119,dominicks,3072,8.030084094,0,1.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/22/1991,119,minute.maid,48064,10.78028874,1,1.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/22/1991,119,tropicana,6016,8.702177866,0,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/22/1991,121,dominicks,6080,8.712759975,0,1.43,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/22/1991,121,minute.maid,91392,11.42291323,1,1.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/22/1991,121,tropicana,6656,8.803273983,0,3.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/22/1991,122,dominicks,5952,8.691482577,0,1.46,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/22/1991,122,minute.maid,96192,11.47410147,1,1.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/22/1991,122,tropicana,7552,8.929567708,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/22/1991,123,dominicks,10240,9.234056899,0,1.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/22/1991,123,minute.maid,160128,11.98372877,1,1.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/22/1991,123,tropicana,4864,8.489616424,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/22/1991,124,dominicks,20736,9.939626599,0,1.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/22/1991,124,minute.maid,101824,11.53100111,1,1.29,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/22/1991,124,tropicana,4864,8.489616424,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/22/1991,126,dominicks,8576,9.056722883,0,1.45,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/22/1991,126,minute.maid,107264,11.58304836,1,1.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/22/1991,126,tropicana,5120,8.540909718,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/22/1991,128,dominicks,6912,8.841014311,0,1.25,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/22/1991,128,minute.maid,208000,12.24529336,1,1.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/22/1991,128,tropicana,8064,8.99516499,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/22/1991,129,dominicks,5248,8.565602331,0,1.44,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/22/1991,129,minute.maid,63808,11.06363385,1,1.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/22/1991,129,tropicana,6144,8.723231275,0,3.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/22/1991,130,dominicks,24256,10.09641929,0,1.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/22/1991,130,minute.maid,198656,12.19932997,1,1.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/22/1991,130,tropicana,2496,7.82244473,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/22/1991,131,dominicks,4800,8.476371197,0,1.44,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/22/1991,131,minute.maid,73344,11.20291598,1,1.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/22/1991,131,tropicana,5888,8.68067166,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/22/1991,132,dominicks,14208,9.561560465,0,1.44,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/22/1991,132,minute.maid,78272,11.26794522,1,1.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/22/1991,132,tropicana,6272,8.743850562,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/22/1991,134,dominicks,8704,9.071537969,0,1.42,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/22/1991,134,minute.maid,39168,10.57561537,1,1.29,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/22/1991,134,tropicana,2624,7.87245515,0,3.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/22/1991,137,dominicks,9152,9.121727714,0,1.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/22/1991,137,minute.maid,121088,11.70427283,1,1.29,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/22/1991,137,tropicana,13376,9.501217335,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/29/1991,2,dominicks,16064,9.684336023,0,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/29/1991,2,minute.maid,2816,7.943072717,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/29/1991,2,tropicana,4160,8.333270353,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/29/1991,5,dominicks,10432,9.252633284,0,1.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/29/1991,5,minute.maid,5184,8.553332238,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/29/1991,5,tropicana,6016,8.702177866,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/29/1991,8,dominicks,53248,10.88271552,0,1.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/29/1991,8,minute.maid,10752,9.282847063,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/29/1991,8,tropicana,7744,8.954673629,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/29/1991,9,dominicks,11328,9.335032816,0,1.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/29/1991,9,minute.maid,3776,8.236420527,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/29/1991,9,tropicana,6016,8.702177866,0,3.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/29/1991,12,dominicks,4416,8.392989588,0,1.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/29/1991,12,minute.maid,6976,8.850230966,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/29/1991,12,tropicana,6528,8.783855897,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/29/1991,14,dominicks,10432,9.252633284,0,1.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/29/1991,14,minute.maid,6656,8.803273983,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/29/1991,14,tropicana,8064,8.99516499,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/29/1991,21,dominicks,15872,9.67231183,0,1.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/29/1991,21,minute.maid,4928,8.502688505,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/29/1991,21,tropicana,2944,7.98752448,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/29/1991,28,dominicks,4928,8.502688505,0,1.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/29/1991,28,minute.maid,2496,7.82244473,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/29/1991,28,tropicana,2432,7.796469243,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/29/1991,32,dominicks,22528,10.02251426,0,1.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/29/1991,32,minute.maid,5056,8.528330936,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/29/1991,32,tropicana,8256,9.018695488,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/29/1991,40,dominicks,18304,9.814874894,0,1.39,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/29/1991,40,minute.maid,6400,8.764053269,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/29/1991,40,tropicana,5312,8.577723691,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/29/1991,44,dominicks,15104,9.622714888,0,1.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/29/1991,44,minute.maid,4160,8.333270353,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/29/1991,44,tropicana,5696,8.647519453,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/29/1991,45,dominicks,6208,8.733594062,0,1.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/29/1991,45,minute.maid,3136,8.050703382,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/29/1991,45,tropicana,5248,8.565602331,0,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/29/1991,47,dominicks,14784,9.601300794,0,1.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/29/1991,47,minute.maid,4224,8.348537825,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/29/1991,47,tropicana,4608,8.435549202,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/29/1991,48,dominicks,3136,8.050703382,0,1.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/29/1991,48,minute.maid,5696,8.647519453,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/29/1991,48,tropicana,4032,8.30201781,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/29/1991,49,dominicks,9792,9.189321005,0,1.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/29/1991,49,minute.maid,2944,7.98752448,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/29/1991,49,tropicana,2880,7.965545573,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/29/1991,50,dominicks,11456,9.346268889,0,1.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/29/1991,50,minute.maid,3968,8.286017468,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/29/1991,50,tropicana,3136,8.050703382,0,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/29/1991,51,dominicks,6912,8.841014311,0,1.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/29/1991,51,minute.maid,3520,8.166216269,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/29/1991,51,tropicana,3968,8.286017468,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/29/1991,52,dominicks,10112,9.221478116,0,1.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/29/1991,52,minute.maid,6528,8.783855897,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/29/1991,52,tropicana,10112,9.221478116,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/29/1991,53,dominicks,28800,10.26813067,0,1.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/29/1991,53,minute.maid,5056,8.528330936,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/29/1991,53,tropicana,9536,9.162829389,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/29/1991,54,dominicks,9152,9.121727714,0,1.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/29/1991,54,minute.maid,4672,8.449342525,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/29/1991,54,tropicana,3904,8.269756948,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/29/1991,56,dominicks,9472,9.156095357,0,1.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/29/1991,56,minute.maid,3648,8.201934351,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/29/1991,56,tropicana,4800,8.476371197,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/29/1991,59,dominicks,14272,9.566054855,0,1.39,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/29/1991,59,minute.maid,4544,8.42156296,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/29/1991,59,tropicana,5568,8.624791202,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/29/1991,62,dominicks,5504,8.61323038,0,1.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/29/1991,62,minute.maid,5440,8.60153434,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/29/1991,62,tropicana,12992,9.472089062,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/29/1991,64,dominicks,10816,9.288781798,0,1.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/29/1991,64,minute.maid,1984,7.592870288,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/29/1991,64,tropicana,1984,7.592870288,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -8/29/1991,67,dominicks,44352,10.69991308,0,1.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/29/1991,67,minute.maid,5056,8.528330936,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/29/1991,67,tropicana,6272,8.743850562,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/29/1991,68,dominicks,17920,9.793672686,0,1.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/29/1991,68,minute.maid,5952,8.691482577,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/29/1991,68,tropicana,4096,8.317766167,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/29/1991,70,dominicks,9984,9.208739091,0,1.39,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/29/1991,70,minute.maid,10752,9.282847063,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/29/1991,70,tropicana,6528,8.783855897,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/29/1991,71,dominicks,12224,9.411156511,0,1.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/29/1991,71,minute.maid,2880,7.965545573,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/29/1991,71,tropicana,2496,7.82244473,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/29/1991,72,dominicks,3072,8.030084094,0,1.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/29/1991,72,minute.maid,4160,8.333270353,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/29/1991,72,tropicana,9088,9.114710141,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/29/1991,73,dominicks,11520,9.351839934,0,1.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/29/1991,73,minute.maid,14272,9.566054855,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/29/1991,73,tropicana,11584,9.357380115,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/29/1991,74,dominicks,65984,11.09716757,0,1.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/29/1991,74,minute.maid,4096,8.317766167,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/29/1991,74,tropicana,4224,8.348537825,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/29/1991,75,dominicks,18240,9.811372264,0,1.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/29/1991,75,minute.maid,4608,8.435549202,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/29/1991,75,tropicana,5120,8.540909718,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/29/1991,76,dominicks,17536,9.77201119,0,1.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/29/1991,76,minute.maid,3264,8.090708716,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/29/1991,76,tropicana,4288,8.363575703,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/29/1991,77,dominicks,9856,9.195835686,0,1.39,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/29/1991,77,minute.maid,10624,9.270870872,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/29/1991,77,tropicana,8960,9.100525506,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/29/1991,78,dominicks,19520,9.87919486,0,1.39,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/29/1991,78,minute.maid,6848,8.831711918,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/29/1991,78,tropicana,5824,8.66974259,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/29/1991,80,dominicks,19904,9.898675996,0,1.39,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/29/1991,80,minute.maid,10496,9.258749511,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/29/1991,80,tropicana,12480,9.431882642,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/29/1991,81,dominicks,9152,9.121727714,0,1.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/29/1991,81,minute.maid,4032,8.30201781,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/29/1991,81,tropicana,4160,8.333270353,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/29/1991,83,dominicks,3136,8.050703382,0,1.39,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/29/1991,83,minute.maid,10112,9.221478116,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/29/1991,83,tropicana,21184,9.961001459,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -8/29/1991,84,dominicks,8192,9.010913347,0,1.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/29/1991,84,minute.maid,3200,8.070906089,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/29/1991,84,tropicana,3520,8.166216269,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -8/29/1991,86,dominicks,18240,9.811372264,0,1.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/29/1991,86,minute.maid,3200,8.070906089,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/29/1991,86,tropicana,4288,8.363575703,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/29/1991,88,dominicks,4480,8.407378325,0,1.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/29/1991,88,minute.maid,2816,7.943072717,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/29/1991,88,tropicana,2432,7.796469243,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/29/1991,89,dominicks,26368,10.17990643,0,1.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/29/1991,89,minute.maid,4160,8.333270353,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/29/1991,89,tropicana,1792,7.491087594,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/29/1991,90,dominicks,7936,8.979164649,0,1.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/29/1991,90,minute.maid,2752,7.920083199,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/29/1991,90,tropicana,3456,8.14786713,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/29/1991,91,dominicks,9920,9.2023082,0,1.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/29/1991,91,minute.maid,2752,7.920083199,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/29/1991,91,tropicana,2944,7.98752448,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/29/1991,92,dominicks,13952,9.543378146,0,1.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/29/1991,92,minute.maid,2752,7.920083199,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/29/1991,92,tropicana,4032,8.30201781,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/29/1991,94,dominicks,7168,8.877381955,0,1.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/29/1991,94,minute.maid,6464,8.7740036,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/29/1991,94,tropicana,5952,8.691482577,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/29/1991,95,dominicks,26368,10.17990643,0,1.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/29/1991,95,minute.maid,2816,7.943072717,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/29/1991,95,tropicana,6080,8.712759975,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/29/1991,97,dominicks,4416,8.392989588,0,1.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/29/1991,97,minute.maid,2560,7.847762538,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/29/1991,97,tropicana,1280,7.154615357,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/29/1991,98,dominicks,23488,10.06424493,0,1.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/29/1991,98,minute.maid,4096,8.317766167,0,2.23,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/29/1991,98,tropicana,4992,8.51559191,0,3.37,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/29/1991,100,dominicks,6656,8.803273983,0,1.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/29/1991,100,minute.maid,5568,8.624791202,0,2.29,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/29/1991,100,tropicana,5760,8.658692754,0,3.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/29/1991,101,dominicks,8320,9.026417534,0,1.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/29/1991,101,minute.maid,3840,8.253227646,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/29/1991,101,tropicana,4736,8.462948177,0,3.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/29/1991,102,dominicks,74752,11.22193125,0,1.38,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/29/1991,102,minute.maid,7104,8.868413285,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/29/1991,102,tropicana,6976,8.850230966,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/29/1991,103,dominicks,23104,10.04776104,0,1.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/29/1991,103,minute.maid,3008,8.009030685,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/29/1991,103,tropicana,2624,7.87245515,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/29/1991,105,dominicks,21632,9.981928979,0,1.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/29/1991,105,minute.maid,4032,8.30201781,0,2.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/29/1991,105,tropicana,2368,7.769800996,0,3.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/29/1991,106,dominicks,11712,9.368369236,0,1.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/29/1991,106,minute.maid,1984,7.592870288,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/29/1991,106,tropicana,1344,7.203405521,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/29/1991,107,dominicks,2752,7.920083199,0,1.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/29/1991,107,minute.maid,4096,8.317766167,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/29/1991,107,tropicana,8704,9.071537969,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/29/1991,109,dominicks,5888,8.68067166,0,1.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/29/1991,109,minute.maid,7744,8.954673629,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/29/1991,109,tropicana,13056,9.477003077,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/29/1991,110,dominicks,8832,9.086136769,0,1.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/29/1991,110,minute.maid,4224,8.348537825,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/29/1991,110,tropicana,4096,8.317766167,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/29/1991,111,dominicks,13312,9.496421163,0,1.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/29/1991,111,minute.maid,2112,7.655390645,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/29/1991,111,tropicana,3776,8.236420527,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/29/1991,112,dominicks,22016,9.999524741,0,1.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/29/1991,112,minute.maid,13824,9.534161491,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/29/1991,112,tropicana,17024,9.742379392,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/29/1991,113,dominicks,11584,9.357380115,0,1.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/29/1991,113,minute.maid,3968,8.286017468,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/29/1991,113,tropicana,4736,8.462948177,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/29/1991,114,dominicks,33408,10.41655067,0,1.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/29/1991,114,minute.maid,5312,8.577723691,0,2.26,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/29/1991,114,tropicana,4544,8.42156296,0,3.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/29/1991,115,dominicks,8384,9.034080407,0,1.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/29/1991,115,minute.maid,4416,8.392989588,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/29/1991,115,tropicana,6656,8.803273983,0,3.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/29/1991,116,dominicks,11840,9.379238908,0,1.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/29/1991,116,minute.maid,2496,7.82244473,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/29/1991,116,tropicana,3840,8.253227646,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/29/1991,117,dominicks,6720,8.812843434,0,1.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/29/1991,117,minute.maid,2560,7.847762538,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/29/1991,117,tropicana,2368,7.769800996,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/29/1991,118,dominicks,7936,8.979164649,0,1.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/29/1991,118,minute.maid,3136,8.050703382,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/29/1991,118,tropicana,5824,8.66974259,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/29/1991,119,dominicks,9536,9.162829389,0,1.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/29/1991,119,minute.maid,3840,8.253227646,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/29/1991,119,tropicana,5824,8.66974259,0,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/29/1991,121,dominicks,27712,10.22962081,0,1.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/29/1991,121,minute.maid,6336,8.754002934,0,2.26,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/29/1991,121,tropicana,7104,8.868413285,0,3.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/29/1991,122,dominicks,8320,9.026417534,0,1.39,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/29/1991,122,minute.maid,12352,9.421573272,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/29/1991,122,tropicana,10048,9.215128889,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/29/1991,123,dominicks,18496,9.825309772,0,1.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/29/1991,123,minute.maid,4992,8.51559191,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/29/1991,123,tropicana,4544,8.42156296,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/29/1991,124,dominicks,19584,9.882468185,0,1.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/29/1991,124,minute.maid,4096,8.317766167,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/29/1991,124,tropicana,4160,8.333270353,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/29/1991,126,dominicks,15104,9.622714888,0,1.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/29/1991,126,minute.maid,5952,8.691482577,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/29/1991,126,tropicana,4864,8.489616424,0,3.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/29/1991,128,dominicks,23936,10.08313888,0,1.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/29/1991,128,minute.maid,4864,8.489616424,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/29/1991,128,tropicana,9280,9.135616826,0,3.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/29/1991,129,dominicks,15040,9.618468598,0,1.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/29/1991,129,minute.maid,5504,8.61323038,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/29/1991,129,tropicana,5760,8.658692754,0,3.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/29/1991,131,dominicks,8256,9.018695488,0,1.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/29/1991,131,minute.maid,5056,8.528330936,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/29/1991,131,tropicana,5440,8.60153434,0,3.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/29/1991,132,dominicks,12992,9.472089062,0,1.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/29/1991,132,minute.maid,7552,8.929567708,0,1.97,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/29/1991,132,tropicana,7360,8.903815212,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/29/1991,137,dominicks,20224,9.914625297,0,1.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/29/1991,137,minute.maid,7488,8.921057018,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/29/1991,137,tropicana,13888,9.538780437,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/5/1991,2,dominicks,12480,9.431882642,0,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/5/1991,2,minute.maid,4288,8.363575703,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/5/1991,2,tropicana,39424,10.58213005,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/5/1991,5,dominicks,9792,9.189321005,0,1.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/5/1991,5,minute.maid,5248,8.565602331,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/5/1991,5,tropicana,50752,10.83470631,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/5/1991,8,dominicks,40576,10.61093204,0,1.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/5/1991,8,minute.maid,6976,8.850230966,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/5/1991,8,tropicana,53184,10.88151288,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/5/1991,9,dominicks,7296,8.895081532,0,1.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/5/1991,9,minute.maid,4608,8.435549202,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/5/1991,9,tropicana,84480,11.3442701,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/5/1991,14,dominicks,8896,9.093357017,0,1.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/5/1991,14,minute.maid,34240,10.44114983,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/5/1991,14,tropicana,70336,11.16103904,1,1.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/5/1991,18,dominicks,8064,8.99516499,0,1.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/5/1991,18,minute.maid,11008,9.30637756,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/5/1991,18,tropicana,59072,10.98651232,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/5/1991,21,dominicks,7616,8.938006577,0,1.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/5/1991,21,minute.maid,6080,8.712759975,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/5/1991,21,tropicana,25408,10.14281936,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/5/1991,28,dominicks,512,6.238324625,0,1.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/5/1991,28,minute.maid,3968,8.286017468,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/5/1991,28,tropicana,25088,10.13014492,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/5/1991,32,dominicks,23808,10.07777694,0,1.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/5/1991,32,minute.maid,7488,8.921057018,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/5/1991,32,tropicana,86720,11.37043982,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/5/1991,33,dominicks,15424,9.643680017,0,1.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/5/1991,33,minute.maid,6080,8.712759975,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/5/1991,33,tropicana,41472,10.63277378,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/5/1991,40,dominicks,8960,9.100525506,0,1.39,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/5/1991,40,minute.maid,6976,8.850230966,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/5/1991,40,tropicana,27584,10.22499117,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/5/1991,44,dominicks,13120,9.481893063,0,1.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/5/1991,44,minute.maid,4928,8.502688505,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/5/1991,44,tropicana,63168,11.05355312,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/5/1991,45,dominicks,5440,8.60153434,0,1.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/5/1991,45,minute.maid,3200,8.070906089,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/5/1991,45,tropicana,28224,10.24792796,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/5/1991,47,dominicks,16192,9.692272572,0,1.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/5/1991,47,minute.maid,6144,8.723231275,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/5/1991,47,tropicana,12544,9.436997743,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/5/1991,48,dominicks,3712,8.219326094,0,1.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/5/1991,48,minute.maid,10304,9.240287448,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/5/1991,48,tropicana,21568,9.978966014,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/5/1991,49,dominicks,2240,7.714231145,0,1.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/5/1991,49,minute.maid,4352,8.378390789,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/5/1991,49,tropicana,30784,10.33475035,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/5/1991,50,dominicks,9216,9.128696383,0,1.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/5/1991,50,minute.maid,3776,8.236420527,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/5/1991,50,tropicana,19264,9.865993348,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/5/1991,51,dominicks,2560,7.847762538,0,1.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/5/1991,51,minute.maid,6528,8.783855897,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/5/1991,51,tropicana,39488,10.58375211,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/5/1991,52,dominicks,9792,9.189321005,0,1.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/5/1991,52,minute.maid,9664,9.17616292,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/5/1991,52,tropicana,64384,11.07262043,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/5/1991,53,dominicks,35072,10.46515837,0,1.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/5/1991,53,minute.maid,4480,8.407378325,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/5/1991,53,tropicana,91520,11.42431281,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/5/1991,54,dominicks,11072,9.312174678,0,1.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/5/1991,54,minute.maid,4160,8.333270353,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/5/1991,54,tropicana,34368,10.44488118,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/5/1991,56,dominicks,10176,9.227787286,0,1.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/5/1991,56,minute.maid,4160,8.333270353,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/5/1991,56,tropicana,28736,10.26590597,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/5/1991,59,dominicks,28992,10.27477521,0,1.39,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/5/1991,59,minute.maid,4480,8.407378325,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/5/1991,59,tropicana,25472,10.14533509,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/5/1991,62,dominicks,3200,8.070906089,0,1.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/5/1991,62,minute.maid,6528,8.783855897,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/5/1991,62,tropicana,59904,11.00049856,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/5/1991,64,dominicks,10176,9.227787286,0,1.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/5/1991,64,minute.maid,2304,7.742402022,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/5/1991,64,tropicana,15552,9.651944527,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/5/1991,67,dominicks,7168,8.877381955,0,1.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/5/1991,67,minute.maid,6848,8.831711918,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/5/1991,67,tropicana,37760,10.53900562,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/5/1991,68,dominicks,7936,8.979164649,0,1.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/5/1991,68,minute.maid,13760,9.529521112,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/5/1991,68,tropicana,49344,10.80657146,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/5/1991,70,dominicks,14016,9.547954813,0,1.39,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/5/1991,70,minute.maid,11008,9.30637756,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/5/1991,70,tropicana,32064,10.37548918,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/5/1991,71,dominicks,10304,9.240287448,0,1.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/5/1991,71,minute.maid,3072,8.030084094,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/5/1991,71,tropicana,40192,10.60142325,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/5/1991,72,dominicks,6080,8.712759975,0,1.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/5/1991,72,minute.maid,4608,8.435549202,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/5/1991,72,tropicana,75520,11.2321528,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/5/1991,73,dominicks,9600,9.169518378,0,1.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/5/1991,73,minute.maid,11328,9.335032816,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/5/1991,73,tropicana,77056,11.25228771,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/5/1991,74,dominicks,40768,10.61565274,0,1.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/5/1991,74,minute.maid,5760,8.658692754,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/5/1991,74,tropicana,53312,10.88391673,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/5/1991,75,dominicks,17600,9.775654181,0,1.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/5/1991,75,minute.maid,5888,8.68067166,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/5/1991,75,tropicana,53888,10.8946631,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/5/1991,76,dominicks,21376,9.970024076,0,1.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/5/1991,76,minute.maid,4416,8.392989588,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/5/1991,76,tropicana,69120,11.1435994,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/5/1991,77,dominicks,6144,8.723231275,0,1.39,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/5/1991,77,minute.maid,13248,9.491601877,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/5/1991,77,tropicana,26752,10.19436452,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/5/1991,78,dominicks,17280,9.757305042,0,1.39,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/5/1991,78,minute.maid,6208,8.733594062,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/5/1991,78,tropicana,46592,10.74918413,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/5/1991,80,dominicks,13696,9.524859098,0,1.39,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/5/1991,80,minute.maid,9664,9.17616292,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/5/1991,80,tropicana,48832,10.79614112,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/5/1991,81,dominicks,15232,9.631153757,0,1.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/5/1991,81,minute.maid,5568,8.624791202,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/5/1991,81,tropicana,56000,10.93310697,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/5/1991,83,dominicks,6848,8.831711918,0,1.39,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/5/1991,83,minute.maid,6080,8.712759975,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/5/1991,83,tropicana,43008,10.66914142,1,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/5/1991,84,dominicks,10496,9.258749511,0,1.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/5/1991,84,minute.maid,4608,8.435549202,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/5/1991,84,tropicana,27648,10.22730867,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/5/1991,86,dominicks,30592,10.32849382,0,1.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/5/1991,86,minute.maid,6592,8.793612072,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/5/1991,86,tropicana,18176,9.807857322,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/5/1991,88,dominicks,1408,7.249925537,0,1.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/5/1991,88,minute.maid,4352,8.378390789,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/5/1991,88,tropicana,28416,10.25470765,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/5/1991,89,dominicks,29504,10.29228113,0,1.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/5/1991,89,minute.maid,4800,8.476371197,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/5/1991,89,tropicana,44928,10.71281649,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/5/1991,90,dominicks,2112,7.655390645,0,1.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/5/1991,90,minute.maid,9984,9.208739091,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/5/1991,90,tropicana,23808,10.07777694,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/5/1991,91,dominicks,9792,9.189321005,0,1.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/5/1991,91,minute.maid,5952,8.691482577,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/5/1991,91,tropicana,31936,10.37148918,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/5/1991,92,dominicks,12032,9.395325046,0,1.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/5/1991,92,minute.maid,4096,8.317766167,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/5/1991,92,tropicana,25088,10.13014492,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/5/1991,93,dominicks,17856,9.790094865,0,1.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/5/1991,93,minute.maid,3904,8.269756948,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/5/1991,93,tropicana,46272,10.74229231,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/5/1991,94,dominicks,7808,8.962904128,0,1.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/5/1991,94,minute.maid,8000,8.987196821,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/5/1991,94,tropicana,24192,10.09377728,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/5/1991,95,dominicks,32000,10.37349118,0,1.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/5/1991,95,minute.maid,3584,8.184234774,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/5/1991,95,tropicana,32384,10.38541975,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/5/1991,97,dominicks,6592,8.793612072,0,1.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/5/1991,97,minute.maid,3392,8.129174997,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/5/1991,97,tropicana,12160,9.405907156,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/5/1991,98,dominicks,6016,8.702177866,0,1.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/5/1991,98,minute.maid,7552,8.929567708,0,2.07,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/5/1991,98,tropicana,64320,11.0716259,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/5/1991,100,dominicks,3008,8.009030685,0,1.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/5/1991,100,minute.maid,8000,8.987196821,0,2.17,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/5/1991,100,tropicana,59520,10.99406767,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/5/1991,101,dominicks,9472,9.156095357,0,1.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/5/1991,101,minute.maid,6464,8.7740036,0,2.1,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/5/1991,101,tropicana,47552,10.76957913,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/5/1991,102,dominicks,30208,10.31586207,0,1.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/5/1991,102,minute.maid,8192,9.010913347,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/5/1991,102,tropicana,43584,10.68244539,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/5/1991,103,dominicks,14272,9.566054855,0,1.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/5/1991,103,minute.maid,4480,8.407378325,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/5/1991,103,tropicana,28160,10.24565781,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/5/1991,105,dominicks,31808,10.36747311,0,1.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/5/1991,105,minute.maid,6272,8.743850562,0,2.11,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/5/1991,105,tropicana,31872,10.36948316,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/5/1991,106,dominicks,9600,9.169518378,0,1.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/5/1991,106,minute.maid,2752,7.920083199,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/5/1991,106,tropicana,15296,9.635346635,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/5/1991,107,dominicks,8576,9.056722883,0,1.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/5/1991,107,minute.maid,6016,8.702177866,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/5/1991,107,tropicana,59072,10.98651232,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/5/1991,109,dominicks,8000,8.987196821,0,1.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/5/1991,109,minute.maid,10112,9.221478116,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/5/1991,109,tropicana,118464,11.6823644,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/5/1991,110,dominicks,2880,7.965545573,0,1.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/5/1991,110,minute.maid,5696,8.647519453,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/5/1991,110,tropicana,37504,10.53220287,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/5/1991,111,dominicks,16320,9.700146629,0,1.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/5/1991,111,minute.maid,9024,9.107642974,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/5/1991,111,tropicana,78464,11.2703952,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/5/1991,112,dominicks,19840,9.895455381,0,1.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/5/1991,112,minute.maid,13120,9.481893063,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/5/1991,112,tropicana,73728,11.20813793,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/5/1991,113,dominicks,10560,9.264828557,0,1.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/5/1991,113,minute.maid,6592,8.793612072,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/5/1991,113,tropicana,73920,11.21073871,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/5/1991,114,dominicks,38848,10.56741187,0,1.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/5/1991,114,minute.maid,7296,8.895081532,0,2.09,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/5/1991,114,tropicana,47680,10.7722673,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/5/1991,115,dominicks,9216,9.128696383,0,1.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/5/1991,115,minute.maid,5056,8.528330936,0,2.13,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/5/1991,115,tropicana,60352,11.00794937,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/5/1991,117,dominicks,8448,9.041685006,0,1.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/5/1991,117,minute.maid,3072,8.030084094,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/5/1991,117,tropicana,23360,10.05878044,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/5/1991,118,dominicks,2624,7.87245515,0,1.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/5/1991,118,minute.maid,4480,8.407378325,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/5/1991,118,tropicana,70464,11.16285722,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/5/1991,119,dominicks,4480,8.407378325,0,1.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/5/1991,119,minute.maid,5696,8.647519453,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/5/1991,119,tropicana,31488,10.3573618,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/5/1991,121,dominicks,14784,9.601300794,0,1.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/5/1991,121,minute.maid,7040,8.859363449,0,2.1,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/5/1991,121,tropicana,34048,10.43552657,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/5/1991,122,dominicks,8896,9.093357017,0,1.39,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/5/1991,122,minute.maid,13696,9.524859098,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/5/1991,122,tropicana,38144,10.54912375,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/5/1991,123,dominicks,18560,9.828764006,0,1.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/5/1991,123,minute.maid,9088,9.114710141,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/5/1991,123,tropicana,76352,11.24310951,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/5/1991,124,dominicks,31744,10.36545901,0,1.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/5/1991,124,minute.maid,4352,8.378390789,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/5/1991,124,tropicana,62912,11.0494922,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/5/1991,126,dominicks,3584,8.184234774,0,1.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/5/1991,126,minute.maid,8512,9.049232212,0,2.15,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/5/1991,126,tropicana,67904,11.12585022,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/5/1991,128,dominicks,19776,9.89222436,0,1.38,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/5/1991,128,minute.maid,6720,8.812843434,0,2.23,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/5/1991,128,tropicana,119424,11.69043547,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/5/1991,129,dominicks,12672,9.447150114,0,1.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/5/1991,129,minute.maid,5312,8.577723691,0,2.16,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/5/1991,129,tropicana,46336,10.74367448,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/5/1991,130,dominicks,19072,9.85597657,0,1.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/5/1991,130,minute.maid,10560,9.264828557,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/5/1991,130,tropicana,71808,11.18175117,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/5/1991,131,dominicks,9024,9.107642974,0,1.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/5/1991,131,minute.maid,5568,8.624791202,0,2.17,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/5/1991,131,tropicana,51456,10.84848235,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/5/1991,132,dominicks,13632,9.520175249,0,1.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/5/1991,132,minute.maid,8512,9.049232212,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/5/1991,132,tropicana,55296,10.92045585,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/5/1991,134,dominicks,15232,9.631153757,0,1.39,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/5/1991,134,minute.maid,2368,7.769800996,0,2.15,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/5/1991,134,tropicana,8000,8.987196821,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/5/1991,137,dominicks,25472,10.14533509,0,1.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/5/1991,137,minute.maid,8384,9.034080407,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/5/1991,137,tropicana,123648,11.7251941,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/12/1991,2,dominicks,17024,9.742379392,0,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/12/1991,2,minute.maid,18240,9.811372264,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/12/1991,2,tropicana,5632,8.636219898,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/12/1991,5,dominicks,8448,9.041685006,0,1.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/12/1991,5,minute.maid,20672,9.936535407,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/12/1991,5,tropicana,5632,8.636219898,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/12/1991,8,dominicks,25856,10.16029796,0,1.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/12/1991,8,minute.maid,31872,10.36948316,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/12/1991,8,tropicana,6784,8.822322178,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/12/1991,9,dominicks,7936,8.979164649,0,1.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/12/1991,9,minute.maid,35456,10.47604777,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/12/1991,9,tropicana,8128,9.00307017,0,3.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/12/1991,14,dominicks,9472,9.156095357,0,1.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/12/1991,14,minute.maid,21632,9.981928979,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/12/1991,14,tropicana,13184,9.486759252,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/12/1991,18,dominicks,6080,8.712759975,0,1.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/12/1991,18,minute.maid,15232,9.631153757,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/12/1991,18,tropicana,10304,9.240287448,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/12/1991,21,dominicks,17088,9.746131742,0,1.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/12/1991,21,minute.maid,13248,9.491601877,1,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/12/1991,21,tropicana,4480,8.407378325,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/12/1991,28,dominicks,128,4.852030264,0,1.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/12/1991,28,minute.maid,10304,9.240287448,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/12/1991,28,tropicana,3648,8.201934351,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/12/1991,32,dominicks,16192,9.692272572,0,1.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/12/1991,32,minute.maid,44544,10.70423274,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/12/1991,32,tropicana,8896,9.093357017,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/12/1991,33,dominicks,8384,9.034080407,0,1.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/12/1991,33,minute.maid,22848,10.03661887,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/12/1991,33,tropicana,12544,9.436997743,0,3.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/12/1991,40,dominicks,9920,9.2023082,0,1.39,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/12/1991,40,minute.maid,23616,10.06967973,1,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/12/1991,40,tropicana,6400,8.764053269,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/12/1991,44,dominicks,12288,9.416378455,0,1.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/12/1991,44,minute.maid,34304,10.44301724,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/12/1991,44,tropicana,7680,8.946374826,0,3.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/12/1991,45,dominicks,5376,8.589699882,0,1.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/12/1991,45,minute.maid,12160,9.405907156,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/12/1991,45,tropicana,6912,8.841014311,0,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/12/1991,47,dominicks,16384,9.704060528,0,1.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/12/1991,47,minute.maid,15552,9.651944527,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/12/1991,47,tropicana,5568,8.624791202,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/12/1991,48,dominicks,2880,7.965545573,0,1.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/12/1991,48,minute.maid,16320,9.700146629,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/12/1991,48,tropicana,5504,8.61323038,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/12/1991,49,dominicks,2368,7.769800996,0,1.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/12/1991,49,minute.maid,16384,9.704060528,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/12/1991,49,tropicana,3648,8.201934351,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/12/1991,50,dominicks,8832,9.086136769,0,1.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/12/1991,50,minute.maid,14848,9.605620455,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/12/1991,50,tropicana,2560,7.847762538,0,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/12/1991,51,dominicks,6016,8.702177866,0,1.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/12/1991,51,minute.maid,22976,10.04220547,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/12/1991,51,tropicana,5248,8.565602331,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/12/1991,52,dominicks,6336,8.754002934,0,1.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/12/1991,52,minute.maid,29632,10.29661014,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/12/1991,52,tropicana,12160,9.405907156,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/12/1991,53,dominicks,2304,7.742402022,0,1.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/12/1991,53,minute.maid,48448,10.78824634,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/12/1991,53,tropicana,16256,9.69621735,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/12/1991,54,dominicks,8128,9.00307017,0,1.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/12/1991,54,minute.maid,12800,9.45720045,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/12/1991,54,tropicana,6272,8.743850562,0,3.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/12/1991,56,dominicks,11968,9.3899917,0,1.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/12/1991,56,minute.maid,14784,9.601300794,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/12/1991,56,tropicana,6656,8.803273983,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/12/1991,59,dominicks,23360,10.05878044,0,1.39,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/12/1991,59,minute.maid,9536,9.162829389,1,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/12/1991,59,tropicana,4992,8.51559191,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/12/1991,62,dominicks,2688,7.896552702,0,1.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/12/1991,62,minute.maid,23168,10.0505273,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/12/1991,62,tropicana,15360,9.639522007,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/12/1991,64,dominicks,10048,9.215128889,0,1.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/12/1991,64,minute.maid,9088,9.114710141,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/12/1991,64,tropicana,2176,7.685243608,0,3.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/12/1991,67,dominicks,3648,8.201934351,0,1.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/12/1991,67,minute.maid,24448,10.10430369,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/12/1991,67,tropicana,6080,8.712759975,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/12/1991,68,dominicks,5440,8.60153434,0,1.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/12/1991,68,minute.maid,27328,10.2156671,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/12/1991,68,tropicana,7744,8.954673629,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/12/1991,70,dominicks,14400,9.574983486,0,1.39,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/12/1991,70,minute.maid,19968,9.901886271,1,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/12/1991,70,tropicana,7424,8.912473275,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/12/1991,71,dominicks,10240,9.234056899,0,1.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/12/1991,71,minute.maid,16960,9.738612909,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/12/1991,71,tropicana,19008,9.852615222,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/12/1991,72,dominicks,9536,9.162829389,0,1.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/12/1991,72,minute.maid,29824,10.30306872,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/12/1991,72,tropicana,11200,9.323669057,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/12/1991,73,dominicks,11392,9.340666634,0,1.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/12/1991,73,minute.maid,56064,10.93424917,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/12/1991,73,tropicana,9856,9.195835686,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/12/1991,74,dominicks,34752,10.4559924,0,1.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/12/1991,74,minute.maid,17344,9.761001904,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/12/1991,74,tropicana,8128,9.00307017,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/12/1991,75,dominicks,15872,9.67231183,0,1.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/12/1991,75,minute.maid,23936,10.08313888,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/12/1991,75,tropicana,9088,9.114710141,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/12/1991,76,dominicks,16128,9.688312171,0,1.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/12/1991,76,minute.maid,26816,10.196754,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/12/1991,76,tropicana,6656,8.803273983,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/12/1991,77,dominicks,8256,9.018695488,0,1.39,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/12/1991,77,minute.maid,16128,9.688312171,1,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/12/1991,77,tropicana,8512,9.049232212,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/12/1991,78,dominicks,17984,9.797237753,0,1.39,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/12/1991,78,minute.maid,7744,8.954673629,1,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/12/1991,78,tropicana,9024,9.107642974,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/12/1991,80,dominicks,11968,9.3899917,0,1.39,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/12/1991,80,minute.maid,10560,9.264828557,1,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/12/1991,80,tropicana,10944,9.30054664,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/12/1991,81,dominicks,11904,9.384629757,0,1.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/12/1991,81,minute.maid,22848,10.03661887,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/12/1991,81,tropicana,5504,8.61323038,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/12/1991,83,dominicks,10944,9.30054664,0,1.39,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/12/1991,83,minute.maid,9344,9.142489705,1,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/12/1991,83,tropicana,7552,8.929567708,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/12/1991,84,dominicks,7296,8.895081532,0,1.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/12/1991,84,minute.maid,24960,10.12502982,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/12/1991,84,tropicana,4672,8.449342525,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/12/1991,86,dominicks,16512,9.711842668,0,1.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/12/1991,86,minute.maid,22464,10.01966931,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/12/1991,86,tropicana,5888,8.68067166,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/12/1991,88,dominicks,3328,8.110126802,0,1.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/12/1991,88,minute.maid,14720,9.596962392,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/12/1991,88,tropicana,2688,7.896552702,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/12/1991,89,dominicks,17024,9.742379392,0,1.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/12/1991,89,minute.maid,27968,10.23881628,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/12/1991,89,tropicana,3008,8.009030685,0,3.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/12/1991,90,dominicks,6784,8.822322178,0,1.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/12/1991,90,minute.maid,24256,10.09641929,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/12/1991,90,tropicana,3200,8.070906089,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/12/1991,91,dominicks,3584,8.184234774,0,1.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/12/1991,91,minute.maid,24640,10.11212642,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/12/1991,91,tropicana,3968,8.286017468,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/12/1991,92,dominicks,13312,9.496421163,0,1.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/12/1991,92,minute.maid,18432,9.821843564,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/12/1991,92,tropicana,3776,8.236420527,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/12/1991,93,dominicks,13632,9.520175249,0,1.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/12/1991,93,minute.maid,23488,10.06424493,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/12/1991,93,tropicana,7488,8.921057018,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/12/1991,94,dominicks,12288,9.416378455,0,1.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/12/1991,94,minute.maid,13248,9.491601877,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/12/1991,94,tropicana,6912,8.841014311,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/12/1991,95,dominicks,21440,9.973013615,0,1.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/12/1991,95,minute.maid,12544,9.436997743,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/12/1991,95,tropicana,5568,8.624791202,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/12/1991,97,dominicks,5824,8.66974259,0,1.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/12/1991,97,minute.maid,8448,9.041685006,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/12/1991,97,tropicana,1856,7.526178913,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/12/1991,98,dominicks,3328,8.110126802,0,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/12/1991,98,minute.maid,36032,10.49216271,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/12/1991,98,tropicana,6656,8.803273983,0,2.96,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/12/1991,100,dominicks,5568,8.624791202,0,1.53,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/12/1991,100,minute.maid,44608,10.70566849,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/12/1991,100,tropicana,6656,8.803273983,0,3.06,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/12/1991,101,dominicks,9216,9.128696383,0,1.44,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/12/1991,101,minute.maid,26368,10.17990643,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/12/1991,101,tropicana,7552,8.929567708,0,2.98,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/12/1991,102,dominicks,21504,9.975994243,0,1.45,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/12/1991,102,minute.maid,31808,10.36747311,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/12/1991,102,tropicana,6976,8.850230966,0,2.72,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/12/1991,103,dominicks,15488,9.64782081,0,1.44,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/12/1991,103,minute.maid,16640,9.719564714,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/12/1991,103,tropicana,2880,7.965545573,0,2.75,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/12/1991,104,dominicks,9984,9.208739091,0,1.39,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/12/1991,104,minute.maid,15424,9.643680017,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/12/1991,104,tropicana,3968,8.286017468,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/12/1991,105,dominicks,28544,10.25920204,0,1.44,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/12/1991,105,minute.maid,22720,10.03100087,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/12/1991,105,tropicana,3456,8.14786713,0,2.94,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/12/1991,106,dominicks,7936,8.979164649,0,1.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/12/1991,106,minute.maid,9472,9.156095357,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/12/1991,106,tropicana,1856,7.526178913,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/12/1991,107,dominicks,15168,9.626943225,0,1.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/12/1991,107,minute.maid,25792,10.15781965,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/12/1991,107,tropicana,10880,9.29468152,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/12/1991,109,dominicks,5376,8.589699882,0,1.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/12/1991,109,minute.maid,56512,10.94220828,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/12/1991,109,tropicana,16064,9.684336023,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/12/1991,110,dominicks,4416,8.392989588,0,1.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/12/1991,110,minute.maid,22720,10.03100087,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/12/1991,110,tropicana,5312,8.577723691,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/12/1991,111,dominicks,14400,9.574983486,0,1.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/12/1991,111,minute.maid,75456,11.23130498,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/12/1991,111,tropicana,7168,8.877381955,0,3.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/12/1991,112,dominicks,20032,9.905086274,0,1.41,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/12/1991,112,minute.maid,33472,10.41846455,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/12/1991,112,tropicana,17536,9.77201119,0,2.87,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/12/1991,113,dominicks,9856,9.195835686,0,1.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/12/1991,113,minute.maid,28096,10.2433825,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/12/1991,113,tropicana,7744,8.954673629,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/12/1991,114,dominicks,23232,10.05328592,0,1.45,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/12/1991,114,minute.maid,45184,10.71849832,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/12/1991,114,tropicana,4608,8.435549202,0,2.91,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/12/1991,115,dominicks,10240,9.234056899,0,1.44,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/12/1991,115,minute.maid,28480,10.25695737,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/12/1991,115,tropicana,7616,8.938006577,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/12/1991,116,dominicks,12736,9.452187908,0,1.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/12/1991,116,minute.maid,12992,9.472089062,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/12/1991,116,tropicana,5568,8.624791202,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/12/1991,117,dominicks,6336,8.754002934,0,1.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/12/1991,117,minute.maid,13312,9.496421163,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/12/1991,117,tropicana,2752,7.920083199,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/12/1991,118,dominicks,3520,8.166216269,0,1.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/12/1991,118,minute.maid,24704,10.11472045,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/12/1991,118,tropicana,7232,8.886270902,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/12/1991,119,dominicks,8000,8.987196821,0,1.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/12/1991,119,minute.maid,16320,9.700146629,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/12/1991,119,tropicana,7616,8.938006577,0,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/12/1991,121,dominicks,7488,8.921057018,0,1.47,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/12/1991,121,minute.maid,27840,10.23422911,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/12/1991,121,tropicana,6784,8.822322178,0,3.06,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/12/1991,122,dominicks,7296,8.895081532,0,1.46,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/12/1991,122,minute.maid,26432,10.18233068,1,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/12/1991,122,tropicana,11648,9.36288977,0,2.64,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/12/1991,123,dominicks,19072,9.85597657,0,1.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/12/1991,123,minute.maid,55872,10.93081864,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/12/1991,123,tropicana,6848,8.831711918,0,3.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/12/1991,124,dominicks,34496,10.44859865,0,1.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/12/1991,124,minute.maid,41728,10.63892765,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/12/1991,124,tropicana,6272,8.743850562,0,3.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/12/1991,126,dominicks,16832,9.731037116,0,1.44,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/12/1991,126,minute.maid,31168,10.34714721,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/12/1991,126,tropicana,9600,9.169518378,0,2.93,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/12/1991,128,dominicks,19392,9.872615889,0,1.43,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/12/1991,128,minute.maid,61248,11.02268648,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/12/1991,128,tropicana,11648,9.36288977,0,2.91,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/12/1991,129,dominicks,10112,9.221478116,0,1.43,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/12/1991,129,minute.maid,21888,9.99369382,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/12/1991,129,tropicana,7808,8.962904128,0,2.92,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/12/1991,130,dominicks,22592,10.02535114,0,1.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/12/1991,130,minute.maid,32192,10.37947325,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/12/1991,130,tropicana,5248,8.565602331,0,3.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/12/1991,131,dominicks,7936,8.979164649,0,1.42,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/12/1991,131,minute.maid,20096,9.908276069,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/12/1991,131,tropicana,6208,8.733594062,0,2.94,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/12/1991,132,dominicks,14272,9.566054855,0,1.43,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/12/1991,132,minute.maid,33408,10.41655067,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/12/1991,132,tropicana,9088,9.114710141,0,2.84,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/12/1991,134,dominicks,9280,9.135616826,0,1.43,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/12/1991,134,minute.maid,13120,9.481893063,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/12/1991,134,tropicana,2752,7.920083199,0,2.81,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/12/1991,137,dominicks,33856,10.42987152,0,1.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/12/1991,137,minute.maid,46976,10.75739211,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/12/1991,137,tropicana,20480,9.927204079,0,3.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/19/1991,2,dominicks,13440,9.505990614,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/19/1991,2,minute.maid,7360,8.903815212,0,1.95,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/19/1991,2,tropicana,9024,9.107642974,1,2.68,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/19/1991,8,dominicks,24064,10.08847223,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/19/1991,8,minute.maid,5312,8.577723691,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/19/1991,8,tropicana,8000,8.987196821,1,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/19/1991,9,dominicks,15168,9.626943225,1,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/19/1991,9,minute.maid,6592,8.793612072,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/19/1991,9,tropicana,10496,9.258749511,1,2.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/19/1991,12,dominicks,14080,9.55251063,1,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/19/1991,12,minute.maid,14528,9.583833101,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/19/1991,12,tropicana,12608,9.442086812,1,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/19/1991,14,dominicks,9792,9.189321005,1,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/19/1991,14,minute.maid,8640,9.064157862,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/19/1991,14,tropicana,16192,9.692272572,1,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/19/1991,18,dominicks,18304,9.814874894,1,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/19/1991,18,minute.maid,8320,9.026417534,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/19/1991,18,tropicana,10368,9.246479419,1,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/19/1991,21,dominicks,22080,10.0024275,1,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/19/1991,21,minute.maid,5312,8.577723691,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/19/1991,21,tropicana,5952,8.691482577,1,2.55,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/19/1991,28,dominicks,6400,8.764053269,1,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/19/1991,28,minute.maid,3136,8.050703382,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/19/1991,28,tropicana,4800,8.476371197,1,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/19/1991,32,dominicks,19328,9.869310101,1,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/19/1991,32,minute.maid,10432,9.252633284,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/19/1991,32,tropicana,14656,9.592605087,1,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/19/1991,33,dominicks,16384,9.704060528,1,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/19/1991,33,minute.maid,11136,9.317938383,0,1.94,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/19/1991,33,tropicana,12096,9.400630098,1,2.73,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/19/1991,40,dominicks,8704,9.071537969,1,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/19/1991,40,minute.maid,4800,8.476371197,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/19/1991,40,tropicana,4800,8.476371197,1,2.56,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/19/1991,44,dominicks,8064,8.99516499,1,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/19/1991,44,minute.maid,6720,8.812843434,0,1.95,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/19/1991,44,tropicana,11136,9.317938383,1,2.73,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/19/1991,45,dominicks,7488,8.921057018,1,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/19/1991,45,minute.maid,9664,9.17616292,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/19/1991,45,tropicana,6656,8.803273983,1,2.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/19/1991,47,dominicks,11328,9.335032816,1,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/19/1991,47,minute.maid,8640,9.064157862,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/19/1991,47,tropicana,5056,8.528330936,1,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/19/1991,48,dominicks,3712,8.219326094,1,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/19/1991,48,minute.maid,6208,8.733594062,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/19/1991,48,tropicana,6528,8.783855897,1,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/19/1991,49,dominicks,8128,9.00307017,1,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/19/1991,49,minute.maid,3520,8.166216269,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/19/1991,49,tropicana,5824,8.66974259,1,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/19/1991,50,dominicks,6016,8.702177866,1,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/19/1991,50,minute.maid,3712,8.219326094,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/19/1991,50,tropicana,4736,8.462948177,1,2.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/19/1991,51,dominicks,13504,9.510741217,1,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/19/1991,51,minute.maid,4416,8.392989588,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/19/1991,51,tropicana,5120,8.540909718,1,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/19/1991,52,dominicks,6976,8.850230966,1,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/19/1991,52,minute.maid,11456,9.346268889,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/19/1991,52,tropicana,17024,9.742379392,1,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/19/1991,53,dominicks,17216,9.753594463,1,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/19/1991,53,minute.maid,11008,9.30637756,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/19/1991,53,tropicana,16000,9.680344001,1,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/19/1991,54,dominicks,7808,8.962904128,1,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/19/1991,54,minute.maid,6528,8.783855897,0,1.94,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/19/1991,54,tropicana,8640,9.064157862,1,2.72,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/19/1991,56,dominicks,14144,9.557045785,1,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/19/1991,56,minute.maid,5440,8.60153434,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/19/1991,56,tropicana,8448,9.041685006,1,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/19/1991,59,dominicks,8512,9.049232212,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/19/1991,59,minute.maid,5440,8.60153434,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/19/1991,59,tropicana,5824,8.66974259,1,2.54,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/19/1991,62,dominicks,5056,8.528330936,1,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/19/1991,62,minute.maid,7232,8.886270902,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/19/1991,62,tropicana,17920,9.793672686,1,2.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/19/1991,64,dominicks,10624,9.270870872,1,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/19/1991,64,minute.maid,2688,7.896552702,0,1.96,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/19/1991,64,tropicana,3136,8.050703382,1,2.62,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/19/1991,67,dominicks,6592,8.793612072,1,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/19/1991,67,minute.maid,4992,8.51559191,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/19/1991,67,tropicana,4160,8.333270353,1,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/19/1991,70,dominicks,14784,9.601300794,1,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/19/1991,70,minute.maid,8576,9.056722883,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/19/1991,70,tropicana,9408,9.14931567,1,2.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/19/1991,71,dominicks,18944,9.849242538,1,1.58,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/19/1991,71,minute.maid,6720,8.812843434,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/19/1991,71,tropicana,17344,9.761001904,1,2.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/19/1991,72,dominicks,15296,9.635346635,1,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/19/1991,72,minute.maid,8320,9.026417534,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/19/1991,72,tropicana,12608,9.442086812,1,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/19/1991,73,dominicks,24768,10.11730778,1,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/19/1991,73,minute.maid,12480,9.431882642,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/19/1991,73,tropicana,9600,9.169518378,1,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/19/1991,74,dominicks,23808,10.07777694,1,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/19/1991,74,minute.maid,8384,9.034080407,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/19/1991,74,tropicana,12544,9.436997743,1,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/19/1991,75,dominicks,13696,9.524859098,1,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/19/1991,75,minute.maid,11904,9.384629757,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/19/1991,75,tropicana,11648,9.36288977,1,2.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/19/1991,76,dominicks,12800,9.45720045,1,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/19/1991,76,minute.maid,5504,8.61323038,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/19/1991,76,tropicana,9216,9.128696383,1,2.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/19/1991,77,dominicks,10112,9.221478116,1,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/19/1991,77,minute.maid,6592,8.793612072,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/19/1991,77,tropicana,12288,9.416378455,1,2.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/19/1991,78,dominicks,15552,9.651944527,1,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/19/1991,78,minute.maid,4800,8.476371197,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/19/1991,78,tropicana,8640,9.064157862,1,2.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/19/1991,80,dominicks,9024,9.107642974,1,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/19/1991,80,minute.maid,8320,9.026417534,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/19/1991,80,tropicana,12800,9.45720045,1,2.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/19/1991,81,dominicks,7744,8.954673629,1,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/19/1991,81,minute.maid,7616,8.938006577,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/19/1991,81,tropicana,9024,9.107642974,1,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/19/1991,83,dominicks,8896,9.093357017,1,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/19/1991,83,minute.maid,6016,8.702177866,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/19/1991,83,tropicana,8192,9.010913347,1,2.49,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/19/1991,84,dominicks,9600,9.169518378,1,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/19/1991,84,minute.maid,5376,8.589699882,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/19/1991,84,tropicana,5504,8.61323038,1,2.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/19/1991,86,dominicks,9792,9.189321005,1,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/19/1991,86,minute.maid,6016,8.702177866,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/19/1991,86,tropicana,6592,8.793612072,1,2.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/19/1991,88,dominicks,8512,9.049232212,1,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/19/1991,88,minute.maid,5440,8.60153434,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/19/1991,88,tropicana,4032,8.30201781,1,2.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/19/1991,89,dominicks,25088,10.13014492,1,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/19/1991,89,minute.maid,4352,8.378390789,0,1.96,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/19/1991,89,tropicana,3968,8.286017468,1,2.72,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/19/1991,90,dominicks,6144,8.723231275,1,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/19/1991,90,minute.maid,6016,8.702177866,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/19/1991,90,tropicana,2816,7.943072717,1,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/19/1991,91,dominicks,7616,8.938006577,1,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/19/1991,91,minute.maid,4096,8.317766167,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/19/1991,91,tropicana,5376,8.589699882,1,2.49,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/19/1991,92,dominicks,23360,10.05878044,1,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/19/1991,92,minute.maid,5824,8.66974259,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/19/1991,92,tropicana,3584,8.184234774,1,2.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/19/1991,93,dominicks,11904,9.384629757,1,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/19/1991,93,minute.maid,7232,8.886270902,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/19/1991,93,tropicana,11520,9.351839934,1,2.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/19/1991,94,dominicks,13760,9.529521112,1,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/19/1991,94,minute.maid,8128,9.00307017,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/19/1991,94,tropicana,9728,9.182763604,1,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/19/1991,95,dominicks,23680,10.07238609,1,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/19/1991,95,minute.maid,5120,8.540909718,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/19/1991,95,tropicana,4608,8.435549202,1,2.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/19/1991,97,dominicks,6912,8.841014311,1,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/19/1991,97,minute.maid,4288,8.363575703,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/19/1991,97,tropicana,2944,7.98752448,1,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/19/1991,98,dominicks,12864,9.462187991,1,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/19/1991,98,minute.maid,5888,8.68067166,0,1.93,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/19/1991,98,tropicana,6848,8.831711918,1,2.61,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/19/1991,100,dominicks,22464,10.01966931,1,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/19/1991,100,minute.maid,7872,8.971067439,0,1.94,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/19/1991,100,tropicana,10048,9.215128889,1,2.78,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/19/1991,101,dominicks,13504,9.510741217,1,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/19/1991,101,minute.maid,11712,9.368369236,0,1.94,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/19/1991,101,tropicana,10944,9.30054664,1,2.73,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/19/1991,102,dominicks,26624,10.18956834,1,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/19/1991,102,minute.maid,11840,9.379238908,0,1.95,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/19/1991,102,tropicana,8960,9.100525506,1,2.61,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/19/1991,103,dominicks,20032,9.905086274,1,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/19/1991,103,minute.maid,3136,8.050703382,0,1.95,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/19/1991,103,tropicana,3200,8.070906089,1,2.57,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/19/1991,104,dominicks,11584,9.357380115,1,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/19/1991,104,minute.maid,4544,8.42156296,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/19/1991,104,tropicana,4608,8.435549202,1,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/19/1991,105,dominicks,25344,10.1402973,1,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/19/1991,105,minute.maid,5312,8.577723691,0,1.97,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/19/1991,105,tropicana,4032,8.30201781,1,2.75,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/19/1991,106,dominicks,8576,9.056722883,1,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/19/1991,106,minute.maid,2432,7.796469243,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/19/1991,106,tropicana,3840,8.253227646,1,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/19/1991,107,dominicks,14912,9.609921537,1,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/19/1991,107,minute.maid,7552,8.929567708,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/19/1991,107,tropicana,15168,9.626943225,1,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/19/1991,109,dominicks,10944,9.30054664,1,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/19/1991,109,minute.maid,8960,9.100525506,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/19/1991,109,tropicana,19776,9.89222436,1,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/19/1991,110,dominicks,16192,9.692272572,1,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/19/1991,110,minute.maid,5120,8.540909718,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/19/1991,110,tropicana,6912,8.841014311,1,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/19/1991,111,dominicks,37184,10.52363384,1,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/19/1991,111,minute.maid,6528,8.783855897,0,1.95,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/19/1991,111,tropicana,5184,8.553332238,1,2.72,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/19/1991,112,dominicks,9600,9.169518378,1,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/19/1991,112,minute.maid,10240,9.234056899,0,1.96,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/19/1991,112,tropicana,18112,9.804329981,1,2.56,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/19/1991,113,dominicks,21696,9.984883191,1,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/19/1991,113,minute.maid,8960,9.100525506,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/19/1991,113,tropicana,10432,9.252633284,1,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/19/1991,114,dominicks,36800,10.51325312,1,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/19/1991,114,minute.maid,6272,8.743850562,0,1.96,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/19/1991,114,tropicana,6016,8.702177866,1,2.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/19/1991,116,dominicks,11904,9.384629757,1,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/19/1991,116,minute.maid,3904,8.269756948,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/19/1991,116,tropicana,5888,8.68067166,1,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/19/1991,117,dominicks,9600,9.169518378,1,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/19/1991,117,minute.maid,4096,8.317766167,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/19/1991,117,tropicana,4608,8.435549202,1,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/19/1991,118,dominicks,9024,9.107642974,1,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/19/1991,118,minute.maid,5376,8.589699882,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/19/1991,118,tropicana,12928,9.467150781,1,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/19/1991,119,dominicks,4736,8.462948177,1,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/19/1991,119,minute.maid,6016,8.702177866,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/19/1991,119,tropicana,8576,9.056722883,1,2.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/19/1991,121,dominicks,10240,9.234056899,1,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/19/1991,121,minute.maid,14656,9.592605087,0,1.95,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/19/1991,121,tropicana,10240,9.234056899,1,2.67,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/19/1991,122,dominicks,10240,9.234056899,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/19/1991,122,minute.maid,8320,9.026417534,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/19/1991,122,tropicana,11136,9.317938383,1,2.55,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/19/1991,123,dominicks,11584,9.357380115,1,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/19/1991,123,minute.maid,8512,9.049232212,0,1.94,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/19/1991,123,tropicana,6528,8.783855897,1,2.71,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/19/1991,124,dominicks,31232,10.34919849,1,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/19/1991,124,minute.maid,8192,9.010913347,0,1.93,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/19/1991,124,tropicana,9536,9.162829389,1,2.84,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/19/1991,126,dominicks,23168,10.0505273,1,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/19/1991,126,minute.maid,7424,8.912473275,0,1.95,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/19/1991,126,tropicana,13376,9.501217335,1,2.68,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/19/1991,128,dominicks,17344,9.761001904,1,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/19/1991,128,minute.maid,20352,9.920934466,0,1.95,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/19/1991,128,tropicana,13440,9.505990614,1,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/19/1991,129,dominicks,6720,8.812843434,1,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/19/1991,129,minute.maid,9792,9.189321005,0,1.93,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/19/1991,129,tropicana,9280,9.135616826,1,2.68,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/19/1991,130,dominicks,52928,10.87668778,1,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/19/1991,130,minute.maid,25792,10.15781965,0,1.9,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/19/1991,130,tropicana,5760,8.658692754,1,2.73,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/19/1991,131,dominicks,8512,9.049232212,1,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/19/1991,131,minute.maid,10816,9.288781798,0,1.95,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/19/1991,131,tropicana,7936,8.979164649,1,2.71,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/19/1991,132,dominicks,19776,9.89222436,1,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/19/1991,132,minute.maid,8512,9.049232212,0,1.95,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/19/1991,132,tropicana,9728,9.182763604,1,2.64,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/19/1991,134,dominicks,10752,9.282847063,1,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/19/1991,134,minute.maid,2880,7.965545573,0,1.97,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/19/1991,134,tropicana,3968,8.286017468,1,2.63,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/19/1991,137,dominicks,31104,10.34509171,1,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/19/1991,137,minute.maid,12160,9.405907156,0,1.93,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/19/1991,137,tropicana,22784,10.03381381,1,2.74,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/26/1991,2,dominicks,10112,9.221478116,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/26/1991,2,minute.maid,7808,8.962904128,0,1.83,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/26/1991,2,tropicana,6016,8.702177866,0,3.44,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/26/1991,5,dominicks,6912,8.841014311,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/26/1991,5,minute.maid,12352,9.421573272,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/26/1991,5,tropicana,6400,8.764053269,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/26/1991,8,dominicks,15680,9.660141294,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/26/1991,8,minute.maid,33344,10.41463313,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/26/1991,8,tropicana,6592,8.793612072,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/26/1991,9,dominicks,8384,9.034080407,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/26/1991,9,minute.maid,8704,9.071537969,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/26/1991,9,tropicana,7168,8.877381955,0,3.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/26/1991,12,dominicks,7680,8.946374826,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/26/1991,12,minute.maid,19840,9.895455381,0,1.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/26/1991,12,tropicana,5056,8.528330936,0,3.59,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/26/1991,14,dominicks,8128,9.00307017,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/26/1991,14,minute.maid,11840,9.379238908,0,1.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/26/1991,14,tropicana,10240,9.234056899,0,3.59,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/26/1991,18,dominicks,12992,9.472089062,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/26/1991,18,minute.maid,15808,9.66827142,0,1.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/26/1991,18,tropicana,7744,8.954673629,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/26/1991,21,dominicks,13248,9.491601877,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/26/1991,21,minute.maid,5696,8.647519453,0,1.82,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/26/1991,21,tropicana,3520,8.166216269,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/26/1991,28,dominicks,3840,8.253227646,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/26/1991,28,minute.maid,4800,8.476371197,0,1.89,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/26/1991,28,tropicana,3392,8.129174997,0,3.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/26/1991,32,dominicks,16896,9.734832187,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/26/1991,32,minute.maid,19328,9.869310101,0,1.89,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/26/1991,32,tropicana,10816,9.288781798,0,3.59,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/26/1991,33,dominicks,12352,9.421573272,0,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/26/1991,33,minute.maid,9024,9.107642974,0,1.86,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/26/1991,33,tropicana,8704,9.071537969,0,3.41,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/26/1991,40,dominicks,8256,9.018695488,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/26/1991,40,minute.maid,6144,8.723231275,0,1.82,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/26/1991,40,tropicana,3584,8.184234774,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/26/1991,44,dominicks,9728,9.182763604,0,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/26/1991,44,minute.maid,12608,9.442086812,0,1.85,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/26/1991,44,tropicana,8384,9.034080407,0,3.21,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/26/1991,45,dominicks,7232,8.886270902,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/26/1991,45,minute.maid,8640,9.064157862,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/26/1991,45,tropicana,4800,8.476371197,0,3.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/26/1991,47,dominicks,10112,9.221478116,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/26/1991,47,minute.maid,11520,9.351839934,0,1.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/26/1991,47,tropicana,4288,8.363575703,0,3.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/26/1991,48,dominicks,3008,8.009030685,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/26/1991,48,minute.maid,10944,9.30054664,0,1.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/26/1991,48,tropicana,4608,8.435549202,0,3.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/26/1991,49,dominicks,7616,8.938006577,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/26/1991,49,minute.maid,6528,8.783855897,0,1.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/26/1991,49,tropicana,3008,8.009030685,0,3.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/26/1991,50,dominicks,7232,8.886270902,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/26/1991,50,minute.maid,6016,8.702177866,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/26/1991,50,tropicana,2816,7.943072717,0,3.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/26/1991,51,dominicks,7104,8.868413285,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/26/1991,51,minute.maid,7744,8.954673629,0,1.89,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/26/1991,51,tropicana,4160,8.333270353,0,3.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/26/1991,52,dominicks,7104,8.868413285,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/26/1991,52,minute.maid,14016,9.547954813,0,1.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/26/1991,52,tropicana,11008,9.30637756,0,3.59,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/26/1991,53,dominicks,12160,9.405907156,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/26/1991,53,minute.maid,17984,9.797237753,0,1.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/26/1991,53,tropicana,9024,9.107642974,0,3.59,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/26/1991,54,dominicks,8192,9.010913347,0,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/26/1991,54,minute.maid,6912,8.841014311,0,1.85,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/26/1991,54,tropicana,4416,8.392989588,0,3.27,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/26/1991,56,dominicks,10816,9.288781798,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/26/1991,56,minute.maid,7040,8.859363449,0,1.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/26/1991,56,tropicana,4416,8.392989588,0,3.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/26/1991,59,dominicks,7808,8.962904128,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/26/1991,59,minute.maid,5888,8.68067166,0,1.83,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/26/1991,59,tropicana,5504,8.61323038,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/26/1991,62,dominicks,4992,8.51559191,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/26/1991,62,minute.maid,13952,9.543378146,0,1.89,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/26/1991,62,tropicana,12416,9.426741242,0,3.59,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/26/1991,64,dominicks,7680,8.946374826,0,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/26/1991,64,minute.maid,6464,8.7740036,0,1.83,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/26/1991,64,tropicana,1920,7.560080465,0,3.14,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/26/1991,67,dominicks,6400,8.764053269,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/26/1991,67,minute.maid,8000,8.987196821,0,1.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/26/1991,67,tropicana,5248,8.565602331,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/26/1991,68,dominicks,13056,9.477003077,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/26/1991,68,minute.maid,19584,9.882468185,0,1.89,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/26/1991,68,tropicana,6784,8.822322178,0,3.59,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/26/1991,70,dominicks,12224,9.411156511,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/26/1991,70,minute.maid,11008,9.30637756,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/26/1991,70,tropicana,6336,8.754002934,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/26/1991,71,dominicks,10112,9.221478116,0,1.58,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/26/1991,71,minute.maid,9344,9.142489705,0,1.89,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/26/1991,71,tropicana,10048,9.215128889,0,3.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/26/1991,72,dominicks,2176,7.685243608,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/26/1991,72,minute.maid,17024,9.742379392,0,1.89,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/26/1991,72,tropicana,8064,8.99516499,0,3.59,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/26/1991,73,dominicks,22784,10.03381381,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/26/1991,73,minute.maid,30080,10.31161578,0,1.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/26/1991,73,tropicana,8512,9.049232212,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/26/1991,74,dominicks,27136,10.20861654,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/26/1991,74,minute.maid,13184,9.486759252,0,1.89,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/26/1991,74,tropicana,5376,8.589699882,0,3.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/26/1991,75,dominicks,14912,9.609921537,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/26/1991,75,minute.maid,16128,9.688312171,0,1.89,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/26/1991,75,tropicana,6784,8.822322178,0,3.59,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/26/1991,76,dominicks,4416,8.392989588,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/26/1991,76,minute.maid,10496,9.258749511,0,1.89,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/26/1991,76,tropicana,5760,8.658692754,0,3.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/26/1991,77,dominicks,5312,8.577723691,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/26/1991,77,minute.maid,6784,8.822322178,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/26/1991,77,tropicana,8256,9.018695488,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/26/1991,78,dominicks,14080,9.55251063,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/26/1991,78,minute.maid,6400,8.764053269,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/26/1991,78,tropicana,5312,8.577723691,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/26/1991,80,dominicks,5824,8.66974259,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/26/1991,80,minute.maid,10560,9.264828557,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/26/1991,80,tropicana,9088,9.114710141,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/26/1991,81,dominicks,9728,9.182763604,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/26/1991,81,minute.maid,9408,9.14931567,0,1.89,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/26/1991,81,tropicana,7744,8.954673629,0,3.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/26/1991,83,dominicks,7808,8.962904128,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/26/1991,83,minute.maid,7360,8.903815212,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/26/1991,83,tropicana,6144,8.723231275,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/26/1991,84,dominicks,9664,9.17616292,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/26/1991,84,minute.maid,8448,9.041685006,0,1.89,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/26/1991,84,tropicana,3840,8.253227646,0,3.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/26/1991,86,dominicks,12480,9.431882642,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/26/1991,86,minute.maid,8640,9.064157862,0,1.89,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/26/1991,86,tropicana,4928,8.502688505,0,3.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/26/1991,88,dominicks,7360,8.903815212,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/26/1991,88,minute.maid,7168,8.877381955,0,1.89,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/26/1991,88,tropicana,2816,7.943072717,0,3.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/26/1991,89,dominicks,10304,9.240287448,0,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/26/1991,89,minute.maid,6784,8.822322178,0,1.83,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/26/1991,89,tropicana,2112,7.655390645,0,3.19,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/26/1991,90,dominicks,7936,8.979164649,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/26/1991,90,minute.maid,9856,9.195835686,0,1.89,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/26/1991,90,tropicana,2176,7.685243608,0,3.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/26/1991,91,dominicks,9280,9.135616826,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/26/1991,91,minute.maid,6528,8.783855897,0,1.89,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/26/1991,91,tropicana,3200,8.070906089,0,3.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/26/1991,92,dominicks,11712,9.368369236,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/26/1991,92,minute.maid,8128,9.00307017,0,1.89,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/26/1991,92,tropicana,3264,8.090708716,0,3.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/26/1991,93,dominicks,6592,8.793612072,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/26/1991,93,minute.maid,6464,8.7740036,0,1.89,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/26/1991,93,tropicana,5824,8.66974259,0,3.59,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/26/1991,94,dominicks,15104,9.622714888,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/26/1991,94,minute.maid,12352,9.421573272,0,1.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/26/1991,94,tropicana,8256,9.018695488,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/26/1991,95,dominicks,9536,9.162829389,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/26/1991,95,minute.maid,12480,9.431882642,0,1.89,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/26/1991,95,tropicana,2688,7.896552702,0,3.59,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/26/1991,97,dominicks,4416,8.392989588,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/26/1991,97,minute.maid,6272,8.743850562,0,1.89,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/26/1991,97,tropicana,1472,7.294377299,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/26/1991,98,dominicks,18432,9.821843564,0,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/26/1991,98,minute.maid,10432,9.252633284,0,1.83,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/26/1991,98,tropicana,5248,8.565602331,0,3.21,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/26/1991,100,dominicks,14912,9.609921537,0,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/26/1991,100,minute.maid,13632,9.520175249,0,1.83,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/26/1991,100,tropicana,4928,8.502688505,0,3.42,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/26/1991,101,dominicks,11136,9.317938383,0,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/26/1991,101,minute.maid,8128,9.00307017,0,1.84,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/26/1991,101,tropicana,6080,8.712759975,0,3.22,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/26/1991,102,dominicks,24704,10.11472045,0,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/26/1991,102,minute.maid,14144,9.557045785,0,1.84,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/26/1991,102,tropicana,6720,8.812843434,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/26/1991,103,dominicks,11264,9.329367078,0,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/26/1991,103,minute.maid,12992,9.472089062,0,1.86,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/26/1991,103,tropicana,3008,8.009030685,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/26/1991,104,dominicks,6656,8.803273983,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/26/1991,104,minute.maid,6528,8.783855897,0,1.89,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/26/1991,104,tropicana,3840,8.253227646,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/26/1991,105,dominicks,20352,9.920934466,0,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/26/1991,105,minute.maid,12800,9.45720045,0,1.83,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/26/1991,105,tropicana,2880,7.965545573,0,3.22,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/26/1991,106,dominicks,6656,8.803273983,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/26/1991,106,minute.maid,4352,8.378390789,0,1.89,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/26/1991,106,tropicana,2240,7.714231145,0,3.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/26/1991,107,dominicks,2048,7.624618986,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/26/1991,107,minute.maid,14976,9.614204199,0,1.89,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/26/1991,107,tropicana,10880,9.29468152,0,3.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/26/1991,109,dominicks,9472,9.156095357,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/26/1991,109,minute.maid,17600,9.775654181,0,1.89,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/26/1991,109,tropicana,11648,9.36288977,0,3.59,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/26/1991,110,dominicks,9664,9.17616292,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/26/1991,110,minute.maid,7488,8.921057018,0,1.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/26/1991,110,tropicana,4416,8.392989588,0,3.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/26/1991,111,dominicks,10496,9.258749511,0,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/26/1991,111,minute.maid,17984,9.797237753,0,1.83,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/26/1991,111,tropicana,2688,7.896552702,0,3.47,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/26/1991,112,dominicks,13952,9.543378146,0,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/26/1991,112,minute.maid,16192,9.692272572,0,1.84,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/26/1991,112,tropicana,16384,9.704060528,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/26/1991,113,dominicks,19584,9.882468185,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/26/1991,113,minute.maid,11648,9.36288977,0,1.89,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/26/1991,113,tropicana,6720,8.812843434,0,3.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/26/1991,114,dominicks,27584,10.22499117,0,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/26/1991,114,minute.maid,13952,9.543378146,0,1.84,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/26/1991,114,tropicana,3840,8.253227646,0,3.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/26/1991,115,dominicks,11392,9.340666634,0,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/26/1991,115,minute.maid,14464,9.579418083,0,1.83,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/26/1991,115,tropicana,6976,8.850230966,0,3.21,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/26/1991,116,dominicks,8960,9.100525506,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/26/1991,116,minute.maid,8064,8.99516499,0,1.89,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/26/1991,116,tropicana,3392,8.129174997,0,3.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/26/1991,117,dominicks,7360,8.903815212,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/26/1991,117,minute.maid,7104,8.868413285,0,1.89,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/26/1991,117,tropicana,3008,8.009030685,0,3.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/26/1991,118,dominicks,6976,8.850230966,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/26/1991,118,minute.maid,3648,8.201934351,0,1.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/26/1991,118,tropicana,4672,8.449342525,0,3.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/26/1991,119,dominicks,3328,8.110126802,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/26/1991,119,minute.maid,9920,9.2023082,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/26/1991,119,tropicana,6656,8.803273983,0,3.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/26/1991,121,dominicks,8832,9.086136769,0,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/26/1991,121,minute.maid,16384,9.704060528,0,1.85,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/26/1991,121,tropicana,7424,8.912473275,0,3.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/26/1991,122,dominicks,10112,9.221478116,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/26/1991,122,minute.maid,10496,9.258749511,0,1.82,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/26/1991,122,tropicana,11328,9.335032816,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/26/1991,123,dominicks,10432,9.252633284,0,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/26/1991,123,minute.maid,32128,10.3774832,0,1.85,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/26/1991,123,tropicana,5056,8.528330936,0,3.43,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/26/1991,124,dominicks,46464,10.7464331,0,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/26/1991,124,minute.maid,10624,9.270870872,0,1.84,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/26/1991,124,tropicana,4992,8.51559191,0,3.44,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/26/1991,126,dominicks,18048,9.800790154,0,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/26/1991,126,minute.maid,10688,9.276876896,0,1.84,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/26/1991,126,tropicana,6464,8.7740036,0,3.22,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/26/1991,128,dominicks,16448,9.707959168,0,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/26/1991,128,minute.maid,16192,9.692272572,0,1.83,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/26/1991,128,tropicana,8448,9.041685006,0,3.4,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/26/1991,129,dominicks,6144,8.723231275,0,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/26/1991,129,minute.maid,16000,9.680344001,0,1.85,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/26/1991,129,tropicana,5120,8.540909718,0,3.18,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/26/1991,130,dominicks,52096,10.86084345,0,1.58,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/26/1991,130,minute.maid,16896,9.734832187,0,1.82,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/26/1991,130,tropicana,2752,7.920083199,0,3.4,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/26/1991,131,dominicks,8064,8.99516499,0,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/26/1991,131,minute.maid,10432,9.252633284,0,1.85,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/26/1991,131,tropicana,6016,8.702177866,0,3.23,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/26/1991,132,dominicks,20864,9.945780465,0,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/26/1991,132,minute.maid,12672,9.447150114,0,1.85,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/26/1991,132,tropicana,6208,8.733594062,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/26/1991,134,dominicks,5184,8.553332238,0,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/26/1991,134,minute.maid,4736,8.462948177,0,1.85,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/26/1991,134,tropicana,2176,7.685243608,0,3.05,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/26/1991,137,dominicks,4288,8.363575703,0,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/26/1991,137,minute.maid,16576,9.715711145,0,1.84,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/26/1991,137,tropicana,15616,9.656051309,0,3.41,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/3/1991,2,dominicks,9088,9.114710141,0,1.56,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/3/1991,2,minute.maid,13504,9.510741217,0,1.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/3/1991,2,tropicana,7744,8.954673629,0,3.14,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/3/1991,5,dominicks,8256,9.018695488,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/3/1991,5,minute.maid,12032,9.395325046,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/3/1991,5,tropicana,5440,8.60153434,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/3/1991,8,dominicks,16576,9.715711145,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/3/1991,8,minute.maid,13504,9.510741217,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/3/1991,8,tropicana,5248,8.565602331,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/3/1991,9,dominicks,11328,9.335032816,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/3/1991,9,minute.maid,12544,9.436997743,0,1.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/3/1991,9,tropicana,7104,8.868413285,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/3/1991,12,dominicks,11712,9.368369236,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/3/1991,12,minute.maid,23360,10.05878044,0,1.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/3/1991,12,tropicana,6912,8.841014311,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/3/1991,14,dominicks,7424,8.912473275,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/3/1991,14,minute.maid,14912,9.609921537,0,1.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/3/1991,14,tropicana,10432,9.252633284,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/3/1991,18,dominicks,14592,9.588228712,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/3/1991,18,minute.maid,16640,9.719564714,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/3/1991,18,tropicana,7168,8.877381955,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/3/1991,21,dominicks,12608,9.442086812,0,1.54,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/3/1991,21,minute.maid,7744,8.954673629,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/3/1991,21,tropicana,3648,8.201934351,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/3/1991,32,dominicks,19840,9.895455381,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/3/1991,32,minute.maid,15424,9.643680017,0,1.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/3/1991,32,tropicana,8000,8.987196821,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/3/1991,33,dominicks,15040,9.618468598,0,1.55,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/3/1991,33,minute.maid,12800,9.45720045,0,1.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/3/1991,33,tropicana,9344,9.142489705,0,3.14,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/3/1991,40,dominicks,9280,9.135616826,0,1.53,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/3/1991,40,minute.maid,7552,8.929567708,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/3/1991,40,tropicana,3968,8.286017468,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/3/1991,44,dominicks,9984,9.208739091,0,1.54,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/3/1991,44,minute.maid,11456,9.346268889,0,1.79,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/3/1991,44,tropicana,7616,8.938006577,0,2.97,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/3/1991,45,dominicks,6592,8.793612072,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/3/1991,45,minute.maid,11136,9.317938383,0,1.79,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/3/1991,45,tropicana,5120,8.540909718,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/3/1991,47,dominicks,11264,9.329367078,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/3/1991,47,minute.maid,11712,9.368369236,0,1.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/3/1991,47,tropicana,4672,8.449342525,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/3/1991,48,dominicks,2624,7.87245515,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/3/1991,48,minute.maid,10560,9.264828557,0,1.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/3/1991,48,tropicana,4736,8.462948177,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/3/1991,49,dominicks,7680,8.946374826,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/3/1991,49,minute.maid,8512,9.049232212,0,1.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/3/1991,49,tropicana,3328,8.110126802,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/3/1991,50,dominicks,6528,8.783855897,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/3/1991,50,minute.maid,7104,8.868413285,0,1.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/3/1991,50,tropicana,3136,8.050703382,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/3/1991,51,dominicks,8448,9.041685006,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/3/1991,51,minute.maid,9024,9.107642974,0,1.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/3/1991,51,tropicana,4288,8.363575703,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/3/1991,52,dominicks,7104,8.868413285,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/3/1991,52,minute.maid,22784,10.03381381,0,1.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/3/1991,52,tropicana,11712,9.368369236,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/3/1991,53,dominicks,9344,9.142489705,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/3/1991,53,minute.maid,24704,10.11472045,0,1.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/3/1991,53,tropicana,10624,9.270870872,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/3/1991,54,dominicks,6720,8.812843434,0,1.58,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/3/1991,54,minute.maid,9920,9.2023082,0,1.79,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/3/1991,54,tropicana,4608,8.435549202,0,2.97,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/3/1991,56,dominicks,9536,9.162829389,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/3/1991,56,minute.maid,7360,8.903815212,0,1.79,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/3/1991,56,tropicana,4672,8.449342525,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/3/1991,59,dominicks,11904,9.384629757,0,1.55,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/3/1991,59,minute.maid,6976,8.850230966,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/3/1991,59,tropicana,3328,8.110126802,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/3/1991,64,dominicks,6144,8.723231275,0,1.57,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/3/1991,64,minute.maid,6080,8.712759975,0,1.79,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/3/1991,64,tropicana,2304,7.742402022,0,2.97,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/3/1991,67,dominicks,15680,9.660141294,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/3/1991,67,minute.maid,9152,9.121727714,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/3/1991,67,tropicana,5056,8.528330936,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/3/1991,68,dominicks,10624,9.270870872,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/3/1991,68,minute.maid,16960,9.738612909,0,1.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/3/1991,68,tropicana,7104,8.868413285,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/3/1991,70,dominicks,10624,9.270870872,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/3/1991,70,minute.maid,13312,9.496421163,0,1.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/3/1991,70,tropicana,6080,8.712759975,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/3/1991,71,dominicks,8000,8.987196821,0,1.58,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/3/1991,71,minute.maid,9664,9.17616292,0,1.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/3/1991,71,tropicana,4032,8.30201781,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/3/1991,72,dominicks,3968,8.286017468,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/3/1991,72,minute.maid,14848,9.605620455,0,1.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/3/1991,72,tropicana,9664,9.17616292,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/3/1991,73,dominicks,23680,10.07238609,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/3/1991,73,minute.maid,19904,9.898675996,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/3/1991,73,tropicana,8000,8.987196821,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/3/1991,74,dominicks,22208,10.00820786,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/3/1991,74,minute.maid,16768,9.727227587,0,1.79,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/3/1991,74,tropicana,7936,8.979164649,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/3/1991,75,dominicks,12800,9.45720045,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/3/1991,75,minute.maid,17088,9.746131742,0,1.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/3/1991,75,tropicana,5760,8.658692754,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/3/1991,76,dominicks,8640,9.064157862,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/3/1991,76,minute.maid,18944,9.849242538,0,1.79,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/3/1991,76,tropicana,5760,8.658692754,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/3/1991,77,dominicks,5824,8.66974259,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/3/1991,77,minute.maid,9280,9.135616826,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/3/1991,77,tropicana,5824,8.66974259,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/3/1991,78,dominicks,10816,9.288781798,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/3/1991,78,minute.maid,8000,8.987196821,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/3/1991,78,tropicana,5504,8.61323038,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/3/1991,80,dominicks,8768,9.078864009,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/3/1991,80,minute.maid,13184,9.486759252,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/3/1991,80,tropicana,8256,9.018695488,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/3/1991,81,dominicks,12608,9.442086812,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/3/1991,81,minute.maid,10304,9.240287448,0,1.79,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/3/1991,81,tropicana,7168,8.877381955,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/3/1991,83,dominicks,46272,10.74229231,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/3/1991,83,minute.maid,10880,9.29468152,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/3/1991,83,tropicana,6528,8.783855897,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/3/1991,84,dominicks,9728,9.182763604,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/3/1991,84,minute.maid,10048,9.215128889,0,1.79,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/3/1991,84,tropicana,3584,8.184234774,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/3/1991,86,dominicks,13376,9.501217335,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/3/1991,86,minute.maid,10176,9.227787286,0,1.79,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/3/1991,86,tropicana,4608,8.435549202,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/3/1991,89,dominicks,11648,9.36288977,0,1.56,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/3/1991,89,minute.maid,12032,9.395325046,0,1.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/3/1991,89,tropicana,2496,7.82244473,0,2.97,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/3/1991,90,dominicks,8064,8.99516499,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/3/1991,90,minute.maid,11968,9.3899917,0,1.79,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/3/1991,90,tropicana,2304,7.742402022,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/3/1991,91,dominicks,9536,9.162829389,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/3/1991,91,minute.maid,6400,8.764053269,0,1.79,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/3/1991,91,tropicana,3584,8.184234774,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/3/1991,92,dominicks,9408,9.14931567,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/3/1991,92,minute.maid,11392,9.340666634,0,1.79,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/3/1991,92,tropicana,2880,7.965545573,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/3/1991,93,dominicks,4352,8.378390789,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/3/1991,93,minute.maid,7360,8.903815212,0,1.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/3/1991,93,tropicana,5888,8.68067166,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/3/1991,94,dominicks,13120,9.481893063,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/3/1991,94,minute.maid,10816,9.288781798,0,1.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/3/1991,94,tropicana,4352,8.378390789,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/3/1991,97,dominicks,4672,8.449342525,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/3/1991,97,minute.maid,8192,9.010913347,0,1.79,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/3/1991,97,tropicana,1408,7.249925537,0,3.16,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/3/1991,98,dominicks,15872,9.67231183,0,1.54,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/3/1991,98,minute.maid,23616,10.06967973,0,1.79,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/3/1991,98,tropicana,6464,8.7740036,0,2.96,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/3/1991,100,dominicks,20672,9.936535407,0,1.54,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/3/1991,100,minute.maid,19328,9.869310101,0,1.79,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/3/1991,100,tropicana,3328,8.110126802,0,3.14,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/3/1991,101,dominicks,13504,9.510741217,0,1.54,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/3/1991,101,minute.maid,11328,9.335032816,0,1.79,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/3/1991,101,tropicana,5952,8.691482577,0,2.95,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/3/1991,102,dominicks,26560,10.1871616,0,1.55,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/3/1991,102,minute.maid,16896,9.734832187,0,1.79,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/3/1991,102,tropicana,5888,8.68067166,0,2.97,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/3/1991,103,dominicks,13248,9.491601877,0,1.55,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/3/1991,103,minute.maid,6656,8.803273983,0,1.79,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/3/1991,103,tropicana,2368,7.769800996,0,2.97,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/3/1991,104,dominicks,5824,8.66974259,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/3/1991,104,minute.maid,7872,8.971067439,0,1.79,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/3/1991,104,tropicana,3392,8.129174997,0,3.11,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/3/1991,105,dominicks,21120,9.957975738,0,1.55,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/3/1991,105,minute.maid,17152,9.749870064,0,1.79,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/3/1991,105,tropicana,3520,8.166216269,0,2.96,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/3/1991,106,dominicks,6784,8.822322178,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/3/1991,106,minute.maid,6720,8.812843434,0,1.79,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/3/1991,106,tropicana,2432,7.796469243,0,3.1,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/3/1991,107,dominicks,5760,8.658692754,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/3/1991,107,minute.maid,17280,9.757305042,0,1.79,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/3/1991,107,tropicana,10240,9.234056899,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/3/1991,109,dominicks,5888,8.68067166,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/3/1991,109,minute.maid,16064,9.684336023,0,1.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/3/1991,109,tropicana,11840,9.379238908,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/3/1991,111,dominicks,26560,10.1871616,0,1.53,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/3/1991,111,minute.maid,19264,9.865993348,0,1.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/3/1991,111,tropicana,2304,7.742402022,0,3.12,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/3/1991,112,dominicks,11840,9.379238908,0,1.55,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/3/1991,112,minute.maid,20032,9.905086274,0,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/3/1991,112,tropicana,10048,9.215128889,0,2.97,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/3/1991,113,dominicks,9728,9.182763604,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/3/1991,113,minute.maid,12800,9.45720045,0,1.79,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/3/1991,113,tropicana,7424,8.912473275,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/3/1991,114,dominicks,28032,10.24110199,0,1.55,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/3/1991,114,minute.maid,16000,9.680344001,0,1.79,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/3/1991,114,tropicana,4288,8.363575703,0,2.97,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/3/1991,115,dominicks,7168,8.877381955,0,1.55,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/3/1991,115,minute.maid,11392,9.340666634,0,1.79,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/3/1991,115,tropicana,7744,8.954673629,0,2.97,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/3/1991,116,dominicks,9472,9.156095357,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/3/1991,116,minute.maid,7296,8.895081532,0,1.79,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/3/1991,116,tropicana,7168,8.877381955,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/3/1991,117,dominicks,5504,8.61323038,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/3/1991,117,minute.maid,6656,8.803273983,0,1.79,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/3/1991,117,tropicana,3008,8.009030685,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/3/1991,118,dominicks,6848,8.831711918,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/3/1991,118,minute.maid,16320,9.700146629,0,1.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/3/1991,118,tropicana,5248,8.565602331,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/3/1991,119,dominicks,5824,8.66974259,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/3/1991,119,minute.maid,10368,9.246479419,0,1.79,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/3/1991,119,tropicana,6528,8.783855897,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/3/1991,121,dominicks,8128,9.00307017,0,1.55,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/3/1991,121,minute.maid,26368,10.17990643,0,1.79,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/3/1991,121,tropicana,7488,8.921057018,0,2.97,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/3/1991,122,dominicks,11648,9.36288977,0,1.55,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/3/1991,122,minute.maid,11392,9.340666634,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/3/1991,122,tropicana,6976,8.850230966,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/3/1991,123,dominicks,11136,9.317938383,0,1.54,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/3/1991,123,minute.maid,13504,9.510741217,0,1.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/3/1991,123,tropicana,4416,8.392989588,0,3.13,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/3/1991,124,dominicks,40064,10.59823345,0,1.55,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/3/1991,124,minute.maid,15040,9.618468598,0,1.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/3/1991,124,tropicana,4800,8.476371197,0,3.14,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/3/1991,126,dominicks,14720,9.596962392,0,1.57,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/3/1991,126,minute.maid,11520,9.351839934,0,1.79,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/3/1991,126,tropicana,6400,8.764053269,0,2.97,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/3/1991,128,dominicks,17600,9.775654181,0,1.55,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/3/1991,128,minute.maid,20992,9.951896692,0,1.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/3/1991,128,tropicana,10240,9.234056899,0,3.14,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/3/1991,129,dominicks,8704,9.071537969,0,1.54,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/3/1991,129,minute.maid,9344,9.142489705,0,1.79,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/3/1991,129,tropicana,6208,8.733594062,0,2.97,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/3/1991,130,dominicks,12672,9.447150114,0,1.52,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/3/1991,130,minute.maid,72192,11.18708452,0,1.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/3/1991,130,tropicana,4032,8.30201781,0,3.14,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/3/1991,131,dominicks,8512,9.049232212,0,1.54,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/3/1991,131,minute.maid,8704,9.071537969,0,1.79,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/3/1991,131,tropicana,6784,8.822322178,0,2.97,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/3/1991,132,dominicks,17664,9.779283949,0,1.55,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/3/1991,132,minute.maid,14912,9.609921537,0,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/3/1991,132,tropicana,5504,8.61323038,0,2.97,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/3/1991,137,dominicks,9024,9.107642974,0,1.53,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/3/1991,137,minute.maid,22912,10.03941607,0,1.79,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/3/1991,137,tropicana,16256,9.69621735,0,3.14,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/10/1991,2,dominicks,22848,10.03661887,1,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/10/1991,2,minute.maid,10048,9.215128889,0,1.91,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/10/1991,2,tropicana,6784,8.822322178,0,3.07,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/10/1991,5,dominicks,28672,10.26367632,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/10/1991,5,minute.maid,13440,9.505990614,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/10/1991,5,tropicana,8128,9.00307017,0,2.94,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/10/1991,8,dominicks,49664,10.8130356,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/10/1991,8,minute.maid,13504,9.510741217,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/10/1991,8,tropicana,6592,8.793612072,0,2.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/10/1991,9,dominicks,38528,10.55914053,1,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/10/1991,9,minute.maid,10432,9.252633284,0,1.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/10/1991,9,tropicana,6976,8.850230966,0,2.94,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/10/1991,12,dominicks,69760,11.15281606,1,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/10/1991,12,minute.maid,20480,9.927204079,0,1.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/10/1991,12,tropicana,9344,9.142489705,0,3.07,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/10/1991,14,dominicks,27776,10.23192762,1,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/10/1991,14,minute.maid,10368,9.246479419,0,1.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/10/1991,14,tropicana,13568,9.515469358,0,3.07,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/10/1991,18,dominicks,48192,10.78294831,1,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/10/1991,18,minute.maid,12736,9.452187908,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/10/1991,18,tropicana,7168,8.877381955,0,2.94,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/10/1991,21,dominicks,27136,10.20861654,1,1.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/10/1991,21,minute.maid,7104,8.868413285,0,1.78,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/10/1991,21,tropicana,3456,8.14786713,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/10/1991,32,dominicks,46848,10.7546636,1,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/10/1991,32,minute.maid,10368,9.246479419,0,1.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/10/1991,32,tropicana,12096,9.400630098,0,3.07,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/10/1991,40,dominicks,42496,10.65716523,1,1.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/10/1991,40,minute.maid,8448,9.041685006,0,1.8,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/10/1991,40,tropicana,3456,8.14786713,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/10/1991,44,dominicks,28608,10.26144168,1,1.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/10/1991,44,minute.maid,9408,9.14931567,0,1.9,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/10/1991,44,tropicana,9216,9.128696383,0,2.94,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/10/1991,45,dominicks,15808,9.66827142,1,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/10/1991,45,minute.maid,10368,9.246479419,0,1.79,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/10/1991,45,tropicana,5696,8.647519453,0,2.94,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/10/1991,47,dominicks,26496,10.18474906,1,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/10/1991,47,minute.maid,15488,9.64782081,0,1.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/10/1991,47,tropicana,3392,8.129174997,0,2.94,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/10/1991,48,dominicks,12416,9.426741242,1,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/10/1991,48,minute.maid,10112,9.221478116,0,1.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/10/1991,48,tropicana,6016,8.702177866,0,2.94,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/10/1991,49,dominicks,16768,9.727227587,1,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/10/1991,49,minute.maid,7872,8.971067439,0,1.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/10/1991,49,tropicana,3392,8.129174997,0,2.94,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/10/1991,51,dominicks,27584,10.22499117,1,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/10/1991,51,minute.maid,8512,9.049232212,0,1.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/10/1991,51,tropicana,3904,8.269756948,0,3.04,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/10/1991,52,dominicks,16192,9.692272572,1,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/10/1991,52,minute.maid,10560,9.264828557,0,1.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/10/1991,52,tropicana,11584,9.357380115,0,3.07,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/10/1991,53,dominicks,45888,10.73395892,1,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/10/1991,53,minute.maid,18752,9.839055692,0,1.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/10/1991,53,tropicana,9920,9.2023082,0,3.07,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/10/1991,54,dominicks,22656,10.02818,1,1.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/10/1991,54,minute.maid,8448,9.041685006,0,1.88,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/10/1991,54,tropicana,6400,8.764053269,0,2.94,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/10/1991,56,dominicks,25792,10.15781965,1,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/10/1991,56,minute.maid,5632,8.636219898,0,1.79,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/10/1991,56,tropicana,4608,8.435549202,0,2.94,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/10/1991,59,dominicks,25088,10.13014492,1,1.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/10/1991,59,minute.maid,5056,8.528330936,0,1.8,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/10/1991,59,tropicana,4608,8.435549202,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/10/1991,62,dominicks,15872,9.67231183,1,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/10/1991,62,minute.maid,11392,9.340666634,0,1.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/10/1991,62,tropicana,13376,9.501217335,0,3.07,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/10/1991,64,dominicks,16512,9.711842668,1,1.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/10/1991,64,minute.maid,5760,8.658692754,0,1.93,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/10/1991,64,tropicana,1344,7.203405521,0,2.94,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/10/1991,67,dominicks,25088,10.13014492,1,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/10/1991,67,minute.maid,7744,8.954673629,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/10/1991,67,tropicana,4864,8.489616424,0,2.94,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/10/1991,68,dominicks,27328,10.2156671,1,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/10/1991,68,minute.maid,15168,9.626943225,0,1.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/10/1991,68,tropicana,7168,8.877381955,0,3.07,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/10/1991,70,dominicks,27264,10.21332243,1,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/10/1991,70,minute.maid,10368,9.246479419,0,1.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/10/1991,70,tropicana,5760,8.658692754,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/10/1991,71,dominicks,33984,10.43364511,1,1.58,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/10/1991,71,minute.maid,9280,9.135616826,0,1.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/10/1991,71,tropicana,4224,8.348537825,0,3.07,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/10/1991,72,dominicks,21248,9.964018052,1,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/10/1991,72,minute.maid,15744,9.664214619,0,1.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/10/1991,72,tropicana,8000,8.987196821,0,3.07,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/10/1991,73,dominicks,68544,11.13523115,1,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/10/1991,73,minute.maid,17344,9.761001904,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/10/1991,73,tropicana,7488,8.921057018,0,2.94,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/10/1991,74,dominicks,57216,10.95458886,1,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/10/1991,74,minute.maid,11392,9.340666634,0,1.79,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/10/1991,74,tropicana,7936,8.979164649,0,2.94,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/10/1991,75,dominicks,36672,10.5097688,1,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/10/1991,75,minute.maid,12928,9.467150781,0,1.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/10/1991,75,tropicana,6144,8.723231275,0,3.07,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/10/1991,76,dominicks,57408,10.95793895,1,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/10/1991,76,minute.maid,11904,9.384629757,0,1.79,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/10/1991,76,tropicana,6144,8.723231275,0,2.94,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/10/1991,77,dominicks,15552,9.651944527,1,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/10/1991,77,minute.maid,8128,9.00307017,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/10/1991,77,tropicana,5568,8.624791202,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/10/1991,78,dominicks,37120,10.52191119,1,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/10/1991,78,minute.maid,8512,9.049232212,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/10/1991,78,tropicana,5184,8.553332238,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/10/1991,80,dominicks,23552,10.06696602,1,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/10/1991,80,minute.maid,12480,9.431882642,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/10/1991,80,tropicana,9728,9.182763604,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/10/1991,81,dominicks,42304,10.65263692,1,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/10/1991,81,minute.maid,10432,9.252633284,0,1.79,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/10/1991,81,tropicana,7360,8.903815212,0,2.94,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/10/1991,83,dominicks,52224,10.86329744,1,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/10/1991,83,minute.maid,9344,9.142489705,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/10/1991,83,tropicana,5824,8.66974259,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/10/1991,84,dominicks,29760,10.30092049,1,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/10/1991,84,minute.maid,9216,9.128696383,0,1.79,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/10/1991,84,tropicana,4864,8.489616424,0,2.94,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/10/1991,86,dominicks,44800,10.70996342,1,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/10/1991,86,minute.maid,7936,8.979164649,0,1.79,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/10/1991,86,tropicana,4992,8.51559191,0,2.94,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/10/1991,88,dominicks,27840,10.23422911,1,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/10/1991,88,minute.maid,6272,8.743850562,0,1.79,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/10/1991,88,tropicana,3264,8.090708716,0,2.94,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/10/1991,89,dominicks,46208,10.74090822,1,1.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/10/1991,89,minute.maid,9408,9.14931567,0,1.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/10/1991,89,tropicana,2496,7.82244473,0,2.94,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/10/1991,90,dominicks,34752,10.4559924,1,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/10/1991,90,minute.maid,8640,9.064157862,0,1.79,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/10/1991,90,tropicana,2112,7.655390645,0,3.07,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/10/1991,91,dominicks,34368,10.44488118,1,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/10/1991,91,minute.maid,4864,8.489616424,0,1.79,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/10/1991,91,tropicana,3648,8.201934351,0,2.94,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/10/1991,92,dominicks,32192,10.37947325,1,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/10/1991,92,minute.maid,7744,8.954673629,0,1.79,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/10/1991,92,tropicana,3904,8.269756948,0,2.94,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/10/1991,94,dominicks,23040,10.04498712,1,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/10/1991,94,minute.maid,10880,9.29468152,0,1.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/10/1991,94,tropicana,6464,8.7740036,0,2.94,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/10/1991,95,dominicks,26432,10.18233068,1,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/10/1991,95,minute.maid,11648,9.36288977,0,1.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/10/1991,95,tropicana,1920,7.560080465,0,3.07,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/10/1991,97,dominicks,14080,9.55251063,1,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/10/1991,97,minute.maid,5184,8.553332238,0,1.79,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/10/1991,97,tropicana,1408,7.249925537,0,3.1,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/10/1991,98,dominicks,54656,10.90881428,1,1.49,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/10/1991,98,minute.maid,7936,8.979164649,0,1.88,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/10/1991,98,tropicana,6400,8.764053269,0,2.94,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/10/1991,100,dominicks,60288,11.00688836,1,1.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/10/1991,100,minute.maid,14144,9.557045785,0,1.9,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/10/1991,100,tropicana,5440,8.60153434,0,3.07,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/10/1991,101,dominicks,38016,10.5457624,1,1.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/10/1991,101,minute.maid,7744,8.954673629,0,1.87,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/10/1991,101,tropicana,8128,9.00307017,0,2.94,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/10/1991,102,dominicks,82624,11.32205547,1,1.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/10/1991,102,minute.maid,11264,9.329367078,0,1.83,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/10/1991,102,tropicana,6784,8.822322178,0,2.94,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/10/1991,103,dominicks,28736,10.26590597,1,1.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/10/1991,103,minute.maid,7296,8.895081532,0,1.85,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/10/1991,103,tropicana,1920,7.560080465,0,2.94,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/10/1991,104,dominicks,18560,9.828764006,1,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/10/1991,104,minute.maid,6144,8.723231275,0,1.79,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/10/1991,104,tropicana,2560,7.847762538,0,3.07,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/10/1991,105,dominicks,46208,10.74090822,1,1.49,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/10/1991,105,minute.maid,10432,9.252633284,0,1.9,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/10/1991,105,tropicana,4096,8.317766167,0,2.94,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/10/1991,106,dominicks,18624,9.832206351,1,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/10/1991,106,minute.maid,4608,8.435549202,0,1.79,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/10/1991,106,tropicana,1792,7.491087594,0,3.08,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/10/1991,107,dominicks,34304,10.44301724,1,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/10/1991,107,minute.maid,9088,9.114710141,0,1.79,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/10/1991,107,tropicana,11968,9.3899917,0,2.94,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/10/1991,109,dominicks,42560,10.65867012,1,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/10/1991,109,minute.maid,16576,9.715711145,0,1.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/10/1991,109,tropicana,14080,9.55251063,0,3.07,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/10/1991,110,dominicks,30208,10.31586207,1,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/10/1991,110,minute.maid,9408,9.14931567,0,1.79,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/10/1991,110,tropicana,5376,8.589699882,0,2.94,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/10/1991,111,dominicks,108992,11.59902976,1,1.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/10/1991,111,minute.maid,14848,9.605620455,0,1.98,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/10/1991,111,tropicana,3200,8.070906089,0,3.07,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/10/1991,112,dominicks,27392,10.21800628,1,1.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/10/1991,112,minute.maid,18240,9.811372264,0,1.72,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/10/1991,112,tropicana,9536,9.162829389,0,2.94,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/10/1991,113,dominicks,37568,10.5339079,1,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/10/1991,113,minute.maid,10176,9.227787286,0,1.79,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/10/1991,113,tropicana,12160,9.405907156,0,2.94,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/10/1991,114,dominicks,71552,11.17817974,1,1.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/10/1991,114,minute.maid,12480,9.431882642,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/10/1991,114,tropicana,5376,8.589699882,0,2.94,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/10/1991,115,dominicks,31808,10.36747311,1,1.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/10/1991,115,minute.maid,5504,8.61323038,0,2,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/10/1991,115,tropicana,7104,8.868413285,0,2.94,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/10/1991,116,dominicks,29568,10.29444797,1,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/10/1991,116,minute.maid,7936,8.979164649,0,1.79,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/10/1991,116,tropicana,5440,8.60153434,0,2.94,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/10/1991,117,dominicks,19584,9.882468185,1,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/10/1991,117,minute.maid,5504,8.61323038,0,1.79,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/10/1991,117,tropicana,4032,8.30201781,0,2.94,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/10/1991,118,dominicks,16768,9.727227587,1,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/10/1991,118,minute.maid,13056,9.477003077,0,1.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/10/1991,118,tropicana,7360,8.903815212,0,3.07,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/10/1991,119,dominicks,17984,9.797237753,1,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/10/1991,119,minute.maid,10368,9.246479419,0,1.79,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/10/1991,119,tropicana,7808,8.962904128,0,2.94,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/10/1991,121,dominicks,32000,10.37349118,1,1.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/10/1991,121,minute.maid,10304,9.240287448,0,1.94,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/10/1991,121,tropicana,7744,8.954673629,0,2.94,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/10/1991,122,dominicks,40576,10.61093204,1,1.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/10/1991,122,minute.maid,11904,9.384629757,0,1.81,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/10/1991,122,tropicana,8576,9.056722883,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/10/1991,123,dominicks,75968,11.23806748,1,1.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/10/1991,123,minute.maid,11072,9.312174678,0,1.93,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/10/1991,123,tropicana,5312,8.577723691,0,3.07,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/10/1991,124,dominicks,61248,11.02268648,1,1.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/10/1991,124,minute.maid,10112,9.221478116,0,1.95,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/10/1991,124,tropicana,5504,8.61323038,0,3.07,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/10/1991,126,dominicks,27136,10.20861654,1,1.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/10/1991,126,minute.maid,9664,9.17616292,0,1.93,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/10/1991,126,tropicana,8768,9.078864009,0,2.94,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/10/1991,128,dominicks,79424,11.28255587,1,1.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/10/1991,128,minute.maid,18048,9.800790154,0,1.88,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/10/1991,128,tropicana,11072,9.312174678,0,3.07,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/10/1991,129,dominicks,20160,9.911455722,1,1.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/10/1991,129,minute.maid,8448,9.041685006,0,1.87,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/10/1991,129,tropicana,7296,8.895081532,0,2.94,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/10/1991,130,dominicks,95552,11.46742588,1,1.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/10/1991,130,minute.maid,45568,10.726961,0,1.4,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/10/1991,130,tropicana,4160,8.333270353,0,3.07,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/10/1991,131,dominicks,34240,10.44114983,1,1.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/10/1991,131,minute.maid,10176,9.227787286,0,1.92,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/10/1991,131,tropicana,7680,8.946374826,0,2.94,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/10/1991,132,dominicks,62720,11.04643566,1,1.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/10/1991,132,minute.maid,12672,9.447150114,0,1.85,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/10/1991,132,tropicana,6144,8.723231275,0,2.94,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/10/1991,134,dominicks,20032,9.905086274,1,1.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/10/1991,134,minute.maid,3968,8.286017468,0,1.95,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/10/1991,134,tropicana,2112,7.655390645,0,2.73,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/10/1991,137,dominicks,47488,10.76823233,1,1.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/10/1991,137,minute.maid,27456,10.22034,0,1.94,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/10/1991,137,tropicana,17152,9.749870064,0,3.07,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/17/1991,2,dominicks,6976,8.850230966,0,1.65,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/17/1991,2,minute.maid,135936,11.81993947,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/17/1991,2,tropicana,6784,8.822322178,0,3.07,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/17/1991,8,dominicks,10752,9.282847063,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/17/1991,8,minute.maid,335808,12.72429485,1,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/17/1991,8,tropicana,5888,8.68067166,0,2.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/17/1991,9,dominicks,6464,8.7740036,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/17/1991,9,minute.maid,228224,12.33808288,1,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/17/1991,9,tropicana,8192,9.010913347,0,2.94,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/17/1991,12,dominicks,9792,9.189321005,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/17/1991,12,minute.maid,262144,12.47664925,1,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/17/1991,12,tropicana,7488,8.921057018,0,3.07,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/17/1991,14,dominicks,5760,8.658692754,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/17/1991,14,minute.maid,146176,11.89256665,1,1.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/17/1991,14,tropicana,11328,9.335032816,0,3.07,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/17/1991,18,dominicks,9728,9.182763604,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/17/1991,18,minute.maid,255680,12.45168194,1,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/17/1991,18,tropicana,8384,9.034080407,0,2.94,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/17/1991,21,dominicks,7360,8.903815212,0,1.51,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/17/1991,21,minute.maid,171520,12.05245516,1,1.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/17/1991,21,tropicana,2688,7.896552702,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/17/1991,28,dominicks,5504,8.61323038,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/17/1991,28,minute.maid,97792,11.49059805,1,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/17/1991,28,tropicana,2880,7.965545573,0,2.94,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/17/1991,32,dominicks,14272,9.566054855,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/17/1991,32,minute.maid,194624,12.17882477,1,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/17/1991,32,tropicana,10112,9.221478116,0,3.07,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/17/1991,33,dominicks,7424,8.912473275,0,1.56,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/17/1991,33,minute.maid,99968,11.51260541,1,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/17/1991,33,tropicana,11840,9.379238908,0,3.07,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/17/1991,40,dominicks,7616,8.938006577,0,1.52,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/17/1991,40,minute.maid,213952,12.27350697,1,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/17/1991,40,tropicana,3200,8.070906089,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/17/1991,44,dominicks,5760,8.658692754,0,1.52,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/17/1991,44,minute.maid,192128,12.1659171,1,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/17/1991,44,tropicana,8512,9.049232212,0,2.94,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/17/1991,45,dominicks,2816,7.943072717,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/17/1991,45,minute.maid,80064,11.29058159,1,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/17/1991,45,tropicana,5056,8.528330936,0,2.94,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/17/1991,47,dominicks,6016,8.702177866,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/17/1991,47,minute.maid,142080,11.86414556,1,1.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/17/1991,47,tropicana,3776,8.236420527,0,2.94,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/17/1991,48,dominicks,3072,8.030084094,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 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-10/17/1991,54,minute.maid,106880,11.57946199,1,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/17/1991,54,tropicana,4736,8.462948177,0,2.94,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/17/1991,56,dominicks,4544,8.42156296,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/17/1991,56,minute.maid,124480,11.73190034,1,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/17/1991,56,tropicana,4096,8.317766167,0,2.94,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 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-10/17/1991,62,tropicana,13760,9.529521112,0,3.07,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/17/1991,64,dominicks,9088,9.114710141,0,1.51,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/17/1991,64,minute.maid,109696,11.60546818,1,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/17/1991,64,tropicana,1920,7.560080465,0,2.94,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/17/1991,67,dominicks,2432,7.796469243,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 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-10/17/1991,71,tropicana,5888,8.68067166,0,3.07,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/17/1991,72,dominicks,4416,8.392989588,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/17/1991,72,minute.maid,207104,12.24097636,1,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/17/1991,72,tropicana,8064,8.99516499,0,3.07,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/17/1991,73,dominicks,17216,9.753594463,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/17/1991,73,minute.maid,408192,12.91949293,1,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/17/1991,73,tropicana,8320,9.026417534,0,2.94,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/17/1991,74,dominicks,12480,9.431882642,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/17/1991,74,minute.maid,304384,12.62604534,1,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/17/1991,74,tropicana,7296,8.895081532,0,2.94,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/17/1991,75,dominicks,7680,8.946374826,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/17/1991,75,minute.maid,197440,12.19319002,1,1.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/17/1991,75,tropicana,7360,8.903815212,0,3.07,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/17/1991,76,dominicks,13056,9.477003077,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/17/1991,76,minute.maid,212672,12.26750635,1,1.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/17/1991,76,tropicana,6400,8.764053269,0,2.94,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/17/1991,77,dominicks,5888,8.68067166,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/17/1991,77,minute.maid,114624,11.64941249,1,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/17/1991,77,tropicana,6848,8.831711918,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/17/1991,78,dominicks,7424,8.912473275,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/17/1991,78,minute.maid,222208,12.31136916,1,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/17/1991,78,tropicana,6272,8.743850562,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/17/1991,80,dominicks,6528,8.783855897,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/17/1991,80,minute.maid,197120,12.19156796,1,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/17/1991,80,tropicana,9088,9.114710141,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/17/1991,81,dominicks,12480,9.431882642,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/17/1991,81,minute.maid,161408,11.9916906,1,1.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/17/1991,81,tropicana,8704,9.071537969,0,2.94,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/17/1991,83,dominicks,7552,8.929567708,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/17/1991,83,minute.maid,315136,12.66075957,1,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 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-10/17/1991,86,minute.maid,157760,11.96883017,1,1.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/17/1991,86,tropicana,6080,8.712759975,0,2.94,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/17/1991,88,dominicks,4736,8.462948177,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/17/1991,88,minute.maid,102208,11.53476523,1,1.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/17/1991,88,tropicana,3072,8.030084094,0,2.94,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 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-10/17/1991,92,minute.maid,205824,12.23477671,1,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/17/1991,92,tropicana,3008,8.009030685,0,2.94,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/17/1991,93,dominicks,7040,8.859363449,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/17/1991,93,minute.maid,123136,11.72104472,1,1.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/17/1991,93,tropicana,7616,8.938006577,0,3.07,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/17/1991,94,dominicks,6912,8.841014311,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/17/1991,94,minute.maid,151296,11.92699346,1,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/17/1991,94,tropicana,5568,8.624791202,0,2.94,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/17/1991,95,dominicks,6400,8.764053269,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/17/1991,95,minute.maid,152896,11.93751323,1,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/17/1991,95,tropicana,2240,7.714231145,0,3.07,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/17/1991,97,dominicks,7872,8.971067439,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/17/1991,97,minute.maid,81664,11.31036855,1,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/17/1991,97,tropicana,1792,7.491087594,0,3.1,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/17/1991,98,dominicks,14592,9.588228712,0,1.53,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/17/1991,98,minute.maid,271808,12.51285121,1,1.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/17/1991,98,tropicana,6912,8.841014311,0,2.94,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/17/1991,100,dominicks,10624,9.270870872,0,1.63,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/17/1991,100,minute.maid,272000,12.51355735,1,1.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/17/1991,100,tropicana,4736,8.462948177,0,3.07,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 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-10/17/1991,104,minute.maid,104000,11.55214618,1,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/17/1991,104,tropicana,3328,8.110126802,0,3.1,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/17/1991,105,dominicks,15488,9.64782081,0,1.53,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/17/1991,105,minute.maid,194880,12.18013926,1,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/17/1991,105,tropicana,3264,8.090708716,0,2.94,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 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-10/17/1991,107,tropicana,11392,9.340666634,0,2.94,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/17/1991,109,dominicks,5888,8.68067166,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/17/1991,109,minute.maid,144000,11.87756858,1,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/17/1991,109,tropicana,16064,9.684336023,0,3.07,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/17/1991,110,dominicks,8064,8.99516499,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 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-10/17/1991,115,minute.maid,154240,11.94626511,1,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/17/1991,115,tropicana,7168,8.877381955,0,2.94,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/17/1991,116,dominicks,13696,9.524859098,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/17/1991,116,minute.maid,140416,11.85236472,1,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/17/1991,116,tropicana,5376,8.589699882,0,2.94,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/17/1991,117,dominicks,6976,8.850230966,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/17/1991,117,minute.maid,105280,11.56437875,1,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/17/1991,117,tropicana,4864,8.489616424,0,2.94,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/17/1991,118,dominicks,1984,7.592870288,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/17/1991,118,minute.maid,207872,12.24467779,1,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 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-10/17/1991,121,minute.maid,197312,12.19254151,1,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/17/1991,121,tropicana,10624,9.270870872,0,2.94,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/17/1991,122,dominicks,15616,9.656051309,0,1.51,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/17/1991,122,minute.maid,96064,11.47276992,1,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/17/1991,122,tropicana,8448,9.041685006,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 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-10/17/1991,137,minute.maid,206720,12.2391205,1,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/17/1991,137,tropicana,21184,9.961001459,0,3.07,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/24/1991,2,dominicks,4160,8.333270353,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/24/1991,2,minute.maid,5056,8.528330936,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/24/1991,2,tropicana,6272,8.743850562,0,3.07,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/24/1991,5,dominicks,4416,8.392989588,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/24/1991,5,minute.maid,5824,8.66974259,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/24/1991,5,tropicana,7232,8.886270902,0,2.94,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/24/1991,8,dominicks,9792,9.189321005,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/24/1991,8,minute.maid,13120,9.481893063,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/24/1991,8,tropicana,6336,8.754002934,0,2.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/24/1991,9,dominicks,5632,8.636219898,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/24/1991,9,minute.maid,5888,8.68067166,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/24/1991,9,tropicana,8000,8.987196821,0,2.94,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/24/1991,12,dominicks,4992,8.51559191,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/24/1991,12,minute.maid,11904,9.384629757,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/24/1991,12,tropicana,9792,9.189321005,0,3.07,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/24/1991,14,dominicks,3456,8.14786713,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/24/1991,14,minute.maid,6080,8.712759975,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/24/1991,14,tropicana,12288,9.416378455,0,3.07,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/24/1991,18,dominicks,8512,9.049232212,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/24/1991,18,minute.maid,13824,9.534161491,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/24/1991,18,tropicana,8640,9.064157862,0,2.94,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/24/1991,21,dominicks,9792,9.189321005,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/24/1991,21,minute.maid,6912,8.841014311,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/24/1991,21,tropicana,3392,8.129174997,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/24/1991,28,dominicks,2048,7.624618986,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/24/1991,28,minute.maid,3328,8.110126802,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/24/1991,28,tropicana,4160,8.333270353,0,2.94,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/24/1991,32,dominicks,6848,8.831711918,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/24/1991,32,minute.maid,9472,9.156095357,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/24/1991,32,tropicana,11776,9.373818841,0,3.07,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/24/1991,33,dominicks,4544,8.42156296,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/24/1991,33,minute.maid,5632,8.636219898,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/24/1991,33,tropicana,12032,9.395325046,0,3.07,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/24/1991,40,dominicks,7296,8.895081532,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/24/1991,40,minute.maid,9280,9.135616826,0,1.78,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/24/1991,40,tropicana,3712,8.219326094,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/24/1991,44,dominicks,3840,8.253227646,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/24/1991,44,minute.maid,9216,9.128696383,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/24/1991,44,tropicana,8128,9.00307017,0,2.94,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/24/1991,45,dominicks,3648,8.201934351,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/24/1991,45,minute.maid,4224,8.348537825,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/24/1991,45,tropicana,5632,8.636219898,0,2.94,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/24/1991,47,dominicks,7424,8.912473275,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/24/1991,47,minute.maid,4736,8.462948177,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/24/1991,47,tropicana,4480,8.407378325,0,2.94,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/24/1991,48,dominicks,3776,8.236420527,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/24/1991,48,minute.maid,4800,8.476371197,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/24/1991,48,tropicana,5504,8.61323038,0,2.94,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/24/1991,49,dominicks,4864,8.489616424,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/24/1991,49,minute.maid,6144,8.723231275,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/24/1991,49,tropicana,4288,8.363575703,0,2.94,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/24/1991,50,dominicks,4032,8.30201781,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/24/1991,50,minute.maid,8064,8.99516499,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/24/1991,50,tropicana,3584,8.184234774,0,2.94,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/24/1991,51,dominicks,4224,8.348537825,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/24/1991,51,minute.maid,4160,8.333270353,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/24/1991,51,tropicana,3840,8.253227646,0,3.04,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/24/1991,52,dominicks,3648,8.201934351,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/24/1991,52,minute.maid,8704,9.071537969,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/24/1991,52,tropicana,8832,9.086136769,0,3.07,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/24/1991,53,dominicks,6528,8.783855897,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/24/1991,53,minute.maid,8256,9.018695488,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/24/1991,53,tropicana,9088,9.114710141,0,3.07,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/24/1991,54,dominicks,3392,8.129174997,0,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/24/1991,54,minute.maid,4736,8.462948177,0,2.24,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/24/1991,54,tropicana,6400,8.764053269,0,2.94,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/24/1991,56,dominicks,3136,8.050703382,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/24/1991,56,minute.maid,5760,8.658692754,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/24/1991,56,tropicana,5248,8.565602331,0,2.94,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/24/1991,59,dominicks,4864,8.489616424,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/24/1991,59,minute.maid,6208,8.733594062,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/24/1991,59,tropicana,5056,8.528330936,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/24/1991,62,dominicks,2944,7.98752448,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/24/1991,62,minute.maid,6400,8.764053269,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/24/1991,62,tropicana,13056,9.477003077,0,3.07,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/24/1991,64,dominicks,4288,8.363575703,0,1.89,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/24/1991,64,minute.maid,2432,7.796469243,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/24/1991,64,tropicana,1856,7.526178913,0,2.94,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/24/1991,67,dominicks,4800,8.476371197,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/24/1991,67,minute.maid,8128,9.00307017,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/24/1991,67,tropicana,6016,8.702177866,0,2.94,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/24/1991,68,dominicks,4160,8.333270353,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/24/1991,68,minute.maid,9536,9.162829389,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/24/1991,68,tropicana,7232,8.886270902,0,3.07,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/24/1991,70,dominicks,12416,9.426741242,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/24/1991,70,minute.maid,8192,9.010913347,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/24/1991,70,tropicana,7040,8.859363449,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/24/1991,72,dominicks,2752,7.920083199,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/24/1991,72,minute.maid,6656,8.803273983,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/24/1991,72,tropicana,7808,8.962904128,0,3.07,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/24/1991,73,dominicks,13568,9.515469358,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/24/1991,73,minute.maid,13888,9.538780437,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/24/1991,73,tropicana,8064,8.99516499,0,2.94,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/24/1991,74,dominicks,12736,9.452187908,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/24/1991,74,minute.maid,9408,9.14931567,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/24/1991,74,tropicana,7296,8.895081532,0,2.94,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/24/1991,75,dominicks,4160,8.333270353,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/24/1991,75,minute.maid,7488,8.921057018,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/24/1991,75,tropicana,5632,8.636219898,0,3.07,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/24/1991,76,dominicks,6720,8.812843434,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/24/1991,76,minute.maid,4352,8.378390789,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/24/1991,76,tropicana,6080,8.712759975,0,2.94,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/24/1991,77,dominicks,5632,8.636219898,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/24/1991,77,minute.maid,8512,9.049232212,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/24/1991,77,tropicana,6592,8.793612072,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/24/1991,78,dominicks,8000,8.987196821,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/24/1991,78,minute.maid,12864,9.462187991,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/24/1991,78,tropicana,5376,8.589699882,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/24/1991,80,dominicks,4608,8.435549202,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/24/1991,80,minute.maid,12608,9.442086812,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/24/1991,80,tropicana,8256,9.018695488,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/24/1991,81,dominicks,5760,8.658692754,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/24/1991,81,minute.maid,12224,9.411156511,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/24/1991,81,tropicana,6784,8.822322178,0,2.94,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/24/1991,83,dominicks,11008,9.30637756,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/24/1991,83,minute.maid,16640,9.719564714,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/24/1991,83,tropicana,8000,8.987196821,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/24/1991,84,dominicks,5504,8.61323038,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/24/1991,84,minute.maid,9792,9.189321005,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/24/1991,84,tropicana,5824,8.66974259,0,2.94,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/24/1991,86,dominicks,7168,8.877381955,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/24/1991,86,minute.maid,6848,8.831711918,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/24/1991,86,tropicana,6208,8.733594062,0,2.94,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/24/1991,88,dominicks,3840,8.253227646,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/24/1991,88,minute.maid,6464,8.7740036,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/24/1991,88,tropicana,4544,8.42156296,0,2.94,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/24/1991,89,dominicks,5824,8.66974259,0,1.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/24/1991,89,minute.maid,6592,8.793612072,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/24/1991,89,tropicana,3264,8.090708716,0,2.94,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/24/1991,90,dominicks,3648,8.201934351,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/24/1991,90,minute.maid,4480,8.407378325,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/24/1991,90,tropicana,2304,7.742402022,0,3.07,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/24/1991,91,dominicks,3456,8.14786713,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/24/1991,91,minute.maid,6016,8.702177866,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/24/1991,91,tropicana,2880,7.965545573,0,2.94,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/24/1991,92,dominicks,4608,8.435549202,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/24/1991,92,minute.maid,3008,8.009030685,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/24/1991,92,tropicana,3136,8.050703382,0,2.94,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/24/1991,93,dominicks,3968,8.286017468,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/24/1991,93,minute.maid,7296,8.895081532,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/24/1991,93,tropicana,7168,8.877381955,0,3.07,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/24/1991,94,dominicks,7936,8.979164649,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/24/1991,94,minute.maid,9984,9.208739091,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/24/1991,94,tropicana,5696,8.647519453,0,2.94,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/24/1991,95,dominicks,4096,8.317766167,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/24/1991,95,minute.maid,4224,8.348537825,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/24/1991,95,tropicana,2688,7.896552702,0,3.07,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/24/1991,97,dominicks,3904,8.269756948,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/24/1991,97,minute.maid,4992,8.51559191,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/24/1991,97,tropicana,832,6.723832441,0,3.1,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/24/1991,98,dominicks,10432,9.252633284,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/24/1991,98,minute.maid,4160,8.333270353,0,2.26,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/24/1991,98,tropicana,7104,8.868413285,0,2.94,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/24/1991,100,dominicks,5440,8.60153434,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/24/1991,100,minute.maid,5824,8.66974259,0,2.3,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/24/1991,100,tropicana,6656,8.803273983,0,3.07,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/24/1991,101,dominicks,5120,8.540909718,0,1.89,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/24/1991,101,minute.maid,5248,8.565602331,0,2.25,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/24/1991,101,tropicana,8192,9.010913347,0,2.94,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/24/1991,102,dominicks,18112,9.804329981,0,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/24/1991,102,minute.maid,17792,9.786504197,0,1.98,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/24/1991,102,tropicana,6016,8.702177866,0,2.94,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/24/1991,103,dominicks,17856,9.790094865,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/24/1991,103,minute.maid,4096,8.317766167,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/24/1991,103,tropicana,3072,8.030084094,0,2.94,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/24/1991,104,dominicks,5120,8.540909718,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/24/1991,104,minute.maid,3904,8.269756948,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/24/1991,104,tropicana,4032,8.30201781,0,3.08,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/24/1991,105,dominicks,10752,9.282847063,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/24/1991,105,minute.maid,4608,8.435549202,0,2.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/24/1991,105,tropicana,3328,8.110126802,0,2.94,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/24/1991,106,dominicks,3712,8.219326094,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/24/1991,106,minute.maid,3648,8.201934351,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/24/1991,106,tropicana,1472,7.294377299,0,3.08,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/24/1991,107,dominicks,5312,8.577723691,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/24/1991,107,minute.maid,9600,9.169518378,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/24/1991,107,tropicana,10368,9.246479419,0,2.94,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/24/1991,109,dominicks,3264,8.090708716,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/24/1991,109,minute.maid,12992,9.472089062,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/24/1991,109,tropicana,11968,9.3899917,0,3.07,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/24/1991,110,dominicks,8128,9.00307017,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/24/1991,110,minute.maid,4672,8.449342525,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/24/1991,110,tropicana,5568,8.624791202,0,2.94,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/24/1991,111,dominicks,5696,8.647519453,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/24/1991,111,minute.maid,5312,8.577723691,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/24/1991,111,tropicana,2944,7.98752448,0,3.07,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/24/1991,112,dominicks,8832,9.086136769,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/24/1991,112,minute.maid,11648,9.36288977,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/24/1991,112,tropicana,12224,9.411156511,0,2.94,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/24/1991,113,dominicks,6976,8.850230966,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/24/1991,113,minute.maid,6208,8.733594062,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/24/1991,113,tropicana,6400,8.764053269,0,2.94,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/24/1991,114,dominicks,10816,9.288781798,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/24/1991,114,minute.maid,5248,8.565602331,0,2.26,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/24/1991,114,tropicana,5760,8.658692754,0,2.94,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/24/1991,115,dominicks,3392,8.129174997,0,1.89,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/24/1991,115,minute.maid,4224,8.348537825,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/24/1991,115,tropicana,7168,8.877381955,0,2.94,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/24/1991,116,dominicks,6848,8.831711918,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/24/1991,116,minute.maid,3968,8.286017468,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/24/1991,116,tropicana,5440,8.60153434,0,2.94,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/24/1991,117,dominicks,3776,8.236420527,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/24/1991,117,minute.maid,3840,8.253227646,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/24/1991,117,tropicana,3904,8.269756948,0,2.94,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/24/1991,118,dominicks,4608,8.435549202,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/24/1991,118,minute.maid,5376,8.589699882,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/24/1991,118,tropicana,3840,8.253227646,0,3.07,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/24/1991,119,dominicks,2816,7.943072717,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/24/1991,119,minute.maid,4736,8.462948177,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/24/1991,119,tropicana,6848,8.831711918,0,2.94,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/24/1991,121,dominicks,5056,8.528330936,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/24/1991,121,minute.maid,8384,9.034080407,0,2.1,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/24/1991,121,tropicana,13568,9.515469358,0,2.94,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/24/1991,122,dominicks,9600,9.169518378,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/24/1991,122,minute.maid,13824,9.534161491,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/24/1991,122,tropicana,8256,9.018695488,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/24/1991,123,dominicks,4352,8.378390789,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/24/1991,123,minute.maid,8512,9.049232212,0,2.16,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/24/1991,123,tropicana,6464,8.7740036,0,3.07,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/24/1991,124,dominicks,5312,8.577723691,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/24/1991,124,minute.maid,5568,8.624791202,0,2.08,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/24/1991,124,tropicana,4672,8.449342525,0,3.07,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/24/1991,126,dominicks,4224,8.348537825,0,1.89,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/24/1991,126,minute.maid,10304,9.240287448,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/24/1991,126,tropicana,6912,8.841014311,0,2.94,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/24/1991,128,dominicks,6016,8.702177866,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/24/1991,128,minute.maid,6592,8.793612072,0,2.36,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/24/1991,128,tropicana,10752,9.282847063,0,3.07,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/24/1991,129,dominicks,4480,8.407378325,0,1.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/24/1991,129,minute.maid,6016,8.702177866,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/24/1991,129,tropicana,7552,8.929567708,0,2.94,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/24/1991,130,dominicks,8704,9.071537969,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/24/1991,130,minute.maid,6592,8.793612072,0,2.38,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/24/1991,130,tropicana,5120,8.540909718,0,3.07,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/24/1991,131,dominicks,3840,8.253227646,0,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/24/1991,131,minute.maid,6080,8.712759975,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/24/1991,131,tropicana,7872,8.971067439,0,2.94,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/24/1991,132,dominicks,15360,9.639522007,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/24/1991,132,minute.maid,13632,9.520175249,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/24/1991,132,tropicana,6016,8.702177866,0,2.94,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/24/1991,134,dominicks,3904,8.269756948,0,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/24/1991,134,minute.maid,4544,8.42156296,0,2.22,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/24/1991,134,tropicana,2752,7.920083199,0,2.73,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/24/1991,137,dominicks,6464,8.7740036,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/24/1991,137,minute.maid,9792,9.189321005,0,2.31,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/24/1991,137,tropicana,16064,9.684336023,0,3.07,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/31/1991,2,dominicks,3328,8.110126802,0,1.83,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/31/1991,2,minute.maid,27968,10.23881628,0,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/31/1991,2,tropicana,5312,8.577723691,0,3.07,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/31/1991,5,dominicks,1856,7.526178913,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/31/1991,5,minute.maid,50112,10.82201578,0,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/31/1991,5,tropicana,7168,8.877381955,0,2.94,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/31/1991,8,dominicks,7104,8.868413285,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/31/1991,8,minute.maid,49664,10.8130356,0,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/31/1991,8,tropicana,5888,8.68067166,0,2.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/31/1991,9,dominicks,1088,6.992096427,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/31/1991,9,minute.maid,60992,11.01849799,0,1.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/31/1991,9,tropicana,7552,8.929567708,0,2.94,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/31/1991,12,dominicks,2496,7.82244473,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/31/1991,12,minute.maid,102400,11.53664199,0,1.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/31/1991,12,tropicana,7104,8.868413285,0,3.07,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/31/1991,14,dominicks,2112,7.655390645,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/31/1991,14,minute.maid,31552,10.35939226,0,1.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/31/1991,14,tropicana,10240,9.234056899,0,3.07,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/31/1991,18,dominicks,4480,8.407378325,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/31/1991,18,minute.maid,36736,10.51151248,0,1.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/31/1991,18,tropicana,6720,8.812843434,0,2.94,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/31/1991,21,dominicks,5632,8.636219898,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/31/1991,21,minute.maid,19968,9.901886271,0,1.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/31/1991,21,tropicana,3520,8.166216269,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/31/1991,28,dominicks,4288,8.363575703,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/31/1991,28,minute.maid,3392,8.129174997,0,1.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/31/1991,28,tropicana,3776,8.236420527,0,2.94,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/31/1991,32,dominicks,5376,8.589699882,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/31/1991,32,minute.maid,30976,10.34096799,0,1.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/31/1991,32,tropicana,13248,9.491601877,0,3.07,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/31/1991,33,dominicks,4480,8.407378325,0,1.83,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/31/1991,33,minute.maid,22464,10.01966931,0,1.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/31/1991,33,tropicana,11456,9.346268889,0,3.07,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/31/1991,40,dominicks,3328,8.110126802,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/31/1991,40,minute.maid,42688,10.66167313,0,1.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/31/1991,40,tropicana,2944,7.98752448,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/31/1991,44,dominicks,2048,7.624618986,0,1.82,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/31/1991,44,minute.maid,48704,10.79351644,0,1.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/31/1991,44,tropicana,8960,9.100525506,0,2.94,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/31/1991,45,dominicks,3648,8.201934351,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/31/1991,45,minute.maid,15680,9.660141294,0,1.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/31/1991,45,tropicana,5312,8.577723691,0,2.94,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/31/1991,47,dominicks,2944,7.98752448,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/31/1991,47,minute.maid,29696,10.29876764,0,1.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/31/1991,47,tropicana,3584,8.184234774,0,2.94,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/31/1991,48,dominicks,3584,8.184234774,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/31/1991,48,minute.maid,16768,9.727227587,0,1.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/31/1991,48,tropicana,4672,8.449342525,0,2.94,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/31/1991,49,dominicks,2112,7.655390645,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/31/1991,49,minute.maid,21376,9.970024076,0,1.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/31/1991,49,tropicana,3136,8.050703382,0,2.94,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/31/1991,50,dominicks,2432,7.796469243,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/31/1991,50,minute.maid,19840,9.895455381,0,1.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/31/1991,50,tropicana,2752,7.920083199,0,2.94,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/31/1991,51,dominicks,4864,8.489616424,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/31/1991,51,minute.maid,6464,8.7740036,0,1.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/31/1991,51,tropicana,5056,8.528330936,0,3.04,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/31/1991,52,dominicks,3712,8.219326094,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/31/1991,52,minute.maid,36736,10.51151248,0,1.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/31/1991,52,tropicana,11456,9.346268889,0,3.07,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/31/1991,53,dominicks,4736,8.462948177,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/31/1991,53,minute.maid,56896,10.94898032,0,1.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/31/1991,53,tropicana,8576,9.056722883,0,3.07,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/31/1991,54,dominicks,1792,7.491087594,0,1.81,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/31/1991,54,minute.maid,30976,10.34096799,0,1.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/31/1991,54,tropicana,4992,8.51559191,0,2.94,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/31/1991,56,dominicks,2624,7.87245515,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/31/1991,56,minute.maid,22848,10.03661887,0,1.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/31/1991,56,tropicana,6016,8.702177866,0,2.94,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -10/31/1991,59,dominicks,1728,7.454719949,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/31/1991,59,minute.maid,37248,10.52535353,0,1.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/31/1991,59,tropicana,4416,8.392989588,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -10/31/1991,62,dominicks,576,6.356107661,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/31/1991,62,minute.maid,29824,10.30306872,0,1.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/31/1991,62,tropicana,13056,9.477003077,0,3.07,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -10/31/1991,64,dominicks,2176,7.685243608,0,1.81,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/31/1991,64,minute.maid,18176,9.807857322,0,1.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/31/1991,64,tropicana,2176,7.685243608,0,2.94,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -10/31/1991,67,dominicks,1408,7.249925537,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/31/1991,67,minute.maid,39360,10.58050535,0,1.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/31/1991,67,tropicana,5120,8.540909718,0,2.94,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -10/31/1991,68,dominicks,4032,8.30201781,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/31/1991,68,minute.maid,43072,10.67062841,0,1.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/31/1991,68,tropicana,7104,8.868413285,0,3.07,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -10/31/1991,70,dominicks,4608,8.435549202,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/31/1991,70,minute.maid,32192,10.37947325,0,1.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/31/1991,70,tropicana,6016,8.702177866,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -10/31/1991,71,dominicks,2560,7.847762538,0,1.83,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/31/1991,71,minute.maid,24256,10.09641929,0,1.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/31/1991,71,tropicana,4352,8.378390789,0,3.07,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -10/31/1991,72,dominicks,1408,7.249925537,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/31/1991,72,minute.maid,44352,10.69991308,0,1.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/31/1991,72,tropicana,7104,8.868413285,0,3.07,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -10/31/1991,73,dominicks,9152,9.121727714,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/31/1991,73,minute.maid,65472,11.08937785,0,1.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/31/1991,73,tropicana,7552,8.929567708,0,2.94,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -10/31/1991,74,dominicks,6336,8.754002934,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/31/1991,74,minute.maid,40192,10.60142325,0,1.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/31/1991,74,tropicana,7104,8.868413285,0,2.94,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -10/31/1991,75,dominicks,3200,8.070906089,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/31/1991,75,minute.maid,42944,10.66765222,0,1.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/31/1991,75,tropicana,5248,8.565602331,0,3.07,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -10/31/1991,76,dominicks,3392,8.129174997,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/31/1991,76,minute.maid,54528,10.90646961,0,1.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/31/1991,76,tropicana,5376,8.589699882,0,2.94,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -10/31/1991,77,dominicks,3328,8.110126802,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/31/1991,77,minute.maid,24832,10.11988842,0,1.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/31/1991,77,tropicana,6016,8.702177866,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -10/31/1991,78,dominicks,1984,7.592870288,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/31/1991,78,minute.maid,44672,10.70710219,0,1.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/31/1991,78,tropicana,4160,8.333270353,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -10/31/1991,80,dominicks,2176,7.685243608,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/31/1991,80,minute.maid,35392,10.47424109,0,1.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/31/1991,80,tropicana,8192,9.010913347,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -10/31/1991,81,dominicks,3264,8.090708716,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/31/1991,81,minute.maid,28480,10.25695737,0,1.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/31/1991,81,tropicana,8256,9.018695488,0,2.94,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -10/31/1991,84,dominicks,2816,7.943072717,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/31/1991,84,minute.maid,27264,10.21332243,0,1.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/31/1991,84,tropicana,4224,8.348537825,0,2.94,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -10/31/1991,86,dominicks,4480,8.407378325,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/31/1991,86,minute.maid,25152,10.1326927,0,1.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/31/1991,86,tropicana,5568,8.624791202,0,2.94,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -10/31/1991,88,dominicks,3520,8.166216269,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/31/1991,88,minute.maid,16064,9.684336023,0,1.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/31/1991,88,tropicana,2752,7.920083199,0,2.94,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -10/31/1991,89,dominicks,4224,8.348537825,0,1.77,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/31/1991,89,minute.maid,32576,10.3913311,0,1.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/31/1991,89,tropicana,2944,7.98752448,0,2.94,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -10/31/1991,90,dominicks,2688,7.896552702,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/31/1991,90,minute.maid,16192,9.692272572,0,1.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/31/1991,90,tropicana,2432,7.796469243,0,3.07,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -10/31/1991,91,dominicks,2432,7.796469243,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/31/1991,91,minute.maid,37120,10.52191119,0,1.49,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/31/1991,91,tropicana,4288,8.363575703,0,2.94,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -10/31/1991,92,dominicks,2240,7.714231145,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/31/1991,92,minute.maid,28672,10.26367632,0,1.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/31/1991,92,tropicana,2368,7.769800996,0,2.94,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -10/31/1991,93,dominicks,3520,8.166216269,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/31/1991,93,minute.maid,28032,10.24110199,0,1.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/31/1991,93,tropicana,9728,9.182763604,0,3.07,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -10/31/1991,94,dominicks,4416,8.392989588,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/31/1991,94,minute.maid,32704,10.39525267,0,1.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/31/1991,94,tropicana,5504,8.61323038,0,2.94,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -10/31/1991,95,dominicks,1472,7.294377299,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/31/1991,95,minute.maid,30016,10.30948585,0,1.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/31/1991,95,tropicana,2816,7.943072717,0,3.07,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -10/31/1991,97,dominicks,1792,7.491087594,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/31/1991,97,minute.maid,13760,9.529521112,0,1.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/31/1991,97,tropicana,1088,6.992096427,0,3.11,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/31/1991,98,dominicks,4992,8.51559191,0,1.81,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/31/1991,98,minute.maid,31104,10.34509171,0,1.49,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/31/1991,98,tropicana,6336,8.754002934,0,2.94,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/31/1991,100,dominicks,4864,8.489616424,0,1.88,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/31/1991,100,minute.maid,42688,10.66167313,0,1.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/31/1991,100,tropicana,5952,8.691482577,0,3.07,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/31/1991,101,dominicks,4416,8.392989588,0,1.8,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/31/1991,101,minute.maid,31936,10.37148918,0,1.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/31/1991,101,tropicana,8128,9.00307017,0,2.94,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/31/1991,102,dominicks,6656,8.803273983,0,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/31/1991,102,minute.maid,71424,11.17638923,0,1.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/31/1991,102,tropicana,6592,8.793612072,0,2.94,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/31/1991,103,dominicks,3904,8.269756948,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/31/1991,103,minute.maid,23744,10.07508515,0,1.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/31/1991,103,tropicana,2752,7.920083199,0,2.94,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/31/1991,104,dominicks,3392,8.129174997,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/31/1991,104,minute.maid,15488,9.64782081,0,1.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/31/1991,104,tropicana,3072,8.030084094,0,3.08,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/31/1991,105,dominicks,3904,8.269756948,0,1.83,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/31/1991,105,minute.maid,43904,10.68976071,0,1.49,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/31/1991,105,tropicana,2624,7.87245515,0,2.94,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/31/1991,106,dominicks,2496,7.82244473,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/31/1991,106,minute.maid,16960,9.738612909,0,1.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/31/1991,106,tropicana,2368,7.769800996,0,3.09,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/31/1991,107,dominicks,1984,7.592870288,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/31/1991,107,minute.maid,53376,10.88511649,0,1.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/31/1991,107,tropicana,10176,9.227787286,0,2.94,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/31/1991,109,dominicks,1984,7.592870288,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/31/1991,109,minute.maid,34304,10.44301724,0,1.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/31/1991,109,tropicana,12544,9.436997743,0,3.07,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/31/1991,110,dominicks,3776,8.236420527,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/31/1991,110,minute.maid,30976,10.34096799,0,1.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/31/1991,110,tropicana,5568,8.624791202,0,2.94,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/31/1991,111,dominicks,2816,7.943072717,0,1.8,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/31/1991,111,minute.maid,101248,11.52532823,0,1.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/31/1991,111,tropicana,3840,8.253227646,0,3.07,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/31/1991,112,dominicks,3456,8.14786713,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/31/1991,112,minute.maid,56320,10.93880499,0,1.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/31/1991,112,tropicana,12352,9.421573272,0,2.94,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -10/31/1991,113,dominicks,4416,8.392989588,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/31/1991,113,minute.maid,27200,10.21097225,0,1.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/31/1991,113,tropicana,8576,9.056722883,0,2.94,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/31/1991,114,dominicks,7808,8.962904128,0,1.8,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/31/1991,114,minute.maid,40320,10.6046029,0,1.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/31/1991,114,tropicana,5248,8.565602331,0,2.94,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/31/1991,115,dominicks,2624,7.87245515,0,1.77,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/31/1991,115,minute.maid,25408,10.14281936,0,1.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/31/1991,115,tropicana,6784,8.822322178,0,2.94,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/31/1991,116,dominicks,2560,7.847762538,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/31/1991,116,minute.maid,28032,10.24110199,0,1.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/31/1991,116,tropicana,4864,8.489616424,0,2.94,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/31/1991,117,dominicks,2048,7.624618986,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/31/1991,117,minute.maid,17856,9.790094865,0,1.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/31/1991,117,tropicana,4224,8.348537825,0,2.94,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/31/1991,118,dominicks,1600,7.377758908,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/31/1991,118,minute.maid,51072,10.84099168,0,1.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/31/1991,118,tropicana,5056,8.528330936,0,3.07,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/31/1991,119,dominicks,3136,8.050703382,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/31/1991,119,minute.maid,21824,9.990765561,0,1.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/31/1991,119,tropicana,6016,8.702177866,0,2.94,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/31/1991,121,dominicks,3648,8.201934351,0,1.78,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/31/1991,121,minute.maid,26048,10.16769627,0,1.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/31/1991,121,tropicana,7808,8.962904128,0,2.94,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/31/1991,122,dominicks,7168,8.877381955,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/31/1991,122,minute.maid,28288,10.25019297,0,1.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/31/1991,122,tropicana,7680,8.946374826,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/31/1991,123,dominicks,3200,8.070906089,0,1.86,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/31/1991,123,minute.maid,97088,11.48337306,0,1.48,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/31/1991,123,tropicana,5888,8.68067166,0,3.07,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/31/1991,124,dominicks,9024,9.107642974,0,1.81,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/31/1991,124,minute.maid,27520,10.22266829,0,1.48,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/31/1991,124,tropicana,5888,8.68067166,0,3.07,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/31/1991,126,dominicks,2560,7.847762538,0,1.83,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/31/1991,126,minute.maid,37632,10.53561003,0,1.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/31/1991,126,tropicana,6464,8.7740036,0,2.94,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/31/1991,128,dominicks,6976,8.850230966,0,1.8,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/31/1991,128,minute.maid,8000,8.987196821,0,1.5,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/31/1991,128,tropicana,13120,9.481893063,0,3.07,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/31/1991,129,dominicks,2688,7.896552702,0,1.82,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/31/1991,129,minute.maid,25920,10.16277015,0,1.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/31/1991,129,tropicana,6976,8.850230966,0,2.94,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/31/1991,130,dominicks,4736,8.462948177,0,1.81,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/31/1991,130,minute.maid,106816,11.57886301,0,1.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/31/1991,130,tropicana,3328,8.110126802,0,3.07,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/31/1991,131,dominicks,2112,7.655390645,0,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/31/1991,131,minute.maid,30656,10.33058368,0,1.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/31/1991,131,tropicana,6656,8.803273983,0,2.94,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/31/1991,132,dominicks,12288,9.416378455,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/31/1991,132,minute.maid,59200,10.98867682,0,1.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/31/1991,132,tropicana,5888,8.68067166,0,2.94,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/31/1991,134,dominicks,3008,8.009030685,0,1.61,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/31/1991,134,minute.maid,13312,9.496421163,0,1.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/31/1991,134,tropicana,3136,8.050703382,0,2.73,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/31/1991,137,dominicks,4224,8.348537825,0,1.84,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/31/1991,137,minute.maid,50688,10.83344448,0,1.48,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/31/1991,137,tropicana,16512,9.711842668,0,3.07,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/31/1991,83,dominicks,4480,8.407378325,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/31/1991,83,minute.maid,63808,11.06363385,0,1.49,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -10/31/1991,83,tropicana,5184,8.553332238,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/7/1991,2,dominicks,12096,9.400630098,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/7/1991,2,minute.maid,4736,8.462948177,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/7/1991,2,tropicana,9216,9.128696383,0,3.11,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/7/1991,5,dominicks,6528,8.783855897,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/7/1991,5,minute.maid,5184,8.553332238,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/7/1991,5,tropicana,7872,8.971067439,0,2.94,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/7/1991,8,dominicks,9216,9.128696383,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/7/1991,8,minute.maid,10880,9.29468152,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/7/1991,8,tropicana,6080,8.712759975,0,2.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/7/1991,9,dominicks,8000,8.987196821,1,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/7/1991,9,minute.maid,3072,8.030084094,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/7/1991,9,tropicana,7104,8.868413285,0,2.94,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/7/1991,12,dominicks,15360,9.639522007,1,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/7/1991,12,minute.maid,5632,8.636219898,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/7/1991,12,tropicana,7616,8.938006577,0,3.07,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/7/1991,14,dominicks,12096,9.400630098,1,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/7/1991,14,minute.maid,5056,8.528330936,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/7/1991,14,tropicana,15040,9.618468598,0,3.07,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/7/1991,18,dominicks,13056,9.477003077,1,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/7/1991,18,minute.maid,9024,9.107642974,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/7/1991,18,tropicana,8896,9.093357017,0,2.94,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/7/1991,21,dominicks,9984,9.208739091,1,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/7/1991,21,minute.maid,6272,8.743850562,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/7/1991,21,tropicana,4608,8.435549202,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/7/1991,28,dominicks,3904,8.269756948,1,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/7/1991,28,minute.maid,3200,8.070906089,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/7/1991,28,tropicana,4928,8.502688505,0,2.94,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/7/1991,32,dominicks,14912,9.609921537,1,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/7/1991,32,minute.maid,5312,8.577723691,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/7/1991,32,tropicana,12288,9.416378455,0,3.07,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/7/1991,33,dominicks,10240,9.234056899,1,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/7/1991,33,minute.maid,5824,8.66974259,0,2.31,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/7/1991,33,tropicana,10880,9.29468152,0,3.12,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/7/1991,40,dominicks,10112,9.221478116,1,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/7/1991,40,minute.maid,7488,8.921057018,0,1.83,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/7/1991,40,tropicana,4416,8.392989588,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/7/1991,44,dominicks,3392,8.129174997,1,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/7/1991,44,minute.maid,4544,8.42156296,0,2.22,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/7/1991,44,tropicana,13248,9.491601877,0,2.96,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/7/1991,45,dominicks,10176,9.227787286,1,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/7/1991,45,minute.maid,2560,7.847762538,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/7/1991,45,tropicana,7360,8.903815212,0,2.94,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/7/1991,47,dominicks,12608,9.442086812,1,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/7/1991,47,minute.maid,3968,8.286017468,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/7/1991,47,tropicana,5440,8.60153434,0,2.94,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/7/1991,48,dominicks,5888,8.68067166,1,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/7/1991,48,minute.maid,5056,8.528330936,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/7/1991,48,tropicana,8128,9.00307017,0,2.94,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/7/1991,49,dominicks,8704,9.071537969,1,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/7/1991,49,minute.maid,2560,7.847762538,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/7/1991,49,tropicana,5760,8.658692754,0,2.94,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/7/1991,50,dominicks,10432,9.252633284,1,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/7/1991,50,minute.maid,2624,7.87245515,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/7/1991,50,tropicana,6400,8.764053269,0,2.94,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/7/1991,51,dominicks,5120,8.540909718,1,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/7/1991,51,minute.maid,3968,8.286017468,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/7/1991,51,tropicana,5504,8.61323038,0,3.04,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/7/1991,53,dominicks,18368,9.818365299,1,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/7/1991,53,minute.maid,4096,8.317766167,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/7/1991,53,tropicana,9728,9.182763604,0,3.07,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/7/1991,54,dominicks,8192,9.010913347,1,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/7/1991,54,minute.maid,3904,8.269756948,0,2.21,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/7/1991,54,tropicana,8896,9.093357017,0,2.96,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/7/1991,56,dominicks,9152,9.121727714,1,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/7/1991,56,minute.maid,2944,7.98752448,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/7/1991,56,tropicana,4352,8.378390789,0,2.94,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/7/1991,59,dominicks,7552,8.929567708,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/7/1991,59,minute.maid,4992,8.51559191,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/7/1991,59,tropicana,5952,8.691482577,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/7/1991,62,dominicks,8256,9.018695488,1,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/7/1991,62,minute.maid,5696,8.647519453,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/7/1991,62,tropicana,17536,9.77201119,0,3.07,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/7/1991,64,dominicks,13952,9.543378146,1,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/7/1991,64,minute.maid,3584,8.184234774,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/7/1991,64,tropicana,3968,8.286017468,0,2.96,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/7/1991,67,dominicks,5696,8.647519453,1,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/7/1991,67,minute.maid,5312,8.577723691,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/7/1991,67,tropicana,6592,8.793612072,0,2.94,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 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-11/7/1991,72,minute.maid,4288,8.363575703,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/7/1991,72,tropicana,10432,9.252633284,0,3.07,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/7/1991,73,dominicks,16576,9.715711145,1,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/7/1991,73,minute.maid,30016,10.30948585,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/7/1991,73,tropicana,12864,9.462187991,0,2.94,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 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-11/7/1991,75,tropicana,6976,8.850230966,0,3.07,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/7/1991,76,dominicks,13312,9.496421163,1,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/7/1991,76,minute.maid,2880,7.965545573,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/7/1991,76,tropicana,6912,8.841014311,0,2.94,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/7/1991,77,dominicks,9728,9.182763604,1,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/7/1991,77,minute.maid,8192,9.010913347,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/7/1991,77,tropicana,7744,8.954673629,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/7/1991,78,dominicks,6400,8.764053269,1,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/7/1991,78,minute.maid,8448,9.041685006,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/7/1991,78,tropicana,7296,8.895081532,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 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-11/7/1991,81,tropicana,9024,9.107642974,0,2.94,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/7/1991,86,dominicks,12736,9.452187908,1,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/7/1991,86,minute.maid,4224,8.348537825,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/7/1991,86,tropicana,7744,8.954673629,0,2.94,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/7/1991,88,dominicks,6208,8.733594062,1,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/7/1991,88,minute.maid,3328,8.110126802,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/7/1991,88,tropicana,4288,8.363575703,0,2.94,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/7/1991,89,dominicks,12224,9.411156511,1,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/7/1991,89,minute.maid,1920,7.560080465,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/7/1991,89,tropicana,2816,7.943072717,0,2.95,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 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-11/7/1991,91,tropicana,2816,7.943072717,0,2.94,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/7/1991,92,dominicks,15360,9.639522007,1,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/7/1991,92,minute.maid,3520,8.166216269,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/7/1991,92,tropicana,3648,8.201934351,0,2.94,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/7/1991,93,dominicks,3584,8.184234774,1,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/7/1991,93,minute.maid,3776,8.236420527,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/7/1991,93,tropicana,10240,9.234056899,0,3.07,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/7/1991,94,dominicks,10624,9.270870872,1,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/7/1991,94,minute.maid,8064,8.99516499,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/7/1991,94,tropicana,8128,9.00307017,0,2.94,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/7/1991,95,dominicks,10752,9.282847063,1,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/7/1991,95,minute.maid,2816,7.943072717,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/7/1991,95,tropicana,3520,8.166216269,0,3.07,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/7/1991,97,dominicks,6912,8.841014311,1,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/7/1991,97,minute.maid,1728,7.454719949,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/7/1991,97,tropicana,1920,7.560080465,0,3.09,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/7/1991,98,dominicks,17344,9.761001904,1,1.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/7/1991,98,minute.maid,4736,8.462948177,0,2.26,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/7/1991,98,tropicana,9664,9.17616292,0,2.96,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/7/1991,100,dominicks,19392,9.872615889,1,1.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/7/1991,100,minute.maid,4288,8.363575703,0,2.35,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/7/1991,100,tropicana,11264,9.329367078,0,3.13,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/7/1991,101,dominicks,10304,9.240287448,1,1.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/7/1991,101,minute.maid,4096,8.317766167,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/7/1991,101,tropicana,9536,9.162829389,0,2.95,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/7/1991,102,dominicks,25344,10.1402973,1,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/7/1991,102,minute.maid,10368,9.246479419,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/7/1991,102,tropicana,13696,9.524859098,0,2.95,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/7/1991,103,dominicks,10944,9.30054664,1,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/7/1991,103,minute.maid,4032,8.30201781,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/7/1991,103,tropicana,4096,8.317766167,0,2.96,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/7/1991,104,dominicks,6592,8.793612072,1,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/7/1991,104,minute.maid,3968,8.286017468,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/7/1991,104,tropicana,4416,8.392989588,0,3.07,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/7/1991,105,dominicks,19264,9.865993348,1,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/7/1991,105,minute.maid,4160,8.333270353,0,2.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/7/1991,105,tropicana,4352,8.378390789,0,2.96,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/7/1991,106,dominicks,6528,8.783855897,1,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/7/1991,106,minute.maid,2304,7.742402022,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/7/1991,106,tropicana,2752,7.920083199,0,3.09,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/7/1991,107,dominicks,13120,9.481893063,1,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/7/1991,107,minute.maid,7168,8.877381955,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/7/1991,107,tropicana,12352,9.421573272,0,2.94,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/7/1991,109,dominicks,9280,9.135616826,1,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/7/1991,109,minute.maid,8384,9.034080407,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/7/1991,109,tropicana,17664,9.779283949,0,3.07,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/7/1991,110,dominicks,9536,9.162829389,1,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/7/1991,110,minute.maid,3200,8.070906089,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/7/1991,110,tropicana,6400,8.764053269,0,2.94,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/7/1991,111,dominicks,13824,9.534161491,1,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/7/1991,111,minute.maid,3840,8.253227646,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/7/1991,111,tropicana,7808,8.962904128,0,3.13,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/7/1991,112,dominicks,9408,9.14931567,1,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/7/1991,112,minute.maid,10496,9.258749511,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/7/1991,112,tropicana,16256,9.69621735,0,2.95,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/7/1991,113,dominicks,7040,8.859363449,1,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/7/1991,113,minute.maid,4288,8.363575703,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/7/1991,113,tropicana,8576,9.056722883,0,2.94,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/7/1991,114,dominicks,22784,10.03381381,1,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/7/1991,114,minute.maid,4288,8.363575703,0,2.26,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/7/1991,114,tropicana,7104,8.868413285,0,2.95,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/7/1991,115,dominicks,7104,8.868413285,1,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/7/1991,115,minute.maid,4416,8.392989588,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/7/1991,115,tropicana,9408,9.14931567,0,2.95,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/7/1991,116,dominicks,12416,9.426741242,1,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/7/1991,116,minute.maid,2816,7.943072717,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/7/1991,116,tropicana,8064,8.99516499,0,2.94,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/7/1991,117,dominicks,10304,9.240287448,1,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/7/1991,117,minute.maid,2368,7.769800996,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/7/1991,117,tropicana,4544,8.42156296,0,2.94,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/7/1991,118,dominicks,3008,8.009030685,1,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/7/1991,118,minute.maid,3968,8.286017468,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/7/1991,118,tropicana,6848,8.831711918,0,3.07,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/7/1991,119,dominicks,4864,8.489616424,1,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/7/1991,119,minute.maid,3904,8.269756948,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/7/1991,119,tropicana,8064,8.99516499,0,2.94,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/7/1991,121,dominicks,7552,8.929567708,1,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/7/1991,121,minute.maid,6144,8.723231275,0,2.23,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/7/1991,121,tropicana,9600,9.169518378,0,2.95,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/7/1991,122,dominicks,11200,9.323669057,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/7/1991,122,minute.maid,11072,9.312174678,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/7/1991,122,tropicana,10688,9.276876896,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/7/1991,123,dominicks,15360,9.639522007,1,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/7/1991,123,minute.maid,4352,8.378390789,0,2.37,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/7/1991,123,tropicana,6144,8.723231275,0,3.12,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/7/1991,124,dominicks,28992,10.27477521,1,1.68,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/7/1991,124,minute.maid,3712,8.219326094,0,2.15,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/7/1991,124,tropicana,7360,8.903815212,0,3.1,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/7/1991,126,dominicks,10944,9.30054664,1,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/7/1991,126,minute.maid,5248,8.565602331,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/7/1991,126,tropicana,10560,9.264828557,0,2.96,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/7/1991,128,dominicks,10560,9.264828557,1,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/7/1991,128,minute.maid,6144,8.723231275,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/7/1991,128,tropicana,11072,9.312174678,0,3.11,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/7/1991,129,dominicks,6272,8.743850562,1,1.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/7/1991,129,minute.maid,5056,8.528330936,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/7/1991,129,tropicana,9344,9.142489705,0,2.96,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/7/1991,130,dominicks,32000,10.37349118,1,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/7/1991,130,minute.maid,14400,9.574983486,0,1.9,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/7/1991,130,tropicana,5120,8.540909718,0,3.07,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/7/1991,131,dominicks,9280,9.135616826,1,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/7/1991,131,minute.maid,4288,8.363575703,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/7/1991,131,tropicana,9792,9.189321005,0,2.95,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/7/1991,132,dominicks,13184,9.486759252,1,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/7/1991,132,minute.maid,9664,9.17616292,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/7/1991,132,tropicana,9408,9.14931567,0,2.95,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/7/1991,134,dominicks,6848,8.831711918,1,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/7/1991,134,minute.maid,2240,7.714231145,0,2.26,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/7/1991,134,tropicana,3776,8.236420527,0,2.86,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/7/1991,137,dominicks,27072,10.20625526,1,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/7/1991,137,minute.maid,6784,8.822322178,0,2.35,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/7/1991,137,tropicana,22528,10.02251426,0,3.11,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/7/1991,83,dominicks,8384,9.034080407,1,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/7/1991,83,minute.maid,8064,8.99516499,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/7/1991,83,tropicana,7104,8.868413285,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/7/1991,84,dominicks,12224,9.411156511,1,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/7/1991,84,minute.maid,5056,8.528330936,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/7/1991,84,tropicana,5120,8.540909718,0,2.94,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/14/1991,2,dominicks,6208,8.733594062,0,1.76,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/14/1991,2,minute.maid,7808,8.962904128,0,2.14,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/14/1991,2,tropicana,7296,8.895081532,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/14/1991,5,dominicks,6080,8.712759975,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/14/1991,5,minute.maid,8384,9.034080407,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/14/1991,5,tropicana,7552,8.929567708,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/14/1991,8,dominicks,12608,9.442086812,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/14/1991,8,minute.maid,9984,9.208739091,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/14/1991,8,tropicana,6848,8.831711918,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/14/1991,9,dominicks,3456,8.14786713,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/14/1991,9,minute.maid,4672,8.449342525,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/14/1991,9,tropicana,10496,9.258749511,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/14/1991,12,dominicks,8832,9.086136769,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/14/1991,12,minute.maid,19264,9.865993348,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/14/1991,12,tropicana,7936,8.979164649,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/14/1991,14,dominicks,6400,8.764053269,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/14/1991,14,minute.maid,8192,9.010913347,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/14/1991,14,tropicana,12928,9.467150781,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/14/1991,18,dominicks,8896,9.093357017,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/14/1991,18,minute.maid,11904,9.384629757,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/14/1991,18,tropicana,8640,9.064157862,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/14/1991,21,dominicks,10432,9.252633284,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/14/1991,21,minute.maid,6720,8.812843434,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/14/1991,21,tropicana,4992,8.51559191,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/14/1991,28,dominicks,4608,8.435549202,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/14/1991,28,minute.maid,4672,8.449342525,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/14/1991,28,tropicana,5504,8.61323038,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/14/1991,32,dominicks,11904,9.384629757,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/14/1991,32,minute.maid,10688,9.276876896,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/14/1991,32,tropicana,12928,9.467150781,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/14/1991,33,dominicks,7872,8.971067439,0,1.76,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/14/1991,33,minute.maid,9344,9.142489705,0,2.15,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/14/1991,33,tropicana,11328,9.335032816,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/14/1991,40,dominicks,9920,9.2023082,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/14/1991,40,minute.maid,7104,8.868413285,0,1.84,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/14/1991,40,tropicana,5376,8.589699882,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/14/1991,44,dominicks,3968,8.286017468,0,1.73,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/14/1991,44,minute.maid,8000,8.987196821,0,2.07,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/14/1991,44,tropicana,10624,9.270870872,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/14/1991,45,dominicks,5760,8.658692754,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/14/1991,45,minute.maid,4352,8.378390789,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/14/1991,45,tropicana,6592,8.793612072,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/14/1991,47,dominicks,7104,8.868413285,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/14/1991,47,minute.maid,6848,8.831711918,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/14/1991,47,tropicana,5184,8.553332238,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/14/1991,48,dominicks,2112,7.655390645,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/14/1991,48,minute.maid,6336,8.754002934,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/14/1991,48,tropicana,4608,8.435549202,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -11/14/1991,49,dominicks,7040,8.859363449,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/14/1991,49,minute.maid,4800,8.476371197,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/14/1991,49,tropicana,4864,8.489616424,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/14/1991,50,dominicks,4416,8.392989588,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/14/1991,50,minute.maid,4800,8.476371197,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/14/1991,50,tropicana,4224,8.348537825,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/14/1991,51,dominicks,6720,8.812843434,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/14/1991,51,minute.maid,4224,8.348537825,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/14/1991,51,tropicana,6720,8.812843434,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/14/1991,53,dominicks,8384,9.034080407,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/14/1991,53,minute.maid,5632,8.636219898,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/14/1991,53,tropicana,10816,9.288781798,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/14/1991,54,dominicks,4928,8.502688505,0,1.73,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/14/1991,54,minute.maid,6912,8.841014311,0,2.06,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/14/1991,54,tropicana,7552,8.929567708,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/14/1991,56,dominicks,7040,8.859363449,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/14/1991,56,minute.maid,6528,8.783855897,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/14/1991,56,tropicana,6528,8.783855897,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/14/1991,59,dominicks,8128,9.00307017,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/14/1991,59,minute.maid,5824,8.66974259,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/14/1991,59,tropicana,6656,8.803273983,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/14/1991,62,dominicks,3264,8.090708716,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/14/1991,62,minute.maid,9856,9.195835686,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/14/1991,62,tropicana,13440,9.505990614,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/14/1991,64,dominicks,7424,8.912473275,0,1.72,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/14/1991,64,minute.maid,4544,8.42156296,0,2.14,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/14/1991,64,tropicana,3584,8.184234774,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/14/1991,67,dominicks,4672,8.449342525,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/14/1991,67,minute.maid,7360,8.903815212,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/14/1991,67,tropicana,5952,8.691482577,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/14/1991,68,dominicks,9344,9.142489705,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/14/1991,68,minute.maid,14080,9.55251063,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/14/1991,68,tropicana,7296,8.895081532,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/14/1991,70,dominicks,10304,9.240287448,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/14/1991,70,minute.maid,10624,9.270870872,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/14/1991,70,tropicana,7232,8.886270902,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/14/1991,71,dominicks,7104,8.868413285,0,1.71,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/14/1991,71,minute.maid,4096,8.317766167,0,2.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/14/1991,71,tropicana,5824,8.66974259,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/14/1991,72,dominicks,6656,8.803273983,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/14/1991,72,minute.maid,9920,9.2023082,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/14/1991,72,tropicana,10112,9.221478116,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/14/1991,73,dominicks,17664,9.779283949,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/14/1991,73,minute.maid,14208,9.561560465,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/14/1991,73,tropicana,13184,9.486759252,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/14/1991,74,dominicks,8384,9.034080407,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/14/1991,74,minute.maid,6400,8.764053269,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/14/1991,74,tropicana,9344,9.142489705,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/14/1991,75,dominicks,9728,9.182763604,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/14/1991,75,minute.maid,8768,9.078864009,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/14/1991,75,tropicana,5952,8.691482577,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/14/1991,76,dominicks,8896,9.093357017,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/14/1991,76,minute.maid,6336,8.754002934,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/14/1991,76,tropicana,7424,8.912473275,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/14/1991,77,dominicks,6848,8.831711918,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/14/1991,77,minute.maid,9152,9.121727714,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/14/1991,77,tropicana,10176,9.227787286,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/14/1991,78,dominicks,10112,9.221478116,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/14/1991,78,minute.maid,10880,9.29468152,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/14/1991,78,tropicana,9280,9.135616826,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/14/1991,80,dominicks,6720,8.812843434,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/14/1991,80,minute.maid,13888,9.538780437,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/14/1991,80,tropicana,13440,9.505990614,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/14/1991,81,dominicks,8896,9.093357017,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/14/1991,81,minute.maid,7296,8.895081532,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/14/1991,81,tropicana,8064,8.99516499,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/14/1991,86,dominicks,12224,9.411156511,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/14/1991,86,minute.maid,5632,8.636219898,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/14/1991,86,tropicana,8256,9.018695488,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/14/1991,88,dominicks,6016,8.702177866,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/14/1991,88,minute.maid,5504,8.61323038,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/14/1991,88,tropicana,4736,8.462948177,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/14/1991,89,dominicks,5888,8.68067166,0,1.72,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/14/1991,89,minute.maid,4288,8.363575703,0,2.13,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/14/1991,89,tropicana,3584,8.184234774,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/14/1991,90,dominicks,2816,7.943072717,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/14/1991,90,minute.maid,6144,8.723231275,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/14/1991,90,tropicana,2624,7.87245515,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/14/1991,91,dominicks,6656,8.803273983,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/14/1991,91,minute.maid,5120,8.540909718,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/14/1991,91,tropicana,4544,8.42156296,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/14/1991,92,dominicks,4864,8.489616424,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/14/1991,92,minute.maid,4416,8.392989588,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/14/1991,92,tropicana,3776,8.236420527,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/14/1991,93,dominicks,4928,8.502688505,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/14/1991,93,minute.maid,9472,9.156095357,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/14/1991,93,tropicana,12224,9.411156511,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/14/1991,94,dominicks,9792,9.189321005,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/14/1991,94,minute.maid,9536,9.162829389,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/14/1991,94,tropicana,5952,8.691482577,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/14/1991,95,dominicks,5760,8.658692754,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/14/1991,95,minute.maid,5120,8.540909718,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/14/1991,95,tropicana,3200,8.070906089,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/14/1991,97,dominicks,4736,8.462948177,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/14/1991,97,minute.maid,3904,8.269756948,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/14/1991,97,tropicana,1664,7.416979621,0,3.12,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/14/1991,98,dominicks,15040,9.618468598,0,1.75,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/14/1991,98,minute.maid,9216,9.128696383,0,2.03,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/14/1991,98,tropicana,13120,9.481893063,0,2.98,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/14/1991,100,dominicks,19840,9.895455381,0,1.74,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/14/1991,100,minute.maid,10304,9.240287448,0,2.13,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/14/1991,100,tropicana,12096,9.400630098,0,3.19,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/14/1991,101,dominicks,7104,8.868413285,0,1.75,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/14/1991,101,minute.maid,6272,8.743850562,0,2.12,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/14/1991,101,tropicana,8448,9.041685006,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/14/1991,102,dominicks,22656,10.02818,0,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/14/1991,102,minute.maid,8896,9.093357017,0,1.95,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/14/1991,102,tropicana,7680,8.946374826,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/14/1991,103,dominicks,9024,9.107642974,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/14/1991,103,minute.maid,4160,8.333270353,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/14/1991,103,tropicana,4096,8.317766167,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/14/1991,104,dominicks,8896,9.093357017,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/14/1991,104,minute.maid,3520,8.166216269,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/14/1991,104,tropicana,5696,8.647519453,0,3.14,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/14/1991,105,dominicks,15168,9.626943225,0,1.73,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/14/1991,105,minute.maid,6912,8.841014311,0,2.09,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/14/1991,105,tropicana,4800,8.476371197,0,2.97,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/14/1991,106,dominicks,6400,8.764053269,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/14/1991,106,minute.maid,4416,8.392989588,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/14/1991,106,tropicana,2240,7.714231145,0,3.1,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/14/1991,107,dominicks,3776,8.236420527,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/14/1991,107,minute.maid,10624,9.270870872,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/14/1991,107,tropicana,12544,9.436997743,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/14/1991,109,dominicks,5376,8.589699882,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/14/1991,109,minute.maid,12032,9.395325046,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/14/1991,109,tropicana,17088,9.746131742,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/14/1991,110,dominicks,8576,9.056722883,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/14/1991,110,minute.maid,5632,8.636219898,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/14/1991,110,tropicana,7168,8.877381955,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/14/1991,111,dominicks,10496,9.258749511,0,1.73,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/14/1991,111,minute.maid,11264,9.329367078,0,2.11,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/14/1991,111,tropicana,5696,8.647519453,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/14/1991,112,dominicks,10496,9.258749511,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/14/1991,112,minute.maid,12928,9.467150781,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/14/1991,112,tropicana,15488,9.64782081,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/14/1991,113,dominicks,8384,9.034080407,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/14/1991,113,minute.maid,7104,8.868413285,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/14/1991,113,tropicana,8768,9.078864009,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/14/1991,114,dominicks,19328,9.869310101,0,1.73,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/14/1991,114,minute.maid,9984,9.208739091,0,2.08,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/14/1991,114,tropicana,4096,8.317766167,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/14/1991,115,dominicks,4480,8.407378325,0,1.74,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/14/1991,115,minute.maid,7168,8.877381955,0,2.1,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/14/1991,115,tropicana,9536,9.162829389,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/14/1991,116,dominicks,8384,9.034080407,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/14/1991,116,minute.maid,4352,8.378390789,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/14/1991,116,tropicana,6144,8.723231275,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/14/1991,117,dominicks,6080,8.712759975,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/14/1991,117,minute.maid,4864,8.489616424,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/14/1991,117,tropicana,4864,8.489616424,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/14/1991,118,dominicks,3776,8.236420527,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/14/1991,118,minute.maid,7296,8.895081532,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/14/1991,118,tropicana,4800,8.476371197,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/14/1991,119,dominicks,4352,8.378390789,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/14/1991,119,minute.maid,5568,8.624791202,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/14/1991,119,tropicana,5888,8.68067166,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/14/1991,121,dominicks,7872,8.971067439,0,1.73,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/14/1991,121,minute.maid,7872,8.971067439,0,2.1,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/14/1991,121,tropicana,8832,9.086136769,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/14/1991,122,dominicks,11584,9.357380115,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/14/1991,122,minute.maid,11328,9.335032816,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/14/1991,122,tropicana,12096,9.400630098,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/14/1991,123,dominicks,7680,8.946374826,0,1.74,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/14/1991,123,minute.maid,9024,9.107642974,0,2.16,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/14/1991,123,tropicana,7168,8.877381955,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/14/1991,124,dominicks,17664,9.779283949,0,1.75,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/14/1991,124,minute.maid,7680,8.946374826,0,2.04,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/14/1991,124,tropicana,3840,8.253227646,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/14/1991,126,dominicks,10240,9.234056899,0,1.74,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/14/1991,126,minute.maid,8640,9.064157862,0,2.14,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/14/1991,126,tropicana,11008,9.30637756,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/14/1991,128,dominicks,11392,9.340666634,0,1.74,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/14/1991,128,minute.maid,12928,9.467150781,0,2.15,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/14/1991,128,tropicana,10176,9.227787286,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/14/1991,129,dominicks,4992,8.51559191,0,1.73,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/14/1991,129,minute.maid,9024,9.107642974,0,2.09,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/14/1991,129,tropicana,8448,9.041685006,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/14/1991,130,dominicks,8448,9.041685006,0,1.76,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/14/1991,130,minute.maid,31424,10.35532721,0,2.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/14/1991,130,tropicana,4224,8.348537825,0,3.07,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/14/1991,131,dominicks,8384,9.034080407,0,1.73,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/14/1991,131,minute.maid,6336,8.754002934,0,2.12,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/14/1991,131,tropicana,7680,8.946374826,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/14/1991,132,dominicks,17728,9.78290059,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/14/1991,132,minute.maid,11200,9.323669057,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/14/1991,132,tropicana,7936,8.979164649,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/14/1991,134,dominicks,4416,8.392989588,0,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/14/1991,134,minute.maid,3904,8.269756948,0,2.12,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/14/1991,134,tropicana,3840,8.253227646,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/14/1991,137,dominicks,18176,9.807857322,0,1.7,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/14/1991,137,minute.maid,16704,9.723403491,0,2.07,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/14/1991,137,tropicana,20032,9.905086274,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/14/1991,83,dominicks,10496,9.258749511,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/14/1991,83,minute.maid,6976,8.850230966,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/14/1991,83,tropicana,6848,8.831711918,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/14/1991,84,dominicks,6336,8.754002934,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/14/1991,84,minute.maid,6656,8.803273983,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/14/1991,84,tropicana,5056,8.528330936,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/21/1991,2,dominicks,3008,8.009030685,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/21/1991,2,minute.maid,12480,9.431882642,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/21/1991,2,tropicana,34240,10.44114983,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/21/1991,5,dominicks,3456,8.14786713,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/21/1991,5,minute.maid,10112,9.221478116,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/21/1991,5,tropicana,69504,11.14913958,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/21/1991,8,dominicks,16448,9.707959168,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/21/1991,8,minute.maid,9216,9.128696383,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/21/1991,8,tropicana,54016,10.89703558,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/21/1991,9,dominicks,1728,7.454719949,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/21/1991,9,minute.maid,30400,10.32219789,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/21/1991,9,tropicana,75712,11.23469195,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/21/1991,12,dominicks,3456,8.14786713,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/21/1991,12,minute.maid,18560,9.828764006,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/21/1991,12,tropicana,110848,11.61591517,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/21/1991,14,dominicks,1664,7.416979621,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/21/1991,14,minute.maid,15488,9.64782081,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/21/1991,14,tropicana,66880,11.11065525,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/21/1991,21,dominicks,11328,9.335032816,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/21/1991,21,minute.maid,7296,8.895081532,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/21/1991,21,tropicana,25280,10.13776885,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/21/1991,28,dominicks,2816,7.943072717,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/21/1991,28,minute.maid,4224,8.348537825,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/21/1991,28,tropicana,31808,10.36747311,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/21/1991,32,dominicks,2816,7.943072717,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/21/1991,32,minute.maid,15488,9.64782081,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/21/1991,32,tropicana,100352,11.51643928,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/21/1991,33,dominicks,3008,8.009030685,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/21/1991,33,minute.maid,12736,9.452187908,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/21/1991,33,tropicana,61696,11.02997438,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/21/1991,40,dominicks,9472,9.156095357,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/21/1991,40,minute.maid,8000,8.987196821,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/21/1991,40,tropicana,41408,10.63122938,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/21/1991,44,dominicks,4352,8.378390789,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/21/1991,44,minute.maid,11136,9.317938383,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/21/1991,44,tropicana,57408,10.95793895,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/21/1991,45,dominicks,4224,8.348537825,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/21/1991,45,minute.maid,4736,8.462948177,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/21/1991,45,tropicana,28928,10.27256526,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/21/1991,49,dominicks,2816,7.943072717,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/21/1991,49,minute.maid,7616,8.938006577,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/21/1991,49,tropicana,18176,9.807857322,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/21/1991,50,dominicks,5696,8.647519453,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/21/1991,50,minute.maid,6656,8.803273983,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/21/1991,50,tropicana,17408,9.76468515,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/21/1991,51,dominicks,3072,8.030084094,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/21/1991,51,minute.maid,6144,8.723231275,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/21/1991,51,tropicana,49856,10.81689413,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/21/1991,52,dominicks,2048,7.624618986,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/21/1991,52,minute.maid,12864,9.462187991,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/21/1991,52,tropicana,95296,11.46474312,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/21/1991,53,dominicks,3840,8.253227646,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/21/1991,53,minute.maid,11328,9.335032816,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/21/1991,53,tropicana,97792,11.49059805,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/21/1991,54,dominicks,4608,8.435549202,0,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/21/1991,54,minute.maid,8064,8.99516499,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/21/1991,54,tropicana,38080,10.54744449,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/21/1991,56,dominicks,4736,8.462948177,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/21/1991,56,minute.maid,6592,8.793612072,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/21/1991,56,tropicana,42816,10.66466714,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/21/1991,59,dominicks,11008,9.30637756,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/21/1991,59,minute.maid,7552,8.929567708,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/21/1991,59,tropicana,36864,10.51499074,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/21/1991,62,dominicks,896,6.797940413,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/21/1991,62,minute.maid,14080,9.55251063,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/21/1991,62,tropicana,74368,11.21678102,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/21/1991,67,dominicks,5312,8.577723691,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/21/1991,67,minute.maid,8064,8.99516499,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/21/1991,67,tropicana,47424,10.76688371,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/21/1991,68,dominicks,3072,8.030084094,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/21/1991,68,minute.maid,12544,9.436997743,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/21/1991,68,tropicana,71104,11.17189887,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/21/1991,70,dominicks,14784,9.601300794,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/21/1991,70,minute.maid,13440,9.505990614,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/21/1991,70,tropicana,23360,10.05878044,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/21/1991,71,dominicks,1920,7.560080465,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/21/1991,71,minute.maid,7040,8.859363449,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/21/1991,71,tropicana,48128,10.78161941,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/21/1991,72,dominicks,1280,7.154615357,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/21/1991,72,minute.maid,10304,9.240287448,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/21/1991,72,tropicana,82944,11.32592096,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/21/1991,73,dominicks,27648,10.22730867,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/21/1991,73,minute.maid,16768,9.727227587,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/21/1991,73,tropicana,46656,10.75055682,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/21/1991,74,dominicks,2816,7.943072717,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/21/1991,74,minute.maid,8896,9.093357017,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/21/1991,74,tropicana,52544,10.86940619,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/21/1991,75,dominicks,2112,7.655390645,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/21/1991,75,minute.maid,14272,9.566054855,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/21/1991,75,tropicana,50304,10.82583988,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/21/1991,76,dominicks,7616,8.938006577,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/21/1991,76,minute.maid,9600,9.169518378,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/21/1991,76,tropicana,61120,11.02059442,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/21/1991,77,dominicks,6848,8.831711918,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/21/1991,77,minute.maid,11008,9.30637756,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/21/1991,77,tropicana,35392,10.47424109,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/21/1991,78,dominicks,11072,9.312174678,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/21/1991,78,minute.maid,9920,9.2023082,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/21/1991,78,tropicana,44288,10.69846904,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/21/1991,80,dominicks,8128,9.00307017,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/21/1991,80,minute.maid,13312,9.496421163,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/21/1991,80,tropicana,57664,10.96238834,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/21/1991,81,dominicks,5696,8.647519453,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/21/1991,81,minute.maid,10496,9.258749511,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/21/1991,81,tropicana,67456,11.11923081,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/21/1991,86,dominicks,6784,8.822322178,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/21/1991,86,minute.maid,6080,8.712759975,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/21/1991,86,tropicana,63552,11.05961375,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/21/1991,88,dominicks,3264,8.090708716,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/21/1991,88,minute.maid,8768,9.078864009,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/21/1991,88,tropicana,11392,9.340666634,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/21/1991,89,dominicks,6464,8.7740036,0,1.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/21/1991,89,minute.maid,8832,9.086136769,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/21/1991,89,tropicana,49024,10.80006525,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/21/1991,90,dominicks,4352,8.378390789,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/21/1991,90,minute.maid,8448,9.041685006,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/21/1991,90,tropicana,36672,10.5097688,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/21/1991,91,dominicks,5184,8.553332238,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/21/1991,91,minute.maid,6016,8.702177866,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/21/1991,91,tropicana,40896,10.61878754,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/21/1991,92,dominicks,7232,8.886270902,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/21/1991,92,minute.maid,8576,9.056722883,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/21/1991,92,tropicana,45376,10.72273861,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/21/1991,93,dominicks,3520,8.166216269,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/21/1991,93,minute.maid,11328,9.335032816,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/21/1991,93,tropicana,57472,10.95905315,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/21/1991,94,dominicks,8384,9.034080407,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/21/1991,94,minute.maid,9728,9.182763604,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/21/1991,94,tropicana,27584,10.22499117,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/21/1991,95,dominicks,896,6.797940413,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/21/1991,95,minute.maid,5056,8.528330936,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/21/1991,95,tropicana,21696,9.984883191,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/21/1991,97,dominicks,4224,8.348537825,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/21/1991,97,minute.maid,7424,8.912473275,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/21/1991,97,tropicana,15680,9.660141294,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/21/1991,98,dominicks,10112,9.221478116,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/21/1991,98,minute.maid,11200,9.323669057,0,1.98,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/21/1991,98,tropicana,80192,11.29217904,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/21/1991,100,dominicks,5952,8.691482577,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/21/1991,100,minute.maid,15168,9.626943225,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/21/1991,100,tropicana,95232,11.4640713,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/21/1991,101,dominicks,6144,8.723231275,0,1.89,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/21/1991,101,minute.maid,8768,9.078864009,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/21/1991,101,tropicana,42688,10.66167313,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/21/1991,102,dominicks,30336,10.32009041,0,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/21/1991,102,minute.maid,12736,9.452187908,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/21/1991,102,tropicana,69504,11.14913958,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/21/1991,103,dominicks,11584,9.357380115,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/21/1991,103,minute.maid,4288,8.363575703,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/21/1991,103,tropicana,30528,10.32639957,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/21/1991,104,dominicks,4160,8.333270353,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/21/1991,104,minute.maid,6848,8.831711918,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/21/1991,104,tropicana,37120,10.52191119,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/21/1991,105,dominicks,9344,9.142489705,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/21/1991,105,minute.maid,9024,9.107642974,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/21/1991,105,tropicana,41984,10.64504387,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/21/1991,106,dominicks,3200,8.070906089,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/21/1991,106,minute.maid,6208,8.733594062,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/21/1991,106,tropicana,18560,9.828764006,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/21/1991,107,dominicks,3008,8.009030685,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/21/1991,107,minute.maid,41664,10.63739273,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/21/1991,107,tropicana,53312,10.88391673,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/21/1991,109,dominicks,1664,7.416979621,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/21/1991,109,minute.maid,17152,9.749870064,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/21/1991,109,tropicana,133504,11.80188672,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/21/1991,110,dominicks,6400,8.764053269,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/21/1991,110,minute.maid,8832,9.086136769,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/21/1991,110,tropicana,44032,10.69267192,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/21/1991,111,dominicks,3584,8.184234774,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/21/1991,111,minute.maid,16064,9.684336023,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/21/1991,111,tropicana,72960,11.19766663,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/21/1991,112,dominicks,12864,9.462187991,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/21/1991,112,minute.maid,13568,9.515469358,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/21/1991,112,tropicana,63040,11.05152473,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/21/1991,113,dominicks,5440,8.60153434,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/21/1991,113,minute.maid,11648,9.36288977,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/21/1991,113,tropicana,82816,11.32437656,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/21/1991,114,dominicks,12672,9.447150114,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/21/1991,114,minute.maid,14272,9.566054855,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/21/1991,114,tropicana,76032,11.23890958,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/21/1991,115,dominicks,2752,7.920083199,0,1.89,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/21/1991,115,minute.maid,9216,9.128696383,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/21/1991,115,tropicana,59776,10.99835952,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/21/1991,116,dominicks,5696,8.647519453,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/21/1991,116,minute.maid,9216,9.128696383,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/21/1991,116,tropicana,30656,10.33058368,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/21/1991,117,dominicks,4416,8.392989588,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/21/1991,117,minute.maid,6592,8.793612072,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/21/1991,117,tropicana,31552,10.35939226,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/21/1991,118,dominicks,2624,7.87245515,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/21/1991,118,minute.maid,51776,10.854682,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/21/1991,118,tropicana,21824,9.990765561,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/21/1991,119,dominicks,4800,8.476371197,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/21/1991,119,minute.maid,9536,9.162829389,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/21/1991,119,tropicana,44416,10.70135504,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/21/1991,121,dominicks,6848,8.831711918,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/21/1991,121,minute.maid,9536,9.162829389,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/21/1991,121,tropicana,53568,10.88870715,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/21/1991,122,dominicks,12480,9.431882642,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/21/1991,122,minute.maid,11904,9.384629757,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/21/1991,122,tropicana,54592,10.90764263,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/21/1991,123,dominicks,3712,8.219326094,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/21/1991,123,minute.maid,11200,9.323669057,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/21/1991,123,tropicana,104704,11.5588926,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/21/1991,124,dominicks,5504,8.61323038,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/21/1991,124,minute.maid,15616,9.656051309,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/21/1991,124,tropicana,62144,11.03720955,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/21/1991,126,dominicks,7552,8.929567708,0,1.89,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/21/1991,126,minute.maid,16064,9.684336023,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/21/1991,126,tropicana,81408,11.30722883,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/21/1991,128,dominicks,2176,7.685243608,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/21/1991,128,minute.maid,12800,9.45720045,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/21/1991,128,tropicana,147136,11.89911261,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/21/1991,129,dominicks,4032,8.30201781,0,1.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/21/1991,129,minute.maid,12672,9.447150114,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/21/1991,129,tropicana,42304,10.65263692,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/21/1991,130,dominicks,6656,8.803273983,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/21/1991,130,minute.maid,26560,10.1871616,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/21/1991,130,tropicana,89600,11.4031106,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/21/1991,131,dominicks,4608,8.435549202,0,1.89,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/21/1991,131,minute.maid,10752,9.282847063,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/21/1991,131,tropicana,56896,10.94898032,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/21/1991,132,dominicks,20672,9.936535407,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/21/1991,132,minute.maid,10880,9.29468152,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/21/1991,132,tropicana,63232,11.05456578,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/21/1991,134,dominicks,5056,8.528330936,0,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/21/1991,134,minute.maid,4096,8.317766167,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/21/1991,134,tropicana,21440,9.973013615,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/21/1991,137,dominicks,3584,8.184234774,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/21/1991,137,minute.maid,31808,10.36747311,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/21/1991,137,tropicana,123136,11.72104472,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/21/1991,18,dominicks,10112,9.221478116,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/21/1991,18,minute.maid,13440,9.505990614,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/21/1991,18,tropicana,69312,11.14637333,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/21/1991,64,dominicks,4352,8.378390789,0,1.89,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/21/1991,64,minute.maid,5056,8.528330936,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/21/1991,64,tropicana,19328,9.869310101,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/21/1991,83,dominicks,15744,9.664214619,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/21/1991,83,minute.maid,8832,9.086136769,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/21/1991,83,tropicana,61376,11.02477416,1,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/21/1991,84,dominicks,5056,8.528330936,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/21/1991,84,minute.maid,8704,9.071537969,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/21/1991,84,tropicana,38272,10.55247384,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/28/1991,2,dominicks,19456,9.875910785,1,1.5,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/28/1991,2,minute.maid,9664,9.17616292,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/28/1991,2,tropicana,7168,8.877381955,0,2.64,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -11/28/1991,5,dominicks,25856,10.16029796,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/28/1991,5,minute.maid,8384,9.034080407,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/28/1991,5,tropicana,8960,9.100525506,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -11/28/1991,8,dominicks,27968,10.23881628,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/28/1991,8,minute.maid,7680,8.946374826,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/28/1991,8,tropicana,10368,9.246479419,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -11/28/1991,9,dominicks,18816,9.842462851,1,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/28/1991,9,minute.maid,8960,9.100525506,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/28/1991,9,tropicana,9920,9.2023082,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -11/28/1991,12,dominicks,39296,10.57887801,1,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/28/1991,12,minute.maid,13824,9.534161491,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/28/1991,12,tropicana,6592,8.793612072,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -11/28/1991,14,dominicks,17600,9.775654181,1,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/28/1991,14,minute.maid,9344,9.142489705,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/28/1991,14,tropicana,11136,9.317938383,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -11/28/1991,21,dominicks,17472,9.768354879,1,1.5,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/28/1991,21,minute.maid,7296,8.895081532,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/28/1991,21,tropicana,6144,8.723231275,0,2.53,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -11/28/1991,28,dominicks,9728,9.182763604,1,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/28/1991,28,minute.maid,3456,8.14786713,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/28/1991,28,tropicana,4096,8.317766167,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -11/28/1991,32,dominicks,31424,10.35532721,1,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/28/1991,32,minute.maid,13184,9.486759252,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/28/1991,32,tropicana,16320,9.700146629,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -11/28/1991,33,dominicks,18496,9.825309772,1,1.5,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/28/1991,33,minute.maid,10816,9.288781798,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/28/1991,33,tropicana,13632,9.520175249,0,2.84,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -11/28/1991,40,dominicks,30336,10.32009041,1,1.5,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/28/1991,40,minute.maid,5888,8.68067166,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/28/1991,40,tropicana,3968,8.286017468,0,2.55,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -11/28/1991,44,dominicks,19264,9.865993348,1,1.5,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/28/1991,44,minute.maid,10496,9.258749511,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/28/1991,44,tropicana,11200,9.323669057,0,2.72,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -11/28/1991,45,dominicks,17024,9.742379392,1,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/28/1991,45,minute.maid,4160,8.333270353,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/28/1991,45,tropicana,7232,8.886270902,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -11/28/1991,47,dominicks,18112,9.804329981,1,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/28/1991,47,minute.maid,7616,8.938006577,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/28/1991,47,tropicana,5312,8.577723691,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -11/28/1991,49,dominicks,8768,9.078864009,1,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/28/1991,49,minute.maid,5504,8.61323038,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/28/1991,49,tropicana,4224,8.348537825,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -11/28/1991,50,dominicks,16000,9.680344001,1,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/28/1991,50,minute.maid,5184,8.553332238,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/28/1991,50,tropicana,3072,8.030084094,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -11/28/1991,51,dominicks,9408,9.14931567,1,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/28/1991,51,minute.maid,5888,8.68067166,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/28/1991,51,tropicana,7616,8.938006577,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -11/28/1991,52,dominicks,47808,10.77494827,1,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/28/1991,52,minute.maid,12416,9.426741242,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/28/1991,52,tropicana,14336,9.570529135,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -11/28/1991,53,dominicks,22848,10.03661887,1,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/28/1991,53,minute.maid,12288,9.416378455,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/28/1991,53,tropicana,13888,9.538780437,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -11/28/1991,54,dominicks,16896,9.734832187,1,1.5,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/28/1991,54,minute.maid,6144,8.723231275,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/28/1991,54,tropicana,7488,8.921057018,0,2.62,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -11/28/1991,56,dominicks,15552,9.651944527,1,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/28/1991,56,minute.maid,5632,8.636219898,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/28/1991,56,tropicana,7360,8.903815212,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -11/28/1991,59,dominicks,19968,9.901886271,1,1.5,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/28/1991,59,minute.maid,5056,8.528330936,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/28/1991,59,tropicana,7872,8.971067439,0,2.6,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -11/28/1991,62,dominicks,13120,9.481893063,1,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/28/1991,62,minute.maid,11264,9.329367078,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/28/1991,62,tropicana,15488,9.64782081,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -11/28/1991,67,dominicks,7168,8.877381955,1,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/28/1991,67,minute.maid,6272,8.743850562,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/28/1991,67,tropicana,9280,9.135616826,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -11/28/1991,68,dominicks,25600,10.15034763,1,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/28/1991,68,minute.maid,9472,9.156095357,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/28/1991,68,tropicana,4864,8.489616424,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -11/28/1991,70,dominicks,19264,9.865993348,1,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/28/1991,70,minute.maid,7104,8.868413285,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/28/1991,70,tropicana,5568,8.624791202,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -11/28/1991,71,dominicks,25728,10.15533517,1,1.5,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/28/1991,71,minute.maid,5568,8.624791202,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/28/1991,71,tropicana,4544,8.42156296,0,2.71,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -11/28/1991,72,dominicks,21504,9.975994243,1,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/28/1991,72,minute.maid,10432,9.252633284,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/28/1991,72,tropicana,14016,9.547954813,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -11/28/1991,73,dominicks,70656,11.16557831,1,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/28/1991,73,minute.maid,11456,9.346268889,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/28/1991,73,tropicana,8128,9.00307017,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -11/28/1991,74,dominicks,46336,10.74367448,1,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/28/1991,74,minute.maid,8000,8.987196821,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/28/1991,74,tropicana,9408,9.14931567,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -11/28/1991,75,dominicks,21632,9.981928979,1,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/28/1991,75,minute.maid,12736,9.452187908,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/28/1991,75,tropicana,8512,9.049232212,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -11/28/1991,76,dominicks,50240,10.8245668,1,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/28/1991,76,minute.maid,6656,8.803273983,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/28/1991,76,tropicana,11712,9.368369236,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -11/28/1991,77,dominicks,16320,9.700146629,1,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/28/1991,77,minute.maid,6656,8.803273983,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/28/1991,77,tropicana,8192,9.010913347,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -11/28/1991,78,dominicks,28480,10.25695737,1,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/28/1991,78,minute.maid,8576,9.056722883,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/28/1991,78,tropicana,7488,8.921057018,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -11/28/1991,80,dominicks,13504,9.510741217,1,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/28/1991,80,minute.maid,10304,9.240287448,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/28/1991,80,tropicana,10176,9.227787286,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -11/28/1991,81,dominicks,29760,10.30092049,1,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/28/1991,81,minute.maid,8000,8.987196821,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/28/1991,81,tropicana,12992,9.472089062,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -11/28/1991,86,dominicks,27392,10.21800628,1,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/28/1991,86,minute.maid,5696,8.647519453,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/28/1991,86,tropicana,11776,9.373818841,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -11/28/1991,88,dominicks,24000,10.08580911,1,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/28/1991,88,minute.maid,5056,8.528330936,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/28/1991,88,tropicana,4352,8.378390789,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -11/28/1991,89,dominicks,35840,10.48681987,1,1.5,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/28/1991,89,minute.maid,9664,9.17616292,0,1.7,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/28/1991,89,tropicana,5632,8.636219898,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -11/28/1991,90,dominicks,19072,9.85597657,1,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/28/1991,90,minute.maid,5312,8.577723691,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/28/1991,90,tropicana,4224,8.348537825,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -11/28/1991,91,dominicks,23360,10.05878044,1,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/28/1991,91,minute.maid,4928,8.502688505,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/28/1991,91,tropicana,6080,8.712759975,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -11/28/1991,92,dominicks,34112,10.43740451,1,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/28/1991,92,minute.maid,3776,8.236420527,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/28/1991,92,tropicana,4992,8.51559191,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -11/28/1991,93,dominicks,29184,10.28137589,1,1.5,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/28/1991,93,minute.maid,6464,8.7740036,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/28/1991,93,tropicana,10752,9.282847063,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -11/28/1991,94,dominicks,19712,9.888982866,1,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/28/1991,94,minute.maid,8640,9.064157862,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/28/1991,94,tropicana,7168,8.877381955,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -11/28/1991,95,dominicks,5888,8.68067166,1,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/28/1991,95,minute.maid,4928,8.502688505,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/28/1991,95,tropicana,4224,8.348537825,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -11/28/1991,97,dominicks,12096,9.400630098,1,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/28/1991,97,minute.maid,4352,8.378390789,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/28/1991,97,tropicana,1664,7.416979621,0,3.12,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -11/28/1991,98,dominicks,25152,10.1326927,1,1.51,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/28/1991,98,minute.maid,8000,8.987196821,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/28/1991,98,tropicana,13120,9.481893063,0,2.57,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -11/28/1991,100,dominicks,36032,10.49216271,1,1.5,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/28/1991,100,minute.maid,11648,9.36288977,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/28/1991,100,tropicana,17728,9.78290059,0,2.58,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -11/28/1991,101,dominicks,21568,9.978966014,1,1.5,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/28/1991,101,minute.maid,7744,8.954673629,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/28/1991,101,tropicana,9344,9.142489705,0,2.75,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -11/28/1991,102,dominicks,63808,11.06363385,1,1.5,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/28/1991,102,minute.maid,8768,9.078864009,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/28/1991,102,tropicana,11968,9.3899917,0,2.6,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -11/28/1991,103,dominicks,24512,10.10691807,1,1.5,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/28/1991,103,minute.maid,3904,8.269756948,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/28/1991,103,tropicana,4928,8.502688505,0,2.53,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -11/28/1991,104,dominicks,10240,9.234056899,1,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/28/1991,104,minute.maid,8448,9.041685006,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/28/1991,104,tropicana,4608,8.435549202,0,3.11,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -11/28/1991,105,dominicks,39808,10.59182318,1,1.51,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/28/1991,105,minute.maid,7040,8.859363449,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/28/1991,105,tropicana,9152,9.121727714,0,2.56,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -11/28/1991,106,dominicks,13504,9.510741217,1,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/28/1991,106,minute.maid,3968,8.286017468,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/28/1991,106,tropicana,2688,7.896552702,0,3.12,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -11/28/1991,107,dominicks,26304,10.1774763,1,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/28/1991,107,minute.maid,8384,9.034080407,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/28/1991,107,tropicana,12160,9.405907156,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -11/28/1991,109,dominicks,35968,10.49038493,1,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/28/1991,109,minute.maid,12800,9.45720045,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/28/1991,109,tropicana,12544,9.436997743,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -11/28/1991,110,dominicks,28672,10.26367632,1,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/28/1991,110,minute.maid,6272,8.743850562,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/28/1991,110,tropicana,6528,8.783855897,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -11/28/1991,111,dominicks,74240,11.21505837,1,1.5,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/28/1991,111,minute.maid,8640,9.064157862,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/28/1991,111,tropicana,5120,8.540909718,0,2.44,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -11/28/1991,112,dominicks,23104,10.04776104,1,1.51,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/28/1991,112,minute.maid,10560,9.264828557,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/28/1991,112,tropicana,13760,9.529521112,0,2.77,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -11/28/1991,113,dominicks,24832,10.11988842,1,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/28/1991,113,minute.maid,5952,8.691482577,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/28/1991,113,tropicana,9088,9.114710141,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -11/28/1991,114,dominicks,77184,11.25394746,1,1.5,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/28/1991,114,minute.maid,10304,9.240287448,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/28/1991,114,tropicana,9216,9.128696383,0,2.51,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -11/28/1991,115,dominicks,28864,10.27035042,1,1.5,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/28/1991,115,minute.maid,6976,8.850230966,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/28/1991,115,tropicana,11648,9.36288977,0,2.62,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -11/28/1991,116,dominicks,13696,9.524859098,1,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/28/1991,116,minute.maid,6400,8.764053269,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/28/1991,116,tropicana,4352,8.378390789,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -11/28/1991,117,dominicks,16512,9.711842668,1,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/28/1991,117,minute.maid,5504,8.61323038,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/28/1991,117,tropicana,5760,8.658692754,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -11/28/1991,118,dominicks,16192,9.692272572,1,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/28/1991,118,minute.maid,14912,9.609921537,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/28/1991,118,tropicana,6144,8.723231275,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -11/28/1991,119,dominicks,18048,9.800790154,1,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/28/1991,119,minute.maid,7104,8.868413285,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/28/1991,119,tropicana,8256,9.018695488,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -11/28/1991,121,dominicks,36992,10.51845695,1,1.51,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/28/1991,121,minute.maid,8064,8.99516499,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/28/1991,121,tropicana,9600,9.169518378,0,2.74,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -11/28/1991,122,dominicks,29760,10.30092049,1,1.5,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/28/1991,122,minute.maid,10240,9.234056899,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/28/1991,122,tropicana,11136,9.317938383,0,2.33,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -11/28/1991,123,dominicks,49088,10.80136989,1,1.5,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/28/1991,123,minute.maid,10816,9.288781798,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/28/1991,123,tropicana,13056,9.477003077,0,2.36,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -11/28/1991,124,dominicks,34752,10.4559924,1,1.5,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/28/1991,124,minute.maid,12544,9.436997743,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/28/1991,124,tropicana,13056,9.477003077,0,2.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -11/28/1991,126,dominicks,23936,10.08313888,1,1.5,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/28/1991,126,minute.maid,9792,9.189321005,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/28/1991,126,tropicana,10176,9.227787286,0,2.76,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -11/28/1991,128,dominicks,37760,10.53900562,1,1.5,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/28/1991,128,minute.maid,20352,9.920934466,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/28/1991,128,tropicana,23552,10.06696602,0,2.6,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -11/28/1991,129,dominicks,23744,10.07508515,1,1.5,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/28/1991,129,minute.maid,7360,8.903815212,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/28/1991,129,tropicana,8576,9.056722883,0,2.77,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -11/28/1991,130,dominicks,61952,11.03411517,1,1.5,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/28/1991,130,minute.maid,16000,9.680344001,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/28/1991,130,tropicana,12864,9.462187991,0,2.31,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -11/28/1991,131,dominicks,33280,10.4127119,1,1.5,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/28/1991,131,minute.maid,7744,8.954673629,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/28/1991,131,tropicana,8384,9.034080407,0,2.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -11/28/1991,132,dominicks,41728,10.63892765,1,1.5,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/28/1991,132,minute.maid,10496,9.258749511,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/28/1991,132,tropicana,8000,8.987196821,0,2.84,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -11/28/1991,134,dominicks,12160,9.405907156,1,1.5,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/28/1991,134,minute.maid,5376,8.589699882,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/28/1991,134,tropicana,3648,8.201934351,0,2.72,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -11/28/1991,137,dominicks,35648,10.48144832,1,1.5,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/28/1991,137,minute.maid,22656,10.02818,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/28/1991,137,tropicana,24768,10.11730778,0,2.84,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -11/28/1991,18,dominicks,32704,10.39525267,1,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/28/1991,18,minute.maid,10432,9.252633284,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/28/1991,18,tropicana,7488,8.921057018,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -11/28/1991,64,dominicks,18048,9.800790154,1,1.5,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/28/1991,64,minute.maid,3584,8.184234774,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/28/1991,64,tropicana,3904,8.269756948,0,2.55,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -11/28/1991,83,dominicks,43968,10.69121738,1,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/28/1991,83,minute.maid,6272,8.743850562,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/28/1991,83,tropicana,8000,8.987196821,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -11/28/1991,84,dominicks,20160,9.911455722,1,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/28/1991,84,minute.maid,7488,8.921057018,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -11/28/1991,84,tropicana,6464,8.7740036,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/5/1991,2,dominicks,16768,9.727227587,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/5/1991,2,minute.maid,7168,8.877381955,0,2.06,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/5/1991,2,tropicana,6080,8.712759975,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/5/1991,5,dominicks,25728,10.15533517,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/5/1991,5,minute.maid,11456,9.346268889,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/5/1991,5,tropicana,6912,8.841014311,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/5/1991,8,dominicks,37824,10.5406991,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/5/1991,8,minute.maid,7296,8.895081532,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/5/1991,8,tropicana,5568,8.624791202,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/5/1991,9,dominicks,22080,10.0024275,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/5/1991,9,minute.maid,7424,8.912473275,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/5/1991,9,tropicana,6848,8.831711918,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/5/1991,12,dominicks,58560,10.97780715,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/5/1991,12,minute.maid,9024,9.107642974,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/5/1991,12,tropicana,5248,8.565602331,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/5/1991,14,dominicks,20352,9.920934466,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/5/1991,14,minute.maid,7808,8.962904128,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/5/1991,14,tropicana,10752,9.282847063,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/5/1991,21,dominicks,17024,9.742379392,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/5/1991,21,minute.maid,6656,8.803273983,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/5/1991,21,tropicana,4224,8.348537825,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/5/1991,28,dominicks,14208,9.561560465,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/5/1991,28,minute.maid,4224,8.348537825,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/5/1991,28,tropicana,4288,8.363575703,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/5/1991,32,dominicks,44480,10.70279493,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/5/1991,32,minute.maid,10176,9.227787286,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/5/1991,32,tropicana,8384,9.034080407,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/5/1991,33,dominicks,18048,9.800790154,0,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/5/1991,33,minute.maid,8576,9.056722883,0,2.04,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/5/1991,33,tropicana,10752,9.282847063,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/5/1991,40,dominicks,27840,10.23422911,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/5/1991,40,minute.maid,7104,8.868413285,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/5/1991,40,tropicana,3776,8.236420527,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/5/1991,44,dominicks,30784,10.33475035,0,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/5/1991,44,minute.maid,7360,8.903815212,0,2.08,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/5/1991,44,tropicana,8576,9.056722883,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/5/1991,45,dominicks,11776,9.373818841,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/5/1991,45,minute.maid,4608,8.435549202,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/5/1991,45,tropicana,4736,8.462948177,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/5/1991,47,dominicks,16192,9.692272572,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/5/1991,47,minute.maid,4096,8.317766167,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/5/1991,47,tropicana,3072,8.030084094,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/5/1991,49,dominicks,10240,9.234056899,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/5/1991,49,minute.maid,3904,8.269756948,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/5/1991,49,tropicana,3264,8.090708716,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/5/1991,50,dominicks,9600,9.169518378,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/5/1991,50,minute.maid,5120,8.540909718,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/5/1991,50,tropicana,3328,8.110126802,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/5/1991,51,dominicks,21760,9.987828701,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/5/1991,51,minute.maid,5248,8.565602331,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/5/1991,51,tropicana,4864,8.489616424,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/5/1991,52,dominicks,21120,9.957975738,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/5/1991,52,minute.maid,10752,9.282847063,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/5/1991,52,tropicana,7104,8.868413285,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/5/1991,53,dominicks,25024,10.12759064,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/5/1991,53,minute.maid,10560,9.264828557,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/5/1991,53,tropicana,8576,9.056722883,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/5/1991,54,dominicks,15872,9.67231183,0,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/5/1991,54,minute.maid,6080,8.712759975,0,2.03,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/5/1991,54,tropicana,4800,8.476371197,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/5/1991,56,dominicks,14016,9.547954813,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/5/1991,56,minute.maid,5632,8.636219898,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/5/1991,56,tropicana,5568,8.624791202,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/5/1991,59,dominicks,18240,9.811372264,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/5/1991,59,minute.maid,4288,8.363575703,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/5/1991,59,tropicana,6912,8.841014311,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/5/1991,62,dominicks,17280,9.757305042,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/5/1991,62,minute.maid,10240,9.234056899,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/5/1991,62,tropicana,10944,9.30054664,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/5/1991,67,dominicks,95936,11.47143658,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/5/1991,67,minute.maid,6720,8.812843434,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/5/1991,67,tropicana,4480,8.407378325,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/5/1991,68,dominicks,20032,9.905086274,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/5/1991,68,minute.maid,9280,9.135616826,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/5/1991,68,tropicana,5632,8.636219898,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/5/1991,70,dominicks,13824,9.534161491,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/5/1991,70,minute.maid,8000,8.987196821,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/5/1991,70,tropicana,5504,8.61323038,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/5/1991,71,dominicks,26944,10.20151592,0,1.58,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/5/1991,71,minute.maid,3712,8.219326094,0,2.04,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/5/1991,71,tropicana,3776,8.236420527,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/5/1991,72,dominicks,29056,10.27698028,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/5/1991,72,minute.maid,9344,9.142489705,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/5/1991,72,tropicana,6464,8.7740036,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/5/1991,73,dominicks,68160,11.12961316,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/5/1991,73,minute.maid,12032,9.395325046,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/5/1991,73,tropicana,7680,8.946374826,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/5/1991,74,dominicks,36544,10.50627229,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/5/1991,74,minute.maid,7360,8.903815212,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/5/1991,74,tropicana,6208,8.733594062,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/5/1991,75,dominicks,17984,9.797237753,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/5/1991,75,minute.maid,9792,9.189321005,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/5/1991,75,tropicana,5056,8.528330936,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/5/1991,76,dominicks,41856,10.64199044,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/5/1991,76,minute.maid,3968,8.286017468,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/5/1991,76,tropicana,5696,8.647519453,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/5/1991,78,dominicks,21952,9.996613531,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/5/1991,78,minute.maid,7616,8.938006577,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/5/1991,78,tropicana,3840,8.253227646,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/5/1991,80,dominicks,14144,9.557045785,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/5/1991,80,minute.maid,10560,9.264828557,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/5/1991,80,tropicana,8064,8.99516499,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/5/1991,81,dominicks,14464,9.579418083,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/5/1991,81,minute.maid,5696,8.647519453,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/5/1991,81,tropicana,6464,8.7740036,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/5/1991,86,dominicks,20992,9.951896692,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/5/1991,86,minute.maid,4800,8.476371197,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/5/1991,86,tropicana,5120,8.540909718,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/5/1991,88,dominicks,19584,9.882468185,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/5/1991,88,minute.maid,4352,8.378390789,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/5/1991,88,tropicana,3264,8.090708716,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/5/1991,89,dominicks,26944,10.20151592,0,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/5/1991,89,minute.maid,8576,9.056722883,0,1.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/5/1991,89,tropicana,2624,7.87245515,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/5/1991,90,dominicks,19904,9.898675996,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/5/1991,90,minute.maid,6464,8.7740036,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/5/1991,90,tropicana,2752,7.920083199,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/5/1991,91,dominicks,28160,10.24565781,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/5/1991,91,minute.maid,3072,8.030084094,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/5/1991,91,tropicana,3904,8.269756948,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/5/1991,92,dominicks,31168,10.34714721,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/5/1991,92,minute.maid,5696,8.647519453,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/5/1991,92,tropicana,3648,8.201934351,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/5/1991,93,dominicks,235520,12.36955112,0,0.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/5/1991,93,minute.maid,5952,8.691482577,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/5/1991,93,tropicana,7936,8.979164649,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/5/1991,94,dominicks,15424,9.643680017,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/5/1991,94,minute.maid,8384,9.034080407,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/5/1991,94,tropicana,8384,9.034080407,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/5/1991,95,dominicks,17536,9.77201119,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/5/1991,95,minute.maid,6336,8.754002934,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/5/1991,95,tropicana,2752,7.920083199,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/5/1991,97,dominicks,6848,8.831711918,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/5/1991,97,minute.maid,3456,8.14786713,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/5/1991,97,tropicana,960,6.866933285,0,3.15,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/5/1991,98,dominicks,39488,10.58375211,0,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/5/1991,98,minute.maid,6272,8.743850562,0,2.08,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/5/1991,98,tropicana,6336,8.754002934,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/5/1991,100,dominicks,50176,10.8232921,0,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/5/1991,100,minute.maid,8320,9.026417534,0,2.06,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/5/1991,100,tropicana,7680,8.946374826,0,3.19,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/5/1991,101,dominicks,21056,9.954940834,0,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/5/1991,101,minute.maid,7232,8.886270902,0,2.04,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/5/1991,101,tropicana,8768,9.078864009,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/5/1991,102,dominicks,52544,10.86940619,0,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/5/1991,102,minute.maid,9856,9.195835686,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/5/1991,102,tropicana,11712,9.368369236,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/5/1991,103,dominicks,28352,10.25245285,0,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/5/1991,103,minute.maid,4672,8.449342525,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/5/1991,103,tropicana,2560,7.847762538,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/5/1991,104,dominicks,9216,9.128696383,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/5/1991,104,minute.maid,4224,8.348537825,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/5/1991,104,tropicana,1856,7.526178913,0,3.1,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/5/1991,105,dominicks,37312,10.52707027,0,1.58,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/5/1991,105,minute.maid,6528,8.783855897,0,2.06,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/5/1991,105,tropicana,3904,8.269756948,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/5/1991,106,dominicks,11392,9.340666634,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/5/1991,106,minute.maid,3520,8.166216269,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/5/1991,106,tropicana,2240,7.714231145,0,3.1,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/5/1991,107,dominicks,18624,9.832206351,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/5/1991,107,minute.maid,7808,8.962904128,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/5/1991,107,tropicana,10944,9.30054664,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/5/1991,109,dominicks,27904,10.23652533,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/5/1991,109,minute.maid,12608,9.442086812,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/5/1991,109,tropicana,11328,9.335032816,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/5/1991,110,dominicks,22400,10.01681624,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/5/1991,110,minute.maid,8384,9.034080407,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/5/1991,110,tropicana,4544,8.42156296,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/5/1991,111,dominicks,101696,11.52974325,0,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/5/1991,111,minute.maid,17088,9.746131742,0,1.75,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/5/1991,111,tropicana,2368,7.769800996,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/5/1991,112,dominicks,29312,10.28575227,0,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/5/1991,112,minute.maid,11648,9.36288977,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/5/1991,112,tropicana,12928,9.467150781,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/5/1991,113,dominicks,33984,10.43364511,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/5/1991,113,minute.maid,7296,8.895081532,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/5/1991,113,tropicana,7360,8.903815212,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/5/1991,114,dominicks,61056,11.01954676,0,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/5/1991,114,minute.maid,8768,9.078864009,0,2.04,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/5/1991,114,tropicana,6656,8.803273983,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/5/1991,115,dominicks,22912,10.03941607,0,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/5/1991,115,minute.maid,9280,9.135616826,0,1.92,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/5/1991,115,tropicana,6528,8.783855897,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/5/1991,116,dominicks,13824,9.534161491,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/5/1991,116,minute.maid,5568,8.624791202,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/5/1991,116,tropicana,5504,8.61323038,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/5/1991,117,dominicks,15424,9.643680017,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/5/1991,117,minute.maid,4672,8.449342525,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/5/1991,117,tropicana,3584,8.184234774,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/5/1991,118,dominicks,13952,9.543378146,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/5/1991,118,minute.maid,7040,8.859363449,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/5/1991,118,tropicana,4736,8.462948177,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/5/1991,119,dominicks,14528,9.583833101,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/5/1991,119,minute.maid,6336,8.754002934,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/5/1991,119,tropicana,5504,8.61323038,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/5/1991,121,dominicks,31488,10.3573618,0,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/5/1991,121,minute.maid,8640,9.064157862,0,2.06,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/5/1991,121,tropicana,8064,8.99516499,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/5/1991,122,dominicks,32064,10.37548918,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/5/1991,122,minute.maid,10432,9.252633284,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/5/1991,122,tropicana,8320,9.026417534,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/5/1991,123,dominicks,48384,10.78692446,0,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/5/1991,123,minute.maid,7808,8.962904128,0,2.07,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/5/1991,123,tropicana,5440,8.60153434,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/5/1991,124,dominicks,39040,10.57234204,0,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/5/1991,124,minute.maid,8896,9.093357017,0,2.05,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/5/1991,124,tropicana,4480,8.407378325,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/5/1991,126,dominicks,16384,9.704060528,0,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/5/1991,126,minute.maid,9984,9.208739091,0,2.07,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/5/1991,126,tropicana,5888,8.68067166,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/5/1991,128,dominicks,54336,10.90294227,0,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/5/1991,128,minute.maid,16768,9.727227587,0,2.06,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/5/1991,128,tropicana,12416,9.426741242,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/5/1991,129,dominicks,21120,9.957975738,0,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/5/1991,129,minute.maid,5824,8.66974259,0,2.06,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/5/1991,129,tropicana,7040,8.859363449,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/5/1991,130,dominicks,59456,10.99299182,0,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/5/1991,130,minute.maid,8704,9.071537969,0,2.06,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/5/1991,130,tropicana,4864,8.489616424,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/5/1991,131,dominicks,29760,10.30092049,0,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/5/1991,131,minute.maid,5952,8.691482577,0,2.04,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/5/1991,131,tropicana,5824,8.66974259,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/5/1991,132,dominicks,41664,10.63739273,0,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/5/1991,132,minute.maid,11456,9.346268889,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/5/1991,132,tropicana,6144,8.723231275,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/5/1991,134,dominicks,12992,9.472089062,0,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/5/1991,134,minute.maid,2112,7.655390645,0,2.03,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/5/1991,134,tropicana,3008,8.009030685,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/5/1991,137,dominicks,32320,10.38344151,0,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/5/1991,137,minute.maid,21184,9.961001459,0,2.07,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/5/1991,137,tropicana,17088,9.746131742,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/5/1991,18,dominicks,41920,10.64351832,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/5/1991,18,minute.maid,10496,9.258749511,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/5/1991,18,tropicana,8256,9.018695488,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/5/1991,64,dominicks,19968,9.901886271,0,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/5/1991,64,minute.maid,3136,8.050703382,0,2.03,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/5/1991,64,tropicana,2176,7.685243608,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/5/1991,77,dominicks,13824,9.534161491,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/5/1991,77,minute.maid,8704,9.071537969,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/5/1991,77,tropicana,6080,8.712759975,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/5/1991,83,dominicks,43328,10.67655436,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/5/1991,83,minute.maid,7936,8.979164649,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/5/1991,83,tropicana,6592,8.793612072,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/5/1991,84,dominicks,16768,9.727227587,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/5/1991,84,minute.maid,5760,8.658692754,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/5/1991,84,tropicana,4096,8.317766167,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/12/1991,2,dominicks,13568,9.515469358,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/12/1991,2,minute.maid,4480,8.407378325,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/12/1991,2,tropicana,5120,8.540909718,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/12/1991,5,dominicks,23552,10.06696602,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/12/1991,5,minute.maid,5952,8.691482577,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/12/1991,5,tropicana,6656,8.803273983,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/12/1991,8,dominicks,33664,10.4241843,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/12/1991,8,minute.maid,8192,9.010913347,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/12/1991,8,tropicana,4864,8.489616424,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/12/1991,9,dominicks,10368,9.246479419,1,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/12/1991,9,minute.maid,4160,8.333270353,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/12/1991,9,tropicana,7104,8.868413285,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/12/1991,12,dominicks,46336,10.74367448,1,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/12/1991,12,minute.maid,6464,8.7740036,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/12/1991,12,tropicana,6720,8.812843434,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/12/1991,14,dominicks,15744,9.664214619,1,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/12/1991,14,minute.maid,3904,8.269756948,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/12/1991,14,tropicana,12416,9.426741242,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/12/1991,21,dominicks,14080,9.55251063,1,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/12/1991,21,minute.maid,6080,8.712759975,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/12/1991,21,tropicana,3392,8.129174997,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/12/1991,28,dominicks,10880,9.29468152,1,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/12/1991,28,minute.maid,3008,8.009030685,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/12/1991,28,tropicana,4224,8.348537825,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/12/1991,32,dominicks,41344,10.62968259,1,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/12/1991,32,minute.maid,6656,8.803273983,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/12/1991,32,tropicana,8960,9.100525506,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/12/1991,33,dominicks,15616,9.656051309,1,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/12/1991,33,minute.maid,4608,8.435549202,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/12/1991,33,tropicana,8000,8.987196821,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/12/1991,40,dominicks,24448,10.10430369,1,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/12/1991,40,minute.maid,5696,8.647519453,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/12/1991,40,tropicana,5312,8.577723691,0,2.73,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/12/1991,44,dominicks,31040,10.34303197,1,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/12/1991,44,minute.maid,4608,8.435549202,0,2.26,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/12/1991,44,tropicana,8704,9.071537969,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/12/1991,45,dominicks,14400,9.574983486,1,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/12/1991,45,minute.maid,1984,7.592870288,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/12/1991,45,tropicana,5632,8.636219898,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/12/1991,47,dominicks,15488,9.64782081,1,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/12/1991,47,minute.maid,4096,8.317766167,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/12/1991,47,tropicana,3072,8.030084094,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/12/1991,48,dominicks,7872,8.971067439,1,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/12/1991,48,minute.maid,4800,8.476371197,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/12/1991,48,tropicana,5120,8.540909718,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/12/1991,50,dominicks,13888,9.538780437,1,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/12/1991,50,minute.maid,2368,7.769800996,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/12/1991,50,tropicana,2880,7.965545573,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/12/1991,51,dominicks,21312,9.967025573,1,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/12/1991,51,minute.maid,3008,8.009030685,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/12/1991,51,tropicana,6080,8.712759975,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/12/1991,52,dominicks,19072,9.85597657,1,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/12/1991,52,minute.maid,8128,9.00307017,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/12/1991,52,tropicana,9088,9.114710141,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/12/1991,53,dominicks,21824,9.990765561,1,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/12/1991,53,minute.maid,3392,8.129174997,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/12/1991,53,tropicana,7808,8.962904128,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/12/1991,54,dominicks,16960,9.738612909,1,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/12/1991,54,minute.maid,3648,8.201934351,0,2.26,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/12/1991,54,tropicana,4800,8.476371197,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/12/1991,56,dominicks,17728,9.78290059,1,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/12/1991,56,minute.maid,3648,8.201934351,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/12/1991,56,tropicana,6528,8.783855897,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/12/1991,59,dominicks,18944,9.849242538,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/12/1991,59,minute.maid,5504,8.61323038,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/12/1991,59,tropicana,5248,8.565602331,0,2.73,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/12/1991,62,dominicks,11008,9.30637756,1,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/12/1991,62,minute.maid,7232,8.886270902,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/12/1991,62,tropicana,13504,9.510741217,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/12/1991,67,dominicks,32000,10.37349118,1,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/12/1991,67,minute.maid,6464,8.7740036,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/12/1991,67,tropicana,5504,8.61323038,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/12/1991,68,dominicks,9024,9.107642974,1,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/12/1991,68,minute.maid,4864,8.489616424,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/12/1991,68,tropicana,6144,8.723231275,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/12/1991,70,dominicks,14848,9.605620455,1,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/12/1991,70,minute.maid,8960,9.100525506,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/12/1991,70,tropicana,5760,8.658692754,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/12/1991,71,dominicks,25408,10.14281936,1,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/12/1991,71,minute.maid,2368,7.769800996,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/12/1991,71,tropicana,3904,8.269756948,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/12/1991,72,dominicks,21824,9.990765561,1,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/12/1991,72,minute.maid,5824,8.66974259,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/12/1991,72,tropicana,8512,9.049232212,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/12/1991,73,dominicks,31424,10.35532721,1,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/12/1991,73,minute.maid,9344,9.142489705,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/12/1991,73,tropicana,5888,8.68067166,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/12/1991,75,dominicks,26368,10.17990643,1,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/12/1991,75,minute.maid,4096,8.317766167,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/12/1991,75,tropicana,6208,8.733594062,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/12/1991,76,dominicks,31488,10.3573618,1,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/12/1991,76,minute.maid,3456,8.14786713,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/12/1991,76,tropicana,6080,8.712759975,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/12/1991,78,dominicks,21184,9.961001459,1,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/12/1991,78,minute.maid,8576,9.056722883,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/12/1991,78,tropicana,5696,8.647519453,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/12/1991,80,dominicks,10304,9.240287448,1,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/12/1991,80,minute.maid,11264,9.329367078,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/12/1991,80,tropicana,8448,9.041685006,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/12/1991,81,dominicks,10752,9.282847063,1,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/12/1991,81,minute.maid,3456,8.14786713,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/12/1991,81,tropicana,8448,9.041685006,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/12/1991,86,dominicks,22848,10.03661887,1,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/12/1991,86,minute.maid,1728,7.454719949,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/12/1991,86,tropicana,11136,9.317938383,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/12/1991,88,dominicks,22016,9.999524741,1,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/12/1991,88,minute.maid,3968,8.286017468,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/12/1991,88,tropicana,2880,7.965545573,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/12/1991,89,dominicks,32576,10.3913311,1,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/12/1991,89,minute.maid,3136,8.050703382,0,2.26,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/12/1991,89,tropicana,3072,8.030084094,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/12/1991,91,dominicks,24704,10.11472045,1,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/12/1991,91,minute.maid,2816,7.943072717,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/12/1991,91,tropicana,3712,8.219326094,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/12/1991,92,dominicks,22912,10.03941607,1,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/12/1991,92,minute.maid,3456,8.14786713,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/12/1991,92,tropicana,3968,8.286017468,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/12/1991,93,dominicks,17600,9.775654181,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/12/1991,93,minute.maid,4160,8.333270353,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/12/1991,93,tropicana,7872,8.971067439,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/12/1991,95,dominicks,18368,9.818365299,1,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/12/1991,95,minute.maid,3328,8.110126802,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/12/1991,95,tropicana,2880,7.965545573,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/12/1991,97,dominicks,9216,9.128696383,1,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/12/1991,97,minute.maid,1984,7.592870288,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/12/1991,97,tropicana,1088,6.992096427,0,3.15,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/12/1991,98,dominicks,26304,10.1774763,1,1.59,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/12/1991,98,minute.maid,4736,8.462948177,0,2.26,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/12/1991,98,tropicana,6336,8.754002934,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/12/1991,100,dominicks,44992,10.71423998,1,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/12/1991,100,minute.maid,2688,7.896552702,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/12/1991,100,tropicana,5952,8.691482577,0,3.19,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/12/1991,101,dominicks,31488,10.3573618,1,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/12/1991,101,minute.maid,3904,8.269756948,0,2.26,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/12/1991,101,tropicana,8896,9.093357017,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/12/1991,102,dominicks,58176,10.97122818,1,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/12/1991,102,minute.maid,8192,9.010913347,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/12/1991,102,tropicana,8512,9.049232212,0,2.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/12/1991,103,dominicks,21760,9.987828701,1,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/12/1991,103,minute.maid,2944,7.98752448,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/12/1991,103,tropicana,2048,7.624618986,0,2.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/12/1991,104,dominicks,9728,9.182763604,1,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/12/1991,104,minute.maid,3584,8.184234774,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/12/1991,104,tropicana,3968,8.286017468,0,3.08,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/12/1991,105,dominicks,25216,10.13523399,1,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/12/1991,105,minute.maid,3840,8.253227646,0,2.26,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/12/1991,105,tropicana,3840,8.253227646,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/12/1991,106,dominicks,11136,9.317938383,1,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/12/1991,106,minute.maid,2816,7.943072717,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/12/1991,106,tropicana,1728,7.454719949,0,3.08,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/12/1991,107,dominicks,18880,9.84585844,1,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/12/1991,107,minute.maid,5248,8.565602331,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/12/1991,107,tropicana,12672,9.447150114,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/12/1991,109,dominicks,31872,10.36948316,1,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/12/1991,109,minute.maid,8384,9.034080407,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/12/1991,109,tropicana,13376,9.501217335,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/12/1991,110,dominicks,26432,10.18233068,1,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/12/1991,110,minute.maid,4352,8.378390789,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/12/1991,110,tropicana,4864,8.489616424,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/12/1991,111,dominicks,86976,11.3733875,1,1.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/12/1991,111,minute.maid,3968,8.286017468,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/12/1991,111,tropicana,3648,8.201934351,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/12/1991,112,dominicks,26496,10.18474906,1,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/12/1991,112,minute.maid,12224,9.411156511,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/12/1991,112,tropicana,10816,9.288781798,0,2.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/12/1991,113,dominicks,30400,10.32219789,1,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/12/1991,113,minute.maid,4032,8.30201781,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/12/1991,113,tropicana,6144,8.723231275,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/12/1991,114,dominicks,60544,11.01112565,1,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/12/1991,114,minute.maid,5632,8.636219898,0,2.26,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/12/1991,114,tropicana,5568,8.624791202,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/12/1991,115,dominicks,23552,10.06696602,1,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/12/1991,115,minute.maid,5184,8.553332238,0,2.22,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/12/1991,115,tropicana,7552,8.929567708,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/12/1991,116,dominicks,16256,9.69621735,1,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/12/1991,116,minute.maid,2880,7.965545573,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/12/1991,116,tropicana,5056,8.528330936,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/12/1991,117,dominicks,14784,9.601300794,1,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/12/1991,117,minute.maid,2240,7.714231145,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/12/1991,117,tropicana,4096,8.317766167,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/12/1991,118,dominicks,9984,9.208739091,1,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/12/1991,118,minute.maid,4032,8.30201781,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/12/1991,118,tropicana,5568,8.624791202,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/12/1991,119,dominicks,8896,9.093357017,1,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/12/1991,119,minute.maid,5440,8.60153434,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/12/1991,119,tropicana,6784,8.822322178,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/12/1991,121,dominicks,28928,10.27256526,1,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/12/1991,121,minute.maid,5312,8.577723691,0,2.26,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/12/1991,121,tropicana,8320,9.026417534,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/12/1991,122,dominicks,27584,10.22499117,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/12/1991,122,minute.maid,10880,9.29468152,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/12/1991,122,tropicana,8768,9.078864009,0,2.73,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/12/1991,123,dominicks,32256,10.38145935,1,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/12/1991,123,minute.maid,5184,8.553332238,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/12/1991,123,tropicana,6976,8.850230966,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/12/1991,124,dominicks,43136,10.67211319,1,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/12/1991,124,minute.maid,3520,8.166216269,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/12/1991,124,tropicana,4096,8.317766167,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/12/1991,126,dominicks,20480,9.927204079,1,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/12/1991,126,minute.maid,6400,8.764053269,0,2.26,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/12/1991,126,tropicana,7040,8.859363449,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/12/1991,128,dominicks,50752,10.83470631,1,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/12/1991,128,minute.maid,7744,8.954673629,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/12/1991,128,tropicana,9664,9.17616292,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/12/1991,129,dominicks,16960,9.738612909,1,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/12/1991,129,minute.maid,5120,8.540909718,0,2.26,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/12/1991,129,tropicana,7296,8.895081532,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/12/1991,130,dominicks,70016,11.15647907,1,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/12/1991,130,minute.maid,4544,8.42156296,0,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/12/1991,130,tropicana,2944,7.98752448,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/12/1991,131,dominicks,27200,10.21097225,1,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/12/1991,131,minute.maid,4416,8.392989588,0,2.26,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/12/1991,131,tropicana,6272,8.743850562,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/12/1991,132,dominicks,43776,10.686841,1,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/12/1991,132,minute.maid,9280,9.135616826,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/12/1991,132,tropicana,7040,8.859363449,0,2.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/12/1991,134,dominicks,14080,9.55251063,1,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/12/1991,134,minute.maid,2304,7.742402022,0,2.14,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/12/1991,134,tropicana,3648,8.201934351,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/12/1991,137,dominicks,31616,10.3614186,1,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/12/1991,137,minute.maid,7424,8.912473275,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/12/1991,137,tropicana,15296,9.635346635,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/12/1991,18,dominicks,33280,10.4127119,1,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/12/1991,18,minute.maid,7616,8.938006577,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/12/1991,18,tropicana,9024,9.107642974,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/12/1991,49,dominicks,9408,9.14931567,1,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/12/1991,49,minute.maid,2880,7.965545573,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/12/1991,49,tropicana,4352,8.378390789,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/12/1991,64,dominicks,14336,9.570529135,1,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/12/1991,64,minute.maid,3136,8.050703382,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/12/1991,64,tropicana,2176,7.685243608,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/12/1991,74,dominicks,42560,10.65867012,1,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/12/1991,74,minute.maid,4928,8.502688505,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/12/1991,74,tropicana,8320,9.026417534,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/12/1991,77,dominicks,14976,9.614204199,1,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/12/1991,77,minute.maid,8320,9.026417534,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/12/1991,77,tropicana,6016,8.702177866,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/12/1991,83,dominicks,36672,10.5097688,1,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/12/1991,83,minute.maid,5888,8.68067166,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/12/1991,83,tropicana,6592,8.793612072,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/12/1991,84,dominicks,15552,9.651944527,1,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/12/1991,84,minute.maid,5568,8.624791202,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/12/1991,84,tropicana,6016,8.702177866,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/12/1991,90,dominicks,19648,9.885730831,1,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/12/1991,90,minute.maid,3328,8.110126802,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/12/1991,90,tropicana,2688,7.896552702,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/12/1991,94,dominicks,14400,9.574983486,1,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/12/1991,94,minute.maid,9344,9.142489705,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/12/1991,94,tropicana,7936,8.979164649,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/19/1991,2,dominicks,6080,8.712759975,0,1.61,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/19/1991,2,minute.maid,5952,8.691482577,0,2.22,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/19/1991,2,tropicana,8320,9.026417534,0,2.74,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/19/1991,5,dominicks,2944,7.98752448,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/19/1991,5,minute.maid,8512,9.049232212,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/19/1991,5,tropicana,8192,9.010913347,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/19/1991,8,dominicks,17728,9.78290059,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/19/1991,8,minute.maid,6080,8.712759975,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/19/1991,8,tropicana,7232,8.886270902,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/19/1991,12,dominicks,7104,8.868413285,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/19/1991,12,minute.maid,5568,8.624791202,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/19/1991,12,tropicana,8576,9.056722883,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/19/1991,21,dominicks,6528,8.783855897,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/19/1991,21,minute.maid,4608,8.435549202,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/19/1991,21,tropicana,3904,8.269756948,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/19/1991,32,dominicks,6848,8.831711918,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/19/1991,32,minute.maid,7168,8.877381955,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/19/1991,32,tropicana,13760,9.529521112,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/19/1991,40,dominicks,8448,9.041685006,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/19/1991,40,minute.maid,6592,8.793612072,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/19/1991,40,tropicana,6336,8.754002934,0,2.61,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/19/1991,44,dominicks,9856,9.195835686,0,1.6,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/19/1991,44,minute.maid,6272,8.743850562,0,2.12,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/19/1991,44,tropicana,12032,9.395325046,0,2.74,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/19/1991,45,dominicks,6336,8.754002934,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/19/1991,45,minute.maid,2880,7.965545573,0,2.26,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/19/1991,45,tropicana,6592,8.793612072,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/19/1991,47,dominicks,7616,8.938006577,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/19/1991,47,minute.maid,5888,8.68067166,0,2.26,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/19/1991,47,tropicana,6080,8.712759975,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/19/1991,48,dominicks,3072,8.030084094,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/19/1991,48,minute.maid,5568,8.624791202,0,2.26,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/19/1991,48,tropicana,6528,8.783855897,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/19/1991,51,dominicks,3136,8.050703382,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/19/1991,51,minute.maid,4032,8.30201781,0,2.26,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/19/1991,51,tropicana,6080,8.712759975,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/19/1991,52,dominicks,3776,8.236420527,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/19/1991,52,minute.maid,9088,9.114710141,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/19/1991,52,tropicana,13888,9.538780437,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/19/1991,53,dominicks,6464,8.7740036,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/19/1991,53,minute.maid,6016,8.702177866,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/19/1991,53,tropicana,11328,9.335032816,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/19/1991,54,dominicks,7168,8.877381955,0,1.62,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/19/1991,54,minute.maid,4096,8.317766167,0,2.12,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/19/1991,54,tropicana,7424,8.912473275,0,2.71,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/19/1991,56,dominicks,5120,8.540909718,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/19/1991,56,minute.maid,4800,8.476371197,0,2.26,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/19/1991,56,tropicana,4032,8.30201781,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/19/1991,59,dominicks,8320,9.026417534,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/19/1991,59,minute.maid,4544,8.42156296,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/19/1991,59,tropicana,4800,8.476371197,0,2.55,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/19/1991,62,dominicks,2752,7.920083199,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/19/1991,62,minute.maid,7552,8.929567708,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/19/1991,62,tropicana,21056,9.954940834,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/19/1991,67,dominicks,1536,7.336936914,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/19/1991,67,minute.maid,4416,8.392989588,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/19/1991,67,tropicana,4864,8.489616424,0,2.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/19/1991,70,dominicks,9856,9.195835686,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/19/1991,70,minute.maid,9536,9.162829389,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/19/1991,70,tropicana,8320,9.026417534,0,2.73,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/19/1991,71,dominicks,5952,8.691482577,0,1.6,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/19/1991,71,minute.maid,3136,8.050703382,0,2.18,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/19/1991,71,tropicana,5760,8.658692754,0,2.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/19/1991,72,dominicks,4864,8.489616424,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/19/1991,72,minute.maid,4480,8.407378325,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/19/1991,72,tropicana,9088,9.114710141,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/19/1991,73,dominicks,31808,10.36747311,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/19/1991,73,minute.maid,9536,9.162829389,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/19/1991,73,tropicana,7040,8.859363449,0,2.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/19/1991,75,dominicks,6656,8.803273983,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/19/1991,75,minute.maid,5056,8.528330936,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/19/1991,75,tropicana,7040,8.859363449,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/19/1991,76,dominicks,2752,7.920083199,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/19/1991,76,minute.maid,3712,8.219326094,0,2.26,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/19/1991,76,tropicana,7616,8.938006577,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/19/1991,78,dominicks,5312,8.577723691,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/19/1991,78,minute.maid,6336,8.754002934,0,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/19/1991,78,tropicana,6528,8.783855897,0,2.73,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/19/1991,80,dominicks,2432,7.796469243,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/19/1991,80,minute.maid,9024,9.107642974,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/19/1991,80,tropicana,12352,9.421573272,0,2.73,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/19/1991,81,dominicks,9664,9.17616292,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/19/1991,81,minute.maid,6400,8.764053269,0,2.26,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/19/1991,81,tropicana,9728,9.182763604,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/19/1991,89,dominicks,5760,8.658692754,0,1.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/19/1991,89,minute.maid,3328,8.110126802,0,2.11,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/19/1991,89,tropicana,2816,7.943072717,0,2.74,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/19/1991,92,dominicks,4992,8.51559191,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/19/1991,92,minute.maid,3712,8.219326094,0,2.26,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/19/1991,92,tropicana,4288,8.363575703,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/19/1991,93,dominicks,3968,8.286017468,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/19/1991,93,minute.maid,3968,8.286017468,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/19/1991,93,tropicana,10176,9.227787286,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/19/1991,97,dominicks,5248,8.565602331,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/19/1991,97,minute.maid,2752,7.920083199,0,2.26,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/19/1991,97,tropicana,2240,7.714231145,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/19/1991,98,dominicks,7872,8.971067439,0,1.61,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/19/1991,98,minute.maid,4992,8.51559191,0,2.13,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/19/1991,98,tropicana,7296,8.895081532,0,2.71,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/19/1991,100,dominicks,4864,8.489616424,0,1.6,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/19/1991,100,minute.maid,4736,8.462948177,0,2.19,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/19/1991,100,tropicana,5888,8.68067166,0,2.88,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/19/1991,101,dominicks,7872,8.971067439,0,1.61,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/19/1991,101,minute.maid,3520,8.166216269,0,2.17,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/19/1991,101,tropicana,11008,9.30637756,0,2.83,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/19/1991,102,dominicks,33792,10.42797937,0,1.62,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/19/1991,102,minute.maid,9216,9.128696383,0,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/19/1991,102,tropicana,9408,9.14931567,0,2.74,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/19/1991,103,dominicks,5440,8.60153434,0,1.61,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/19/1991,103,minute.maid,2624,7.87245515,0,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/19/1991,103,tropicana,3008,8.009030685,0,2.73,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/19/1991,104,dominicks,6400,8.764053269,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/19/1991,104,minute.maid,3840,8.253227646,0,2.26,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/19/1991,104,tropicana,5056,8.528330936,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/19/1991,105,dominicks,9536,9.162829389,0,1.61,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/19/1991,105,minute.maid,4928,8.502688505,0,2.13,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/19/1991,105,tropicana,4288,8.363575703,0,2.72,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/19/1991,106,dominicks,3264,8.090708716,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/19/1991,106,minute.maid,2496,7.82244473,0,2.26,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/19/1991,106,tropicana,2560,7.847762538,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/19/1991,107,dominicks,7552,8.929567708,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/19/1991,107,minute.maid,5056,8.528330936,0,2.26,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/19/1991,107,tropicana,13888,9.538780437,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/19/1991,109,dominicks,2688,7.896552702,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/19/1991,109,minute.maid,10112,9.221478116,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/19/1991,109,tropicana,20352,9.920934466,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/19/1991,110,dominicks,4544,8.42156296,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/19/1991,110,minute.maid,5632,8.636219898,0,2.26,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/19/1991,110,tropicana,6848,8.831711918,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/19/1991,111,dominicks,9152,9.121727714,0,1.61,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/19/1991,111,minute.maid,4288,8.363575703,0,2.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/19/1991,111,tropicana,3392,8.129174997,0,2.75,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/19/1991,112,dominicks,6656,8.803273983,0,1.6,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/19/1991,112,minute.maid,10176,9.227787286,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/19/1991,112,tropicana,15296,9.635346635,0,2.71,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/19/1991,113,dominicks,4672,8.449342525,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/19/1991,113,minute.maid,6720,8.812843434,0,2.26,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/19/1991,113,tropicana,9344,9.142489705,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/19/1991,114,dominicks,11200,9.323669057,0,1.6,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/19/1991,114,minute.maid,5312,8.577723691,0,2.12,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/19/1991,114,tropicana,6144,8.723231275,0,2.68,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/19/1991,115,dominicks,3392,8.129174997,0,1.61,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/19/1991,115,minute.maid,6080,8.712759975,0,2.17,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/19/1991,115,tropicana,9984,9.208739091,0,2.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/19/1991,116,dominicks,5952,8.691482577,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/19/1991,116,minute.maid,4032,8.30201781,0,2.26,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/19/1991,116,tropicana,6144,8.723231275,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/19/1991,117,dominicks,5248,8.565602331,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/19/1991,117,minute.maid,2880,7.965545573,0,2.26,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/19/1991,117,tropicana,3968,8.286017468,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/19/1991,118,dominicks,4288,8.363575703,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/19/1991,118,minute.maid,4416,8.392989588,0,2.26,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/19/1991,118,tropicana,6464,8.7740036,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/19/1991,119,dominicks,2304,7.742402022,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/19/1991,119,minute.maid,4032,8.30201781,0,2.26,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/19/1991,119,tropicana,9152,9.121727714,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/19/1991,121,dominicks,5504,8.61323038,0,1.61,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/19/1991,121,minute.maid,6720,8.812843434,0,2.16,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/19/1991,121,tropicana,11328,9.335032816,0,2.71,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/19/1991,122,dominicks,10304,9.240287448,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/19/1991,122,minute.maid,11456,9.346268889,0,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/19/1991,122,tropicana,10432,9.252633284,0,2.58,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/19/1991,123,dominicks,3840,8.253227646,0,1.61,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/19/1991,123,minute.maid,4736,8.462948177,0,2.22,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/19/1991,123,tropicana,5824,8.66974259,0,2.72,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/19/1991,124,dominicks,10688,9.276876896,0,1.61,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/19/1991,124,minute.maid,4992,8.51559191,0,2.2,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/19/1991,124,tropicana,9216,9.128696383,0,2.73,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/19/1991,126,dominicks,8256,9.018695488,0,1.63,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/19/1991,126,minute.maid,6976,8.850230966,0,2.13,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/19/1991,126,tropicana,8704,9.071537969,0,2.71,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/19/1991,128,dominicks,9536,9.162829389,0,1.62,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/19/1991,128,minute.maid,5504,8.61323038,0,2.23,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/19/1991,128,tropicana,11520,9.351839934,0,2.8,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/19/1991,129,dominicks,7424,8.912473275,0,1.61,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/19/1991,129,minute.maid,8128,9.00307017,0,2.14,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/19/1991,129,tropicana,9792,9.189321005,0,2.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/19/1991,130,dominicks,20352,9.920934466,0,1.61,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/19/1991,130,minute.maid,4032,8.30201781,0,2.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/19/1991,130,tropicana,17600,9.775654181,0,2.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/19/1991,131,dominicks,6144,8.723231275,0,1.61,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/19/1991,131,minute.maid,6912,8.841014311,0,2.15,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/19/1991,131,tropicana,9152,9.121727714,0,2.8,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/19/1991,132,dominicks,13888,9.538780437,0,1.61,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/19/1991,132,minute.maid,8448,9.041685006,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/19/1991,132,tropicana,10880,9.29468152,0,2.68,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/19/1991,134,dominicks,4288,8.363575703,0,1.61,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/19/1991,134,minute.maid,4032,8.30201781,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/19/1991,134,tropicana,4800,8.476371197,0,2.71,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/19/1991,137,dominicks,19136,9.859326657,0,1.62,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/19/1991,137,minute.maid,8256,9.018695488,0,2.22,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/19/1991,137,tropicana,21696,9.984883191,0,2.83,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/19/1991,9,dominicks,3008,8.009030685,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/19/1991,9,minute.maid,2816,7.943072717,0,2.26,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/19/1991,9,tropicana,8512,9.049232212,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/19/1991,14,dominicks,3264,8.090708716,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/19/1991,14,minute.maid,6080,8.712759975,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/19/1991,14,tropicana,14592,9.588228712,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/19/1991,18,dominicks,3776,8.236420527,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/19/1991,18,minute.maid,7488,8.921057018,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/19/1991,18,tropicana,6656,8.803273983,0,2.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/19/1991,28,dominicks,2944,7.98752448,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/19/1991,28,minute.maid,3648,8.201934351,0,2.26,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/19/1991,28,tropicana,5632,8.636219898,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/19/1991,49,dominicks,3968,8.286017468,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/19/1991,49,minute.maid,2944,7.98752448,0,2.26,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/19/1991,49,tropicana,5248,8.565602331,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/19/1991,50,dominicks,4096,8.317766167,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/19/1991,50,minute.maid,2368,7.769800996,0,2.26,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/19/1991,50,tropicana,5440,8.60153434,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/19/1991,64,dominicks,6720,8.812843434,0,1.61,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/19/1991,64,minute.maid,3456,8.14786713,0,2.13,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/19/1991,64,tropicana,5440,8.60153434,0,2.7,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/19/1991,68,dominicks,2240,7.714231145,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/19/1991,68,minute.maid,5696,8.647519453,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/19/1991,68,tropicana,5376,8.589699882,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/19/1991,74,dominicks,8576,9.056722883,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/19/1991,74,minute.maid,5952,8.691482577,0,2.26,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/19/1991,74,tropicana,8256,9.018695488,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/19/1991,77,dominicks,6848,8.831711918,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/19/1991,77,minute.maid,5376,8.589699882,0,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/19/1991,77,tropicana,8832,9.086136769,0,2.73,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/19/1991,83,dominicks,9472,9.156095357,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/19/1991,83,minute.maid,8256,9.018695488,0,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/19/1991,83,tropicana,8320,9.026417534,0,2.73,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/19/1991,84,dominicks,6464,8.7740036,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/19/1991,84,minute.maid,6464,8.7740036,0,2.26,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/19/1991,84,tropicana,5376,8.589699882,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/19/1991,86,dominicks,5760,8.658692754,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/19/1991,86,minute.maid,2880,7.965545573,0,2.26,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/19/1991,86,tropicana,13824,9.534161491,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/19/1991,88,dominicks,3840,8.253227646,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/19/1991,88,minute.maid,4032,8.30201781,0,2.26,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/19/1991,88,tropicana,4416,8.392989588,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/19/1991,90,dominicks,8192,9.010913347,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/19/1991,90,minute.maid,4352,8.378390789,0,2.26,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/19/1991,90,tropicana,3200,8.070906089,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/19/1991,91,dominicks,6784,8.822322178,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/19/1991,91,minute.maid,4736,8.462948177,0,2.26,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/19/1991,91,tropicana,4480,8.407378325,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/19/1991,94,dominicks,12736,9.452187908,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/19/1991,94,minute.maid,10304,9.240287448,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/19/1991,94,tropicana,9472,9.156095357,0,2.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/19/1991,95,dominicks,13440,9.505990614,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/19/1991,95,minute.maid,2880,7.965545573,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/19/1991,95,tropicana,3712,8.219326094,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/26/1991,2,dominicks,10432,9.252633284,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/26/1991,2,minute.maid,21696,9.984883191,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/26/1991,2,tropicana,17728,9.78290059,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -12/26/1991,21,dominicks,9984,9.208739091,1,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/26/1991,21,minute.maid,7744,8.954673629,1,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/26/1991,21,tropicana,7232,8.886270902,0,2.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -12/26/1991,33,dominicks,16064,9.684336023,1,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/26/1991,33,minute.maid,22400,10.01681624,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/26/1991,33,tropicana,26432,10.18233068,0,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -12/26/1991,40,dominicks,14464,9.579418083,1,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/26/1991,40,minute.maid,7808,8.962904128,1,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/26/1991,40,tropicana,8768,9.078864009,0,2.39,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -12/26/1991,44,dominicks,20672,9.936535407,1,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/26/1991,44,minute.maid,17216,9.753594463,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/26/1991,44,tropicana,19008,9.852615222,0,2.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -12/26/1991,48,dominicks,6912,8.841014311,1,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/26/1991,48,minute.maid,7360,8.903815212,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/26/1991,48,tropicana,9216,9.128696383,0,2.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -12/26/1991,54,dominicks,14016,9.547954813,1,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/26/1991,54,minute.maid,17152,9.749870064,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/26/1991,54,tropicana,11520,9.351839934,0,2.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -12/26/1991,56,dominicks,10944,9.30054664,1,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/26/1991,56,minute.maid,17984,9.797237753,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/26/1991,56,tropicana,7936,8.979164649,0,2.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -12/26/1991,59,dominicks,16832,9.731037116,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/26/1991,59,minute.maid,13632,9.520175249,1,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/26/1991,59,tropicana,8128,9.00307017,0,2.39,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -12/26/1991,75,dominicks,15296,9.635346635,1,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/26/1991,75,minute.maid,17152,9.749870064,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/26/1991,75,tropicana,15296,9.635346635,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -12/26/1991,76,dominicks,15360,9.639522007,1,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/26/1991,76,minute.maid,31808,10.36747311,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/26/1991,76,tropicana,13376,9.501217335,0,2.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -12/26/1991,78,dominicks,8256,9.018695488,1,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/26/1991,78,minute.maid,7360,8.903815212,1,1.85,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/26/1991,78,tropicana,10624,9.270870872,0,2.39,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -12/26/1991,89,dominicks,13184,9.486759252,1,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/26/1991,89,minute.maid,19072,9.85597657,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/26/1991,89,tropicana,13696,9.524859098,0,2.24,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -12/26/1991,92,dominicks,9216,9.128696383,1,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/26/1991,92,minute.maid,16384,9.704060528,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/26/1991,92,tropicana,7680,8.946374826,0,2.39,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -12/26/1991,97,dominicks,8128,9.00307017,1,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/26/1991,97,minute.maid,4672,8.449342525,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/26/1991,97,tropicana,5504,8.61323038,0,2.39,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -12/26/1991,98,dominicks,20160,9.911455722,1,1.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/26/1991,98,minute.maid,36736,10.51151248,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/26/1991,98,tropicana,11584,9.357380115,0,2.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -12/26/1991,100,dominicks,11648,9.36288977,1,1.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/26/1991,100,minute.maid,33792,10.42797937,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/26/1991,100,tropicana,13248,9.491601877,0,2.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -12/26/1991,101,dominicks,9472,9.156095357,1,1.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/26/1991,101,minute.maid,12288,9.416378455,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/26/1991,101,tropicana,17088,9.746131742,0,2.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -12/26/1991,102,dominicks,28480,10.25695737,1,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/26/1991,102,minute.maid,38784,10.56576307,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/26/1991,102,tropicana,12160,9.405907156,0,2.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -12/26/1991,104,dominicks,8832,9.086136769,1,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/26/1991,104,minute.maid,10944,9.30054664,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/26/1991,104,tropicana,12288,9.416378455,0,2.39,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -12/26/1991,105,dominicks,20736,9.939626599,1,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/26/1991,105,minute.maid,17280,9.757305042,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/26/1991,105,tropicana,8576,9.056722883,0,2.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -12/26/1991,106,dominicks,6528,8.783855897,1,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/26/1991,106,minute.maid,7360,8.903815212,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/26/1991,106,tropicana,4928,8.502688505,0,2.39,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -12/26/1991,111,dominicks,11072,9.312174678,1,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/26/1991,111,minute.maid,70144,11.15830555,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/26/1991,111,tropicana,14208,9.561560465,0,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -12/26/1991,115,dominicks,7296,8.895081532,1,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/26/1991,115,minute.maid,32128,10.3774832,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/26/1991,115,tropicana,17984,9.797237753,0,2.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -12/26/1991,121,dominicks,18304,9.814874894,1,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/26/1991,121,minute.maid,19328,9.869310101,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/26/1991,121,tropicana,16704,9.723403491,0,2.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -12/26/1991,122,dominicks,16448,9.707959168,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/26/1991,122,minute.maid,30080,10.31161578,1,1.85,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/26/1991,122,tropicana,16512,9.711842668,0,2.39,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -12/26/1991,123,dominicks,26496,10.18474906,1,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/26/1991,123,minute.maid,48384,10.78692446,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/26/1991,123,tropicana,15808,9.66827142,0,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -12/26/1991,124,dominicks,35840,10.48681987,1,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/26/1991,124,minute.maid,22976,10.04220547,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/26/1991,124,tropicana,14080,9.55251063,0,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -12/26/1991,126,dominicks,14336,9.570529135,1,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/26/1991,126,minute.maid,24448,10.10430369,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/26/1991,126,tropicana,33728,10.42608363,0,2.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -12/26/1991,128,dominicks,47104,10.7601132,1,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/26/1991,128,minute.maid,35136,10.46698153,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/26/1991,128,tropicana,28480,10.25695737,0,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -12/26/1991,130,dominicks,37696,10.53730927,1,1.68,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/26/1991,130,minute.maid,33728,10.42608363,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/26/1991,130,tropicana,43840,10.68830192,0,2.1,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -12/26/1991,131,dominicks,10432,9.252633284,1,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/26/1991,131,minute.maid,32576,10.3913311,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/26/1991,131,tropicana,13376,9.501217335,0,2.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -12/26/1991,132,dominicks,19456,9.875910785,1,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/26/1991,132,minute.maid,38080,10.54744449,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/26/1991,132,tropicana,14912,9.609921537,0,2.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -12/26/1991,134,dominicks,8192,9.010913347,1,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/26/1991,134,minute.maid,6464,8.7740036,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/26/1991,134,tropicana,8896,9.093357017,0,2.39,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -12/26/1991,137,dominicks,26304,10.1774763,1,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/26/1991,137,minute.maid,33024,10.40498985,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/26/1991,137,tropicana,47744,10.77360868,0,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -12/26/1991,5,dominicks,5888,8.68067166,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/26/1991,5,minute.maid,27968,10.23881628,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/26/1991,5,tropicana,13440,9.505990614,0,2.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -12/26/1991,8,dominicks,25088,10.13014492,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/26/1991,8,minute.maid,15040,9.618468598,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/26/1991,8,tropicana,15232,9.631153757,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -12/26/1991,9,dominicks,8192,9.010913347,1,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/26/1991,9,minute.maid,34752,10.4559924,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/26/1991,9,tropicana,15936,9.67633598,0,2.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -12/26/1991,12,dominicks,14272,9.566054855,1,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/26/1991,12,minute.maid,42048,10.6465671,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/26/1991,12,tropicana,21696,9.984883191,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -12/26/1991,14,dominicks,12992,9.472089062,1,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/26/1991,14,minute.maid,16960,9.738612909,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/26/1991,14,tropicana,18496,9.825309772,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -12/26/1991,18,dominicks,21312,9.967025573,1,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/26/1991,18,minute.maid,18176,9.807857322,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/26/1991,18,tropicana,15296,9.635346635,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -12/26/1991,28,dominicks,4672,8.449342525,1,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/26/1991,28,minute.maid,6976,8.850230966,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/26/1991,28,tropicana,6016,8.702177866,0,2.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -12/26/1991,32,dominicks,34688,10.45414909,1,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/26/1991,32,minute.maid,24576,10.10952564,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/26/1991,32,tropicana,30976,10.34096799,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -12/26/1991,45,dominicks,10304,9.240287448,1,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/26/1991,45,minute.maid,8768,9.078864009,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/26/1991,45,tropicana,10368,9.246479419,0,2.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -12/26/1991,47,dominicks,14976,9.614204199,1,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/26/1991,47,minute.maid,13760,9.529521112,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/26/1991,47,tropicana,7040,8.859363449,0,2.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -12/26/1991,49,dominicks,8576,9.056722883,1,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/26/1991,49,minute.maid,11008,9.30637756,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/26/1991,49,tropicana,5888,8.68067166,0,2.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -12/26/1991,50,dominicks,11712,9.368369236,1,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/26/1991,50,minute.maid,10816,9.288781798,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/26/1991,50,tropicana,5120,8.540909718,0,2.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -12/26/1991,51,dominicks,5952,8.691482577,1,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/26/1991,51,minute.maid,11840,9.379238908,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/26/1991,51,tropicana,8256,9.018695488,0,2.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -12/26/1991,52,dominicks,5440,8.60153434,1,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/26/1991,52,minute.maid,21184,9.961001459,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/26/1991,52,tropicana,21504,9.975994243,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -12/26/1991,53,dominicks,11648,9.36288977,1,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/26/1991,53,minute.maid,27072,10.20625526,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/26/1991,53,tropicana,26176,10.17259824,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -12/26/1991,62,dominicks,6272,8.743850562,1,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/26/1991,62,minute.maid,19200,9.862665558,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/26/1991,62,tropicana,28160,10.24565781,0,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -12/26/1991,64,dominicks,14144,9.557045785,1,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/26/1991,64,minute.maid,11264,9.329367078,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/26/1991,64,tropicana,5632,8.636219898,0,2.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -12/26/1991,67,dominicks,21696,9.984883191,1,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/26/1991,67,minute.maid,10112,9.221478116,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/26/1991,67,tropicana,8384,9.034080407,0,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -12/26/1991,68,dominicks,16000,9.680344001,1,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/26/1991,68,minute.maid,20032,9.905086274,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/26/1991,68,tropicana,14592,9.588228712,0,2.39,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -12/26/1991,70,dominicks,10176,9.227787286,1,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/26/1991,70,minute.maid,10944,9.30054664,1,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/26/1991,70,tropicana,10496,9.258749511,0,2.39,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -12/26/1991,71,dominicks,23680,10.07238609,1,1.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/26/1991,71,minute.maid,15872,9.67231183,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/26/1991,71,tropicana,19136,9.859326657,0,2.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -12/26/1991,72,dominicks,6080,8.712759975,1,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/26/1991,72,minute.maid,27136,10.20861654,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/26/1991,72,tropicana,23040,10.04498712,0,2.39,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -12/26/1991,73,dominicks,43968,10.69121738,1,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/26/1991,73,minute.maid,13120,9.481893063,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/26/1991,73,tropicana,10048,9.215128889,0,2.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -12/26/1991,74,dominicks,27008,10.2038884,1,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/26/1991,74,minute.maid,17792,9.786504197,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/26/1991,74,tropicana,18240,9.811372264,0,2.39,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -12/26/1991,77,dominicks,8704,9.071537969,1,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/26/1991,77,minute.maid,11776,9.373818841,1,1.85,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/26/1991,77,tropicana,14016,9.547954813,0,2.39,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -12/26/1991,80,dominicks,8128,9.00307017,1,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/26/1991,80,minute.maid,11520,9.351839934,1,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/26/1991,80,tropicana,25024,10.12759064,0,2.39,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -12/26/1991,81,dominicks,11072,9.312174678,1,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/26/1991,81,minute.maid,14336,9.570529135,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/26/1991,81,tropicana,17536,9.77201119,0,2.39,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -12/26/1991,83,dominicks,9664,9.17616292,1,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/26/1991,83,minute.maid,7872,8.971067439,1,1.85,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/26/1991,83,tropicana,10880,9.29468152,0,2.39,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -12/26/1991,84,dominicks,11712,9.368369236,1,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/26/1991,84,minute.maid,19456,9.875910785,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/26/1991,84,tropicana,12096,9.400630098,0,2.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -12/26/1991,86,dominicks,10432,9.252633284,1,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/26/1991,86,minute.maid,19648,9.885730831,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/26/1991,86,tropicana,13568,9.515469358,0,2.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -12/26/1991,88,dominicks,15296,9.635346635,1,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/26/1991,88,minute.maid,19712,9.888982866,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/26/1991,88,tropicana,8320,9.026417534,0,2.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -12/26/1991,90,dominicks,5504,8.61323038,1,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/26/1991,90,minute.maid,17280,9.757305042,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/26/1991,90,tropicana,4352,8.378390789,0,2.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -12/26/1991,91,dominicks,8192,9.010913347,1,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/26/1991,91,minute.maid,9728,9.182763604,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/26/1991,91,tropicana,6400,8.764053269,0,2.39,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -12/26/1991,93,dominicks,15104,9.622714888,1,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/26/1991,93,minute.maid,15936,9.67633598,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/26/1991,93,tropicana,16512,9.711842668,0,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -12/26/1991,94,dominicks,10304,9.240287448,1,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/26/1991,94,minute.maid,12928,9.467150781,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/26/1991,94,tropicana,13312,9.496421163,0,2.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -12/26/1991,95,dominicks,12928,9.467150781,1,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/26/1991,95,minute.maid,13120,9.481893063,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/26/1991,95,tropicana,8256,9.018695488,0,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -12/26/1991,103,dominicks,11840,9.379238908,1,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/26/1991,103,minute.maid,5824,8.66974259,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/26/1991,103,tropicana,5120,8.540909718,0,2.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -12/26/1991,107,dominicks,13376,9.501217335,1,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/26/1991,107,minute.maid,19648,9.885730831,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/26/1991,107,tropicana,20416,9.924074186,0,2.39,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -12/26/1991,109,dominicks,9920,9.2023082,1,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/26/1991,109,minute.maid,24512,10.10691807,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/26/1991,109,tropicana,30400,10.32219789,0,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -12/26/1991,110,dominicks,12608,9.442086812,1,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/26/1991,110,minute.maid,20992,9.951896692,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/26/1991,110,tropicana,9408,9.14931567,0,2.39,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -12/26/1991,112,dominicks,13632,9.520175249,1,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/26/1991,112,minute.maid,42368,10.65414864,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/26/1991,112,tropicana,28032,10.24110199,0,2.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -12/26/1991,113,dominicks,24000,10.08580911,1,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/26/1991,113,minute.maid,23936,10.08313888,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/26/1991,113,tropicana,11456,9.346268889,0,2.39,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -12/26/1991,114,dominicks,30208,10.31586207,1,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/26/1991,114,minute.maid,33984,10.43364511,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/26/1991,114,tropicana,12736,9.452187908,0,2.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -12/26/1991,116,dominicks,9536,9.162829389,1,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/26/1991,116,minute.maid,9344,9.142489705,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/26/1991,116,tropicana,7360,8.903815212,0,2.39,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -12/26/1991,117,dominicks,9280,9.135616826,1,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/26/1991,117,minute.maid,14464,9.579418083,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/26/1991,117,tropicana,8832,9.086136769,0,2.39,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -12/26/1991,118,dominicks,10240,9.234056899,1,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/26/1991,118,minute.maid,18560,9.828764006,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/26/1991,118,tropicana,16768,9.727227587,0,2.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -12/26/1991,119,dominicks,8832,9.086136769,1,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/26/1991,119,minute.maid,12352,9.421573272,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/26/1991,119,tropicana,11584,9.357380115,0,2.39,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -12/26/1991,129,dominicks,7104,8.868413285,1,1.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/26/1991,129,minute.maid,21952,9.996613531,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -12/26/1991,129,tropicana,14336,9.570529135,0,2.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/2/1992,2,dominicks,11712,9.368369236,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/2/1992,2,minute.maid,12032,9.395325046,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/2/1992,2,tropicana,13120,9.481893063,0,2.35,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/2/1992,5,dominicks,6848,8.831711918,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/2/1992,5,minute.maid,24000,10.08580911,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/2/1992,5,tropicana,12160,9.405907156,0,2.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/2/1992,8,dominicks,13184,9.486759252,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/2/1992,8,minute.maid,9472,9.156095357,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/2/1992,8,tropicana,47040,10.75875358,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/2/1992,9,dominicks,11648,9.36288977,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/2/1992,9,minute.maid,14144,9.557045785,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/2/1992,9,tropicana,19200,9.862665558,0,2.39,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/2/1992,12,dominicks,11904,9.384629757,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/2/1992,12,minute.maid,20288,9.917784857,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/2/1992,12,tropicana,46976,10.75739211,0,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/2/1992,14,dominicks,6656,8.803273983,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/2/1992,14,minute.maid,14848,9.605620455,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/2/1992,14,tropicana,20224,9.914625297,0,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/2/1992,18,dominicks,8704,9.071537969,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/2/1992,18,minute.maid,15488,9.64782081,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/2/1992,18,tropicana,15744,9.664214619,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/2/1992,21,dominicks,11648,9.36288977,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/2/1992,21,minute.maid,6400,8.764053269,0,1.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/2/1992,21,tropicana,6144,8.723231275,0,2.35,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/2/1992,28,dominicks,5312,8.577723691,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/2/1992,28,minute.maid,7040,8.859363449,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/2/1992,28,tropicana,6400,8.764053269,0,2.39,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/2/1992,32,dominicks,12864,9.462187991,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/2/1992,32,minute.maid,20416,9.924074186,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/2/1992,32,tropicana,17984,9.797237753,0,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/2/1992,33,dominicks,11456,9.346268889,0,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/2/1992,33,minute.maid,14592,9.588228712,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/2/1992,33,tropicana,20544,9.930324207,0,2.34,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/2/1992,40,dominicks,9152,9.121727714,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/2/1992,40,minute.maid,9920,9.2023082,0,1.85,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/2/1992,40,tropicana,10176,9.227787286,0,2.34,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/2/1992,44,dominicks,11392,9.340666634,0,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/2/1992,44,minute.maid,18944,9.849242538,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/2/1992,44,tropicana,18432,9.821843564,0,2.35,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/2/1992,45,dominicks,5696,8.647519453,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/2/1992,45,minute.maid,7936,8.979164649,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/2/1992,45,tropicana,8192,9.010913347,0,2.39,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/2/1992,47,dominicks,10560,9.264828557,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/2/1992,47,minute.maid,13184,9.486759252,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/2/1992,47,tropicana,6016,8.702177866,0,2.39,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/2/1992,48,dominicks,4160,8.333270353,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/2/1992,48,minute.maid,7360,8.903815212,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/2/1992,48,tropicana,10752,9.282847063,0,2.39,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/2/1992,49,dominicks,7872,8.971067439,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/2/1992,49,minute.maid,11264,9.329367078,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/2/1992,49,tropicana,7680,8.946374826,0,2.39,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/2/1992,50,dominicks,10432,9.252633284,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/2/1992,50,minute.maid,7744,8.954673629,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/2/1992,50,tropicana,5056,8.528330936,0,2.39,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/2/1992,51,dominicks,5760,8.658692754,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/2/1992,51,minute.maid,10944,9.30054664,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/2/1992,51,tropicana,10496,9.258749511,0,2.39,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/2/1992,52,dominicks,5184,8.553332238,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/2/1992,52,minute.maid,19584,9.882468185,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/2/1992,52,tropicana,24384,10.10168246,0,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/2/1992,53,dominicks,9024,9.107642974,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/2/1992,53,minute.maid,24192,10.09377728,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/2/1992,53,tropicana,19968,9.901886271,0,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/2/1992,54,dominicks,7744,8.954673629,0,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/2/1992,54,minute.maid,10880,9.29468152,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/2/1992,54,tropicana,11904,9.384629757,0,2.35,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/2/1992,56,dominicks,8192,9.010913347,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/2/1992,56,minute.maid,10432,9.252633284,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/2/1992,56,tropicana,9024,9.107642974,0,2.39,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/2/1992,59,dominicks,8064,8.99516499,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/2/1992,59,minute.maid,6784,8.822322178,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/2/1992,59,tropicana,8000,8.987196821,0,2.35,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 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-1/2/1992,75,minute.maid,15296,9.635346635,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/2/1992,75,tropicana,16640,9.719564714,0,2.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -1/2/1992,76,dominicks,16384,9.704060528,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/2/1992,76,minute.maid,14464,9.579418083,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/2/1992,76,tropicana,12416,9.426741242,0,2.39,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 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-1/2/1992,78,tropicana,10752,9.282847063,0,2.39,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -1/2/1992,80,dominicks,7552,8.929567708,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -1/2/1992,80,minute.maid,12800,9.45720045,0,1.85,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -1/2/1992,80,tropicana,26816,10.196754,0,2.39,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -1/2/1992,81,dominicks,4992,8.51559191,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 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-1/2/1992,84,dominicks,11072,9.312174678,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -1/2/1992,84,minute.maid,13568,9.515469358,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -1/2/1992,84,tropicana,9280,9.135616826,0,2.39,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -1/2/1992,86,dominicks,15872,9.67231183,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/2/1992,86,minute.maid,6464,8.7740036,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/2/1992,86,tropicana,13824,9.534161491,0,2.39,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/2/1992,88,dominicks,15424,9.643680017,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -1/2/1992,88,minute.maid,11136,9.317938383,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -1/2/1992,88,tropicana,8384,9.034080407,0,2.39,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -1/2/1992,89,dominicks,9984,9.208739091,0,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/2/1992,89,minute.maid,7872,8.971067439,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/2/1992,89,tropicana,29184,10.28137589,0,2,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -1/2/1992,90,dominicks,6592,8.793612072,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -1/2/1992,90,minute.maid,22272,10.01108556,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -1/2/1992,90,tropicana,9152,9.121727714,0,2.39,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 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-1/2/1992,123,minute.maid,39296,10.57887801,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/2/1992,123,tropicana,11584,9.357380115,0,2.34,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/2/1992,124,dominicks,21568,9.978966014,0,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/2/1992,124,minute.maid,18240,9.811372264,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/2/1992,124,tropicana,12672,9.447150114,0,2.34,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 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-1/2/1992,130,minute.maid,29632,10.29661014,0,1.95,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/2/1992,130,tropicana,44096,10.69412435,0,2.05,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/2/1992,131,dominicks,5888,8.68067166,0,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/2/1992,131,minute.maid,14784,9.601300794,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/2/1992,131,tropicana,12288,9.416378455,0,2.35,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 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-1/9/1992,14,minute.maid,7744,8.954673629,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/9/1992,14,tropicana,18240,9.811372264,0,2.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/9/1992,18,dominicks,2240,7.714231145,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/9/1992,18,minute.maid,5632,8.636219898,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/9/1992,18,tropicana,12096,9.400630098,0,2.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/9/1992,21,dominicks,3520,8.166216269,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/9/1992,21,minute.maid,5568,8.624791202,0,2.02,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/9/1992,21,tropicana,3840,8.253227646,0,2.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/9/1992,28,dominicks,1664,7.416979621,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/9/1992,28,minute.maid,3776,8.236420527,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/9/1992,28,tropicana,5248,8.565602331,0,2.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/9/1992,32,dominicks,4672,8.449342525,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/9/1992,32,minute.maid,8064,8.99516499,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/9/1992,32,tropicana,20288,9.917784857,0,2.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/9/1992,33,dominicks,6016,8.702177866,0,1.76,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/9/1992,33,minute.maid,9088,9.114710141,0,2.08,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/9/1992,33,tropicana,19840,9.895455381,0,2.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/9/1992,40,dominicks,3008,8.009030685,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/9/1992,40,minute.maid,4352,8.378390789,0,1.93,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/9/1992,40,tropicana,5248,8.565602331,0,2.29,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/9/1992,44,dominicks,2304,7.742402022,0,1.71,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/9/1992,44,minute.maid,7424,8.912473275,0,2.15,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/9/1992,44,tropicana,16576,9.715711145,0,2.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/9/1992,45,dominicks,3328,8.110126802,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/9/1992,45,minute.maid,3904,8.269756948,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/9/1992,45,tropicana,9408,9.14931567,0,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/9/1992,47,dominicks,7296,8.895081532,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/9/1992,47,minute.maid,7680,8.946374826,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/9/1992,47,tropicana,5504,8.61323038,0,2.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/9/1992,48,dominicks,2368,7.769800996,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/9/1992,48,minute.maid,5504,8.61323038,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/9/1992,48,tropicana,8064,8.99516499,0,2.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/9/1992,49,dominicks,2240,7.714231145,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/9/1992,49,minute.maid,3584,8.184234774,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/9/1992,49,tropicana,3968,8.286017468,0,2.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/9/1992,50,dominicks,4864,8.489616424,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/9/1992,50,minute.maid,2816,7.943072717,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/9/1992,50,tropicana,4864,8.489616424,0,2.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/9/1992,51,dominicks,1280,7.154615357,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/9/1992,51,minute.maid,4608,8.435549202,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/9/1992,51,tropicana,7296,8.895081532,0,2.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/9/1992,52,dominicks,3072,8.030084094,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/9/1992,52,minute.maid,10944,9.30054664,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/9/1992,52,tropicana,23936,10.08313888,0,2.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/9/1992,53,dominicks,3968,8.286017468,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/9/1992,53,minute.maid,9472,9.156095357,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/9/1992,53,tropicana,17408,9.76468515,0,2.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/9/1992,54,dominicks,3712,8.219326094,0,1.76,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/9/1992,54,minute.maid,6016,8.702177866,0,2.14,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/9/1992,54,tropicana,7168,8.877381955,0,2.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/9/1992,56,dominicks,3264,8.090708716,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/9/1992,56,minute.maid,6272,8.743850562,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/9/1992,56,tropicana,6016,8.702177866,0,2.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/9/1992,59,dominicks,2048,7.624618986,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/9/1992,59,minute.maid,4288,8.363575703,0,1.97,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/9/1992,59,tropicana,5952,8.691482577,0,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/9/1992,62,dominicks,2112,7.655390645,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/9/1992,62,minute.maid,8320,9.026417534,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/9/1992,62,tropicana,27264,10.21332243,0,2.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/9/1992,64,dominicks,4288,8.363575703,0,1.74,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/9/1992,64,minute.maid,4672,8.449342525,0,2.05,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/9/1992,64,tropicana,4416,8.392989588,0,2.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/9/1992,67,dominicks,1152,7.049254841,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/9/1992,67,minute.maid,3776,8.236420527,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/9/1992,67,tropicana,6592,8.793612072,0,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/9/1992,70,dominicks,9536,9.162829389,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -1/9/1992,70,minute.maid,8000,8.987196821,0,1.85,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -1/9/1992,70,tropicana,8512,9.049232212,0,2.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -1/9/1992,71,dominicks,3072,8.030084094,0,1.74,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/9/1992,71,minute.maid,3328,8.110126802,0,2.1,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/9/1992,71,tropicana,9728,9.182763604,0,2.29,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/9/1992,72,dominicks,4416,8.392989588,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/9/1992,72,minute.maid,6592,8.793612072,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/9/1992,72,tropicana,15104,9.622714888,0,2.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/9/1992,73,dominicks,7552,8.929567708,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/9/1992,73,minute.maid,6592,8.793612072,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/9/1992,73,tropicana,11392,9.340666634,0,2.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/9/1992,74,dominicks,2496,7.82244473,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/9/1992,74,minute.maid,5504,8.61323038,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/9/1992,74,tropicana,10240,9.234056899,0,2.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 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-1/9/1992,111,minute.maid,8320,9.026417534,0,2.2,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/9/1992,111,tropicana,8576,9.056722883,0,2.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/9/1992,112,dominicks,4800,8.476371197,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/9/1992,112,minute.maid,8704,9.071537969,0,2.17,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/9/1992,112,tropicana,17472,9.768354879,0,2.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 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-1/9/1992,116,minute.maid,5184,8.553332238,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/9/1992,116,tropicana,8128,9.00307017,0,2.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/9/1992,117,dominicks,2560,7.847762538,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/9/1992,117,minute.maid,5312,8.577723691,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/9/1992,117,tropicana,7296,8.895081532,0,2.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 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-1/9/1992,122,minute.maid,8640,9.064157862,0,2.01,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/9/1992,122,tropicana,11648,9.36288977,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/9/1992,123,dominicks,4096,8.317766167,0,1.77,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/9/1992,123,minute.maid,8576,9.056722883,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/9/1992,123,tropicana,11584,9.357380115,0,2.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 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-1/9/1992,126,tropicana,13824,9.534161491,0,2.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/9/1992,128,dominicks,5952,8.691482577,0,1.74,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/9/1992,128,minute.maid,8576,9.056722883,0,2.12,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/9/1992,128,tropicana,22208,10.00820786,0,2.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/9/1992,129,dominicks,1920,7.560080465,0,1.75,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/9/1992,129,minute.maid,6208,8.733594062,0,2.14,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/9/1992,129,tropicana,11520,9.351839934,0,2.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/9/1992,130,dominicks,8320,9.026417534,0,1.78,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/9/1992,130,minute.maid,9344,9.142489705,0,2.12,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/9/1992,130,tropicana,12800,9.45720045,0,2.14,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/9/1992,131,dominicks,2816,7.943072717,0,1.77,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/9/1992,131,minute.maid,6976,8.850230966,0,2.12,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/9/1992,131,tropicana,8640,9.064157862,0,2.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/9/1992,132,dominicks,8576,9.056722883,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/9/1992,132,minute.maid,8768,9.078864009,0,2.13,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 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-1/16/1992,124,minute.maid,15168,9.626943225,1,2.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/16/1992,124,tropicana,7744,8.954673629,0,2.53,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/16/1992,126,dominicks,5696,8.647519453,0,1.77,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/16/1992,126,minute.maid,18432,9.821843564,1,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/16/1992,126,tropicana,13120,9.481893063,0,2.37,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 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-1/16/1992,131,minute.maid,14080,9.55251063,1,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/16/1992,131,tropicana,11328,9.335032816,0,2.38,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/16/1992,132,dominicks,16256,9.69621735,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/16/1992,132,minute.maid,14784,9.601300794,1,2.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/16/1992,132,tropicana,10368,9.246479419,0,2.44,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 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-1/16/1992,137,tropicana,35520,10.4778512,0,2.37,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/23/1992,2,dominicks,13632,9.520175249,0,1.47,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/23/1992,2,minute.maid,6848,8.831711918,1,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/23/1992,2,tropicana,3520,8.166216269,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/23/1992,5,dominicks,16768,9.727227587,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/23/1992,5,minute.maid,11392,9.340666634,1,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/23/1992,5,tropicana,5888,8.68067166,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/23/1992,8,dominicks,19008,9.852615222,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/23/1992,8,minute.maid,11712,9.368369236,1,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/23/1992,8,tropicana,5056,8.528330936,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 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-1/23/1992,12,tropicana,6656,8.803273983,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/23/1992,14,dominicks,13184,9.486759252,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/23/1992,14,minute.maid,8256,9.018695488,1,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/23/1992,14,tropicana,8832,9.086136769,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/23/1992,18,dominicks,18880,9.84585844,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/23/1992,18,minute.maid,14016,9.547954813,1,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/23/1992,18,tropicana,6208,8.733594062,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/23/1992,21,dominicks,18176,9.807857322,0,1.46,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/23/1992,21,minute.maid,6976,8.850230966,1,2.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/23/1992,21,tropicana,3392,8.129174997,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/23/1992,28,dominicks,960,6.866933285,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/23/1992,28,minute.maid,5120,8.540909718,1,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/23/1992,28,tropicana,3648,8.201934351,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/23/1992,32,dominicks,30336,10.32009041,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/23/1992,32,minute.maid,23168,10.0505273,1,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/23/1992,32,tropicana,8064,8.99516499,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/23/1992,33,dominicks,18688,9.835636886,0,1.47,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/23/1992,33,minute.maid,8448,9.041685006,1,2.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/23/1992,33,tropicana,10240,9.234056899,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/23/1992,40,dominicks,16000,9.680344001,0,1.43,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/23/1992,40,minute.maid,5248,8.565602331,1,2.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/23/1992,40,tropicana,4480,8.407378325,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/23/1992,44,dominicks,15424,9.643680017,0,1.47,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/23/1992,44,minute.maid,11392,9.340666634,1,2.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/23/1992,44,tropicana,6272,8.743850562,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/23/1992,45,dominicks,8960,9.100525506,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/23/1992,45,minute.maid,3520,8.166216269,1,2.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/23/1992,45,tropicana,3520,8.166216269,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/23/1992,47,dominicks,14144,9.557045785,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/23/1992,47,minute.maid,6144,8.723231275,1,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/23/1992,47,tropicana,3072,8.030084094,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/23/1992,48,dominicks,4736,8.462948177,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/23/1992,48,minute.maid,5184,8.553332238,1,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/23/1992,48,tropicana,5504,8.61323038,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/23/1992,49,dominicks,8128,9.00307017,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/23/1992,49,minute.maid,4224,8.348537825,1,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/23/1992,49,tropicana,3264,8.090708716,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/23/1992,50,dominicks,5696,8.647519453,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/23/1992,50,minute.maid,4992,8.51559191,1,2.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/23/1992,50,tropicana,1920,7.560080465,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -1/23/1992,51,dominicks,12288,9.416378455,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/23/1992,51,minute.maid,5376,8.589699882,1,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/23/1992,51,tropicana,4544,8.42156296,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/23/1992,52,dominicks,8960,9.100525506,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/23/1992,52,minute.maid,9984,9.208739091,1,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/23/1992,52,tropicana,7168,8.877381955,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/23/1992,53,dominicks,17536,9.77201119,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/23/1992,53,minute.maid,10368,9.246479419,1,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/23/1992,53,tropicana,8832,9.086136769,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/23/1992,54,dominicks,9600,9.169518378,0,1.55,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/23/1992,54,minute.maid,5440,8.60153434,1,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/23/1992,54,tropicana,5568,8.624791202,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -1/23/1992,56,dominicks,13504,9.510741217,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/23/1992,56,minute.maid,8832,9.086136769,1,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/23/1992,56,tropicana,4160,8.333270353,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -1/23/1992,59,dominicks,9536,9.162829389,0,1.46,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/23/1992,59,minute.maid,7424,8.912473275,1,2.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/23/1992,59,tropicana,4608,8.435549202,0,2.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -1/23/1992,62,dominicks,6144,8.723231275,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/23/1992,62,minute.maid,14720,9.596962392,1,2.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/23/1992,62,tropicana,11520,9.351839934,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -1/23/1992,64,dominicks,6656,8.803273983,0,1.47,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/23/1992,64,minute.maid,4480,8.407378325,1,2.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/23/1992,64,tropicana,2048,7.624618986,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -1/23/1992,67,dominicks,8128,9.00307017,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/23/1992,67,minute.maid,5248,8.565602331,1,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/23/1992,67,tropicana,4352,8.378390789,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -1/23/1992,68,dominicks,13888,9.538780437,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -1/23/1992,68,minute.maid,9216,9.128696383,1,2.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -1/23/1992,68,tropicana,5056,8.528330936,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -1/23/1992,70,dominicks,13824,9.534161491,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -1/23/1992,70,minute.maid,10432,9.252633284,1,2.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -1/23/1992,70,tropicana,6592,8.793612072,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -1/23/1992,71,dominicks,22464,10.01966931,0,1.45,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/23/1992,71,minute.maid,9536,9.162829389,1,2.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/23/1992,71,tropicana,3776,8.236420527,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/23/1992,72,dominicks,17408,9.76468515,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/23/1992,72,minute.maid,8192,9.010913347,1,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/23/1992,72,tropicana,7040,8.859363449,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/23/1992,73,dominicks,44928,10.71281649,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/23/1992,73,minute.maid,17152,9.749870064,1,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/23/1992,73,tropicana,6400,8.764053269,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -1/23/1992,74,dominicks,46272,10.74229231,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/23/1992,74,minute.maid,9408,9.14931567,1,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -1/23/1992,74,tropicana,6272,8.743850562,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 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-1/23/1992,104,minute.maid,5120,8.540909718,1,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -1/23/1992,104,tropicana,3264,8.090708716,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -1/23/1992,105,dominicks,27008,10.2038884,0,1.52,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/23/1992,105,minute.maid,8384,9.034080407,1,2.49,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/23/1992,105,tropicana,3520,8.166216269,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -1/23/1992,106,dominicks,10240,9.234056899,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/23/1992,106,minute.maid,3072,8.030084094,1,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/23/1992,106,tropicana,1664,7.416979621,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -1/23/1992,107,dominicks,15872,9.67231183,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/23/1992,107,minute.maid,9152,9.121727714,1,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/23/1992,107,tropicana,10048,9.215128889,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/23/1992,109,dominicks,9216,9.128696383,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/23/1992,109,minute.maid,14848,9.605620455,1,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/23/1992,109,tropicana,11712,9.368369236,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/23/1992,110,dominicks,14016,9.547954813,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/23/1992,110,minute.maid,9728,9.182763604,1,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/23/1992,110,tropicana,4800,8.476371197,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/23/1992,111,dominicks,50048,10.82073782,0,1.36,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/23/1992,111,minute.maid,17216,9.753594463,1,2.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/23/1992,111,tropicana,3072,8.030084094,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/23/1992,112,dominicks,15232,9.631153757,0,1.5,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/23/1992,112,minute.maid,9536,9.162829389,1,2.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/23/1992,112,tropicana,9472,9.156095357,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/23/1992,113,dominicks,14784,9.601300794,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/23/1992,113,minute.maid,14656,9.592605087,1,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/23/1992,113,tropicana,6464,8.7740036,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/23/1992,114,dominicks,40896,10.61878754,0,1.47,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/23/1992,114,minute.maid,6336,8.754002934,1,2.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/23/1992,114,tropicana,5248,8.565602331,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/23/1992,115,dominicks,12224,9.411156511,0,1.47,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/23/1992,115,minute.maid,7296,8.895081532,1,2.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/23/1992,115,tropicana,5504,8.61323038,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/23/1992,116,dominicks,9152,9.121727714,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/23/1992,116,minute.maid,7040,8.859363449,1,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/23/1992,116,tropicana,3456,8.14786713,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/23/1992,117,dominicks,14464,9.579418083,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/23/1992,117,minute.maid,5056,8.528330936,1,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/23/1992,117,tropicana,3776,8.236420527,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/23/1992,118,dominicks,14080,9.55251063,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/23/1992,118,minute.maid,7424,8.912473275,1,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/23/1992,118,tropicana,4288,8.363575703,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/23/1992,119,dominicks,5504,8.61323038,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/23/1992,119,minute.maid,6976,8.850230966,1,2.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/23/1992,119,tropicana,6848,8.831711918,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/23/1992,121,dominicks,10880,9.29468152,0,1.52,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/23/1992,121,minute.maid,14016,9.547954813,1,2.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/23/1992,121,tropicana,6528,8.783855897,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/23/1992,122,dominicks,16832,9.731037116,0,1.43,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/23/1992,122,minute.maid,7680,8.946374826,1,2.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/23/1992,122,tropicana,9600,9.169518378,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/23/1992,123,dominicks,14976,9.614204199,0,1.46,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/23/1992,123,minute.maid,12608,9.442086812,1,2.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/23/1992,123,tropicana,4224,8.348537825,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/23/1992,124,dominicks,18304,9.814874894,0,1.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/23/1992,124,minute.maid,13888,9.538780437,1,2.38,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/23/1992,124,tropicana,5056,8.528330936,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/23/1992,126,dominicks,12992,9.472089062,0,1.47,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/23/1992,126,minute.maid,15744,9.664214619,1,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/23/1992,126,tropicana,6400,8.764053269,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/23/1992,128,dominicks,47296,10.764181,0,1.53,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/23/1992,128,minute.maid,9152,9.121727714,1,1.91,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/23/1992,128,tropicana,7488,8.921057018,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/23/1992,129,dominicks,9408,9.14931567,0,1.52,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/23/1992,129,minute.maid,7296,8.895081532,1,2.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/23/1992,129,tropicana,7040,8.859363449,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/23/1992,130,dominicks,40704,10.61408165,0,1.46,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/23/1992,130,minute.maid,8000,8.987196821,1,1.48,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/23/1992,130,tropicana,3008,8.009030685,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/23/1992,131,dominicks,15360,9.639522007,0,1.48,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/23/1992,131,minute.maid,10688,9.276876896,1,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/23/1992,131,tropicana,5696,8.647519453,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/23/1992,132,dominicks,18112,9.804329981,0,1.46,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/23/1992,132,minute.maid,11328,9.335032816,1,2.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/23/1992,132,tropicana,6464,8.7740036,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/23/1992,134,dominicks,5632,8.636219898,0,1.54,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/23/1992,134,minute.maid,5440,8.60153434,1,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/23/1992,134,tropicana,3840,8.253227646,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/23/1992,137,dominicks,20480,9.927204079,0,1.55,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/23/1992,137,minute.maid,21440,9.973013615,1,2.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/23/1992,137,tropicana,14848,9.605620455,0,3.18,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/30/1992,2,dominicks,45120,10.71708089,0,1.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/30/1992,2,minute.maid,3968,8.286017468,0,2.61,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/30/1992,2,tropicana,5504,8.61323038,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -1/30/1992,5,dominicks,52160,10.8620712,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/30/1992,5,minute.maid,5824,8.66974259,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/30/1992,5,tropicana,7424,8.912473275,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -1/30/1992,8,dominicks,121664,11.70901843,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/30/1992,8,minute.maid,7936,8.979164649,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/30/1992,8,tropicana,6080,8.712759975,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -1/30/1992,9,dominicks,67648,11.12207307,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/30/1992,9,minute.maid,3392,8.129174997,0,2.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/30/1992,9,tropicana,7616,8.938006577,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -1/30/1992,12,dominicks,108224,11.59195843,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/30/1992,12,minute.maid,12416,9.426741242,0,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/30/1992,12,tropicana,7552,8.929567708,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -1/30/1992,14,dominicks,35264,10.47061789,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/30/1992,14,minute.maid,5376,8.589699882,0,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/30/1992,14,tropicana,10048,9.215128889,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -1/30/1992,18,dominicks,65408,11.08839985,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/30/1992,18,minute.maid,5824,8.66974259,0,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/30/1992,18,tropicana,6464,8.7740036,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -1/30/1992,21,dominicks,47232,10.76282691,0,1.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/30/1992,21,minute.maid,3392,8.129174997,0,2.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/30/1992,21,tropicana,3712,8.219326094,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -1/30/1992,28,dominicks,21376,9.970024076,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/30/1992,28,minute.maid,2752,7.920083199,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/30/1992,28,tropicana,4160,8.333270353,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -1/30/1992,32,dominicks,86144,11.36377559,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/30/1992,32,minute.maid,7872,8.971067439,0,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/30/1992,32,tropicana,10496,9.258749511,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -1/30/1992,33,dominicks,46336,10.74367448,0,1.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/30/1992,33,minute.maid,5632,8.636219898,0,2.57,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/30/1992,33,tropicana,9472,9.156095357,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -1/30/1992,40,dominicks,73792,11.2090056,0,1.29,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/30/1992,40,minute.maid,2624,7.87245515,0,2.39,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/30/1992,40,tropicana,4992,8.51559191,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -1/30/1992,44,dominicks,65280,11.08644099,0,1.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/30/1992,44,minute.maid,4160,8.333270353,0,2.55,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/30/1992,44,tropicana,9536,9.162829389,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -1/30/1992,45,dominicks,27008,10.2038884,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/30/1992,45,minute.maid,2944,7.98752448,0,2.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/30/1992,45,tropicana,5696,8.647519453,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -1/30/1992,47,dominicks,40960,10.62035126,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/30/1992,47,minute.maid,3968,8.286017468,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/30/1992,47,tropicana,2944,7.98752448,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -1/30/1992,48,dominicks,31616,10.3614186,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/30/1992,48,minute.maid,3456,8.14786713,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/30/1992,48,tropicana,4672,8.449342525,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -1/30/1992,49,dominicks,21888,9.99369382,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/30/1992,49,minute.maid,2560,7.847762538,0,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/30/1992,49,tropicana,2816,7.943072717,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -1/30/1992,51,dominicks,45568,10.726961,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/30/1992,51,minute.maid,4288,8.363575703,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/30/1992,51,tropicana,4352,8.378390789,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -1/30/1992,52,dominicks,28160,10.24565781,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/30/1992,52,minute.maid,6464,8.7740036,0,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/30/1992,52,tropicana,10048,9.215128889,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -1/30/1992,53,dominicks,60800,11.01534507,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/30/1992,53,minute.maid,6720,8.812843434,0,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -1/30/1992,53,tropicana,7808,8.962904128,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 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-1/30/1992,71,minute.maid,2240,7.714231145,0,2.55,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/30/1992,71,tropicana,4352,8.378390789,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -1/30/1992,72,dominicks,63680,11.06162582,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/30/1992,72,minute.maid,4736,8.462948177,0,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -1/30/1992,72,tropicana,7488,8.921057018,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 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-1/30/1992,76,minute.maid,4096,8.317766167,0,2.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/30/1992,76,tropicana,6336,8.754002934,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -1/30/1992,77,dominicks,36480,10.50451944,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/30/1992,77,minute.maid,3712,8.219326094,0,2.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -1/30/1992,77,tropicana,6784,8.822322178,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 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-1/30/1992,86,dominicks,72832,11.1959107,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/30/1992,86,minute.maid,2560,7.847762538,0,2.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/30/1992,86,tropicana,5760,8.658692754,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -1/30/1992,88,dominicks,52608,10.87062348,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -1/30/1992,88,minute.maid,2816,7.943072717,0,2.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 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-1/30/1992,90,minute.maid,3456,8.14786713,0,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -1/30/1992,90,tropicana,1856,7.526178913,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -1/30/1992,91,dominicks,58752,10.98108047,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -1/30/1992,91,minute.maid,3200,8.070906089,0,2.49,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -1/30/1992,91,tropicana,4288,8.363575703,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 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-1/30/1992,95,minute.maid,3840,8.253227646,0,2.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/30/1992,95,tropicana,1984,7.592870288,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -1/30/1992,97,dominicks,29120,10.2791805,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/30/1992,97,minute.maid,2368,7.769800996,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -1/30/1992,97,tropicana,1920,7.560080465,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 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-1/30/1992,102,minute.maid,8128,9.00307017,0,2.45,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -1/30/1992,102,tropicana,6464,8.7740036,0,2.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -1/30/1992,103,dominicks,71744,11.18085951,0,1.29,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -1/30/1992,103,minute.maid,2240,7.714231145,0,2.45,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -1/30/1992,103,tropicana,2240,7.714231145,0,2.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 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-1/30/1992,107,minute.maid,4288,8.363575703,0,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/30/1992,107,tropicana,10368,9.246479419,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -1/30/1992,109,dominicks,55488,10.92392206,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/30/1992,109,minute.maid,9536,9.162829389,0,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/30/1992,109,tropicana,14080,9.55251063,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -1/30/1992,110,dominicks,36160,10.49570882,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/30/1992,110,minute.maid,2560,7.847762538,0,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/30/1992,110,tropicana,3072,8.030084094,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -1/30/1992,111,dominicks,248000,12.42118403,0,1.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/30/1992,111,minute.maid,5248,8.565602331,0,2.59,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/30/1992,111,tropicana,3520,8.166216269,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -1/30/1992,112,dominicks,48576,10.79088486,0,1.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/30/1992,112,minute.maid,6912,8.841014311,0,2.45,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/30/1992,112,tropicana,10560,9.264828557,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -1/30/1992,113,dominicks,73856,11.20987253,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/30/1992,113,minute.maid,15808,9.66827142,0,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/30/1992,113,tropicana,7040,8.859363449,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -1/30/1992,114,dominicks,138816,11.84090459,0,1.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/30/1992,114,minute.maid,3968,8.286017468,0,2.53,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/30/1992,114,tropicana,5184,8.553332238,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -1/30/1992,115,dominicks,39936,10.59503345,0,1.29,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/30/1992,115,minute.maid,4608,8.435549202,0,2.54,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/30/1992,115,tropicana,7104,8.868413285,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -1/30/1992,116,dominicks,49088,10.80136989,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/30/1992,116,minute.maid,5376,8.589699882,0,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/30/1992,116,tropicana,6656,8.803273983,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -1/30/1992,117,dominicks,35648,10.48144832,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/30/1992,117,minute.maid,3008,8.009030685,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/30/1992,117,tropicana,3328,8.110126802,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -1/30/1992,118,dominicks,45952,10.73535265,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/30/1992,118,minute.maid,3904,8.269756948,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/30/1992,118,tropicana,3840,8.253227646,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -1/30/1992,119,dominicks,24192,10.09377728,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/30/1992,119,minute.maid,3904,8.269756948,0,2.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/30/1992,119,tropicana,7296,8.895081532,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -1/30/1992,121,dominicks,44416,10.70135504,0,1.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/30/1992,121,minute.maid,10752,9.282847063,0,2.53,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/30/1992,121,tropicana,9152,9.121727714,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -1/30/1992,122,dominicks,55040,10.91581547,0,1.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/30/1992,122,minute.maid,6784,8.822322178,0,2.43,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/30/1992,122,tropicana,10112,9.221478116,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -1/30/1992,123,dominicks,103936,11.5515306,0,1.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/30/1992,123,minute.maid,7680,8.946374826,0,2.21,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/30/1992,123,tropicana,6912,8.841014311,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -1/30/1992,124,dominicks,95488,11.46675586,0,1.29,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/30/1992,124,minute.maid,4032,8.30201781,0,2.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/30/1992,124,tropicana,5568,8.624791202,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -1/30/1992,126,dominicks,37504,10.53220287,0,1.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/30/1992,126,minute.maid,10496,9.258749511,0,2.53,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/30/1992,126,tropicana,8576,9.056722883,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -1/30/1992,128,dominicks,90752,11.41588579,0,1.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/30/1992,128,minute.maid,6016,8.702177866,0,2.57,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/30/1992,128,tropicana,7680,8.946374826,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -1/30/1992,129,dominicks,29888,10.30521234,0,1.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/30/1992,129,minute.maid,5376,8.589699882,0,2.53,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/30/1992,129,tropicana,6400,8.764053269,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -1/30/1992,130,dominicks,129408,11.77072548,0,1.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/30/1992,130,minute.maid,6464,8.7740036,0,2.53,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/30/1992,130,tropicana,3904,8.269756948,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -1/30/1992,131,dominicks,56512,10.94220828,0,1.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/30/1992,131,minute.maid,7616,8.938006577,0,2.54,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/30/1992,131,tropicana,7232,8.886270902,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -1/30/1992,132,dominicks,86336,11.36600194,0,1.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/30/1992,132,minute.maid,7360,8.903815212,0,2.45,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/30/1992,132,tropicana,8000,8.987196821,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -1/30/1992,134,dominicks,33664,10.4241843,0,1.29,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/30/1992,134,minute.maid,3072,8.030084094,0,2.52,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/30/1992,134,tropicana,4032,8.30201781,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -1/30/1992,137,dominicks,75072,11.22620293,0,1.29,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/30/1992,137,minute.maid,13824,9.534161491,0,2.55,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -1/30/1992,137,tropicana,17408,9.76468515,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/6/1992,2,dominicks,9984,9.208739091,0,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/6/1992,2,minute.maid,5888,8.68067166,0,2.26,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/6/1992,2,tropicana,6720,8.812843434,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/6/1992,5,dominicks,16640,9.719564714,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/6/1992,5,minute.maid,7488,8.921057018,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/6/1992,5,tropicana,5632,8.636219898,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/6/1992,8,dominicks,38848,10.56741187,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/6/1992,8,minute.maid,5184,8.553332238,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/6/1992,8,tropicana,10496,9.258749511,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/6/1992,9,dominicks,7232,8.886270902,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/6/1992,9,minute.maid,5568,8.624791202,0,2.66,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/6/1992,9,tropicana,7296,8.895081532,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/6/1992,12,dominicks,29312,10.28575227,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/6/1992,12,minute.maid,6016,8.702177866,0,2.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/6/1992,12,tropicana,9216,9.128696383,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/6/1992,14,dominicks,6464,8.7740036,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/6/1992,14,minute.maid,8640,9.064157862,0,2.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/6/1992,14,tropicana,12672,9.447150114,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/6/1992,18,dominicks,16640,9.719564714,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/6/1992,18,minute.maid,5248,8.565602331,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/6/1992,18,tropicana,7488,8.921057018,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/6/1992,21,dominicks,10240,9.234056899,0,1.31,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/6/1992,21,minute.maid,3392,8.129174997,0,2.13,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/6/1992,21,tropicana,4288,8.363575703,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/6/1992,28,dominicks,6592,8.793612072,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/6/1992,28,minute.maid,6080,8.712759975,0,2.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/6/1992,28,tropicana,5056,8.528330936,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/6/1992,32,dominicks,11264,9.329367078,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/6/1992,32,minute.maid,5376,8.589699882,0,2.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/6/1992,32,tropicana,10368,9.246479419,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/6/1992,33,dominicks,14720,9.596962392,0,1.41,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/6/1992,33,minute.maid,7488,8.921057018,0,2.27,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/6/1992,33,tropicana,10176,9.227787286,0,3.18,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/6/1992,40,dominicks,6912,8.841014311,0,1.31,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/6/1992,40,minute.maid,3904,8.269756948,0,2.14,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/6/1992,40,tropicana,4800,8.476371197,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/6/1992,44,dominicks,10880,9.29468152,0,1.32,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/6/1992,44,minute.maid,5184,8.553332238,0,2.37,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/6/1992,44,tropicana,8448,9.041685006,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/6/1992,45,dominicks,5376,8.589699882,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/6/1992,45,minute.maid,2624,7.87245515,0,2.66,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/6/1992,45,tropicana,6784,8.822322178,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/6/1992,47,dominicks,10048,9.215128889,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/6/1992,47,minute.maid,4352,8.378390789,0,2.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/6/1992,47,tropicana,3776,8.236420527,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/6/1992,48,dominicks,12352,9.421573272,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/6/1992,48,minute.maid,5696,8.647519453,0,2.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/6/1992,48,tropicana,5696,8.647519453,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/6/1992,49,dominicks,7168,8.877381955,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/6/1992,49,minute.maid,2560,7.847762538,0,2.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/6/1992,49,tropicana,4224,8.348537825,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/6/1992,50,dominicks,4032,8.30201781,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/6/1992,50,minute.maid,1792,7.491087594,0,2.66,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/6/1992,50,tropicana,3648,8.201934351,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/6/1992,51,dominicks,4224,8.348537825,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/6/1992,51,minute.maid,2944,7.98752448,0,2.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/6/1992,51,tropicana,4608,8.435549202,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/6/1992,52,dominicks,6720,8.812843434,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/6/1992,52,minute.maid,6784,8.822322178,0,2.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/6/1992,52,tropicana,9408,9.14931567,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/6/1992,53,dominicks,12928,9.467150781,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/6/1992,53,minute.maid,7232,8.886270902,0,2.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/6/1992,53,tropicana,9984,9.208739091,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/6/1992,54,dominicks,12416,9.426741242,0,1.35,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/6/1992,54,minute.maid,4928,8.502688505,0,2.21,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/6/1992,54,tropicana,8128,9.00307017,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/6/1992,56,dominicks,10240,9.234056899,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/6/1992,56,minute.maid,4224,8.348537825,0,2.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/6/1992,56,tropicana,5760,8.658692754,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/6/1992,59,dominicks,7488,8.921057018,0,1.33,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/6/1992,59,minute.maid,4224,8.348537825,0,2.13,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/6/1992,59,tropicana,5376,8.589699882,0,2.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/6/1992,62,dominicks,5504,8.61323038,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/6/1992,62,minute.maid,8000,8.987196821,0,2.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/6/1992,62,tropicana,13824,9.534161491,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/6/1992,64,dominicks,8448,9.041685006,0,1.36,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/6/1992,64,minute.maid,3328,8.110126802,0,2.06,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/6/1992,64,tropicana,3904,8.269756948,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/6/1992,67,dominicks,21632,9.981928979,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/6/1992,67,minute.maid,3328,8.110126802,0,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/6/1992,67,tropicana,5696,8.647519453,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/6/1992,68,dominicks,9152,9.121727714,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/6/1992,68,minute.maid,6080,8.712759975,0,2.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/6/1992,68,tropicana,6144,8.723231275,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/6/1992,70,dominicks,11200,9.323669057,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/6/1992,70,minute.maid,6272,8.743850562,0,2.25,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/6/1992,70,tropicana,8320,9.026417534,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/6/1992,72,dominicks,11456,9.346268889,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/6/1992,72,minute.maid,6080,8.712759975,0,2.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/6/1992,72,tropicana,10432,9.252633284,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/6/1992,73,dominicks,45696,10.72976605,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/6/1992,73,minute.maid,5568,8.624791202,0,2.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/6/1992,73,tropicana,6720,8.812843434,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/6/1992,74,dominicks,26368,10.17990643,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/6/1992,74,minute.maid,5440,8.60153434,0,2.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/6/1992,74,tropicana,6720,8.812843434,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/6/1992,75,dominicks,11776,9.373818841,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/6/1992,75,minute.maid,3456,8.14786713,0,2.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/6/1992,75,tropicana,6080,8.712759975,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/6/1992,76,dominicks,7936,8.979164649,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/6/1992,76,minute.maid,3008,8.009030685,0,2.66,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/6/1992,76,tropicana,7744,8.954673629,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/6/1992,77,dominicks,8704,9.071537969,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/6/1992,77,minute.maid,4544,8.42156296,0,2.25,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/6/1992,77,tropicana,7040,8.859363449,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/6/1992,78,dominicks,17856,9.790094865,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/6/1992,78,minute.maid,10240,9.234056899,0,2.25,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/6/1992,78,tropicana,6912,8.841014311,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/6/1992,80,dominicks,6400,8.764053269,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/6/1992,80,minute.maid,6400,8.764053269,0,2.25,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/6/1992,80,tropicana,9600,9.169518378,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/6/1992,81,dominicks,9152,9.121727714,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/6/1992,81,minute.maid,4096,8.317766167,0,2.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/6/1992,81,tropicana,8384,9.034080407,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/6/1992,83,dominicks,10240,9.234056899,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/6/1992,83,minute.maid,3008,8.009030685,0,2.25,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/6/1992,83,tropicana,8256,9.018695488,0,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/6/1992,84,dominicks,22208,10.00820786,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/6/1992,84,minute.maid,4864,8.489616424,0,2.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/6/1992,84,tropicana,6272,8.743850562,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/6/1992,86,dominicks,14848,9.605620455,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/6/1992,86,minute.maid,3136,8.050703382,0,2.66,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/6/1992,86,tropicana,5120,8.540909718,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/6/1992,88,dominicks,6272,8.743850562,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/6/1992,88,minute.maid,4800,8.476371197,0,2.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/6/1992,88,tropicana,4800,8.476371197,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/6/1992,89,dominicks,9600,9.169518378,0,1.32,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/6/1992,89,minute.maid,2432,7.796469243,0,2.29,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/6/1992,89,tropicana,2432,7.796469243,0,2.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/6/1992,90,dominicks,17408,9.76468515,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/6/1992,90,minute.maid,2880,7.965545573,0,2.79,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/6/1992,90,tropicana,2368,7.769800996,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/6/1992,91,dominicks,8192,9.010913347,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/6/1992,91,minute.maid,2624,7.87245515,0,2.66,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/6/1992,91,tropicana,3904,8.269756948,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/6/1992,92,dominicks,8960,9.100525506,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/6/1992,92,minute.maid,2176,7.685243608,0,2.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/6/1992,92,tropicana,3776,8.236420527,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -2/6/1992,93,dominicks,9792,9.189321005,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/6/1992,93,minute.maid,14656,9.592605087,0,2.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/6/1992,93,tropicana,9664,9.17616292,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/6/1992,94,dominicks,3072,8.030084094,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/6/1992,94,minute.maid,5568,8.624791202,0,2.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/6/1992,94,tropicana,6720,8.812843434,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/6/1992,95,dominicks,20864,9.945780465,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/6/1992,95,minute.maid,5056,8.528330936,0,2.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/6/1992,95,tropicana,4096,8.317766167,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -2/6/1992,97,dominicks,5376,8.589699882,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/6/1992,97,minute.maid,1984,7.592870288,0,2.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/6/1992,97,tropicana,1856,7.526178913,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -2/6/1992,98,dominicks,9664,9.17616292,0,1.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/6/1992,98,minute.maid,7488,8.921057018,0,1.9,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/6/1992,98,tropicana,9472,9.156095357,0,2.97,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -2/6/1992,100,dominicks,16512,9.711842668,0,1.33,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/6/1992,100,minute.maid,4352,8.378390789,0,2.28,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/6/1992,100,tropicana,7744,8.954673629,0,3.19,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/6/1992,101,dominicks,12608,9.442086812,0,1.29,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/6/1992,101,minute.maid,4928,8.502688505,0,2.42,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/6/1992,101,tropicana,8448,9.041685006,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/6/1992,102,dominicks,17152,9.749870064,0,1.36,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/6/1992,102,minute.maid,6080,8.712759975,0,2.2,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/6/1992,102,tropicana,7616,8.938006577,0,2.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -2/6/1992,103,dominicks,25792,10.15781965,0,1.34,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/6/1992,103,minute.maid,2688,7.896552702,0,2.24,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/6/1992,103,tropicana,2560,7.847762538,0,2.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -2/6/1992,104,dominicks,6272,8.743850562,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/6/1992,104,minute.maid,2944,7.98752448,0,2.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/6/1992,104,tropicana,4032,8.30201781,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/6/1992,105,dominicks,10816,9.288781798,0,1.31,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/6/1992,105,minute.maid,2624,7.87245515,0,2.36,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/6/1992,105,tropicana,2816,7.943072717,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/6/1992,106,dominicks,10496,9.258749511,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/6/1992,106,minute.maid,2112,7.655390645,0,2.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/6/1992,106,tropicana,2048,7.624618986,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/6/1992,107,dominicks,7296,8.895081532,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/6/1992,107,minute.maid,5248,8.565602331,0,2.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/6/1992,107,tropicana,11200,9.323669057,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/6/1992,109,dominicks,6464,8.7740036,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/6/1992,109,minute.maid,11712,9.368369236,0,2.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/6/1992,109,tropicana,14976,9.614204199,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/6/1992,110,dominicks,5056,8.528330936,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/6/1992,110,minute.maid,3008,8.009030685,0,2.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/6/1992,110,tropicana,4480,8.407378325,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/6/1992,111,dominicks,12288,9.416378455,0,1.33,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/6/1992,111,minute.maid,4032,8.30201781,0,2.4,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/6/1992,111,tropicana,3648,8.201934351,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/6/1992,112,dominicks,7936,8.979164649,0,1.34,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/6/1992,112,minute.maid,8000,8.987196821,0,2.25,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/6/1992,112,tropicana,13632,9.520175249,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/6/1992,113,dominicks,20224,9.914625297,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/6/1992,113,minute.maid,4352,8.378390789,0,2.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/6/1992,113,tropicana,7360,8.903815212,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/6/1992,114,dominicks,32576,10.3913311,0,1.38,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/6/1992,114,minute.maid,3968,8.286017468,0,2.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/6/1992,114,tropicana,5056,8.528330936,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/6/1992,115,dominicks,12288,9.416378455,0,1.42,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/6/1992,115,minute.maid,5312,8.577723691,0,2.33,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/6/1992,115,tropicana,7936,8.979164649,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/6/1992,116,dominicks,22976,10.04220547,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/6/1992,116,minute.maid,3520,8.166216269,0,2.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/6/1992,116,tropicana,5376,8.589699882,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/6/1992,117,dominicks,7680,8.946374826,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/6/1992,117,minute.maid,2880,7.965545573,0,2.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/6/1992,117,tropicana,5632,8.636219898,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/6/1992,118,dominicks,4928,8.502688505,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/6/1992,118,minute.maid,7680,8.946374826,0,2.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/6/1992,118,tropicana,4800,8.476371197,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/6/1992,119,dominicks,4544,8.42156296,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/6/1992,119,minute.maid,3328,8.110126802,0,2.66,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/6/1992,119,tropicana,7424,8.912473275,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/6/1992,121,dominicks,17728,9.78290059,0,1.35,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/6/1992,121,minute.maid,7808,8.962904128,0,2.34,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/6/1992,121,tropicana,10624,9.270870872,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/6/1992,122,dominicks,9792,9.189321005,0,1.35,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/6/1992,122,minute.maid,6848,8.831711918,0,2.14,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/6/1992,122,tropicana,10624,9.270870872,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/6/1992,123,dominicks,34688,10.45414909,0,1.33,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/6/1992,123,minute.maid,9472,9.156095357,0,2,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/6/1992,123,tropicana,4800,8.476371197,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/6/1992,124,dominicks,15040,9.618468598,0,1.37,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/6/1992,124,minute.maid,6720,8.812843434,0,2.12,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/6/1992,124,tropicana,7936,8.979164649,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/6/1992,126,dominicks,8000,8.987196821,0,1.36,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/6/1992,126,minute.maid,8384,9.034080407,0,2.38,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/6/1992,126,tropicana,8384,9.034080407,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/6/1992,128,dominicks,18048,9.800790154,0,1.34,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/6/1992,128,minute.maid,5440,8.60153434,0,2.35,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/6/1992,128,tropicana,8640,9.064157862,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/6/1992,129,dominicks,4736,8.462948177,0,1.34,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/6/1992,129,minute.maid,4416,8.392989588,0,2.43,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/6/1992,129,tropicana,6336,8.754002934,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/6/1992,130,dominicks,14464,9.579418083,0,1.37,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/6/1992,130,minute.maid,4096,8.317766167,0,2.2,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/6/1992,130,tropicana,3328,8.110126802,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/6/1992,131,dominicks,23296,10.05603695,0,1.35,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/6/1992,131,minute.maid,4160,8.333270353,0,2.31,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/6/1992,131,tropicana,7936,8.979164649,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/6/1992,132,dominicks,31296,10.35124557,0,1.35,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/6/1992,132,minute.maid,8256,9.018695488,0,2.16,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/6/1992,132,tropicana,9792,9.189321005,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/6/1992,134,dominicks,4608,8.435549202,0,1.34,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/6/1992,134,minute.maid,2944,7.98752448,0,2.25,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/6/1992,134,tropicana,3392,8.129174997,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/6/1992,137,dominicks,26880,10.1991378,0,1.3,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/6/1992,137,minute.maid,10112,9.221478116,0,2.42,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/6/1992,137,tropicana,18752,9.839055692,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/13/1992,2,dominicks,4800,8.476371197,0,1.82,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/13/1992,2,minute.maid,6208,8.733594062,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/13/1992,2,tropicana,20224,9.914625297,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/13/1992,5,dominicks,1344,7.203405521,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/13/1992,5,minute.maid,8320,9.026417534,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/13/1992,5,tropicana,33600,10.42228135,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/13/1992,8,dominicks,6144,8.723231275,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/13/1992,8,minute.maid,7168,8.877381955,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/13/1992,8,tropicana,39040,10.57234204,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/13/1992,9,dominicks,1152,7.049254841,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/13/1992,9,minute.maid,4736,8.462948177,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/13/1992,9,tropicana,36736,10.51151248,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/13/1992,12,dominicks,18880,9.84585844,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/13/1992,12,minute.maid,9600,9.169518378,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/13/1992,12,tropicana,57088,10.95234922,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/13/1992,14,dominicks,2112,7.655390645,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/13/1992,14,minute.maid,15424,9.643680017,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/13/1992,14,tropicana,43008,10.66914142,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/13/1992,18,dominicks,4608,8.435549202,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/13/1992,18,minute.maid,8128,9.00307017,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/13/1992,18,tropicana,23104,10.04776104,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/13/1992,21,dominicks,19712,9.888982866,0,1,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/13/1992,21,minute.maid,4800,8.476371197,0,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/13/1992,21,tropicana,11520,9.351839934,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/13/1992,28,dominicks,1408,7.249925537,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/13/1992,28,minute.maid,4032,8.30201781,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/13/1992,28,tropicana,16128,9.688312171,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/13/1992,32,dominicks,11904,9.384629757,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/13/1992,32,minute.maid,10176,9.227787286,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/13/1992,32,tropicana,38592,10.56080028,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/13/1992,33,dominicks,5312,8.577723691,0,1.76,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/13/1992,33,minute.maid,10368,9.246479419,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/13/1992,33,tropicana,38592,10.56080028,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/13/1992,40,dominicks,4160,8.333270353,0,1.56,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/13/1992,40,minute.maid,4864,8.489616424,0,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/13/1992,40,tropicana,16704,9.723403491,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/13/1992,44,dominicks,17536,9.77201119,0,1.07,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/13/1992,44,minute.maid,6400,8.764053269,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/13/1992,44,tropicana,34240,10.44114983,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/13/1992,45,dominicks,2560,7.847762538,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/13/1992,45,minute.maid,4544,8.42156296,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/13/1992,45,tropicana,17984,9.797237753,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/13/1992,47,dominicks,8640,9.064157862,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/13/1992,47,minute.maid,6272,8.743850562,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/13/1992,47,tropicana,15296,9.635346635,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/13/1992,48,dominicks,4928,8.502688505,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/13/1992,48,minute.maid,7680,8.946374826,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/13/1992,48,tropicana,24064,10.08847223,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/13/1992,49,dominicks,2752,7.920083199,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/13/1992,49,minute.maid,6592,8.793612072,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/13/1992,49,tropicana,18304,9.814874894,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/13/1992,50,dominicks,2240,7.714231145,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/13/1992,50,minute.maid,4416,8.392989588,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/13/1992,50,tropicana,17280,9.757305042,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/13/1992,51,dominicks,2112,7.655390645,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/13/1992,51,minute.maid,3392,8.129174997,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/13/1992,51,tropicana,24000,10.08580911,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/13/1992,52,dominicks,3648,8.201934351,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 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-2/13/1992,88,tropicana,21952,9.996613531,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/13/1992,89,dominicks,2432,7.796469243,0,1.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/13/1992,89,minute.maid,3776,8.236420527,0,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/13/1992,89,tropicana,24704,10.11472045,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/13/1992,90,dominicks,21184,9.961001459,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/13/1992,90,minute.maid,4736,8.462948177,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/13/1992,90,tropicana,12288,9.416378455,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/13/1992,91,dominicks,3008,8.009030685,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/13/1992,91,minute.maid,4160,8.333270353,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -2/13/1992,91,tropicana,25152,10.1326927,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 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-2/13/1992,93,tropicana,45696,10.72976605,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -2/13/1992,94,dominicks,8576,9.056722883,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/13/1992,94,minute.maid,19712,9.888982866,0,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/13/1992,94,tropicana,33216,10.41078697,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -2/13/1992,95,dominicks,3520,8.166216269,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 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-2/13/1992,107,minute.maid,7936,8.979164649,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/13/1992,107,tropicana,39232,10.57724802,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/13/1992,109,dominicks,2496,7.82244473,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/13/1992,109,minute.maid,13888,9.538780437,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/13/1992,109,tropicana,76480,11.24478455,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 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-2/13/1992,111,tropicana,19520,9.87919486,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/13/1992,112,dominicks,5888,8.68067166,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/13/1992,112,minute.maid,10688,9.276876896,0,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/13/1992,112,tropicana,56192,10.93652968,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/13/1992,113,dominicks,2112,7.655390645,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/13/1992,113,minute.maid,6336,8.754002934,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/13/1992,113,tropicana,46144,10.73952222,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/13/1992,114,dominicks,80128,11.29138064,0,1.02,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/13/1992,114,minute.maid,7360,8.903815212,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/13/1992,114,tropicana,20096,9.908276069,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/13/1992,115,dominicks,2240,7.714231145,0,1.73,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/13/1992,115,minute.maid,8000,8.987196821,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/13/1992,115,tropicana,36032,10.49216271,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/13/1992,116,dominicks,2496,7.82244473,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/13/1992,116,minute.maid,4160,8.333270353,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/13/1992,116,tropicana,27072,10.20625526,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/13/1992,117,dominicks,3584,8.184234774,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/13/1992,117,minute.maid,4608,8.435549202,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/13/1992,117,tropicana,22464,10.01966931,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/13/1992,118,dominicks,4352,8.378390789,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/13/1992,118,minute.maid,10752,9.282847063,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/13/1992,118,tropicana,22144,10.00532186,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/13/1992,119,dominicks,2368,7.769800996,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/13/1992,119,minute.maid,4928,8.502688505,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/13/1992,119,tropicana,17408,9.76468515,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/13/1992,121,dominicks,2752,7.920083199,0,1.82,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/13/1992,121,minute.maid,8320,9.026417534,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/13/1992,121,tropicana,29504,10.29228113,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/13/1992,122,dominicks,11200,9.323669057,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/13/1992,122,minute.maid,9792,9.189321005,0,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/13/1992,122,tropicana,40512,10.60935351,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/13/1992,123,dominicks,3584,8.184234774,0,1.82,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/13/1992,123,minute.maid,9152,9.121727714,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/13/1992,123,tropicana,63424,11.05759762,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/13/1992,124,dominicks,8256,9.018695488,0,1.73,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/13/1992,124,minute.maid,7488,8.921057018,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/13/1992,124,tropicana,40576,10.61093204,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/13/1992,126,dominicks,15744,9.664214619,0,1.1,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/13/1992,126,minute.maid,14272,9.566054855,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/13/1992,126,tropicana,54464,10.90529521,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/13/1992,128,dominicks,72256,11.18797065,0,1.07,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/13/1992,128,minute.maid,10176,9.227787286,0,1.98,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/13/1992,128,tropicana,81856,11.31271689,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/13/1992,129,dominicks,40256,10.60301434,0,1,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/13/1992,129,minute.maid,5312,8.577723691,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/13/1992,129,tropicana,24128,10.09112827,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/13/1992,130,dominicks,127232,11.75376747,0,1,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/13/1992,130,minute.maid,9984,9.208739091,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/13/1992,130,tropicana,50176,10.8232921,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/13/1992,131,dominicks,2368,7.769800996,0,1.56,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/13/1992,131,minute.maid,4096,8.317766167,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/13/1992,131,tropicana,35328,10.47243113,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/13/1992,132,dominicks,61952,11.03411517,0,1.01,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/13/1992,132,minute.maid,9728,9.182763604,0,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/13/1992,132,tropicana,34112,10.43740451,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/13/1992,134,dominicks,26752,10.19436452,0,1.08,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/13/1992,134,minute.maid,3712,8.219326094,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/13/1992,134,tropicana,13760,9.529521112,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/13/1992,137,dominicks,42688,10.66167313,0,1.05,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/13/1992,137,minute.maid,17344,9.761001904,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/13/1992,137,tropicana,99968,11.51260541,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/20/1992,2,dominicks,11776,9.373818841,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/20/1992,2,minute.maid,72256,11.18797065,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/20/1992,2,tropicana,5056,8.528330936,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/20/1992,5,dominicks,4608,8.435549202,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/20/1992,5,minute.maid,99904,11.511965,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/20/1992,5,tropicana,5376,8.589699882,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/20/1992,8,dominicks,13632,9.520175249,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/20/1992,8,minute.maid,216064,12.28332994,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/20/1992,8,tropicana,4480,8.407378325,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/20/1992,9,dominicks,7168,8.877381955,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/20/1992,9,minute.maid,112192,11.62796697,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/20/1992,9,tropicana,7040,8.859363449,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/20/1992,14,dominicks,9600,9.169518378,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/20/1992,14,minute.maid,89472,11.40168101,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/20/1992,14,tropicana,8064,8.99516499,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/20/1992,18,dominicks,10816,9.288781798,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/20/1992,18,minute.maid,125504,11.74009291,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/20/1992,18,tropicana,4736,8.462948177,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/20/1992,21,dominicks,9600,9.169518378,0,0.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/20/1992,21,minute.maid,87296,11.37705992,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/20/1992,21,tropicana,3456,8.14786713,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/20/1992,28,dominicks,2048,7.624618986,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/20/1992,28,minute.maid,56256,10.93766798,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/20/1992,28,tropicana,4992,8.51559191,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/20/1992,32,dominicks,16576,9.715711145,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/20/1992,32,minute.maid,138368,11.83767208,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/20/1992,32,tropicana,10240,9.234056899,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/20/1992,33,dominicks,12864,9.462187991,0,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/20/1992,33,minute.maid,84544,11.34502739,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/20/1992,33,tropicana,8768,9.078864009,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/20/1992,40,dominicks,6336,8.754002934,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/20/1992,40,minute.maid,102720,11.53976212,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/20/1992,40,tropicana,4032,8.30201781,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/20/1992,44,dominicks,7296,8.895081532,0,0.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/20/1992,44,minute.maid,123968,11.72777875,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/20/1992,44,tropicana,7040,8.859363449,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/20/1992,45,dominicks,8000,8.987196821,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/20/1992,45,minute.maid,42880,10.6661608,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/20/1992,45,tropicana,3776,8.236420527,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/20/1992,47,dominicks,10752,9.282847063,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/20/1992,47,minute.maid,79680,11.28577389,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/20/1992,47,tropicana,3584,8.184234774,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/20/1992,48,dominicks,7168,8.877381955,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/20/1992,48,minute.maid,62720,11.04643566,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/20/1992,48,tropicana,4928,8.502688505,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/20/1992,49,dominicks,5824,8.66974259,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/20/1992,49,minute.maid,69696,11.15189821,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/20/1992,49,tropicana,4352,8.378390789,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/20/1992,50,dominicks,6720,8.812843434,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/20/1992,50,minute.maid,62912,11.0494922,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/20/1992,50,tropicana,2688,7.896552702,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/20/1992,51,dominicks,6720,8.812843434,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/20/1992,51,minute.maid,92992,11.44026875,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/20/1992,51,tropicana,3456,8.14786713,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/20/1992,52,dominicks,6592,8.793612072,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/20/1992,52,minute.maid,92032,11.42989162,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/20/1992,52,tropicana,6016,8.702177866,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/20/1992,53,dominicks,23680,10.07238609,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/20/1992,53,minute.maid,120896,11.70268595,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/20/1992,53,tropicana,8448,9.041685006,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/20/1992,54,dominicks,5568,8.624791202,0,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/20/1992,54,minute.maid,97216,11.48469059,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/20/1992,54,tropicana,5376,8.589699882,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/20/1992,56,dominicks,11776,9.373818841,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/20/1992,56,minute.maid,63040,11.05152473,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/20/1992,56,tropicana,4032,8.30201781,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/20/1992,59,dominicks,4864,8.489616424,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/20/1992,59,minute.maid,89152,11.39809806,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/20/1992,59,tropicana,5056,8.528330936,0,2.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/20/1992,62,dominicks,9664,9.17616292,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/20/1992,62,minute.maid,57984,10.96792239,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/20/1992,62,tropicana,8512,9.049232212,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/20/1992,64,dominicks,8960,9.100525506,0,0.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/20/1992,64,minute.maid,60288,11.00688836,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/20/1992,64,tropicana,2240,7.714231145,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/20/1992,67,dominicks,6336,8.754002934,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/20/1992,67,minute.maid,105664,11.56801953,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/20/1992,67,tropicana,4672,8.449342525,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/20/1992,68,dominicks,15552,9.651944527,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/20/1992,68,minute.maid,112512,11.63081516,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/20/1992,68,tropicana,3392,8.129174997,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/20/1992,70,dominicks,8320,9.026417534,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/20/1992,70,minute.maid,144256,11.87934478,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/20/1992,70,tropicana,6848,8.831711918,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/20/1992,71,dominicks,7936,8.979164649,0,1.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/20/1992,71,minute.maid,464128,13.04791566,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/20/1992,71,tropicana,3456,8.14786713,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/20/1992,72,dominicks,9600,9.169518378,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/20/1992,72,minute.maid,117888,11.6774903,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 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-2/20/1992,104,dominicks,5568,8.624791202,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/20/1992,104,minute.maid,85824,11.36005397,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/20/1992,104,tropicana,3264,8.090708716,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -2/20/1992,105,dominicks,28224,10.24792796,0,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/20/1992,105,minute.maid,127040,11.75225728,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/20/1992,105,tropicana,2944,7.98752448,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/20/1992,106,dominicks,4864,8.489616424,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/20/1992,106,minute.maid,74112,11.21333274,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/20/1992,106,tropicana,1472,7.294377299,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/20/1992,107,dominicks,11392,9.340666634,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/20/1992,107,minute.maid,141376,11.85917829,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/20/1992,107,tropicana,9152,9.121727714,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/20/1992,109,dominicks,7360,8.903815212,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/20/1992,109,minute.maid,121280,11.7058572,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/20/1992,109,tropicana,13952,9.543378146,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/20/1992,110,dominicks,4608,8.435549202,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/20/1992,110,minute.maid,118464,11.6823644,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/20/1992,110,tropicana,3584,8.184234774,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/20/1992,111,dominicks,15680,9.660141294,0,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/20/1992,111,minute.maid,374400,12.83308002,1,1.27,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/20/1992,111,tropicana,2240,7.714231145,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/20/1992,112,dominicks,8448,9.041685006,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/20/1992,112,minute.maid,75520,11.2321528,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/20/1992,112,tropicana,9088,9.114710141,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/20/1992,113,dominicks,9024,9.107642974,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/20/1992,113,minute.maid,124992,11.73600501,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/20/1992,113,tropicana,6272,8.743850562,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/20/1992,115,dominicks,12544,9.436997743,0,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/20/1992,115,minute.maid,89664,11.40382463,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/20/1992,115,tropicana,4480,8.407378325,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/20/1992,116,dominicks,11456,9.346268889,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/20/1992,116,minute.maid,88000,11.38509209,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/20/1992,116,tropicana,4096,8.317766167,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/20/1992,117,dominicks,7936,8.979164649,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/20/1992,117,minute.maid,76864,11.24979291,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/20/1992,117,tropicana,3072,8.030084094,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/20/1992,118,dominicks,8128,9.00307017,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/20/1992,118,minute.maid,91904,11.42849983,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/20/1992,118,tropicana,4544,8.42156296,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/20/1992,119,dominicks,7680,8.946374826,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/20/1992,119,minute.maid,50560,10.83091603,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/20/1992,119,tropicana,5440,8.60153434,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/20/1992,121,dominicks,6976,8.850230966,0,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/20/1992,121,minute.maid,105280,11.56437875,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/20/1992,121,tropicana,8512,9.049232212,0,2.98,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/20/1992,122,dominicks,12032,9.395325046,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/20/1992,122,minute.maid,96960,11.4820538,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/20/1992,122,tropicana,8960,9.100525506,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/20/1992,123,dominicks,16832,9.731037116,0,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/20/1992,123,minute.maid,147968,11.90475131,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/20/1992,123,tropicana,4480,8.407378325,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/20/1992,124,dominicks,29312,10.28575227,0,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/20/1992,124,minute.maid,134400,11.80857571,1,1.96,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/20/1992,124,tropicana,7040,8.859363449,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/20/1992,126,dominicks,18432,9.821843564,0,0.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/20/1992,126,minute.maid,132800,11.79659952,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/20/1992,126,tropicana,5568,8.624791202,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/20/1992,128,dominicks,32128,10.3774832,0,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/20/1992,128,minute.maid,192640,12.16857844,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/20/1992,128,tropicana,7552,8.929567708,0,3.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/20/1992,130,dominicks,19520,9.87919486,0,0.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/20/1992,130,minute.maid,311104,12.64788254,1,1.26,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/20/1992,130,tropicana,5056,8.528330936,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/20/1992,131,dominicks,8960,9.100525506,0,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/20/1992,131,minute.maid,125568,11.74060272,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/20/1992,131,tropicana,5888,8.68067166,0,2.98,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/20/1992,132,dominicks,18240,9.811372264,0,0.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/20/1992,132,minute.maid,198144,12.19674932,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/20/1992,132,tropicana,6656,8.803273983,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/20/1992,134,dominicks,7552,8.929567708,0,1.82,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/20/1992,134,minute.maid,78016,11.26466921,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/20/1992,134,tropicana,2240,7.714231145,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/20/1992,137,dominicks,23616,10.06967973,0,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/20/1992,137,minute.maid,154304,11.94667996,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/20/1992,137,tropicana,15872,9.67231183,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/27/1992,2,dominicks,11584,9.357380115,0,1.54,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/27/1992,2,minute.maid,11520,9.351839934,0,2.11,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/27/1992,2,tropicana,43584,10.68244539,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -2/27/1992,5,dominicks,12672,9.447150114,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/27/1992,5,minute.maid,6976,8.850230966,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/27/1992,5,tropicana,54272,10.90176372,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -2/27/1992,8,dominicks,9792,9.189321005,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/27/1992,8,minute.maid,15040,9.618468598,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/27/1992,8,tropicana,61760,11.03101119,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -2/27/1992,9,dominicks,4672,8.449342525,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/27/1992,9,minute.maid,5248,8.565602331,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/27/1992,9,tropicana,67072,11.11352195,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -2/27/1992,12,dominicks,23936,10.08313888,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/27/1992,12,minute.maid,9856,9.195835686,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/27/1992,12,tropicana,115712,11.65885962,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -2/27/1992,14,dominicks,7360,8.903815212,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/27/1992,14,minute.maid,7552,8.929567708,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/27/1992,14,tropicana,57792,10.96460564,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -2/27/1992,18,dominicks,14720,9.596962392,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/27/1992,18,minute.maid,9280,9.135616826,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/27/1992,18,tropicana,64960,11.08152698,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -2/27/1992,21,dominicks,14976,9.614204199,0,1.15,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/27/1992,21,minute.maid,5312,8.577723691,0,2.04,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/27/1992,21,tropicana,28800,10.26813067,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -2/27/1992,28,dominicks,1664,7.416979621,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/27/1992,28,minute.maid,5184,8.553332238,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/27/1992,28,tropicana,23424,10.06151642,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -2/27/1992,32,dominicks,14400,9.574983486,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/27/1992,32,minute.maid,9728,9.182763604,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/27/1992,32,tropicana,87680,11.3814491,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -2/27/1992,33,dominicks,6272,8.743850562,0,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/27/1992,33,minute.maid,8192,9.010913347,0,2.16,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/27/1992,33,tropicana,76672,11.24729186,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -2/27/1992,40,dominicks,9856,9.195835686,0,1.51,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/27/1992,40,minute.maid,6336,8.754002934,0,2.05,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/27/1992,40,tropicana,38208,10.5508002,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -2/27/1992,44,dominicks,4480,8.407378325,0,1.23,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/27/1992,44,minute.maid,14400,9.574983486,0,2.08,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/27/1992,44,tropicana,74752,11.22193125,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -2/27/1992,45,dominicks,5696,8.647519453,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/27/1992,45,minute.maid,4224,8.348537825,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/27/1992,45,tropicana,26752,10.19436452,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -2/27/1992,47,dominicks,10048,9.215128889,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/27/1992,47,minute.maid,7168,8.877381955,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/27/1992,47,tropicana,27328,10.2156671,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -2/27/1992,48,dominicks,4544,8.42156296,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/27/1992,48,minute.maid,7616,8.938006577,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/27/1992,48,tropicana,30144,10.31374118,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -2/27/1992,49,dominicks,7744,8.954673629,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/27/1992,49,minute.maid,6528,8.783855897,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/27/1992,49,tropicana,21888,9.99369382,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -2/27/1992,50,dominicks,5248,8.565602331,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/27/1992,50,minute.maid,5056,8.528330936,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/27/1992,50,tropicana,23040,10.04498712,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -2/27/1992,51,dominicks,16320,9.700146629,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/27/1992,51,minute.maid,5248,8.565602331,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/27/1992,51,tropicana,42560,10.65867012,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -2/27/1992,52,dominicks,8640,9.064157862,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/27/1992,52,minute.maid,10368,9.246479419,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/27/1992,52,tropicana,67264,11.11638045,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -2/27/1992,53,dominicks,9280,9.135616826,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/27/1992,53,minute.maid,12032,9.395325046,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/27/1992,53,tropicana,89344,11.40024937,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -2/27/1992,54,dominicks,4672,8.449342525,0,1.64,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/27/1992,54,minute.maid,6272,8.743850562,0,2.17,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/27/1992,54,tropicana,46272,10.74229231,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -2/27/1992,56,dominicks,6144,8.723231275,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/27/1992,56,minute.maid,4672,8.449342525,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/27/1992,56,tropicana,32320,10.38344151,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -2/27/1992,59,dominicks,9536,9.162829389,0,1.5,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/27/1992,59,minute.maid,4288,8.363575703,0,2.03,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/27/1992,59,tropicana,39040,10.57234204,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -2/27/1992,62,dominicks,4544,8.42156296,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/27/1992,62,minute.maid,7680,8.946374826,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/27/1992,62,tropicana,68288,11.13148934,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -2/27/1992,64,dominicks,5632,8.636219898,0,1.11,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/27/1992,64,minute.maid,5184,8.553332238,0,2.13,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/27/1992,64,tropicana,22592,10.02535114,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -2/27/1992,67,dominicks,6976,8.850230966,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/27/1992,67,minute.maid,6592,8.793612072,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/27/1992,67,tropicana,43648,10.68391274,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -2/27/1992,68,dominicks,9856,9.195835686,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/27/1992,68,minute.maid,11456,9.346268889,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/27/1992,68,tropicana,67200,11.11542853,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -2/27/1992,70,dominicks,12672,9.447150114,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/27/1992,70,minute.maid,12224,9.411156511,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/27/1992,70,tropicana,31168,10.34714721,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -2/27/1992,71,dominicks,2304,7.742402022,0,1.62,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/27/1992,71,minute.maid,5504,8.61323038,0,1.94,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/27/1992,71,tropicana,288384,12.5720482,1,1.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -2/27/1992,72,dominicks,10112,9.221478116,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/27/1992,72,minute.maid,10688,9.276876896,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/27/1992,72,tropicana,60352,11.00794937,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -2/27/1992,73,dominicks,18240,9.811372264,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/27/1992,73,minute.maid,26368,10.17990643,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/27/1992,73,tropicana,60480,11.01006801,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -2/27/1992,74,dominicks,15424,9.643680017,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/27/1992,74,minute.maid,11840,9.379238908,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/27/1992,74,tropicana,76864,11.24979291,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -2/27/1992,75,dominicks,9664,9.17616292,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/27/1992,75,minute.maid,7808,8.962904128,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/27/1992,75,tropicana,54528,10.90646961,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -2/27/1992,76,dominicks,10176,9.227787286,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/27/1992,76,minute.maid,6272,8.743850562,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/27/1992,76,tropicana,66560,11.10585908,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -2/27/1992,77,dominicks,8256,9.018695488,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/27/1992,77,minute.maid,5760,8.658692754,0,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/27/1992,77,tropicana,37760,10.53900562,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -2/27/1992,78,dominicks,6912,8.841014311,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/27/1992,78,minute.maid,5696,8.647519453,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/27/1992,78,tropicana,42176,10.64960662,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -2/27/1992,80,dominicks,6464,8.7740036,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/27/1992,80,minute.maid,8192,9.010913347,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/27/1992,80,tropicana,53888,10.8946631,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -2/27/1992,81,dominicks,18176,9.807857322,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/27/1992,81,minute.maid,8768,9.078864009,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/27/1992,81,tropicana,40960,10.62035126,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -2/27/1992,83,dominicks,12864,9.462187991,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/27/1992,83,minute.maid,17472,9.768354879,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/27/1992,83,tropicana,32064,10.37548918,1,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -2/27/1992,84,dominicks,11264,9.329367078,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/27/1992,84,minute.maid,6848,8.831711918,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/27/1992,84,tropicana,44288,10.69846904,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -2/27/1992,86,dominicks,27712,10.22962081,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/27/1992,86,minute.maid,5312,8.577723691,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/27/1992,86,tropicana,51456,10.84848235,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -2/27/1992,88,dominicks,14208,9.561560465,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/27/1992,88,minute.maid,12992,9.472089062,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/27/1992,88,tropicana,22400,10.01681624,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -2/27/1992,89,dominicks,10624,9.270870872,0,1.58,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/27/1992,89,minute.maid,11840,9.379238908,0,2.1,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/27/1992,89,tropicana,35712,10.48324205,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -2/27/1992,90,dominicks,19840,9.895455381,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/27/1992,90,minute.maid,6528,8.783855897,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -2/27/1992,90,tropicana,40960,10.62035126,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 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-2/27/1992,100,tropicana,74560,11.21935945,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -2/27/1992,101,dominicks,12864,9.462187991,0,1.54,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/27/1992,101,minute.maid,14080,9.55251063,0,2.11,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/27/1992,101,tropicana,37824,10.5406991,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -2/27/1992,102,dominicks,24000,10.08580911,0,1.53,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 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-2/27/1992,105,tropicana,53568,10.88870715,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -2/27/1992,106,dominicks,6976,8.850230966,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/27/1992,106,minute.maid,3904,8.269756948,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/27/1992,106,tropicana,18624,9.832206351,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -2/27/1992,107,dominicks,7040,8.859363449,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/27/1992,107,minute.maid,10688,9.276876896,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/27/1992,107,tropicana,75072,11.22620293,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -2/27/1992,109,dominicks,4288,8.363575703,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/27/1992,109,minute.maid,15040,9.618468598,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/27/1992,109,tropicana,86272,11.36526038,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -2/27/1992,110,dominicks,7296,8.895081532,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/27/1992,110,minute.maid,8768,9.078864009,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/27/1992,110,tropicana,38336,10.55414468,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -2/27/1992,111,dominicks,45760,10.73116563,0,1.57,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/27/1992,111,minute.maid,30080,10.31161578,0,1.27,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/27/1992,111,tropicana,72320,11.188856,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -2/27/1992,112,dominicks,8448,9.041685006,0,1.58,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/27/1992,112,minute.maid,9600,9.169518378,0,2.1,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/27/1992,112,tropicana,74432,11.21764124,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -2/27/1992,113,dominicks,9344,9.142489705,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/27/1992,113,minute.maid,13888,9.538780437,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/27/1992,113,tropicana,54336,10.90294227,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -2/27/1992,114,dominicks,31744,10.36545901,0,1.55,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/27/1992,114,minute.maid,8960,9.100525506,0,2,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/27/1992,114,tropicana,71296,11.1745955,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -2/27/1992,115,dominicks,9792,9.189321005,0,1.51,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/27/1992,115,minute.maid,15552,9.651944527,0,2.11,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/27/1992,115,tropicana,54784,10.91115346,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -2/27/1992,116,dominicks,10560,9.264828557,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/27/1992,116,minute.maid,16512,9.711842668,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/27/1992,116,tropicana,30400,10.32219789,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -2/27/1992,117,dominicks,6528,8.783855897,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/27/1992,117,minute.maid,4032,8.30201781,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/27/1992,117,tropicana,41024,10.62191254,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -2/27/1992,118,dominicks,4352,8.378390789,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/27/1992,118,minute.maid,14400,9.574983486,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/27/1992,118,tropicana,60992,11.01849799,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -2/27/1992,119,dominicks,6464,8.7740036,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/27/1992,119,minute.maid,11968,9.3899917,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/27/1992,119,tropicana,33280,10.4127119,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -2/27/1992,121,dominicks,10816,9.288781798,0,1.53,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/27/1992,121,minute.maid,20608,9.933434629,0,2.04,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/27/1992,121,tropicana,42240,10.65112292,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -2/27/1992,122,dominicks,13760,9.529521112,0,1.51,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/27/1992,122,minute.maid,9152,9.121727714,0,2.06,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/27/1992,122,tropicana,52288,10.86452218,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -2/27/1992,123,dominicks,12288,9.416378455,0,1.54,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/27/1992,123,minute.maid,6592,8.793612072,0,2.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/27/1992,123,tropicana,88704,11.39306026,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -2/27/1992,124,dominicks,21056,9.954940834,0,1.57,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/27/1992,124,minute.maid,8192,9.010913347,0,2.08,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/27/1992,124,tropicana,70144,11.15830555,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -2/27/1992,126,dominicks,15744,9.664214619,0,1.16,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/27/1992,126,minute.maid,17664,9.779283949,0,2.11,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/27/1992,126,tropicana,65088,11.08349548,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -2/27/1992,128,dominicks,16320,9.700146629,0,1.53,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/27/1992,128,minute.maid,24256,10.09641929,0,2.05,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/27/1992,128,tropicana,116864,11.66876615,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -2/27/1992,129,dominicks,18176,9.807857322,0,1.5,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/27/1992,129,minute.maid,8192,9.010913347,0,2.12,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/27/1992,129,tropicana,44800,10.70996342,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -2/27/1992,130,dominicks,35072,10.46515837,0,1.22,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/27/1992,130,minute.maid,10368,9.246479419,0,1.93,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/27/1992,130,tropicana,101568,11.5284838,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -2/27/1992,131,dominicks,14272,9.566054855,0,1.55,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/27/1992,131,minute.maid,7808,8.962904128,0,2.11,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/27/1992,131,tropicana,51072,10.84099168,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -2/27/1992,132,dominicks,36672,10.5097688,0,1.16,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/27/1992,132,minute.maid,7488,8.921057018,0,2.08,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/27/1992,132,tropicana,51520,10.84972536,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -2/27/1992,134,dominicks,3776,8.236420527,0,1.57,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/27/1992,134,minute.maid,3392,8.129174997,0,2.25,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/27/1992,134,tropicana,34176,10.43927892,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -2/27/1992,137,dominicks,26816,10.196754,0,1.55,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/27/1992,137,minute.maid,19328,9.869310101,0,2.16,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -2/27/1992,137,tropicana,130624,11.78007825,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/5/1992,2,dominicks,51264,10.84474403,1,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/5/1992,2,minute.maid,5824,8.66974259,0,2.35,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/5/1992,2,tropicana,25728,10.15533517,0,1.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/5/1992,5,dominicks,48640,10.79220152,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/5/1992,5,minute.maid,9984,9.208739091,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/5/1992,5,tropicana,33600,10.42228135,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/5/1992,8,dominicks,86912,11.37265139,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/5/1992,8,minute.maid,11840,9.379238908,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/5/1992,8,tropicana,15360,9.639522007,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/5/1992,9,dominicks,48640,10.79220152,1,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/5/1992,9,minute.maid,5568,8.624791202,0,2.66,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/5/1992,9,tropicana,16128,9.688312171,0,1.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/5/1992,12,dominicks,75776,11.2355369,1,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/5/1992,12,minute.maid,6976,8.850230966,0,2.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/5/1992,12,tropicana,92800,11.43820192,0,1.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/5/1992,14,dominicks,31872,10.36948316,1,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/5/1992,14,minute.maid,6208,8.733594062,0,2.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/5/1992,14,tropicana,38144,10.54912375,0,1.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/5/1992,18,dominicks,68288,11.13148934,1,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/5/1992,18,minute.maid,9152,9.121727714,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/5/1992,18,tropicana,16896,9.734832187,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/5/1992,21,dominicks,50496,10.8296494,1,1.39,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/5/1992,21,minute.maid,5248,8.565602331,0,2.12,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/5/1992,21,tropicana,11712,9.368369236,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/5/1992,28,dominicks,14592,9.588228712,1,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/5/1992,28,minute.maid,4032,8.30201781,0,2.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/5/1992,28,tropicana,4672,8.449342525,0,1.79,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/5/1992,32,dominicks,81600,11.30958454,1,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/5/1992,32,minute.maid,8896,9.093357017,0,2.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/5/1992,32,tropicana,15616,9.656051309,0,1.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/5/1992,33,dominicks,42944,10.66765222,1,1.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/5/1992,33,minute.maid,6464,8.7740036,0,2.32,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/5/1992,33,tropicana,33088,10.40692596,0,1.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/5/1992,40,dominicks,54208,10.90058378,1,1.39,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/5/1992,40,minute.maid,4544,8.42156296,0,2.12,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/5/1992,40,tropicana,8960,9.100525506,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/5/1992,44,dominicks,58496,10.97671366,1,1.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/5/1992,44,minute.maid,7744,8.954673629,0,2.3,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/5/1992,44,tropicana,41792,10.64046021,0,1.79,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/5/1992,45,dominicks,23296,10.05603695,1,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/5/1992,45,minute.maid,3264,8.090708716,0,2.66,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/5/1992,45,tropicana,26688,10.19196931,0,1.79,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/5/1992,47,dominicks,41984,10.64504387,1,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/5/1992,47,minute.maid,7744,8.954673629,0,2.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/5/1992,47,tropicana,3840,8.253227646,0,1.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/5/1992,48,dominicks,15488,9.64782081,1,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/5/1992,48,minute.maid,6528,8.783855897,0,2.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/5/1992,48,tropicana,6848,8.831711918,0,1.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/5/1992,49,dominicks,28352,10.25245285,1,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/5/1992,49,minute.maid,4160,8.333270353,0,2.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/5/1992,49,tropicana,13696,9.524859098,0,1.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/5/1992,50,dominicks,28608,10.26144168,1,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/5/1992,50,minute.maid,2688,7.896552702,0,2.66,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/5/1992,50,tropicana,7040,8.859363449,0,1.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/5/1992,51,dominicks,48960,10.79875892,1,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/5/1992,51,minute.maid,3712,8.219326094,0,2.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/5/1992,51,tropicana,20288,9.917784857,0,1.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/5/1992,52,dominicks,49152,10.80267282,1,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/5/1992,52,minute.maid,7936,8.979164649,0,2.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/5/1992,52,tropicana,36224,10.49747716,0,1.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/5/1992,53,dominicks,65088,11.08349548,1,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/5/1992,53,minute.maid,10688,9.276876896,0,2.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/5/1992,53,tropicana,54400,10.90411943,0,1.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/5/1992,54,dominicks,41728,10.63892765,1,1.38,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/5/1992,54,minute.maid,6016,8.702177866,0,2.28,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/5/1992,54,tropicana,14720,9.596962392,0,1.79,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/5/1992,56,dominicks,36800,10.51325312,1,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/5/1992,56,minute.maid,3904,8.269756948,0,2.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/5/1992,56,tropicana,11392,9.340666634,0,1.79,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/5/1992,59,dominicks,52544,10.86940619,1,1.39,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/5/1992,59,minute.maid,4288,8.363575703,0,2.16,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/5/1992,59,tropicana,14720,9.596962392,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/5/1992,62,dominicks,26496,10.18474906,1,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/5/1992,62,minute.maid,9408,9.14931567,0,2.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/5/1992,62,tropicana,11904,9.384629757,0,1.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/5/1992,64,dominicks,26496,10.18474906,1,1.39,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/5/1992,64,minute.maid,3456,8.14786713,0,2.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/5/1992,64,tropicana,3904,8.269756948,0,1.79,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/5/1992,67,dominicks,60032,11.00263303,1,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/5/1992,67,minute.maid,5312,8.577723691,0,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/5/1992,67,tropicana,9472,9.156095357,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/5/1992,68,dominicks,42752,10.66317126,1,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/5/1992,68,minute.maid,9472,9.156095357,0,2.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/5/1992,68,tropicana,57856,10.96571244,0,1.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/5/1992,70,dominicks,59648,10.9962159,1,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/5/1992,70,minute.maid,8128,9.00307017,0,2.25,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/5/1992,70,tropicana,18368,9.818365299,0,1.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/5/1992,71,dominicks,35392,10.47424109,1,1.39,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/5/1992,71,minute.maid,4928,8.502688505,0,2.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/5/1992,71,tropicana,175872,12.07751174,0,1.43,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/5/1992,72,dominicks,51968,10.85838342,1,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/5/1992,72,minute.maid,8512,9.049232212,0,2.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/5/1992,72,tropicana,26368,10.17990643,0,1.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/5/1992,73,dominicks,124032,11.72829488,1,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/5/1992,73,minute.maid,11904,9.384629757,0,2.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/5/1992,73,tropicana,31360,10.35328847,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/5/1992,74,dominicks,88192,11.38727154,1,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/5/1992,74,minute.maid,4992,8.51559191,0,2.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/5/1992,74,tropicana,58816,10.98216921,0,1.79,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/5/1992,75,dominicks,46912,10.75602879,1,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/5/1992,75,minute.maid,5568,8.624791202,0,2.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/5/1992,75,tropicana,10112,9.221478116,0,1.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/5/1992,76,dominicks,86528,11.36822334,1,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/5/1992,76,minute.maid,7104,8.868413285,0,2.66,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/5/1992,76,tropicana,22592,10.02535114,0,1.79,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/5/1992,77,dominicks,37888,10.54238972,1,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/5/1992,77,minute.maid,4800,8.476371197,0,2.25,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/5/1992,77,tropicana,18560,9.828764006,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/5/1992,78,dominicks,56640,10.94447073,1,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/5/1992,78,minute.maid,5888,8.68067166,0,2.25,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/5/1992,78,tropicana,30848,10.3368272,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/5/1992,80,dominicks,48192,10.78294831,1,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/5/1992,80,minute.maid,7360,8.903815212,0,2.25,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/5/1992,80,tropicana,10176,9.227787286,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/5/1992,81,dominicks,57088,10.95234922,1,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/5/1992,81,minute.maid,5888,8.68067166,0,2.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/5/1992,81,tropicana,21312,9.967025573,0,1.79,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/5/1992,83,dominicks,105216,11.56377066,1,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/5/1992,83,minute.maid,5632,8.636219898,0,2.25,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/5/1992,83,tropicana,11648,9.36288977,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/5/1992,84,dominicks,57664,10.96238834,1,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/5/1992,84,minute.maid,4992,8.51559191,0,2.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/5/1992,84,tropicana,22272,10.01108556,0,1.79,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/5/1992,86,dominicks,90944,11.41799921,1,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/5/1992,86,minute.maid,11904,9.384629757,0,2.66,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/5/1992,86,tropicana,11392,9.340666634,0,1.79,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/5/1992,88,dominicks,40576,10.61093204,1,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/5/1992,88,minute.maid,13696,9.524859098,0,2.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/5/1992,88,tropicana,5952,8.691482577,0,1.79,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/5/1992,89,dominicks,95488,11.46675586,1,1.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/5/1992,89,minute.maid,4096,8.317766167,0,2.18,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/5/1992,89,tropicana,10560,9.264828557,0,1.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/5/1992,90,dominicks,65728,11.09328029,1,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/5/1992,90,minute.maid,5696,8.647519453,0,2.79,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/5/1992,90,tropicana,9984,9.208739091,0,1.79,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/5/1992,91,dominicks,55360,10.92161259,1,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/5/1992,91,minute.maid,4672,8.449342525,0,2.66,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/5/1992,91,tropicana,3648,8.201934351,0,1.79,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/5/1992,92,dominicks,70208,11.15921754,1,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/5/1992,92,minute.maid,4480,8.407378325,0,2.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/5/1992,92,tropicana,12096,9.400630098,0,1.79,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/5/1992,93,dominicks,38912,10.56905797,1,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/5/1992,93,minute.maid,4864,8.489616424,0,2.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/5/1992,93,tropicana,54272,10.90176372,0,1.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/5/1992,95,dominicks,61568,11.02789753,1,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/5/1992,95,minute.maid,4672,8.449342525,0,2.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/5/1992,95,tropicana,3904,8.269756948,0,1.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/5/1992,97,dominicks,32128,10.3774832,1,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/5/1992,97,minute.maid,2688,7.896552702,0,2.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/5/1992,97,tropicana,5632,8.636219898,0,1.79,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/5/1992,98,dominicks,99328,11.50618278,1,1.39,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/5/1992,98,minute.maid,5760,8.658692754,0,2.27,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/5/1992,98,tropicana,7104,8.868413285,0,1.78,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/5/1992,100,dominicks,107968,11.58959017,1,1.39,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/5/1992,100,minute.maid,7744,8.954673629,0,2.24,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/5/1992,100,tropicana,11392,9.340666634,0,1.79,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/5/1992,101,dominicks,44160,10.69557468,1,1.39,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/5/1992,101,minute.maid,9152,9.121727714,0,2.31,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/5/1992,101,tropicana,6400,8.764053269,0,1.79,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/5/1992,102,dominicks,121408,11.70691205,1,1.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/5/1992,102,minute.maid,9024,9.107642974,0,2.14,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/5/1992,102,tropicana,36672,10.5097688,0,1.94,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/5/1992,103,dominicks,44352,10.69991308,1,1.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/5/1992,103,minute.maid,25792,10.15781965,0,1.51,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/5/1992,103,tropicana,6208,8.733594062,0,1.79,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/5/1992,104,dominicks,43328,10.67655436,1,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/5/1992,104,minute.maid,4736,8.462948177,0,2.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/5/1992,104,tropicana,10176,9.227787286,0,1.79,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/5/1992,105,dominicks,95872,11.47076925,1,1.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/5/1992,105,minute.maid,3520,8.166216269,0,2.32,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/5/1992,105,tropicana,27264,10.21332243,0,1.79,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/5/1992,106,dominicks,35136,10.46698153,1,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/5/1992,106,minute.maid,3712,8.219326094,0,2.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/5/1992,106,tropicana,11584,9.357380115,0,1.79,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/5/1992,107,dominicks,61568,11.02789753,1,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/5/1992,107,minute.maid,11840,9.379238908,0,2.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/5/1992,107,tropicana,10496,9.258749511,0,1.79,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/5/1992,109,dominicks,61952,11.03411517,1,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/5/1992,109,minute.maid,14528,9.583833101,0,2.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/5/1992,109,tropicana,28480,10.25695737,0,1.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/5/1992,110,dominicks,51968,10.85838342,1,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/5/1992,110,minute.maid,9152,9.121727714,0,2.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/5/1992,110,tropicana,3776,8.236420527,0,1.79,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/5/1992,111,dominicks,188608,12.14742607,1,1.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/5/1992,111,minute.maid,5824,8.66974259,0,2.28,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/5/1992,111,tropicana,15488,9.64782081,0,1.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/5/1992,112,dominicks,44416,10.70135504,1,1.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/5/1992,112,minute.maid,11520,9.351839934,0,2.14,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/5/1992,112,tropicana,36480,10.50451944,0,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/5/1992,113,dominicks,64704,11.0775783,1,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/5/1992,113,minute.maid,4480,8.407378325,0,2.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/5/1992,113,tropicana,37888,10.54238972,0,1.79,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/5/1992,114,dominicks,97728,11.48994339,1,1.39,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/5/1992,114,minute.maid,14400,9.574983486,0,2.01,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/5/1992,114,tropicana,20800,9.942708266,0,1.79,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/5/1992,115,dominicks,49664,10.8130356,1,1.39,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/5/1992,115,minute.maid,5056,8.528330936,0,2.3,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/5/1992,115,tropicana,30592,10.32849382,0,1.79,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/5/1992,116,dominicks,40448,10.60777248,1,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/5/1992,116,minute.maid,6528,8.783855897,0,2.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/5/1992,116,tropicana,17856,9.790094865,0,1.79,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/5/1992,117,dominicks,35200,10.46880136,1,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/5/1992,117,minute.maid,4544,8.42156296,0,2.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/5/1992,117,tropicana,21120,9.957975738,0,1.79,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/5/1992,118,dominicks,43968,10.69121738,1,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/5/1992,118,minute.maid,9792,9.189321005,0,2.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/5/1992,118,tropicana,17856,9.790094865,0,1.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/5/1992,119,dominicks,30272,10.31797847,1,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/5/1992,119,minute.maid,9792,9.189321005,0,2.66,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/5/1992,119,tropicana,7168,8.877381955,0,1.79,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/5/1992,121,dominicks,61632,11.0289365,1,1.39,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/5/1992,121,minute.maid,7488,8.921057018,0,2.32,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/5/1992,121,tropicana,31616,10.3614186,0,1.79,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/5/1992,122,dominicks,54656,10.90881428,1,1.39,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/5/1992,122,minute.maid,7104,8.868413285,0,2.15,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/5/1992,122,tropicana,25664,10.15284451,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/5/1992,123,dominicks,107264,11.58304836,1,1.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/5/1992,123,minute.maid,8640,9.064157862,0,2.42,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/5/1992,123,tropicana,14656,9.592605087,0,1.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/5/1992,124,dominicks,82688,11.32282977,1,1.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/5/1992,124,minute.maid,7744,8.954673629,0,2.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/5/1992,124,tropicana,6848,8.831711918,0,3.15,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/5/1992,126,dominicks,47936,10.77762207,1,1.39,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/5/1992,126,minute.maid,33664,10.4241843,0,1.92,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/5/1992,126,tropicana,16832,9.731037116,0,1.79,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/5/1992,128,dominicks,99264,11.50553825,1,1.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/5/1992,128,minute.maid,16000,9.680344001,0,2.32,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/5/1992,128,tropicana,17344,9.761001904,0,1.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/5/1992,129,dominicks,40576,10.61093204,1,1.39,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/5/1992,129,minute.maid,6784,8.822322178,0,2.28,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/5/1992,129,tropicana,12544,9.436997743,0,1.79,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/5/1992,130,dominicks,158912,11.97610587,1,1.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/5/1992,130,minute.maid,9408,9.14931567,0,2.33,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/5/1992,130,tropicana,25088,10.13014492,0,1.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/5/1992,131,dominicks,51264,10.84474403,1,1.39,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/5/1992,131,minute.maid,6720,8.812843434,0,2.27,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/5/1992,131,tropicana,13120,9.481893063,0,1.79,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/5/1992,132,dominicks,97920,11.4919061,1,1.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/5/1992,132,minute.maid,9344,9.142489705,0,2.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/5/1992,132,tropicana,10048,9.215128889,0,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/5/1992,134,dominicks,44672,10.70710219,1,1.39,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/5/1992,134,minute.maid,6720,8.812843434,0,2.27,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/5/1992,134,tropicana,3392,8.129174997,0,1.79,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/5/1992,137,dominicks,63744,11.06263034,1,1.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/5/1992,137,minute.maid,12672,9.447150114,0,2.37,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/5/1992,137,tropicana,108480,11.5943211,0,1.79,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/12/1992,2,dominicks,14976,9.614204199,0,1.44,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/12/1992,2,minute.maid,19392,9.872615889,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/12/1992,2,tropicana,31808,10.36747311,0,1.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/12/1992,5,dominicks,13248,9.491601877,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/12/1992,5,minute.maid,32832,10.39915893,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/12/1992,5,tropicana,24448,10.10430369,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/12/1992,8,dominicks,24512,10.10691807,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/12/1992,8,minute.maid,25472,10.14533509,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/12/1992,8,tropicana,54976,10.91465201,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/12/1992,9,dominicks,10944,9.30054664,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/12/1992,9,minute.maid,24448,10.10430369,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/12/1992,9,tropicana,30464,10.32430094,0,1.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/12/1992,12,dominicks,16512,9.711842668,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/12/1992,12,minute.maid,38144,10.54912375,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/12/1992,12,tropicana,76736,11.24812624,0,1.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/12/1992,14,dominicks,10496,9.258749511,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/12/1992,14,minute.maid,24832,10.11988842,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/12/1992,14,tropicana,28736,10.26590597,0,1.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/12/1992,18,dominicks,16832,9.731037116,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/12/1992,18,minute.maid,21632,9.981928979,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/12/1992,18,tropicana,39808,10.59182318,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/12/1992,21,dominicks,17088,9.746131742,0,1.45,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/12/1992,21,minute.maid,15296,9.635346635,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/12/1992,21,tropicana,22528,10.02251426,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/12/1992,28,dominicks,6528,8.783855897,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/12/1992,28,minute.maid,9216,9.128696383,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/12/1992,28,tropicana,11840,9.379238908,0,1.79,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/12/1992,32,dominicks,15488,9.64782081,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/12/1992,32,minute.maid,20672,9.936535407,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/12/1992,32,tropicana,64640,11.07658869,0,1.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/12/1992,33,dominicks,20288,9.917784857,0,1.46,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/12/1992,33,minute.maid,21888,9.99369382,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/12/1992,33,tropicana,49792,10.81560961,0,1.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/12/1992,40,dominicks,13504,9.510741217,0,1.43,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/12/1992,40,minute.maid,16000,9.680344001,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/12/1992,40,tropicana,31872,10.36948316,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/12/1992,44,dominicks,8384,9.034080407,0,1.44,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/12/1992,44,minute.maid,18560,9.828764006,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/12/1992,44,tropicana,83072,11.32746298,0,1.79,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/12/1992,45,dominicks,6080,8.712759975,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/12/1992,45,minute.maid,7872,8.971067439,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/12/1992,45,tropicana,39808,10.59182318,0,1.79,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/12/1992,47,dominicks,12032,9.395325046,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/12/1992,47,minute.maid,21056,9.954940834,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/12/1992,47,tropicana,14464,9.579418083,0,1.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/12/1992,48,dominicks,8256,9.018695488,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/12/1992,48,minute.maid,14976,9.614204199,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/12/1992,48,tropicana,20608,9.933434629,0,1.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/12/1992,49,dominicks,7936,8.979164649,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/12/1992,49,minute.maid,13696,9.524859098,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/12/1992,49,tropicana,14400,9.574983486,0,1.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/12/1992,50,dominicks,10304,9.240287448,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/12/1992,50,minute.maid,13760,9.529521112,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/12/1992,50,tropicana,7040,8.859363449,0,1.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/12/1992,51,dominicks,8384,9.034080407,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/12/1992,51,minute.maid,11136,9.317938383,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/12/1992,51,tropicana,39552,10.58537154,0,1.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/12/1992,52,dominicks,9472,9.156095357,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/12/1992,52,minute.maid,26304,10.1774763,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/12/1992,52,tropicana,44288,10.69846904,0,1.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/12/1992,53,dominicks,23872,10.0804615,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/12/1992,53,minute.maid,25216,10.13523399,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/12/1992,53,tropicana,53952,10.89585004,0,1.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/12/1992,54,dominicks,8448,9.041685006,0,1.48,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/12/1992,54,minute.maid,15296,9.635346635,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/12/1992,54,tropicana,21952,9.996613531,0,1.79,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/12/1992,56,dominicks,11008,9.30637756,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/12/1992,56,minute.maid,15552,9.651944527,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/12/1992,56,tropicana,28992,10.27477521,0,1.79,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/12/1992,59,dominicks,10880,9.29468152,0,1.45,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/12/1992,59,minute.maid,13632,9.520175249,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/12/1992,59,tropicana,24000,10.08580911,0,1.79,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/12/1992,62,dominicks,6656,8.803273983,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/12/1992,62,minute.maid,24448,10.10430369,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/12/1992,62,tropicana,35072,10.46515837,0,1.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/12/1992,64,dominicks,7040,8.859363449,0,1.44,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/12/1992,64,minute.maid,8896,9.093357017,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/12/1992,64,tropicana,13184,9.486759252,0,1.79,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/12/1992,67,dominicks,8448,9.041685006,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/12/1992,67,minute.maid,10560,9.264828557,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/12/1992,67,tropicana,47296,10.764181,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/12/1992,68,dominicks,12288,9.416378455,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/12/1992,68,minute.maid,21504,9.975994243,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/12/1992,68,tropicana,59328,10.99083665,0,1.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/12/1992,70,dominicks,19776,9.89222436,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/12/1992,70,minute.maid,16960,9.738612909,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/12/1992,70,tropicana,39744,10.59021417,0,1.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/12/1992,71,dominicks,28992,10.27477521,0,1.48,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/12/1992,71,minute.maid,25024,10.12759064,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/12/1992,71,tropicana,15040,9.618468598,0,1.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/12/1992,72,dominicks,18560,9.828764006,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/12/1992,72,minute.maid,21056,9.954940834,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/12/1992,72,tropicana,43840,10.68830192,0,1.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/12/1992,73,dominicks,66432,11.10393415,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/12/1992,73,minute.maid,27072,10.20625526,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/12/1992,73,tropicana,66048,11.09813703,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/12/1992,74,dominicks,43776,10.686841,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/12/1992,74,minute.maid,18880,9.84585844,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/12/1992,74,tropicana,37440,10.53049493,0,1.79,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/12/1992,75,dominicks,16512,9.711842668,0,1.58,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/12/1992,75,minute.maid,19136,9.859326657,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/12/1992,75,tropicana,34688,10.45414909,0,1.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/12/1992,76,dominicks,24768,10.11730778,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/12/1992,76,minute.maid,33024,10.40498985,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/12/1992,76,tropicana,36160,10.49570882,0,1.79,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/12/1992,77,dominicks,13056,9.477003077,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/12/1992,77,minute.maid,15808,9.66827142,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/12/1992,77,tropicana,23168,10.0505273,0,1.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/12/1992,78,dominicks,10432,9.252633284,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/12/1992,78,minute.maid,12928,9.467150781,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/12/1992,78,tropicana,52416,10.86696717,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/12/1992,80,dominicks,8768,9.078864009,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/12/1992,80,minute.maid,23168,10.0505273,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/12/1992,80,tropicana,26496,10.18474906,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/12/1992,81,dominicks,14144,9.557045785,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/12/1992,81,minute.maid,28416,10.25470765,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/12/1992,81,tropicana,27520,10.22266829,0,1.79,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/12/1992,83,dominicks,36480,10.50451944,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/12/1992,83,minute.maid,10624,9.270870872,1,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/12/1992,83,tropicana,39360,10.58050535,0,1.79,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/12/1992,84,dominicks,8000,8.987196821,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/12/1992,84,minute.maid,21504,9.975994243,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/12/1992,84,tropicana,27136,10.20861654,0,1.79,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/12/1992,86,dominicks,13120,9.481893063,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/12/1992,86,minute.maid,20864,9.945780465,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/12/1992,86,tropicana,52352,10.86574542,0,1.79,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/12/1992,88,dominicks,10240,9.234056899,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/12/1992,88,minute.maid,17280,9.757305042,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/12/1992,88,tropicana,22080,10.0024275,0,1.79,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/12/1992,89,dominicks,13184,9.486759252,0,1.47,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/12/1992,89,minute.maid,14336,9.570529135,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/12/1992,89,tropicana,37632,10.53561003,0,1.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/12/1992,90,dominicks,12352,9.421573272,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/12/1992,90,minute.maid,17728,9.78290059,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/12/1992,90,tropicana,30976,10.34096799,0,1.79,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/12/1992,91,dominicks,9280,9.135616826,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/12/1992,91,minute.maid,17344,9.761001904,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/12/1992,91,tropicana,34944,10.46150206,0,1.79,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/12/1992,92,dominicks,27072,10.20625526,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/12/1992,92,minute.maid,9408,9.14931567,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/12/1992,92,tropicana,36736,10.51151248,0,1.79,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/12/1992,93,dominicks,15296,9.635346635,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/12/1992,93,minute.maid,22208,10.00820786,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/12/1992,93,tropicana,37760,10.53900562,0,1.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/12/1992,94,dominicks,12480,9.431882642,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/12/1992,94,minute.maid,21696,9.984883191,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/12/1992,94,tropicana,23360,10.05878044,0,1.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/12/1992,95,dominicks,10624,9.270870872,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/12/1992,95,minute.maid,14720,9.596962392,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/12/1992,95,tropicana,34880,10.45966888,0,1.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/12/1992,97,dominicks,8256,9.018695488,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/12/1992,97,minute.maid,7232,8.886270902,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/12/1992,97,tropicana,23744,10.07508515,0,1.79,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/12/1992,98,dominicks,21888,9.99369382,0,1.46,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/12/1992,98,minute.maid,22848,10.03661887,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/12/1992,98,tropicana,56448,10.94107514,0,1.8,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/12/1992,100,dominicks,17088,9.746131742,0,1.45,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/12/1992,100,minute.maid,20928,9.948843254,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/12/1992,100,tropicana,59776,10.99835952,0,1.79,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/12/1992,101,dominicks,14400,9.574983486,0,1.44,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/12/1992,101,minute.maid,19776,9.89222436,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/12/1992,101,tropicana,34560,10.45045222,0,1.79,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/12/1992,102,dominicks,22720,10.03100087,0,1.47,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/12/1992,102,minute.maid,34112,10.43740451,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/12/1992,102,tropicana,64256,11.07063038,0,1.79,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/12/1992,103,dominicks,13632,9.520175249,0,1.44,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/12/1992,103,minute.maid,15360,9.639522007,1,1.92,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/12/1992,103,tropicana,25984,10.16523624,0,1.79,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/12/1992,104,dominicks,8576,9.056722883,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/12/1992,104,minute.maid,14080,9.55251063,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/12/1992,104,tropicana,35520,10.4778512,0,1.79,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/12/1992,105,dominicks,19200,9.862665558,0,1.45,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/12/1992,105,minute.maid,17344,9.761001904,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/12/1992,105,tropicana,40384,10.60618895,0,1.79,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/12/1992,106,dominicks,6528,8.783855897,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/12/1992,106,minute.maid,8000,8.987196821,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/12/1992,106,tropicana,10624,9.270870872,0,1.79,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/12/1992,107,dominicks,11456,9.346268889,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/12/1992,107,minute.maid,32128,10.3774832,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/12/1992,107,tropicana,37376,10.52878407,0,1.79,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/12/1992,109,dominicks,8064,8.99516499,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/12/1992,109,minute.maid,38912,10.56905797,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/12/1992,109,tropicana,49792,10.81560961,0,1.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/12/1992,110,dominicks,11904,9.384629757,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/12/1992,110,minute.maid,21376,9.970024076,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/12/1992,110,tropicana,11776,9.373818841,0,1.79,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/12/1992,111,dominicks,108928,11.59844239,0,1.44,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/12/1992,111,minute.maid,23744,10.07508515,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/12/1992,111,tropicana,57792,10.96460564,0,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/12/1992,112,dominicks,15296,9.635346635,0,1.46,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/12/1992,112,minute.maid,27776,10.23192762,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/12/1992,112,tropicana,37952,10.54407748,0,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/12/1992,113,dominicks,12480,9.431882642,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/12/1992,113,minute.maid,32768,10.39720771,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/12/1992,113,tropicana,39808,10.59182318,0,1.79,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/12/1992,114,dominicks,33280,10.4127119,0,1.45,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/12/1992,114,minute.maid,28160,10.24565781,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/12/1992,114,tropicana,68416,11.13336199,0,1.79,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/12/1992,115,dominicks,6656,8.803273983,0,1.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/12/1992,115,minute.maid,18688,9.835636886,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/12/1992,115,tropicana,49984,10.81945823,0,1.79,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/12/1992,116,dominicks,14976,9.614204199,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/12/1992,116,minute.maid,13696,9.524859098,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/12/1992,116,tropicana,42112,10.64808802,0,1.79,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/12/1992,117,dominicks,12736,9.452187908,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/12/1992,117,minute.maid,12544,9.436997743,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/12/1992,117,tropicana,25344,10.1402973,0,1.79,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/12/1992,118,dominicks,9856,9.195835686,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/12/1992,118,minute.maid,27008,10.2038884,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/12/1992,118,tropicana,37888,10.54238972,0,1.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/12/1992,119,dominicks,8384,9.034080407,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/12/1992,119,minute.maid,10688,9.276876896,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/12/1992,119,tropicana,24256,10.09641929,0,1.79,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/12/1992,121,dominicks,25280,10.13776885,0,1.43,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/12/1992,121,minute.maid,19904,9.898675996,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/12/1992,121,tropicana,52544,10.86940619,0,1.79,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/12/1992,122,dominicks,15552,9.651944527,0,1.43,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/12/1992,122,minute.maid,23488,10.06424493,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/12/1992,122,tropicana,41408,10.63122938,0,1.79,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/12/1992,123,dominicks,23168,10.0505273,0,1.48,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/12/1992,123,minute.maid,40768,10.61565274,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/12/1992,123,tropicana,42176,10.64960662,0,1.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/12/1992,124,dominicks,51648,10.85220675,0,1.41,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/12/1992,124,minute.maid,19200,9.862665558,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/12/1992,124,tropicana,37376,10.52878407,0,1.8,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/12/1992,126,dominicks,17536,9.77201119,0,1.44,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/12/1992,126,minute.maid,25024,10.12759064,1,1.98,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/12/1992,126,tropicana,60928,11.01744812,0,1.79,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/12/1992,128,dominicks,37760,10.53900562,0,1.43,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/12/1992,128,minute.maid,50048,10.82073782,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/12/1992,128,tropicana,47936,10.77762207,0,1.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/12/1992,129,dominicks,11712,9.368369236,0,1.45,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/12/1992,129,minute.maid,13376,9.501217335,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/12/1992,129,tropicana,24512,10.10691807,0,1.79,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/12/1992,130,dominicks,25024,10.12759064,0,1.51,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/12/1992,130,minute.maid,39552,10.58537154,1,1.98,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/12/1992,130,tropicana,62848,11.04847439,0,1.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/12/1992,131,dominicks,11584,9.357380115,0,1.45,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/12/1992,131,minute.maid,28032,10.24110199,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/12/1992,131,tropicana,27840,10.23422911,0,1.79,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/12/1992,132,dominicks,27200,10.21097225,0,1.42,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/12/1992,132,minute.maid,25024,10.12759064,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/12/1992,132,tropicana,52928,10.87668778,0,1.78,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/12/1992,134,dominicks,10624,9.270870872,0,1.46,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/12/1992,134,minute.maid,17984,9.797237753,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/12/1992,134,tropicana,7744,8.954673629,0,1.79,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/12/1992,137,dominicks,33024,10.40498985,0,1.42,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/12/1992,137,minute.maid,29824,10.30306872,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/12/1992,137,tropicana,103936,11.5515306,0,1.79,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/19/1992,2,dominicks,30784,10.33475035,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/19/1992,2,minute.maid,9536,9.162829389,0,2.1,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/19/1992,2,tropicana,20736,9.939626599,0,1.91,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/19/1992,5,dominicks,29248,10.28356647,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/19/1992,5,minute.maid,8128,9.00307017,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/19/1992,5,tropicana,22784,10.03381381,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/19/1992,8,dominicks,58048,10.96902553,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/19/1992,8,minute.maid,16384,9.704060528,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/19/1992,8,tropicana,34368,10.44488118,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/19/1992,9,dominicks,23168,10.0505273,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/19/1992,9,minute.maid,5632,8.636219898,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/19/1992,9,tropicana,25408,10.14281936,0,1.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/19/1992,12,dominicks,46336,10.74367448,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/19/1992,12,minute.maid,31808,10.36747311,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/19/1992,12,tropicana,33920,10.43176009,0,1.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/19/1992,14,dominicks,17856,9.790094865,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/19/1992,14,minute.maid,9088,9.114710141,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/19/1992,14,tropicana,21248,9.964018052,0,1.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/19/1992,18,dominicks,46272,10.74229231,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/19/1992,18,minute.maid,10624,9.270870872,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/19/1992,18,tropicana,16320,9.700146629,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/19/1992,21,dominicks,21440,9.973013615,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/19/1992,21,minute.maid,6144,8.723231275,0,2.01,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/19/1992,21,tropicana,17600,9.775654181,0,1.72,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/19/1992,28,dominicks,9728,9.182763604,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/19/1992,28,minute.maid,7296,8.895081532,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/19/1992,28,tropicana,4352,8.378390789,0,1.79,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/19/1992,32,dominicks,51264,10.84474403,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/19/1992,32,minute.maid,10816,9.288781798,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/19/1992,32,tropicana,20480,9.927204079,0,1.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/19/1992,33,dominicks,28544,10.25920204,0,1.59,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/19/1992,33,minute.maid,9920,9.2023082,0,2.16,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/19/1992,33,tropicana,24384,10.10168246,0,1.96,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/19/1992,40,dominicks,30144,10.31374118,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/19/1992,40,minute.maid,7040,8.859363449,0,2.02,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/19/1992,40,tropicana,5888,8.68067166,0,2.03,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/19/1992,44,dominicks,34432,10.44674164,0,1.59,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/19/1992,44,minute.maid,7040,8.859363449,0,2.15,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/19/1992,44,tropicana,35712,10.48324205,0,1.91,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/19/1992,45,dominicks,18176,9.807857322,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/19/1992,45,minute.maid,6464,8.7740036,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/19/1992,45,tropicana,13568,9.515469358,0,1.79,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/19/1992,47,dominicks,21056,9.954940834,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/19/1992,47,minute.maid,8320,9.026417534,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/19/1992,47,tropicana,12096,9.400630098,0,1.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/19/1992,48,dominicks,17792,9.786504197,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/19/1992,48,minute.maid,6656,8.803273983,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/19/1992,48,tropicana,7616,8.938006577,0,1.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/19/1992,49,dominicks,8448,9.041685006,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/19/1992,49,minute.maid,4992,8.51559191,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/19/1992,49,tropicana,18944,9.849242538,0,1.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/19/1992,50,dominicks,18560,9.828764006,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/19/1992,50,minute.maid,5376,8.589699882,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/19/1992,50,tropicana,7360,8.903815212,0,1.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/19/1992,51,dominicks,26240,10.17504024,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/19/1992,51,minute.maid,16896,9.734832187,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/19/1992,51,tropicana,15488,9.64782081,0,1.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/19/1992,52,dominicks,27904,10.23652533,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/19/1992,52,minute.maid,12544,9.436997743,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/19/1992,52,tropicana,25024,10.12759064,0,1.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/19/1992,53,dominicks,36800,10.51325312,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/19/1992,53,minute.maid,11072,9.312174678,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/19/1992,53,tropicana,17280,9.757305042,0,1.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/19/1992,54,dominicks,26688,10.19196931,0,1.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/19/1992,54,minute.maid,7168,8.877381955,0,2.11,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/19/1992,54,tropicana,7104,8.868413285,0,2.25,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/19/1992,56,dominicks,26048,10.16769627,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/19/1992,56,minute.maid,4032,8.30201781,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/19/1992,56,tropicana,12928,9.467150781,0,1.79,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/19/1992,59,dominicks,27264,10.21332243,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/19/1992,59,minute.maid,4992,8.51559191,0,2.06,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/19/1992,59,tropicana,13824,9.534161491,0,1.9,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/19/1992,62,dominicks,10752,9.282847063,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/19/1992,62,minute.maid,8512,9.049232212,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/19/1992,62,tropicana,25856,10.16029796,0,1.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/19/1992,64,dominicks,12032,9.395325046,0,1.59,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/19/1992,64,minute.maid,3392,8.129174997,0,2.17,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/19/1992,64,tropicana,13888,9.538780437,0,1.79,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/19/1992,67,dominicks,36032,10.49216271,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/19/1992,67,minute.maid,6592,8.793612072,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/19/1992,67,tropicana,11328,9.335032816,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/19/1992,68,dominicks,19904,9.898675996,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/19/1992,68,minute.maid,94464,11.45597409,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/19/1992,68,tropicana,11136,9.317938383,0,1.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/19/1992,70,dominicks,35392,10.47424109,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/19/1992,70,minute.maid,35328,10.47243113,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/19/1992,70,tropicana,24256,10.09641929,0,1.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/19/1992,71,dominicks,59904,11.00049856,0,1.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/19/1992,71,minute.maid,6784,8.822322178,0,2.07,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/19/1992,71,tropicana,19776,9.89222436,0,1.92,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/19/1992,72,dominicks,24768,10.11730778,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/19/1992,72,minute.maid,12416,9.426741242,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/19/1992,72,tropicana,9664,9.17616292,0,1.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/19/1992,73,dominicks,78720,11.27365253,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/19/1992,73,minute.maid,23680,10.07238609,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/19/1992,73,tropicana,25152,10.1326927,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/19/1992,74,dominicks,53184,10.88151288,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/19/1992,74,minute.maid,21952,9.996613531,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/19/1992,74,tropicana,32192,10.37947325,0,1.79,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 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-3/19/1992,78,minute.maid,5248,8.565602331,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/19/1992,78,tropicana,31552,10.35939226,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/19/1992,80,dominicks,28736,10.26590597,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/19/1992,80,minute.maid,8320,9.026417534,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -3/19/1992,80,tropicana,15936,9.67633598,0,1.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 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-3/19/1992,98,minute.maid,17728,9.78290059,0,1.38,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/19/1992,98,tropicana,7488,8.921057018,0,2.83,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/19/1992,100,dominicks,65920,11.09619717,0,1.59,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/19/1992,100,minute.maid,11136,9.317938383,0,2.12,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/19/1992,100,tropicana,14528,9.583833101,0,1.87,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/19/1992,101,dominicks,34752,10.4559924,0,1.59,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/19/1992,101,minute.maid,7424,8.912473275,0,2.07,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/19/1992,101,tropicana,13056,9.477003077,0,1.88,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/19/1992,102,dominicks,75072,11.22620293,0,1.59,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/19/1992,102,minute.maid,20736,9.939626599,0,2.04,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/19/1992,102,tropicana,8768,9.078864009,0,2.19,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/19/1992,103,dominicks,31552,10.35939226,0,1.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/19/1992,103,minute.maid,6912,8.841014311,0,2.05,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/19/1992,103,tropicana,23936,10.08313888,0,1.53,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/19/1992,104,dominicks,21248,9.964018052,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/19/1992,104,minute.maid,4736,8.462948177,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/19/1992,104,tropicana,9088,9.114710141,0,1.79,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/19/1992,105,dominicks,56512,10.94220828,0,1.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/19/1992,105,minute.maid,6272,8.743850562,0,2.09,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/19/1992,105,tropicana,10816,9.288781798,0,1.94,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/19/1992,106,dominicks,17856,9.790094865,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/19/1992,106,minute.maid,6848,8.831711918,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/19/1992,106,tropicana,3392,8.129174997,0,1.79,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/19/1992,107,dominicks,19904,9.898675996,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/19/1992,107,minute.maid,29888,10.30521234,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/19/1992,107,tropicana,36224,10.49747716,0,1.79,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/19/1992,109,dominicks,44032,10.69267192,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/19/1992,109,minute.maid,12864,9.462187991,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/19/1992,109,tropicana,21888,9.99369382,0,1.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/19/1992,110,dominicks,20736,9.939626599,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/19/1992,110,minute.maid,6528,8.783855897,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/19/1992,110,tropicana,9408,9.14931567,0,1.79,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/19/1992,111,dominicks,86080,11.36303238,0,1.58,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/19/1992,111,minute.maid,11520,9.351839934,0,2.14,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/19/1992,111,tropicana,32384,10.38541975,0,1.71,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/19/1992,112,dominicks,25600,10.15034763,0,1.59,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/19/1992,112,minute.maid,11456,9.346268889,0,2.07,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/19/1992,112,tropicana,27968,10.23881628,0,1.97,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/19/1992,113,dominicks,44672,10.70710219,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/19/1992,113,minute.maid,8512,9.049232212,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/19/1992,113,tropicana,11712,9.368369236,0,1.79,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/19/1992,114,dominicks,74432,11.21764124,0,1.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/19/1992,114,minute.maid,8704,9.071537969,0,2.13,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/19/1992,114,tropicana,16640,9.719564714,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/19/1992,115,dominicks,28160,10.24565781,0,1.59,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/19/1992,115,minute.maid,6336,8.754002934,0,2.14,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/19/1992,115,tropicana,19456,9.875910785,0,1.91,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/19/1992,116,dominicks,33216,10.41078697,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/19/1992,116,minute.maid,7040,8.859363449,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/19/1992,116,tropicana,7936,8.979164649,0,1.79,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/19/1992,117,dominicks,21376,9.970024076,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/19/1992,117,minute.maid,4864,8.489616424,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/19/1992,117,tropicana,15680,9.660141294,0,1.79,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/19/1992,118,dominicks,27520,10.22266829,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/19/1992,118,minute.maid,7296,8.895081532,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/19/1992,118,tropicana,14336,9.570529135,0,1.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/19/1992,119,dominicks,15680,9.660141294,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/19/1992,119,minute.maid,6592,8.793612072,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/19/1992,119,tropicana,14400,9.574983486,0,1.79,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/19/1992,121,dominicks,29504,10.29228113,0,1.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/19/1992,121,minute.maid,11968,9.3899917,0,2.14,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/19/1992,121,tropicana,38656,10.56245728,0,1.91,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/19/1992,122,dominicks,29568,10.29444797,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/19/1992,122,minute.maid,9536,9.162829389,0,2.04,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/19/1992,122,tropicana,16320,9.700146629,0,2,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/19/1992,123,dominicks,63808,11.06363385,0,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/19/1992,123,minute.maid,14400,9.574983486,0,2.16,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/19/1992,123,tropicana,15808,9.66827142,0,1.88,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/19/1992,124,dominicks,56576,10.94334015,0,1.59,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/19/1992,124,minute.maid,16640,9.719564714,0,2.1,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/19/1992,124,tropicana,17088,9.746131742,0,1.86,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/19/1992,126,dominicks,32704,10.39525267,0,1.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/19/1992,126,minute.maid,17856,9.790094865,0,2.08,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/19/1992,126,tropicana,15680,9.660141294,0,2.35,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/19/1992,128,dominicks,53888,10.8946631,0,1.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/19/1992,128,minute.maid,31232,10.34919849,0,2.07,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/19/1992,128,tropicana,18560,9.828764006,0,2.01,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/19/1992,129,dominicks,24000,10.08580911,0,1.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/19/1992,129,minute.maid,8896,9.093357017,0,2.08,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/19/1992,129,tropicana,7168,8.877381955,0,2.21,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/19/1992,130,dominicks,109248,11.60137581,0,1.59,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/19/1992,130,minute.maid,12096,9.400630098,0,2.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/19/1992,130,tropicana,7488,8.921057018,0,2.04,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/19/1992,131,dominicks,31744,10.36545901,0,1.59,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/19/1992,131,minute.maid,14528,9.583833101,0,2.06,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/19/1992,131,tropicana,13184,9.486759252,0,1.82,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/19/1992,132,dominicks,65152,11.08447828,0,1.59,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/19/1992,132,minute.maid,13888,9.538780437,0,2.04,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/19/1992,132,tropicana,7424,8.912473275,0,2.17,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/19/1992,134,dominicks,19072,9.85597657,0,1.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/19/1992,134,minute.maid,12352,9.421573272,0,1.75,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/19/1992,134,tropicana,13824,9.534161491,0,1.9,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/19/1992,137,dominicks,45056,10.71566144,0,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/19/1992,137,minute.maid,24064,10.08847223,0,2.12,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/19/1992,137,tropicana,40256,10.60301434,0,1.95,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/26/1992,2,dominicks,12480,9.431882642,0,1.6,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/26/1992,2,minute.maid,5312,8.577723691,0,2.28,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/26/1992,2,tropicana,15168,9.626943225,0,2.81,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -3/26/1992,5,dominicks,4608,8.435549202,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/26/1992,5,minute.maid,6464,8.7740036,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/26/1992,5,tropicana,19008,9.852615222,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -3/26/1992,8,dominicks,13952,9.543378146,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/26/1992,8,minute.maid,20480,9.927204079,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/26/1992,8,tropicana,10752,9.282847063,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -3/26/1992,9,dominicks,2688,7.896552702,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/26/1992,9,minute.maid,3840,8.253227646,0,2.66,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/26/1992,9,tropicana,14272,9.566054855,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -3/26/1992,12,dominicks,9024,9.107642974,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/26/1992,12,minute.maid,27456,10.22034,0,2.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/26/1992,12,tropicana,17664,9.779283949,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -3/26/1992,14,dominicks,5952,8.691482577,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/26/1992,14,minute.maid,9408,9.14931567,0,2.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/26/1992,14,tropicana,26048,10.16769627,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -3/26/1992,18,dominicks,8448,9.041685006,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/26/1992,18,minute.maid,7104,8.868413285,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/26/1992,18,tropicana,14208,9.561560465,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -3/26/1992,21,dominicks,8704,9.071537969,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/26/1992,21,minute.maid,14144,9.557045785,0,1.3,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/26/1992,21,tropicana,9152,9.121727714,0,2.56,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -3/26/1992,28,dominicks,4928,8.502688505,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/26/1992,28,minute.maid,3008,8.009030685,0,2.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/26/1992,28,tropicana,7168,8.877381955,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -3/26/1992,32,dominicks,12224,9.411156511,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/26/1992,32,minute.maid,8512,9.049232212,0,2.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/26/1992,32,tropicana,15552,9.651944527,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -3/26/1992,33,dominicks,8320,9.026417534,0,1.71,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/26/1992,33,minute.maid,8960,9.100525506,0,2.24,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/26/1992,33,tropicana,14848,9.605620455,0,2.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -3/26/1992,40,dominicks,6784,8.822322178,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/26/1992,40,minute.maid,3584,8.184234774,0,2.13,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/26/1992,40,tropicana,13504,9.510741217,0,2.54,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -3/26/1992,44,dominicks,5696,8.647519453,0,1.67,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/26/1992,44,minute.maid,5312,8.577723691,0,2.34,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/26/1992,44,tropicana,21760,9.987828701,0,2.75,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -3/26/1992,45,dominicks,5440,8.60153434,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/26/1992,45,minute.maid,3328,8.110126802,0,2.66,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/26/1992,45,tropicana,10240,9.234056899,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -3/26/1992,47,dominicks,6272,8.743850562,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/26/1992,47,minute.maid,4992,8.51559191,0,2.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/26/1992,47,tropicana,11264,9.329367078,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -3/26/1992,48,dominicks,4928,8.502688505,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/26/1992,48,minute.maid,6272,8.743850562,0,2.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/26/1992,48,tropicana,8832,9.086136769,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -3/26/1992,49,dominicks,4288,8.363575703,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/26/1992,49,minute.maid,13952,9.543378146,0,2.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/26/1992,49,tropicana,6080,8.712759975,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -3/26/1992,50,dominicks,3264,8.090708716,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/26/1992,50,minute.maid,3648,8.201934351,0,2.66,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/26/1992,50,tropicana,6144,8.723231275,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -3/26/1992,51,dominicks,9600,9.169518378,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/26/1992,51,minute.maid,10688,9.276876896,0,2.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/26/1992,51,tropicana,8512,9.049232212,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -3/26/1992,52,dominicks,11584,9.357380115,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/26/1992,52,minute.maid,8000,8.987196821,0,2.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/26/1992,52,tropicana,19648,9.885730831,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -3/26/1992,53,dominicks,4736,8.462948177,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/26/1992,53,minute.maid,37632,10.53561003,0,2.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/26/1992,53,tropicana,15488,9.64782081,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -3/26/1992,54,dominicks,7744,8.954673629,0,1.67,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/26/1992,54,minute.maid,3648,8.201934351,0,2.47,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/26/1992,54,tropicana,12608,9.442086812,0,2.92,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -3/26/1992,56,dominicks,5760,8.658692754,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/26/1992,56,minute.maid,3328,8.110126802,0,2.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/26/1992,56,tropicana,14912,9.609921537,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -3/26/1992,59,dominicks,4224,8.348537825,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/26/1992,59,minute.maid,3520,8.166216269,0,2.09,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/26/1992,59,tropicana,9024,9.107642974,0,2.57,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -3/26/1992,62,dominicks,2816,7.943072717,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/26/1992,62,minute.maid,6784,8.822322178,0,2.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/26/1992,62,tropicana,20864,9.945780465,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -3/26/1992,64,dominicks,3392,8.129174997,0,1.62,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/26/1992,64,minute.maid,9728,9.182763604,0,1.54,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/26/1992,64,tropicana,6336,8.754002934,0,2.53,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -3/26/1992,67,dominicks,4928,8.502688505,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/26/1992,67,minute.maid,4544,8.42156296,0,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/26/1992,67,tropicana,9792,9.189321005,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -3/26/1992,68,dominicks,16256,9.69621735,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/26/1992,68,minute.maid,8832,9.086136769,0,2.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/26/1992,68,tropicana,14016,9.547954813,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -3/26/1992,70,dominicks,24896,10.12246243,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/26/1992,70,minute.maid,36608,10.50802208,0,2.25,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/26/1992,70,tropicana,13120,9.481893063,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -3/26/1992,71,dominicks,13632,9.520175249,0,1.61,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/26/1992,71,minute.maid,4864,8.489616424,0,2.27,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/26/1992,71,tropicana,9472,9.156095357,0,2.8,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -3/26/1992,72,dominicks,13568,9.515469358,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/26/1992,72,minute.maid,8256,9.018695488,0,2.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/26/1992,72,tropicana,11200,9.323669057,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -3/26/1992,73,dominicks,19968,9.901886271,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/26/1992,73,minute.maid,38848,10.56741187,0,2.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/26/1992,73,tropicana,15936,9.67633598,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -3/26/1992,74,dominicks,10752,9.282847063,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/26/1992,74,minute.maid,320,5.768320996,0,2.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/26/1992,74,tropicana,13504,9.510741217,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -3/26/1992,75,dominicks,8128,9.00307017,0,1.61,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/26/1992,75,minute.maid,2880,7.965545573,0,2.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/26/1992,75,tropicana,4352,8.378390789,0,3.13,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -3/26/1992,76,dominicks,12224,9.411156511,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/26/1992,76,minute.maid,26432,10.18233068,0,2.66,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/26/1992,76,tropicana,11072,9.312174678,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -3/26/1992,77,dominicks,8384,9.034080407,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/26/1992,77,minute.maid,4736,8.462948177,0,2.25,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/26/1992,77,tropicana,13824,9.534161491,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -3/26/1992,78,dominicks,5824,8.66974259,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/26/1992,78,minute.maid,5312,8.577723691,0,2.25,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/26/1992,78,tropicana,12096,9.400630098,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -3/26/1992,81,dominicks,9280,9.135616826,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/26/1992,81,minute.maid,7232,8.886270902,0,2.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/26/1992,81,tropicana,12224,9.411156511,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -3/26/1992,83,dominicks,26304,10.1774763,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/26/1992,83,minute.maid,5504,8.61323038,0,2.25,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/26/1992,83,tropicana,9920,9.2023082,0,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -3/26/1992,84,dominicks,5120,8.540909718,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/26/1992,84,minute.maid,13824,9.534161491,0,2.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/26/1992,84,tropicana,11200,9.323669057,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -3/26/1992,86,dominicks,9984,9.208739091,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/26/1992,86,minute.maid,14400,9.574983486,0,2.66,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/26/1992,86,tropicana,13696,9.524859098,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -3/26/1992,88,dominicks,6592,8.793612072,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/26/1992,88,minute.maid,5312,8.577723691,0,2.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/26/1992,88,tropicana,9536,9.162829389,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -3/26/1992,89,dominicks,10560,9.264828557,0,1.61,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/26/1992,89,minute.maid,9984,9.208739091,0,1.54,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/26/1992,89,tropicana,5248,8.565602331,0,2.74,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -3/26/1992,90,dominicks,37184,10.52363384,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/26/1992,90,minute.maid,23744,10.07508515,0,2.79,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/26/1992,90,tropicana,9024,9.107642974,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -3/26/1992,91,dominicks,16512,9.711842668,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/26/1992,91,minute.maid,4800,8.476371197,0,2.66,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/26/1992,91,tropicana,6592,8.793612072,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -3/26/1992,92,dominicks,15104,9.622714888,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/26/1992,92,minute.maid,3968,8.286017468,0,2.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/26/1992,92,tropicana,6784,8.822322178,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -3/26/1992,93,dominicks,7616,8.938006577,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/26/1992,93,minute.maid,8000,8.987196821,0,2.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/26/1992,93,tropicana,12608,9.442086812,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -3/26/1992,94,dominicks,5440,8.60153434,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/26/1992,94,minute.maid,7488,8.921057018,0,2.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/26/1992,94,tropicana,13312,9.496421163,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -3/26/1992,95,dominicks,5440,8.60153434,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/26/1992,95,minute.maid,15168,9.626943225,0,2.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/26/1992,95,tropicana,13632,9.520175249,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -3/26/1992,97,dominicks,5184,8.553332238,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/26/1992,97,minute.maid,4928,8.502688505,0,2.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/26/1992,97,tropicana,4288,8.363575703,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -3/26/1992,98,dominicks,11264,9.329367078,0,1.65,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/26/1992,98,minute.maid,71552,11.17817974,0,0.88,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/26/1992,98,tropicana,15680,9.660141294,0,2.76,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -3/26/1992,100,dominicks,11584,9.357380115,0,1.62,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/26/1992,100,minute.maid,5888,8.68067166,0,2.2,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/26/1992,100,tropicana,13312,9.496421163,0,2.73,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -3/26/1992,101,dominicks,9920,9.2023082,0,1.65,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/26/1992,101,minute.maid,5632,8.636219898,0,2.28,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/26/1992,101,tropicana,16448,9.707959168,0,2.77,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -3/26/1992,102,dominicks,19968,9.901886271,0,1.62,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/26/1992,102,minute.maid,8832,9.086136769,0,2.13,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/26/1992,102,tropicana,15168,9.626943225,0,2.74,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -3/26/1992,103,dominicks,15680,9.660141294,0,1.61,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/26/1992,103,minute.maid,35072,10.46515837,0,2,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/26/1992,103,tropicana,11136,9.317938383,0,2.71,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -3/26/1992,104,dominicks,4160,8.333270353,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/26/1992,104,minute.maid,4928,8.502688505,0,2.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/26/1992,104,tropicana,6720,8.812843434,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -3/26/1992,105,dominicks,10048,9.215128889,0,1.63,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/26/1992,105,minute.maid,6272,8.743850562,0,2.32,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/26/1992,105,tropicana,8960,9.100525506,0,2.85,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -3/26/1992,106,dominicks,7744,8.954673629,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/26/1992,106,minute.maid,2688,7.896552702,0,2.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/26/1992,106,tropicana,6144,8.723231275,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -3/26/1992,107,dominicks,3904,8.269756948,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/26/1992,107,minute.maid,60224,11.00582622,0,2.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/26/1992,107,tropicana,18880,9.84585844,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -3/26/1992,109,dominicks,3584,8.184234774,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/26/1992,109,minute.maid,11072,9.312174678,0,2.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/26/1992,109,tropicana,21376,9.970024076,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -3/26/1992,110,dominicks,7744,8.954673629,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/26/1992,110,minute.maid,3840,8.253227646,0,2.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/26/1992,110,tropicana,9472,9.156095357,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -3/26/1992,111,dominicks,20672,9.936535407,0,1.67,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/26/1992,111,minute.maid,26176,10.17259824,0,2.04,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/26/1992,111,tropicana,9856,9.195835686,0,2.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -3/26/1992,112,dominicks,8832,9.086136769,0,1.61,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/26/1992,112,minute.maid,9024,9.107642974,0,2.13,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/26/1992,112,tropicana,23744,10.07508515,0,2.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -3/26/1992,113,dominicks,13376,9.501217335,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/26/1992,113,minute.maid,5760,8.658692754,0,2.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/26/1992,113,tropicana,14912,9.609921537,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -3/26/1992,114,dominicks,14720,9.596962392,0,1.63,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/26/1992,114,minute.maid,42752,10.66317126,0,1.05,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/26/1992,114,tropicana,7232,8.886270902,0,2.68,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -3/26/1992,115,dominicks,4224,8.348537825,0,1.68,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/26/1992,115,minute.maid,6016,8.702177866,0,2.32,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/26/1992,115,tropicana,11648,9.36288977,0,2.87,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -3/26/1992,116,dominicks,9344,9.142489705,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/26/1992,116,minute.maid,3648,8.201934351,0,2.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/26/1992,116,tropicana,13568,9.515469358,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -3/26/1992,117,dominicks,6784,8.822322178,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/26/1992,117,minute.maid,9152,9.121727714,0,2.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/26/1992,117,tropicana,10176,9.227787286,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -3/26/1992,118,dominicks,9408,9.14931567,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/26/1992,118,minute.maid,5376,8.589699882,0,2.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/26/1992,118,tropicana,9984,9.208739091,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -3/26/1992,119,dominicks,3968,8.286017468,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/26/1992,119,minute.maid,3584,8.184234774,0,2.66,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/26/1992,119,tropicana,11264,9.329367078,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -3/26/1992,121,dominicks,3968,8.286017468,0,1.62,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/26/1992,121,minute.maid,7936,8.979164649,0,2.38,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/26/1992,121,tropicana,21120,9.957975738,0,2.78,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -3/26/1992,122,dominicks,11200,9.323669057,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/26/1992,122,minute.maid,10240,9.234056899,0,2.13,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/26/1992,122,tropicana,19776,9.89222436,0,2.54,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -3/26/1992,123,dominicks,6208,8.733594062,0,1.65,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/26/1992,123,minute.maid,5504,8.61323038,0,2.43,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/26/1992,123,tropicana,13248,9.491601877,0,2.85,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -3/26/1992,124,dominicks,27648,10.22730867,0,1.62,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/26/1992,124,minute.maid,4480,8.407378325,0,2.25,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/26/1992,124,tropicana,2048,7.624618986,0,2.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -3/26/1992,126,dominicks,9728,9.182763604,0,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/26/1992,126,minute.maid,12288,9.416378455,0,2.34,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/26/1992,126,tropicana,10944,9.30054664,0,2.74,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -3/26/1992,128,dominicks,11008,9.30637756,0,1.66,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/26/1992,128,minute.maid,7744,8.954673629,0,2.27,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/26/1992,128,tropicana,18880,9.84585844,0,2.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -3/26/1992,129,dominicks,5440,8.60153434,0,1.65,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/26/1992,129,minute.maid,6656,8.803273983,0,2.37,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/26/1992,129,tropicana,17088,9.746131742,0,2.75,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -3/26/1992,130,dominicks,15744,9.664214619,0,1.68,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/26/1992,130,minute.maid,8896,9.093357017,0,2.18,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/26/1992,130,tropicana,14272,9.566054855,0,2.71,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -3/26/1992,131,dominicks,7936,8.979164649,0,1.71,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/26/1992,131,minute.maid,4992,8.51559191,0,2.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/26/1992,131,tropicana,10560,9.264828557,0,2.79,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -3/26/1992,132,dominicks,17408,9.76468515,0,1.61,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/26/1992,132,minute.maid,10112,9.221478116,0,2.12,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/26/1992,132,tropicana,10176,9.227787286,0,2.74,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -3/26/1992,134,dominicks,5504,8.61323038,0,1.72,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/26/1992,134,minute.maid,16000,9.680344001,0,1.38,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/26/1992,134,tropicana,8576,9.056722883,0,2.74,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -3/26/1992,137,dominicks,22528,10.02251426,0,1.64,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/26/1992,137,minute.maid,9152,9.121727714,0,2.38,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -3/26/1992,137,tropicana,35392,10.47424109,0,2.76,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/2/1992,2,dominicks,3264,8.090708716,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/2/1992,2,minute.maid,14528,9.583833101,1,1.9,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/2/1992,2,tropicana,28096,10.2433825,1,2.5,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/2/1992,5,dominicks,3136,8.050703382,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/2/1992,5,minute.maid,36800,10.51325312,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/2/1992,5,tropicana,15808,9.66827142,1,2.5,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/2/1992,8,dominicks,15168,9.626943225,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/2/1992,8,minute.maid,34688,10.45414909,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/2/1992,8,tropicana,20096,9.908276069,1,2.5,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/2/1992,9,dominicks,13184,9.486759252,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/2/1992,9,minute.maid,15488,9.64782081,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/2/1992,9,tropicana,18112,9.804329981,1,2.5,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/2/1992,12,dominicks,2816,7.943072717,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/2/1992,12,minute.maid,38208,10.5508002,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/2/1992,12,tropicana,33216,10.41078697,1,2.5,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/2/1992,14,dominicks,2368,7.769800996,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/2/1992,14,minute.maid,22528,10.02251426,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/2/1992,14,tropicana,32576,10.3913311,1,2.5,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/2/1992,18,dominicks,10176,9.227787286,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/2/1992,18,minute.maid,21440,9.973013615,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/2/1992,18,tropicana,20672,9.936535407,1,2.5,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/2/1992,21,dominicks,10624,9.270870872,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/2/1992,21,minute.maid,14848,9.605620455,1,1.65,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/2/1992,21,tropicana,8960,9.100525506,1,2.5,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/2/1992,28,dominicks,2944,7.98752448,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/2/1992,28,minute.maid,7232,8.886270902,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/2/1992,28,tropicana,8960,9.100525506,1,2.5,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/2/1992,32,dominicks,4992,8.51559191,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/2/1992,32,minute.maid,29696,10.29876764,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/2/1992,32,tropicana,36416,10.50276352,1,2.5,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/2/1992,33,dominicks,4544,8.42156296,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/2/1992,33,minute.maid,26112,10.17015026,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/2/1992,33,tropicana,31232,10.34919849,1,2.5,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/2/1992,40,dominicks,9600,9.169518378,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/2/1992,40,minute.maid,12672,9.447150114,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/2/1992,40,tropicana,18816,9.842462851,1,2.5,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/2/1992,44,dominicks,3648,8.201934351,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/2/1992,44,minute.maid,22976,10.04220547,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/2/1992,44,tropicana,39296,10.57887801,1,2.5,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/2/1992,45,dominicks,4288,8.363575703,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/2/1992,45,minute.maid,12480,9.431882642,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/2/1992,45,tropicana,12992,9.472089062,1,2.5,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/2/1992,47,dominicks,4096,8.317766167,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/2/1992,47,minute.maid,17152,9.749870064,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/2/1992,47,tropicana,8960,9.100525506,1,2.5,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/2/1992,48,dominicks,2624,7.87245515,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/2/1992,48,minute.maid,11264,9.329367078,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/2/1992,48,tropicana,14976,9.614204199,1,2.5,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/2/1992,49,dominicks,4736,8.462948177,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/2/1992,49,minute.maid,13120,9.481893063,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/2/1992,49,tropicana,12480,9.431882642,1,2.5,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/2/1992,50,dominicks,3904,8.269756948,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/2/1992,50,minute.maid,12480,9.431882642,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/2/1992,50,tropicana,12288,9.416378455,1,2.5,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/2/1992,51,dominicks,2368,7.769800996,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/2/1992,51,minute.maid,24256,10.09641929,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/2/1992,51,tropicana,17728,9.78290059,1,2.5,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/2/1992,52,dominicks,3072,8.030084094,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/2/1992,52,minute.maid,26048,10.16769627,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/2/1992,52,tropicana,38080,10.54744449,1,2.5,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/2/1992,53,dominicks,2176,7.685243608,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/2/1992,53,minute.maid,34240,10.44114983,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/2/1992,53,tropicana,52544,10.86940619,1,2.5,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/2/1992,54,dominicks,1728,7.454719949,0,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/2/1992,54,minute.maid,17536,9.77201119,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/2/1992,54,tropicana,11456,9.346268889,1,2.5,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/2/1992,56,dominicks,3776,8.236420527,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/2/1992,56,minute.maid,13568,9.515469358,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/2/1992,56,tropicana,22144,10.00532186,1,2.5,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/2/1992,59,dominicks,6784,8.822322178,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/2/1992,59,minute.maid,10496,9.258749511,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/2/1992,59,tropicana,13888,9.538780437,1,2.5,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/2/1992,62,dominicks,1088,6.992096427,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/2/1992,62,minute.maid,22400,10.01681624,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/2/1992,62,tropicana,36800,10.51325312,1,2.5,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/2/1992,64,dominicks,3776,8.236420527,0,1.89,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/2/1992,64,minute.maid,7552,8.929567708,1,1.98,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/2/1992,64,tropicana,10624,9.270870872,1,2.5,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/2/1992,67,dominicks,2048,7.624618986,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/2/1992,67,minute.maid,10432,9.252633284,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/2/1992,67,tropicana,16832,9.731037116,1,2.5,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/2/1992,68,dominicks,8704,9.071537969,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/2/1992,68,minute.maid,22976,10.04220547,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/2/1992,68,tropicana,22976,10.04220547,1,2.5,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/2/1992,70,dominicks,26240,10.17504024,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/2/1992,70,minute.maid,14912,9.609921537,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/2/1992,70,tropicana,12032,9.395325046,1,2.5,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/2/1992,71,dominicks,3840,8.253227646,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/2/1992,71,minute.maid,22336,10.01395501,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/2/1992,71,tropicana,34560,10.45045222,1,2.5,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/2/1992,72,dominicks,2496,7.82244473,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/2/1992,72,minute.maid,32192,10.37947325,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/2/1992,72,tropicana,38272,10.55247384,1,2.5,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/2/1992,73,dominicks,18560,9.828764006,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/2/1992,73,minute.maid,19776,9.89222436,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/2/1992,73,tropicana,21952,9.996613531,1,2.5,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/2/1992,74,dominicks,7808,8.962904128,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/2/1992,74,minute.maid,21632,9.981928979,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/2/1992,74,tropicana,23488,10.06424493,1,2.5,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/2/1992,75,dominicks,3264,8.090708716,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/2/1992,75,minute.maid,19776,9.89222436,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/2/1992,75,tropicana,18176,9.807857322,1,2.5,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/2/1992,76,dominicks,6016,8.702177866,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/2/1992,76,minute.maid,19328,9.869310101,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/2/1992,76,tropicana,19968,9.901886271,1,2.5,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/2/1992,77,dominicks,7616,8.938006577,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/2/1992,77,minute.maid,13440,9.505990614,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/2/1992,77,tropicana,15104,9.622714888,1,2.5,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/2/1992,78,dominicks,8192,9.010913347,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/2/1992,78,minute.maid,14016,9.547954813,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/2/1992,78,tropicana,11648,9.36288977,1,2.5,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/2/1992,80,dominicks,7616,8.938006577,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/2/1992,80,minute.maid,15168,9.626943225,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/2/1992,80,tropicana,27136,10.20861654,1,2.5,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/2/1992,81,dominicks,6272,8.743850562,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/2/1992,81,minute.maid,16256,9.69621735,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/2/1992,81,tropicana,23168,10.0505273,1,2.5,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/2/1992,83,dominicks,11712,9.368369236,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/2/1992,83,minute.maid,10560,9.264828557,1,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/2/1992,83,tropicana,14464,9.579418083,1,2.5,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/2/1992,84,dominicks,6592,8.793612072,0,1.58,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/2/1992,84,minute.maid,24896,10.12246243,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/2/1992,84,tropicana,11968,9.3899917,1,2.5,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/2/1992,86,dominicks,10048,9.215128889,0,1.58,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/2/1992,86,minute.maid,19776,9.89222436,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/2/1992,86,tropicana,23168,10.0505273,1,2.5,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/2/1992,88,dominicks,4160,8.333270353,0,1.58,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/2/1992,88,minute.maid,18624,9.832206351,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/2/1992,88,tropicana,11712,9.368369236,1,2.5,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/2/1992,89,dominicks,10496,9.258749511,0,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/2/1992,89,minute.maid,17920,9.793672686,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/2/1992,89,tropicana,8896,9.093357017,1,2.5,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/2/1992,90,dominicks,3328,8.110126802,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/2/1992,90,minute.maid,11584,9.357380115,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/2/1992,90,tropicana,13632,9.520175249,1,2.5,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/2/1992,91,dominicks,2816,7.943072717,0,1.58,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/2/1992,91,minute.maid,15808,9.66827142,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/2/1992,91,tropicana,12352,9.421573272,1,2.5,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/2/1992,92,dominicks,6016,8.702177866,0,1.58,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/2/1992,92,minute.maid,14528,9.583833101,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/2/1992,92,tropicana,16896,9.734832187,1,2.5,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/2/1992,93,dominicks,4736,8.462948177,0,1.58,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/2/1992,93,minute.maid,20800,9.942708266,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/2/1992,93,tropicana,33408,10.41655067,1,2.5,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/2/1992,94,dominicks,7808,8.962904128,0,1.58,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/2/1992,94,minute.maid,14208,9.561560465,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/2/1992,94,tropicana,17856,9.790094865,1,2.5,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/2/1992,95,dominicks,2624,7.87245515,0,1.58,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/2/1992,95,minute.maid,21568,9.978966014,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/2/1992,95,tropicana,17152,9.749870064,1,2.5,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/2/1992,97,dominicks,2944,7.98752448,0,1.58,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/2/1992,97,minute.maid,24448,10.10430369,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/2/1992,97,tropicana,7872,8.971067439,1,2.5,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/2/1992,98,dominicks,10112,9.221478116,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/2/1992,98,minute.maid,28032,10.24110199,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/2/1992,98,tropicana,23808,10.07777694,1,2.5,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/2/1992,100,dominicks,5760,8.658692754,0,1.89,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/2/1992,100,minute.maid,35008,10.46333189,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/2/1992,100,tropicana,21760,9.987828701,1,2.5,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/2/1992,101,dominicks,7104,8.868413285,0,1.89,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/2/1992,101,minute.maid,15424,9.643680017,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/2/1992,101,tropicana,19136,9.859326657,1,2.5,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/2/1992,102,dominicks,16128,9.688312171,0,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/2/1992,102,minute.maid,34816,10.45783233,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/2/1992,102,tropicana,23168,10.0505273,1,2.5,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/2/1992,103,dominicks,8512,9.049232212,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/2/1992,103,minute.maid,16960,9.738612909,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/2/1992,103,tropicana,7040,8.859363449,1,2.5,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/2/1992,104,dominicks,3776,8.236420527,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/2/1992,104,minute.maid,13120,9.481893063,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/2/1992,104,tropicana,16576,9.715711145,1,2.5,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/2/1992,105,dominicks,9088,9.114710141,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/2/1992,105,minute.maid,11520,9.351839934,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/2/1992,105,tropicana,12800,9.45720045,1,2.5,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/2/1992,106,dominicks,2432,7.796469243,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/2/1992,106,minute.maid,10752,9.282847063,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/2/1992,106,tropicana,4608,8.435549202,1,2.5,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/2/1992,107,dominicks,3584,8.184234774,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/2/1992,107,minute.maid,38848,10.56741187,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/2/1992,107,tropicana,33792,10.42797937,1,2.5,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/2/1992,109,dominicks,1664,7.416979621,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/2/1992,109,minute.maid,32320,10.38344151,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/2/1992,109,tropicana,46848,10.7546636,1,2.5,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/2/1992,110,dominicks,2560,7.847762538,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/2/1992,110,minute.maid,17088,9.746131742,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/2/1992,110,tropicana,13760,9.529521112,1,2.5,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/2/1992,111,dominicks,6656,8.803273983,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/2/1992,111,minute.maid,62208,11.03823889,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/2/1992,111,tropicana,11136,9.317938383,1,2.5,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/2/1992,112,dominicks,8000,8.987196821,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/2/1992,112,minute.maid,27072,10.20625526,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/2/1992,112,tropicana,30528,10.32639957,1,2.5,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/2/1992,113,dominicks,4032,8.30201781,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/2/1992,113,minute.maid,43904,10.68976071,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/2/1992,113,tropicana,23424,10.06151642,1,2.5,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/2/1992,114,dominicks,10816,9.288781798,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/2/1992,114,minute.maid,38784,10.56576307,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/2/1992,114,tropicana,16128,9.688312171,1,2.5,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/2/1992,115,dominicks,3328,8.110126802,0,1.89,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/2/1992,115,minute.maid,17920,9.793672686,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/2/1992,115,tropicana,23936,10.08313888,1,2.5,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/2/1992,116,dominicks,5120,8.540909718,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/2/1992,116,minute.maid,14784,9.601300794,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/2/1992,116,tropicana,12480,9.431882642,1,2.5,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/2/1992,117,dominicks,3776,8.236420527,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/2/1992,117,minute.maid,12800,9.45720045,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/2/1992,117,tropicana,12288,9.416378455,1,2.5,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/2/1992,118,dominicks,1856,7.526178913,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/2/1992,118,minute.maid,29760,10.30092049,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/2/1992,118,tropicana,25024,10.12759064,1,2.5,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/2/1992,119,dominicks,4480,8.407378325,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/2/1992,119,minute.maid,8000,8.987196821,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/2/1992,119,tropicana,18624,9.832206351,1,2.5,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/2/1992,121,dominicks,3968,8.286017468,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/2/1992,121,minute.maid,27584,10.22499117,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/2/1992,121,tropicana,25344,10.1402973,1,2.5,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/2/1992,122,dominicks,15168,9.626943225,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/2/1992,122,minute.maid,18688,9.835636886,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/2/1992,122,tropicana,15168,9.626943225,1,2.5,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/2/1992,123,dominicks,3904,8.269756948,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/2/1992,123,minute.maid,37376,10.52878407,1,1.89,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/2/1992,123,tropicana,23040,10.04498712,1,2.5,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/2/1992,124,dominicks,5376,8.589699882,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/2/1992,124,minute.maid,32192,10.37947325,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/2/1992,124,tropicana,24448,10.10430369,1,2.5,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/2/1992,126,dominicks,5568,8.624791202,0,1.89,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/2/1992,126,minute.maid,33664,10.4241843,1,1.86,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/2/1992,126,tropicana,43904,10.68976071,1,2.5,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/2/1992,128,dominicks,8192,9.010913347,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/2/1992,128,minute.maid,27840,10.23422911,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/2/1992,128,tropicana,64448,11.07361398,1,2.5,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/2/1992,129,dominicks,4928,8.502688505,0,1.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/2/1992,129,minute.maid,12416,9.426741242,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/2/1992,129,tropicana,17024,9.742379392,1,2.5,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/2/1992,130,dominicks,9216,9.128696383,0,1.98,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/2/1992,130,minute.maid,31296,10.35124557,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/2/1992,130,tropicana,19136,9.859326657,1,2.5,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/2/1992,131,dominicks,5888,8.68067166,0,1.89,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/2/1992,131,minute.maid,12992,9.472089062,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/2/1992,131,tropicana,19200,9.862665558,1,2.5,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/2/1992,132,dominicks,12288,9.416378455,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/2/1992,132,minute.maid,31680,10.36344085,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/2/1992,132,tropicana,30400,10.32219789,1,2.5,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/2/1992,134,dominicks,4416,8.392989588,0,1.89,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/2/1992,134,minute.maid,8128,9.00307017,1,1.74,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/2/1992,134,tropicana,15936,9.67633598,1,2.5,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/2/1992,137,dominicks,8832,9.086136769,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/2/1992,137,minute.maid,35520,10.4778512,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/2/1992,137,tropicana,100864,11.52152835,1,2.5,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/9/1992,2,dominicks,8768,9.078864009,0,1.48,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/9/1992,2,minute.maid,12416,9.426741242,0,2.12,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/9/1992,2,tropicana,12416,9.426741242,0,2.58,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/9/1992,5,dominicks,13184,9.486759252,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/9/1992,5,minute.maid,12928,9.467150781,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/9/1992,5,tropicana,14144,9.557045785,0,2.5,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/9/1992,8,dominicks,14592,9.588228712,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/9/1992,8,minute.maid,22400,10.01681624,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/9/1992,8,tropicana,16192,9.692272572,0,2.5,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/9/1992,9,dominicks,26496,10.18474906,0,1.58,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/9/1992,9,minute.maid,7104,8.868413285,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/9/1992,9,tropicana,16384,9.704060528,0,2.5,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/9/1992,12,dominicks,25216,10.13523399,0,1.58,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/9/1992,12,minute.maid,28992,10.27477521,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/9/1992,12,tropicana,16256,9.69621735,0,2.5,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/9/1992,14,dominicks,8768,9.078864009,0,1.58,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/9/1992,14,minute.maid,13056,9.477003077,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/9/1992,14,tropicana,17536,9.77201119,0,2.5,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/9/1992,18,dominicks,19712,9.888982866,0,1.58,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/9/1992,18,minute.maid,17984,9.797237753,0,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/9/1992,18,tropicana,18112,9.804329981,0,2.5,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/9/1992,21,dominicks,16000,9.680344001,0,1.43,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/9/1992,21,minute.maid,6656,8.803273983,0,2.04,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/9/1992,21,tropicana,6016,8.702177866,0,2.52,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/9/1992,28,dominicks,4224,8.348537825,0,1.58,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/9/1992,28,minute.maid,6144,8.723231275,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/9/1992,28,tropicana,7040,8.859363449,0,2.5,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/9/1992,32,dominicks,20928,9.948843254,0,1.58,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/9/1992,32,minute.maid,14464,9.579418083,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/9/1992,32,tropicana,31168,10.34714721,0,2.5,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/9/1992,33,dominicks,8384,9.034080407,0,1.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/9/1992,33,minute.maid,14272,9.566054855,0,2.12,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/9/1992,33,tropicana,21376,9.970024076,0,2.63,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/9/1992,40,dominicks,3456,8.14786713,0,1.45,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/9/1992,40,minute.maid,10688,9.276876896,0,2.02,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/9/1992,40,tropicana,12992,9.472089062,0,2.51,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/9/1992,44,dominicks,7680,8.946374826,0,1.45,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/9/1992,44,minute.maid,7360,8.903815212,0,2.22,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/9/1992,44,tropicana,11904,9.384629757,0,2.64,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/9/1992,45,dominicks,3264,8.090708716,0,1.58,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/9/1992,45,minute.maid,8384,9.034080407,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/9/1992,45,tropicana,7872,8.971067439,0,2.5,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/9/1992,47,dominicks,9152,9.121727714,0,1.58,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/9/1992,47,minute.maid,11840,9.379238908,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/9/1992,47,tropicana,7104,8.868413285,0,2.5,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/9/1992,48,dominicks,3456,8.14786713,0,1.58,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/9/1992,48,minute.maid,9728,9.182763604,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/9/1992,48,tropicana,9408,9.14931567,0,2.5,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/9/1992,49,dominicks,6080,8.712759975,0,1.58,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/9/1992,49,minute.maid,6528,8.783855897,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/9/1992,49,tropicana,7360,8.903815212,0,2.5,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/9/1992,50,dominicks,5440,8.60153434,0,1.58,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/9/1992,50,minute.maid,7744,8.954673629,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/9/1992,50,tropicana,5760,8.658692754,0,2.5,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/9/1992,51,dominicks,4544,8.42156296,0,1.58,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/9/1992,51,minute.maid,7744,8.954673629,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/9/1992,51,tropicana,12416,9.426741242,0,2.5,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/9/1992,52,dominicks,3520,8.166216269,0,1.58,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/9/1992,52,minute.maid,18432,9.821843564,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/9/1992,52,tropicana,22720,10.03100087,0,2.5,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/9/1992,53,dominicks,25216,10.13523399,0,1.58,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/9/1992,53,minute.maid,20352,9.920934466,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/9/1992,53,tropicana,14784,9.601300794,0,2.5,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/9/1992,54,dominicks,7360,8.903815212,0,1.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/9/1992,54,minute.maid,4288,8.363575703,0,2.22,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/9/1992,54,tropicana,5824,8.66974259,0,2.64,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/9/1992,56,dominicks,6720,8.812843434,0,1.58,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/9/1992,56,minute.maid,4928,8.502688505,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/9/1992,56,tropicana,8832,9.086136769,0,2.5,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/9/1992,59,dominicks,9344,9.142489705,0,1.32,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/9/1992,59,minute.maid,8896,9.093357017,0,2.02,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/9/1992,59,tropicana,10368,9.246479419,0,2.51,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/9/1992,62,dominicks,3392,8.129174997,0,1.58,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/9/1992,62,minute.maid,11968,9.3899917,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/9/1992,62,tropicana,22400,10.01681624,0,2.5,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/9/1992,64,dominicks,4032,8.30201781,0,1.54,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/9/1992,64,minute.maid,5312,8.577723691,0,2.12,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/9/1992,64,tropicana,7616,8.938006577,0,2.57,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/9/1992,67,dominicks,10304,9.240287448,0,1.58,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/9/1992,67,minute.maid,11008,9.30637756,0,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/9/1992,67,tropicana,7040,8.859363449,0,2.5,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/9/1992,68,dominicks,11584,9.357380115,0,1.58,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/9/1992,68,minute.maid,18880,9.84585844,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/9/1992,68,tropicana,11456,9.346268889,0,2.5,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/9/1992,70,dominicks,12672,9.447150114,0,1.58,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/9/1992,70,minute.maid,13312,9.496421163,0,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/9/1992,70,tropicana,9472,9.156095357,0,2.5,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/9/1992,71,dominicks,19520,9.87919486,0,1.37,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/9/1992,71,minute.maid,11584,9.357380115,0,2.07,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/9/1992,71,tropicana,11776,9.373818841,0,2.64,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/9/1992,72,dominicks,9856,9.195835686,0,1.58,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/9/1992,72,minute.maid,17216,9.753594463,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/9/1992,72,tropicana,15936,9.67633598,0,2.5,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/9/1992,73,dominicks,49216,10.80397405,0,1.58,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/9/1992,73,minute.maid,17280,9.757305042,0,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/9/1992,73,tropicana,13504,9.510741217,0,2.5,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/9/1992,74,dominicks,51648,10.85220675,0,1.58,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/9/1992,74,minute.maid,12608,9.442086812,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/9/1992,74,tropicana,15424,9.643680017,0,2.5,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/9/1992,75,dominicks,9856,9.195835686,0,1.5,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/9/1992,75,minute.maid,12736,9.452187908,0,2.11,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/9/1992,75,tropicana,10304,9.240287448,0,2.62,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/9/1992,76,dominicks,10752,9.282847063,0,1.58,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/9/1992,76,minute.maid,10624,9.270870872,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/9/1992,76,tropicana,9984,9.208739091,0,2.5,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/9/1992,77,dominicks,11008,9.30637756,0,1.58,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/9/1992,77,minute.maid,8832,9.086136769,0,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/9/1992,77,tropicana,10624,9.270870872,0,2.5,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/9/1992,78,dominicks,8704,9.071537969,0,1.58,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/9/1992,78,minute.maid,15808,9.66827142,0,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/9/1992,78,tropicana,12736,9.452187908,0,2.5,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/9/1992,80,dominicks,9728,9.182763604,0,1.58,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/9/1992,80,minute.maid,12672,9.447150114,0,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/9/1992,80,tropicana,11904,9.384629757,0,2.5,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/9/1992,81,dominicks,9792,9.189321005,0,1.58,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/9/1992,81,minute.maid,14592,9.588228712,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/9/1992,81,tropicana,12608,9.442086812,0,2.5,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/9/1992,83,dominicks,43072,10.67062841,0,1.58,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/9/1992,83,minute.maid,10240,9.234056899,0,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/9/1992,83,tropicana,11392,9.340666634,0,2.5,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 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-4/9/1992,89,minute.maid,12608,9.442086812,0,2.04,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/9/1992,89,tropicana,6208,8.733594062,0,2.56,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/9/1992,90,dominicks,25664,10.15284451,0,1.58,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/9/1992,90,minute.maid,6656,8.803273983,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/9/1992,90,tropicana,8576,9.056722883,0,2.5,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 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-4/9/1992,100,tropicana,12416,9.426741242,0,2.58,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/9/1992,101,dominicks,8960,9.100525506,0,1.37,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/9/1992,101,minute.maid,14208,9.561560465,0,2.09,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/9/1992,101,tropicana,13120,9.481893063,0,2.56,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/9/1992,102,dominicks,24960,10.12502982,0,1.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/9/1992,102,minute.maid,18496,9.825309772,0,2.07,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/9/1992,102,tropicana,23104,10.04776104,0,2.55,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/9/1992,103,dominicks,7168,8.877381955,0,1.43,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/9/1992,103,minute.maid,5824,8.66974259,0,2.1,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/9/1992,103,tropicana,3904,8.269756948,0,2.59,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/9/1992,104,dominicks,7680,8.946374826,0,1.58,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/9/1992,104,minute.maid,7360,8.903815212,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/9/1992,104,tropicana,7232,8.886270902,0,2.5,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/9/1992,105,dominicks,16320,9.700146629,0,1.5,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/9/1992,105,minute.maid,10112,9.221478116,0,2.08,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/9/1992,105,tropicana,8000,8.987196821,0,2.53,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/9/1992,106,dominicks,10240,9.234056899,0,1.58,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/9/1992,106,minute.maid,4544,8.42156296,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/9/1992,106,tropicana,4032,8.30201781,0,2.5,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/9/1992,107,dominicks,22144,10.00532186,0,1.58,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/9/1992,107,minute.maid,10304,9.240287448,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/9/1992,107,tropicana,18688,9.835636886,0,2.5,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/9/1992,109,dominicks,8192,9.010913347,0,1.58,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/9/1992,109,minute.maid,21376,9.970024076,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/9/1992,109,tropicana,24384,10.10168246,0,2.5,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/9/1992,110,dominicks,6912,8.841014311,0,1.58,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/9/1992,110,minute.maid,8512,9.049232212,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/9/1992,110,tropicana,8384,9.034080407,0,2.5,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/9/1992,111,dominicks,70784,11.16738827,0,1.32,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/9/1992,111,minute.maid,17280,9.757305042,0,2.1,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/9/1992,111,tropicana,8128,9.00307017,0,2.6,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/9/1992,112,dominicks,10048,9.215128889,0,1.45,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/9/1992,112,minute.maid,17024,9.742379392,0,2.08,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/9/1992,112,tropicana,19840,9.895455381,0,2.6,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/9/1992,113,dominicks,6848,8.831711918,0,1.58,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/9/1992,113,minute.maid,10944,9.30054664,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/9/1992,113,tropicana,17280,9.757305042,0,2.5,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/9/1992,114,dominicks,46400,10.74505474,0,1.55,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/9/1992,114,minute.maid,13440,9.505990614,0,2.09,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/9/1992,114,tropicana,12736,9.452187908,0,2.58,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/9/1992,115,dominicks,6144,8.723231275,0,1.47,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/9/1992,115,minute.maid,10560,9.264828557,0,2.07,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/9/1992,115,tropicana,12224,9.411156511,0,2.58,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/9/1992,116,dominicks,8960,9.100525506,0,1.58,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/9/1992,116,minute.maid,9920,9.2023082,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/9/1992,116,tropicana,9472,9.156095357,0,2.5,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/9/1992,117,dominicks,6400,8.764053269,0,1.58,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/9/1992,117,minute.maid,6720,8.812843434,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/9/1992,117,tropicana,7360,8.903815212,0,2.5,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/9/1992,118,dominicks,11136,9.317938383,0,1.58,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/9/1992,118,minute.maid,11328,9.335032816,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/9/1992,118,tropicana,18432,9.821843564,0,2.5,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/9/1992,119,dominicks,6080,8.712759975,0,1.58,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/9/1992,119,minute.maid,8640,9.064157862,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/9/1992,119,tropicana,11328,9.335032816,0,2.5,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/9/1992,121,dominicks,3776,8.236420527,0,1.56,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/9/1992,121,minute.maid,11584,9.357380115,0,2.14,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/9/1992,121,tropicana,22080,10.0024275,0,2.61,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/9/1992,122,dominicks,19456,9.875910785,0,1.42,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/9/1992,122,minute.maid,11712,9.368369236,0,2.05,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/9/1992,122,tropicana,10368,9.246479419,0,2.51,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/9/1992,123,dominicks,12160,9.405907156,0,1.54,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/9/1992,123,minute.maid,14592,9.588228712,0,1.94,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/9/1992,123,tropicana,10432,9.252633284,0,2.61,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/9/1992,124,dominicks,7424,8.912473275,0,1.41,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/9/1992,124,minute.maid,13440,9.505990614,0,2.1,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/9/1992,124,tropicana,13760,9.529521112,0,2.6,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/9/1992,126,dominicks,6912,8.841014311,0,1.47,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/9/1992,126,minute.maid,16128,9.688312171,0,2.1,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/9/1992,126,tropicana,24320,10.09905434,0,2.54,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/9/1992,128,dominicks,10688,9.276876896,0,1.5,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/9/1992,128,minute.maid,13376,9.501217335,0,2.16,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/9/1992,128,tropicana,24768,10.11730778,0,2.59,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/9/1992,129,dominicks,7104,8.868413285,0,1.53,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/9/1992,129,minute.maid,10880,9.29468152,0,2.13,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/9/1992,129,tropicana,13632,9.520175249,0,2.6,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/9/1992,130,dominicks,18560,9.828764006,0,1.41,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/9/1992,130,minute.maid,21568,9.978966014,0,2.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/9/1992,130,tropicana,3776,8.236420527,0,2.75,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/9/1992,131,dominicks,7424,8.912473275,0,1.45,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/9/1992,131,minute.maid,14208,9.561560465,0,2.14,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/9/1992,131,tropicana,9408,9.14931567,0,2.63,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/9/1992,132,dominicks,17216,9.753594463,0,1.42,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/9/1992,132,minute.maid,14464,9.579418083,0,2.09,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/9/1992,132,tropicana,13504,9.510741217,0,2.58,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/9/1992,134,dominicks,8768,9.078864009,0,1.42,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/9/1992,134,minute.maid,5568,8.624791202,0,2.11,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/9/1992,134,tropicana,10112,9.221478116,0,2.56,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/9/1992,137,dominicks,8832,9.086136769,0,1.47,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/9/1992,137,minute.maid,19904,9.898675996,0,2.14,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/9/1992,137,tropicana,69056,11.14267305,0,2.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/16/1992,2,dominicks,70848,11.16829202,1,1.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/16/1992,2,minute.maid,5376,8.589699882,0,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/16/1992,2,tropicana,5376,8.589699882,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/16/1992,5,dominicks,67712,11.1230187,1,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/16/1992,5,minute.maid,7424,8.912473275,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/16/1992,5,tropicana,9600,9.169518378,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/16/1992,8,dominicks,145088,11.88509573,1,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/16/1992,8,minute.maid,7808,8.962904128,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/16/1992,8,tropicana,6528,8.783855897,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/16/1992,9,dominicks,72576,11.19238957,1,1.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/16/1992,9,minute.maid,3776,8.236420527,0,2.66,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/16/1992,9,tropicana,7168,8.877381955,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/16/1992,12,dominicks,100160,11.51452419,1,1.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/16/1992,12,minute.maid,8960,9.100525506,0,2.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/16/1992,12,tropicana,6656,8.803273983,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/16/1992,14,dominicks,45760,10.73116563,1,1.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/16/1992,14,minute.maid,6080,8.712759975,0,2.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/16/1992,14,tropicana,10176,9.227787286,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/16/1992,18,dominicks,82880,11.32514906,1,1.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/16/1992,18,minute.maid,8896,9.093357017,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/16/1992,18,tropicana,9024,9.107642974,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/16/1992,21,dominicks,70848,11.16829202,1,1.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/16/1992,21,minute.maid,4864,8.489616424,0,2.25,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/16/1992,21,tropicana,5632,8.636219898,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/16/1992,28,dominicks,25152,10.1326927,1,1.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/16/1992,28,minute.maid,4352,8.378390789,0,2.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/16/1992,28,tropicana,3968,8.286017468,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/16/1992,32,dominicks,102592,11.53851524,1,1.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/16/1992,32,minute.maid,6720,8.812843434,0,2.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/16/1992,32,tropicana,8960,9.100525506,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/16/1992,33,dominicks,49280,10.8052736,1,1.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/16/1992,33,minute.maid,5312,8.577723691,0,2.78,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/16/1992,33,tropicana,8256,9.018695488,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/16/1992,40,dominicks,75200,11.22790651,1,1.29,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/16/1992,40,minute.maid,5312,8.577723691,0,2.25,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/16/1992,40,tropicana,6080,8.712759975,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/16/1992,44,dominicks,70016,11.15647907,1,1.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/16/1992,44,minute.maid,5952,8.691482577,0,2.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/16/1992,44,tropicana,11392,9.340666634,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/16/1992,45,dominicks,30464,10.32430094,1,1.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/16/1992,45,minute.maid,4160,8.333270353,0,2.66,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/16/1992,45,tropicana,4352,8.378390789,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/16/1992,47,dominicks,54912,10.91348718,1,1.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/16/1992,47,minute.maid,3904,8.269756948,0,2.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/16/1992,47,tropicana,4160,8.333270353,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/16/1992,48,dominicks,30144,10.31374118,1,1.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/16/1992,48,minute.maid,6336,8.754002934,0,2.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/16/1992,48,tropicana,5120,8.540909718,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/16/1992,49,dominicks,33152,10.40885833,1,1.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/16/1992,49,minute.maid,4096,8.317766167,0,2.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/16/1992,49,tropicana,4672,8.449342525,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/16/1992,50,dominicks,30080,10.31161578,1,1.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/16/1992,50,minute.maid,2752,7.920083199,0,2.66,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/16/1992,50,tropicana,3712,8.219326094,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/16/1992,51,dominicks,59840,10.99942961,1,1.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/16/1992,51,minute.maid,3072,8.030084094,0,2.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/16/1992,51,tropicana,3904,8.269756948,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/16/1992,52,dominicks,42496,10.65716523,1,1.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/16/1992,52,minute.maid,8704,9.071537969,0,2.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/16/1992,52,tropicana,10944,9.30054664,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/16/1992,53,dominicks,70528,11.16376507,1,1.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/16/1992,53,minute.maid,5888,8.68067166,0,2.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/16/1992,53,tropicana,16704,9.723403491,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/16/1992,54,dominicks,43136,10.67211319,1,1.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/16/1992,54,minute.maid,5056,8.528330936,0,2.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/16/1992,54,tropicana,6336,8.754002934,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/16/1992,56,dominicks,39552,10.58537154,1,1.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/16/1992,56,minute.maid,3008,8.009030685,0,2.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/16/1992,56,tropicana,5056,8.528330936,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/16/1992,59,dominicks,51456,10.84848235,1,1.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/16/1992,59,minute.maid,4672,8.449342525,0,2.25,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/16/1992,59,tropicana,9664,9.17616292,0,2.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/16/1992,62,dominicks,24640,10.11212642,1,1.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/16/1992,62,minute.maid,8256,9.018695488,0,2.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/16/1992,62,tropicana,14848,9.605620455,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/16/1992,64,dominicks,46208,10.74090822,1,1.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/16/1992,64,minute.maid,2880,7.965545573,0,2.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/16/1992,64,tropicana,3136,8.050703382,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/16/1992,67,dominicks,73600,11.20640031,1,1.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/16/1992,67,minute.maid,4864,8.489616424,0,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/16/1992,67,tropicana,6528,8.783855897,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/16/1992,68,dominicks,65408,11.08839985,1,1.29,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/16/1992,68,minute.maid,7168,8.877381955,0,2.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/16/1992,68,tropicana,5952,8.691482577,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 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-4/16/1992,71,tropicana,5504,8.61323038,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/16/1992,72,dominicks,58880,10.98325675,1,1.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/16/1992,72,minute.maid,6592,8.793612072,0,2.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/16/1992,72,tropicana,9536,9.162829389,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/16/1992,73,dominicks,141888,11.86279329,1,1.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/16/1992,73,minute.maid,8256,9.018695488,0,2.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/16/1992,73,tropicana,8896,9.093357017,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/16/1992,74,dominicks,134528,11.80952764,1,1.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/16/1992,74,minute.maid,5184,8.553332238,0,2.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/16/1992,74,tropicana,8256,9.018695488,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 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-4/16/1992,78,minute.maid,5888,8.68067166,0,2.25,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/16/1992,78,tropicana,7488,8.921057018,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/16/1992,80,dominicks,53504,10.8875117,1,1.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/16/1992,80,minute.maid,7744,8.954673629,0,2.25,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/16/1992,80,tropicana,11328,9.335032816,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 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-4/16/1992,83,tropicana,7168,8.877381955,0,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/16/1992,84,dominicks,56448,10.94107514,1,1.29,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/16/1992,84,minute.maid,4160,8.333270353,0,2.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/16/1992,84,tropicana,5440,8.60153434,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/16/1992,86,dominicks,79168,11.27932746,1,1.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/16/1992,86,minute.maid,4096,8.317766167,0,2.66,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/16/1992,86,tropicana,5696,8.647519453,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/16/1992,88,dominicks,54272,10.90176372,1,1.29,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/16/1992,88,minute.maid,3840,8.253227646,0,2.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/16/1992,88,tropicana,3584,8.184234774,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/16/1992,89,dominicks,106496,11.57586271,1,1.29,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/16/1992,89,minute.maid,4032,8.30201781,0,2.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/16/1992,89,tropicana,3520,8.166216269,0,2.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/16/1992,91,dominicks,82240,11.31739708,1,1.29,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/16/1992,91,minute.maid,3968,8.286017468,0,2.66,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/16/1992,91,tropicana,4096,8.317766167,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/16/1992,92,dominicks,64448,11.07361398,1,1.29,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/16/1992,92,minute.maid,4032,8.30201781,0,2.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/16/1992,92,tropicana,3328,8.110126802,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/16/1992,93,dominicks,64640,11.07658869,1,1.29,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/16/1992,93,minute.maid,6400,8.764053269,0,2.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/16/1992,93,tropicana,7424,8.912473275,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/16/1992,94,dominicks,32768,10.39720771,1,1.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/16/1992,94,minute.maid,8192,9.010913347,0,2.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/16/1992,94,tropicana,7808,8.962904128,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/16/1992,95,dominicks,76480,11.24478455,1,1.29,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/16/1992,95,minute.maid,3200,8.070906089,0,2.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/16/1992,95,tropicana,4672,8.449342525,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/16/1992,97,dominicks,37376,10.52878407,1,1.29,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/16/1992,97,minute.maid,2048,7.624618986,0,2.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 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-4/16/1992,100,minute.maid,4800,8.476371197,0,2.66,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/16/1992,100,tropicana,6272,8.743850562,0,2.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/16/1992,101,dominicks,70400,11.16194854,1,1.29,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/16/1992,101,minute.maid,3968,8.286017468,0,2.66,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/16/1992,101,tropicana,6848,8.831711918,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/16/1992,102,dominicks,171136,12.05021384,1,1.29,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/16/1992,102,minute.maid,6592,8.793612072,0,2.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/16/1992,102,tropicana,6656,8.803273983,0,2.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/16/1992,103,dominicks,67968,11.12679229,1,1.29,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/16/1992,103,minute.maid,3136,8.050703382,0,2.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/16/1992,103,tropicana,2112,7.655390645,0,2.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/16/1992,104,dominicks,50176,10.8232921,1,1.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/16/1992,104,minute.maid,3136,8.050703382,0,2.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/16/1992,104,tropicana,5120,8.540909718,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/16/1992,105,dominicks,103104,11.54349347,1,1.29,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/16/1992,105,minute.maid,3968,8.286017468,0,2.66,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/16/1992,105,tropicana,3136,8.050703382,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/16/1992,106,dominicks,38464,10.55747802,1,1.29,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/16/1992,106,minute.maid,2304,7.742402022,0,2.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/16/1992,106,tropicana,1408,7.249925537,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/16/1992,107,dominicks,67392,11.1182816,1,1.29,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/16/1992,107,minute.maid,7360,8.903815212,0,2.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/16/1992,107,tropicana,10176,9.227787286,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/16/1992,109,dominicks,55744,10.92852506,1,1.29,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/16/1992,109,minute.maid,10624,9.270870872,0,2.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/16/1992,109,tropicana,16128,9.688312171,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/16/1992,110,dominicks,58112,10.97012746,1,1.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/16/1992,110,minute.maid,3648,8.201934351,0,2.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/16/1992,110,tropicana,4608,8.435549202,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/16/1992,111,dominicks,245248,12.41002522,1,1.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/16/1992,111,minute.maid,5952,8.691482577,0,2.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/16/1992,111,tropicana,3648,8.201934351,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/16/1992,112,dominicks,47296,10.764181,1,1.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/16/1992,112,minute.maid,10432,9.252633284,0,2.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/16/1992,112,tropicana,13696,9.524859098,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/16/1992,113,dominicks,67648,11.12207307,1,1.29,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/16/1992,113,minute.maid,5632,8.636219898,0,2.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/16/1992,113,tropicana,8448,9.041685006,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/16/1992,114,dominicks,148288,11.90691161,1,1.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/16/1992,114,minute.maid,4480,8.407378325,0,2.66,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/16/1992,114,tropicana,6400,8.764053269,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/16/1992,115,dominicks,46272,10.74229231,1,1.29,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/16/1992,115,minute.maid,5440,8.60153434,0,2.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/16/1992,115,tropicana,8384,9.034080407,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/16/1992,116,dominicks,52800,10.87426647,1,1.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/16/1992,116,minute.maid,3328,8.110126802,0,2.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/16/1992,116,tropicana,3904,8.269756948,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/16/1992,117,dominicks,40512,10.60935351,1,1.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/16/1992,117,minute.maid,2688,7.896552702,0,2.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/16/1992,117,tropicana,3264,8.090708716,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/16/1992,118,dominicks,46976,10.75739211,1,1.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/16/1992,118,minute.maid,3648,8.201934351,0,2.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/16/1992,118,tropicana,6208,8.733594062,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/16/1992,119,dominicks,40768,10.61565274,1,1.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/16/1992,119,minute.maid,3392,8.129174997,0,2.66,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/16/1992,119,tropicana,5504,8.61323038,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/16/1992,121,dominicks,65408,11.08839985,1,1.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/16/1992,121,minute.maid,7616,8.938006577,0,2.66,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/16/1992,121,tropicana,11200,9.323669057,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/16/1992,122,dominicks,78912,11.27608859,1,1.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/16/1992,122,minute.maid,9984,9.208739091,0,2.25,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/16/1992,122,tropicana,9856,9.195835686,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/16/1992,123,dominicks,133568,11.80236599,1,1.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/16/1992,123,minute.maid,7360,8.903815212,0,2.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/16/1992,123,tropicana,6592,8.793612072,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/16/1992,124,dominicks,118336,11.68128332,1,1.29,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/16/1992,124,minute.maid,4672,8.449342525,0,2.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/16/1992,124,tropicana,5504,8.61323038,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/16/1992,126,dominicks,85632,11.35781432,1,1.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/16/1992,126,minute.maid,9280,9.135616826,0,2.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/16/1992,126,tropicana,9024,9.107642974,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/16/1992,128,dominicks,109888,11.60721694,1,1.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/16/1992,128,minute.maid,8512,9.049232212,0,2.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/16/1992,128,tropicana,10112,9.221478116,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/16/1992,129,dominicks,44928,10.71281649,1,1.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/16/1992,129,minute.maid,6464,8.7740036,0,2.66,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/16/1992,129,tropicana,8640,9.064157862,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/16/1992,130,dominicks,145920,11.89081381,1,1.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/16/1992,130,minute.maid,5312,8.577723691,0,2.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/16/1992,130,tropicana,3200,8.070906089,0,3.12,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/16/1992,131,dominicks,55936,10.93196346,1,1.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/16/1992,131,minute.maid,6400,8.764053269,0,2.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/16/1992,131,tropicana,7488,8.921057018,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/16/1992,132,dominicks,130816,11.78154704,1,1.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/16/1992,132,minute.maid,8064,8.99516499,0,2.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/16/1992,132,tropicana,8448,9.041685006,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/16/1992,134,dominicks,46336,10.74367448,1,1.29,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/16/1992,134,minute.maid,2752,7.920083199,0,2.66,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/16/1992,134,tropicana,4544,8.42156296,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/16/1992,137,dominicks,88448,11.39017009,1,1.29,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/16/1992,137,minute.maid,9728,9.182763604,0,2.79,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/16/1992,137,tropicana,23680,10.07238609,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/23/1992,2,dominicks,18560,9.828764006,0,1.42,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/23/1992,2,minute.maid,19008,9.852615222,1,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/23/1992,2,tropicana,9792,9.189321005,0,2.67,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/23/1992,5,dominicks,18880,9.84585844,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/23/1992,5,minute.maid,34176,10.43927892,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/23/1992,5,tropicana,10112,9.221478116,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/23/1992,8,dominicks,43712,10.68537794,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/23/1992,8,minute.maid,48064,10.78028874,1,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/23/1992,8,tropicana,8320,9.026417534,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/23/1992,9,dominicks,8256,9.018695488,0,1.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/23/1992,9,minute.maid,40576,10.61093204,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/23/1992,9,tropicana,9984,9.208739091,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/23/1992,12,dominicks,21952,9.996613531,0,1.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/23/1992,12,minute.maid,44608,10.70566849,1,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/23/1992,12,tropicana,12224,9.411156511,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/23/1992,14,dominicks,12800,9.45720045,0,1.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/23/1992,14,minute.maid,26560,10.1871616,1,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/23/1992,14,tropicana,14848,9.605620455,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/23/1992,18,dominicks,38400,10.55581274,0,1.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/23/1992,18,minute.maid,29824,10.30306872,1,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/23/1992,18,tropicana,10560,9.264828557,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/23/1992,21,dominicks,14976,9.614204199,0,1.34,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/23/1992,21,minute.maid,27328,10.2156671,1,1.68,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/23/1992,21,tropicana,6144,8.723231275,0,2.24,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/23/1992,28,dominicks,6528,8.783855897,0,1.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/23/1992,28,minute.maid,15232,9.631153757,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/23/1992,28,tropicana,5184,8.553332238,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/23/1992,32,dominicks,16640,9.719564714,0,1.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/23/1992,32,minute.maid,42112,10.64808802,1,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/23/1992,32,tropicana,11520,9.351839934,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/23/1992,33,dominicks,4288,8.363575703,0,1.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/23/1992,33,minute.maid,29440,10.29010957,1,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/23/1992,33,tropicana,10752,9.282847063,0,2.82,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/23/1992,40,dominicks,17920,9.793672686,0,1.34,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/23/1992,40,minute.maid,34816,10.45783233,1,1.68,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/23/1992,40,tropicana,7360,8.903815212,0,2.27,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/23/1992,44,dominicks,9536,9.162829389,0,1.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/23/1992,44,minute.maid,26880,10.1991378,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/23/1992,44,tropicana,12800,9.45720045,0,2.61,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/23/1992,45,dominicks,7488,8.921057018,0,1.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/23/1992,45,minute.maid,14144,9.557045785,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/23/1992,45,tropicana,6016,8.702177866,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/23/1992,47,dominicks,9920,9.2023082,0,1.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/23/1992,47,minute.maid,23808,10.07777694,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/23/1992,47,tropicana,4544,8.42156296,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/23/1992,48,dominicks,8320,9.026417534,0,1.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/23/1992,48,minute.maid,16192,9.692272572,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/23/1992,48,tropicana,6784,8.822322178,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 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-4/23/1992,50,tropicana,3456,8.14786713,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/23/1992,51,dominicks,9280,9.135616826,0,1.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/23/1992,51,minute.maid,23104,10.04776104,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/23/1992,51,tropicana,6656,8.803273983,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/23/1992,52,dominicks,6080,8.712759975,0,1.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/23/1992,52,minute.maid,32704,10.39525267,1,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/23/1992,52,tropicana,17408,9.76468515,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/23/1992,53,dominicks,25728,10.15533517,0,1.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/23/1992,53,minute.maid,26560,10.1871616,1,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/23/1992,53,tropicana,22080,10.0024275,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 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-4/23/1992,56,tropicana,8640,9.064157862,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/23/1992,59,dominicks,9728,9.182763604,0,1.38,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/23/1992,59,minute.maid,24128,10.09112827,1,1.68,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/23/1992,59,tropicana,7488,8.921057018,0,2.21,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/23/1992,62,dominicks,3648,8.201934351,0,1.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/23/1992,62,minute.maid,29440,10.29010957,1,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/23/1992,62,tropicana,17536,9.77201119,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/23/1992,64,dominicks,12288,9.416378455,0,1.36,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/23/1992,64,minute.maid,14080,9.55251063,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/23/1992,64,tropicana,2304,7.742402022,0,2.76,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/23/1992,67,dominicks,3584,8.184234774,0,1.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/23/1992,67,minute.maid,41216,10.62658181,1,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/23/1992,67,tropicana,6848,8.831711918,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -4/23/1992,68,dominicks,24192,10.09377728,0,1.29,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/23/1992,68,minute.maid,21120,9.957975738,1,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/23/1992,68,tropicana,8640,9.064157862,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -4/23/1992,70,dominicks,20736,9.939626599,0,1.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/23/1992,70,minute.maid,34048,10.43552657,1,1.68,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/23/1992,70,tropicana,9728,9.182763604,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -4/23/1992,71,dominicks,45504,10.72555551,0,1.32,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/23/1992,71,minute.maid,28416,10.25470765,1,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/23/1992,71,tropicana,8832,9.086136769,0,2.71,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -4/23/1992,72,dominicks,11776,9.373818841,0,1.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/23/1992,72,minute.maid,31616,10.3614186,1,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/23/1992,72,tropicana,10240,9.234056899,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -4/23/1992,73,dominicks,22016,9.999524741,0,1.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/23/1992,73,minute.maid,54848,10.912321,1,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/23/1992,73,tropicana,12096,9.400630098,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -4/23/1992,74,dominicks,33280,10.4127119,0,1.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/23/1992,74,minute.maid,19712,9.888982866,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/23/1992,74,tropicana,12736,9.452187908,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -4/23/1992,75,dominicks,9216,9.128696383,0,1.51,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/23/1992,75,minute.maid,20672,9.936535407,1,2.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/23/1992,75,tropicana,9728,9.182763604,0,2.64,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -4/23/1992,76,dominicks,16192,9.692272572,0,1.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/23/1992,76,minute.maid,39168,10.57561537,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/23/1992,76,tropicana,8064,8.99516499,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -4/23/1992,77,dominicks,11456,9.346268889,0,1.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/23/1992,77,minute.maid,21760,9.987828701,1,1.68,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/23/1992,77,tropicana,9920,9.2023082,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -4/23/1992,78,dominicks,21376,9.970024076,0,1.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/23/1992,78,minute.maid,31296,10.35124557,1,1.68,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/23/1992,78,tropicana,8448,9.041685006,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -4/23/1992,80,dominicks,12096,9.400630098,0,1.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/23/1992,80,minute.maid,48128,10.78161941,1,1.68,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/23/1992,80,tropicana,15488,9.64782081,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -4/23/1992,81,dominicks,16576,9.715711145,0,1.29,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/23/1992,81,minute.maid,24128,10.09112827,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/23/1992,81,tropicana,10368,9.246479419,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -4/23/1992,83,dominicks,69440,11.14821835,0,1.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/23/1992,83,minute.maid,29952,10.30735138,1,1.68,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/23/1992,83,tropicana,8640,9.064157862,0,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -4/23/1992,84,dominicks,8064,8.99516499,0,1.29,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/23/1992,84,minute.maid,27648,10.22730867,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -4/23/1992,84,tropicana,6272,8.743850562,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 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-4/23/1992,88,tropicana,5440,8.60153434,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/23/1992,89,dominicks,15872,9.67231183,0,1.38,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/23/1992,89,minute.maid,34496,10.44859865,1,1.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/23/1992,89,tropicana,4672,8.449342525,0,2.45,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -4/23/1992,90,dominicks,15936,9.67633598,0,1.29,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/23/1992,90,minute.maid,18496,9.825309772,1,2.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/23/1992,90,tropicana,4480,8.407378325,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -4/23/1992,91,dominicks,14272,9.566054855,0,1.29,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/23/1992,91,minute.maid,27840,10.23422911,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -4/23/1992,91,tropicana,5056,8.528330936,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 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-4/23/1992,93,tropicana,13952,9.543378146,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/23/1992,94,dominicks,6912,8.841014311,0,1.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/23/1992,94,minute.maid,18368,9.818365299,1,1.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/23/1992,94,tropicana,7424,8.912473275,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -4/23/1992,95,dominicks,15360,9.639522007,0,1.29,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/23/1992,95,minute.maid,20160,9.911455722,1,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/23/1992,95,tropicana,5568,8.624791202,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/23/1992,97,dominicks,6720,8.812843434,0,1.29,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/23/1992,97,minute.maid,13568,9.515469358,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/23/1992,97,tropicana,2496,7.82244473,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/23/1992,98,dominicks,16576,9.715711145,0,1.44,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/23/1992,98,minute.maid,37696,10.53730927,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/23/1992,98,tropicana,9856,9.195835686,0,2.62,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/23/1992,100,dominicks,8384,9.034080407,0,1.45,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/23/1992,100,minute.maid,45248,10.71991375,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/23/1992,100,tropicana,8128,9.00307017,0,2.66,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/23/1992,101,dominicks,24832,10.11988842,0,1.37,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/23/1992,101,minute.maid,22464,10.01966931,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/23/1992,101,tropicana,8128,9.00307017,0,2.67,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/23/1992,102,dominicks,59776,10.99835952,0,1.37,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/23/1992,102,minute.maid,56512,10.94220828,1,1.79,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/23/1992,102,tropicana,9536,9.162829389,0,2.48,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/23/1992,103,dominicks,13376,9.501217335,0,1.34,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/23/1992,103,minute.maid,24704,10.11472045,1,1.79,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/23/1992,103,tropicana,4416,8.392989588,0,2.45,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/23/1992,104,dominicks,9088,9.114710141,0,1.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/23/1992,104,minute.maid,16896,9.734832187,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/23/1992,104,tropicana,3840,8.253227646,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/23/1992,105,dominicks,25472,10.14533509,0,1.39,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/23/1992,105,minute.maid,29696,10.29876764,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/23/1992,105,tropicana,5248,8.565602331,0,2.57,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/23/1992,106,dominicks,16576,9.715711145,0,1.29,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/23/1992,106,minute.maid,13440,9.505990614,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/23/1992,106,tropicana,2304,7.742402022,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/23/1992,107,dominicks,15808,9.66827142,0,1.29,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/23/1992,107,minute.maid,44608,10.70566849,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/23/1992,107,tropicana,12544,9.436997743,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/23/1992,109,dominicks,9088,9.114710141,0,1.29,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/23/1992,109,minute.maid,50688,10.83344448,1,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/23/1992,109,tropicana,14848,9.605620455,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/23/1992,110,dominicks,22272,10.01108556,0,1.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/23/1992,110,minute.maid,19136,9.859326657,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/23/1992,110,tropicana,5504,8.61323038,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/23/1992,111,dominicks,57600,10.96127785,0,1.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/23/1992,111,minute.maid,51392,10.8472378,1,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/23/1992,111,tropicana,6336,8.754002934,0,2.73,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/23/1992,112,dominicks,10240,9.234056899,0,1.33,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/23/1992,112,minute.maid,43136,10.67211319,1,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/23/1992,112,tropicana,16000,9.680344001,0,2.48,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/23/1992,113,dominicks,11712,9.368369236,0,1.29,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/23/1992,113,minute.maid,35392,10.47424109,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/23/1992,113,tropicana,12224,9.411156511,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/23/1992,114,dominicks,30528,10.32639957,0,1.36,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/23/1992,114,minute.maid,45696,10.72976605,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/23/1992,114,tropicana,7232,8.886270902,0,2.58,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/23/1992,115,dominicks,8000,8.987196821,0,1.45,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/23/1992,115,minute.maid,30976,10.34096799,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/23/1992,115,tropicana,10816,9.288781798,0,2.65,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/23/1992,116,dominicks,14912,9.609921537,0,1.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/23/1992,116,minute.maid,22144,10.00532186,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/23/1992,116,tropicana,6400,8.764053269,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/23/1992,117,dominicks,10368,9.246479419,0,1.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/23/1992,117,minute.maid,17408,9.76468515,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/23/1992,117,tropicana,5696,8.647519453,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/23/1992,118,dominicks,12096,9.400630098,0,1.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/23/1992,118,minute.maid,29952,10.30735138,1,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/23/1992,118,tropicana,8768,9.078864009,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/23/1992,119,dominicks,15936,9.67633598,0,1.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/23/1992,119,minute.maid,13760,9.529521112,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/23/1992,119,tropicana,9920,9.2023082,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/23/1992,121,dominicks,3648,8.201934351,0,1.36,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/23/1992,121,minute.maid,35136,10.46698153,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/23/1992,121,tropicana,12992,9.472089062,0,2.59,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/23/1992,122,dominicks,16192,9.692272572,0,1.35,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/23/1992,122,minute.maid,40128,10.59982962,1,1.68,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/23/1992,122,tropicana,10624,9.270870872,0,2.24,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/23/1992,123,dominicks,16320,9.700146629,0,1.41,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/23/1992,123,minute.maid,49856,10.81689413,1,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/23/1992,123,tropicana,8640,9.064157862,0,2.73,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/23/1992,124,dominicks,47296,10.764181,0,1.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/23/1992,124,minute.maid,23424,10.06151642,1,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/23/1992,124,tropicana,5376,8.589699882,0,2.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/23/1992,126,dominicks,12096,9.400630098,0,1.38,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/23/1992,126,minute.maid,36608,10.50802208,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/23/1992,126,tropicana,8320,9.026417534,0,2.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/23/1992,128,dominicks,16896,9.734832187,0,1.47,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/23/1992,128,minute.maid,54912,10.91348718,1,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/23/1992,128,tropicana,15936,9.67633598,0,2.86,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/23/1992,129,dominicks,9536,9.162829389,0,1.36,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/23/1992,129,minute.maid,24384,10.10168246,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/23/1992,129,tropicana,10176,9.227787286,0,2.59,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/23/1992,130,dominicks,69120,11.1435994,0,1.45,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/23/1992,130,minute.maid,16896,9.734832187,1,2.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/23/1992,130,tropicana,8000,8.987196821,0,2.62,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/23/1992,131,dominicks,17536,9.77201119,0,1.37,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/23/1992,131,minute.maid,29952,10.30735138,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/23/1992,131,tropicana,10496,9.258749511,0,2.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/23/1992,132,dominicks,37376,10.52878407,0,1.33,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/23/1992,132,minute.maid,45568,10.726961,1,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/23/1992,132,tropicana,8576,9.056722883,0,2.58,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/23/1992,134,dominicks,8896,9.093357017,0,1.39,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/23/1992,134,minute.maid,15936,9.67633598,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/23/1992,134,tropicana,4864,8.489616424,0,2.53,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/23/1992,137,dominicks,40384,10.60618895,0,1.35,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/23/1992,137,minute.maid,49792,10.81560961,1,2.09,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/23/1992,137,tropicana,25728,10.15533517,0,2.74,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/30/1992,2,dominicks,9152,9.121727714,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/30/1992,2,minute.maid,3904,8.269756948,0,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/30/1992,2,tropicana,16960,9.738612909,1,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -4/30/1992,5,dominicks,6208,8.733594062,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/30/1992,5,minute.maid,4160,8.333270353,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/30/1992,5,tropicana,31872,10.36948316,1,2.24,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -4/30/1992,8,dominicks,20608,9.933434629,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/30/1992,8,minute.maid,7360,8.903815212,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/30/1992,8,tropicana,30784,10.33475035,1,2.16,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -4/30/1992,9,dominicks,6720,8.812843434,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/30/1992,9,minute.maid,2880,7.965545573,0,2.66,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/30/1992,9,tropicana,45952,10.73535265,1,2.24,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -4/30/1992,12,dominicks,12416,9.426741242,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/30/1992,12,minute.maid,4224,8.348537825,0,2.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/30/1992,12,tropicana,44096,10.69412435,1,2.39,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -4/30/1992,14,dominicks,7744,8.954673629,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/30/1992,14,minute.maid,4224,8.348537825,0,2.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/30/1992,14,tropicana,35712,10.48324205,1,2.39,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -4/30/1992,18,dominicks,13568,9.515469358,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/30/1992,18,minute.maid,6208,8.733594062,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/30/1992,18,tropicana,36736,10.51151248,1,2.16,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -4/30/1992,21,dominicks,12224,9.411156511,0,1.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/30/1992,21,minute.maid,3328,8.110126802,0,2.25,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/30/1992,21,tropicana,27712,10.22962081,1,1.94,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -4/30/1992,28,dominicks,2816,7.943072717,0,1.89,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/30/1992,28,minute.maid,2304,7.742402022,0,2.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/30/1992,28,tropicana,6656,8.803273983,1,2.24,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -4/30/1992,32,dominicks,12288,9.416378455,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/30/1992,32,minute.maid,6272,8.743850562,0,2.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/30/1992,32,tropicana,33280,10.4127119,1,2.39,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -4/30/1992,33,dominicks,8960,9.100525506,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/30/1992,33,minute.maid,4736,8.462948177,0,2.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/30/1992,33,tropicana,37120,10.52191119,1,2.39,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -4/30/1992,40,dominicks,9728,9.182763604,0,1.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/30/1992,40,minute.maid,3840,8.253227646,0,2.25,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/30/1992,40,tropicana,35072,10.46515837,1,1.94,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -4/30/1992,44,dominicks,6784,8.822322178,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/30/1992,44,minute.maid,4928,8.502688505,0,2.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/30/1992,44,tropicana,50624,10.83218105,1,2.24,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -4/30/1992,45,dominicks,4928,8.502688505,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/30/1992,45,minute.maid,2688,7.896552702,0,2.66,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/30/1992,45,tropicana,14336,9.570529135,1,2.24,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -4/30/1992,47,dominicks,8064,8.99516499,0,1.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/30/1992,47,minute.maid,3264,8.090708716,0,2.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/30/1992,47,tropicana,12288,9.416378455,1,2.24,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -4/30/1992,48,dominicks,3904,8.269756948,0,1.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/30/1992,48,minute.maid,4096,8.317766167,0,2.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/30/1992,48,tropicana,15360,9.639522007,1,2.24,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -4/30/1992,49,dominicks,5056,8.528330936,0,1.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/30/1992,49,minute.maid,2432,7.796469243,0,2.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/30/1992,49,tropicana,16640,9.719564714,1,2.24,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -4/30/1992,50,dominicks,2240,7.714231145,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/30/1992,50,minute.maid,1856,7.526178913,0,2.66,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/30/1992,50,tropicana,11520,9.351839934,1,2.24,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -4/30/1992,51,dominicks,5696,8.647519453,0,1.89,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/30/1992,51,minute.maid,2496,7.82244473,0,2.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/30/1992,51,tropicana,16768,9.727227587,1,2.31,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -4/30/1992,52,dominicks,5632,8.636219898,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/30/1992,52,minute.maid,5248,8.565602331,0,2.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/30/1992,52,tropicana,47488,10.76823233,1,2.39,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -4/30/1992,53,dominicks,6592,8.793612072,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/30/1992,53,minute.maid,4288,8.363575703,0,2.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/30/1992,53,tropicana,57984,10.96792239,1,2.39,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -4/30/1992,54,dominicks,7616,8.938006577,0,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/30/1992,54,minute.maid,3136,8.050703382,0,2.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/30/1992,54,tropicana,22976,10.04220547,1,2.24,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -4/30/1992,56,dominicks,4416,8.392989588,0,1.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/30/1992,56,minute.maid,2496,7.82244473,0,2.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/30/1992,56,tropicana,15872,9.67231183,1,2.24,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -4/30/1992,59,dominicks,6208,8.733594062,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/30/1992,59,minute.maid,3392,8.129174997,0,2.25,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/30/1992,59,tropicana,28736,10.26590597,1,1.94,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -4/30/1992,62,dominicks,3264,8.090708716,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/30/1992,62,minute.maid,6208,8.733594062,0,2.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/30/1992,62,tropicana,39680,10.58860256,1,2.39,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -4/30/1992,64,dominicks,6336,8.754002934,0,1.89,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/30/1992,64,minute.maid,2368,7.769800996,0,2.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/30/1992,64,tropicana,7936,8.979164649,1,2.24,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -4/30/1992,67,dominicks,7040,8.859363449,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 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-4/30/1992,86,minute.maid,2752,7.920083199,0,2.66,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/30/1992,86,tropicana,23232,10.05328592,1,2.24,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -4/30/1992,88,dominicks,9088,9.114710141,0,1.89,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/30/1992,88,minute.maid,3136,8.050703382,0,2.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -4/30/1992,88,tropicana,11136,9.317938383,1,2.24,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 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-4/30/1992,92,minute.maid,3392,8.129174997,0,2.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/30/1992,92,tropicana,16960,9.738612909,1,2.24,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -4/30/1992,93,dominicks,9024,9.107642974,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/30/1992,93,minute.maid,4672,8.449342525,0,2.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -4/30/1992,93,tropicana,34560,10.45045222,1,2.39,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 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-4/30/1992,95,tropicana,16832,9.731037116,1,2.39,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -4/30/1992,97,dominicks,4864,8.489616424,0,1.89,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/30/1992,97,minute.maid,1856,7.526178913,0,2.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/30/1992,97,tropicana,12480,9.431882642,1,2.24,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -4/30/1992,98,dominicks,16768,9.727227587,0,1.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/30/1992,98,minute.maid,4096,8.317766167,0,2.66,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/30/1992,98,tropicana,30080,10.31161578,1,2.24,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -4/30/1992,100,dominicks,9984,9.208739091,0,1.89,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/30/1992,100,minute.maid,4288,8.363575703,0,2.66,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/30/1992,100,tropicana,37888,10.54238972,1,2.24,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -4/30/1992,101,dominicks,14976,9.614204199,0,1.89,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/30/1992,101,minute.maid,6144,8.723231275,0,2.66,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/30/1992,101,tropicana,21376,9.970024076,1,2.24,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -4/30/1992,102,dominicks,41792,10.64046021,0,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/30/1992,102,minute.maid,3840,8.253227646,0,2.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/30/1992,102,tropicana,34368,10.44488118,1,2.16,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -4/30/1992,103,dominicks,7040,8.859363449,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/30/1992,103,minute.maid,1984,7.592870288,0,2.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/30/1992,103,tropicana,15680,9.660141294,1,2.16,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -4/30/1992,104,dominicks,4992,8.51559191,0,1.89,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/30/1992,104,minute.maid,2304,7.742402022,0,2.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/30/1992,104,tropicana,10944,9.30054664,1,2.24,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -4/30/1992,105,dominicks,12480,9.431882642,0,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/30/1992,105,minute.maid,2560,7.847762538,0,2.66,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/30/1992,105,tropicana,25408,10.14281936,1,2.24,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -4/30/1992,106,dominicks,7552,8.929567708,0,1.89,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/30/1992,106,minute.maid,2752,7.920083199,0,2.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/30/1992,106,tropicana,6400,8.764053269,1,2.24,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -4/30/1992,107,dominicks,4352,8.378390789,0,1.89,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/30/1992,107,minute.maid,4864,8.489616424,0,2.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/30/1992,107,tropicana,38464,10.55747802,1,2.24,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -4/30/1992,109,dominicks,3712,8.219326094,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/30/1992,109,minute.maid,8768,9.078864009,0,2.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/30/1992,109,tropicana,68544,11.13523115,1,2.39,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -4/30/1992,110,dominicks,6336,8.754002934,0,1.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/30/1992,110,minute.maid,2560,7.847762538,0,2.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/30/1992,110,tropicana,22656,10.02818,1,2.24,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -4/30/1992,111,dominicks,17280,9.757305042,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/30/1992,111,minute.maid,3584,8.184234774,0,2.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/30/1992,111,tropicana,34624,10.45230236,1,2.39,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -4/30/1992,112,dominicks,11520,9.351839934,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/30/1992,112,minute.maid,5952,8.691482577,0,2.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/30/1992,112,tropicana,36992,10.51845695,1,2.16,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -4/30/1992,113,dominicks,9600,9.169518378,0,1.89,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/30/1992,113,minute.maid,3328,8.110126802,0,2.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/30/1992,113,tropicana,38848,10.56741187,1,2.24,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -4/30/1992,114,dominicks,19200,9.862665558,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/30/1992,114,minute.maid,3776,8.236420527,0,2.66,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/30/1992,114,tropicana,33152,10.40885833,1,2.24,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -4/30/1992,115,dominicks,5824,8.66974259,0,1.89,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/30/1992,115,minute.maid,4096,8.317766167,0,2.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/30/1992,115,tropicana,37440,10.53049493,1,2.24,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -4/30/1992,116,dominicks,7232,8.886270902,0,1.89,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/30/1992,116,minute.maid,2432,7.796469243,0,2.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/30/1992,116,tropicana,19776,9.89222436,1,2.24,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -4/30/1992,117,dominicks,7040,8.859363449,0,1.89,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/30/1992,117,minute.maid,1408,7.249925537,0,2.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/30/1992,117,tropicana,15872,9.67231183,1,2.24,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -4/30/1992,118,dominicks,5504,8.61323038,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/30/1992,118,minute.maid,4096,8.317766167,0,2.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/30/1992,118,tropicana,23872,10.0804615,1,2.39,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -4/30/1992,119,dominicks,4544,8.42156296,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/30/1992,119,minute.maid,3072,8.030084094,0,2.66,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/30/1992,119,tropicana,26048,10.16769627,1,2.24,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -4/30/1992,121,dominicks,5952,8.691482577,0,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/30/1992,121,minute.maid,4992,8.51559191,0,2.66,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/30/1992,121,tropicana,38208,10.5508002,1,2.24,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -4/30/1992,122,dominicks,15424,9.643680017,0,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/30/1992,122,minute.maid,6272,8.743850562,0,2.25,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/30/1992,122,tropicana,33984,10.43364511,1,1.94,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -4/30/1992,123,dominicks,12288,9.416378455,0,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/30/1992,123,minute.maid,4224,8.348537825,0,2.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/30/1992,123,tropicana,23296,10.05603695,1,2.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -4/30/1992,124,dominicks,22208,10.00820786,0,1.98,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/30/1992,124,minute.maid,4032,8.30201781,0,2.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/30/1992,124,tropicana,22400,10.01681624,1,2.39,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -4/30/1992,126,dominicks,5952,8.691482577,0,1.89,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/30/1992,126,minute.maid,5760,8.658692754,0,2.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/30/1992,126,tropicana,36672,10.5097688,1,2.24,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -4/30/1992,128,dominicks,14528,9.583833101,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/30/1992,128,minute.maid,4416,8.392989588,0,2.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/30/1992,128,tropicana,62208,11.03823889,1,2.39,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -4/30/1992,129,dominicks,5504,8.61323038,0,1.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/30/1992,129,minute.maid,3392,8.129174997,0,2.66,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/30/1992,129,tropicana,36416,10.50276352,1,2.24,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -4/30/1992,130,dominicks,37312,10.52707027,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/30/1992,130,minute.maid,4544,8.42156296,0,2.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/30/1992,130,tropicana,33216,10.41078697,1,2.39,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -4/30/1992,131,dominicks,4352,8.378390789,0,1.89,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/30/1992,131,minute.maid,3840,8.253227646,0,2.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/30/1992,131,tropicana,32960,10.40304998,1,2.24,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -4/30/1992,132,dominicks,19200,9.862665558,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/30/1992,132,minute.maid,5504,8.61323038,0,2.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/30/1992,132,tropicana,37888,10.54238972,1,2.16,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -4/30/1992,134,dominicks,6784,8.822322178,0,1.89,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/30/1992,134,minute.maid,2496,7.82244473,0,2.66,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/30/1992,134,tropicana,13760,9.529521112,1,2.24,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -4/30/1992,137,dominicks,7232,8.886270902,0,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/30/1992,137,minute.maid,8448,9.041685006,0,2.79,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -4/30/1992,137,tropicana,80384,11.29457043,1,2.39,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/7/1992,2,dominicks,9600,9.169518378,0,2,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/7/1992,2,minute.maid,6336,8.754002934,0,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/7/1992,2,tropicana,8320,9.026417534,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/7/1992,5,dominicks,5952,8.691482577,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/7/1992,5,minute.maid,5952,8.691482577,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/7/1992,5,tropicana,9280,9.135616826,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/7/1992,8,dominicks,18752,9.839055692,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/7/1992,8,minute.maid,6272,8.743850562,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/7/1992,8,tropicana,18048,9.800790154,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/7/1992,9,dominicks,4736,8.462948177,0,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/7/1992,9,minute.maid,4224,8.348537825,0,2.66,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/7/1992,9,tropicana,7424,8.912473275,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/7/1992,12,dominicks,8768,9.078864009,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/7/1992,12,minute.maid,7360,8.903815212,0,2.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/7/1992,12,tropicana,8448,9.041685006,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/7/1992,14,dominicks,6528,8.783855897,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/7/1992,14,minute.maid,5568,8.624791202,0,2.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/7/1992,14,tropicana,13824,9.534161491,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/7/1992,18,dominicks,9984,9.208739091,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/7/1992,18,minute.maid,11456,9.346268889,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/7/1992,18,tropicana,8768,9.078864009,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/7/1992,21,dominicks,16768,9.727227587,0,1.63,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/7/1992,21,minute.maid,3392,8.129174997,0,2.25,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/7/1992,21,tropicana,3968,8.286017468,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/7/1992,32,dominicks,10432,9.252633284,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/7/1992,32,minute.maid,7808,8.962904128,0,2.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/7/1992,32,tropicana,15040,9.618468598,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/7/1992,33,dominicks,9856,9.195835686,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/7/1992,33,minute.maid,6080,8.712759975,0,2.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/7/1992,33,tropicana,11712,9.368369236,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/7/1992,40,dominicks,13952,9.543378146,0,1.64,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/7/1992,40,minute.maid,5568,8.624791202,0,2.25,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/7/1992,40,tropicana,6272,8.743850562,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/7/1992,44,dominicks,9216,9.128696383,0,1.93,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/7/1992,44,minute.maid,6976,8.850230966,0,2.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/7/1992,44,tropicana,11840,9.379238908,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/7/1992,45,dominicks,5184,8.553332238,0,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/7/1992,45,minute.maid,3456,8.14786713,0,2.66,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/7/1992,45,tropicana,6016,8.702177866,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/7/1992,47,dominicks,7872,8.971067439,0,1.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/7/1992,47,minute.maid,4032,8.30201781,0,2.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/7/1992,47,tropicana,3968,8.286017468,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/7/1992,48,dominicks,3776,8.236420527,0,1.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/7/1992,48,minute.maid,5568,8.624791202,0,2.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/7/1992,48,tropicana,5568,8.624791202,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/7/1992,49,dominicks,4544,8.42156296,0,1.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/7/1992,49,minute.maid,2624,7.87245515,0,2.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/7/1992,49,tropicana,3584,8.184234774,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/7/1992,50,dominicks,5504,8.61323038,0,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/7/1992,50,minute.maid,3136,8.050703382,0,2.66,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/7/1992,50,tropicana,3712,8.219326094,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/7/1992,51,dominicks,6656,8.803273983,0,1.89,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/7/1992,51,minute.maid,4480,8.407378325,0,2.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/7/1992,51,tropicana,3712,8.219326094,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/7/1992,52,dominicks,3776,8.236420527,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/7/1992,52,minute.maid,7552,8.929567708,0,2.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/7/1992,52,tropicana,11712,9.368369236,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/7/1992,53,dominicks,6848,8.831711918,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/7/1992,53,minute.maid,8320,9.026417534,0,2.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/7/1992,53,tropicana,11840,9.379238908,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/7/1992,54,dominicks,6400,8.764053269,0,1.92,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/7/1992,54,minute.maid,3776,8.236420527,0,2.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/7/1992,54,tropicana,6848,8.831711918,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/7/1992,56,dominicks,4608,8.435549202,0,1.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/7/1992,56,minute.maid,4352,8.378390789,0,2.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/7/1992,56,tropicana,6912,8.841014311,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/7/1992,59,dominicks,7808,8.962904128,0,1.61,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/7/1992,59,minute.maid,2624,7.87245515,0,2.25,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/7/1992,59,tropicana,5504,8.61323038,0,2.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/7/1992,62,dominicks,4416,8.392989588,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/7/1992,62,minute.maid,8128,9.00307017,0,2.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/7/1992,62,tropicana,12608,9.442086812,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/7/1992,64,dominicks,5824,8.66974259,0,1.9,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/7/1992,64,minute.maid,2560,7.847762538,0,2.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/7/1992,64,tropicana,2496,7.82244473,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/7/1992,67,dominicks,23744,10.07508515,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/7/1992,67,minute.maid,5056,8.528330936,0,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/7/1992,67,tropicana,5184,8.553332238,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/7/1992,68,dominicks,10304,9.240287448,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/7/1992,68,minute.maid,7872,8.971067439,0,2.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/7/1992,68,tropicana,5504,8.61323038,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/7/1992,70,dominicks,14656,9.592605087,0,1.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/7/1992,70,minute.maid,8256,9.018695488,0,2.25,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/7/1992,70,tropicana,10368,9.246479419,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/7/1992,71,dominicks,6784,8.822322178,0,2,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/7/1992,71,minute.maid,3392,8.129174997,0,2.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/7/1992,71,tropicana,5504,8.61323038,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/7/1992,72,dominicks,6272,8.743850562,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/7/1992,72,minute.maid,4480,8.407378325,0,2.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/7/1992,72,tropicana,9408,9.14931567,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/7/1992,73,dominicks,18304,9.814874894,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/7/1992,73,minute.maid,9920,9.2023082,0,2.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/7/1992,73,tropicana,9280,9.135616826,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/7/1992,75,dominicks,9152,9.121727714,0,2.01,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/7/1992,75,minute.maid,6912,8.841014311,0,2.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/7/1992,75,tropicana,7168,8.877381955,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/7/1992,76,dominicks,9280,9.135616826,0,1.89,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/7/1992,76,minute.maid,4864,8.489616424,0,2.66,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/7/1992,76,tropicana,6464,8.7740036,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/7/1992,77,dominicks,5376,8.589699882,0,1.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/7/1992,77,minute.maid,3328,8.110126802,0,2.25,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/7/1992,77,tropicana,9728,9.182763604,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/7/1992,78,dominicks,10752,9.282847063,0,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/7/1992,78,minute.maid,6464,8.7740036,0,2.25,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/7/1992,78,tropicana,6080,8.712759975,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/7/1992,80,dominicks,8320,9.026417534,0,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/7/1992,80,minute.maid,7552,8.929567708,0,2.25,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/7/1992,80,tropicana,10368,9.246479419,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/7/1992,81,dominicks,7936,8.979164649,0,1.89,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/7/1992,81,minute.maid,5696,8.647519453,0,2.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/7/1992,81,tropicana,8000,8.987196821,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/7/1992,83,dominicks,46080,10.7381343,0,1.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/7/1992,83,minute.maid,4288,8.363575703,0,2.25,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/7/1992,83,tropicana,6144,8.723231275,0,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/7/1992,84,dominicks,7168,8.877381955,0,1.89,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/7/1992,84,minute.maid,6784,8.822322178,0,2.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/7/1992,84,tropicana,5120,8.540909718,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/7/1992,86,dominicks,9664,9.17616292,0,1.89,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/7/1992,86,minute.maid,2432,7.796469243,0,2.66,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/7/1992,86,tropicana,6528,8.783855897,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/7/1992,88,dominicks,6976,8.850230966,0,1.89,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/7/1992,88,minute.maid,5184,8.553332238,0,2.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/7/1992,88,tropicana,4672,8.449342525,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/7/1992,89,dominicks,15424,9.643680017,0,1.72,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/7/1992,89,minute.maid,3904,8.269756948,0,2.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/7/1992,89,tropicana,3712,8.219326094,0,2.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/7/1992,90,dominicks,5120,8.540909718,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/7/1992,90,minute.maid,3008,8.009030685,0,2.79,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/7/1992,90,tropicana,2496,7.82244473,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/7/1992,91,dominicks,5376,8.589699882,0,1.89,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/7/1992,91,minute.maid,3072,8.030084094,0,2.66,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/7/1992,91,tropicana,3648,8.201934351,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/7/1992,92,dominicks,7680,8.946374826,0,1.89,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/7/1992,92,minute.maid,3264,8.090708716,0,2.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/7/1992,92,tropicana,3520,8.166216269,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/7/1992,93,dominicks,6976,8.850230966,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/7/1992,93,minute.maid,6720,8.812843434,0,2.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/7/1992,93,tropicana,8640,9.064157862,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/7/1992,94,dominicks,9536,9.162829389,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/7/1992,94,minute.maid,5952,8.691482577,0,2.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/7/1992,94,tropicana,5696,8.647519453,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/7/1992,95,dominicks,10240,9.234056899,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/7/1992,95,minute.maid,4608,8.435549202,0,2.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/7/1992,95,tropicana,5312,8.577723691,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/7/1992,97,dominicks,3840,8.253227646,0,1.89,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/7/1992,97,minute.maid,1728,7.454719949,0,2.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/7/1992,97,tropicana,2624,7.87245515,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/7/1992,98,dominicks,13184,9.486759252,0,1.91,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/7/1992,98,minute.maid,3776,8.236420527,0,2.66,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/7/1992,98,tropicana,7680,8.946374826,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 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-5/7/1992,103,minute.maid,4352,8.378390789,0,2.39,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/7/1992,103,tropicana,3264,8.090708716,0,2.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/7/1992,104,dominicks,3520,8.166216269,0,1.89,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/7/1992,104,minute.maid,2752,7.920083199,0,2.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/7/1992,104,tropicana,3904,8.269756948,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 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-5/7/1992,106,tropicana,2752,7.920083199,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/7/1992,107,dominicks,9216,9.128696383,0,1.89,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/7/1992,107,minute.maid,7040,8.859363449,0,2.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/7/1992,107,tropicana,11328,9.335032816,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/7/1992,109,dominicks,4352,8.378390789,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/7/1992,109,minute.maid,9600,9.169518378,0,2.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/7/1992,109,tropicana,14144,9.557045785,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/7/1992,110,dominicks,5376,8.589699882,0,1.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/7/1992,110,minute.maid,3072,8.030084094,0,2.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/7/1992,110,tropicana,3520,8.166216269,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/7/1992,111,dominicks,10688,9.276876896,0,2.01,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/7/1992,111,minute.maid,3520,8.166216269,0,2.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/7/1992,111,tropicana,3776,8.236420527,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/7/1992,112,dominicks,13120,9.481893063,0,1.72,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/7/1992,112,minute.maid,7936,8.979164649,0,2.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/7/1992,112,tropicana,13824,9.534161491,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/7/1992,113,dominicks,12672,9.447150114,0,1.89,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/7/1992,113,minute.maid,5248,8.565602331,0,2.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/7/1992,113,tropicana,9088,9.114710141,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/7/1992,114,dominicks,18112,9.804329981,0,1.92,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/7/1992,114,minute.maid,5376,8.589699882,0,2.66,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/7/1992,114,tropicana,7296,8.895081532,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/7/1992,115,dominicks,3776,8.236420527,0,1.92,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/7/1992,115,minute.maid,6080,8.712759975,0,2.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/7/1992,115,tropicana,12864,9.462187991,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/7/1992,116,dominicks,8896,9.093357017,0,1.89,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/7/1992,116,minute.maid,4480,8.407378325,0,2.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/7/1992,116,tropicana,6080,8.712759975,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/7/1992,117,dominicks,8512,9.049232212,0,1.89,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/7/1992,117,minute.maid,3584,8.184234774,0,2.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/7/1992,117,tropicana,4736,8.462948177,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/7/1992,118,dominicks,5120,8.540909718,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/7/1992,118,minute.maid,6528,8.783855897,0,2.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/7/1992,118,tropicana,5568,8.624791202,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/7/1992,119,dominicks,6336,8.754002934,0,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/7/1992,119,minute.maid,4416,8.392989588,0,2.66,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/7/1992,119,tropicana,7104,8.868413285,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/7/1992,121,dominicks,6016,8.702177866,0,1.91,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/7/1992,121,minute.maid,5632,8.636219898,0,2.66,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/7/1992,121,tropicana,10880,9.29468152,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/7/1992,122,dominicks,16960,9.738612909,0,1.61,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/7/1992,122,minute.maid,7616,8.938006577,0,2.25,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/7/1992,122,tropicana,8640,9.064157862,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/7/1992,123,dominicks,9024,9.107642974,0,2.02,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/7/1992,123,minute.maid,6656,8.803273983,0,2.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/7/1992,123,tropicana,6272,8.743850562,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/7/1992,124,dominicks,19712,9.888982866,0,2.01,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/7/1992,124,minute.maid,6144,8.723231275,0,2.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/7/1992,124,tropicana,5504,8.61323038,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/7/1992,126,dominicks,7296,8.895081532,0,1.92,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/7/1992,126,minute.maid,5440,8.60153434,0,2.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/7/1992,126,tropicana,12608,9.442086812,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/7/1992,128,dominicks,10816,9.288781798,0,2.02,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/7/1992,128,minute.maid,7040,8.859363449,0,2.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/7/1992,128,tropicana,12032,9.395325046,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/7/1992,129,dominicks,8640,9.064157862,0,1.92,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/7/1992,129,minute.maid,5952,8.691482577,0,2.66,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/7/1992,129,tropicana,8832,9.086136769,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/7/1992,130,dominicks,14272,9.566054855,0,2.01,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/7/1992,130,minute.maid,4160,8.333270353,0,2.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/7/1992,130,tropicana,4864,8.489616424,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/7/1992,131,dominicks,5376,8.589699882,0,1.91,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/7/1992,131,minute.maid,6336,8.754002934,0,2.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/7/1992,131,tropicana,7296,8.895081532,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/7/1992,132,dominicks,20736,9.939626599,0,1.71,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/7/1992,132,minute.maid,8512,9.049232212,0,2.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/7/1992,132,tropicana,7744,8.954673629,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/7/1992,134,dominicks,5376,8.589699882,0,1.91,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/7/1992,134,minute.maid,3200,8.070906089,0,2.66,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/7/1992,134,tropicana,3840,8.253227646,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/7/1992,137,dominicks,9472,9.156095357,0,2.01,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/7/1992,137,minute.maid,10688,9.276876896,0,2.79,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/7/1992,137,tropicana,30464,10.32430094,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/14/1992,2,dominicks,4800,8.476371197,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/14/1992,2,minute.maid,5440,8.60153434,0,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/14/1992,2,tropicana,6912,8.841014311,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/14/1992,5,dominicks,4160,8.333270353,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/14/1992,5,minute.maid,6528,8.783855897,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/14/1992,5,tropicana,7680,8.946374826,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/14/1992,8,dominicks,20160,9.911455722,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/14/1992,8,minute.maid,6400,8.764053269,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/14/1992,8,tropicana,12864,9.462187991,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/14/1992,9,dominicks,2624,7.87245515,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/14/1992,9,minute.maid,3392,8.129174997,0,2.66,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/14/1992,9,tropicana,8896,9.093357017,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/14/1992,12,dominicks,4288,8.363575703,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/14/1992,12,minute.maid,6720,8.812843434,0,2.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/14/1992,12,tropicana,8192,9.010913347,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/14/1992,14,dominicks,4928,8.502688505,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/14/1992,14,minute.maid,6976,8.850230966,0,2.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/14/1992,14,tropicana,14080,9.55251063,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/14/1992,18,dominicks,17856,9.790094865,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/14/1992,18,minute.maid,9536,9.162829389,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/14/1992,18,tropicana,9216,9.128696383,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/14/1992,21,dominicks,11904,9.384629757,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/14/1992,21,minute.maid,3136,8.050703382,0,2.25,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/14/1992,21,tropicana,5184,8.553332238,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/14/1992,28,dominicks,7232,8.886270902,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/14/1992,28,minute.maid,7040,8.859363449,0,2.66,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/14/1992,28,tropicana,9344,9.142489705,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/14/1992,32,dominicks,6272,8.743850562,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/14/1992,32,minute.maid,8192,9.010913347,0,2.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/14/1992,32,tropicana,11520,9.351839934,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/14/1992,33,dominicks,5632,8.636219898,0,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/14/1992,33,minute.maid,4928,8.502688505,0,2.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/14/1992,33,tropicana,11904,9.384629757,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/14/1992,40,dominicks,11072,9.312174678,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/14/1992,40,minute.maid,4736,8.462948177,0,2.25,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/14/1992,40,tropicana,6272,8.743850562,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/14/1992,44,dominicks,5696,8.647519453,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/14/1992,44,minute.maid,6528,8.783855897,0,2.66,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/14/1992,44,tropicana,12096,9.400630098,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/14/1992,45,dominicks,3584,8.184234774,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/14/1992,45,minute.maid,3904,8.269756948,0,2.66,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/14/1992,45,tropicana,6336,8.754002934,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/14/1992,47,dominicks,6208,8.733594062,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/14/1992,47,minute.maid,3776,8.236420527,0,2.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/14/1992,47,tropicana,4224,8.348537825,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/14/1992,48,dominicks,4032,8.30201781,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/14/1992,48,minute.maid,5568,8.624791202,0,2.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/14/1992,48,tropicana,6080,8.712759975,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/14/1992,49,dominicks,3392,8.129174997,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/14/1992,49,minute.maid,3072,8.030084094,0,2.66,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/14/1992,49,tropicana,3648,8.201934351,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/14/1992,50,dominicks,4864,8.489616424,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/14/1992,50,minute.maid,3072,8.030084094,0,2.66,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/14/1992,50,tropicana,3840,8.253227646,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/14/1992,51,dominicks,3456,8.14786713,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/14/1992,51,minute.maid,3648,8.201934351,0,2.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/14/1992,51,tropicana,4608,8.435549202,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/14/1992,52,dominicks,2816,7.943072717,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/14/1992,52,minute.maid,7232,8.886270902,0,2.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/14/1992,52,tropicana,11328,9.335032816,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/14/1992,53,dominicks,7808,8.962904128,0,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/14/1992,53,minute.maid,6656,8.803273983,0,2.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/14/1992,53,tropicana,10944,9.30054664,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/14/1992,54,dominicks,4672,8.449342525,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/14/1992,54,minute.maid,4160,8.333270353,0,2.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/14/1992,54,tropicana,5568,8.624791202,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/14/1992,56,dominicks,4672,8.449342525,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/14/1992,56,minute.maid,2816,7.943072717,0,2.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/14/1992,56,tropicana,6784,8.822322178,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/14/1992,59,dominicks,6400,8.764053269,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/14/1992,59,minute.maid,4608,8.435549202,0,2.25,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/14/1992,59,tropicana,6336,8.754002934,0,2.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/14/1992,62,dominicks,2688,7.896552702,0,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/14/1992,62,minute.maid,7168,8.877381955,0,2.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/14/1992,62,tropicana,13120,9.481893063,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/14/1992,64,dominicks,3584,8.184234774,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/14/1992,64,minute.maid,2688,7.896552702,0,2.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/14/1992,64,tropicana,3328,8.110126802,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/14/1992,67,dominicks,23040,10.04498712,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/14/1992,67,minute.maid,5120,8.540909718,0,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/14/1992,67,tropicana,5952,8.691482577,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/14/1992,68,dominicks,6464,8.7740036,0,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/14/1992,68,minute.maid,6528,8.783855897,0,2.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/14/1992,68,tropicana,6464,8.7740036,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/14/1992,70,dominicks,15040,9.618468598,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/14/1992,70,minute.maid,7552,8.929567708,0,2.25,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/14/1992,70,tropicana,8896,9.093357017,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/14/1992,71,dominicks,3392,8.129174997,0,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/14/1992,71,minute.maid,2880,7.965545573,0,2.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/14/1992,71,tropicana,6656,8.803273983,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/14/1992,72,dominicks,4096,8.317766167,0,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/14/1992,72,minute.maid,5248,8.565602331,0,2.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/14/1992,72,tropicana,9024,9.107642974,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/14/1992,73,dominicks,26688,10.19196931,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/14/1992,73,minute.maid,8896,9.093357017,0,2.39,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/14/1992,73,tropicana,8448,9.041685006,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/14/1992,74,dominicks,12288,9.416378455,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/14/1992,74,minute.maid,10880,9.29468152,0,2.66,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/14/1992,74,tropicana,16640,9.719564714,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/14/1992,75,dominicks,3968,8.286017468,0,2.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/14/1992,75,minute.maid,4928,8.502688505,0,2.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/14/1992,75,tropicana,7168,8.877381955,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/14/1992,76,dominicks,6144,8.723231275,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/14/1992,76,minute.maid,7232,8.886270902,0,2.66,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/14/1992,76,tropicana,7296,8.895081532,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/14/1992,77,dominicks,7936,8.979164649,0,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/14/1992,77,minute.maid,4416,8.392989588,0,2.25,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/14/1992,77,tropicana,8576,9.056722883,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/14/1992,78,dominicks,10560,9.264828557,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/14/1992,78,minute.maid,5696,8.647519453,0,2.25,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/14/1992,78,tropicana,8896,9.093357017,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/14/1992,80,dominicks,9664,9.17616292,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/14/1992,80,minute.maid,7872,8.971067439,0,2.25,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/14/1992,80,tropicana,11904,9.384629757,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/14/1992,81,dominicks,4928,8.502688505,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/14/1992,81,minute.maid,5760,8.658692754,0,2.66,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/14/1992,81,tropicana,10048,9.215128889,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -5/14/1992,83,dominicks,52224,10.86329744,0,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/14/1992,83,minute.maid,5504,8.61323038,0,2.25,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/14/1992,83,tropicana,9024,9.107642974,0,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -5/14/1992,84,dominicks,6784,8.822322178,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/14/1992,84,minute.maid,4864,8.489616424,0,2.66,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/14/1992,84,tropicana,5888,8.68067166,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -5/14/1992,86,dominicks,6656,8.803273983,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/14/1992,86,minute.maid,3840,8.253227646,0,2.66,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/14/1992,86,tropicana,7168,8.877381955,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -5/14/1992,88,dominicks,4352,8.378390789,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/14/1992,88,minute.maid,3712,8.219326094,0,2.66,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/14/1992,88,tropicana,4288,8.363575703,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -5/14/1992,89,dominicks,13312,9.496421163,0,1.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/14/1992,89,minute.maid,5440,8.60153434,0,2.39,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/14/1992,89,tropicana,3904,8.269756948,0,2.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -5/14/1992,90,dominicks,1152,7.049254841,0,2.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/14/1992,90,minute.maid,3456,8.14786713,0,2.79,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/14/1992,90,tropicana,2048,7.624618986,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -5/14/1992,91,dominicks,4672,8.449342525,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/14/1992,91,minute.maid,3456,8.14786713,0,2.66,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/14/1992,91,tropicana,5440,8.60153434,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -5/14/1992,92,dominicks,6656,8.803273983,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/14/1992,92,minute.maid,3712,8.219326094,0,2.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/14/1992,92,tropicana,2688,7.896552702,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/14/1992,93,dominicks,4928,8.502688505,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/14/1992,93,minute.maid,6336,8.754002934,0,2.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/14/1992,93,tropicana,8448,9.041685006,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/14/1992,94,dominicks,8448,9.041685006,0,1.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/14/1992,94,minute.maid,6656,8.803273983,0,2.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/14/1992,94,tropicana,6720,8.812843434,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/14/1992,95,dominicks,2176,7.685243608,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/14/1992,95,minute.maid,231808,12.35366472,0,1.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/14/1992,95,tropicana,4672,8.449342525,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/14/1992,97,dominicks,2816,7.943072717,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/14/1992,97,minute.maid,2368,7.769800996,0,2.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/14/1992,97,tropicana,2176,7.685243608,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/14/1992,98,dominicks,8000,8.987196821,0,1.98,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/14/1992,98,minute.maid,4096,8.317766167,0,2.66,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/14/1992,98,tropicana,7488,8.921057018,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/14/1992,100,dominicks,7552,8.929567708,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/14/1992,100,minute.maid,5056,8.528330936,0,2.66,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/14/1992,100,tropicana,8832,9.086136769,0,2.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/14/1992,101,dominicks,11328,9.335032816,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/14/1992,101,minute.maid,5760,8.658692754,0,2.66,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/14/1992,101,tropicana,8256,9.018695488,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/14/1992,102,dominicks,25600,10.15034763,0,1.79,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/14/1992,102,minute.maid,7168,8.877381955,0,2.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/14/1992,102,tropicana,10176,9.227787286,0,2.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/14/1992,104,dominicks,7296,8.895081532,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/14/1992,104,minute.maid,4480,8.407378325,0,2.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/14/1992,104,tropicana,6656,8.803273983,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/14/1992,105,dominicks,6720,8.812843434,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/14/1992,105,minute.maid,3520,8.166216269,0,2.65,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/14/1992,105,tropicana,4480,8.407378325,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/14/1992,106,dominicks,4160,8.333270353,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/14/1992,106,minute.maid,4544,8.42156296,0,2.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/14/1992,106,tropicana,2304,7.742402022,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/14/1992,107,dominicks,6592,8.793612072,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/14/1992,107,minute.maid,6720,8.812843434,0,2.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/14/1992,107,tropicana,13824,9.534161491,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/14/1992,109,dominicks,2176,7.685243608,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/14/1992,109,minute.maid,11328,9.335032816,0,2.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/14/1992,109,tropicana,15040,9.618468598,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/14/1992,110,dominicks,3520,8.166216269,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/14/1992,110,minute.maid,3712,8.219326094,0,2.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/14/1992,110,tropicana,4352,8.378390789,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/14/1992,111,dominicks,5376,8.589699882,0,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/14/1992,111,minute.maid,5760,8.658692754,0,2.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/14/1992,111,tropicana,4416,8.392989588,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/14/1992,112,dominicks,10752,9.282847063,0,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/14/1992,112,minute.maid,9024,9.107642974,0,2.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/14/1992,112,tropicana,11136,9.317938383,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/14/1992,113,dominicks,10624,9.270870872,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/14/1992,113,minute.maid,5568,8.624791202,0,2.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/14/1992,113,tropicana,9920,9.2023082,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/14/1992,114,dominicks,8832,9.086136769,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/14/1992,114,minute.maid,3968,8.286017468,0,2.66,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/14/1992,114,tropicana,6336,8.754002934,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/14/1992,115,dominicks,2816,7.943072717,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/14/1992,115,minute.maid,5632,8.636219898,0,2.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/14/1992,115,tropicana,7296,8.895081532,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/14/1992,116,dominicks,5952,8.691482577,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/14/1992,116,minute.maid,3840,8.253227646,0,2.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/14/1992,116,tropicana,10304,9.240287448,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/14/1992,117,dominicks,5696,8.647519453,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/14/1992,117,minute.maid,3456,8.14786713,0,2.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/14/1992,117,tropicana,4992,8.51559191,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/14/1992,118,dominicks,2560,7.847762538,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/14/1992,118,minute.maid,4864,8.489616424,0,2.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/14/1992,118,tropicana,5504,8.61323038,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/14/1992,119,dominicks,5120,8.540909718,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/14/1992,119,minute.maid,4032,8.30201781,0,2.66,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/14/1992,119,tropicana,6784,8.822322178,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/14/1992,121,dominicks,4096,8.317766167,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/14/1992,121,minute.maid,6336,8.754002934,0,2.66,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/14/1992,121,tropicana,9344,9.142489705,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/14/1992,122,dominicks,14848,9.605620455,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/14/1992,122,minute.maid,8192,9.010913347,0,2.25,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/14/1992,122,tropicana,9920,9.2023082,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/14/1992,123,dominicks,3776,8.236420527,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/14/1992,123,minute.maid,6912,8.841014311,0,2.78,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/14/1992,123,tropicana,5696,8.647519453,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/14/1992,124,dominicks,7552,8.929567708,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/14/1992,124,minute.maid,4800,8.476371197,0,2.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/14/1992,124,tropicana,7424,8.912473275,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/14/1992,126,dominicks,5120,8.540909718,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/14/1992,126,minute.maid,5632,8.636219898,0,2.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/14/1992,126,tropicana,8896,9.093357017,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/14/1992,128,dominicks,8320,9.026417534,0,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/14/1992,128,minute.maid,8768,9.078864009,0,2.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/14/1992,128,tropicana,12864,9.462187991,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/14/1992,129,dominicks,4864,8.489616424,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/14/1992,129,minute.maid,4736,8.462948177,0,2.66,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/14/1992,129,tropicana,9408,9.14931567,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/14/1992,130,dominicks,8704,9.071537969,0,2.07,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/14/1992,130,minute.maid,5376,8.589699882,0,2.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/14/1992,130,tropicana,3712,8.219326094,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/14/1992,131,dominicks,7104,8.868413285,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/14/1992,131,minute.maid,6464,8.7740036,0,2.66,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/14/1992,131,tropicana,5952,8.691482577,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/14/1992,132,dominicks,18368,9.818365299,0,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/14/1992,132,minute.maid,9408,9.14931567,0,2.39,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/14/1992,132,tropicana,7552,8.929567708,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/14/1992,134,dominicks,3584,8.184234774,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/14/1992,134,minute.maid,2944,7.98752448,0,2.66,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/14/1992,134,tropicana,3328,8.110126802,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/14/1992,137,dominicks,5952,8.691482577,0,2.09,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/14/1992,137,minute.maid,9728,9.182763604,0,2.79,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/14/1992,137,tropicana,27904,10.23652533,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/21/1992,2,dominicks,9664,9.17616292,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/21/1992,2,minute.maid,22400,10.01681624,1,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/21/1992,2,tropicana,6976,8.850230966,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/21/1992,5,dominicks,23488,10.06424493,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/21/1992,5,minute.maid,30656,10.33058368,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/21/1992,5,tropicana,8704,9.071537969,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/21/1992,8,dominicks,18688,9.835636886,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/21/1992,8,minute.maid,54592,10.90764263,1,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/21/1992,8,tropicana,7168,8.877381955,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/21/1992,9,dominicks,6080,8.712759975,0,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/21/1992,9,minute.maid,43392,10.67803037,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/21/1992,9,tropicana,7040,8.859363449,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -5/21/1992,12,dominicks,26240,10.17504024,0,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/21/1992,12,minute.maid,43072,10.67062841,1,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/21/1992,12,tropicana,7296,8.895081532,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/21/1992,14,dominicks,14656,9.592605087,0,1.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/21/1992,14,minute.maid,25664,10.15284451,1,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/21/1992,14,tropicana,11136,9.317938383,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -5/21/1992,18,dominicks,29568,10.29444797,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/21/1992,18,minute.maid,29568,10.29444797,1,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/21/1992,18,tropicana,8000,8.987196821,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/21/1992,21,dominicks,10816,9.288781798,0,1.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/21/1992,21,minute.maid,37632,10.53561003,1,1.68,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/21/1992,21,tropicana,4032,8.30201781,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/21/1992,28,dominicks,4672,8.449342525,0,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/21/1992,28,minute.maid,12800,9.45720045,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/21/1992,28,tropicana,4224,8.348537825,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -5/21/1992,32,dominicks,21632,9.981928979,0,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/21/1992,32,minute.maid,43328,10.67655436,1,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/21/1992,32,tropicana,10816,9.288781798,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/21/1992,33,dominicks,20992,9.951896692,0,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/21/1992,33,minute.maid,19328,9.869310101,1,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/21/1992,33,tropicana,6272,8.743850562,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/21/1992,40,dominicks,6400,8.764053269,0,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/21/1992,40,minute.maid,43392,10.67803037,1,1.68,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/21/1992,40,tropicana,5440,8.60153434,0,2.59,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/21/1992,44,dominicks,24384,10.10168246,0,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/21/1992,44,minute.maid,32192,10.37947325,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/21/1992,44,tropicana,10368,9.246479419,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/21/1992,45,dominicks,10560,9.264828557,0,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/21/1992,45,minute.maid,12736,9.452187908,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/21/1992,45,tropicana,5248,8.565602331,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/21/1992,47,dominicks,10368,9.246479419,0,1.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/21/1992,47,minute.maid,19904,9.898675996,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/21/1992,47,tropicana,3776,8.236420527,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/21/1992,48,dominicks,6592,8.793612072,0,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/21/1992,48,minute.maid,15360,9.639522007,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/21/1992,48,tropicana,5120,8.540909718,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/21/1992,49,dominicks,6848,8.831711918,0,1.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/21/1992,49,minute.maid,15616,9.656051309,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/21/1992,49,tropicana,3648,8.201934351,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -5/21/1992,50,dominicks,12032,9.395325046,0,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/21/1992,50,minute.maid,12928,9.467150781,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/21/1992,50,tropicana,3712,8.219326094,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/21/1992,51,dominicks,11392,9.340666634,0,1.69,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/21/1992,51,minute.maid,21248,9.964018052,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/21/1992,51,tropicana,3968,8.286017468,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/21/1992,52,dominicks,7488,8.921057018,0,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/21/1992,52,minute.maid,33920,10.43176009,1,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/21/1992,52,tropicana,10368,9.246479419,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/21/1992,53,dominicks,34816,10.45783233,0,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/21/1992,53,minute.maid,35392,10.47424109,1,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/21/1992,53,tropicana,9472,9.156095357,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/21/1992,54,dominicks,12800,9.45720045,0,1.68,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/21/1992,54,minute.maid,16320,9.700146629,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/21/1992,54,tropicana,5248,8.565602331,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/21/1992,56,dominicks,11712,9.368369236,0,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/21/1992,56,minute.maid,17792,9.786504197,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/21/1992,56,tropicana,5632,8.636219898,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/21/1992,59,dominicks,3520,8.166216269,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/21/1992,59,minute.maid,39552,10.58537154,1,1.68,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/21/1992,59,tropicana,5184,8.553332238,0,2.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/21/1992,62,dominicks,12736,9.452187908,0,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/21/1992,62,minute.maid,23168,10.0505273,1,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/21/1992,62,tropicana,12416,9.426741242,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/21/1992,64,dominicks,6720,8.812843434,0,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/21/1992,64,minute.maid,12864,9.462187991,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/21/1992,64,tropicana,2112,7.655390645,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/21/1992,67,dominicks,21952,9.996613531,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/21/1992,67,minute.maid,18304,9.814874894,1,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/21/1992,67,tropicana,3968,8.286017468,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/21/1992,68,dominicks,20416,9.924074186,0,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/21/1992,68,minute.maid,23488,10.06424493,1,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/21/1992,68,tropicana,4544,8.42156296,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/21/1992,70,dominicks,7680,8.946374826,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/21/1992,70,minute.maid,44480,10.70279493,1,1.68,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/21/1992,70,tropicana,8512,9.049232212,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -5/21/1992,71,dominicks,59968,11.00156637,0,1.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/21/1992,71,minute.maid,21952,9.996613531,1,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/21/1992,71,tropicana,5440,8.60153434,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -5/21/1992,72,dominicks,3968,8.286017468,0,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/21/1992,72,minute.maid,35712,10.48324205,1,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/21/1992,72,tropicana,8960,9.100525506,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -5/21/1992,73,dominicks,24320,10.09905434,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/21/1992,73,minute.maid,43392,10.67803037,1,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/21/1992,73,tropicana,6272,8.743850562,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -5/21/1992,74,dominicks,42048,10.6465671,0,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/21/1992,74,minute.maid,31104,10.34509171,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/21/1992,74,tropicana,8192,9.010913347,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -5/21/1992,75,dominicks,13760,9.529521112,0,1.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/21/1992,75,minute.maid,24384,10.10168246,1,2.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/21/1992,75,tropicana,6080,8.712759975,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -5/21/1992,76,dominicks,20096,9.908276069,0,1.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/21/1992,76,minute.maid,33088,10.40692596,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/21/1992,76,tropicana,6336,8.754002934,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -5/21/1992,77,dominicks,5120,8.540909718,0,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/21/1992,77,minute.maid,26496,10.18474906,1,1.68,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/21/1992,77,tropicana,7808,8.962904128,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -5/21/1992,78,dominicks,10112,9.221478116,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/21/1992,78,minute.maid,37696,10.53730927,1,1.68,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/21/1992,78,tropicana,4928,8.502688505,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 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-5/21/1992,105,dominicks,19648,9.885730831,0,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/21/1992,105,minute.maid,23552,10.06696602,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/21/1992,105,tropicana,3776,8.236420527,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/21/1992,106,dominicks,7872,8.971067439,0,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/21/1992,106,minute.maid,15296,9.635346635,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/21/1992,106,tropicana,1856,7.526178913,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/21/1992,107,dominicks,27264,10.21332243,0,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/21/1992,107,minute.maid,37248,10.52535353,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/21/1992,107,tropicana,11776,9.373818841,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/21/1992,109,dominicks,13376,9.501217335,0,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/21/1992,109,minute.maid,46336,10.74367448,1,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/21/1992,109,tropicana,15168,9.626943225,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/21/1992,110,dominicks,9664,9.17616292,0,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/21/1992,110,minute.maid,21056,9.954940834,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/21/1992,110,tropicana,5120,8.540909718,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/21/1992,111,dominicks,51712,10.85344514,0,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/21/1992,111,minute.maid,54656,10.90881428,1,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/21/1992,111,tropicana,2816,7.943072717,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/21/1992,112,dominicks,8768,9.078864009,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/21/1992,112,minute.maid,48768,10.79482964,1,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/21/1992,112,tropicana,11392,9.340666634,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/21/1992,113,dominicks,17728,9.78290059,0,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/21/1992,113,minute.maid,32768,10.39720771,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/21/1992,113,tropicana,8064,8.99516499,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/21/1992,114,dominicks,22720,10.03100087,0,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/21/1992,114,minute.maid,45568,10.726961,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/21/1992,114,tropicana,5504,8.61323038,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/21/1992,115,dominicks,13184,9.486759252,0,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/21/1992,115,minute.maid,34496,10.44859865,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/21/1992,115,tropicana,8448,9.041685006,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/21/1992,116,dominicks,10880,9.29468152,0,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/21/1992,116,minute.maid,18880,9.84585844,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/21/1992,116,tropicana,5504,8.61323038,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/21/1992,117,dominicks,7680,8.946374826,0,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/21/1992,117,minute.maid,20800,9.942708266,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/21/1992,117,tropicana,4032,8.30201781,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/21/1992,118,dominicks,10816,9.288781798,0,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/21/1992,118,minute.maid,31104,10.34509171,1,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/21/1992,118,tropicana,4352,8.378390789,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/21/1992,119,dominicks,10944,9.30054664,0,1.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/21/1992,119,minute.maid,14208,9.561560465,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/21/1992,119,tropicana,7296,8.895081532,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -5/21/1992,121,dominicks,27584,10.22499117,0,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/21/1992,121,minute.maid,20672,9.936535407,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/21/1992,121,tropicana,10560,9.264828557,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/21/1992,122,dominicks,12544,9.436997743,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/21/1992,122,minute.maid,41536,10.6343158,1,1.68,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/21/1992,122,tropicana,7296,8.895081532,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/21/1992,123,dominicks,17024,9.742379392,0,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/21/1992,123,minute.maid,45632,10.7283645,1,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/21/1992,123,tropicana,4608,8.435549202,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/21/1992,124,dominicks,28352,10.25245285,0,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/21/1992,124,minute.maid,22592,10.02535114,1,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/21/1992,124,tropicana,6784,8.822322178,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/21/1992,126,dominicks,15296,9.635346635,0,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/21/1992,126,minute.maid,36096,10.49393734,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/21/1992,126,tropicana,6848,8.831711918,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/21/1992,128,dominicks,27008,10.2038884,0,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/21/1992,128,minute.maid,48320,10.78560083,1,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/21/1992,128,tropicana,10112,9.221478116,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/21/1992,129,dominicks,2944,7.98752448,0,1.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/21/1992,129,minute.maid,35200,10.46880136,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/21/1992,129,tropicana,8320,9.026417534,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/21/1992,130,dominicks,44160,10.69557468,0,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/21/1992,130,minute.maid,29248,10.28356647,1,2.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/21/1992,130,tropicana,3584,8.184234774,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/21/1992,131,dominicks,11968,9.3899917,0,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/21/1992,131,minute.maid,34048,10.43552657,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/21/1992,131,tropicana,5440,8.60153434,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/21/1992,132,dominicks,31680,10.36344085,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/21/1992,132,minute.maid,51584,10.85096683,1,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/21/1992,132,tropicana,7232,8.886270902,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/21/1992,134,dominicks,12672,9.447150114,0,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/21/1992,134,minute.maid,19776,9.89222436,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/21/1992,134,tropicana,3712,8.219326094,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/21/1992,137,dominicks,41856,10.64199044,0,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/21/1992,137,minute.maid,45760,10.73116563,1,2.09,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/21/1992,137,tropicana,19904,9.898675996,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/28/1992,2,dominicks,45568,10.726961,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/28/1992,2,minute.maid,3968,8.286017468,0,2.84,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/28/1992,2,tropicana,7232,8.886270902,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -5/28/1992,5,dominicks,60480,11.01006801,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/28/1992,5,minute.maid,6656,8.803273983,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/28/1992,5,tropicana,9920,9.2023082,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -5/28/1992,8,dominicks,133824,11.80428078,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/28/1992,8,minute.maid,8128,9.00307017,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/28/1992,8,tropicana,9024,9.107642974,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -5/28/1992,12,dominicks,90304,11.41093704,0,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/28/1992,12,minute.maid,6080,8.712759975,0,2.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/28/1992,12,tropicana,8960,9.100525506,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -5/28/1992,18,dominicks,84672,11.34654025,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/28/1992,18,minute.maid,7744,8.954673629,0,2.39,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/28/1992,18,tropicana,9280,9.135616826,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -5/28/1992,21,dominicks,48512,10.78956647,0,1.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/28/1992,21,minute.maid,2880,7.965545573,0,2.32,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/28/1992,21,tropicana,6272,8.743850562,0,2.62,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -5/28/1992,32,dominicks,94272,11.4539395,0,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/28/1992,32,minute.maid,5824,8.66974259,0,2.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/28/1992,32,tropicana,10048,9.215128889,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -5/28/1992,33,dominicks,54336,10.90294227,0,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/28/1992,33,minute.maid,5568,8.624791202,0,2.83,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/28/1992,33,tropicana,10624,9.270870872,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -5/28/1992,40,dominicks,78016,11.26466921,0,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/28/1992,40,minute.maid,4992,8.51559191,0,2.31,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/28/1992,40,tropicana,5568,8.624791202,0,2.63,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -5/28/1992,44,dominicks,77824,11.26220515,0,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/28/1992,44,minute.maid,5248,8.565602331,0,2.67,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/28/1992,44,tropicana,12224,9.411156511,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -5/28/1992,45,dominicks,28224,10.24792796,0,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/28/1992,45,minute.maid,2624,7.87245515,0,2.66,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/28/1992,45,tropicana,5376,8.589699882,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -5/28/1992,47,dominicks,44224,10.69702291,0,1.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/28/1992,47,minute.maid,3328,8.110126802,0,2.66,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/28/1992,47,tropicana,4352,8.378390789,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -5/28/1992,48,dominicks,21120,9.957975738,0,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/28/1992,48,minute.maid,4224,8.348537825,0,2.66,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/28/1992,48,tropicana,6080,8.712759975,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -5/28/1992,50,dominicks,44672,10.70710219,0,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/28/1992,50,minute.maid,2624,7.87245515,0,2.66,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/28/1992,50,tropicana,5504,8.61323038,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -5/28/1992,51,dominicks,57536,10.96016612,0,1.69,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/28/1992,51,minute.maid,3712,8.219326094,0,2.66,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/28/1992,51,tropicana,5504,8.61323038,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -5/28/1992,52,dominicks,53888,10.8946631,0,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/28/1992,52,minute.maid,7488,8.921057018,0,2.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/28/1992,52,tropicana,14336,9.570529135,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -5/28/1992,53,dominicks,86208,11.36451826,0,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/28/1992,53,minute.maid,4352,8.378390789,0,2.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/28/1992,53,tropicana,9728,9.182763604,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -5/28/1992,54,dominicks,42368,10.65414864,0,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/28/1992,54,minute.maid,2752,7.920083199,0,2.67,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/28/1992,54,tropicana,6592,8.793612072,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -5/28/1992,56,dominicks,39808,10.59182318,0,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/28/1992,56,minute.maid,3200,8.070906089,0,2.66,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/28/1992,56,tropicana,8448,9.041685006,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -5/28/1992,59,dominicks,53888,10.8946631,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/28/1992,59,minute.maid,3136,8.050703382,0,2.26,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/28/1992,59,tropicana,5824,8.66974259,0,2.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -5/28/1992,62,dominicks,29120,10.2791805,0,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/28/1992,62,minute.maid,5568,8.624791202,0,2.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/28/1992,62,tropicana,16256,9.69621735,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -5/28/1992,64,dominicks,40896,10.61878754,0,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/28/1992,64,minute.maid,1856,7.526178913,0,2.66,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/28/1992,64,tropicana,2496,7.82244473,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -5/28/1992,67,dominicks,70848,11.16829202,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/28/1992,67,minute.maid,4544,8.42156296,0,2.39,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/28/1992,67,tropicana,5632,8.636219898,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -5/28/1992,68,dominicks,58240,10.97232768,0,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/28/1992,68,minute.maid,5888,8.68067166,0,2.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -5/28/1992,68,tropicana,5504,8.61323038,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 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-5/28/1992,78,minute.maid,3264,8.090708716,0,2.25,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/28/1992,78,tropicana,7040,8.859363449,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -5/28/1992,80,dominicks,51968,10.85838342,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/28/1992,80,minute.maid,6208,8.733594062,0,2.25,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -5/28/1992,80,tropicana,12032,9.395325046,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 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-5/28/1992,92,minute.maid,4352,8.378390789,0,2.66,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/28/1992,92,tropicana,4608,8.435549202,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -5/28/1992,93,dominicks,48448,10.78824634,0,1.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/28/1992,93,minute.maid,5120,8.540909718,0,2.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/28/1992,93,tropicana,9472,9.156095357,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -5/28/1992,94,dominicks,33024,10.40498985,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/28/1992,94,minute.maid,6592,8.793612072,0,2.39,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/28/1992,94,tropicana,8576,9.056722883,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -5/28/1992,95,dominicks,67904,11.12585022,0,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/28/1992,95,minute.maid,1600,7.377758908,0,2.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/28/1992,95,tropicana,5120,8.540909718,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -5/28/1992,97,dominicks,32256,10.38145935,0,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/28/1992,97,minute.maid,2624,7.87245515,0,2.66,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/28/1992,97,tropicana,2752,7.920083199,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -5/28/1992,98,dominicks,103744,11.54968161,0,1.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/28/1992,98,minute.maid,4096,8.317766167,0,2.66,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/28/1992,98,tropicana,7552,8.929567708,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -5/28/1992,100,dominicks,92800,11.43820192,0,1.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/28/1992,100,minute.maid,3584,8.184234774,0,2.67,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/28/1992,100,tropicana,9408,9.14931567,0,2.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -5/28/1992,101,dominicks,58304,10.97342598,0,1.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/28/1992,101,minute.maid,5056,8.528330936,0,2.66,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/28/1992,101,tropicana,7552,8.929567708,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -5/28/1992,102,dominicks,165376,12.01597695,0,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/28/1992,102,minute.maid,7232,8.886270902,0,2.41,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/28/1992,102,tropicana,8064,8.99516499,0,2.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -5/28/1992,103,dominicks,49728,10.81432343,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/28/1992,103,minute.maid,3968,8.286017468,0,2.41,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/28/1992,103,tropicana,3136,8.050703382,0,2.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -5/28/1992,104,dominicks,45184,10.71849832,0,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/28/1992,104,minute.maid,3392,8.129174997,0,2.66,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/28/1992,104,tropicana,4608,8.435549202,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -5/28/1992,105,dominicks,86784,11.37117755,0,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/28/1992,105,minute.maid,2688,7.896552702,0,2.66,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/28/1992,105,tropicana,4224,8.348537825,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -5/28/1992,106,dominicks,28224,10.24792796,0,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/28/1992,106,minute.maid,2304,7.742402022,0,2.66,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/28/1992,106,tropicana,2176,7.685243608,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -5/28/1992,107,dominicks,80256,11.29297681,0,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/28/1992,107,minute.maid,6528,8.783855897,0,2.66,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/28/1992,107,tropicana,15616,9.656051309,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -5/28/1992,109,dominicks,58880,10.98325675,0,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/28/1992,109,minute.maid,9600,9.169518378,0,2.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/28/1992,109,tropicana,16128,9.688312171,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -5/28/1992,110,dominicks,39872,10.5934296,0,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/28/1992,110,minute.maid,3904,8.269756948,0,2.66,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/28/1992,110,tropicana,5568,8.624791202,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -5/28/1992,111,dominicks,199616,12.2041508,0,1.68,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/28/1992,111,minute.maid,4544,8.42156296,0,2.82,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/28/1992,111,tropicana,3456,8.14786713,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -5/28/1992,112,dominicks,41280,10.6281334,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/28/1992,112,minute.maid,9280,9.135616826,0,2.43,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/28/1992,112,tropicana,14528,9.583833101,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -5/28/1992,113,dominicks,69120,11.1435994,0,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/28/1992,113,minute.maid,4800,8.476371197,0,2.66,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/28/1992,113,tropicana,11008,9.30637756,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -5/28/1992,114,dominicks,108928,11.59844239,0,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/28/1992,114,minute.maid,3776,8.236420527,0,2.66,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/28/1992,114,tropicana,6208,8.733594062,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -5/28/1992,115,dominicks,51136,10.84224403,0,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/28/1992,115,minute.maid,6016,8.702177866,0,2.66,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/28/1992,115,tropicana,9600,9.169518378,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -5/28/1992,116,dominicks,60800,11.01534507,0,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/28/1992,116,minute.maid,3200,8.070906089,0,2.66,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/28/1992,116,tropicana,6080,8.712759975,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -5/28/1992,117,dominicks,40000,10.59663473,0,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/28/1992,117,minute.maid,2752,7.920083199,0,2.66,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/28/1992,117,tropicana,3840,8.253227646,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -5/28/1992,118,dominicks,59456,10.99299182,0,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/28/1992,118,minute.maid,4928,8.502688505,0,2.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/28/1992,118,tropicana,5824,8.66974259,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -5/28/1992,121,dominicks,66880,11.11065525,0,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/28/1992,121,minute.maid,6144,8.723231275,0,2.67,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/28/1992,121,tropicana,13184,9.486759252,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -5/28/1992,122,dominicks,48064,10.78028874,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/28/1992,122,minute.maid,7872,8.971067439,0,2.26,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/28/1992,122,tropicana,14272,9.566054855,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -5/28/1992,123,dominicks,80000,11.28978191,0,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/28/1992,123,minute.maid,4928,8.502688505,0,2.83,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/28/1992,123,tropicana,6144,8.723231275,0,3.16,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -5/28/1992,124,dominicks,92672,11.43682166,0,1.68,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/28/1992,124,minute.maid,1984,7.592870288,0,2.75,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/28/1992,124,tropicana,7680,8.946374826,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -5/28/1992,126,dominicks,74432,11.21764124,0,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/28/1992,126,minute.maid,7104,8.868413285,0,2.66,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/28/1992,126,tropicana,8832,9.086136769,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -5/28/1992,128,dominicks,90368,11.4116455,0,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/28/1992,128,minute.maid,5952,8.691482577,0,2.82,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/28/1992,128,tropicana,10944,9.30054664,0,3.18,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -5/28/1992,129,dominicks,50368,10.82711133,0,1.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/28/1992,129,minute.maid,6272,8.743850562,0,2.66,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/28/1992,129,tropicana,9920,9.2023082,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -5/28/1992,130,dominicks,159360,11.97892107,0,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/28/1992,130,minute.maid,3008,8.009030685,0,2.83,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/28/1992,130,tropicana,5376,8.589699882,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -5/28/1992,131,dominicks,65152,11.08447828,0,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/28/1992,131,minute.maid,4352,8.378390789,0,2.67,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/28/1992,131,tropicana,8320,9.026417534,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -5/28/1992,132,dominicks,92864,11.43889134,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/28/1992,132,minute.maid,5120,8.540909718,0,2.41,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/28/1992,132,tropicana,8128,9.00307017,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -5/28/1992,134,dominicks,47360,10.76553327,0,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/28/1992,134,minute.maid,3072,8.030084094,0,2.67,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/28/1992,134,tropicana,3840,8.253227646,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -5/28/1992,137,dominicks,89600,11.4031106,0,1.68,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/28/1992,137,minute.maid,7040,8.859363449,0,2.81,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -5/28/1992,137,tropicana,23104,10.04776104,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/4/1992,2,dominicks,20992,9.951896692,0,1.74,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/4/1992,2,minute.maid,3264,8.090708716,0,2.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/4/1992,2,tropicana,51520,10.84972536,1,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/4/1992,5,dominicks,20416,9.924074186,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/4/1992,5,minute.maid,4416,8.392989588,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/4/1992,5,tropicana,91968,11.42919597,1,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/4/1992,8,dominicks,63488,11.05860619,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/4/1992,8,minute.maid,4928,8.502688505,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/4/1992,8,tropicana,84992,11.35031241,1,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/4/1992,9,dominicks,20992,9.951896692,0,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/4/1992,9,minute.maid,2368,7.769800996,0,2.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/4/1992,9,tropicana,80320,11.29377394,1,2.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/4/1992,12,dominicks,42112,10.64808802,0,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/4/1992,12,minute.maid,4096,8.317766167,0,2.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/4/1992,12,tropicana,84416,11.34351224,1,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/4/1992,14,dominicks,22528,10.02251426,0,1.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/4/1992,14,minute.maid,4672,8.449342525,0,2.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/4/1992,14,tropicana,76864,11.24979291,1,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/4/1992,18,dominicks,40768,10.61565274,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/4/1992,18,minute.maid,4736,8.462948177,0,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/4/1992,18,tropicana,83008,11.32669227,1,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/4/1992,21,dominicks,19968,9.901886271,0,1.7,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/4/1992,21,minute.maid,2880,7.965545573,0,2.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/4/1992,21,tropicana,50752,10.83470631,1,2.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/4/1992,28,dominicks,9984,9.208739091,0,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/4/1992,28,minute.maid,2048,7.624618986,0,2.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/4/1992,28,tropicana,36096,10.49393734,1,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/4/1992,32,dominicks,37312,10.52707027,0,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/4/1992,32,minute.maid,3968,8.286017468,0,2.89,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/4/1992,32,tropicana,98816,11.50101481,1,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/4/1992,33,dominicks,21248,9.964018052,0,1.76,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/4/1992,33,minute.maid,4032,8.30201781,0,2.89,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/4/1992,33,tropicana,69312,11.14637333,1,2.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/4/1992,40,dominicks,33536,10.42037477,0,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/4/1992,40,minute.maid,3392,8.129174997,0,2.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/4/1992,40,tropicana,65280,11.08644099,1,2.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/4/1992,44,dominicks,38720,10.56411154,0,1.72,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/4/1992,44,minute.maid,5120,8.540909718,0,2.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/4/1992,44,tropicana,98624,11.49906992,1,2.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/4/1992,48,dominicks,10944,9.30054664,0,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/4/1992,48,minute.maid,2752,7.920083199,0,2.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/4/1992,48,tropicana,34944,10.46150206,1,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/4/1992,49,dominicks,21312,9.967025573,0,1.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/4/1992,49,minute.maid,2048,7.624618986,0,2.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/4/1992,49,tropicana,40832,10.61722137,1,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/4/1992,50,dominicks,19200,9.862665558,0,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/4/1992,50,minute.maid,2112,7.655390645,0,2.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/4/1992,50,tropicana,40000,10.59663473,1,2.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/4/1992,51,dominicks,17344,9.761001904,0,1.69,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/4/1992,51,minute.maid,2240,7.714231145,0,2.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/4/1992,51,tropicana,55616,10.92622621,1,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/4/1992,52,dominicks,23744,10.07508515,0,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/4/1992,52,minute.maid,3904,8.269756948,0,2.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/4/1992,52,tropicana,103552,11.54782918,1,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/4/1992,53,dominicks,30272,10.31797847,0,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/4/1992,53,minute.maid,3584,8.184234774,0,2.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/4/1992,53,tropicana,96640,11.47874801,1,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/4/1992,54,dominicks,21696,9.984883191,0,1.71,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/4/1992,54,minute.maid,3712,8.219326094,0,2.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/4/1992,54,tropicana,38784,10.56576307,1,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/4/1992,56,dominicks,17664,9.779283949,0,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/4/1992,56,minute.maid,1920,7.560080465,0,2.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/4/1992,56,tropicana,51584,10.85096683,1,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/4/1992,59,dominicks,19136,9.859326657,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/4/1992,59,minute.maid,3264,8.090708716,0,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/4/1992,59,tropicana,68800,11.13895902,1,2.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/4/1992,62,dominicks,11840,9.379238908,0,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/4/1992,62,minute.maid,5056,8.528330936,0,2.89,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/4/1992,62,tropicana,73984,11.21160413,1,2.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/4/1992,64,dominicks,22080,10.0024275,0,1.74,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/4/1992,64,minute.maid,1792,7.491087594,0,2.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/4/1992,64,tropicana,29248,10.28356647,1,2.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/4/1992,67,dominicks,34560,10.45045222,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/4/1992,67,minute.maid,3840,8.253227646,0,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/4/1992,67,tropicana,51072,10.84099168,1,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/4/1992,68,dominicks,25728,10.15533517,0,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/4/1992,68,minute.maid,4224,8.348537825,0,2.89,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/4/1992,68,tropicana,72064,11.18530989,1,2.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/4/1992,71,dominicks,60480,11.01006801,0,1.7,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/4/1992,71,minute.maid,3136,8.050703382,0,2.89,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/4/1992,71,tropicana,103104,11.54349347,1,2.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/4/1992,72,dominicks,28864,10.27035042,0,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/4/1992,72,minute.maid,3712,8.219326094,0,2.89,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/4/1992,72,tropicana,78656,11.27283919,1,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/4/1992,73,dominicks,75072,11.22620293,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/4/1992,73,minute.maid,4224,8.348537825,0,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/4/1992,73,tropicana,86656,11.36970154,1,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/4/1992,74,dominicks,44736,10.70853383,0,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/4/1992,74,minute.maid,3520,8.166216269,0,2.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/4/1992,74,tropicana,76416,11.24394738,1,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/4/1992,75,dominicks,24896,10.12246243,0,1.74,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/4/1992,75,minute.maid,3136,8.050703382,0,2.89,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/4/1992,75,tropicana,55296,10.92045585,1,2.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/4/1992,76,dominicks,34624,10.45230236,0,1.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/4/1992,76,minute.maid,2496,7.82244473,0,2.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/4/1992,76,tropicana,68224,11.13055169,1,2.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/4/1992,77,dominicks,20544,9.930324207,0,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/4/1992,77,minute.maid,4096,8.317766167,0,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/4/1992,77,tropicana,48320,10.78560083,1,2.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/4/1992,78,dominicks,39296,10.57887801,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/4/1992,78,minute.maid,3072,8.030084094,0,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/4/1992,78,tropicana,63616,11.06062029,1,2.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/4/1992,80,dominicks,20480,9.927204079,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/4/1992,80,minute.maid,6144,8.723231275,0,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/4/1992,80,tropicana,84736,11.34729582,1,2.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/4/1992,81,dominicks,26560,10.1871616,0,1.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/4/1992,81,minute.maid,3392,8.129174997,0,2.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/4/1992,81,tropicana,68416,11.13336199,1,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/4/1992,83,dominicks,60480,11.01006801,0,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/4/1992,83,minute.maid,3712,8.219326094,0,2.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/4/1992,83,tropicana,75264,11.22875721,1,2.49,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/4/1992,84,dominicks,25664,10.15284451,0,1.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/4/1992,84,minute.maid,3456,8.14786713,0,2.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/4/1992,84,tropicana,69504,11.14913958,1,2.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/4/1992,86,dominicks,32448,10.38739409,0,1.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/4/1992,86,minute.maid,2176,7.685243608,0,2.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/4/1992,86,tropicana,55360,10.92161259,1,2.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/4/1992,88,dominicks,26880,10.1991378,0,1.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/4/1992,88,minute.maid,3392,8.129174997,0,2.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/4/1992,88,tropicana,27008,10.2038884,1,2.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/4/1992,89,dominicks,42432,10.65565807,0,1.7,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/4/1992,89,minute.maid,2816,7.943072717,0,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/4/1992,89,tropicana,58560,10.97780715,1,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/4/1992,91,dominicks,35776,10.48503256,0,1.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/4/1992,91,minute.maid,1984,7.592870288,0,2.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/4/1992,91,tropicana,61824,11.03204692,1,2.49,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/4/1992,92,dominicks,27200,10.21097225,0,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/4/1992,92,minute.maid,3072,8.030084094,0,2.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 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-6/4/1992,94,minute.maid,4416,8.392989588,0,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/4/1992,94,tropicana,49728,10.81432343,1,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/4/1992,95,dominicks,30848,10.3368272,0,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/4/1992,95,minute.maid,1856,7.526178913,0,2.89,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/4/1992,95,tropicana,87552,11.37998818,1,2.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/4/1992,97,dominicks,15424,9.643680017,0,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/4/1992,97,minute.maid,1920,7.560080465,0,2.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/4/1992,97,tropicana,24896,10.12246243,1,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/4/1992,98,dominicks,54272,10.90176372,0,1.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/4/1992,98,minute.maid,2560,7.847762538,0,2.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/4/1992,98,tropicana,62720,11.04643566,1,2.49,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/4/1992,100,dominicks,38912,10.56905797,0,1.72,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/4/1992,100,minute.maid,3456,8.14786713,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/4/1992,100,tropicana,74048,11.21246881,1,2.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/4/1992,101,dominicks,34176,10.43927892,0,1.73,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/4/1992,101,minute.maid,3264,8.090708716,0,2.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/4/1992,101,tropicana,59392,10.99191482,1,2.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/4/1992,102,dominicks,70016,11.15647907,0,1.71,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/4/1992,102,minute.maid,3456,8.14786713,0,2.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/4/1992,102,tropicana,76352,11.24310951,1,2.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/4/1992,103,dominicks,16064,9.684336023,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/4/1992,103,minute.maid,2816,7.943072717,0,2.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/4/1992,103,tropicana,43328,10.67655436,1,2.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/4/1992,104,dominicks,19648,9.885730831,0,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/4/1992,104,minute.maid,2880,7.965545573,0,2.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/4/1992,104,tropicana,44352,10.69991308,1,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/4/1992,105,dominicks,34688,10.45414909,0,1.71,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/4/1992,105,minute.maid,1920,7.560080465,0,2.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/4/1992,105,tropicana,47552,10.76957913,1,2.49,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/4/1992,106,dominicks,14784,9.601300794,0,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/4/1992,106,minute.maid,1664,7.416979621,0,2.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/4/1992,106,tropicana,24192,10.09377728,1,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/4/1992,107,dominicks,36992,10.51845695,0,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/4/1992,107,minute.maid,5120,8.540909718,0,2.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/4/1992,107,tropicana,97792,11.49059805,1,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/4/1992,109,dominicks,27392,10.21800628,0,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/4/1992,109,minute.maid,8256,9.018695488,0,2.89,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/4/1992,109,tropicana,128192,11.76128442,1,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/4/1992,110,dominicks,13696,9.524859098,0,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/4/1992,110,minute.maid,1664,7.416979621,0,2.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/4/1992,110,tropicana,61568,11.02789753,1,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/4/1992,111,dominicks,84416,11.34351224,0,1.73,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/4/1992,111,minute.maid,2624,7.87245515,0,2.89,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/4/1992,111,tropicana,63680,11.06162582,1,2.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/4/1992,112,dominicks,23040,10.04498712,0,1.7,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/4/1992,112,minute.maid,6912,8.841014311,0,2.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/4/1992,112,tropicana,74176,11.21419593,1,2.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/4/1992,113,dominicks,35648,10.48144832,0,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/4/1992,113,minute.maid,3328,8.110126802,0,2.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/4/1992,113,tropicana,76608,11.24645679,1,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/4/1992,114,dominicks,42240,10.65112292,0,1.72,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/4/1992,114,minute.maid,2880,7.965545573,0,2.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/4/1992,114,tropicana,62400,11.04132055,1,2.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/4/1992,115,dominicks,23040,10.04498712,0,1.72,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/4/1992,115,minute.maid,4672,8.449342525,0,2.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/4/1992,115,tropicana,78912,11.27608859,1,2.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/4/1992,116,dominicks,28672,10.26367632,0,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/4/1992,116,minute.maid,2496,7.82244473,0,2.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/4/1992,116,tropicana,53312,10.88391673,1,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/4/1992,118,dominicks,12864,9.462187991,0,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/4/1992,118,minute.maid,3648,8.201934351,0,2.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/4/1992,118,tropicana,81280,11.30565526,1,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/4/1992,119,dominicks,17344,9.761001904,0,1.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/4/1992,119,minute.maid,3520,8.166216269,0,2.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/4/1992,119,tropicana,46336,10.74367448,1,2.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/4/1992,121,dominicks,26816,10.196754,0,1.71,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/4/1992,121,minute.maid,4096,8.317766167,0,2.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/4/1992,121,tropicana,95232,11.4640713,1,2.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/4/1992,122,dominicks,29440,10.29010957,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/4/1992,122,minute.maid,8960,9.100525506,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/4/1992,122,tropicana,51392,10.8472378,1,2.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/4/1992,123,dominicks,32128,10.3774832,0,1.72,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/4/1992,123,minute.maid,3904,8.269756948,0,2.89,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/4/1992,123,tropicana,72448,11.19062434,1,2.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/4/1992,124,dominicks,50048,10.82073782,0,1.75,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/4/1992,124,minute.maid,2304,7.742402022,0,2.89,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/4/1992,124,tropicana,63488,11.05860619,1,2.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/4/1992,126,dominicks,24640,10.11212642,0,1.72,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/4/1992,126,minute.maid,5440,8.60153434,0,2.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/4/1992,126,tropicana,96704,11.47941005,1,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/4/1992,128,dominicks,27968,10.23881628,0,1.74,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/4/1992,128,minute.maid,4672,8.449342525,0,2.89,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/4/1992,128,tropicana,115520,11.65719895,1,2.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/4/1992,129,dominicks,23360,10.05878044,0,1.73,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/4/1992,129,minute.maid,4800,8.476371197,0,2.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/4/1992,129,tropicana,63424,11.05759762,1,2.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/4/1992,130,dominicks,57728,10.9634976,0,1.72,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/4/1992,130,minute.maid,3072,8.030084094,0,2.89,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/4/1992,130,tropicana,99264,11.50553825,1,2.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/4/1992,131,dominicks,33536,10.42037477,0,1.73,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/4/1992,131,minute.maid,2944,7.98752448,0,2.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/4/1992,131,tropicana,86272,11.36526038,1,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/4/1992,132,dominicks,41216,10.62658181,0,1.7,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/4/1992,132,minute.maid,5056,8.528330936,0,2.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/4/1992,132,tropicana,72000,11.1844214,1,2.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/4/1992,134,dominicks,19072,9.85597657,0,1.71,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/4/1992,134,minute.maid,2240,7.714231145,0,2.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/4/1992,134,tropicana,33984,10.43364511,1,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/4/1992,137,dominicks,42944,10.66765222,0,1.74,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/4/1992,137,minute.maid,6272,8.743850562,0,2.89,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/4/1992,137,tropicana,145088,11.88509573,1,2.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/11/1992,2,dominicks,6592,8.793612072,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/11/1992,2,minute.maid,4352,8.378390789,0,2.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/11/1992,2,tropicana,22272,10.01108556,0,2.21,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/11/1992,5,dominicks,6336,8.754002934,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/11/1992,5,minute.maid,5696,8.647519453,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/11/1992,5,tropicana,44096,10.69412435,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/11/1992,8,dominicks,71040,11.17099838,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/11/1992,8,minute.maid,5440,8.60153434,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/11/1992,8,tropicana,14144,9.557045785,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/11/1992,9,dominicks,6464,8.7740036,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/11/1992,9,minute.maid,1920,7.560080465,0,2.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/11/1992,9,tropicana,38464,10.55747802,0,2.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/11/1992,12,dominicks,22528,10.02251426,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/11/1992,12,minute.maid,6080,8.712759975,0,2.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/11/1992,12,tropicana,38528,10.55914053,0,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/11/1992,14,dominicks,5440,8.60153434,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/11/1992,14,minute.maid,5504,8.61323038,0,2.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/11/1992,14,tropicana,37888,10.54238972,0,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/11/1992,18,dominicks,12800,9.45720045,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/11/1992,18,minute.maid,5184,8.553332238,0,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/11/1992,18,tropicana,29632,10.29661014,0,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/11/1992,21,dominicks,16128,9.688312171,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/11/1992,21,minute.maid,3072,8.030084094,0,2.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/11/1992,21,tropicana,8832,9.086136769,0,2.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/11/1992,28,dominicks,4352,8.378390789,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/11/1992,28,minute.maid,3136,8.050703382,0,2.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/11/1992,28,tropicana,11008,9.30637756,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/11/1992,32,dominicks,8000,8.987196821,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/11/1992,32,minute.maid,5440,8.60153434,0,2.89,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/11/1992,32,tropicana,58624,10.97889945,0,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/11/1992,33,dominicks,7680,8.946374826,0,2.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/11/1992,33,minute.maid,5440,8.60153434,0,2.89,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/11/1992,33,tropicana,32384,10.38541975,0,2.32,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/11/1992,40,dominicks,10944,9.30054664,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/11/1992,40,minute.maid,2496,7.82244473,0,2.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/11/1992,40,tropicana,17088,9.746131742,0,2.23,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/11/1992,44,dominicks,13888,9.538780437,0,1.98,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/11/1992,44,minute.maid,6400,8.764053269,0,2.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/11/1992,44,tropicana,27840,10.23422911,0,2.21,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/11/1992,45,dominicks,3712,8.219326094,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/11/1992,45,minute.maid,4480,8.407378325,0,2.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/11/1992,45,tropicana,14208,9.561560465,0,2.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/11/1992,47,dominicks,5760,8.658692754,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/11/1992,47,minute.maid,3904,8.269756948,0,2.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/11/1992,47,tropicana,11392,9.340666634,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/11/1992,48,dominicks,4160,8.333270353,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/11/1992,48,minute.maid,4288,8.363575703,0,2.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/11/1992,48,tropicana,15104,9.622714888,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/11/1992,49,dominicks,4672,8.449342525,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/11/1992,49,minute.maid,2432,7.796469243,0,2.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/11/1992,49,tropicana,16192,9.692272572,0,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/11/1992,50,dominicks,5824,8.66974259,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/11/1992,50,minute.maid,2240,7.714231145,0,2.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/11/1992,50,tropicana,12288,9.416378455,0,2.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/11/1992,51,dominicks,5312,8.577723691,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/11/1992,51,minute.maid,2688,7.896552702,0,2.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/11/1992,51,tropicana,27264,10.21332243,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/11/1992,52,dominicks,4096,8.317766167,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/11/1992,52,minute.maid,6400,8.764053269,0,2.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/11/1992,52,tropicana,47552,10.76957913,0,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/11/1992,54,dominicks,5824,8.66974259,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/11/1992,54,minute.maid,4224,8.348537825,0,2.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/11/1992,54,tropicana,15040,9.618468598,0,2.25,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/11/1992,56,dominicks,5824,8.66974259,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/11/1992,56,minute.maid,2944,7.98752448,0,2.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/11/1992,56,tropicana,14080,9.55251063,0,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/11/1992,59,dominicks,6656,8.803273983,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/11/1992,59,minute.maid,3584,8.184234774,0,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/11/1992,59,tropicana,22080,10.0024275,0,2.17,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/11/1992,64,dominicks,11840,9.379238908,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/11/1992,64,minute.maid,2432,7.796469243,0,2.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/11/1992,64,tropicana,6784,8.822322178,0,2.16,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/11/1992,67,dominicks,19776,9.89222436,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/11/1992,67,minute.maid,3456,8.14786713,0,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/11/1992,67,tropicana,13632,9.520175249,0,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/11/1992,68,dominicks,9088,9.114710141,0,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/11/1992,68,minute.maid,5760,8.658692754,0,2.89,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/11/1992,68,tropicana,35200,10.46880136,0,2.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/11/1992,70,dominicks,22720,10.03100087,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/11/1992,70,minute.maid,6272,8.743850562,0,2.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/11/1992,70,tropicana,16512,9.711842668,0,2.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/11/1992,71,dominicks,13440,9.505990614,0,1.84,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/11/1992,71,minute.maid,2368,7.769800996,0,2.89,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/11/1992,71,tropicana,46336,10.74367448,0,2.16,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/11/1992,72,dominicks,13824,9.534161491,0,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/11/1992,72,minute.maid,3520,8.166216269,0,2.89,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/11/1992,72,tropicana,24192,10.09377728,0,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/11/1992,73,dominicks,27136,10.20861654,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/11/1992,73,minute.maid,6976,8.850230966,0,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/11/1992,73,tropicana,24448,10.10430369,0,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/11/1992,74,dominicks,14912,9.609921537,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/11/1992,74,minute.maid,5376,8.589699882,0,2.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/11/1992,74,tropicana,31232,10.34919849,0,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/11/1992,75,dominicks,7104,8.868413285,0,2.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/11/1992,75,minute.maid,3520,8.166216269,0,2.89,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/11/1992,75,tropicana,22336,10.01395501,0,2.26,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/11/1992,76,dominicks,30016,10.30948585,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/11/1992,76,minute.maid,3904,8.269756948,0,2.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/11/1992,76,tropicana,18304,9.814874894,0,2.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 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-6/11/1992,81,minute.maid,6016,8.702177866,0,2.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/11/1992,81,tropicana,19392,9.872615889,0,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/11/1992,83,dominicks,14336,9.570529135,0,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/11/1992,83,minute.maid,5056,8.528330936,0,2.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/11/1992,83,tropicana,13632,9.520175249,0,2.49,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/11/1992,84,dominicks,6912,8.841014311,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/11/1992,84,minute.maid,4608,8.435549202,0,2.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/11/1992,84,tropicana,25856,10.16029796,0,2.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/11/1992,86,dominicks,4864,8.489616424,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/11/1992,86,minute.maid,2112,7.655390645,0,2.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 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-6/11/1992,89,minute.maid,2624,7.87245515,0,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/11/1992,89,tropicana,7680,8.946374826,0,2.18,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/11/1992,90,dominicks,3264,8.090708716,0,2.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/11/1992,90,minute.maid,3648,8.201934351,0,2.89,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/11/1992,90,tropicana,19136,9.859326657,0,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 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-6/11/1992,92,tropicana,18304,9.814874894,0,2.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/11/1992,93,dominicks,5184,8.553332238,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/11/1992,93,minute.maid,4800,8.476371197,0,2.89,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/11/1992,93,tropicana,47296,10.764181,0,2.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/11/1992,94,dominicks,9600,9.169518378,0,1.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/11/1992,94,minute.maid,6336,8.754002934,0,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/11/1992,94,tropicana,14848,9.605620455,0,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/11/1992,95,dominicks,19776,9.89222436,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/11/1992,95,minute.maid,7552,8.929567708,0,2.89,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/11/1992,95,tropicana,23744,10.07508515,0,2.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 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-6/11/1992,98,tropicana,19840,9.895455381,0,2.23,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/11/1992,100,dominicks,6848,8.831711918,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/11/1992,100,minute.maid,4864,8.489616424,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/11/1992,100,tropicana,40512,10.60935351,0,2.19,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/11/1992,101,dominicks,11840,9.379238908,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/11/1992,101,minute.maid,4736,8.462948177,0,2.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/11/1992,101,tropicana,21120,9.957975738,0,2.22,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/11/1992,102,dominicks,42624,10.66017275,0,1.79,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/11/1992,102,minute.maid,4416,8.392989588,0,2.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/11/1992,102,tropicana,19136,9.859326657,0,2.23,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/11/1992,103,dominicks,7936,8.979164649,0,1.79,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/11/1992,103,minute.maid,3072,8.030084094,0,2.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/11/1992,103,tropicana,6016,8.702177866,0,2.21,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/11/1992,104,dominicks,5696,8.647519453,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/11/1992,104,minute.maid,3200,8.070906089,0,2.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/11/1992,104,tropicana,12224,9.411156511,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/11/1992,105,dominicks,9728,9.182763604,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/11/1992,105,minute.maid,3264,8.090708716,0,2.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/11/1992,105,tropicana,22016,9.999524741,0,2.12,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/11/1992,106,dominicks,6464,8.7740036,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/11/1992,106,minute.maid,2048,7.624618986,0,2.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/11/1992,106,tropicana,11520,9.351839934,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/11/1992,107,dominicks,13632,9.520175249,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/11/1992,107,minute.maid,5760,8.658692754,0,2.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/11/1992,107,tropicana,22848,10.03661887,0,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/11/1992,109,dominicks,2688,7.896552702,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/11/1992,109,minute.maid,10368,9.246479419,0,2.89,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/11/1992,109,tropicana,39104,10.57398004,0,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/11/1992,110,dominicks,4928,8.502688505,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/11/1992,110,minute.maid,3008,8.009030685,0,2.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/11/1992,110,tropicana,21888,9.99369382,0,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/11/1992,111,dominicks,28544,10.25920204,0,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/11/1992,111,minute.maid,3072,8.030084094,0,2.89,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/11/1992,111,tropicana,41856,10.64199044,0,2.17,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/11/1992,112,dominicks,9216,9.128696383,0,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/11/1992,112,minute.maid,7488,8.921057018,0,2.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/11/1992,112,tropicana,42176,10.64960662,0,2.24,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/11/1992,113,dominicks,16064,9.684336023,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/11/1992,113,minute.maid,5184,8.553332238,0,2.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/11/1992,113,tropicana,30464,10.32430094,0,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/11/1992,114,dominicks,12096,9.400630098,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/11/1992,114,minute.maid,7680,8.946374826,0,2.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/11/1992,114,tropicana,16960,9.738612909,0,2.21,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/11/1992,115,dominicks,9152,9.121727714,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/11/1992,115,minute.maid,4416,8.392989588,0,2.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/11/1992,115,tropicana,22656,10.02818,0,2.26,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/11/1992,116,dominicks,10304,9.240287448,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/11/1992,116,minute.maid,2624,7.87245515,0,2.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/11/1992,116,tropicana,12160,9.405907156,0,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/11/1992,117,dominicks,5120,8.540909718,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/11/1992,117,minute.maid,2624,7.87245515,0,2.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/11/1992,117,tropicana,10944,9.30054664,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/11/1992,118,dominicks,7296,8.895081532,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/11/1992,118,minute.maid,5120,8.540909718,0,2.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/11/1992,118,tropicana,26624,10.18956834,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/11/1992,119,dominicks,5376,8.589699882,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/11/1992,119,minute.maid,3264,8.090708716,0,2.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/11/1992,119,tropicana,25920,10.16277015,0,2.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/11/1992,121,dominicks,7936,8.979164649,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/11/1992,121,minute.maid,8192,9.010913347,0,2.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/11/1992,121,tropicana,24000,10.08580911,0,2.3,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/11/1992,122,dominicks,17216,9.753594463,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/11/1992,122,minute.maid,8128,9.00307017,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/11/1992,122,tropicana,12032,9.395325046,0,2.31,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/11/1992,123,dominicks,8384,9.034080407,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/11/1992,123,minute.maid,5568,8.624791202,0,2.89,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/11/1992,123,tropicana,30016,10.30948585,0,2.21,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/11/1992,124,dominicks,17280,9.757305042,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/11/1992,124,minute.maid,2816,7.943072717,0,2.89,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/11/1992,124,tropicana,20608,9.933434629,0,2.28,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/11/1992,126,dominicks,11456,9.346268889,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/11/1992,126,minute.maid,5696,8.647519453,0,2.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/11/1992,126,tropicana,32384,10.38541975,0,2.27,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/11/1992,128,dominicks,12672,9.447150114,0,2.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/11/1992,128,minute.maid,5696,8.647519453,0,2.89,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/11/1992,128,tropicana,45184,10.71849832,0,2.17,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/11/1992,129,dominicks,5824,8.66974259,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/11/1992,129,minute.maid,4928,8.502688505,0,2.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/11/1992,129,tropicana,33344,10.41463313,0,2.28,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/11/1992,130,dominicks,18560,9.828764006,0,2.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/11/1992,130,minute.maid,4864,8.489616424,0,2.89,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/11/1992,130,tropicana,17280,9.757305042,0,2.18,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/11/1992,131,dominicks,7808,8.962904128,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/11/1992,131,minute.maid,4288,8.363575703,0,2.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/11/1992,131,tropicana,31936,10.37148918,0,2.22,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/11/1992,132,dominicks,21504,9.975994243,0,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/11/1992,132,minute.maid,6656,8.803273983,0,2.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/11/1992,132,tropicana,15104,9.622714888,0,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/11/1992,134,dominicks,7360,8.903815212,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/11/1992,134,minute.maid,2816,7.943072717,0,2.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/11/1992,134,tropicana,13312,9.496421163,0,2.2,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/11/1992,137,dominicks,15040,9.618468598,0,2.09,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/11/1992,137,minute.maid,7296,8.895081532,0,2.89,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/11/1992,137,tropicana,100160,11.51452419,0,2.25,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/18/1992,2,dominicks,4992,8.51559191,0,2.05,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/18/1992,2,minute.maid,4480,8.407378325,0,2.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/18/1992,2,tropicana,46144,10.73952222,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/18/1992,9,dominicks,2496,7.82244473,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/18/1992,9,minute.maid,1856,7.526178913,0,2.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/18/1992,9,tropicana,95232,11.4640713,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/18/1992,12,dominicks,3584,8.184234774,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/18/1992,12,minute.maid,4544,8.42156296,0,2.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/18/1992,12,tropicana,89024,11.39666128,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/18/1992,14,dominicks,2560,7.847762538,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/18/1992,14,minute.maid,4224,8.348537825,0,2.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/18/1992,14,tropicana,73728,11.20813793,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/18/1992,18,dominicks,9664,9.17616292,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/18/1992,18,minute.maid,5952,8.691482577,0,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/18/1992,18,tropicana,63232,11.05456578,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/18/1992,21,dominicks,7296,8.895081532,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/18/1992,21,minute.maid,2496,7.82244473,0,2.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/18/1992,21,tropicana,27328,10.2156671,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/18/1992,28,dominicks,2624,7.87245515,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/18/1992,28,minute.maid,2816,7.943072717,0,2.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/18/1992,28,tropicana,25728,10.15533517,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/18/1992,33,dominicks,5440,8.60153434,0,2.04,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/18/1992,33,minute.maid,5568,8.624791202,0,2.89,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/18/1992,33,tropicana,44992,10.71423998,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/18/1992,40,dominicks,7616,8.938006577,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/18/1992,40,minute.maid,3328,8.110126802,0,2.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/18/1992,40,tropicana,43648,10.68391274,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/18/1992,44,dominicks,3520,8.166216269,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/18/1992,44,minute.maid,4864,8.489616424,0,2.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/18/1992,44,tropicana,81088,11.30329026,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/18/1992,45,dominicks,2816,7.943072717,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/18/1992,45,minute.maid,2752,7.920083199,0,2.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/18/1992,45,tropicana,31936,10.37148918,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/18/1992,47,dominicks,3840,8.253227646,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/18/1992,47,minute.maid,3456,8.14786713,0,2.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/18/1992,47,tropicana,28672,10.26367632,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/18/1992,48,dominicks,3776,8.236420527,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/18/1992,48,minute.maid,4224,8.348537825,0,2.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/18/1992,48,tropicana,22912,10.03941607,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/18/1992,49,dominicks,1856,7.526178913,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/18/1992,49,minute.maid,1792,7.491087594,0,2.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/18/1992,49,tropicana,36352,10.5010045,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/18/1992,50,dominicks,2112,7.655390645,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/18/1992,50,minute.maid,1792,7.491087594,0,2.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/18/1992,50,tropicana,31936,10.37148918,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/18/1992,52,dominicks,3392,8.129174997,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/18/1992,52,minute.maid,5696,8.647519453,0,2.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/18/1992,52,tropicana,103872,11.55091465,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/18/1992,53,dominicks,4992,8.51559191,0,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/18/1992,53,minute.maid,12608,9.442086812,0,2.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/18/1992,53,tropicana,101120,11.52406321,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/18/1992,54,dominicks,1856,7.526178913,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/18/1992,54,minute.maid,3520,8.166216269,0,2.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/18/1992,54,tropicana,42112,10.64808802,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/18/1992,56,dominicks,3328,8.110126802,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/18/1992,56,minute.maid,2880,7.965545573,0,2.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/18/1992,56,tropicana,38720,10.56411154,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/18/1992,59,dominicks,4224,8.348537825,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/18/1992,59,minute.maid,2752,7.920083199,0,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/18/1992,59,tropicana,41024,10.62191254,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/18/1992,62,dominicks,1984,7.592870288,0,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/18/1992,62,minute.maid,5504,8.61323038,0,2.89,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/18/1992,62,tropicana,87552,11.37998818,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/18/1992,64,dominicks,8192,9.010913347,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/18/1992,64,minute.maid,3200,8.070906089,0,2.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/18/1992,64,tropicana,15488,9.64782081,1,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/18/1992,68,dominicks,6272,8.743850562,0,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/18/1992,68,minute.maid,4096,8.317766167,0,2.89,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/18/1992,68,tropicana,62080,11.03617916,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/18/1992,70,dominicks,15616,9.656051309,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/18/1992,70,minute.maid,5120,8.540909718,0,2.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 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-6/18/1992,73,minute.maid,6144,8.723231275,0,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/18/1992,73,tropicana,76288,11.24227093,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/18/1992,74,dominicks,14464,9.579418083,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/18/1992,74,minute.maid,4864,8.489616424,0,2.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/18/1992,74,tropicana,42112,10.64808802,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 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-6/18/1992,76,tropicana,71168,11.17279856,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/18/1992,77,dominicks,6976,8.850230966,0,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/18/1992,77,minute.maid,3968,8.286017468,0,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/18/1992,77,tropicana,38400,10.55581274,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/18/1992,78,dominicks,10240,9.234056899,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/18/1992,78,minute.maid,2880,7.965545573,0,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/18/1992,78,tropicana,52288,10.86452218,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/18/1992,80,dominicks,4096,8.317766167,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/18/1992,80,minute.maid,6656,8.803273983,0,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/18/1992,80,tropicana,51776,10.854682,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -6/18/1992,81,dominicks,3840,8.253227646,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/18/1992,81,minute.maid,4608,8.435549202,0,2.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/18/1992,81,tropicana,61632,11.0289365,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/18/1992,83,dominicks,8704,9.071537969,0,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/18/1992,83,minute.maid,6528,8.783855897,0,2.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/18/1992,83,tropicana,31168,10.34714721,1,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/18/1992,84,dominicks,2368,7.769800996,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/18/1992,84,minute.maid,2816,7.943072717,0,2.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/18/1992,84,tropicana,49792,10.81560961,1,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -6/18/1992,86,dominicks,3264,8.090708716,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/18/1992,86,minute.maid,2368,7.769800996,0,2.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/18/1992,86,tropicana,66112,11.09910555,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -6/18/1992,88,dominicks,3712,8.219326094,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/18/1992,88,minute.maid,3008,8.009030685,0,2.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/18/1992,88,tropicana,35392,10.47424109,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -6/18/1992,89,dominicks,17152,9.749870064,0,1.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/18/1992,89,minute.maid,2560,7.847762538,0,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/18/1992,89,tropicana,52608,10.87062348,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -6/18/1992,90,dominicks,2048,7.624618986,0,2.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/18/1992,90,minute.maid,3456,8.14786713,0,2.89,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/18/1992,90,tropicana,46784,10.75329654,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -6/18/1992,91,dominicks,24064,10.08847223,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/18/1992,91,minute.maid,13504,9.510741217,0,2.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/18/1992,91,tropicana,44416,10.70135504,1,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/18/1992,92,dominicks,2048,7.624618986,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/18/1992,92,minute.maid,2624,7.87245515,0,2.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/18/1992,92,tropicana,34816,10.45783233,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/18/1992,93,dominicks,3072,8.030084094,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/18/1992,93,minute.maid,4736,8.462948177,0,2.89,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/18/1992,93,tropicana,81280,11.30565526,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -6/18/1992,94,dominicks,6720,8.812843434,0,1.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/18/1992,94,minute.maid,5888,8.68067166,0,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/18/1992,94,tropicana,32192,10.37947325,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -6/18/1992,95,dominicks,4608,8.435549202,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/18/1992,95,minute.maid,2496,7.82244473,0,2.89,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/18/1992,95,tropicana,91520,11.42431281,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -6/18/1992,97,dominicks,2176,7.685243608,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/18/1992,97,minute.maid,1536,7.336936914,0,2.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/18/1992,97,tropicana,22400,10.01681624,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -6/18/1992,98,dominicks,15808,9.66827142,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/18/1992,98,minute.maid,4224,8.348537825,0,2.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/18/1992,98,tropicana,54080,10.89821971,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -6/18/1992,100,dominicks,4800,8.476371197,0,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/18/1992,100,minute.maid,5184,8.553332238,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/18/1992,100,tropicana,80704,11.29854342,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -6/18/1992,101,dominicks,5824,8.66974259,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/18/1992,101,minute.maid,3264,8.090708716,0,2.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/18/1992,101,tropicana,57088,10.95234922,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -6/18/1992,102,dominicks,27968,10.23881628,0,1.79,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/18/1992,102,minute.maid,4736,8.462948177,0,2.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/18/1992,102,tropicana,62208,11.03823889,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -6/18/1992,103,dominicks,6144,8.723231275,0,1.79,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/18/1992,103,minute.maid,2368,7.769800996,0,2.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/18/1992,103,tropicana,26496,10.18474906,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -6/18/1992,104,dominicks,3840,8.253227646,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/18/1992,104,minute.maid,2624,7.87245515,0,2.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/18/1992,104,tropicana,31680,10.36344085,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/18/1992,105,dominicks,5056,8.528330936,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/18/1992,105,minute.maid,2752,7.920083199,0,2.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/18/1992,105,tropicana,51968,10.85838342,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/18/1992,106,dominicks,1920,7.560080465,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/18/1992,106,minute.maid,1472,7.294377299,0,2.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/18/1992,106,tropicana,22784,10.03381381,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -6/18/1992,107,dominicks,6336,8.754002934,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/18/1992,107,minute.maid,4608,8.435549202,0,2.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/18/1992,107,tropicana,82432,11.31972899,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/18/1992,109,dominicks,3328,8.110126802,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/18/1992,109,minute.maid,8512,9.049232212,0,2.89,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/18/1992,109,tropicana,99456,11.50747061,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/18/1992,110,dominicks,1792,7.491087594,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/18/1992,110,minute.maid,3008,8.009030685,0,2.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/18/1992,110,tropicana,33088,10.40692596,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/18/1992,111,dominicks,4096,8.317766167,0,2.03,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/18/1992,111,minute.maid,3072,8.030084094,0,2.89,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/18/1992,111,tropicana,118528,11.6829045,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/18/1992,112,dominicks,6784,8.822322178,0,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/18/1992,112,minute.maid,6272,8.743850562,0,2.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/18/1992,112,tropicana,68736,11.13802836,1,1.99,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/18/1992,113,dominicks,4544,8.42156296,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/18/1992,113,minute.maid,4416,8.392989588,0,2.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/18/1992,113,tropicana,99968,11.51260541,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/18/1992,114,dominicks,9024,9.107642974,0,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/18/1992,114,minute.maid,18496,9.825309772,0,1.38,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/18/1992,114,tropicana,60544,11.01112565,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/18/1992,115,dominicks,1792,7.491087594,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/18/1992,115,minute.maid,4224,8.348537825,0,2.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/18/1992,115,tropicana,55936,10.93196346,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/18/1992,116,dominicks,7360,8.903815212,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/18/1992,116,minute.maid,2752,7.920083199,0,2.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/18/1992,116,tropicana,36992,10.51845695,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/18/1992,117,dominicks,3968,8.286017468,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/18/1992,117,minute.maid,2752,7.920083199,0,2.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/18/1992,117,tropicana,25920,10.16277015,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/18/1992,118,dominicks,2944,7.98752448,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/18/1992,118,minute.maid,2752,7.920083199,0,2.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/18/1992,118,tropicana,54592,10.90764263,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/18/1992,119,dominicks,3776,8.236420527,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/18/1992,119,minute.maid,2304,7.742402022,0,2.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/18/1992,119,tropicana,37440,10.53049493,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/18/1992,121,dominicks,4352,8.378390789,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/18/1992,121,minute.maid,6656,8.803273983,0,2.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/18/1992,121,tropicana,49664,10.8130356,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/18/1992,122,dominicks,10944,9.30054664,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/18/1992,122,minute.maid,6848,8.831711918,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/18/1992,122,tropicana,42240,10.65112292,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/18/1992,123,dominicks,4096,8.317766167,0,2.03,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/18/1992,123,minute.maid,5760,8.658692754,0,2.6,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/18/1992,123,tropicana,95296,11.46474312,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/18/1992,124,dominicks,9792,9.189321005,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/18/1992,124,minute.maid,3072,8.030084094,0,2.89,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/18/1992,124,tropicana,51712,10.85344514,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/18/1992,126,dominicks,3392,8.129174997,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/18/1992,126,minute.maid,4544,8.42156296,0,2.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/18/1992,126,tropicana,74752,11.22193125,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/18/1992,128,dominicks,5824,8.66974259,0,2.05,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/18/1992,128,minute.maid,4352,8.378390789,0,2.89,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/18/1992,128,tropicana,69888,11.15464924,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/18/1992,129,dominicks,2944,7.98752448,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/18/1992,129,minute.maid,5120,8.540909718,0,2.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/18/1992,129,tropicana,39872,10.5934296,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/18/1992,130,dominicks,10176,9.227787286,0,2.02,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/18/1992,130,minute.maid,3072,8.030084094,0,2.89,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/18/1992,130,tropicana,116928,11.66931364,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/18/1992,131,dominicks,3840,8.253227646,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/18/1992,131,minute.maid,4480,8.407378325,0,2.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/18/1992,131,tropicana,53440,10.88631481,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/18/1992,132,dominicks,10624,9.270870872,0,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/18/1992,132,minute.maid,6784,8.822322178,0,2.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/18/1992,132,tropicana,47872,10.77628606,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/18/1992,134,dominicks,2368,7.769800996,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/18/1992,134,minute.maid,2112,7.655390645,0,2.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/18/1992,134,tropicana,33408,10.41655067,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/18/1992,137,dominicks,7232,8.886270902,0,2.04,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/18/1992,137,minute.maid,8768,9.078864009,0,2.89,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/18/1992,137,tropicana,92608,11.43613081,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/25/1992,2,dominicks,8064,8.99516499,0,1.24,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/25/1992,2,minute.maid,3840,8.253227646,0,2.52,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/25/1992,2,tropicana,4352,8.378390789,1,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -6/25/1992,5,dominicks,1408,7.249925537,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/25/1992,5,minute.maid,5696,8.647519453,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/25/1992,5,tropicana,7296,8.895081532,1,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -6/25/1992,8,dominicks,15360,9.639522007,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/25/1992,8,minute.maid,5888,8.68067166,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/25/1992,8,tropicana,7488,8.921057018,1,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -6/25/1992,9,dominicks,1088,6.992096427,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/25/1992,9,minute.maid,1984,7.592870288,0,2.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/25/1992,9,tropicana,6528,8.783855897,1,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -6/25/1992,12,dominicks,15040,9.618468598,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/25/1992,12,minute.maid,4224,8.348537825,0,2.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/25/1992,12,tropicana,5824,8.66974259,1,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -6/25/1992,14,dominicks,2688,7.896552702,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/25/1992,14,minute.maid,6848,8.831711918,0,2.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/25/1992,14,tropicana,11008,9.30637756,1,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -6/25/1992,18,dominicks,11136,9.317938383,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/25/1992,18,minute.maid,5120,8.540909718,0,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/25/1992,18,tropicana,6720,8.812843434,1,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -6/25/1992,21,dominicks,6400,8.764053269,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/25/1992,21,minute.maid,2816,7.943072717,0,2.38,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/25/1992,21,tropicana,3200,8.070906089,1,2.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -6/25/1992,28,dominicks,2432,7.796469243,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/25/1992,28,minute.maid,2624,7.87245515,0,2.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/25/1992,28,tropicana,4096,8.317766167,1,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -6/25/1992,32,dominicks,3072,8.030084094,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/25/1992,32,minute.maid,4736,8.462948177,0,2.89,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/25/1992,32,tropicana,11520,9.351839934,1,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -6/25/1992,33,dominicks,3456,8.14786713,0,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/25/1992,33,minute.maid,6080,8.712759975,0,2.42,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/25/1992,33,tropicana,9664,9.17616292,1,3.12,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -6/25/1992,40,dominicks,7232,8.886270902,0,1.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/25/1992,40,minute.maid,4160,8.333270353,0,2.33,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/25/1992,40,tropicana,5568,8.624791202,1,2.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -6/25/1992,44,dominicks,3008,8.009030685,0,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/25/1992,44,minute.maid,6656,8.803273983,0,2.42,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/25/1992,44,tropicana,10688,9.276876896,1,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -6/25/1992,45,dominicks,2048,7.624618986,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/25/1992,45,minute.maid,1792,7.491087594,0,2.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/25/1992,45,tropicana,7488,8.921057018,1,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -6/25/1992,47,dominicks,2624,7.87245515,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/25/1992,47,minute.maid,3648,8.201934351,0,2.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/25/1992,47,tropicana,6784,8.822322178,1,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -6/25/1992,48,dominicks,1792,7.491087594,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/25/1992,48,minute.maid,3840,8.253227646,0,2.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/25/1992,48,tropicana,5312,8.577723691,1,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -6/25/1992,49,dominicks,1088,6.992096427,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/25/1992,49,minute.maid,2112,7.655390645,0,2.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/25/1992,49,tropicana,3584,8.184234774,1,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -6/25/1992,50,dominicks,2432,7.796469243,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/25/1992,50,minute.maid,2240,7.714231145,0,2.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/25/1992,50,tropicana,2688,7.896552702,1,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -6/25/1992,51,dominicks,20736,9.939626599,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/25/1992,51,minute.maid,3008,8.009030685,0,2.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/25/1992,51,tropicana,4416,8.392989588,1,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -6/25/1992,52,dominicks,2624,7.87245515,0,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/25/1992,52,minute.maid,5888,8.68067166,0,2.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/25/1992,52,tropicana,11072,9.312174678,1,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -6/25/1992,53,dominicks,14336,9.570529135,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/25/1992,53,minute.maid,7616,8.938006577,0,2.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/25/1992,53,tropicana,12160,9.405907156,1,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -6/25/1992,54,dominicks,2816,7.943072717,0,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/25/1992,54,minute.maid,4096,8.317766167,0,2.46,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/25/1992,54,tropicana,5888,8.68067166,1,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -6/25/1992,56,dominicks,3712,8.219326094,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/25/1992,56,minute.maid,1728,7.454719949,0,2.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/25/1992,56,tropicana,7232,8.886270902,1,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -6/25/1992,59,dominicks,5440,8.60153434,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/25/1992,59,minute.maid,2624,7.87245515,0,2.24,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/25/1992,59,tropicana,29696,10.29876764,1,2.01,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -6/25/1992,62,dominicks,1856,7.526178913,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/25/1992,62,minute.maid,3904,8.269756948,0,2.89,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/25/1992,62,tropicana,10816,9.288781798,1,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -6/25/1992,64,dominicks,6720,8.812843434,0,1.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/25/1992,64,minute.maid,2368,7.769800996,0,2.53,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/25/1992,64,tropicana,2048,7.624618986,1,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -6/25/1992,67,dominicks,4352,8.378390789,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/25/1992,67,minute.maid,3136,8.050703382,0,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/25/1992,67,tropicana,3456,8.14786713,1,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -6/25/1992,68,dominicks,7744,8.954673629,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/25/1992,68,minute.maid,6848,8.831711918,0,2.89,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/25/1992,68,tropicana,6464,8.7740036,1,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -6/25/1992,70,dominicks,11712,9.368369236,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/25/1992,70,minute.maid,5440,8.60153434,0,2.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/25/1992,70,tropicana,8192,9.010913347,1,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -6/25/1992,71,dominicks,3392,8.129174997,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/25/1992,71,minute.maid,2560,7.847762538,0,2.4,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/25/1992,71,tropicana,4672,8.449342525,1,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -6/25/1992,72,dominicks,3136,8.050703382,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/25/1992,72,minute.maid,6656,8.803273983,0,2.89,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/25/1992,72,tropicana,9792,9.189321005,1,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -6/25/1992,73,dominicks,21696,9.984883191,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/25/1992,73,minute.maid,5696,8.647519453,0,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/25/1992,73,tropicana,12800,9.45720045,1,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -6/25/1992,74,dominicks,6784,8.822322178,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/25/1992,74,minute.maid,5696,8.647519453,0,2.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/25/1992,74,tropicana,7744,8.954673629,1,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -6/25/1992,75,dominicks,3136,8.050703382,0,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/25/1992,75,minute.maid,3904,8.269756948,0,2.6,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/25/1992,75,tropicana,6464,8.7740036,1,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -6/25/1992,76,dominicks,10944,9.30054664,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/25/1992,76,minute.maid,3264,8.090708716,0,2.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/25/1992,76,tropicana,7744,8.954673629,1,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -6/25/1992,77,dominicks,7488,8.921057018,0,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/25/1992,77,minute.maid,3136,8.050703382,0,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/25/1992,77,tropicana,6656,8.803273983,1,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -6/25/1992,78,dominicks,8960,9.100525506,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -6/25/1992,78,minute.maid,3392,8.129174997,0,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 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-6/25/1992,81,minute.maid,4608,8.435549202,0,2.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/25/1992,81,tropicana,8384,9.034080407,1,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -6/25/1992,83,dominicks,10816,9.288781798,0,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/25/1992,83,minute.maid,4928,8.502688505,0,2.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -6/25/1992,83,tropicana,8960,9.100525506,1,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 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-6/25/1992,91,minute.maid,1536,7.336936914,0,2.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/25/1992,91,tropicana,4288,8.363575703,1,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -6/25/1992,92,dominicks,2240,7.714231145,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/25/1992,92,minute.maid,4096,8.317766167,0,2.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -6/25/1992,92,tropicana,3904,8.269756948,1,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 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-6/25/1992,104,minute.maid,2496,7.82244473,0,2.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/25/1992,104,tropicana,3328,8.110126802,1,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -6/25/1992,105,dominicks,3136,8.050703382,0,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/25/1992,105,minute.maid,3840,8.253227646,0,2.4,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -6/25/1992,105,tropicana,4800,8.476371197,1,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 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-6/25/1992,107,tropicana,11712,9.368369236,1,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -6/25/1992,109,dominicks,2240,7.714231145,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/25/1992,109,minute.maid,8128,9.00307017,0,2.89,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/25/1992,109,tropicana,14400,9.574983486,1,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -6/25/1992,110,dominicks,1280,7.154615357,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/25/1992,110,minute.maid,2944,7.98752448,0,2.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/25/1992,110,tropicana,4992,8.51559191,1,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -6/25/1992,111,dominicks,3520,8.166216269,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/25/1992,111,minute.maid,4352,8.378390789,0,2.38,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/25/1992,111,tropicana,3648,8.201934351,1,3.16,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -6/25/1992,112,dominicks,7104,8.868413285,0,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/25/1992,112,minute.maid,7296,8.895081532,0,2.32,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/25/1992,112,tropicana,11136,9.317938383,1,2.88,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -6/25/1992,113,dominicks,8512,9.049232212,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/25/1992,113,minute.maid,5568,8.624791202,0,2.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/25/1992,113,tropicana,6336,8.754002934,1,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -6/25/1992,114,dominicks,9600,9.169518378,0,1.8,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/25/1992,114,minute.maid,28032,10.24110199,0,1.18,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/25/1992,114,tropicana,2368,7.769800996,1,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -6/25/1992,115,dominicks,2048,7.624618986,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/25/1992,115,minute.maid,5568,8.624791202,0,2.41,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/25/1992,115,tropicana,7616,8.938006577,1,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -6/25/1992,116,dominicks,3776,8.236420527,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/25/1992,116,minute.maid,2944,7.98752448,0,2.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/25/1992,116,tropicana,4416,8.392989588,1,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -6/25/1992,117,dominicks,2752,7.920083199,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/25/1992,117,minute.maid,3008,8.009030685,0,2.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/25/1992,117,tropicana,4544,8.42156296,1,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -6/25/1992,118,dominicks,3264,8.090708716,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/25/1992,118,minute.maid,4416,8.392989588,0,2.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/25/1992,118,tropicana,3648,8.201934351,1,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -6/25/1992,119,dominicks,2176,7.685243608,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/25/1992,119,minute.maid,2624,7.87245515,0,2.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/25/1992,119,tropicana,5824,8.66974259,1,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -6/25/1992,121,dominicks,2048,7.624618986,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/25/1992,121,minute.maid,7168,8.877381955,0,2.41,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/25/1992,121,tropicana,8768,9.078864009,1,2.96,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -6/25/1992,122,dominicks,18432,9.821843564,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/25/1992,122,minute.maid,7552,8.929567708,0,2.23,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/25/1992,122,tropicana,8256,9.018695488,1,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -6/25/1992,123,dominicks,7488,8.921057018,0,1.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/25/1992,123,minute.maid,15744,9.664214619,0,2.07,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/25/1992,123,tropicana,4608,8.435549202,1,3.18,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -6/25/1992,124,dominicks,8960,9.100525506,0,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/25/1992,124,minute.maid,4736,8.462948177,0,2.4,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/25/1992,124,tropicana,6336,8.754002934,1,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -6/25/1992,126,dominicks,2944,7.98752448,0,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/25/1992,126,minute.maid,5440,8.60153434,0,2.46,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/25/1992,126,tropicana,7552,8.929567708,1,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -6/25/1992,128,dominicks,6848,8.831711918,0,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/25/1992,128,minute.maid,5632,8.636219898,0,2.51,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/25/1992,128,tropicana,8320,9.026417534,1,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -6/25/1992,129,dominicks,1984,7.592870288,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/25/1992,129,minute.maid,7232,8.886270902,0,2.4,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/25/1992,129,tropicana,6208,8.733594062,1,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -6/25/1992,130,dominicks,5184,8.553332238,0,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/25/1992,130,minute.maid,4352,8.378390789,0,2.51,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/25/1992,130,tropicana,2880,7.965545573,1,3.08,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -6/25/1992,131,dominicks,3008,8.009030685,0,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/25/1992,131,minute.maid,4800,8.476371197,0,2.46,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/25/1992,131,tropicana,6784,8.822322178,1,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -6/25/1992,132,dominicks,12288,9.416378455,0,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/25/1992,132,minute.maid,5120,8.540909718,0,2.32,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/25/1992,132,tropicana,6400,8.764053269,1,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -6/25/1992,134,dominicks,2752,7.920083199,0,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/25/1992,134,minute.maid,2944,7.98752448,0,2.36,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/25/1992,134,tropicana,4160,8.333270353,1,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -6/25/1992,137,dominicks,12416,9.426741242,0,1.98,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/25/1992,137,minute.maid,7040,8.859363449,0,2.55,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -6/25/1992,137,tropicana,15040,9.618468598,1,3.13,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/2/1992,2,dominicks,7360,8.903815212,0,1.61,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/2/1992,2,minute.maid,13312,9.496421163,1,2,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/2/1992,2,tropicana,17280,9.757305042,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/2/1992,5,dominicks,4672,8.449342525,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/2/1992,5,minute.maid,39680,10.58860256,1,2.01,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/2/1992,5,tropicana,12928,9.467150781,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/2/1992,8,dominicks,17728,9.78290059,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/2/1992,8,minute.maid,23872,10.0804615,1,2.02,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/2/1992,8,tropicana,12352,9.421573272,0,2.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/2/1992,9,dominicks,8000,8.987196821,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/2/1992,9,minute.maid,6976,8.850230966,1,2.01,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/2/1992,9,tropicana,11328,9.335032816,0,2.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/2/1992,12,dominicks,12096,9.400630098,0,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/2/1992,12,minute.maid,23616,10.06967973,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/2/1992,12,tropicana,23808,10.07777694,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/2/1992,14,dominicks,3200,8.070906089,0,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/2/1992,14,minute.maid,19008,9.852615222,1,2.03,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/2/1992,14,tropicana,22208,10.00820786,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/2/1992,18,dominicks,11648,9.36288977,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/2/1992,18,minute.maid,12224,9.411156511,1,2.01,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/2/1992,18,tropicana,13760,9.529521112,0,2.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/2/1992,21,dominicks,10112,9.221478116,0,1.75,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/2/1992,21,minute.maid,12480,9.431882642,1,2.15,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/2/1992,21,tropicana,5760,8.658692754,0,2.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/2/1992,28,dominicks,2240,7.714231145,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/2/1992,28,minute.maid,7424,8.912473275,1,2.04,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/2/1992,28,tropicana,3904,8.269756948,0,2.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/2/1992,32,dominicks,13312,9.496421163,0,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/2/1992,32,minute.maid,28416,10.25470765,1,2,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/2/1992,32,tropicana,17536,9.77201119,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/2/1992,33,dominicks,6272,8.743850562,0,1.72,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/2/1992,33,minute.maid,21632,9.981928979,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/2/1992,33,tropicana,28032,10.24110199,0,2.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/2/1992,40,dominicks,5760,8.658692754,0,1.76,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/2/1992,40,minute.maid,21184,9.961001459,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/2/1992,40,tropicana,6144,8.723231275,0,2.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/2/1992,44,dominicks,29184,10.28137589,0,1.41,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/2/1992,44,minute.maid,18368,9.818365299,1,2.01,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/2/1992,44,tropicana,24512,10.10691807,0,2.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/2/1992,45,dominicks,2944,7.98752448,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/2/1992,45,minute.maid,4992,8.51559191,1,2.04,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/2/1992,45,tropicana,10752,9.282847063,0,2.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/2/1992,47,dominicks,5120,8.540909718,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/2/1992,47,minute.maid,17344,9.761001904,1,2.02,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/2/1992,47,tropicana,6784,8.822322178,0,2.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/2/1992,48,dominicks,3072,8.030084094,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/2/1992,48,minute.maid,15744,9.664214619,1,2.01,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/2/1992,48,tropicana,7424,8.912473275,0,2.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/2/1992,49,dominicks,10304,9.240287448,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/2/1992,49,minute.maid,8064,8.99516499,1,2.01,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/2/1992,49,tropicana,6400,8.764053269,0,2.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/2/1992,50,dominicks,3968,8.286017468,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/2/1992,50,minute.maid,17024,9.742379392,1,2,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/2/1992,50,tropicana,6080,8.712759975,0,2.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/2/1992,51,dominicks,3008,8.009030685,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/2/1992,51,minute.maid,19072,9.85597657,1,2.01,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/2/1992,51,tropicana,8064,8.99516499,0,2.69,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/2/1992,53,dominicks,11840,9.379238908,0,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/2/1992,53,minute.maid,17920,9.793672686,1,2.01,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/2/1992,53,tropicana,27136,10.20861654,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/2/1992,54,dominicks,3840,8.253227646,0,1.74,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/2/1992,54,minute.maid,9344,9.142489705,1,1.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/2/1992,54,tropicana,10432,9.252633284,0,2.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/2/1992,56,dominicks,5696,8.647519453,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/2/1992,56,minute.maid,14720,9.596962392,1,2.02,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/2/1992,56,tropicana,6464,8.7740036,0,2.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/2/1992,59,dominicks,6016,8.702177866,0,1.63,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/2/1992,59,minute.maid,11072,9.312174678,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/2/1992,59,tropicana,7808,8.962904128,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/2/1992,62,dominicks,4608,8.435549202,0,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/2/1992,62,minute.maid,15424,9.643680017,1,2.03,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/2/1992,62,tropicana,32320,10.38344151,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/2/1992,64,dominicks,7424,8.912473275,0,1.81,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/2/1992,64,minute.maid,7872,8.971067439,1,2,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/2/1992,64,tropicana,4096,8.317766167,0,2.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/2/1992,67,dominicks,4544,8.42156296,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/2/1992,67,minute.maid,8128,9.00307017,1,2.02,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/2/1992,67,tropicana,7936,8.979164649,0,2.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/2/1992,68,dominicks,11968,9.3899917,0,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/2/1992,68,minute.maid,25152,10.1326927,1,2,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/2/1992,68,tropicana,27264,10.21332243,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/2/1992,70,dominicks,14144,9.557045785,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/2/1992,70,minute.maid,11648,9.36288977,1,2.03,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/2/1992,70,tropicana,11648,9.36288977,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/2/1992,71,dominicks,10752,9.282847063,0,1.72,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/2/1992,71,minute.maid,14528,9.583833101,1,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/2/1992,71,tropicana,23488,10.06424493,0,2.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/2/1992,72,dominicks,6976,8.850230966,0,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/2/1992,72,minute.maid,33792,10.42797937,1,2.01,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/2/1992,72,tropicana,10368,9.246479419,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/2/1992,73,dominicks,23552,10.06696602,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/2/1992,73,minute.maid,26944,10.20151592,1,2,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/2/1992,73,tropicana,17536,9.77201119,0,2.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/2/1992,75,dominicks,34112,10.43740451,0,1.1,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/2/1992,75,minute.maid,15040,9.618468598,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/2/1992,75,tropicana,19072,9.85597657,0,2.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/2/1992,76,dominicks,18304,9.814874894,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/2/1992,76,minute.maid,30784,10.33475035,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/2/1992,76,tropicana,12032,9.395325046,0,2.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/2/1992,77,dominicks,6784,8.822322178,0,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/2/1992,77,minute.maid,10176,9.227787286,1,2.04,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/2/1992,77,tropicana,6848,8.831711918,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/2/1992,78,dominicks,12992,9.472089062,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/2/1992,78,minute.maid,9408,9.14931567,1,2.03,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/2/1992,78,tropicana,9216,9.128696383,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/2/1992,80,dominicks,7552,8.929567708,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/2/1992,80,minute.maid,21888,9.99369382,1,2.03,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/2/1992,80,tropicana,13696,9.524859098,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/2/1992,81,dominicks,6336,8.754002934,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/2/1992,81,minute.maid,17152,9.749870064,1,2.03,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/2/1992,81,tropicana,14720,9.596962392,0,2.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/2/1992,83,dominicks,11584,9.357380115,0,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/2/1992,83,minute.maid,33984,10.43364511,1,2,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/2/1992,83,tropicana,7232,8.886270902,0,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/2/1992,84,dominicks,5184,8.553332238,0,1.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/2/1992,84,minute.maid,18176,9.807857322,1,2.04,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/2/1992,84,tropicana,12864,9.462187991,0,2.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/2/1992,86,dominicks,6720,8.812843434,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/2/1992,86,minute.maid,26432,10.18233068,1,2.02,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/2/1992,86,tropicana,16704,9.723403491,0,2.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/2/1992,88,dominicks,4224,8.348537825,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/2/1992,88,minute.maid,13248,9.491601877,1,2.03,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/2/1992,88,tropicana,4352,8.378390789,0,2.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/2/1992,89,dominicks,7680,8.946374826,0,1.58,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/2/1992,89,minute.maid,25664,10.15284451,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/2/1992,89,tropicana,6336,8.754002934,0,2.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/2/1992,90,dominicks,6400,8.764053269,0,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/2/1992,90,minute.maid,12608,9.442086812,1,2.01,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/2/1992,90,tropicana,7168,8.877381955,0,2.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/2/1992,91,dominicks,10048,9.215128889,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/2/1992,91,minute.maid,13440,9.505990614,1,2.02,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/2/1992,91,tropicana,11072,9.312174678,0,2.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/2/1992,92,dominicks,5632,8.636219898,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/2/1992,92,minute.maid,12352,9.421573272,1,2.03,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/2/1992,92,tropicana,5568,8.624791202,0,2.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/2/1992,93,dominicks,7808,8.962904128,0,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/2/1992,93,minute.maid,18496,9.825309772,1,2.01,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/2/1992,93,tropicana,21248,9.964018052,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/2/1992,94,dominicks,7040,8.859363449,0,1.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/2/1992,94,minute.maid,13952,9.543378146,1,2.02,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/2/1992,94,tropicana,8448,9.041685006,0,2.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/2/1992,95,dominicks,6912,8.841014311,0,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/2/1992,95,minute.maid,14976,9.614204199,1,2.02,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/2/1992,95,tropicana,10304,9.240287448,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/2/1992,97,dominicks,3072,8.030084094,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/2/1992,97,minute.maid,7808,8.962904128,1,2.02,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/2/1992,97,tropicana,4160,8.333270353,0,2.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/2/1992,98,dominicks,10816,9.288781798,0,1.82,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/2/1992,98,minute.maid,23936,10.08313888,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/2/1992,98,tropicana,9664,9.17616292,0,2.69,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/2/1992,100,dominicks,6592,8.793612072,0,1.64,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/2/1992,100,minute.maid,32576,10.3913311,1,1.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/2/1992,100,tropicana,11904,9.384629757,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/2/1992,101,dominicks,6016,8.702177866,0,1.72,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/2/1992,101,minute.maid,16000,9.680344001,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/2/1992,101,tropicana,13568,9.515469358,0,2.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/2/1992,102,dominicks,21760,9.987828701,0,1.77,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/2/1992,102,minute.maid,24704,10.11472045,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/2/1992,102,tropicana,11008,9.30637756,0,2.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/2/1992,103,dominicks,8960,9.100525506,0,1.63,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/2/1992,103,minute.maid,9856,9.195835686,1,2,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/2/1992,103,tropicana,3584,8.184234774,0,2.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/2/1992,104,dominicks,4992,8.51559191,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/2/1992,104,minute.maid,10816,9.288781798,1,2.03,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/2/1992,104,tropicana,6720,8.812843434,0,2.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/2/1992,105,dominicks,6464,8.7740036,0,1.87,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/2/1992,105,minute.maid,13952,9.543378146,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/2/1992,105,tropicana,5504,8.61323038,0,2.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/2/1992,106,dominicks,2496,7.82244473,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/2/1992,106,minute.maid,6656,8.803273983,1,2.03,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/2/1992,106,tropicana,4288,8.363575703,0,2.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/2/1992,107,dominicks,10176,9.227787286,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/2/1992,107,minute.maid,20416,9.924074186,1,2.02,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/2/1992,107,tropicana,22592,10.02535114,0,2.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/2/1992,109,dominicks,4864,8.489616424,0,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/2/1992,109,minute.maid,26944,10.20151592,1,2.03,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/2/1992,109,tropicana,44544,10.70423274,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/2/1992,110,dominicks,4224,8.348537825,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/2/1992,110,minute.maid,12800,9.45720045,1,2.03,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/2/1992,110,tropicana,8704,9.071537969,0,2.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/2/1992,111,dominicks,7808,8.962904128,0,1.84,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/2/1992,111,minute.maid,48192,10.78294831,1,2,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/2/1992,111,tropicana,17024,9.742379392,0,2.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/2/1992,112,dominicks,7296,8.895081532,0,1.76,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/2/1992,112,minute.maid,23808,10.07777694,1,2,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/2/1992,112,tropicana,19456,9.875910785,0,2.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/2/1992,113,dominicks,6272,8.743850562,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/2/1992,113,minute.maid,41600,10.63585545,1,2.01,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/2/1992,113,tropicana,14400,9.574983486,0,2.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/2/1992,114,dominicks,12032,9.395325046,0,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/2/1992,114,minute.maid,27264,10.21332243,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/2/1992,114,tropicana,10688,9.276876896,0,2.68,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/2/1992,115,dominicks,3520,8.166216269,0,1.75,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/2/1992,115,minute.maid,31936,10.37148918,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/2/1992,115,tropicana,15360,9.639522007,0,2.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/2/1992,116,dominicks,6848,8.831711918,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/2/1992,116,minute.maid,10432,9.252633284,1,2.02,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/2/1992,116,tropicana,10368,9.246479419,0,2.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/2/1992,117,dominicks,4352,8.378390789,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/2/1992,117,minute.maid,11840,9.379238908,1,2.04,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/2/1992,117,tropicana,7040,8.859363449,0,2.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/2/1992,118,dominicks,6912,8.841014311,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/2/1992,118,minute.maid,23488,10.06424493,1,2.01,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/2/1992,118,tropicana,6272,8.743850562,0,2.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/2/1992,119,dominicks,4672,8.449342525,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/2/1992,119,minute.maid,13312,9.496421163,1,2.04,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/2/1992,119,tropicana,12160,9.405907156,0,2.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/2/1992,121,dominicks,5376,8.589699882,0,1.84,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/2/1992,121,minute.maid,15104,9.622714888,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/2/1992,121,tropicana,20928,9.948843254,0,2.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/2/1992,122,dominicks,12864,9.462187991,0,1.65,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/2/1992,122,minute.maid,23232,10.05328592,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/2/1992,122,tropicana,12288,9.416378455,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/2/1992,123,dominicks,10432,9.252633284,0,1.88,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/2/1992,123,minute.maid,36864,10.51499074,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/2/1992,123,tropicana,25536,10.1478445,0,2.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/2/1992,124,dominicks,13952,9.543378146,0,1.89,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/2/1992,124,minute.maid,18432,9.821843564,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/2/1992,124,tropicana,16768,9.727227587,0,2.66,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/2/1992,126,dominicks,3264,8.090708716,0,1.78,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/2/1992,126,minute.maid,28352,10.25245285,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/2/1992,126,tropicana,19136,9.859326657,0,2.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/2/1992,128,dominicks,7232,8.886270902,0,1.84,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/2/1992,128,minute.maid,31232,10.34919849,1,2,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/2/1992,128,tropicana,20992,9.951896692,0,2.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/2/1992,129,dominicks,3392,8.129174997,0,1.87,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/2/1992,129,minute.maid,19008,9.852615222,1,2,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/2/1992,129,tropicana,9600,9.169518378,0,2.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/2/1992,130,dominicks,27200,10.21097225,0,1.62,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/2/1992,130,minute.maid,43136,10.67211319,1,1.96,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/2/1992,130,tropicana,10752,9.282847063,0,2.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/2/1992,131,dominicks,4672,8.449342525,0,1.79,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/2/1992,131,minute.maid,27584,10.22499117,1,2,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/2/1992,131,tropicana,12224,9.411156511,0,2.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/2/1992,132,dominicks,13696,9.524859098,0,1.72,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/2/1992,132,minute.maid,29696,10.29876764,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/2/1992,132,tropicana,10944,9.30054664,0,2.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/2/1992,134,dominicks,4736,8.462948177,0,1.86,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/2/1992,134,minute.maid,11776,9.373818841,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/2/1992,134,tropicana,5184,8.553332238,0,2.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/2/1992,137,dominicks,6912,8.841014311,0,1.74,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/2/1992,137,minute.maid,32896,10.40110635,1,2,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/2/1992,137,tropicana,43200,10.67359577,0,2.67,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/9/1992,2,dominicks,10048,9.215128889,0,1.4,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/9/1992,2,minute.maid,3776,8.236420527,1,2.33,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/9/1992,2,tropicana,5696,8.647519453,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/9/1992,5,dominicks,19520,9.87919486,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/9/1992,5,minute.maid,6208,8.733594062,1,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/9/1992,5,tropicana,6848,8.831711918,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/9/1992,8,dominicks,24256,10.09641929,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/9/1992,8,minute.maid,6848,8.831711918,1,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/9/1992,8,tropicana,5696,8.647519453,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/9/1992,12,dominicks,5440,8.60153434,0,1.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/9/1992,12,minute.maid,4608,8.435549202,1,2.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/9/1992,12,tropicana,6336,8.754002934,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/9/1992,14,dominicks,11200,9.323669057,0,1.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/9/1992,14,minute.maid,5056,8.528330936,1,2.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/9/1992,14,tropicana,11392,9.340666634,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/9/1992,18,dominicks,18432,9.821843564,0,1.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/9/1992,18,minute.maid,6016,8.702177866,1,2.19,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/9/1992,18,tropicana,8576,9.056722883,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/9/1992,21,dominicks,8256,9.018695488,0,1.45,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/9/1992,21,minute.maid,2944,7.98752448,1,2.23,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/9/1992,21,tropicana,3968,8.286017468,0,2.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/9/1992,28,dominicks,4160,8.333270353,0,1.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/9/1992,28,minute.maid,4160,8.333270353,1,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/9/1992,28,tropicana,4288,8.363575703,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/9/1992,32,dominicks,10368,9.246479419,0,1.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/9/1992,32,minute.maid,8832,9.086136769,1,2.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/9/1992,32,tropicana,15424,9.643680017,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/9/1992,33,dominicks,6528,8.783855897,0,1.4,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/9/1992,33,minute.maid,7232,8.886270902,1,2.37,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/9/1992,33,tropicana,10624,9.270870872,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/9/1992,40,dominicks,5888,8.68067166,0,1.44,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/9/1992,40,minute.maid,6144,8.723231275,1,2.23,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/9/1992,40,tropicana,4096,8.317766167,0,2.74,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/9/1992,44,dominicks,15744,9.664214619,0,1.39,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/9/1992,44,minute.maid,6976,8.850230966,1,2.34,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/9/1992,44,tropicana,8640,9.064157862,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/9/1992,45,dominicks,5888,8.68067166,0,1.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/9/1992,45,minute.maid,3392,8.129174997,1,2.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/9/1992,45,tropicana,6208,8.733594062,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/9/1992,47,dominicks,5696,8.647519453,0,1.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/9/1992,47,minute.maid,3904,8.269756948,1,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/9/1992,47,tropicana,6400,8.764053269,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/9/1992,48,dominicks,4864,8.489616424,0,1.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/9/1992,48,minute.maid,4736,8.462948177,1,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/9/1992,48,tropicana,7808,8.962904128,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/9/1992,49,dominicks,5312,8.577723691,0,1.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/9/1992,49,minute.maid,3520,8.166216269,1,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/9/1992,49,tropicana,3520,8.166216269,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/9/1992,50,dominicks,12288,9.416378455,0,1.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/9/1992,50,minute.maid,3008,8.009030685,1,2.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/9/1992,50,tropicana,4288,8.363575703,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/9/1992,51,dominicks,16576,9.715711145,0,1.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/9/1992,51,minute.maid,3328,8.110126802,1,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/9/1992,51,tropicana,4160,8.333270353,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/9/1992,52,dominicks,9792,9.189321005,0,1.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/9/1992,52,minute.maid,7104,8.868413285,1,2.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/9/1992,52,tropicana,12288,9.416378455,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/9/1992,53,dominicks,18432,9.821843564,0,1.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/9/1992,53,minute.maid,9856,9.195835686,1,2.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/9/1992,53,tropicana,8640,9.064157862,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/9/1992,54,dominicks,8000,8.987196821,0,1.43,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/9/1992,54,minute.maid,5184,8.553332238,1,2.35,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/9/1992,54,tropicana,5888,8.68067166,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/9/1992,56,dominicks,5056,8.528330936,0,1.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/9/1992,56,minute.maid,2688,7.896552702,1,2.19,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/9/1992,56,tropicana,6976,8.850230966,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/9/1992,59,dominicks,12416,9.426741242,0,1.38,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/9/1992,59,minute.maid,3200,8.070906089,1,2.22,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/9/1992,59,tropicana,31616,10.3614186,0,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/9/1992,62,dominicks,8192,9.010913347,0,1.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/9/1992,62,minute.maid,5632,8.636219898,1,2.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/9/1992,62,tropicana,18432,9.821843564,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/9/1992,64,dominicks,7808,8.962904128,0,1.26,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/9/1992,64,minute.maid,3136,8.050703382,1,2.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/9/1992,64,tropicana,2624,7.87245515,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/9/1992,67,dominicks,11712,9.368369236,0,1.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/9/1992,67,minute.maid,3584,8.184234774,1,2.19,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/9/1992,67,tropicana,6912,8.841014311,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/9/1992,68,dominicks,7104,8.868413285,0,1.29,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/9/1992,68,minute.maid,5632,8.636219898,1,2.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/9/1992,68,tropicana,27968,10.23881628,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/9/1992,70,dominicks,19008,9.852615222,0,1.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/9/1992,70,minute.maid,6720,8.812843434,1,2.19,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/9/1992,70,tropicana,8896,9.093357017,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/9/1992,71,dominicks,10432,9.252633284,0,1.36,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/9/1992,71,minute.maid,3072,8.030084094,1,2.44,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/9/1992,71,tropicana,4928,8.502688505,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/9/1992,72,dominicks,8512,9.049232212,0,1.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/9/1992,72,minute.maid,10048,9.215128889,1,2.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/9/1992,72,tropicana,9024,9.107642974,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/9/1992,73,dominicks,17856,9.790094865,0,1.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/9/1992,73,minute.maid,4544,8.42156296,1,2.19,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/9/1992,73,tropicana,8960,9.100525506,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/9/1992,74,dominicks,10688,9.276876896,0,1.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/9/1992,74,minute.maid,7296,8.895081532,1,2.19,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/9/1992,74,tropicana,8576,9.056722883,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/9/1992,75,dominicks,12096,9.400630098,0,1.42,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/9/1992,75,minute.maid,4992,8.51559191,1,2.41,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/9/1992,75,tropicana,6272,8.743850562,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/9/1992,76,dominicks,7808,8.962904128,0,1.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/9/1992,76,minute.maid,3648,8.201934351,1,2.19,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/9/1992,76,tropicana,6144,8.723231275,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/9/1992,77,dominicks,13312,9.496421163,0,1.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/9/1992,77,minute.maid,3200,8.070906089,1,2.19,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/9/1992,77,tropicana,8576,9.056722883,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -7/9/1992,78,dominicks,16256,9.69621735,0,1.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/9/1992,78,minute.maid,3200,8.070906089,1,2.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/9/1992,78,tropicana,5888,8.68067166,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/9/1992,80,dominicks,11840,9.379238908,0,1.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/9/1992,80,minute.maid,7808,8.962904128,1,2.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/9/1992,80,tropicana,11264,9.329367078,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/9/1992,81,dominicks,7424,8.912473275,0,1.29,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/9/1992,81,minute.maid,5632,8.636219898,1,2.19,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/9/1992,81,tropicana,9408,9.14931567,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -7/9/1992,83,dominicks,9280,9.135616826,0,1.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/9/1992,83,minute.maid,5376,8.589699882,1,2.19,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/9/1992,83,tropicana,9984,9.208739091,0,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/9/1992,84,dominicks,4544,8.42156296,0,1.29,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/9/1992,84,minute.maid,4480,8.407378325,1,2.19,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/9/1992,84,tropicana,7360,8.903815212,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -7/9/1992,86,dominicks,11904,9.384629757,0,1.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/9/1992,86,minute.maid,7552,8.929567708,1,2.19,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/9/1992,86,tropicana,13696,9.524859098,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/9/1992,88,dominicks,7360,8.903815212,0,1.29,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/9/1992,88,minute.maid,4416,8.392989588,1,2.19,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/9/1992,88,tropicana,3392,8.129174997,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/9/1992,89,dominicks,5888,8.68067166,0,1.43,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/9/1992,89,minute.maid,3712,8.219326094,1,2.28,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/9/1992,89,tropicana,3392,8.129174997,0,2.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/9/1992,90,dominicks,8896,9.093357017,0,1.29,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/9/1992,90,minute.maid,3584,8.184234774,1,2.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/9/1992,90,tropicana,4800,8.476371197,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 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-7/9/1992,92,tropicana,3584,8.184234774,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/9/1992,93,dominicks,4416,8.392989588,0,1.29,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/9/1992,93,minute.maid,6272,8.743850562,1,2.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/9/1992,93,tropicana,9472,9.156095357,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/9/1992,94,dominicks,4928,8.502688505,0,1.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/9/1992,94,minute.maid,5504,8.61323038,1,2.19,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/9/1992,94,tropicana,4160,8.333270353,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/9/1992,95,dominicks,17600,9.775654181,0,1.29,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/9/1992,95,minute.maid,4224,8.348537825,1,2.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/9/1992,95,tropicana,3008,8.009030685,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/9/1992,97,dominicks,3392,8.129174997,0,1.29,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/9/1992,97,minute.maid,2688,7.896552702,1,2.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/9/1992,97,tropicana,2240,7.714231145,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/9/1992,98,dominicks,21312,9.967025573,0,1.43,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/9/1992,98,minute.maid,4992,8.51559191,1,2.35,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/9/1992,98,tropicana,6400,8.764053269,0,2.95,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/9/1992,100,dominicks,25984,10.16523624,0,1.29,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/9/1992,100,minute.maid,4544,8.42156296,1,2.27,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/9/1992,100,tropicana,8832,9.086136769,0,2.98,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/9/1992,101,dominicks,10624,9.270870872,0,1.45,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/9/1992,101,minute.maid,4160,8.333270353,1,2.35,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/9/1992,101,tropicana,7808,8.962904128,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/9/1992,102,dominicks,24000,10.08580911,0,1.39,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/9/1992,102,minute.maid,4736,8.462948177,1,2.29,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/9/1992,102,tropicana,7296,8.895081532,0,2.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/9/1992,103,dominicks,32064,10.37548918,0,1.26,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/9/1992,103,minute.maid,2176,7.685243608,1,2.26,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/9/1992,103,tropicana,2816,7.943072717,0,2.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/9/1992,104,dominicks,8256,9.018695488,0,1.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/9/1992,104,minute.maid,2304,7.742402022,1,2.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/9/1992,104,tropicana,3904,8.269756948,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/9/1992,105,dominicks,6272,8.743850562,0,1.38,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/9/1992,105,minute.maid,3072,8.030084094,1,2.46,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/9/1992,105,tropicana,3712,8.219326094,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/9/1992,106,dominicks,4480,8.407378325,0,1.29,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/9/1992,106,minute.maid,1920,7.560080465,1,2.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/9/1992,106,tropicana,1728,7.454719949,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/9/1992,110,dominicks,10816,9.288781798,0,1.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/9/1992,110,minute.maid,3392,8.129174997,1,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/9/1992,110,tropicana,3840,8.253227646,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/9/1992,111,dominicks,3392,8.129174997,0,1.5,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/9/1992,111,minute.maid,3200,8.070906089,1,2.35,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/9/1992,111,tropicana,4224,8.348537825,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/9/1992,112,dominicks,17856,9.790094865,0,1.4,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/9/1992,112,minute.maid,6848,8.831711918,1,2.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/9/1992,112,tropicana,9664,9.17616292,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/9/1992,114,dominicks,16384,9.704060528,0,1.32,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/9/1992,114,minute.maid,4096,8.317766167,1,2.37,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/9/1992,114,tropicana,4608,8.435549202,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/9/1992,115,dominicks,7808,8.962904128,0,1.34,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/9/1992,115,minute.maid,5312,8.577723691,1,2.36,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/9/1992,115,tropicana,8384,9.034080407,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/9/1992,117,dominicks,5952,8.691482577,0,1.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/9/1992,117,minute.maid,2880,7.965545573,1,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/9/1992,117,tropicana,5568,8.624791202,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/9/1992,118,dominicks,7872,8.971067439,0,1.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/9/1992,118,minute.maid,7808,8.962904128,1,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/9/1992,118,tropicana,5312,8.577723691,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/9/1992,119,dominicks,6784,8.822322178,0,1.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/9/1992,119,minute.maid,7296,8.895081532,1,2.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/9/1992,119,tropicana,6784,8.822322178,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/9/1992,121,dominicks,15808,9.66827142,0,1.31,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/9/1992,121,minute.maid,5312,8.577723691,1,2.34,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/9/1992,121,tropicana,10816,9.288781798,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/9/1992,122,dominicks,20224,9.914625297,0,1.15,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/9/1992,122,minute.maid,7296,8.895081532,1,2.21,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/9/1992,122,tropicana,11008,9.30637756,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/9/1992,123,dominicks,6336,8.754002934,0,1.39,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/9/1992,123,minute.maid,4096,8.317766167,1,2.32,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/9/1992,123,tropicana,3328,8.110126802,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/9/1992,124,dominicks,12480,9.431882642,0,1.45,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/9/1992,124,minute.maid,4736,8.462948177,1,2.43,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/9/1992,124,tropicana,5824,8.66974259,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/9/1992,126,dominicks,2752,7.920083199,0,1.42,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/9/1992,126,minute.maid,12864,9.462187991,1,2.33,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/9/1992,126,tropicana,14464,9.579418083,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/9/1992,128,dominicks,8448,9.041685006,0,1.62,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/9/1992,128,minute.maid,5568,8.624791202,1,2.32,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/9/1992,128,tropicana,6720,8.812843434,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/9/1992,129,dominicks,12992,9.472089062,0,1.36,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/9/1992,129,minute.maid,11520,9.351839934,1,2.32,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/9/1992,129,tropicana,8576,9.056722883,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/9/1992,130,dominicks,44032,10.69267192,0,1.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/9/1992,130,minute.maid,7744,8.954673629,1,2.1,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/9/1992,130,tropicana,3392,8.129174997,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/9/1992,131,dominicks,11456,9.346268889,0,1.3,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/9/1992,131,minute.maid,8000,8.987196821,1,2.18,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/9/1992,131,tropicana,6400,8.764053269,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/9/1992,132,dominicks,28160,10.24565781,0,1.34,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/9/1992,132,minute.maid,7936,8.979164649,1,2.27,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/9/1992,132,tropicana,5376,8.589699882,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/9/1992,134,dominicks,3840,8.253227646,0,1.39,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/9/1992,134,minute.maid,3008,8.009030685,1,2.34,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/9/1992,134,tropicana,3520,8.166216269,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/9/1992,137,dominicks,32128,10.3774832,0,1.32,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/9/1992,137,minute.maid,11392,9.340666634,1,2.38,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/9/1992,137,tropicana,26624,10.18956834,0,3.15,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/16/1992,2,dominicks,10112,9.221478116,0,1.91,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/16/1992,2,minute.maid,4800,8.476371197,0,2.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/16/1992,2,tropicana,6848,8.831711918,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/16/1992,5,dominicks,7872,8.971067439,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/16/1992,5,minute.maid,7872,8.971067439,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/16/1992,5,tropicana,8064,8.99516499,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/16/1992,8,dominicks,19968,9.901886271,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/16/1992,8,minute.maid,8192,9.010913347,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/16/1992,8,tropicana,7680,8.946374826,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/16/1992,9,dominicks,7296,8.895081532,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/16/1992,9,minute.maid,4096,8.317766167,0,2.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/16/1992,9,tropicana,7424,8.912473275,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/16/1992,12,dominicks,15808,9.66827142,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/16/1992,12,minute.maid,7424,8.912473275,0,2.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/16/1992,12,tropicana,8192,9.010913347,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/16/1992,14,dominicks,6784,8.822322178,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/16/1992,14,minute.maid,6016,8.702177866,0,2.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/16/1992,14,tropicana,12032,9.395325046,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/16/1992,21,dominicks,9024,9.107642974,0,1.76,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/16/1992,21,minute.maid,4032,8.30201781,0,2.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/16/1992,21,tropicana,3456,8.14786713,0,2.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/16/1992,28,dominicks,3648,8.201934351,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/16/1992,28,minute.maid,4160,8.333270353,0,2.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/16/1992,28,tropicana,4288,8.363575703,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/16/1992,32,dominicks,6528,8.783855897,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/16/1992,32,minute.maid,10176,9.227787286,0,2.89,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/16/1992,32,tropicana,12864,9.462187991,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/16/1992,33,dominicks,6080,8.712759975,0,1.93,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/16/1992,33,minute.maid,6272,8.743850562,0,2.89,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/16/1992,33,tropicana,8576,9.056722883,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/16/1992,40,dominicks,11200,9.323669057,0,1.76,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/16/1992,40,minute.maid,4864,8.489616424,0,2.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/16/1992,40,tropicana,4864,8.489616424,0,2.89,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/16/1992,44,dominicks,6656,8.803273983,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/16/1992,44,minute.maid,8128,9.00307017,0,2.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/16/1992,44,tropicana,11456,9.346268889,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/16/1992,45,dominicks,5120,8.540909718,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/16/1992,45,minute.maid,3904,8.269756948,0,2.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/16/1992,45,tropicana,5760,8.658692754,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/16/1992,47,dominicks,6592,8.793612072,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/16/1992,47,minute.maid,4160,8.333270353,0,2.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/16/1992,47,tropicana,3968,8.286017468,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/16/1992,48,dominicks,4096,8.317766167,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/16/1992,48,minute.maid,5632,8.636219898,0,2.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/16/1992,48,tropicana,6720,8.812843434,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/16/1992,50,dominicks,6336,8.754002934,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/16/1992,50,minute.maid,3520,8.166216269,0,2.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/16/1992,50,tropicana,3264,8.090708716,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/16/1992,51,dominicks,3584,8.184234774,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/16/1992,51,minute.maid,7104,8.868413285,0,2.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/16/1992,51,tropicana,4352,8.378390789,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/16/1992,52,dominicks,5184,8.553332238,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/16/1992,52,minute.maid,7552,8.929567708,0,2.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/16/1992,52,tropicana,11456,9.346268889,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/16/1992,53,dominicks,4672,8.449342525,0,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/16/1992,53,minute.maid,8256,9.018695488,0,2.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/16/1992,53,tropicana,12544,9.436997743,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/16/1992,54,dominicks,6784,8.822322178,0,1.88,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/16/1992,54,minute.maid,3392,8.129174997,0,2.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/16/1992,54,tropicana,5248,8.565602331,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/16/1992,56,dominicks,8640,9.064157862,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/16/1992,56,minute.maid,13632,9.520175249,0,2.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/16/1992,56,tropicana,6336,8.754002934,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/16/1992,59,dominicks,4096,8.317766167,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/16/1992,59,minute.maid,2752,7.920083199,0,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/16/1992,59,tropicana,35008,10.46333189,0,1.85,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/16/1992,62,dominicks,6208,8.733594062,0,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/16/1992,62,minute.maid,5888,8.68067166,0,2.89,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/16/1992,62,tropicana,14272,9.566054855,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/16/1992,64,dominicks,4864,8.489616424,0,1.93,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/16/1992,64,minute.maid,2944,7.98752448,0,2.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/16/1992,64,tropicana,2304,7.742402022,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/16/1992,67,dominicks,7744,8.954673629,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/16/1992,67,minute.maid,4992,8.51559191,0,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/16/1992,67,tropicana,5696,8.647519453,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/16/1992,68,dominicks,9984,9.208739091,0,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/16/1992,68,minute.maid,6336,8.754002934,0,2.89,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/16/1992,68,tropicana,5824,8.66974259,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/16/1992,70,dominicks,9472,9.156095357,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/16/1992,70,minute.maid,6656,8.803273983,0,2.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/16/1992,70,tropicana,9280,9.135616826,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/16/1992,71,dominicks,10496,9.258749511,0,1.99,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/16/1992,71,minute.maid,5184,8.553332238,0,2.89,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/16/1992,71,tropicana,7744,8.954673629,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/16/1992,72,dominicks,6016,8.702177866,0,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/16/1992,72,minute.maid,5696,8.647519453,0,2.89,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/16/1992,72,tropicana,9216,9.128696383,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/16/1992,73,dominicks,26752,10.19436452,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/16/1992,73,minute.maid,9024,9.107642974,0,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/16/1992,73,tropicana,9600,9.169518378,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/16/1992,74,dominicks,15232,9.631153757,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/16/1992,74,minute.maid,6528,8.783855897,0,2.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/16/1992,74,tropicana,8768,9.078864009,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 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-7/16/1992,86,minute.maid,3712,8.219326094,0,2.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/16/1992,86,tropicana,8000,8.987196821,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/16/1992,88,dominicks,5632,8.636219898,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/16/1992,88,minute.maid,5696,8.647519453,0,2.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/16/1992,88,tropicana,4032,8.30201781,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 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-7/16/1992,92,minute.maid,3840,8.253227646,0,2.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/16/1992,92,tropicana,4096,8.317766167,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/16/1992,93,dominicks,5248,8.565602331,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/16/1992,93,minute.maid,7424,8.912473275,0,2.89,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/16/1992,93,tropicana,9664,9.17616292,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 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-7/16/1992,98,minute.maid,15360,9.639522007,0,2.22,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/16/1992,98,tropicana,7872,8.971067439,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/16/1992,100,dominicks,11008,9.30637756,0,1.86,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/16/1992,100,minute.maid,5760,8.658692754,0,2.69,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/16/1992,100,tropicana,8576,9.056722883,0,2.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/16/1992,101,dominicks,14464,9.579418083,0,1.09,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/16/1992,101,minute.maid,5312,8.577723691,0,2.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/16/1992,101,tropicana,8832,9.086136769,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/16/1992,102,dominicks,22784,10.03381381,0,1.77,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/16/1992,102,minute.maid,6080,8.712759975,0,2.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/16/1992,102,tropicana,6400,8.764053269,0,2.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/16/1992,103,dominicks,7936,8.979164649,0,1.75,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/16/1992,103,minute.maid,2560,7.847762538,0,2.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/16/1992,103,tropicana,2688,7.896552702,0,2.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/16/1992,104,dominicks,4608,8.435549202,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/16/1992,104,minute.maid,3136,8.050703382,0,2.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/16/1992,104,tropicana,4288,8.363575703,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/16/1992,105,dominicks,9536,9.162829389,0,1.86,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/16/1992,105,minute.maid,3840,8.253227646,0,2.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/16/1992,105,tropicana,3840,8.253227646,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/16/1992,106,dominicks,3584,8.184234774,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/16/1992,106,minute.maid,2944,7.98752448,0,2.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/16/1992,106,tropicana,2048,7.624618986,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/16/1992,107,dominicks,14336,9.570529135,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/16/1992,107,minute.maid,6656,8.803273983,0,2.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/16/1992,107,tropicana,11456,9.346268889,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/16/1992,109,dominicks,4800,8.476371197,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/16/1992,109,minute.maid,33472,10.41846455,0,2.89,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/16/1992,109,tropicana,14848,9.605620455,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/16/1992,110,dominicks,11008,9.30637756,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/16/1992,110,minute.maid,3712,8.219326094,0,2.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/16/1992,110,tropicana,4352,8.378390789,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/16/1992,111,dominicks,9536,9.162829389,0,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/16/1992,111,minute.maid,9088,9.114710141,0,2.89,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/16/1992,111,tropicana,5184,8.553332238,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/16/1992,112,dominicks,10624,9.270870872,0,1.75,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/16/1992,112,minute.maid,8704,9.071537969,0,2.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/16/1992,112,tropicana,10624,9.270870872,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/16/1992,113,dominicks,11008,9.30637756,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/16/1992,113,minute.maid,8064,8.99516499,0,2.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/16/1992,113,tropicana,10112,9.221478116,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/16/1992,114,dominicks,14208,9.561560465,0,1.82,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/16/1992,114,minute.maid,5632,8.636219898,0,2.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/16/1992,114,tropicana,5952,8.691482577,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/16/1992,115,dominicks,7040,8.859363449,0,1.88,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/16/1992,115,minute.maid,5376,8.589699882,0,2.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/16/1992,115,tropicana,8128,9.00307017,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/16/1992,116,dominicks,10176,9.227787286,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/16/1992,116,minute.maid,4352,8.378390789,0,2.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/16/1992,116,tropicana,4928,8.502688505,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/16/1992,117,dominicks,4864,8.489616424,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/16/1992,117,minute.maid,3904,8.269756948,0,2.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/16/1992,117,tropicana,3968,8.286017468,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/16/1992,118,dominicks,7168,8.877381955,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/16/1992,118,minute.maid,5056,8.528330936,0,2.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/16/1992,118,tropicana,5632,8.636219898,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/16/1992,119,dominicks,7808,8.962904128,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/16/1992,119,minute.maid,6464,8.7740036,0,2.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/16/1992,119,tropicana,6976,8.850230966,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/16/1992,121,dominicks,6720,8.812843434,0,1.9,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/16/1992,121,minute.maid,6272,8.743850562,0,2.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/16/1992,121,tropicana,10752,9.282847063,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/16/1992,122,dominicks,12928,9.467150781,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/16/1992,122,minute.maid,9920,9.2023082,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/16/1992,122,tropicana,7872,8.971067439,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/16/1992,123,dominicks,13376,9.501217335,0,1.76,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/16/1992,123,minute.maid,6656,8.803273983,0,2.88,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/16/1992,123,tropicana,5440,8.60153434,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/16/1992,124,dominicks,11264,9.329367078,0,2.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/16/1992,124,minute.maid,6656,8.803273983,0,2.89,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/16/1992,124,tropicana,3520,8.166216269,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/16/1992,126,dominicks,13120,9.481893063,0,1.51,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/16/1992,126,minute.maid,8640,9.064157862,0,2.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/16/1992,126,tropicana,13952,9.543378146,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/16/1992,128,dominicks,8640,9.064157862,0,1.96,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/16/1992,128,minute.maid,8704,9.071537969,0,2.89,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/16/1992,128,tropicana,9728,9.182763604,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/16/1992,129,dominicks,5696,8.647519453,0,1.9,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/16/1992,129,minute.maid,9152,9.121727714,0,2.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/16/1992,129,tropicana,8704,9.071537969,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/16/1992,130,dominicks,16896,9.734832187,0,1.9,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/16/1992,130,minute.maid,4928,8.502688505,0,2.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/16/1992,130,tropicana,4416,8.392989588,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/16/1992,131,dominicks,10752,9.282847063,0,1.78,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/16/1992,131,minute.maid,6336,8.754002934,0,2.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/16/1992,131,tropicana,8256,9.018695488,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/16/1992,132,dominicks,17792,9.786504197,0,1.77,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/16/1992,132,minute.maid,6016,8.702177866,0,2.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/16/1992,132,tropicana,5888,8.68067166,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/16/1992,134,dominicks,12288,9.416378455,0,1.24,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/16/1992,134,minute.maid,3392,8.129174997,0,2.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/16/1992,134,tropicana,3648,8.201934351,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/16/1992,137,dominicks,19328,9.869310101,0,1.5,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/16/1992,137,minute.maid,18432,9.821843564,0,2.89,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/16/1992,137,tropicana,27712,10.22962081,0,3.18,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/23/1992,2,dominicks,9152,9.121727714,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/23/1992,2,minute.maid,24960,10.12502982,1,2.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/23/1992,2,tropicana,4416,8.392989588,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/23/1992,5,dominicks,5184,8.553332238,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/23/1992,5,minute.maid,54528,10.90646961,1,2.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/23/1992,5,tropicana,4992,8.51559191,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/23/1992,8,dominicks,15936,9.67633598,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/23/1992,8,minute.maid,55040,10.91581547,1,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/23/1992,8,tropicana,5440,8.60153434,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/23/1992,9,dominicks,6976,8.850230966,0,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/23/1992,9,minute.maid,50240,10.8245668,1,2.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/23/1992,9,tropicana,5888,8.68067166,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/23/1992,12,dominicks,19840,9.895455381,0,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/23/1992,12,minute.maid,59520,10.99406767,1,2.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/23/1992,12,tropicana,6784,8.822322178,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/23/1992,14,dominicks,5312,8.577723691,0,1.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/23/1992,14,minute.maid,39168,10.57561537,1,2.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/23/1992,14,tropicana,8896,9.093357017,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/23/1992,18,dominicks,15232,9.631153757,0,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/23/1992,18,minute.maid,34368,10.44488118,1,2.29,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/23/1992,18,tropicana,6400,8.764053269,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/23/1992,21,dominicks,6272,8.743850562,0,1.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/23/1992,21,minute.maid,30656,10.33058368,1,2.29,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/23/1992,21,tropicana,3008,8.009030685,0,2.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/23/1992,28,dominicks,5568,8.624791202,0,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/23/1992,28,minute.maid,10944,9.30054664,1,2.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/23/1992,28,tropicana,3328,8.110126802,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/23/1992,32,dominicks,9856,9.195835686,0,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/23/1992,32,minute.maid,58432,10.97561896,1,2.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/23/1992,32,tropicana,8896,9.093357017,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/23/1992,33,dominicks,10176,9.227787286,0,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/23/1992,33,minute.maid,33088,10.40692596,1,2.29,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/23/1992,33,tropicana,9152,9.121727714,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/23/1992,40,dominicks,8256,9.018695488,0,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/23/1992,40,minute.maid,39552,10.58537154,1,2.29,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/23/1992,40,tropicana,2688,7.896552702,0,2.89,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/23/1992,44,dominicks,9024,9.107642974,0,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/23/1992,44,minute.maid,41152,10.62502781,1,2.29,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/23/1992,44,tropicana,6080,8.712759975,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/23/1992,45,dominicks,6016,8.702177866,0,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/23/1992,45,minute.maid,18304,9.814874894,1,2.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/23/1992,45,tropicana,4032,8.30201781,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/23/1992,47,dominicks,12224,9.411156511,0,1.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/23/1992,47,minute.maid,12864,9.462187991,1,2.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/23/1992,47,tropicana,3456,8.14786713,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/23/1992,48,dominicks,6656,8.803273983,0,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/23/1992,48,minute.maid,11968,9.3899917,1,2.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/23/1992,48,tropicana,4800,8.476371197,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/23/1992,49,dominicks,3968,8.286017468,0,1.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/23/1992,49,minute.maid,20672,9.936535407,1,2.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/23/1992,49,tropicana,2560,7.847762538,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/23/1992,50,dominicks,10176,9.227787286,0,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/23/1992,50,minute.maid,10944,9.30054664,1,2.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/23/1992,50,tropicana,2496,7.82244473,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/23/1992,51,dominicks,4096,8.317766167,0,1.69,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/23/1992,51,minute.maid,32896,10.40110635,1,2.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/23/1992,51,tropicana,2368,7.769800996,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/23/1992,52,dominicks,10432,9.252633284,0,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/23/1992,52,minute.maid,19648,9.885730831,1,2.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/23/1992,52,tropicana,8512,9.049232212,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/23/1992,53,dominicks,8640,9.064157862,0,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/23/1992,53,minute.maid,59136,10.98759516,1,2.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/23/1992,53,tropicana,7168,8.877381955,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/23/1992,54,dominicks,5056,8.528330936,0,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/23/1992,54,minute.maid,25088,10.13014492,1,2.29,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/23/1992,54,tropicana,4672,8.449342525,0,2.99,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/23/1992,56,dominicks,3136,8.050703382,0,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/23/1992,56,minute.maid,22016,9.999524741,1,2.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/23/1992,56,tropicana,3200,8.070906089,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/23/1992,59,dominicks,2688,7.896552702,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/23/1992,59,minute.maid,17408,9.76468515,1,2.22,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/23/1992,59,tropicana,52544,10.86940619,0,1.32,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/23/1992,62,dominicks,1856,7.526178913,0,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/23/1992,62,minute.maid,29888,10.30521234,1,2.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/23/1992,62,tropicana,9408,9.14931567,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/23/1992,64,dominicks,7360,8.903815212,0,1.69,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/23/1992,64,minute.maid,15168,9.626943225,1,2.29,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/23/1992,64,tropicana,1728,7.454719949,0,2.99,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/23/1992,67,dominicks,5632,8.636219898,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/23/1992,67,minute.maid,42560,10.65867012,1,2.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/23/1992,67,tropicana,4352,8.378390789,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/23/1992,68,dominicks,8000,8.987196821,0,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/23/1992,68,minute.maid,52032,10.85961419,1,2.29,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/23/1992,68,tropicana,3776,8.236420527,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/23/1992,70,dominicks,7744,8.954673629,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/23/1992,70,minute.maid,37952,10.54407748,1,2.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/23/1992,70,tropicana,9280,9.135616826,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/23/1992,71,dominicks,12160,9.405907156,0,1.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/23/1992,71,minute.maid,45248,10.71991375,1,2.29,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/23/1992,71,tropicana,3776,8.236420527,0,3.19,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/23/1992,72,dominicks,6656,8.803273983,0,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/23/1992,72,minute.maid,62336,11.04029439,1,2.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/23/1992,72,tropicana,6976,8.850230966,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/23/1992,73,dominicks,25344,10.1402973,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/23/1992,73,minute.maid,48000,10.77895629,1,2.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/23/1992,73,tropicana,7616,8.938006577,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/23/1992,74,dominicks,12928,9.467150781,0,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/23/1992,74,minute.maid,39104,10.57398004,1,2.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/23/1992,74,tropicana,8256,9.018695488,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/23/1992,75,dominicks,8000,8.987196821,0,2,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/23/1992,75,minute.maid,80768,11.29933613,1,1.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/23/1992,75,tropicana,7104,8.868413285,0,3.19,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -7/23/1992,76,dominicks,12480,9.431882642,0,1.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/23/1992,76,minute.maid,44480,10.70279493,1,2.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/23/1992,76,tropicana,5120,8.540909718,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -7/23/1992,78,dominicks,5248,8.565602331,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/23/1992,78,minute.maid,37120,10.52191119,1,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/23/1992,78,tropicana,5376,8.589699882,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -7/23/1992,80,dominicks,5888,8.68067166,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/23/1992,80,minute.maid,38976,10.57070135,1,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/23/1992,80,tropicana,9472,9.156095357,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -7/23/1992,83,dominicks,7360,8.903815212,0,1.69,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/23/1992,83,minute.maid,50240,10.8245668,1,2.29,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/23/1992,83,tropicana,6912,8.841014311,0,2.59,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -7/23/1992,86,dominicks,11008,9.30637756,0,1.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/23/1992,86,minute.maid,38016,10.5457624,1,2.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/23/1992,86,tropicana,4224,8.348537825,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -7/23/1992,88,dominicks,1216,7.103322063,0,1.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/23/1992,88,minute.maid,28736,10.26590597,1,2.29,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/23/1992,88,tropicana,2368,7.769800996,0,2.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -7/23/1992,89,dominicks,8832,9.086136769,0,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/23/1992,89,minute.maid,53312,10.88391673,1,2.29,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/23/1992,89,tropicana,3264,8.090708716,0,2.89,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -7/23/1992,90,dominicks,4800,8.476371197,0,1.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/23/1992,90,minute.maid,34048,10.43552657,1,2.29,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/23/1992,90,tropicana,2240,7.714231145,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -7/23/1992,91,dominicks,14720,9.596962392,0,1.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/23/1992,91,minute.maid,23424,10.06151642,1,2.29,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/23/1992,91,tropicana,2944,7.98752448,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -7/23/1992,92,dominicks,6848,8.831711918,0,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/23/1992,92,minute.maid,36224,10.49747716,1,2.29,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/23/1992,92,tropicana,2048,7.624618986,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -7/23/1992,93,dominicks,3136,8.050703382,0,1.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/23/1992,93,minute.maid,51648,10.85220675,1,2.29,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/23/1992,93,tropicana,6080,8.712759975,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -7/23/1992,94,dominicks,6912,8.841014311,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/23/1992,94,minute.maid,27392,10.21800628,1,2.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/23/1992,94,tropicana,5312,8.577723691,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -7/23/1992,95,dominicks,8384,9.034080407,0,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/23/1992,95,minute.maid,63680,11.06162582,1,2.29,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/23/1992,95,tropicana,3136,8.050703382,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -7/23/1992,97,dominicks,3840,8.253227646,0,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/23/1992,97,minute.maid,23936,10.08313888,1,2.29,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/23/1992,97,tropicana,1152,7.049254841,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -7/23/1992,98,dominicks,9408,9.14931567,0,1.68,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/23/1992,98,minute.maid,46272,10.74229231,1,2.29,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/23/1992,98,tropicana,4992,8.51559191,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -7/23/1992,100,dominicks,11520,9.351839934,0,1.68,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/23/1992,100,minute.maid,49152,10.80267282,1,2.29,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/23/1992,100,tropicana,4544,8.42156296,0,2.99,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -7/23/1992,101,dominicks,10176,9.227787286,0,1.69,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/23/1992,101,minute.maid,22528,10.02251426,1,2.29,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/23/1992,101,tropicana,6720,8.812843434,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -7/23/1992,102,dominicks,13056,9.477003077,0,1.68,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/23/1992,102,minute.maid,57856,10.96571244,1,2.29,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/23/1992,102,tropicana,4672,8.449342525,0,2.89,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -7/23/1992,103,dominicks,7744,8.954673629,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/23/1992,103,minute.maid,23936,10.08313888,1,2.29,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/23/1992,103,tropicana,1984,7.592870288,0,2.89,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -7/23/1992,104,dominicks,5120,8.540909718,0,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/23/1992,104,minute.maid,19904,9.898675996,1,2.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/23/1992,104,tropicana,2688,7.896552702,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -7/23/1992,105,dominicks,15488,9.64782081,0,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/23/1992,105,minute.maid,12480,9.431882642,1,2.29,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/23/1992,105,tropicana,3456,8.14786713,0,2.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/23/1992,106,dominicks,6016,8.702177866,0,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/23/1992,106,minute.maid,18304,9.814874894,1,2.29,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/23/1992,106,tropicana,1920,7.560080465,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/23/1992,107,dominicks,11392,9.340666634,0,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/23/1992,107,minute.maid,29184,10.28137589,1,2.29,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/23/1992,107,tropicana,7552,8.929567708,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -7/23/1992,109,dominicks,4672,8.449342525,0,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/23/1992,109,minute.maid,41280,10.6281334,1,2.29,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/23/1992,109,tropicana,8768,9.078864009,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/23/1992,110,dominicks,1344,7.203405521,0,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/23/1992,110,minute.maid,28928,10.27256526,1,2.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/23/1992,110,tropicana,2560,7.847762538,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/23/1992,111,dominicks,16704,9.723403491,0,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/23/1992,111,minute.maid,106816,11.57886301,1,2.29,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/23/1992,111,tropicana,3392,8.129174997,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/23/1992,112,dominicks,4416,8.392989588,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/23/1992,112,minute.maid,41856,10.64199044,1,2.29,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/23/1992,112,tropicana,8704,9.071537969,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/23/1992,113,dominicks,6336,8.754002934,0,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/23/1992,113,minute.maid,63808,11.06363385,1,2.29,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/23/1992,113,tropicana,6144,8.723231275,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/23/1992,114,dominicks,15808,9.66827142,0,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/23/1992,114,minute.maid,45056,10.71566144,1,2.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/23/1992,114,tropicana,3264,8.090708716,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/23/1992,115,dominicks,5504,8.61323038,0,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/23/1992,115,minute.maid,31936,10.37148918,1,2.27,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/23/1992,115,tropicana,4352,8.378390789,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/23/1992,116,dominicks,7872,8.971067439,0,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/23/1992,116,minute.maid,22144,10.00532186,1,2.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/23/1992,116,tropicana,3264,8.090708716,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/23/1992,117,dominicks,6208,8.733594062,0,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/23/1992,117,minute.maid,20416,9.924074186,1,2.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/23/1992,117,tropicana,2752,7.920083199,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/23/1992,118,dominicks,4288,8.363575703,0,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/23/1992,118,minute.maid,48640,10.79220152,1,2.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/23/1992,118,tropicana,3200,8.070906089,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/23/1992,119,dominicks,6592,8.793612072,0,1.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/23/1992,119,minute.maid,22336,10.01395501,1,2.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/23/1992,119,tropicana,5888,8.68067166,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/23/1992,121,dominicks,4608,8.435549202,0,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/23/1992,121,minute.maid,35392,10.47424109,1,2.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/23/1992,121,tropicana,4928,8.502688505,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/23/1992,122,dominicks,9408,9.14931567,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/23/1992,122,minute.maid,40384,10.60618895,1,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/23/1992,122,tropicana,6912,8.841014311,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/23/1992,123,dominicks,10240,9.234056899,0,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/23/1992,123,minute.maid,65600,11.09133098,1,2.29,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/23/1992,123,tropicana,3840,8.253227646,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/23/1992,124,dominicks,20608,9.933434629,0,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/23/1992,124,minute.maid,34880,10.45966888,1,2.28,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/23/1992,124,tropicana,2752,7.920083199,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/23/1992,126,dominicks,5376,8.589699882,0,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/23/1992,126,minute.maid,37568,10.5339079,1,2.29,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/23/1992,126,tropicana,5952,8.691482577,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/23/1992,128,dominicks,2880,7.965545573,0,1.68,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/23/1992,128,minute.maid,96256,11.47476659,1,2.29,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/23/1992,128,tropicana,6656,8.803273983,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/23/1992,129,dominicks,4800,8.476371197,0,1.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/23/1992,129,minute.maid,34240,10.44114983,1,2.29,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/23/1992,129,tropicana,5376,8.589699882,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/23/1992,130,dominicks,18368,9.818365299,0,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/23/1992,130,minute.maid,96768,11.48007164,1,2.29,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/23/1992,130,tropicana,2432,7.796469243,0,3.07,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/23/1992,131,dominicks,6592,8.793612072,0,1.68,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/23/1992,131,minute.maid,48192,10.78294831,1,2.29,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/23/1992,131,tropicana,4480,8.407378325,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/23/1992,132,dominicks,12736,9.452187908,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/23/1992,132,minute.maid,52416,10.86696717,1,2.29,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/23/1992,132,tropicana,5248,8.565602331,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/23/1992,134,dominicks,5376,8.589699882,0,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/23/1992,134,minute.maid,20800,9.942708266,1,2.29,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/23/1992,134,tropicana,2304,7.742402022,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/23/1992,137,dominicks,20416,9.924074186,0,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/23/1992,137,minute.maid,60736,11.01429188,1,2.29,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/23/1992,137,tropicana,17536,9.77201119,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/30/1992,2,dominicks,36288,10.49924239,1,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/30/1992,2,minute.maid,4544,8.42156296,0,2.86,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/30/1992,2,tropicana,4672,8.449342525,0,3.16,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -7/30/1992,5,dominicks,42240,10.65112292,1,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/30/1992,5,minute.maid,6400,8.764053269,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/30/1992,5,tropicana,7360,8.903815212,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -7/30/1992,8,dominicks,76352,11.24310951,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/30/1992,8,minute.maid,6528,8.783855897,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/30/1992,8,tropicana,5632,8.636219898,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -7/30/1992,9,dominicks,43840,10.68830192,1,1.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/30/1992,9,minute.maid,2752,7.920083199,0,2.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/30/1992,9,tropicana,8256,9.018695488,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -7/30/1992,12,dominicks,70016,11.15647907,1,1.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/30/1992,12,minute.maid,7424,8.912473275,0,2.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/30/1992,12,tropicana,7808,8.962904128,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -7/30/1992,14,dominicks,32448,10.38739409,1,1.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/30/1992,14,minute.maid,6528,8.783855897,0,2.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/30/1992,14,tropicana,9472,9.156095357,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -7/30/1992,18,dominicks,63744,11.06263034,1,1.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/30/1992,18,minute.maid,6400,8.764053269,0,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/30/1992,18,tropicana,7168,8.877381955,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -7/30/1992,21,dominicks,33536,10.42037477,1,1.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/30/1992,21,minute.maid,2624,7.87245515,0,2.48,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/30/1992,21,tropicana,3520,8.166216269,0,2.85,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -7/30/1992,28,dominicks,12992,9.472089062,1,1.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/30/1992,28,minute.maid,3456,8.14786713,0,2.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/30/1992,28,tropicana,4160,8.333270353,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -7/30/1992,32,dominicks,60416,11.00900925,1,1.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/30/1992,32,minute.maid,6592,8.793612072,0,2.89,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/30/1992,32,tropicana,9920,9.2023082,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -7/30/1992,33,dominicks,23424,10.06151642,1,1.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/30/1992,33,minute.maid,7104,8.868413285,0,2.87,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/30/1992,33,tropicana,11008,9.30637756,0,3.17,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -7/30/1992,40,dominicks,37056,10.52018556,1,1.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/30/1992,40,minute.maid,3904,8.269756948,0,2.48,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/30/1992,40,tropicana,2752,7.920083199,0,2.86,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -7/30/1992,44,dominicks,48384,10.78692446,1,1.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/30/1992,44,minute.maid,6400,8.764053269,0,2.68,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/30/1992,44,tropicana,9664,9.17616292,0,2.96,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -7/30/1992,45,dominicks,21824,9.990765561,1,1.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/30/1992,45,minute.maid,3584,8.184234774,0,2.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/30/1992,45,tropicana,4800,8.476371197,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -7/30/1992,47,dominicks,31424,10.35532721,1,1.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/30/1992,47,minute.maid,3712,8.219326094,0,2.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/30/1992,47,tropicana,2688,7.896552702,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -7/30/1992,48,dominicks,20224,9.914625297,1,1.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/30/1992,48,minute.maid,4544,8.42156296,0,2.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/30/1992,48,tropicana,5568,8.624791202,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -7/30/1992,49,dominicks,24640,10.11212642,1,1.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/30/1992,49,minute.maid,2816,7.943072717,0,2.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/30/1992,49,tropicana,3008,8.009030685,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -7/30/1992,50,dominicks,26688,10.19196931,1,1.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/30/1992,50,minute.maid,3200,8.070906089,0,2.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/30/1992,50,tropicana,4096,8.317766167,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -7/30/1992,51,dominicks,33024,10.40498985,1,1.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/30/1992,51,minute.maid,2496,7.82244473,0,2.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/30/1992,51,tropicana,4288,8.363575703,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -7/30/1992,52,dominicks,37952,10.54407748,1,1.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/30/1992,52,minute.maid,7296,8.895081532,0,2.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/30/1992,52,tropicana,11200,9.323669057,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -7/30/1992,53,dominicks,64000,11.06663836,1,1.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/30/1992,53,minute.maid,6592,8.793612072,0,2.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/30/1992,53,tropicana,9984,9.208739091,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -7/30/1992,54,dominicks,28480,10.25695737,1,1.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/30/1992,54,minute.maid,4288,8.363575703,0,2.67,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/30/1992,54,tropicana,6016,8.702177866,0,2.96,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -7/30/1992,56,dominicks,30720,10.33266919,1,1.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/30/1992,56,minute.maid,2880,7.965545573,0,2.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/30/1992,56,tropicana,6912,8.841014311,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -7/30/1992,59,dominicks,23808,10.07777694,1,1.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/30/1992,59,minute.maid,5440,8.60153434,0,1.88,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/30/1992,59,tropicana,4800,8.476371197,0,2.57,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -7/30/1992,62,dominicks,16256,9.69621735,1,1.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/30/1992,62,minute.maid,6208,8.733594062,0,2.89,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/30/1992,62,tropicana,11776,9.373818841,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -7/30/1992,64,dominicks,20160,9.911455722,1,1.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/30/1992,64,minute.maid,1600,7.377758908,0,2.68,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/30/1992,64,tropicana,2112,7.655390645,0,2.95,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -7/30/1992,67,dominicks,39936,10.59503345,1,1.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/30/1992,67,minute.maid,3072,8.030084094,0,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/30/1992,67,tropicana,4992,8.51559191,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -7/30/1992,68,dominicks,43904,10.68976071,1,1.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/30/1992,68,minute.maid,5312,8.577723691,0,2.89,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/30/1992,68,tropicana,4160,8.333270353,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -7/30/1992,70,dominicks,40384,10.60618895,1,1.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/30/1992,70,minute.maid,16768,9.727227587,0,2.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/30/1992,70,tropicana,9216,9.128696383,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -7/30/1992,71,dominicks,84800,11.34805082,1,1.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/30/1992,71,minute.maid,3200,8.070906089,0,2.86,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/30/1992,71,tropicana,4224,8.348537825,0,3.17,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -7/30/1992,72,dominicks,53440,10.88631481,1,1.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/30/1992,72,minute.maid,5184,8.553332238,0,2.89,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/30/1992,72,tropicana,5888,8.68067166,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -7/30/1992,73,dominicks,93760,11.44849361,1,1.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/30/1992,73,minute.maid,6720,8.812843434,0,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/30/1992,73,tropicana,8128,9.00307017,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -7/30/1992,74,dominicks,63488,11.05860619,1,1.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/30/1992,74,minute.maid,5440,8.60153434,0,2.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/30/1992,74,tropicana,8384,9.034080407,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -7/30/1992,75,dominicks,153728,11.94294009,1,0.98,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 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-7/30/1992,105,minute.maid,3968,8.286017468,0,2.68,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/30/1992,105,tropicana,4736,8.462948177,0,2.96,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -7/30/1992,106,dominicks,19840,9.895455381,1,1.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/30/1992,106,minute.maid,1920,7.560080465,0,2.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -7/30/1992,106,tropicana,2560,7.847762538,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 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-7/30/1992,109,tropicana,14848,9.605620455,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -7/30/1992,110,dominicks,26176,10.17259824,1,1.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/30/1992,110,minute.maid,3200,8.070906089,0,2.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/30/1992,110,tropicana,3136,8.050703382,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -7/30/1992,111,dominicks,127872,11.75878504,1,1.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/30/1992,111,minute.maid,4544,8.42156296,0,2.86,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/30/1992,111,tropicana,4800,8.476371197,0,3.16,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -7/30/1992,112,dominicks,34112,10.43740451,1,1.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/30/1992,112,minute.maid,7168,8.877381955,0,2.48,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/30/1992,112,tropicana,7552,8.929567708,0,2.84,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -7/30/1992,113,dominicks,48320,10.78560083,1,1.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/30/1992,113,minute.maid,4864,8.489616424,0,2.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/30/1992,113,tropicana,8064,8.99516499,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -7/30/1992,114,dominicks,71744,11.18085951,1,1.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/30/1992,114,minute.maid,4800,8.476371197,0,2.67,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/30/1992,114,tropicana,5312,8.577723691,0,2.97,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -7/30/1992,115,dominicks,37696,10.53730927,1,1.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/30/1992,115,minute.maid,4864,8.489616424,0,2.68,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/30/1992,115,tropicana,5760,8.658692754,0,2.96,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -7/30/1992,116,dominicks,31040,10.34303197,1,1.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/30/1992,116,minute.maid,3520,8.166216269,0,2.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/30/1992,116,tropicana,6080,8.712759975,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -7/30/1992,117,dominicks,29952,10.30735138,1,1.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/30/1992,117,minute.maid,3072,8.030084094,0,2.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/30/1992,117,tropicana,4864,8.489616424,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -7/30/1992,118,dominicks,40384,10.60618895,1,1.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/30/1992,118,minute.maid,4480,8.407378325,0,2.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/30/1992,118,tropicana,5056,8.528330936,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -7/30/1992,119,dominicks,25600,10.15034763,1,1.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/30/1992,119,minute.maid,3840,8.253227646,0,2.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/30/1992,119,tropicana,6528,8.783855897,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -7/30/1992,121,dominicks,30400,10.32219789,1,1.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/30/1992,121,minute.maid,11968,9.3899917,0,2.68,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/30/1992,121,tropicana,9536,9.162829389,0,2.96,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -7/30/1992,122,dominicks,40512,10.60935351,1,1.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/30/1992,122,minute.maid,7936,8.979164649,0,2.28,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/30/1992,122,tropicana,7808,8.962904128,0,2.58,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -7/30/1992,123,dominicks,78528,11.27121053,1,1.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/30/1992,123,minute.maid,3840,8.253227646,0,2.87,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/30/1992,123,tropicana,5312,8.577723691,0,3.17,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -7/30/1992,124,dominicks,58048,10.96902553,1,1.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/30/1992,124,minute.maid,5568,8.624791202,0,2.87,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/30/1992,124,tropicana,2752,7.920083199,0,3.16,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -7/30/1992,126,dominicks,40704,10.61408165,1,1.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/30/1992,126,minute.maid,8320,9.026417534,0,2.68,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/30/1992,126,tropicana,8768,9.078864009,0,2.97,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -7/30/1992,128,dominicks,78016,11.26466921,1,1.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/30/1992,128,minute.maid,6912,8.841014311,0,2.86,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/30/1992,128,tropicana,9152,9.121727714,0,3.16,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -7/30/1992,129,dominicks,35520,10.4778512,1,1.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/30/1992,129,minute.maid,6848,8.831711918,0,2.67,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/30/1992,129,tropicana,8384,9.034080407,0,2.96,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -7/30/1992,130,dominicks,127168,11.75326433,1,1.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/30/1992,130,minute.maid,4160,8.333270353,0,2.86,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/30/1992,130,tropicana,3584,8.184234774,0,3.17,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -7/30/1992,131,dominicks,37440,10.53049493,1,1.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/30/1992,131,minute.maid,5184,8.553332238,0,2.68,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/30/1992,131,tropicana,7808,8.962904128,0,2.96,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -7/30/1992,132,dominicks,58304,10.97342598,1,1.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/30/1992,132,minute.maid,8448,9.041685006,0,2.48,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/30/1992,132,tropicana,6400,8.764053269,0,2.86,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -7/30/1992,134,dominicks,26304,10.1774763,1,1.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/30/1992,134,minute.maid,2688,7.896552702,0,2.68,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/30/1992,134,tropicana,3776,8.236420527,0,2.96,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -7/30/1992,137,dominicks,56576,10.94334015,1,1.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/30/1992,137,minute.maid,13568,9.515469358,0,2.87,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -7/30/1992,137,tropicana,16896,9.734832187,0,3.17,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/6/1992,2,dominicks,3776,8.236420527,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/6/1992,2,minute.maid,3968,8.286017468,1,2.81,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/6/1992,2,tropicana,7168,8.877381955,1,3.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/6/1992,5,dominicks,6592,8.793612072,1,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/6/1992,5,minute.maid,5888,8.68067166,1,2.65,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/6/1992,5,tropicana,8384,9.034080407,1,2.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/6/1992,8,dominicks,17408,9.76468515,1,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/6/1992,8,minute.maid,6208,8.733594062,1,2.45,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/6/1992,8,tropicana,8960,9.100525506,1,2.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/6/1992,9,dominicks,3968,8.286017468,1,1.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/6/1992,9,minute.maid,2112,7.655390645,1,2.65,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/6/1992,9,tropicana,8640,9.064157862,1,2.89,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/6/1992,12,dominicks,10688,9.276876896,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/6/1992,12,minute.maid,5312,8.577723691,1,2.81,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/6/1992,12,tropicana,9472,9.156095357,1,3.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/6/1992,14,dominicks,3520,8.166216269,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/6/1992,14,minute.maid,4864,8.489616424,1,2.81,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/6/1992,14,tropicana,12544,9.436997743,1,3.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/6/1992,18,dominicks,10880,9.29468152,1,1.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/6/1992,18,minute.maid,6080,8.712759975,1,2.45,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/6/1992,18,tropicana,10048,9.215128889,1,2.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/6/1992,21,dominicks,9856,9.195835686,1,1.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/6/1992,21,minute.maid,1984,7.592870288,1,2.45,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/6/1992,21,tropicana,3712,8.219326094,1,2.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/6/1992,28,dominicks,3392,8.129174997,1,1.89,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/6/1992,28,minute.maid,2112,7.655390645,1,2.65,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/6/1992,28,tropicana,5504,8.61323038,1,2.89,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/6/1992,32,dominicks,9664,9.17616292,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/6/1992,32,minute.maid,5568,8.624791202,1,2.81,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/6/1992,32,tropicana,9792,9.189321005,1,3.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/6/1992,33,dominicks,5760,8.658692754,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/6/1992,33,minute.maid,4608,8.435549202,1,2.81,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/6/1992,33,tropicana,8768,9.078864009,1,3.09,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/6/1992,40,dominicks,7872,8.971067439,1,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/6/1992,40,minute.maid,3584,8.184234774,1,2.45,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/6/1992,40,tropicana,4608,8.435549202,1,2.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/6/1992,44,dominicks,4992,8.51559191,1,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/6/1992,44,minute.maid,4992,8.51559191,1,2.65,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/6/1992,44,tropicana,13120,9.481893063,1,2.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/6/1992,45,dominicks,4416,8.392989588,1,1.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/6/1992,45,minute.maid,2048,7.624618986,1,2.65,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/6/1992,45,tropicana,5568,8.624791202,1,2.89,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/6/1992,47,dominicks,5440,8.60153434,1,1.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/6/1992,47,minute.maid,2880,7.965545573,1,2.65,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/6/1992,47,tropicana,5632,8.636219898,1,2.89,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/6/1992,48,dominicks,3392,8.129174997,1,1.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/6/1992,48,minute.maid,3840,8.253227646,1,2.65,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/6/1992,48,tropicana,5056,8.528330936,1,2.89,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/6/1992,49,dominicks,3200,8.070906089,1,1.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/6/1992,49,minute.maid,2240,7.714231145,1,2.65,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/6/1992,49,tropicana,3072,8.030084094,1,2.89,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/6/1992,50,dominicks,4288,8.363575703,1,1.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/6/1992,50,minute.maid,2240,7.714231145,1,2.65,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/6/1992,50,tropicana,5632,8.636219898,1,2.89,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/6/1992,51,dominicks,4224,8.348537825,1,1.89,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/6/1992,51,minute.maid,3456,8.14786713,1,2.75,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/6/1992,51,tropicana,5696,8.647519453,1,2.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/6/1992,52,dominicks,4672,8.449342525,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/6/1992,52,minute.maid,6528,8.783855897,1,2.81,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/6/1992,52,tropicana,15040,9.618468598,1,3.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/6/1992,53,dominicks,4032,8.30201781,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/6/1992,53,minute.maid,4928,8.502688505,1,2.81,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/6/1992,53,tropicana,12096,9.400630098,1,3.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/6/1992,54,dominicks,5504,8.61323038,1,1.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/6/1992,54,minute.maid,2560,7.847762538,1,2.65,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/6/1992,54,tropicana,7232,8.886270902,1,2.89,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/6/1992,56,dominicks,6592,8.793612072,1,1.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/6/1992,56,minute.maid,2816,7.943072717,1,2.65,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/6/1992,56,tropicana,10240,9.234056899,1,2.89,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/6/1992,59,dominicks,3520,8.166216269,1,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/6/1992,59,minute.maid,8064,8.99516499,1,1.67,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/6/1992,59,tropicana,6912,8.841014311,1,2.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/6/1992,62,dominicks,2752,7.920083199,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 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-8/6/1992,72,minute.maid,4288,8.363575703,1,2.81,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/6/1992,72,tropicana,6336,8.754002934,1,3.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/6/1992,73,dominicks,35008,10.46333189,1,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/6/1992,73,minute.maid,6336,8.754002934,1,2.45,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/6/1992,73,tropicana,10304,9.240287448,1,2.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 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-8/6/1992,75,tropicana,10880,9.29468152,1,3.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/6/1992,76,dominicks,17600,9.775654181,1,1.89,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/6/1992,76,minute.maid,3200,8.070906089,1,2.65,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/6/1992,76,tropicana,7552,8.929567708,1,2.89,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/6/1992,78,dominicks,6656,8.803273983,1,1.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/6/1992,78,minute.maid,4992,8.51559191,1,2.19,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/6/1992,78,tropicana,7488,8.921057018,1,2.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/6/1992,80,dominicks,5696,8.647519453,1,1.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/6/1992,80,minute.maid,6528,8.783855897,1,2.19,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/6/1992,80,tropicana,12800,9.45720045,1,2.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 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-8/6/1992,86,tropicana,4736,8.462948177,1,2.89,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/6/1992,88,dominicks,6336,8.754002934,1,1.89,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/6/1992,88,minute.maid,4416,8.392989588,1,2.65,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/6/1992,88,tropicana,4224,8.348537825,1,2.89,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/6/1992,89,dominicks,10176,9.227787286,1,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/6/1992,89,minute.maid,4032,8.30201781,1,2.45,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/6/1992,89,tropicana,3520,8.166216269,1,2.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/6/1992,90,dominicks,5760,8.658692754,1,1.99,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/6/1992,90,minute.maid,2752,7.920083199,1,2.81,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/6/1992,90,tropicana,3264,8.090708716,1,3.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/6/1992,91,dominicks,2240,7.714231145,1,1.89,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/6/1992,91,minute.maid,2048,7.624618986,1,2.65,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/6/1992,91,tropicana,4864,8.489616424,1,2.89,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/6/1992,92,dominicks,6720,8.812843434,1,1.89,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/6/1992,92,minute.maid,3136,8.050703382,1,2.65,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/6/1992,92,tropicana,4288,8.363575703,1,2.89,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/6/1992,93,dominicks,9088,9.114710141,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/6/1992,93,minute.maid,4224,8.348537825,1,2.81,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/6/1992,93,tropicana,10240,9.234056899,1,3.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/6/1992,94,dominicks,7296,8.895081532,1,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/6/1992,94,minute.maid,5184,8.553332238,1,2.45,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/6/1992,94,tropicana,8832,9.086136769,1,2.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/6/1992,95,dominicks,7488,8.921057018,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/6/1992,95,minute.maid,2752,7.920083199,1,2.81,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/6/1992,95,tropicana,5824,8.66974259,1,3.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 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-8/6/1992,98,tropicana,7232,8.886270902,1,2.89,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/6/1992,100,dominicks,8512,9.049232212,1,1.89,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/6/1992,100,minute.maid,2688,7.896552702,1,2.65,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/6/1992,100,tropicana,9344,9.142489705,1,2.89,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -8/6/1992,101,dominicks,6464,8.7740036,1,1.89,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/6/1992,101,minute.maid,2368,7.769800996,1,2.65,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/6/1992,101,tropicana,11072,9.312174678,1,2.89,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/6/1992,102,dominicks,30144,10.31374118,1,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/6/1992,102,minute.maid,3776,8.236420527,1,2.43,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/6/1992,102,tropicana,9600,9.169518378,1,2.79,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/6/1992,103,dominicks,7744,8.954673629,1,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/6/1992,103,minute.maid,2880,7.965545573,1,2.45,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/6/1992,103,tropicana,3584,8.184234774,1,2.79,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/6/1992,104,dominicks,5312,8.577723691,1,1.89,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/6/1992,104,minute.maid,2624,7.87245515,1,2.65,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/6/1992,104,tropicana,7168,8.877381955,1,2.89,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/6/1992,105,dominicks,7552,8.929567708,1,1.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/6/1992,105,minute.maid,2432,7.796469243,1,2.65,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/6/1992,105,tropicana,5184,8.553332238,1,2.89,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/6/1992,106,dominicks,4288,8.363575703,1,1.89,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/6/1992,106,minute.maid,1728,7.454719949,1,2.65,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/6/1992,106,tropicana,3456,8.14786713,1,2.89,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/6/1992,107,dominicks,7552,8.929567708,1,1.89,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/6/1992,107,minute.maid,4800,8.476371197,1,2.65,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/6/1992,107,tropicana,13824,9.534161491,1,2.89,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/6/1992,109,dominicks,5184,8.553332238,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/6/1992,109,minute.maid,8960,9.100525506,1,2.81,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/6/1992,109,tropicana,16128,9.688312171,1,3.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/6/1992,110,dominicks,4416,8.392989588,1,1.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/6/1992,110,minute.maid,2688,7.896552702,1,2.65,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/6/1992,110,tropicana,5504,8.61323038,1,2.89,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/6/1992,111,dominicks,8704,9.071537969,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/6/1992,111,minute.maid,2752,7.920083199,1,2.81,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/6/1992,111,tropicana,4288,8.363575703,1,3.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/6/1992,112,dominicks,9280,9.135616826,1,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/6/1992,112,minute.maid,5632,8.636219898,1,2.45,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/6/1992,112,tropicana,15936,9.67633598,1,2.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/6/1992,113,dominicks,10112,9.221478116,1,1.89,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/6/1992,113,minute.maid,4992,8.51559191,1,2.65,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/6/1992,113,tropicana,8704,9.071537969,1,2.89,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/6/1992,114,dominicks,10944,9.30054664,1,1.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/6/1992,114,minute.maid,3712,8.219326094,1,2.65,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/6/1992,114,tropicana,6144,8.723231275,1,2.89,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/6/1992,115,dominicks,3648,8.201934351,1,1.89,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/6/1992,115,minute.maid,4864,8.489616424,1,2.65,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/6/1992,115,tropicana,10304,9.240287448,1,2.89,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/6/1992,116,dominicks,7296,8.895081532,1,1.89,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/6/1992,116,minute.maid,3008,8.009030685,1,2.65,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/6/1992,116,tropicana,8640,9.064157862,1,2.89,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/6/1992,117,dominicks,5120,8.540909718,1,1.89,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/6/1992,117,minute.maid,2432,7.796469243,1,2.65,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/6/1992,117,tropicana,4352,8.378390789,1,2.89,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/6/1992,118,dominicks,4928,8.502688505,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/6/1992,118,minute.maid,3264,8.090708716,1,2.81,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/6/1992,118,tropicana,4800,8.476371197,1,3.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/6/1992,119,dominicks,4800,8.476371197,1,1.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/6/1992,119,minute.maid,3904,8.269756948,1,2.65,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/6/1992,119,tropicana,8512,9.049232212,1,2.89,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/6/1992,121,dominicks,4544,8.42156296,1,1.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/6/1992,121,minute.maid,5184,8.553332238,1,2.65,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/6/1992,121,tropicana,12928,9.467150781,1,2.89,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/6/1992,122,dominicks,10496,9.258749511,1,1.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/6/1992,122,minute.maid,11520,9.351839934,1,2.19,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/6/1992,122,tropicana,13632,9.520175249,1,2.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/6/1992,123,dominicks,9728,9.182763604,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/6/1992,123,minute.maid,4352,8.378390789,1,2.77,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/6/1992,123,tropicana,6016,8.702177866,1,3.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/6/1992,124,dominicks,11840,9.379238908,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/6/1992,124,minute.maid,3008,8.009030685,1,2.81,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/6/1992,124,tropicana,6016,8.702177866,1,3.09,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/6/1992,126,dominicks,6080,8.712759975,1,1.89,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/6/1992,126,minute.maid,7360,8.903815212,1,2.65,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/6/1992,126,tropicana,12992,9.472089062,1,2.89,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/6/1992,128,dominicks,9344,9.142489705,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/6/1992,128,minute.maid,5056,8.528330936,1,2.8,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/6/1992,128,tropicana,4672,8.449342525,1,3.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/6/1992,129,dominicks,4800,8.476371197,1,1.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/6/1992,129,minute.maid,5440,8.60153434,1,2.65,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/6/1992,129,tropicana,12416,9.426741242,1,2.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/6/1992,130,dominicks,14464,9.579418083,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/6/1992,130,minute.maid,3968,8.286017468,1,2.81,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/6/1992,130,tropicana,4544,8.42156296,1,3.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/6/1992,131,dominicks,6016,8.702177866,1,1.89,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/6/1992,131,minute.maid,4800,8.476371197,1,2.65,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/6/1992,131,tropicana,7488,8.921057018,1,2.89,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/6/1992,132,dominicks,13504,9.510741217,1,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/6/1992,132,minute.maid,5312,8.577723691,1,2.45,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/6/1992,132,tropicana,8512,9.049232212,1,2.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/6/1992,134,dominicks,3840,8.253227646,1,1.89,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/6/1992,134,minute.maid,1920,7.560080465,1,2.65,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/6/1992,134,tropicana,4544,8.42156296,1,2.89,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/6/1992,137,dominicks,12672,9.447150114,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/6/1992,137,minute.maid,10304,9.240287448,1,2.81,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/6/1992,137,tropicana,20096,9.908276069,1,3.09,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/13/1992,2,dominicks,3328,8.110126802,0,1.97,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/13/1992,2,minute.maid,49600,10.81174611,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/13/1992,2,tropicana,5056,8.528330936,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/13/1992,5,dominicks,2112,7.655390645,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/13/1992,5,minute.maid,56384,10.93994071,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/13/1992,5,tropicana,8832,9.086136769,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/13/1992,8,dominicks,17536,9.77201119,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/13/1992,8,minute.maid,94720,11.45868045,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/13/1992,8,tropicana,6080,8.712759975,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/13/1992,9,dominicks,2624,7.87245515,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/13/1992,9,minute.maid,71296,11.1745955,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/13/1992,9,tropicana,5504,8.61323038,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/13/1992,12,dominicks,6208,8.733594062,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/13/1992,12,minute.maid,87680,11.3814491,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/13/1992,12,tropicana,6656,8.803273983,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/13/1992,14,dominicks,2240,7.714231145,0,2.09,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/13/1992,14,minute.maid,46848,10.7546636,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/13/1992,14,tropicana,10624,9.270870872,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/13/1992,18,dominicks,8256,9.018695488,0,1.79,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/13/1992,18,minute.maid,75904,11.23722466,1,1.99,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/13/1992,18,tropicana,6144,8.723231275,0,2.89,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -8/13/1992,21,dominicks,7808,8.962904128,0,1.7,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/13/1992,21,minute.maid,34304,10.44301724,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/13/1992,21,tropicana,2880,7.965545573,0,2.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/13/1992,28,dominicks,3712,8.219326094,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/13/1992,28,minute.maid,27136,10.20861654,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/13/1992,28,tropicana,3712,8.219326094,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/13/1992,32,dominicks,4032,8.30201781,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/13/1992,32,minute.maid,82624,11.32205547,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/13/1992,32,tropicana,6912,8.841014311,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/13/1992,33,dominicks,7232,8.886270902,0,2.01,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/13/1992,33,minute.maid,49408,10.80786763,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/13/1992,33,tropicana,11008,9.30637756,0,3.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/13/1992,40,dominicks,7168,8.877381955,0,1.71,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/13/1992,40,minute.maid,53376,10.88511649,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/13/1992,40,tropicana,3072,8.030084094,0,2.89,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/13/1992,44,dominicks,4928,8.502688505,0,1.89,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/13/1992,44,minute.maid,76416,11.24394738,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/13/1992,44,tropicana,8448,9.041685006,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/13/1992,45,dominicks,3264,8.090708716,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/13/1992,45,minute.maid,26368,10.17990643,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 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-8/13/1992,81,minute.maid,50496,10.8296494,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/13/1992,81,tropicana,6080,8.712759975,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/13/1992,86,dominicks,8832,9.086136769,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/13/1992,86,minute.maid,68864,11.13988882,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/13/1992,86,tropicana,5824,8.66974259,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 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-8/13/1992,113,tropicana,8128,9.00307017,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/13/1992,114,dominicks,7680,8.946374826,0,1.88,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/13/1992,114,minute.maid,72128,11.1861976,1,1.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/13/1992,114,tropicana,5568,8.624791202,0,2.99,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/13/1992,115,dominicks,2176,7.685243608,0,1.9,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/13/1992,115,minute.maid,53696,10.89109379,1,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/13/1992,115,tropicana,7424,8.912473275,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/13/1992,116,dominicks,6528,8.783855897,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/13/1992,116,minute.maid,35648,10.48144832,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/13/1992,116,tropicana,4032,8.30201781,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/13/1992,117,dominicks,5312,8.577723691,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/13/1992,117,minute.maid,32576,10.3913311,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/13/1992,117,tropicana,4480,8.407378325,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/13/1992,118,dominicks,4864,8.489616424,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/13/1992,118,minute.maid,59584,10.99514236,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/13/1992,118,tropicana,5056,8.528330936,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/13/1992,119,dominicks,4992,8.51559191,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/13/1992,119,minute.maid,26752,10.19436452,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/13/1992,119,tropicana,5184,8.553332238,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/13/1992,121,dominicks,3584,8.184234774,0,1.94,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/13/1992,121,minute.maid,62720,11.04643566,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/13/1992,121,tropicana,7808,8.962904128,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/13/1992,122,dominicks,8512,9.049232212,0,1.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/13/1992,122,minute.maid,51840,10.85591733,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/13/1992,122,tropicana,10880,9.29468152,0,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/13/1992,123,dominicks,3456,8.14786713,0,1.97,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/13/1992,123,minute.maid,93952,11.45053929,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/13/1992,123,tropicana,5952,8.691482577,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/13/1992,124,dominicks,13312,9.496421163,0,2.02,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/13/1992,124,minute.maid,65984,11.09716757,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/13/1992,124,tropicana,3072,8.030084094,0,3.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/13/1992,126,dominicks,3456,8.14786713,0,1.9,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/13/1992,126,minute.maid,63488,11.05860619,1,1.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/13/1992,126,tropicana,6144,8.723231275,0,2.99,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/13/1992,128,dominicks,2880,7.965545573,0,2,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/13/1992,128,minute.maid,105536,11.56680741,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/13/1992,128,tropicana,5824,8.66974259,0,3.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/13/1992,129,dominicks,3392,8.129174997,0,1.87,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/13/1992,129,minute.maid,48000,10.77895629,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/13/1992,129,tropicana,8960,9.100525506,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/13/1992,130,dominicks,23488,10.06424493,0,1.93,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/13/1992,130,minute.maid,104832,11.56011435,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/13/1992,130,tropicana,3840,8.253227646,0,3.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/13/1992,131,dominicks,1856,7.526178913,0,1.88,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/13/1992,131,minute.maid,38976,10.57070135,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/13/1992,131,tropicana,5248,8.565602331,0,2.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/13/1992,132,dominicks,12224,9.411156511,0,1.7,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/13/1992,132,minute.maid,69824,11.15373307,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/13/1992,132,tropicana,5440,8.60153434,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/13/1992,134,dominicks,3776,8.236420527,0,1.87,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/13/1992,134,minute.maid,34496,10.44859865,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/13/1992,134,tropicana,3200,8.070906089,0,2.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/13/1992,137,dominicks,4096,8.317766167,0,2.01,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/13/1992,137,minute.maid,83200,11.32900263,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/13/1992,137,tropicana,16512,9.711842668,0,3.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/20/1992,2,dominicks,13824,9.534161491,0,1.36,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/20/1992,2,minute.maid,23488,10.06424493,1,1.94,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/20/1992,2,tropicana,13376,9.501217335,1,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/20/1992,5,dominicks,21248,9.964018052,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/20/1992,5,minute.maid,27072,10.20625526,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/20/1992,5,tropicana,17728,9.78290059,1,2.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/20/1992,8,dominicks,31232,10.34919849,0,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/20/1992,8,minute.maid,55552,10.9250748,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/20/1992,8,tropicana,8576,9.056722883,1,2.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/20/1992,9,dominicks,8192,9.010913347,0,1.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/20/1992,9,minute.maid,27904,10.23652533,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/20/1992,9,tropicana,8832,9.086136769,1,2.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/20/1992,12,dominicks,4288,8.363575703,0,1.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/20/1992,12,minute.maid,56768,10.94672807,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/20/1992,12,tropicana,15424,9.643680017,1,2.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/20/1992,14,dominicks,15488,9.64782081,0,1.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/20/1992,14,minute.maid,29056,10.27698028,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/20/1992,14,tropicana,24256,10.09641929,1,2.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/20/1992,21,dominicks,17024,9.742379392,0,1.28,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/20/1992,21,minute.maid,23552,10.06696602,1,1.94,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/20/1992,21,tropicana,4096,8.317766167,1,2.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/20/1992,28,dominicks,4288,8.363575703,0,1.79,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/20/1992,28,minute.maid,17344,9.761001904,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/20/1992,28,tropicana,7872,8.971067439,1,2.79,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/20/1992,32,dominicks,22656,10.02818,0,1.89,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/20/1992,32,minute.maid,42752,10.66317126,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/20/1992,32,tropicana,25088,10.13014492,1,2.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/20/1992,33,dominicks,14400,9.574983486,0,1.34,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/20/1992,33,minute.maid,34176,10.43927892,1,1.94,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/20/1992,33,tropicana,23872,10.0804615,1,2.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/20/1992,40,dominicks,6848,8.831711918,0,1.4,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/20/1992,40,minute.maid,32512,10.38936453,1,1.96,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/20/1992,40,tropicana,6528,8.783855897,1,2.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/20/1992,44,dominicks,17728,9.78290059,0,1.36,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/20/1992,44,minute.maid,29632,10.29661014,1,1.96,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/20/1992,44,tropicana,27072,10.20625526,1,2.79,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/20/1992,45,dominicks,7616,8.938006577,0,1.79,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/20/1992,45,minute.maid,14592,9.588228712,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/20/1992,45,tropicana,7296,8.895081532,1,2.79,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/20/1992,47,dominicks,15296,9.635346635,0,1.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/20/1992,47,minute.maid,22208,10.00820786,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/20/1992,47,tropicana,6528,8.783855897,1,2.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/20/1992,48,dominicks,11392,9.340666634,0,1.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/20/1992,48,minute.maid,19200,9.862665558,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/20/1992,48,tropicana,7744,8.954673629,1,2.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/20/1992,49,dominicks,6144,8.723231275,0,1.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/20/1992,49,minute.maid,21312,9.967025573,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/20/1992,49,tropicana,4800,8.476371197,1,2.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/20/1992,50,dominicks,11200,9.323669057,0,1.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/20/1992,50,minute.maid,19648,9.885730831,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/20/1992,50,tropicana,3904,8.269756948,1,2.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/20/1992,51,dominicks,11520,9.351839934,0,1.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/20/1992,51,minute.maid,29376,10.28793329,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/20/1992,51,tropicana,11328,9.335032816,1,2.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/20/1992,52,dominicks,7680,8.946374826,0,1.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/20/1992,52,minute.maid,36160,10.49570882,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/20/1992,52,tropicana,29248,10.28356647,1,2.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/20/1992,53,dominicks,19968,9.901886271,0,1.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/20/1992,53,minute.maid,29056,10.27698028,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/20/1992,53,tropicana,24512,10.10691807,1,2.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/20/1992,54,dominicks,10624,9.270870872,0,1.36,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/20/1992,54,minute.maid,21056,9.954940834,1,1.94,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/20/1992,54,tropicana,9408,9.14931567,1,2.79,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/20/1992,56,dominicks,17600,9.775654181,0,1.79,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/20/1992,56,minute.maid,18816,9.842462851,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/20/1992,56,tropicana,9344,9.142489705,1,2.79,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/20/1992,59,dominicks,14144,9.557045785,0,1.3,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/20/1992,59,minute.maid,30848,10.3368272,1,1.94,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/20/1992,59,tropicana,7040,8.859363449,1,2.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/20/1992,62,dominicks,13120,9.481893063,0,1.89,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/20/1992,62,minute.maid,15744,9.664214619,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/20/1992,62,tropicana,30144,10.31374118,1,2.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/20/1992,67,dominicks,15680,9.660141294,0,1.59,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/20/1992,67,minute.maid,31808,10.36747311,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/20/1992,67,tropicana,6016,8.702177866,1,2.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/20/1992,68,dominicks,17728,9.78290059,0,1.89,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/20/1992,68,minute.maid,19520,9.87919486,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/20/1992,68,tropicana,11648,9.36288977,1,2.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/20/1992,70,dominicks,20992,9.951896692,0,1.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/20/1992,70,minute.maid,20096,9.908276069,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/20/1992,70,tropicana,8960,9.100525506,1,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/20/1992,71,dominicks,6656,8.803273983,0,1.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/20/1992,71,minute.maid,96640,11.47874801,1,1.95,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/20/1992,71,tropicana,16768,9.727227587,1,2.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/20/1992,72,dominicks,16000,9.680344001,0,1.89,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/20/1992,72,minute.maid,41664,10.63739273,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/20/1992,72,tropicana,18880,9.84585844,1,2.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/20/1992,73,dominicks,38528,10.55914053,0,1.59,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/20/1992,73,minute.maid,54784,10.91115346,1,1.99,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/20/1992,73,tropicana,10944,9.30054664,1,2.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/20/1992,74,dominicks,41216,10.62658181,0,1.79,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/20/1992,74,minute.maid,50048,10.82073782,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/20/1992,74,tropicana,14336,9.570529135,1,2.79,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/20/1992,75,dominicks,18816,9.842462851,0,1.39,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/20/1992,75,minute.maid,34112,10.43740451,1,1.82,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 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-8/20/1992,77,minute.maid,15232,9.631153757,1,1.99,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/20/1992,77,tropicana,7424,8.912473275,1,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/20/1992,78,dominicks,20864,9.945780465,0,1.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/20/1992,78,minute.maid,40000,10.59663473,1,1.99,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/20/1992,78,tropicana,5696,8.647519453,1,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 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-8/20/1992,81,tropicana,11392,9.340666634,1,2.79,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/20/1992,86,dominicks,35712,10.48324205,0,1.79,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/20/1992,86,minute.maid,20096,9.908276069,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/20/1992,86,tropicana,18816,9.842462851,1,2.79,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/20/1992,88,dominicks,16064,9.684336023,0,1.79,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/20/1992,88,minute.maid,19648,9.885730831,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/20/1992,88,tropicana,5312,8.577723691,1,2.79,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -8/20/1992,89,dominicks,23488,10.06424493,0,1.32,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/20/1992,89,minute.maid,34624,10.45230236,1,1.96,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/20/1992,89,tropicana,3968,8.286017468,1,2.79,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 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-8/20/1992,91,tropicana,6912,8.841014311,1,2.79,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/20/1992,92,dominicks,11584,9.357380115,0,1.79,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/20/1992,92,minute.maid,28672,10.26367632,1,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/20/1992,92,tropicana,5824,8.66974259,1,2.79,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/20/1992,93,dominicks,5696,8.647519453,0,1.89,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/20/1992,93,minute.maid,36992,10.51845695,1,1.99,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/20/1992,93,tropicana,14592,9.588228712,1,2.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/20/1992,94,dominicks,14464,9.579418083,0,1.59,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/20/1992,94,minute.maid,29632,10.29661014,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/20/1992,94,tropicana,8064,8.99516499,1,2.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 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-8/20/1992,97,tropicana,4864,8.489616424,1,2.79,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/20/1992,98,dominicks,20608,9.933434629,0,1.38,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/20/1992,98,minute.maid,30976,10.34096799,1,1.94,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/20/1992,98,tropicana,16704,9.723403491,1,2.79,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/20/1992,100,dominicks,22464,10.01966931,0,1.42,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 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-8/20/1992,103,tropicana,5696,8.647519453,1,2.79,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/20/1992,104,dominicks,18688,9.835636886,0,1.79,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/20/1992,104,minute.maid,18880,9.84585844,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/20/1992,104,tropicana,7232,8.886270902,1,2.79,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/20/1992,105,dominicks,24768,10.11730778,0,1.33,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/20/1992,105,minute.maid,29504,10.29228113,1,1.93,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/20/1992,105,tropicana,5120,8.540909718,1,2.79,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/20/1992,106,dominicks,13056,9.477003077,0,1.79,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/20/1992,106,minute.maid,13184,9.486759252,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/20/1992,106,tropicana,4160,8.333270353,1,2.79,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/20/1992,107,dominicks,23168,10.0505273,0,1.79,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/20/1992,107,minute.maid,47424,10.76688371,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/20/1992,107,tropicana,21504,9.975994243,1,2.79,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/20/1992,109,dominicks,8064,8.99516499,0,1.89,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/20/1992,109,minute.maid,55872,10.93081864,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/20/1992,109,tropicana,44864,10.71139097,1,2.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/20/1992,110,dominicks,14080,9.55251063,0,1.79,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/20/1992,110,minute.maid,15360,9.639522007,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/20/1992,110,tropicana,7552,8.929567708,1,2.79,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/20/1992,111,dominicks,18624,9.832206351,0,1.34,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/20/1992,111,minute.maid,91648,11.42571043,1,1.93,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/20/1992,111,tropicana,8768,9.078864009,1,2.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/20/1992,112,dominicks,22080,10.0024275,0,1.07,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/20/1992,112,minute.maid,18944,9.849242538,1,1.9,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/20/1992,112,tropicana,24960,10.12502982,1,2.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/20/1992,113,dominicks,21632,9.981928979,0,1.79,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/20/1992,113,minute.maid,37440,10.53049493,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/20/1992,113,tropicana,13184,9.486759252,1,2.79,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/20/1992,114,dominicks,32320,10.38344151,0,1.16,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/20/1992,114,minute.maid,27776,10.23192762,1,1.94,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/20/1992,114,tropicana,12160,9.405907156,1,2.79,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/20/1992,115,dominicks,12480,9.431882642,0,1.38,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/20/1992,115,minute.maid,22528,10.02251426,1,1.94,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/20/1992,115,tropicana,20672,9.936535407,1,2.79,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/20/1992,116,dominicks,4608,8.435549202,0,1.79,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/20/1992,116,minute.maid,25408,10.14281936,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/20/1992,116,tropicana,6464,8.7740036,1,2.79,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/20/1992,117,dominicks,15296,9.635346635,0,1.79,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/20/1992,117,minute.maid,20544,9.930324207,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/20/1992,117,tropicana,6144,8.723231275,1,2.79,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/20/1992,119,dominicks,13376,9.501217335,0,1.79,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/20/1992,119,minute.maid,16320,9.700146629,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/20/1992,119,tropicana,7872,8.971067439,1,2.79,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/20/1992,121,dominicks,13888,9.538780437,0,1.35,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/20/1992,121,minute.maid,54272,10.90176372,1,1.93,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/20/1992,121,tropicana,16576,9.715711145,1,2.79,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/20/1992,122,dominicks,14464,9.579418083,0,1.31,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/20/1992,122,minute.maid,34496,10.44859865,1,1.95,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/20/1992,122,tropicana,14400,9.574983486,1,2.59,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/20/1992,123,dominicks,25024,10.12759064,0,1.34,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/20/1992,123,minute.maid,31104,10.34509171,1,1.93,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/20/1992,123,tropicana,27456,10.22034,1,2.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/20/1992,124,dominicks,10688,9.276876896,0,1.55,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/20/1992,124,minute.maid,46080,10.7381343,1,1.93,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/20/1992,124,tropicana,11776,9.373818841,1,2.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/20/1992,126,dominicks,14400,9.574983486,0,1.33,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/20/1992,126,minute.maid,44608,10.70566849,1,1.95,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/20/1992,126,tropicana,13760,9.529521112,1,2.79,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/20/1992,128,dominicks,16576,9.715711145,0,0.94,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/20/1992,128,minute.maid,36224,10.49747716,1,1.94,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/20/1992,128,tropicana,30592,10.32849382,1,2.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/20/1992,129,dominicks,11072,9.312174678,0,1.34,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/20/1992,129,minute.maid,34880,10.45966888,1,1.93,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/20/1992,129,tropicana,15872,9.67231183,1,2.79,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/20/1992,130,dominicks,32128,10.3774832,0,1.33,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/20/1992,130,minute.maid,73920,11.21073871,1,1.87,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/20/1992,130,tropicana,2688,7.896552702,1,2.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/20/1992,131,dominicks,17536,9.77201119,0,1.35,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/20/1992,131,minute.maid,32256,10.38145935,1,1.94,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/20/1992,131,tropicana,12736,9.452187908,1,2.79,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/20/1992,132,dominicks,25024,10.12759064,0,1.33,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/20/1992,132,minute.maid,35456,10.47604777,1,1.95,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/20/1992,132,tropicana,12416,9.426741242,1,2.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/20/1992,134,dominicks,10432,9.252633284,0,1.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/20/1992,134,minute.maid,18176,9.807857322,1,1.94,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/20/1992,134,tropicana,5120,8.540909718,1,2.79,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/20/1992,137,dominicks,27520,10.22266829,0,1.16,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/20/1992,137,minute.maid,59648,10.9962159,1,1.92,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/20/1992,137,tropicana,48192,10.78294831,1,2.79,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/27/1992,2,dominicks,9024,9.107642974,0,1.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/27/1992,2,minute.maid,19008,9.852615222,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/27/1992,2,tropicana,8128,9.00307017,0,2.75,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -8/27/1992,5,dominicks,1856,7.526178913,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/27/1992,5,minute.maid,3840,8.253227646,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/27/1992,5,tropicana,9600,9.169518378,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -8/27/1992,8,dominicks,19200,9.862665558,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/27/1992,8,minute.maid,18688,9.835636886,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/27/1992,8,tropicana,8000,8.987196821,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -8/27/1992,9,dominicks,6528,8.783855897,0,1.29,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/27/1992,9,minute.maid,12224,9.411156511,0,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/27/1992,9,tropicana,11648,9.36288977,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -8/27/1992,12,dominicks,29824,10.30306872,0,1.29,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/27/1992,12,minute.maid,24384,10.10168246,0,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/27/1992,12,tropicana,13504,9.510741217,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -8/27/1992,14,dominicks,8640,9.064157862,0,1.29,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/27/1992,14,minute.maid,19136,9.859326657,0,1.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/27/1992,14,tropicana,21632,9.981928979,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -8/27/1992,21,dominicks,11776,9.373818841,0,1.05,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/27/1992,21,minute.maid,21696,9.984883191,0,1.69,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/27/1992,21,tropicana,3776,8.236420527,0,2.66,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -8/27/1992,28,dominicks,5504,8.61323038,0,1.29,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/27/1992,28,minute.maid,5184,8.553332238,0,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/27/1992,28,tropicana,5312,8.577723691,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -8/27/1992,32,dominicks,5888,8.68067166,0,1.29,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/27/1992,32,minute.maid,25984,10.16523624,0,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/27/1992,32,tropicana,13696,9.524859098,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -8/27/1992,33,dominicks,13120,9.481893063,0,1.32,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/27/1992,33,minute.maid,17088,9.746131742,0,1.69,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/27/1992,33,tropicana,16832,9.731037116,0,2.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -8/27/1992,40,dominicks,15104,9.622714888,0,1.04,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/27/1992,40,minute.maid,10752,9.282847063,0,1.69,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/27/1992,40,tropicana,4928,8.502688505,0,2.7,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -8/27/1992,44,dominicks,11712,9.368369236,0,1.06,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/27/1992,44,minute.maid,13504,9.510741217,0,1.69,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/27/1992,44,tropicana,14784,9.601300794,0,2.75,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -8/27/1992,45,dominicks,9216,9.128696383,0,1.29,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/27/1992,45,minute.maid,8448,9.041685006,0,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/27/1992,45,tropicana,6016,8.702177866,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -8/27/1992,47,dominicks,5184,8.553332238,0,1.29,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/27/1992,47,minute.maid,12544,9.436997743,0,1.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/27/1992,47,tropicana,8256,9.018695488,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -8/27/1992,48,dominicks,1280,7.154615357,0,1.29,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/27/1992,48,minute.maid,17152,9.749870064,0,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/27/1992,48,tropicana,7360,8.903815212,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -8/27/1992,49,dominicks,5376,8.589699882,0,1.29,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/27/1992,49,minute.maid,11584,9.357380115,0,1.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/27/1992,49,tropicana,7360,8.903815212,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -8/27/1992,50,dominicks,5568,8.624791202,0,1.29,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/27/1992,50,minute.maid,8384,9.034080407,0,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/27/1992,50,tropicana,3968,8.286017468,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -8/27/1992,51,dominicks,7360,8.903815212,0,1.29,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/27/1992,51,minute.maid,8128,9.00307017,0,1.69,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/27/1992,51,tropicana,6272,8.743850562,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -8/27/1992,52,dominicks,9280,9.135616826,0,1.29,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/27/1992,52,minute.maid,25152,10.1326927,0,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/27/1992,52,tropicana,16832,9.731037116,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -8/27/1992,53,dominicks,6784,8.822322178,0,1.29,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/27/1992,53,minute.maid,42432,10.65565807,0,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/27/1992,53,tropicana,20544,9.930324207,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -8/27/1992,54,dominicks,6272,8.743850562,0,1.31,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/27/1992,54,minute.maid,13184,9.486759252,0,1.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/27/1992,54,tropicana,7360,8.903815212,0,2.69,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -8/27/1992,56,dominicks,1728,7.454719949,0,1.29,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/27/1992,56,minute.maid,11648,9.36288977,0,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/27/1992,56,tropicana,8576,9.056722883,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -8/27/1992,59,dominicks,6464,8.7740036,0,1.3,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/27/1992,59,minute.maid,7616,8.938006577,0,1.69,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/27/1992,59,tropicana,7488,8.921057018,0,2.55,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -8/27/1992,62,dominicks,5184,8.553332238,0,1.29,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/27/1992,62,minute.maid,19328,9.869310101,0,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/27/1992,62,tropicana,19776,9.89222436,0,3.19,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -8/27/1992,67,dominicks,5312,8.577723691,0,1.29,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/27/1992,67,minute.maid,8640,9.064157862,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/27/1992,67,tropicana,8064,8.99516499,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -8/27/1992,68,dominicks,4352,8.378390789,0,1.29,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/27/1992,68,minute.maid,16640,9.719564714,0,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/27/1992,68,tropicana,10048,9.215128889,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -8/27/1992,70,dominicks,10368,9.246479419,0,1.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/27/1992,70,minute.maid,24576,10.10952564,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/27/1992,70,tropicana,10752,9.282847063,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -8/27/1992,71,dominicks,15424,9.643680017,0,1.29,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/27/1992,71,minute.maid,45632,10.7283645,0,1.69,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/27/1992,71,tropicana,7360,8.903815212,0,3.06,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -8/27/1992,72,dominicks,4800,8.476371197,0,1.29,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/27/1992,72,minute.maid,11200,9.323669057,0,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/27/1992,72,tropicana,13504,9.510741217,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -8/27/1992,73,dominicks,24128,10.09112827,0,1.29,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/27/1992,73,minute.maid,23232,10.05328592,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/27/1992,73,tropicana,12672,9.447150114,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -8/27/1992,74,dominicks,19840,9.895455381,0,1.29,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/27/1992,74,minute.maid,22400,10.01681624,0,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/27/1992,74,tropicana,11648,9.36288977,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -8/27/1992,75,dominicks,13440,9.505990614,0,1.33,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/27/1992,75,minute.maid,23040,10.04498712,0,1.69,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/27/1992,75,tropicana,11264,9.329367078,0,2.7,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -8/27/1992,76,dominicks,5376,8.589699882,0,1.29,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/27/1992,76,minute.maid,2688,7.896552702,0,1.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/27/1992,76,tropicana,5824,8.66974259,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -8/27/1992,77,dominicks,12672,9.447150114,0,1.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/27/1992,77,minute.maid,12480,9.431882642,0,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/27/1992,77,tropicana,11328,9.335032816,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -8/27/1992,78,dominicks,23744,10.07508515,0,1.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/27/1992,78,minute.maid,16832,9.731037116,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/27/1992,78,tropicana,7744,8.954673629,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -8/27/1992,80,dominicks,7104,8.868413285,0,1.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/27/1992,80,minute.maid,15104,9.622714888,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/27/1992,80,tropicana,14400,9.574983486,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -8/27/1992,81,dominicks,13312,9.496421163,0,1.29,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/27/1992,81,minute.maid,16832,9.731037116,0,1.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/27/1992,81,tropicana,9344,9.142489705,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -8/27/1992,86,dominicks,26560,10.1871616,0,1.29,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/27/1992,86,minute.maid,12608,9.442086812,0,1.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/27/1992,86,tropicana,8000,8.987196821,0,2.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -8/27/1992,89,dominicks,12352,9.421573272,0,1.33,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/27/1992,89,minute.maid,10624,9.270870872,0,1.69,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/27/1992,89,tropicana,3712,8.219326094,0,2.66,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -8/27/1992,90,dominicks,5888,8.68067166,0,1.29,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/27/1992,90,minute.maid,14720,9.596962392,0,1.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/27/1992,90,tropicana,6336,8.754002934,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -8/27/1992,91,dominicks,5632,8.636219898,0,1.29,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/27/1992,91,minute.maid,8064,8.99516499,0,1.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/27/1992,91,tropicana,5568,8.624791202,0,2.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -8/27/1992,92,dominicks,1856,7.526178913,0,1.29,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/27/1992,92,minute.maid,21760,9.987828701,0,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/27/1992,92,tropicana,6336,8.754002934,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -8/27/1992,93,dominicks,13120,9.481893063,0,1.29,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/27/1992,93,minute.maid,44032,10.69267192,0,1.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/27/1992,93,tropicana,13888,9.538780437,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -8/27/1992,94,dominicks,9728,9.182763604,0,1.29,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/27/1992,94,minute.maid,14016,9.547954813,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/27/1992,94,tropicana,7936,8.979164649,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -8/27/1992,95,dominicks,14208,9.561560465,0,1.29,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/27/1992,95,minute.maid,18368,9.818365299,0,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/27/1992,95,tropicana,6528,8.783855897,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -8/27/1992,97,dominicks,10752,9.282847063,0,1.29,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/27/1992,97,minute.maid,8704,9.071537969,0,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/27/1992,97,tropicana,3072,8.030084094,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -8/27/1992,98,dominicks,16704,9.723403491,0,1.27,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/27/1992,98,minute.maid,10304,9.240287448,0,1.66,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -8/27/1992,98,tropicana,7488,8.921057018,0,2.75,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 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-8/27/1992,101,tropicana,9536,9.162829389,0,2.77,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -8/27/1992,102,dominicks,14144,9.557045785,0,1.37,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/27/1992,102,minute.maid,40064,10.59823345,0,1.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/27/1992,102,tropicana,8192,9.010913347,0,2.68,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -8/27/1992,103,dominicks,22464,10.01966931,0,1.3,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/27/1992,103,minute.maid,9664,9.17616292,0,1.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/27/1992,103,tropicana,2944,7.98752448,0,2.69,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -8/27/1992,104,dominicks,13376,9.501217335,0,1.29,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/27/1992,104,minute.maid,16704,9.723403491,0,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/27/1992,104,tropicana,5248,8.565602331,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -8/27/1992,105,dominicks,16192,9.692272572,0,1.31,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/27/1992,105,minute.maid,17728,9.78290059,0,1.69,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/27/1992,105,tropicana,5824,8.66974259,0,2.81,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -8/27/1992,106,dominicks,4992,8.51559191,0,1.29,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/27/1992,106,minute.maid,15424,9.643680017,0,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/27/1992,106,tropicana,2816,7.943072717,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -8/27/1992,107,dominicks,12992,9.472089062,0,1.29,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/27/1992,107,minute.maid,22848,10.03661887,0,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/27/1992,107,tropicana,18944,9.849242538,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -8/27/1992,109,dominicks,17984,9.797237753,0,1.29,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/27/1992,109,minute.maid,21888,9.99369382,0,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/27/1992,109,tropicana,19008,9.852615222,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -8/27/1992,110,dominicks,18944,9.849242538,0,1.29,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/27/1992,110,minute.maid,19328,9.869310101,0,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/27/1992,110,tropicana,4608,8.435549202,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -8/27/1992,111,dominicks,3456,8.14786713,0,1.36,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/27/1992,111,minute.maid,57920,10.96681803,0,1.69,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/27/1992,111,tropicana,3904,8.269756948,0,2.73,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -8/27/1992,112,dominicks,7360,8.903815212,0,1.28,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/27/1992,112,minute.maid,13824,9.534161491,0,1.69,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/27/1992,112,tropicana,17856,9.790094865,0,2.73,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -8/27/1992,113,dominicks,9920,9.2023082,0,1.29,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/27/1992,113,minute.maid,11840,9.379238908,0,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/27/1992,113,tropicana,11968,9.3899917,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -8/27/1992,114,dominicks,11520,9.351839934,0,1.29,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/27/1992,114,minute.maid,17472,9.768354879,0,1.69,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/27/1992,114,tropicana,6784,8.822322178,0,2.7,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -8/27/1992,115,dominicks,10880,9.29468152,0,1.3,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/27/1992,115,minute.maid,9920,9.2023082,0,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/27/1992,115,tropicana,10176,9.227787286,0,2.79,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -8/27/1992,116,dominicks,448,6.104793232,0,1.29,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/27/1992,116,minute.maid,11712,9.368369236,0,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/27/1992,116,tropicana,3200,8.070906089,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -8/27/1992,117,dominicks,4800,8.476371197,0,1.29,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/27/1992,117,minute.maid,5312,8.577723691,0,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/27/1992,117,tropicana,3904,8.269756948,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -8/27/1992,118,dominicks,5248,8.565602331,0,1.29,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/27/1992,118,minute.maid,18112,9.804329981,0,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/27/1992,118,tropicana,8256,9.018695488,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -8/27/1992,119,dominicks,7232,8.886270902,0,1.29,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/27/1992,119,minute.maid,11200,9.323669057,0,1.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/27/1992,119,tropicana,7296,8.895081532,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -8/27/1992,121,dominicks,8448,9.041685006,0,1.29,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/27/1992,121,minute.maid,14784,9.601300794,0,1.69,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/27/1992,121,tropicana,14336,9.570529135,0,2.74,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -8/27/1992,122,dominicks,15616,9.656051309,0,0.94,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/27/1992,122,minute.maid,15872,9.67231183,0,1.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/27/1992,122,tropicana,8576,9.056722883,0,2.53,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -8/27/1992,123,dominicks,11456,9.346268889,0,1.34,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/27/1992,123,minute.maid,16256,9.69621735,0,1.69,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/27/1992,123,tropicana,8128,9.00307017,0,2.7,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -8/27/1992,124,dominicks,16960,9.738612909,0,1.05,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/27/1992,124,minute.maid,25856,10.16029796,0,1.69,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/27/1992,124,tropicana,6656,8.803273983,0,2.82,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -8/27/1992,126,dominicks,15744,9.664214619,0,1.04,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/27/1992,126,minute.maid,14720,9.596962392,0,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/27/1992,126,tropicana,13760,9.529521112,0,2.72,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -8/27/1992,128,dominicks,3456,8.14786713,0,1.4,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/27/1992,128,minute.maid,31232,10.34919849,0,1.69,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/27/1992,128,tropicana,12800,9.45720045,0,2.77,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -8/27/1992,129,dominicks,6144,8.723231275,0,1.33,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/27/1992,129,minute.maid,13440,9.505990614,0,1.69,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/27/1992,129,tropicana,10048,9.215128889,0,2.76,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -8/27/1992,130,dominicks,22080,10.0024275,0,1.33,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/27/1992,130,minute.maid,76800,11.24895992,0,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/27/1992,130,tropicana,6720,8.812843434,0,2.63,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -8/27/1992,131,dominicks,4544,8.42156296,0,1.34,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/27/1992,131,minute.maid,19072,9.85597657,0,1.69,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/27/1992,131,tropicana,10176,9.227787286,0,2.75,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -8/27/1992,132,dominicks,18560,9.828764006,0,1.36,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/27/1992,132,minute.maid,17152,9.749870064,0,1.69,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/27/1992,132,tropicana,8192,9.010913347,0,2.65,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -8/27/1992,134,dominicks,9728,9.182763604,0,1.17,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/27/1992,134,minute.maid,15680,9.660141294,0,1.69,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/27/1992,134,tropicana,3456,8.14786713,0,2.81,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -8/27/1992,137,dominicks,8768,9.078864009,0,1.31,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/27/1992,137,minute.maid,58112,10.97012746,0,1.69,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -8/27/1992,137,tropicana,29696,10.29876764,0,2.77,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/3/1992,2,dominicks,2048,7.624618986,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/3/1992,2,minute.maid,11584,9.357380115,0,1.81,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/3/1992,2,tropicana,19456,9.875910785,1,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/3/1992,5,dominicks,3712,8.219326094,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/3/1992,5,minute.maid,6144,8.723231275,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/3/1992,5,tropicana,25664,10.15284451,1,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/3/1992,8,dominicks,12800,9.45720045,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/3/1992,8,minute.maid,14656,9.592605087,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/3/1992,8,tropicana,21760,9.987828701,1,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/3/1992,9,dominicks,2496,7.82244473,0,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/3/1992,9,minute.maid,12032,9.395325046,0,1.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/3/1992,9,tropicana,38144,10.54912375,1,2.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/3/1992,12,dominicks,3584,8.184234774,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/3/1992,12,minute.maid,21568,9.978966014,0,1.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/3/1992,12,tropicana,41984,10.64504387,1,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/3/1992,21,dominicks,4672,8.449342525,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/3/1992,21,minute.maid,11136,9.317938383,0,1.73,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/3/1992,21,tropicana,11392,9.340666634,1,2.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/3/1992,28,dominicks,1280,7.154615357,0,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/3/1992,28,minute.maid,4480,8.407378325,0,1.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/3/1992,28,tropicana,10432,9.252633284,1,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/3/1992,32,dominicks,4928,8.502688505,0,2.09,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/3/1992,32,minute.maid,23488,10.06424493,0,1.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/3/1992,32,tropicana,42176,10.64960662,1,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/3/1992,33,dominicks,3328,8.110126802,0,2.12,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/3/1992,33,minute.maid,16576,9.715711145,0,1.74,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/3/1992,33,tropicana,36800,10.51325312,1,2.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/3/1992,40,dominicks,6400,8.764053269,0,1.82,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/3/1992,40,minute.maid,9600,9.169518378,0,1.76,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/3/1992,40,tropicana,13440,9.505990614,1,2.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/3/1992,44,dominicks,2944,7.98752448,0,2.03,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/3/1992,44,minute.maid,10240,9.234056899,0,1.8,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/3/1992,44,tropicana,45632,10.7283645,1,2.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/3/1992,45,dominicks,3392,8.129174997,0,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/3/1992,45,minute.maid,7616,8.938006577,0,1.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/3/1992,45,tropicana,11008,9.30637756,1,2.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/3/1992,47,dominicks,2240,7.714231145,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/3/1992,47,minute.maid,8704,9.071537969,0,1.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/3/1992,47,tropicana,11136,9.317938383,1,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/3/1992,48,dominicks,1728,7.454719949,0,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/3/1992,48,minute.maid,11520,9.351839934,0,1.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/3/1992,48,tropicana,13120,9.481893063,1,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/3/1992,50,dominicks,2624,7.87245515,0,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/3/1992,50,minute.maid,9024,9.107642974,0,1.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/3/1992,50,tropicana,10496,9.258749511,1,2.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/3/1992,51,dominicks,1472,7.294377299,0,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/3/1992,51,minute.maid,6208,8.733594062,0,1.69,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/3/1992,51,tropicana,18432,9.821843564,1,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/3/1992,52,dominicks,1472,7.294377299,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/3/1992,52,minute.maid,18880,9.84585844,0,1.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/3/1992,52,tropicana,47680,10.7722673,1,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/3/1992,53,dominicks,4416,8.392989588,0,2.09,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/3/1992,53,minute.maid,22272,10.01108556,0,1.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/3/1992,53,tropicana,53568,10.88870715,1,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/3/1992,54,dominicks,2816,7.943072717,0,1.9,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/3/1992,54,minute.maid,9216,9.128696383,0,1.77,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/3/1992,54,tropicana,14656,9.592605087,1,2.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/3/1992,56,dominicks,3392,8.129174997,0,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/3/1992,56,minute.maid,10176,9.227787286,0,1.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/3/1992,56,tropicana,19136,9.859326657,1,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/3/1992,59,dominicks,3008,8.009030685,0,1.67,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/3/1992,59,minute.maid,7040,8.859363449,0,1.74,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/3/1992,59,tropicana,12736,9.452187908,1,2.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/3/1992,62,dominicks,1792,7.491087594,0,2.09,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/3/1992,62,minute.maid,15168,9.626943225,0,1.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/3/1992,62,tropicana,35008,10.46333189,1,2.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/3/1992,67,dominicks,2752,7.920083199,0,1.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/3/1992,67,minute.maid,8064,8.99516499,0,1.69,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/3/1992,67,tropicana,12032,9.395325046,1,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/3/1992,68,dominicks,4416,8.392989588,0,2.09,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/3/1992,68,minute.maid,12480,9.431882642,0,1.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/3/1992,68,tropicana,25984,10.16523624,1,2.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/3/1992,70,dominicks,8128,9.00307017,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/3/1992,70,minute.maid,13248,9.491601877,0,1.69,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/3/1992,70,tropicana,14912,9.609921537,1,2.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/3/1992,71,dominicks,1344,7.203405521,0,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/3/1992,71,minute.maid,57088,10.95234922,0,1.52,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/3/1992,71,tropicana,27776,10.23192762,1,2.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/3/1992,72,dominicks,2752,7.920083199,0,2.09,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/3/1992,72,minute.maid,10048,9.215128889,0,1.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/3/1992,72,tropicana,33536,10.42037477,1,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/3/1992,73,dominicks,17088,9.746131742,0,1.79,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/3/1992,73,minute.maid,16320,9.700146629,0,1.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/3/1992,73,tropicana,35840,10.48681987,1,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/3/1992,74,dominicks,7872,8.971067439,0,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/3/1992,74,minute.maid,13376,9.501217335,0,1.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/3/1992,74,tropicana,24384,10.10168246,1,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/3/1992,75,dominicks,5056,8.528330936,0,1.97,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/3/1992,75,minute.maid,19840,9.895455381,0,1.73,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/3/1992,75,tropicana,32512,10.38936453,1,2.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/3/1992,76,dominicks,2816,7.943072717,0,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/3/1992,76,minute.maid,3008,8.009030685,0,1.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/3/1992,76,tropicana,19008,9.852615222,1,2.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/3/1992,77,dominicks,5888,8.68067166,0,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/3/1992,77,minute.maid,12032,9.395325046,0,1.69,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/3/1992,77,tropicana,15296,9.635346635,1,2.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/3/1992,78,dominicks,4416,8.392989588,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/3/1992,78,minute.maid,17920,9.793672686,0,1.69,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/3/1992,78,tropicana,12416,9.426741242,1,2.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/3/1992,80,dominicks,5760,8.658692754,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/3/1992,80,minute.maid,13184,9.486759252,0,1.69,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/3/1992,80,tropicana,18752,9.839055692,1,2.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/3/1992,81,dominicks,4032,8.30201781,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/3/1992,81,minute.maid,9856,9.195835686,0,1.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/3/1992,81,tropicana,20992,9.951896692,1,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/3/1992,86,dominicks,3904,8.269756948,0,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/3/1992,86,minute.maid,13504,9.510741217,0,1.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/3/1992,86,tropicana,22336,10.01395501,1,2.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/3/1992,88,dominicks,2944,7.98752448,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/3/1992,88,minute.maid,6144,8.723231275,0,1.69,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/3/1992,88,tropicana,9216,9.128696383,1,2.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/3/1992,89,dominicks,6080,8.712759975,0,1.8,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/3/1992,89,minute.maid,11328,9.335032816,0,1.74,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/3/1992,89,tropicana,10496,9.258749511,1,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/3/1992,90,dominicks,512,6.238324625,0,2.09,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/3/1992,90,minute.maid,8640,9.064157862,0,1.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/3/1992,90,tropicana,15040,9.618468598,1,2.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/3/1992,91,dominicks,2688,7.896552702,0,1.99,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/3/1992,91,minute.maid,7808,8.962904128,0,1.69,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/3/1992,91,tropicana,11008,9.30637756,1,2.49,0.255730611,0.144310113,10.53844932,0.144424674,0.108284634,0.024680506,0.289097744,6.292594104,2.571428571,2.089837391,0.681790161 -9/3/1992,92,dominicks,1728,7.454719949,0,1.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/3/1992,92,minute.maid,15424,9.643680017,0,1.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/3/1992,92,tropicana,9728,9.182763604,1,2.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/3/1992,93,dominicks,1728,7.454719949,0,2.09,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/3/1992,93,minute.maid,29952,10.30735138,0,1.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/3/1992,93,tropicana,33280,10.4127119,1,2.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/3/1992,94,dominicks,2688,7.896552702,0,1.79,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/3/1992,94,minute.maid,7872,8.971067439,0,1.69,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/3/1992,94,tropicana,9408,9.14931567,1,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/3/1992,95,dominicks,3776,8.236420527,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/3/1992,95,minute.maid,15552,9.651944527,0,1.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/3/1992,95,tropicana,29440,10.29010957,1,2.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/3/1992,97,dominicks,1152,7.049254841,0,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/3/1992,97,minute.maid,4672,8.449342525,0,1.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/3/1992,97,tropicana,4160,8.333270353,1,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/3/1992,98,dominicks,6016,8.702177866,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/3/1992,98,minute.maid,10688,9.276876896,0,1.7,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/3/1992,98,tropicana,14208,9.561560465,1,2.49,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/3/1992,100,dominicks,5696,8.647519453,0,1.96,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/3/1992,100,minute.maid,18240,9.811372264,0,1.74,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/3/1992,100,tropicana,24896,10.12246243,1,2.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/3/1992,101,dominicks,4480,8.407378325,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/3/1992,101,minute.maid,12672,9.447150114,0,1.74,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/3/1992,101,tropicana,19264,9.865993348,1,2.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/3/1992,102,dominicks,11456,9.346268889,0,1.74,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/3/1992,102,minute.maid,16192,9.692272572,0,1.74,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/3/1992,102,tropicana,22656,10.02818,1,2.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/3/1992,103,dominicks,3648,8.201934351,0,1.82,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/3/1992,103,minute.maid,14016,9.547954813,0,1.72,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/3/1992,103,tropicana,9152,9.121727714,1,2.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/3/1992,104,dominicks,1216,7.103322063,0,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/3/1992,104,minute.maid,12992,9.472089062,0,1.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/3/1992,104,tropicana,11648,9.36288977,1,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/3/1992,105,dominicks,3776,8.236420527,0,2.01,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/3/1992,105,minute.maid,9792,9.189321005,0,1.7,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/3/1992,105,tropicana,11392,9.340666634,1,2.49,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/3/1992,106,dominicks,2560,7.847762538,0,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/3/1992,106,minute.maid,8128,9.00307017,0,1.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/3/1992,106,tropicana,7808,8.962904128,1,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/3/1992,107,dominicks,10816,9.288781798,0,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/3/1992,107,minute.maid,9408,9.14931567,0,1.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/3/1992,107,tropicana,39232,10.57724802,1,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/3/1992,109,dominicks,2304,7.742402022,0,2.09,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/3/1992,109,minute.maid,20864,9.945780465,0,1.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/3/1992,109,tropicana,69440,11.14821835,1,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/3/1992,110,dominicks,1536,7.336936914,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/3/1992,110,minute.maid,15872,9.67231183,0,1.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/3/1992,110,tropicana,12928,9.467150781,1,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/3/1992,111,dominicks,1472,7.294377299,0,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/3/1992,111,minute.maid,13888,9.538780437,0,1.81,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/3/1992,111,tropicana,15872,9.67231183,1,2.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/3/1992,112,dominicks,5056,8.528330936,0,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/3/1992,112,minute.maid,14080,9.55251063,0,1.75,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/3/1992,112,tropicana,32192,10.37947325,1,2.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/3/1992,113,dominicks,2432,7.796469243,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/3/1992,113,minute.maid,10496,9.258749511,0,1.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/3/1992,113,tropicana,26560,10.1871616,1,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/3/1992,114,dominicks,5888,8.68067166,0,1.93,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/3/1992,114,minute.maid,18112,9.804329981,0,1.73,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/3/1992,114,tropicana,23168,10.0505273,1,2.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/3/1992,115,dominicks,1664,7.416979621,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/3/1992,115,minute.maid,10176,9.227787286,0,1.79,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/3/1992,115,tropicana,22528,10.02251426,1,2.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/3/1992,116,dominicks,2816,7.943072717,0,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/3/1992,116,minute.maid,8896,9.093357017,0,1.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/3/1992,116,tropicana,7552,8.929567708,1,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/3/1992,117,dominicks,2880,7.965545573,0,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/3/1992,117,minute.maid,6848,8.831711918,0,1.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/3/1992,117,tropicana,11520,9.351839934,1,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/3/1992,118,dominicks,2048,7.624618986,0,2.09,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/3/1992,118,minute.maid,13376,9.501217335,0,1.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/3/1992,118,tropicana,28672,10.26367632,1,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/3/1992,119,dominicks,2560,7.847762538,0,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/3/1992,119,minute.maid,9472,9.156095357,0,1.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/3/1992,119,tropicana,14720,9.596962392,1,2.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/3/1992,121,dominicks,2944,7.98752448,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/3/1992,121,minute.maid,13376,9.501217335,0,1.77,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/3/1992,121,tropicana,29184,10.28137589,1,2.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/3/1992,122,dominicks,7360,8.903815212,0,1.68,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/3/1992,122,minute.maid,12992,9.472089062,0,1.78,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/3/1992,122,tropicana,21952,9.996613531,1,2.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/3/1992,123,dominicks,3520,8.166216269,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/3/1992,123,minute.maid,12928,9.467150781,0,1.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/3/1992,123,tropicana,41408,10.63122938,1,2.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/3/1992,124,dominicks,5760,8.658692754,0,2.14,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/3/1992,124,minute.maid,8832,9.086136769,0,1.8,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/3/1992,124,tropicana,22208,10.00820786,1,2.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/3/1992,126,dominicks,3648,8.201934351,0,1.94,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/3/1992,126,minute.maid,14016,9.547954813,0,1.76,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/3/1992,126,tropicana,33216,10.41078697,1,2.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/3/1992,128,dominicks,3264,8.090708716,0,2,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/3/1992,128,minute.maid,18240,9.811372264,0,1.76,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/3/1992,128,tropicana,52736,10.87305361,1,2.49,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/3/1992,129,dominicks,2624,7.87245515,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/3/1992,129,minute.maid,14080,9.55251063,0,1.78,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/3/1992,129,tropicana,30784,10.33475035,1,2.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/3/1992,130,dominicks,5184,8.553332238,0,1.98,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/3/1992,130,minute.maid,26240,10.17504024,0,1.73,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/3/1992,130,tropicana,28800,10.26813067,1,2.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/3/1992,131,dominicks,3584,8.184234774,0,1.92,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/3/1992,131,minute.maid,14528,9.583833101,0,1.76,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/3/1992,131,tropicana,22272,10.01108556,1,2.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/3/1992,132,dominicks,9216,9.128696383,0,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/3/1992,132,minute.maid,18240,9.811372264,0,1.75,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/3/1992,132,tropicana,20032,9.905086274,1,2.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/3/1992,134,dominicks,1024,6.931471806,0,2.03,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/3/1992,134,minute.maid,6080,8.712759975,0,1.82,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/3/1992,134,tropicana,9856,9.195835686,1,2.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/3/1992,137,dominicks,2944,7.98752448,0,2,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/3/1992,137,minute.maid,18240,9.811372264,0,1.84,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/3/1992,137,tropicana,81920,11.31349844,1,2.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/10/1992,2,dominicks,1984,7.592870288,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/10/1992,2,minute.maid,26752,10.19436452,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/10/1992,2,tropicana,10048,9.215128889,0,2.64,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/10/1992,5,dominicks,2688,7.896552702,0,1.85,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/10/1992,5,minute.maid,36416,10.50276352,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/10/1992,5,tropicana,9984,9.208739091,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/10/1992,8,dominicks,15296,9.635346635,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/10/1992,8,minute.maid,30144,10.31374118,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/10/1992,8,tropicana,12800,9.45720045,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/10/1992,9,dominicks,1408,7.249925537,0,2.13,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/10/1992,9,minute.maid,30208,10.31586207,1,1.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/10/1992,9,tropicana,14656,9.592605087,0,2.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/10/1992,12,dominicks,4032,8.30201781,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/10/1992,12,minute.maid,53952,10.89585004,1,1.99,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/10/1992,12,tropicana,16640,9.719564714,0,2.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/10/1992,14,dominicks,2368,7.769800996,0,2.24,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/10/1992,14,minute.maid,27072,10.20625526,1,1.99,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/10/1992,14,tropicana,16000,9.680344001,0,2.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/10/1992,21,dominicks,6912,8.841014311,0,1.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/10/1992,21,minute.maid,13248,9.491601877,1,1.99,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/10/1992,21,tropicana,8064,8.99516499,0,2.59,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/10/1992,28,dominicks,1024,6.931471806,0,2.13,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/10/1992,28,minute.maid,13760,9.529521112,1,1.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/10/1992,28,tropicana,7040,8.859363449,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/10/1992,32,dominicks,4672,8.449342525,0,1.94,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/10/1992,32,minute.maid,42304,10.65263692,1,1.99,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/10/1992,32,tropicana,14912,9.609921537,0,2.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/10/1992,33,dominicks,2496,7.82244473,0,2.24,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/10/1992,33,minute.maid,33408,10.41655067,1,1.99,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/10/1992,33,tropicana,17152,9.749870064,0,2.65,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/10/1992,40,dominicks,4672,8.449342525,0,1.92,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/10/1992,40,minute.maid,22976,10.04220547,1,1.99,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/10/1992,40,tropicana,5952,8.691482577,0,2.54,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/10/1992,44,dominicks,1536,7.336936914,0,2.13,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/10/1992,44,minute.maid,42496,10.65716523,1,1.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/10/1992,44,tropicana,16000,9.680344001,0,2.63,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/10/1992,45,dominicks,4032,8.30201781,0,1.85,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/10/1992,45,minute.maid,15168,9.626943225,1,1.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/10/1992,45,tropicana,6336,8.754002934,0,2.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/10/1992,47,dominicks,3072,8.030084094,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/10/1992,47,minute.maid,17408,9.76468515,1,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/10/1992,47,tropicana,5632,8.636219898,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/10/1992,48,dominicks,1472,7.294377299,0,2.13,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/10/1992,48,minute.maid,10624,9.270870872,1,1.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/10/1992,48,tropicana,10816,9.288781798,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/10/1992,49,dominicks,1856,7.526178913,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/10/1992,49,minute.maid,12992,9.472089062,1,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/10/1992,49,tropicana,5504,8.61323038,0,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/10/1992,50,dominicks,1152,7.049254841,0,2.13,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/10/1992,50,minute.maid,11136,9.317938383,1,1.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/10/1992,50,tropicana,7808,8.962904128,0,2.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/10/1992,51,dominicks,2240,7.714231145,0,1.85,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/10/1992,51,minute.maid,20224,9.914625297,1,1.99,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/10/1992,51,tropicana,5248,8.565602331,0,2.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/10/1992,52,dominicks,1280,7.154615357,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/10/1992,52,minute.maid,37184,10.52363384,1,1.99,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/10/1992,52,tropicana,20864,9.945780465,0,2.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/10/1992,53,dominicks,3328,8.110126802,0,2.24,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/10/1992,53,minute.maid,49536,10.81045496,1,1.99,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/10/1992,53,tropicana,18368,9.818365299,0,2.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/10/1992,54,dominicks,3584,8.184234774,0,1.85,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/10/1992,54,minute.maid,23040,10.04498712,1,1.73,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/10/1992,54,tropicana,7616,8.938006577,0,2.66,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/10/1992,56,dominicks,3904,8.269756948,0,1.85,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/10/1992,56,minute.maid,19648,9.885730831,1,1.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/10/1992,56,tropicana,9152,9.121727714,0,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/10/1992,59,dominicks,4928,8.502688505,0,1.66,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/10/1992,59,minute.maid,14400,9.574983486,1,1.99,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/10/1992,59,tropicana,6336,8.754002934,0,2.51,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/10/1992,62,dominicks,2240,7.714231145,0,1.94,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/10/1992,62,minute.maid,27264,10.21332243,1,1.99,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/10/1992,62,tropicana,17984,9.797237753,0,2.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/10/1992,67,dominicks,3264,8.090708716,0,1.92,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/10/1992,67,minute.maid,17536,9.77201119,1,1.99,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/10/1992,67,tropicana,6272,8.743850562,0,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/10/1992,68,dominicks,4800,8.476371197,0,1.94,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/10/1992,68,minute.maid,43584,10.68244539,1,1.99,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/10/1992,68,tropicana,10944,9.30054664,0,2.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/10/1992,70,dominicks,9536,9.162829389,0,1.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/10/1992,70,minute.maid,27840,10.23422911,1,1.99,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/10/1992,70,tropicana,9536,9.162829389,0,2.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/10/1992,71,dominicks,2368,7.769800996,0,2.09,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/10/1992,71,minute.maid,78848,11.27527723,1,1.31,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/10/1992,71,tropicana,10048,9.215128889,0,2.59,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/10/1992,72,dominicks,3328,8.110126802,0,1.94,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/10/1992,72,minute.maid,33856,10.42987152,1,1.99,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/10/1992,72,tropicana,12480,9.431882642,0,2.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/10/1992,74,dominicks,11904,9.384629757,0,1.85,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/10/1992,74,minute.maid,19008,9.852615222,1,1.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/10/1992,74,tropicana,12544,9.436997743,0,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/10/1992,75,dominicks,7104,8.868413285,0,1.94,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/10/1992,75,minute.maid,30144,10.31374118,1,1.99,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/10/1992,75,tropicana,14848,9.605620455,0,2.63,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/10/1992,76,dominicks,1920,7.560080465,0,2.13,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/10/1992,76,minute.maid,45696,10.72976605,1,1.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/10/1992,76,tropicana,6272,8.743850562,0,2.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 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-9/10/1992,78,tropicana,10944,9.30054664,0,2.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/10/1992,80,dominicks,3392,8.129174997,0,1.92,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/10/1992,80,minute.maid,22720,10.03100087,1,1.99,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/10/1992,80,tropicana,13376,9.501217335,0,2.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/10/1992,81,dominicks,4672,8.449342525,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/10/1992,81,minute.maid,25216,10.13523399,1,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/10/1992,81,tropicana,10880,9.29468152,0,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/10/1992,83,dominicks,5248,8.565602331,0,1.92,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/10/1992,83,minute.maid,32640,10.39329381,1,1.99,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/10/1992,83,tropicana,9408,9.14931567,0,2.49,0.200834686,0.145984905,10.45607873,0.050756901,0.098153547,0.107628101,0.330632306,1.346800846,1.6,3.901358126,0.261860719 -9/10/1992,86,dominicks,5824,8.66974259,0,1.85,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/10/1992,86,minute.maid,26176,10.17259824,1,1.99,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/10/1992,86,tropicana,11584,9.357380115,0,2.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/10/1992,88,dominicks,3776,8.236420527,0,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/10/1992,88,minute.maid,15360,9.639522007,1,1.99,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/10/1992,88,tropicana,9280,9.135616826,0,2.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/10/1992,89,dominicks,5824,8.66974259,0,1.85,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/10/1992,89,minute.maid,21568,9.978966014,1,1.99,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/10/1992,89,tropicana,4160,8.333270353,0,2.59,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/10/1992,90,dominicks,2112,7.655390645,0,2.13,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 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-9/10/1992,93,tropicana,16768,9.727227587,0,2.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/10/1992,94,dominicks,4864,8.489616424,0,1.66,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/10/1992,94,minute.maid,10112,9.221478116,1,1.99,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/10/1992,94,tropicana,9344,9.142489705,0,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/10/1992,95,dominicks,3392,8.129174997,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/10/1992,95,minute.maid,39104,10.57398004,1,1.99,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/10/1992,95,tropicana,6784,8.822322178,0,2.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/10/1992,97,dominicks,1152,7.049254841,0,2.09,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/10/1992,97,minute.maid,6336,8.754002934,1,1.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/10/1992,97,tropicana,3648,8.201934351,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/10/1992,98,dominicks,6656,8.803273983,0,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/10/1992,98,minute.maid,29696,10.29876764,1,1.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/10/1992,98,tropicana,8576,9.056722883,0,2.64,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/10/1992,100,dominicks,5056,8.528330936,0,1.94,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/10/1992,100,minute.maid,44800,10.70996342,1,1.83,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/10/1992,100,tropicana,9728,9.182763604,0,2.63,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/10/1992,101,dominicks,3840,8.253227646,0,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/10/1992,101,minute.maid,19968,9.901886271,1,1.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/10/1992,101,tropicana,8960,9.100525506,0,2.6,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/10/1992,102,dominicks,15872,9.67231183,0,1.66,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/10/1992,102,minute.maid,26176,10.17259824,1,1.99,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/10/1992,102,tropicana,12736,9.452187908,0,2.6,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/10/1992,103,dominicks,6272,8.743850562,0,1.92,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/10/1992,103,minute.maid,13760,9.529521112,1,1.99,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/10/1992,103,tropicana,4928,8.502688505,0,2.58,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/10/1992,104,dominicks,1408,7.249925537,0,2.09,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/10/1992,104,minute.maid,15296,9.635346635,1,1.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/10/1992,104,tropicana,6336,8.754002934,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/10/1992,105,dominicks,3840,8.253227646,0,2.13,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/10/1992,105,minute.maid,15552,9.651944527,1,1.99,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/10/1992,105,tropicana,5888,8.68067166,0,2.59,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/10/1992,106,dominicks,640,6.461468176,0,2.24,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/10/1992,106,minute.maid,12224,9.411156511,1,1.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/10/1992,106,tropicana,3968,8.286017468,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/10/1992,107,dominicks,3200,8.070906089,0,2.13,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/10/1992,107,minute.maid,24576,10.10952564,1,1.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/10/1992,107,tropicana,17600,9.775654181,0,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/10/1992,109,dominicks,576,6.356107661,0,2.24,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/10/1992,109,minute.maid,57344,10.9568235,1,1.99,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/10/1992,109,tropicana,26560,10.1871616,0,2.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/10/1992,110,dominicks,1856,7.526178913,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/10/1992,110,minute.maid,21504,9.975994243,1,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/10/1992,110,tropicana,9600,9.169518378,0,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/10/1992,111,dominicks,3712,8.219326094,0,2.09,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/10/1992,111,minute.maid,67392,11.1182816,1,1.99,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/10/1992,111,tropicana,9152,9.121727714,0,2.6,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/10/1992,112,dominicks,5632,8.636219898,0,1.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/10/1992,112,minute.maid,41984,10.64504387,1,1.61,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/10/1992,112,tropicana,12992,9.472089062,0,2.6,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/10/1992,113,dominicks,3328,8.110126802,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/10/1992,113,minute.maid,33600,10.42228135,1,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/10/1992,113,tropicana,14080,9.55251063,0,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/10/1992,114,dominicks,7936,8.979164649,0,1.85,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/10/1992,114,minute.maid,47360,10.76553327,1,1.77,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/10/1992,114,tropicana,6528,8.783855897,0,2.61,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/10/1992,115,dominicks,1920,7.560080465,0,1.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/10/1992,115,minute.maid,24832,10.11988842,1,1.96,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/10/1992,115,tropicana,14272,9.566054855,0,2.57,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/10/1992,116,dominicks,5120,8.540909718,0,1.85,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/10/1992,116,minute.maid,15936,9.67633598,1,1.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/10/1992,116,tropicana,8512,9.049232212,0,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/10/1992,117,dominicks,3264,8.090708716,0,1.85,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/10/1992,117,minute.maid,15360,9.639522007,1,1.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/10/1992,117,tropicana,4672,8.449342525,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/10/1992,118,dominicks,2752,7.920083199,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/10/1992,118,minute.maid,27200,10.21097225,1,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/10/1992,118,tropicana,9728,9.182763604,0,2.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/10/1992,119,dominicks,1856,7.526178913,0,2.13,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/10/1992,119,minute.maid,18688,9.835636886,1,1.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/10/1992,119,tropicana,5184,8.553332238,0,2.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/10/1992,121,dominicks,2752,7.920083199,0,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/10/1992,121,minute.maid,31488,10.3573618,1,1.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/10/1992,121,tropicana,12800,9.45720045,0,2.6,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/10/1992,122,dominicks,10368,9.246479419,0,1.66,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/10/1992,122,minute.maid,27776,10.23192762,1,1.99,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/10/1992,122,tropicana,11136,9.317938383,0,2.51,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/10/1992,123,dominicks,3072,8.030084094,0,2.09,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/10/1992,123,minute.maid,65216,11.08546012,1,1.99,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/10/1992,123,tropicana,9600,9.169518378,0,2.59,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/10/1992,124,dominicks,2816,7.943072717,0,2.24,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/10/1992,124,minute.maid,34944,10.46150206,1,1.99,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/10/1992,124,tropicana,12352,9.421573272,0,2.61,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/10/1992,126,dominicks,3136,8.050703382,0,1.85,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/10/1992,126,minute.maid,32768,10.39720771,1,1.88,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/10/1992,126,tropicana,16704,9.723403491,0,2.61,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/10/1992,128,dominicks,3520,8.166216269,0,1.94,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/10/1992,128,minute.maid,54592,10.90764263,1,1.99,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/10/1992,128,tropicana,25792,10.15781965,0,2.62,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/10/1992,129,dominicks,1664,7.416979621,0,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/10/1992,129,minute.maid,25216,10.13523399,1,1.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/10/1992,129,tropicana,14208,9.561560465,0,2.61,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/10/1992,130,dominicks,8640,9.064157862,0,1.94,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/10/1992,130,minute.maid,59392,10.99191482,1,1.99,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/10/1992,130,tropicana,6656,8.803273983,0,2.66,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/10/1992,131,dominicks,2880,7.965545573,0,1.85,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/10/1992,131,minute.maid,8960,9.100525506,1,1.99,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/10/1992,131,tropicana,13632,9.520175249,0,2.6,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/10/1992,132,dominicks,7744,8.954673629,0,1.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/10/1992,132,minute.maid,34112,10.43740451,1,1.99,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/10/1992,132,tropicana,10304,9.240287448,0,2.58,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/10/1992,134,dominicks,2112,7.655390645,0,2.13,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/10/1992,134,minute.maid,17088,9.746131742,1,1.99,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/10/1992,134,tropicana,6464,8.7740036,0,2.6,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/10/1992,137,dominicks,4928,8.502688505,0,1.94,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/10/1992,137,minute.maid,59264,10.98975732,1,1.99,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/10/1992,137,tropicana,41280,10.6281334,0,2.66,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/17/1992,2,dominicks,4160,8.333270353,0,1.77,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/17/1992,2,minute.maid,3904,8.269756948,0,2.83,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/17/1992,2,tropicana,6336,8.754002934,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/17/1992,5,dominicks,6464,8.7740036,0,1.85,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/17/1992,5,minute.maid,5440,8.60153434,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/17/1992,5,tropicana,8576,9.056722883,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/17/1992,8,dominicks,20992,9.951896692,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/17/1992,8,minute.maid,6208,8.733594062,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/17/1992,8,tropicana,10112,9.221478116,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/17/1992,9,dominicks,3776,8.236420527,0,2.13,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/17/1992,9,minute.maid,11200,9.323669057,0,2.69,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/17/1992,9,tropicana,8192,9.010913347,0,2.99,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/17/1992,12,dominicks,5952,8.691482577,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/17/1992,12,minute.maid,6976,8.850230966,0,2.89,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/17/1992,12,tropicana,10112,9.221478116,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/17/1992,14,dominicks,4736,8.462948177,0,2.24,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/17/1992,14,minute.maid,7104,8.868413285,0,2.89,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/17/1992,14,tropicana,9664,9.17616292,0,3.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/17/1992,21,dominicks,8512,9.049232212,0,1.65,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/17/1992,21,minute.maid,3200,8.070906089,0,2.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/17/1992,21,tropicana,2688,7.896552702,0,2.89,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/17/1992,28,dominicks,960,6.866933285,0,2.13,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/17/1992,28,minute.maid,5184,8.553332238,0,2.69,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/17/1992,28,tropicana,3136,8.050703382,0,2.99,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/17/1992,32,dominicks,7872,8.971067439,0,1.94,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/17/1992,32,minute.maid,7872,8.971067439,0,2.89,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/17/1992,32,tropicana,12800,9.45720045,0,3.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/17/1992,33,dominicks,5120,8.540909718,0,1.97,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/17/1992,33,minute.maid,12672,9.447150114,0,1.84,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/17/1992,33,tropicana,10432,9.252633284,0,3.24,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/17/1992,40,dominicks,6208,8.733594062,0,1.7,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/17/1992,40,minute.maid,3648,8.201934351,0,2.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/17/1992,40,tropicana,4288,8.363575703,0,2.94,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/17/1992,44,dominicks,4416,8.392989588,0,1.77,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/17/1992,44,minute.maid,6336,8.754002934,0,2.58,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/17/1992,44,tropicana,10048,9.215128889,0,3.05,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/17/1992,45,dominicks,4032,8.30201781,0,1.85,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/17/1992,45,minute.maid,3328,8.110126802,0,2.69,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/17/1992,45,tropicana,5248,8.565602331,0,2.99,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/17/1992,47,dominicks,5504,8.61323038,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/17/1992,47,minute.maid,3776,8.236420527,0,2.69,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/17/1992,47,tropicana,4480,8.407378325,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/17/1992,48,dominicks,3072,8.030084094,0,2.13,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/17/1992,48,minute.maid,4864,8.489616424,0,2.69,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/17/1992,48,tropicana,6272,8.743850562,0,2.99,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/17/1992,49,dominicks,1664,7.416979621,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/17/1992,49,minute.maid,10496,9.258749511,0,2.69,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/17/1992,49,tropicana,4736,8.462948177,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/17/1992,50,dominicks,2240,7.714231145,0,2.13,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/17/1992,50,minute.maid,10176,9.227787286,0,2.69,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/17/1992,50,tropicana,2688,7.896552702,0,2.99,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/17/1992,51,dominicks,1344,7.203405521,0,1.85,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/17/1992,51,minute.maid,3584,8.184234774,0,2.79,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/17/1992,51,tropicana,4480,8.407378325,0,3.09,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/17/1992,52,dominicks,3776,8.236420527,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/17/1992,52,minute.maid,8768,9.078864009,0,2.89,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/17/1992,52,tropicana,11072,9.312174678,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/17/1992,53,dominicks,6400,8.764053269,0,2.24,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/17/1992,53,minute.maid,9728,9.182763604,0,2.89,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/17/1992,53,tropicana,9600,9.169518378,0,3.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/17/1992,54,dominicks,4288,8.363575703,0,1.78,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/17/1992,54,minute.maid,4480,8.407378325,0,2.59,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/17/1992,54,tropicana,6912,8.841014311,0,2.91,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/17/1992,56,dominicks,4608,8.435549202,0,1.85,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/17/1992,56,minute.maid,4416,8.392989588,0,2.69,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/17/1992,56,tropicana,7040,8.859363449,0,2.99,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/17/1992,59,dominicks,5760,8.658692754,0,1.59,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/17/1992,59,minute.maid,3008,8.009030685,0,2.29,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/17/1992,59,tropicana,6464,8.7740036,0,2.6,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 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-9/17/1992,64,tropicana,3200,8.070906089,0,3.04,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/17/1992,67,dominicks,4736,8.462948177,0,1.92,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/17/1992,67,minute.maid,12352,9.421573272,0,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/17/1992,67,tropicana,4736,8.462948177,0,2.89,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/17/1992,68,dominicks,4800,8.476371197,0,1.94,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/17/1992,68,minute.maid,6144,8.723231275,0,2.89,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/17/1992,68,tropicana,6336,8.754002934,0,3.19,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/17/1992,70,dominicks,9024,9.107642974,0,1.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/17/1992,70,minute.maid,7040,8.859363449,0,2.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/17/1992,70,tropicana,9856,9.195835686,0,2.59,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 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-9/17/1992,72,tropicana,9920,9.2023082,0,3.19,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/17/1992,73,dominicks,24128,10.09112827,0,1.66,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/17/1992,73,minute.maid,5440,8.60153434,0,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/17/1992,73,tropicana,17280,9.757305042,0,2.89,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/17/1992,74,dominicks,19392,9.872615889,0,1.85,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/17/1992,74,minute.maid,4736,8.462948177,0,2.69,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/17/1992,74,tropicana,8576,9.056722883,0,2.99,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/17/1992,75,dominicks,13504,9.510741217,0,1.68,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/17/1992,75,minute.maid,3968,8.286017468,0,2.81,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/17/1992,75,tropicana,10496,9.258749511,0,3.09,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/17/1992,76,dominicks,7552,8.929567708,0,2.13,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/17/1992,76,minute.maid,3072,8.030084094,0,2.69,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/17/1992,76,tropicana,5888,8.68067166,0,2.99,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/17/1992,77,dominicks,4480,8.407378325,0,1.92,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/17/1992,77,minute.maid,11392,9.340666634,0,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/17/1992,77,tropicana,9408,9.14931567,0,2.59,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/17/1992,78,dominicks,4864,8.489616424,0,1.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/17/1992,78,minute.maid,3136,8.050703382,0,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/17/1992,78,tropicana,8192,9.010913347,0,2.59,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/17/1992,80,dominicks,5504,8.61323038,0,1.92,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/17/1992,80,minute.maid,7040,8.859363449,0,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/17/1992,80,tropicana,10624,9.270870872,0,2.59,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/17/1992,81,dominicks,5824,8.66974259,0,1.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/17/1992,81,minute.maid,6016,8.702177866,0,2.69,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/17/1992,81,tropicana,8320,9.026417534,0,2.99,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/17/1992,84,dominicks,2368,7.769800996,0,1.85,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/17/1992,84,minute.maid,2368,7.769800996,0,2.69,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/17/1992,84,tropicana,5632,8.636219898,0,2.99,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/17/1992,86,dominicks,11008,9.30637756,0,1.85,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/17/1992,86,minute.maid,6528,8.783855897,0,2.69,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 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-9/17/1992,89,minute.maid,4224,8.348537825,0,2.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/17/1992,89,tropicana,4160,8.333270353,0,2.85,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/17/1992,90,dominicks,2688,7.896552702,0,2.13,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/17/1992,90,minute.maid,4288,8.363575703,0,2.89,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/17/1992,90,tropicana,2496,7.82244473,0,3.19,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/17/1992,92,dominicks,2112,7.655390645,0,1.85,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/17/1992,92,minute.maid,2560,7.847762538,0,2.69,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/17/1992,92,tropicana,3648,8.201934351,0,2.99,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/17/1992,93,dominicks,4928,8.502688505,0,2.24,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/17/1992,93,minute.maid,7296,8.895081532,0,2.89,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/17/1992,93,tropicana,10496,9.258749511,0,3.19,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/17/1992,94,dominicks,6400,8.764053269,0,1.66,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/17/1992,94,minute.maid,3712,8.219326094,0,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/17/1992,94,tropicana,5312,8.577723691,0,2.89,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/17/1992,95,dominicks,9344,9.142489705,0,2.09,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/17/1992,95,minute.maid,7616,8.938006577,0,2.89,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/17/1992,95,tropicana,6336,8.754002934,0,3.19,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/17/1992,97,dominicks,2688,7.896552702,0,2.09,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/17/1992,97,minute.maid,6400,8.764053269,0,2.69,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/17/1992,97,tropicana,1792,7.491087594,0,2.99,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/17/1992,98,dominicks,12800,9.45720045,0,1.67,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/17/1992,98,minute.maid,3840,8.253227646,0,2.6,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/17/1992,98,tropicana,7936,8.979164649,0,2.99,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/17/1992,100,dominicks,9152,9.121727714,0,1.72,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/17/1992,100,minute.maid,5952,8.691482577,0,2.61,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/17/1992,100,tropicana,7744,8.954673629,0,2.98,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/17/1992,101,dominicks,3776,8.236420527,0,1.73,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/17/1992,101,minute.maid,3840,8.253227646,0,2.62,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/17/1992,101,tropicana,7552,8.929567708,0,2.99,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/17/1992,102,dominicks,25984,10.16523624,0,1.58,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/17/1992,102,minute.maid,18176,9.807857322,0,1.84,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/17/1992,102,tropicana,10432,9.252633284,0,2.82,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/17/1992,103,dominicks,4352,8.378390789,0,1.68,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/17/1992,103,minute.maid,2048,7.624618986,0,2.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/17/1992,103,tropicana,4288,8.363575703,0,2.96,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/17/1992,104,dominicks,1792,7.491087594,0,2.09,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/17/1992,104,minute.maid,6976,8.850230966,0,2.69,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/17/1992,104,tropicana,3456,8.14786713,0,2.99,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/17/1992,105,dominicks,7040,8.859363449,0,1.79,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/17/1992,105,minute.maid,3840,8.253227646,0,2.62,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/17/1992,105,tropicana,4672,8.449342525,0,3.03,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/17/1992,106,dominicks,4480,8.407378325,0,2.24,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/17/1992,106,minute.maid,2816,7.943072717,0,2.69,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/17/1992,106,tropicana,2560,7.847762538,0,2.99,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/17/1992,107,dominicks,5184,8.553332238,0,2.13,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/17/1992,107,minute.maid,7680,8.946374826,0,2.69,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/17/1992,107,tropicana,11392,9.340666634,0,2.99,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/17/1992,109,dominicks,6464,8.7740036,0,2.24,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/17/1992,109,minute.maid,9664,9.17616292,0,2.89,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/17/1992,109,tropicana,16000,9.680344001,0,3.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/17/1992,110,dominicks,2368,7.769800996,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/17/1992,110,minute.maid,11072,9.312174678,0,2.69,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/17/1992,110,tropicana,4672,8.449342525,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/17/1992,111,dominicks,17664,9.779283949,0,1.52,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/17/1992,111,minute.maid,7296,8.895081532,0,2.13,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/17/1992,111,tropicana,2496,7.82244473,0,3.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/17/1992,112,dominicks,9984,9.208739091,0,1.64,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/17/1992,112,minute.maid,6720,8.812843434,0,2.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/17/1992,112,tropicana,13120,9.481893063,0,2.89,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/17/1992,113,dominicks,4416,8.392989588,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/17/1992,113,minute.maid,13696,9.524859098,0,2.69,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/17/1992,113,tropicana,8768,9.078864009,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/17/1992,114,dominicks,11200,9.323669057,0,1.66,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/17/1992,114,minute.maid,3136,8.050703382,0,2.59,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/17/1992,114,tropicana,7104,8.868413285,0,2.93,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/17/1992,115,dominicks,4928,8.502688505,0,1.69,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/17/1992,115,minute.maid,4096,8.317766167,0,2.65,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/17/1992,115,tropicana,8832,9.086136769,0,2.99,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/17/1992,116,dominicks,7040,8.859363449,0,1.85,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/17/1992,116,minute.maid,2176,7.685243608,0,2.69,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/17/1992,116,tropicana,5568,8.624791202,0,2.99,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/17/1992,117,dominicks,5440,8.60153434,0,1.85,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/17/1992,117,minute.maid,5248,8.565602331,0,2.69,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/17/1992,117,tropicana,4800,8.476371197,0,2.99,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/17/1992,118,dominicks,9216,9.128696383,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/17/1992,118,minute.maid,6272,8.743850562,0,2.89,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/17/1992,118,tropicana,6208,8.733594062,0,3.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/17/1992,119,dominicks,6144,8.723231275,0,2.13,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/17/1992,119,minute.maid,5376,8.589699882,0,2.69,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/17/1992,119,tropicana,6272,8.743850562,0,2.99,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/17/1992,121,dominicks,4544,8.42156296,0,1.66,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/17/1992,121,minute.maid,5504,8.61323038,0,2.6,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/17/1992,121,tropicana,12672,9.447150114,0,2.99,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/17/1992,122,dominicks,14784,9.601300794,0,1.58,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/17/1992,122,minute.maid,7680,8.946374826,0,2.29,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/17/1992,122,tropicana,10112,9.221478116,0,2.62,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/17/1992,123,dominicks,6784,8.822322178,0,1.75,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/17/1992,123,minute.maid,5824,8.66974259,0,2.81,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/17/1992,123,tropicana,5568,8.624791202,0,3.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/17/1992,124,dominicks,11840,9.379238908,0,1.63,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/17/1992,124,minute.maid,4864,8.489616424,0,2.81,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/17/1992,124,tropicana,4416,8.392989588,0,3.23,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/17/1992,126,dominicks,5376,8.589699882,0,1.69,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/17/1992,126,minute.maid,5632,8.636219898,0,2.59,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/17/1992,126,tropicana,12992,9.472089062,0,2.91,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/17/1992,128,dominicks,3712,8.219326094,0,1.68,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/17/1992,128,minute.maid,4736,8.462948177,0,2.82,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/17/1992,128,tropicana,10048,9.215128889,0,3.09,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/17/1992,129,dominicks,6528,8.783855897,0,1.58,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/17/1992,129,minute.maid,8832,9.086136769,0,2.2,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/17/1992,129,tropicana,10432,9.252633284,0,2.99,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/17/1992,130,dominicks,16384,9.704060528,0,1.69,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/17/1992,130,minute.maid,5312,8.577723691,0,2.81,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/17/1992,130,tropicana,3136,8.050703382,0,3.09,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/17/1992,131,dominicks,3520,8.166216269,0,1.6,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/17/1992,131,minute.maid,4736,8.462948177,0,2.62,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/17/1992,131,tropicana,7488,8.921057018,0,2.9,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/17/1992,132,dominicks,13632,9.520175249,0,1.64,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/17/1992,132,minute.maid,5696,8.647519453,0,2.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/17/1992,132,tropicana,6208,8.733594062,0,2.89,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/17/1992,134,dominicks,5376,8.589699882,0,1.62,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/17/1992,134,minute.maid,2304,7.742402022,0,2.59,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/17/1992,134,tropicana,4096,8.317766167,0,3.05,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/17/1992,137,dominicks,13440,9.505990614,0,1.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/17/1992,137,minute.maid,13568,9.515469358,0,2.82,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/17/1992,137,tropicana,20672,9.936535407,0,3.11,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/24/1992,2,dominicks,35264,10.47061789,0,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/24/1992,2,minute.maid,3712,8.219326094,0,2.67,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/24/1992,2,tropicana,16192,9.692272572,1,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -9/24/1992,5,dominicks,40896,10.61878754,0,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/24/1992,5,minute.maid,7680,8.946374826,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/24/1992,5,tropicana,13184,9.486759252,1,2.78,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -9/24/1992,8,dominicks,73856,11.20987253,0,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/24/1992,8,minute.maid,7104,8.868413285,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/24/1992,8,tropicana,10304,9.240287448,1,2.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -9/24/1992,9,dominicks,43392,10.67803037,0,1.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/24/1992,9,minute.maid,4416,8.392989588,0,2.49,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/24/1992,9,tropicana,13056,9.477003077,1,2.79,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -9/24/1992,12,dominicks,73280,11.202043,0,1.49,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/24/1992,12,minute.maid,6528,8.783855897,0,2.69,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/24/1992,12,tropicana,21888,9.99369382,1,2.79,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -9/24/1992,14,dominicks,29120,10.2791805,0,1.49,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/24/1992,14,minute.maid,6208,8.733594062,0,2.69,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/24/1992,14,tropicana,25408,10.14281936,1,2.79,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -9/24/1992,18,dominicks,62528,11.04336974,0,1.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/24/1992,18,minute.maid,6912,8.841014311,0,2.49,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/24/1992,18,tropicana,10880,9.29468152,1,2.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -9/24/1992,21,dominicks,27584,10.22499117,0,1.49,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/24/1992,21,minute.maid,3840,8.253227646,0,2.42,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/24/1992,21,tropicana,3520,8.166216269,1,2.79,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -9/24/1992,28,dominicks,8000,8.987196821,0,1.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/24/1992,28,minute.maid,4032,8.30201781,0,2.49,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/24/1992,28,tropicana,4096,8.317766167,1,2.79,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -9/24/1992,32,dominicks,56000,10.93310697,0,1.49,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/24/1992,32,minute.maid,6464,8.7740036,0,2.69,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/24/1992,32,tropicana,19456,9.875910785,1,2.79,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -9/24/1992,33,dominicks,29888,10.30521234,0,1.49,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/24/1992,33,minute.maid,6656,8.803273983,0,2.73,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/24/1992,33,tropicana,27968,10.23881628,1,2.79,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -9/24/1992,40,dominicks,35392,10.47424109,0,1.49,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/24/1992,40,minute.maid,3456,8.14786713,0,2.46,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/24/1992,40,tropicana,5056,8.528330936,1,2.79,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -9/24/1992,44,dominicks,46080,10.7381343,0,1.49,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/24/1992,44,minute.maid,5632,8.636219898,0,2.53,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/24/1992,44,tropicana,22976,10.04220547,1,2.79,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -9/24/1992,45,dominicks,23488,10.06424493,0,1.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/24/1992,45,minute.maid,3200,8.070906089,0,2.49,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/24/1992,45,tropicana,6976,8.850230966,1,2.78,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -9/24/1992,47,dominicks,28544,10.25920204,0,1.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/24/1992,47,minute.maid,3072,8.030084094,0,2.49,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/24/1992,47,tropicana,7936,8.979164649,1,2.79,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -9/24/1992,48,dominicks,17408,9.76468515,0,1.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/24/1992,48,minute.maid,4736,8.462948177,0,2.49,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/24/1992,48,tropicana,10304,9.240287448,1,2.79,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -9/24/1992,49,dominicks,14080,9.55251063,0,1.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/24/1992,49,minute.maid,2368,7.769800996,0,2.49,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/24/1992,49,tropicana,6400,8.764053269,1,2.79,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -9/24/1992,50,dominicks,24384,10.10168246,0,1.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/24/1992,50,minute.maid,2368,7.769800996,0,2.49,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/24/1992,50,tropicana,5760,8.658692754,1,2.79,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -9/24/1992,51,dominicks,38272,10.55247384,0,1.49,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/24/1992,51,minute.maid,3712,8.219326094,0,2.59,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/24/1992,51,tropicana,6016,8.702177866,1,2.78,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -9/24/1992,52,dominicks,40704,10.61408165,0,1.49,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/24/1992,52,minute.maid,6656,8.803273983,0,2.69,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/24/1992,52,tropicana,33984,10.43364511,1,2.79,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -9/24/1992,53,dominicks,60928,11.01744812,0,1.49,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/24/1992,53,minute.maid,6016,8.702177866,0,2.69,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/24/1992,53,tropicana,36672,10.5097688,1,2.79,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -9/24/1992,54,dominicks,30656,10.33058368,0,1.49,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/24/1992,54,minute.maid,3968,8.286017468,0,2.39,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/24/1992,54,tropicana,5952,8.691482577,1,2.78,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -9/24/1992,56,dominicks,30144,10.31374118,0,1.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/24/1992,56,minute.maid,4480,8.407378325,0,2.49,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/24/1992,56,tropicana,7360,8.903815212,1,2.78,0.192888549,0.237550719,10.83182543,0.578125,0.105927922,0.041356019,0.362168183,4.865720568,0.533333333,2.998578338,0.496126662 -9/24/1992,59,dominicks,27392,10.21800628,0,1.49,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/24/1992,59,minute.maid,3136,8.050703382,0,2.18,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/24/1992,59,tropicana,4416,8.392989588,1,2.72,0.110818913,0.233036093,10.7150398,0.292651757,0.140676118,0.024246568,0.390696107,0.217274596,1,3.331154049,0.395539036 -9/24/1992,62,dominicks,25792,10.15781965,0,1.49,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/24/1992,62,minute.maid,5632,8.636219898,0,2.69,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/24/1992,62,tropicana,28672,10.26367632,1,2.79,0.222534262,0.517760337,11.23619652,0.916699488,0.103979265,0.026510896,0.322765158,5.452685258,0.705882353,2.184049779,0.201722445 -9/24/1992,64,dominicks,24640,10.11212642,0,1.49,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/24/1992,64,minute.maid,2496,7.82244473,0,2.51,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/24/1992,64,tropicana,4544,8.42156296,1,2.79,0.141992021,0.16082131,10.60581744,0.093860685,0.135339885,0.070034198,0.391294542,3.503787688,1.923076923,1.939224403,0.560240328 -9/24/1992,67,dominicks,47616,10.77092412,0,1.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/24/1992,67,minute.maid,3968,8.286017468,0,2.49,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/24/1992,67,tropicana,7616,8.938006577,1,2.79,0.210272984,0.284394654,10.79695991,0.376326075,0.101871469,0.050539779,0.385157627,1.63276453,1.785714286,1.929939975,1.143366607 -9/24/1992,68,dominicks,48512,10.78956647,0,1.49,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/24/1992,68,minute.maid,3968,8.286017468,0,2.69,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/24/1992,68,tropicana,15616,9.656051309,1,2.79,0.181417756,0.159721511,10.1883657,0.218654434,0.108476376,0.220991053,0.330539657,6.650601624,1.538461538,1.626191859,0.125092069 -9/24/1992,70,dominicks,39040,10.57234204,0,1.49,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/24/1992,70,minute.maid,6848,8.831711918,0,2.29,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/24/1992,70,tropicana,10752,9.282847063,1,2.79,0.190235804,0.165669605,10.41235125,0.049271137,0.110542664,0.162860821,0.32772939,3.909329477,0.5,3.527768437,0.367773151 -9/24/1992,71,dominicks,53632,10.88990118,0,1.49,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/24/1992,71,minute.maid,2560,7.847762538,0,2.67,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/24/1992,71,tropicana,15296,9.635346635,1,2.79,0.26807087,0.159588077,10.40483843,0.163882428,0.075013729,0.074828009,0.303101737,6.673879403,1.45,1.818358986,0.266474174 -9/24/1992,72,dominicks,39872,10.5934296,0,1.49,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/24/1992,72,minute.maid,4800,8.476371197,0,2.69,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/24/1992,72,tropicana,21568,9.978966014,1,2.79,0.283727688,0.268724553,10.71219307,0.571315163,0.101522843,0.04593884,0.331130976,3.002118069,0.857142857,1.696152707,0.644907796 -9/24/1992,73,dominicks,104768,11.55950366,0,1.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/24/1992,73,minute.maid,6656,8.803273983,0,2.49,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/24/1992,73,tropicana,9408,9.14931567,1,2.69,0.257450782,0.07305396,10.61496569,0.005144033,0.135340763,0.109213291,0.292748225,2.584605038,1.5,3.228630183,0.18318752 -9/24/1992,74,dominicks,81024,11.30250069,0,1.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/24/1992,74,minute.maid,4864,8.489616424,0,2.49,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/24/1992,74,tropicana,9024,9.107642974,1,2.78,0.307397856,0.07119776,10.48001664,0.15625,0.090196478,0.041542429,0.297796572,7.05964577,0.666666667,2.533639879,0.394653498 -9/24/1992,75,dominicks,49344,10.80657146,0,1.49,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/24/1992,75,minute.maid,5120,8.540909718,0,2.64,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/24/1992,75,tropicana,21120,9.957975738,1,2.79,0.207699493,0.219548453,9.867082871,0.496,0.063964709,0.415999466,0.315583259,7.192666501,2.230769231,1.375126442,0.703181889 -9/24/1992,76,dominicks,49920,10.818177,0,1.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/24/1992,76,minute.maid,4416,8.392989588,0,2.49,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/24/1992,76,tropicana,9728,9.182763604,1,2.79,0.149192423,0.087711787,10.1406129,0.096330275,0.144373905,0.425324028,0.296352584,3.558380317,0.909090909,1.07563146,0.214416531 -9/24/1992,77,dominicks,30592,10.32849382,0,1.49,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/24/1992,77,minute.maid,5312,8.577723691,0,2.29,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/24/1992,77,tropicana,11648,9.36288977,1,2.79,0.10110045,0.376871097,10.98312087,0.624113475,0.118093174,0.073507887,0.414163823,1.875487675,1.176470588,2.510313339,0.554352329 -9/24/1992,78,dominicks,46400,10.74505474,0,1.49,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/24/1992,78,minute.maid,4288,8.363575703,0,2.29,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/24/1992,78,tropicana,5952,8.691482577,1,2.79,0.111947994,0.314432275,10.95917494,0.528312901,0.111221204,0.056086079,0.423952262,0.132096821,1.142857143,1.704244878,0.515669276 -9/24/1992,80,dominicks,30144,10.31374118,0,1.49,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/24/1992,80,minute.maid,5248,8.565602331,0,2.29,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/24/1992,80,tropicana,12928,9.467150781,1,2.79,0.152691263,0.304465687,10.90950929,0.548122392,0.099890045,0.041910238,0.403603604,0.323594391,0.5,1.755245053,0.356535884 -9/24/1992,81,dominicks,32384,10.38541975,0,1.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/24/1992,81,minute.maid,5440,8.60153434,0,2.49,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/24/1992,81,tropicana,13376,9.501217335,1,2.79,0.181118938,0.234201612,10.71993585,0.478846154,0.088337065,0.073961623,0.42954279,3.768907476,0.791666667,1.132215571,0.379390304 -9/24/1992,84,dominicks,40896,10.61878754,0,1.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/24/1992,84,minute.maid,4288,8.363575703,0,2.49,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/24/1992,84,tropicana,6400,8.764053269,1,2.78,0.122100005,0.188094318,10.76561773,0.491441083,0.161592506,0.02963639,0.346358237,0.966161669,1.684210526,1.582358998,0.618634025 -9/24/1992,86,dominicks,49024,10.80006525,0,1.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/24/1992,86,minute.maid,3392,8.129174997,0,2.49,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/24/1992,86,tropicana,14464,9.579418083,1,2.78,0.138756374,0.096763919,10.08897077,0.21005386,0.118656482,0.427866425,0.293206793,2.038235325,0.952380952,1.165745204,0.460779411 -9/24/1992,88,dominicks,23296,10.05603695,0,1.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/24/1992,88,minute.maid,4544,8.42156296,0,2.49,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/24/1992,88,tropicana,6336,8.754002934,1,2.79,0.160414212,0.15163275,10.54980515,0.18957346,0.13516767,0.142928009,0.401899961,4.981954638,1.6,2.087538672,0.489796498 -9/24/1992,89,dominicks,50240,10.8245668,0,1.49,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/24/1992,89,minute.maid,3968,8.286017468,0,2.38,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/24/1992,89,tropicana,4224,8.348537825,1,2.78,0.205811359,0.053349435,10.30811898,0.00250941,0.158335448,0.353053696,0.297890415,7.139074251,1.526315789,1.734594238,0.232061895 -9/24/1992,90,dominicks,52864,10.87547786,0,1.49,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/24/1992,90,minute.maid,3392,8.129174997,0,2.69,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/24/1992,90,tropicana,5312,8.577723691,1,2.79,0.225219573,0.050193454,10.5046565,0.005700872,0.124328331,0.268367489,0.244462674,12.84014961,2.461538462,4.107901798,0.370342571 -9/24/1992,92,dominicks,42240,10.65112292,0,1.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/24/1992,92,minute.maid,2688,7.896552702,0,2.49,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/24/1992,92,tropicana,4416,8.392989588,1,2.78,0.137827632,0.27012665,10.65780038,0.134417808,0.15049505,0.375388716,0.323033209,3.764544229,2.25,1.591835871,0.348654011 -9/24/1992,93,dominicks,41408,10.63122938,0,1.49,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/24/1992,93,minute.maid,4480,8.407378325,0,2.69,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/24/1992,93,tropicana,27264,10.21332243,1,2.79,0.142390194,0.363016383,10.36719012,0.491839763,0.072001002,0.347395301,0.420637376,13.1902194,0.666666667,1.331641281,0.222243272 -9/24/1992,94,dominicks,17472,9.768354879,0,1.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/24/1992,94,minute.maid,4032,8.30201781,0,2.49,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/24/1992,94,tropicana,6400,8.764053269,1,2.86,0.103002197,0.229390404,10.7445158,0.367446809,0.128111377,0.059311691,0.402313223,3.960019335,1.494736842,1.804932958,0.42732801 -9/24/1992,95,dominicks,67648,11.12207307,0,1.49,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/24/1992,95,minute.maid,3520,8.166216269,0,2.69,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/24/1992,95,tropicana,12096,9.400630098,1,2.79,0.230717504,0.085642082,10.38347245,0.042876284,0.105074321,0.096900007,0.292846213,8.551712371,1.428571429,2.007203953,0.489206218 -9/24/1992,97,dominicks,27200,10.21097225,0,1.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/24/1992,97,minute.maid,2432,7.796469243,0,2.49,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/24/1992,97,tropicana,3200,8.070906089,1,2.79,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -9/24/1992,98,dominicks,73152,11.20029475,0,1.49,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/24/1992,98,minute.maid,5760,8.658692754,0,2.45,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/24/1992,98,tropicana,10304,9.240287448,1,2.79,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -9/24/1992,100,dominicks,63296,11.05557742,0,1.49,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/24/1992,100,minute.maid,4288,8.363575703,0,2.47,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/24/1992,100,tropicana,9856,9.195835686,1,2.79,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -9/24/1992,101,dominicks,28224,10.24792796,0,1.49,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/24/1992,101,minute.maid,3584,8.184234774,0,2.47,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/24/1992,101,tropicana,11392,9.340666634,1,2.79,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -9/24/1992,102,dominicks,82240,11.31739708,0,1.49,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/24/1992,102,minute.maid,5888,8.68067166,0,2.29,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/24/1992,102,tropicana,7872,8.971067439,1,2.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -9/24/1992,103,dominicks,36160,10.49570882,0,1.49,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/24/1992,103,minute.maid,2752,7.920083199,0,2.46,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/24/1992,103,tropicana,3072,8.030084094,1,2.79,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -9/24/1992,104,dominicks,16384,9.704060528,0,1.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/24/1992,104,minute.maid,1344,7.203405521,0,2.49,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/24/1992,104,tropicana,3968,8.286017468,1,2.79,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -9/24/1992,105,dominicks,41792,10.64046021,0,1.49,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/24/1992,105,minute.maid,3584,8.184234774,0,2.52,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/24/1992,105,tropicana,7424,8.912473275,1,2.79,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -9/24/1992,106,dominicks,22336,10.01395501,0,1.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/24/1992,106,minute.maid,1984,7.592870288,0,2.49,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/24/1992,106,tropicana,3648,8.201934351,1,2.79,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -9/24/1992,107,dominicks,50432,10.82838117,0,1.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/24/1992,107,minute.maid,7232,8.886270902,0,2.49,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/24/1992,107,tropicana,22400,10.01681624,1,2.79,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -9/24/1992,109,dominicks,64512,11.07460653,0,1.49,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/24/1992,109,minute.maid,8640,9.064157862,0,2.69,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/24/1992,109,tropicana,38848,10.56741187,1,2.79,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -9/24/1992,110,dominicks,22784,10.03381381,0,1.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/24/1992,110,minute.maid,2816,7.943072717,0,2.49,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/24/1992,110,tropicana,6912,8.841014311,1,2.79,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -9/24/1992,111,dominicks,145408,11.88729886,0,1.49,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/24/1992,111,minute.maid,3072,8.030084094,0,2.67,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/24/1992,111,tropicana,8640,9.064157862,1,2.79,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -9/24/1992,112,dominicks,31232,10.34919849,0,1.49,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/24/1992,112,minute.maid,7744,8.954673629,0,2.39,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/24/1992,112,tropicana,21504,9.975994243,1,2.79,0.08972372,0.329898517,10.88265918,0.56065207,0.096660967,0.069710243,0.440164355,3.101112587,0.535714286,3.017842945,0.311666297 -9/24/1992,113,dominicks,37056,10.52018556,0,1.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/24/1992,113,minute.maid,4288,8.363575703,0,2.49,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/24/1992,113,tropicana,15616,9.656051309,1,2.79,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -9/24/1992,114,dominicks,74304,11.21592007,0,1.49,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/24/1992,114,minute.maid,4096,8.317766167,0,2.35,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/24/1992,114,tropicana,6272,8.743850562,1,2.78,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -9/24/1992,115,dominicks,33088,10.40692596,0,1.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/24/1992,115,minute.maid,5056,8.528330936,0,2.49,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/24/1992,115,tropicana,12736,9.452187908,1,2.79,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -9/24/1992,116,dominicks,35712,10.48324205,0,1.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/24/1992,116,minute.maid,3200,8.070906089,0,2.49,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/24/1992,116,tropicana,5824,8.66974259,1,2.78,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -9/24/1992,117,dominicks,28992,10.27477521,0,1.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/24/1992,117,minute.maid,3392,8.129174997,0,2.49,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/24/1992,117,tropicana,6080,8.712759975,1,2.78,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -9/24/1992,118,dominicks,37952,10.54407748,0,1.49,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/24/1992,118,minute.maid,5184,8.553332238,0,2.69,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/24/1992,118,tropicana,15040,9.618468598,1,2.79,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -9/24/1992,119,dominicks,22848,10.03661887,0,1.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/24/1992,119,minute.maid,4608,8.435549202,0,2.49,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/24/1992,119,tropicana,6848,8.831711918,1,2.79,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -9/24/1992,121,dominicks,52288,10.86452218,0,1.49,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/24/1992,121,minute.maid,6528,8.783855897,0,2.47,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/24/1992,121,tropicana,15936,9.67633598,1,2.79,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -9/24/1992,122,dominicks,43456,10.67950421,0,1.49,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/24/1992,122,minute.maid,8960,9.100525506,0,2.2,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/24/1992,122,tropicana,9536,9.162829389,1,2.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -9/24/1992,123,dominicks,79808,11.28737903,0,1.49,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/24/1992,123,minute.maid,4416,8.392989588,0,2.67,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/24/1992,123,tropicana,25152,10.1326927,1,2.79,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -9/24/1992,124,dominicks,66240,11.10103979,0,1.49,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/24/1992,124,minute.maid,5312,8.577723691,0,2.73,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/24/1992,124,tropicana,12096,9.400630098,1,2.79,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -9/24/1992,126,dominicks,39488,10.58375211,0,1.49,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/24/1992,126,minute.maid,5568,8.624791202,0,2.41,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/24/1992,126,tropicana,16128,9.688312171,1,2.78,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -9/24/1992,128,dominicks,75584,11.2329999,0,1.48,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/24/1992,128,minute.maid,6400,8.764053269,0,2.61,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/24/1992,128,tropicana,20864,9.945780465,1,2.79,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -9/24/1992,129,dominicks,36928,10.51672535,0,1.49,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/24/1992,129,minute.maid,5440,8.60153434,0,2.47,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/24/1992,129,tropicana,9664,9.17616292,1,2.79,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -9/24/1992,130,dominicks,115392,11.65609031,0,1.49,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/24/1992,130,minute.maid,5376,8.589699882,0,2.54,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/24/1992,130,tropicana,10688,9.276876896,1,2.79,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -9/24/1992,131,dominicks,45952,10.73535265,0,1.49,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/24/1992,131,minute.maid,5376,8.589699882,0,2.4,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/24/1992,131,tropicana,8576,9.056722883,1,2.78,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -9/24/1992,132,dominicks,57472,10.95905315,0,1.49,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/24/1992,132,minute.maid,6464,8.7740036,0,2.42,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/24/1992,132,tropicana,11008,9.30637756,1,2.79,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -9/24/1992,134,dominicks,20352,9.920934466,0,1.49,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/24/1992,134,minute.maid,2624,7.87245515,0,2.54,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/24/1992,134,tropicana,6848,8.831711918,1,2.79,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -9/24/1992,137,dominicks,55680,10.9273763,0,1.49,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/24/1992,137,minute.maid,11136,9.317938383,0,2.59,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -9/24/1992,137,tropicana,51840,10.85591733,1,2.79,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/1/1992,2,dominicks,8640,9.064157862,0,1.82,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/1/1992,2,minute.maid,41216,10.62658181,1,2.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/1/1992,2,tropicana,5824,8.66974259,0,2.97,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.376926613 -10/1/1992,5,dominicks,6144,8.723231275,0,1.85,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/1/1992,5,minute.maid,50304,10.82583988,1,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/1/1992,5,tropicana,7488,8.921057018,0,2.78,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 -10/1/1992,8,dominicks,16192,9.692272572,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/1/1992,8,minute.maid,65856,11.09522582,1,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/1/1992,8,tropicana,6400,8.764053269,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947 -10/1/1992,9,dominicks,3520,8.166216269,0,2.13,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/1/1992,9,minute.maid,62976,11.05050898,1,2.19,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/1/1992,9,tropicana,5248,8.565602331,0,3.2,0.269119018,0.222172318,10.78715178,0.505747126,0.096830474,0.032618826,0.358994501,1.103278736,0.666666667,1.820474337,0.441268272 -10/1/1992,12,dominicks,15232,9.631153757,0,2.09,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/1/1992,12,minute.maid,68160,11.12961316,1,2.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/1/1992,12,tropicana,8384,9.034080407,0,3.19,0.178341405,0.253412969,9.996659083,0.386627907,0.057212416,0.380697988,0.390941597,9.198733552,1.111111111,0.83930322,0.10599881 -10/1/1992,14,dominicks,4224,8.348537825,0,2.24,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/1/1992,14,minute.maid,55808,10.92967251,1,2.19,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/1/1992,14,tropicana,10688,9.276876896,0,3.41,0.213949275,0.348293024,11.04392933,0.750769908,0.107894294,0.034178744,0.362305746,3.320947571,0.75,2.359891293,0.73181003 -10/1/1992,18,dominicks,11584,9.357380115,0,1.66,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/1/1992,18,minute.maid,73024,11.19854343,1,2.09,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/1/1992,18,tropicana,8448,9.041685006,0,2.69,0.272313368,0.072246456,10.39197554,0.093351946,0.097937631,0.074417144,0.313907719,1.980307092,0.666666667,1.684315129,0.154224764 -10/1/1992,21,dominicks,10496,9.258749511,0,1.7,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/1/1992,21,minute.maid,32896,10.40110635,1,2.19,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/1/1992,21,tropicana,1984,7.592870288,0,2.84,0.066896458,0.17750345,10.71619397,0.123485737,0.164179104,0.105038777,0.406070931,0.267524672,1.05,1.953944431,0.602073176 -10/1/1992,28,dominicks,6976,8.850230966,0,2.13,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/1/1992,28,minute.maid,25344,10.1402973,1,2.19,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/1/1992,28,tropicana,4160,8.333270353,0,3.2,0.213308785,0.233162564,10.79853422,0.445140033,0.103665655,0.055935473,0.389058524,2.912922287,1.727272727,1.820776686,0.814221001 -10/1/1992,32,dominicks,10176,9.227787286,0,1.94,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/1/1992,32,minute.maid,67456,11.11923081,1,2.19,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/1/1992,32,tropicana,9600,9.169518378,0,2.97,0.254953032,0.198259861,10.67447502,0.538346427,0.080251365,0.031938514,0.344152081,6.497971979,0.52173913,3.337253941,0.365659802 -10/1/1992,33,dominicks,6976,8.850230966,0,1.7,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/1/1992,33,minute.maid,48768,10.79482964,1,2.19,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/1/1992,33,tropicana,8640,9.064157862,0,3.03,0.134169965,0.419688004,10.34592726,0.862559242,0.013506356,0.130127179,0.472308288,3.129556199,2.416666667,0.867215261,0.752824617 -10/1/1992,40,dominicks,15552,9.651944527,0,1.41,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/1/1992,40,minute.maid,46784,10.75329654,1,2.19,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/1/1992,40,tropicana,3008,8.009030685,0,2.9,0.1818518,0.072128605,10.55025042,0.068989386,0.126812989,0.044053067,0.306107955,1.086386161,0.4,1.28992007,0.444661211 -10/1/1992,44,dominicks,5056,8.528330936,0,1.72,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/1/1992,44,minute.maid,65024,11.08251171,1,2.19,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/1/1992,44,tropicana,10496,9.258749511,0,2.99,0.190982776,0.329738388,10.86915875,0.64268521,0.116388096,0.037632074,0.340794382,6.134798707,0.615384615,1.64310311,0.539190205 -10/1/1992,45,dominicks,6400,8.764053269,0,1.85,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/1/1992,45,minute.maid,21760,9.987828701,1,2.19,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/1/1992,45,tropicana,4608,8.435549202,0,2.78,0.128857348,0.280150164,10.74537796,0.342017119,0.083717204,0.087233873,0.464112624,3.158816596,1.25,2.891890631,0.887524737 -10/1/1992,47,dominicks,5504,8.61323038,0,1.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/1/1992,47,minute.maid,30912,10.33889974,1,2.19,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/1/1992,47,tropicana,2880,7.965545573,0,2.99,0.125798297,0.140598651,10.63532646,0.335671343,0.145731588,0.120675891,0.398027021,4.910910687,2.028571429,1.967744964,0.794855358 -10/1/1992,48,dominicks,3136,8.050703382,0,2.13,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/1/1992,48,minute.maid,26880,10.1991378,1,2.19,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/1/1992,48,tropicana,5056,8.528330936,0,3.2,0.097921961,0.303260384,10.75602879,0.303356282,0.07848683,0.099491703,0.459682823,4.65068687,1.615384615,2.198071182,0.904416734 -10/1/1992,49,dominicks,5440,8.60153434,0,1.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/1/1992,49,minute.maid,30464,10.32430094,1,2.19,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/1/1992,49,tropicana,3648,8.201934351,0,2.99,0.187473187,0.319949969,10.80675383,0.431708991,0.10247191,0.038352638,0.346411721,3.439418592,2.272727273,1.807235771,0.920568958 -10/1/1992,50,dominicks,4224,8.348537825,0,2.13,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/1/1992,50,minute.maid,38336,10.55414468,1,2.19,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/1/1992,50,tropicana,2880,7.965545573,0,3.2,0.153357376,0.128764278,10.58930796,0.240180587,0.111798396,0.07092564,0.3242127,2.767046304,0.727272727,1.939328534,0.470972765 -10/1/1992,51,dominicks,5248,8.565602331,0,1.85,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/1/1992,51,minute.maid,42560,10.65867012,1,2.19,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/1/1992,51,tropicana,6144,8.723231275,0,2.78,0.176159718,0.171916711,10.62083942,0.316889001,0.130340815,0.025425719,0.313189796,2.102553804,1.25,2.226766425,0.310202569 -10/1/1992,52,dominicks,3392,8.129174997,0,2.09,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/1/1992,52,minute.maid,53376,10.88511649,1,2.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/1/1992,52,tropicana,10688,9.276876896,0,3.19,0.152241195,0.372927296,11.05101698,0.727728684,0.101976511,0.084898613,0.393476864,4.793439152,1.055555556,2.533671515,0.334511569 -10/1/1992,53,dominicks,6208,8.733594062,0,2.24,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/1/1992,53,minute.maid,77184,11.25394746,1,2.19,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/1/1992,53,tropicana,12288,9.416378455,0,3.41,0.300278681,0.2703835,10.60000405,0.441323063,0.113686757,0.065722248,0.312635693,10.45970992,1,2.132004658,0.332950694 -10/1/1992,54,dominicks,4288,8.363575703,0,1.74,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/1/1992,54,minute.maid,31552,10.35939226,1,2.19,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 -10/1/1992,54,tropicana,3840,8.253227646,0,2.78,0.090222278,0.421125644,10.91089788,0.646023263,0.103522408,0.046640859,0.43671875,4.899564267,1.333333333,1.963412365,0.645830051 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-10/1/1992,97,tropicana,1216,7.103322063,0,3.19,0.142433234,0.178182783,10.48374595,0.135732648,0.139324117,0.233612085,0.390218878,16.73439396,1.1875,2.520728232,0.271672959 -10/1/1992,98,dominicks,10368,9.246479419,0,1.78,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/1/1992,98,minute.maid,84864,11.34880525,1,2.19,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/1/1992,98,tropicana,3200,8.070906089,0,2.9,0.24920053,0.051702821,10.57359638,0.009843473,0.125409403,0.164963731,0.299584392,6.23035712,1.5,3.133176717,0.381569368 -10/1/1992,100,dominicks,10624,9.270870872,0,1.73,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/1/1992,100,minute.maid,49024,10.80006525,1,2.19,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/1/1992,100,tropicana,8000,8.987196821,0,2.85,0.136995144,0.049550286,10.0365751,0.011117974,0.216354344,0.564086801,0.267233951,8.369534986,1.115384615,2.109824346,0.197683955 -10/1/1992,101,dominicks,7040,8.859363449,0,1.79,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/1/1992,101,minute.maid,46464,10.7464331,1,2.19,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/1/1992,101,tropicana,5376,8.589699882,0,2.91,0.225035218,0.174741859,10.65993812,0.354573672,0.096042271,0.087422017,0.390065147,4.849622717,0.5,2.200317948,0.342081421 -10/1/1992,102,dominicks,27392,10.21800628,0,1.63,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/1/1992,102,minute.maid,66944,11.11161173,1,2.09,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/1/1992,102,tropicana,7424,8.912473275,0,2.69,0.216626232,0.120657539,10.49385422,0.034301485,0.13893104,0.172158585,0.289944134,5.844058186,0.689655172,2.353846891,0.158903149 -10/1/1992,103,dominicks,6848,8.831711918,0,1.73,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/1/1992,103,minute.maid,26240,10.17504024,1,2.19,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/1/1992,103,tropicana,1728,7.454719949,0,2.94,0.058053966,0.194621102,10.58524512,0.090007348,0.204916868,0.187760899,0.36129428,5.100649474,1.2,3.300102441,0.421982695 -10/1/1992,104,dominicks,4224,8.348537825,0,2.09,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/1/1992,104,minute.maid,30336,10.32009041,1,2.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/1/1992,104,tropicana,3776,8.236420527,0,3.19,0.135286373,0.249684461,10.69885282,0.407496492,0.117514274,0.039405624,0.405306896,11.94482409,1.052631579,1.342004856,0.189129482 -10/1/1992,105,dominicks,9152,9.121727714,0,1.77,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/1/1992,105,minute.maid,38848,10.56741187,1,2.19,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/1/1992,105,tropicana,2432,7.796469243,0,2.97,0.175542126,0.094235589,10.41439317,0.074418605,0.159839527,0.365410594,0.360890119,4.037829402,0.96,3.354376593,0.520134391 -10/1/1992,106,dominicks,2560,7.847762538,0,2.24,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/1/1992,106,minute.maid,17600,9.775654181,1,2.19,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/1/1992,106,tropicana,1600,7.377758908,0,3.41,0.109887349,0.157939308,10.50684678,0.099963249,0.146638759,0.19058587,0.367482993,6.250902694,0.904761905,0.991094709,0.103491755 -10/1/1992,107,dominicks,8000,8.987196821,0,2.13,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/1/1992,107,minute.maid,70080,11.15739273,1,2.19,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/1/1992,107,tropicana,11200,9.323669057,0,3.2,0.261867453,0.273051907,10.82713119,0.533949633,0.094316953,0.025624482,0.311220523,5.821370205,1.19047619,1.994644862,0.300364461 -10/1/1992,109,dominicks,4416,8.392989588,0,2.24,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/1/1992,109,minute.maid,84032,11.33895296,1,2.19,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/1/1992,109,tropicana,14976,9.614204199,0,3.41,0.151055656,0.476916693,11.23319833,0.878804348,0.114171257,0.060687451,0.340806694,7.481499805,0.714285714,2.416943478,0.394100191 -10/1/1992,110,dominicks,4800,8.476371197,0,1.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/1/1992,110,minute.maid,24960,10.12502982,1,2.19,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/1/1992,110,tropicana,3520,8.166216269,0,2.99,0.114956688,0.167553191,10.52085999,0.232741256,0.161950632,0.152944449,0.404314087,8.458996675,0.954545455,2.335458124,0.321927182 -10/1/1992,111,dominicks,23872,10.0804615,0,1.52,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/1/1992,111,minute.maid,56192,10.93652968,1,2.19,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/1/1992,111,tropicana,1984,7.592870288,0,2.9,0.210512842,0.096928919,10.13828296,0.012746711,0.157135934,0.995690759,0.288515406,12.19094513,1.894736842,1.465672465,0.289203431 -10/1/1992,113,dominicks,8128,9.00307017,0,1.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/1/1992,113,minute.maid,67840,11.12490727,1,2.19,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/1/1992,113,tropicana,8256,9.018695488,0,2.99,0.299352545,0.151592429,10.64766049,0.378173027,0.091559829,0.026448309,0.297514876,4.235048902,0.52173913,3.305794933,0.366915963 -10/1/1992,114,dominicks,8384,9.034080407,0,1.72,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/1/1992,114,minute.maid,49792,10.81560961,1,2.19,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/1/1992,114,tropicana,5568,8.624791202,0,2.78,0.182173295,0.094424515,10.34794899,0.009842116,0.111967075,0.441169508,0.307476963,4.935835625,1.454545455,3.239227061,0.147758687 -10/1/1992,115,dominicks,3584,8.184234774,0,1.76,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/1/1992,115,minute.maid,42944,10.66765222,1,2.19,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/1/1992,115,tropicana,5440,8.60153434,0,2.91,0.060280055,0.406312482,10.94748525,0.64424253,0.151332196,0.043971995,0.405279954,4.837982618,0.952380952,1.717685861,0.350483258 -10/1/1992,116,dominicks,9728,9.182763604,0,1.85,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/1/1992,116,minute.maid,37824,10.5406991,1,2.19,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/1/1992,116,tropicana,4352,8.378390789,0,2.78,0.18817339,0.259224748,10.69711335,0.308160505,0.117657192,0.033137058,0.35563513,3.215079635,1.666666667,1.55776418,0.812883335 -10/1/1992,117,dominicks,4160,8.333270353,0,1.85,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/1/1992,117,minute.maid,29312,10.28575227,1,2.19,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/1/1992,117,tropicana,2432,7.796469243,0,2.78,0.110102729,0.249083542,10.76096203,0.352834113,0.106357795,0.049851894,0.439136251,3.434272274,1.4,1.849425028,0.498380462 -10/1/1992,118,dominicks,5120,8.540909718,0,1.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/1/1992,118,minute.maid,50112,10.82201578,1,2.19,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/1/1992,118,tropicana,4992,8.51559191,0,2.99,0.289442379,0.224725883,10.63236378,0.475752773,0.090132827,0.040669145,0.354977468,1.945508831,0.923076923,2.046488588,0.513721734 -10/1/1992,119,dominicks,5888,8.68067166,0,2.13,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/1/1992,119,minute.maid,31296,10.35124557,1,2.19,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/1/1992,119,tropicana,4608,8.435549202,0,3.2,0.121574965,0.279952033,10.75271926,0.459405941,0.089350352,0.049584956,0.462266179,2.213790697,1.153846154,2.71959438,0.722367357 -10/1/1992,121,dominicks,8192,9.010913347,0,1.83,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/1/1992,121,minute.maid,51200,10.84349481,1,2.19,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/1/1992,121,tropicana,8512,9.049232212,0,2.9,0.16358133,0.35061287,10.94992897,0.61086249,0.091693756,0.039173539,0.382304265,3.751225161,1.090909091,2.069345766,0.559533568 -10/1/1992,122,dominicks,13504,9.510741217,0,1.63,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/1/1992,122,minute.maid,36160,10.49570882,1,2.09,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/1/1992,122,tropicana,9344,9.142489705,0,2.69,0.061953911,0.255889066,10.77505285,0.280446271,0.125831821,0.078372885,0.425860127,2.597516352,0.6,2.752490308,0.238737607 -10/1/1992,123,dominicks,6400,8.764053269,0,1.77,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/1/1992,123,minute.maid,66496,11.10489707,1,2.19,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/1/1992,123,tropicana,4800,8.476371197,0,2.94,0.176040942,0.153191489,10.33410045,0.228675136,0.145040289,0.204809699,0.3115727,7.449993976,1.111111111,1.60714281,0.166802611 -10/1/1992,124,dominicks,6400,8.764053269,0,1.81,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/1/1992,124,minute.maid,45760,10.73116563,1,2.19,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/1/1992,124,tropicana,5184,8.553332238,0,3.05,0.119625811,0.261875835,10.25895677,0.416315588,0.12494986,0.572355861,0.348518562,6.328746874,0.727272727,1.793795763,0.202168946 -10/1/1992,126,dominicks,11712,9.368369236,0,1.74,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/1/1992,126,minute.maid,51200,10.84349481,1,2.19,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/1/1992,126,tropicana,9408,9.14931567,0,2.78,0.107002273,0.413222417,10.9808762,0.573626374,0.113699475,0.045018573,0.421959283,5.065200839,1.69047619,1.858260236,0.717799295 -10/1/1992,128,dominicks,5248,8.565602331,0,1.78,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/1/1992,128,minute.maid,65600,11.09133098,1,2.19,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/1/1992,128,tropicana,8832,9.086136769,0,2.86,0.157485189,0.211896894,10.15342882,0.26618705,0.100043497,0.355911221,0.398251192,10.6674474,2,1.409463486,0.259004207 -10/1/1992,129,dominicks,3584,8.184234774,0,1.8,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/1/1992,129,minute.maid,33600,10.42228135,1,2.19,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/1/1992,129,tropicana,7680,8.946374826,0,2.89,0.103412869,0.307842548,10.99986401,0.691439828,0.122080527,0.034128692,0.367876448,7.082314817,1.041666667,2.65687913,0.342210744 -10/1/1992,130,dominicks,14080,9.55251063,0,1.75,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/1/1992,130,minute.maid,85440,11.35556965,1,2.19,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/1/1992,130,tropicana,4992,8.51559191,0,2.9,0.145117314,0.143406846,9.966650127,0.080565371,0.111081513,0.984896319,0.337029744,17.85595081,1.636363636,0.951815576,0.094561751 -10/1/1992,131,dominicks,5312,8.577723691,0,1.75,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/1/1992,131,minute.maid,41792,10.64046021,1,2.19,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/1/1992,131,tropicana,5888,8.68067166,0,2.78,0.17065481,0.2713958,10.79353697,0.455995496,0.090100341,0.074656427,0.414841412,3.159728192,0.625,1.49003586,0.383226786 -10/1/1992,132,dominicks,14080,9.55251063,0,1.72,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/1/1992,132,minute.maid,47808,10.77494827,1,2.19,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/1/1992,132,tropicana,6272,8.743850562,0,2.86,0.139617348,0.264528441,10.61915425,0.148870968,0.129984231,0.273974204,0.352754754,2.312046161,1.5625,2.795615017,0.478339596 -10/1/1992,134,dominicks,6976,8.850230966,0,1.7,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/1/1992,134,minute.maid,23040,10.04498712,1,2.19,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/1/1992,134,tropicana,2944,7.98752448,0,3,0.090152653,0.247949139,10.7225182,0.346153846,0.154877575,0.105855856,0.388116308,6.678129633,1.972222222,3.224540507,0.657090005 -10/1/1992,137,dominicks,7040,8.859363449,0,1.75,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/1/1992,137,minute.maid,73536,11.20553036,1,2.19,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 -10/1/1992,137,tropicana,27904,10.23652533,0,2.88,0.20960245,0.528362014,10.96648993,0.86073903,0.092996052,0.11324985,0.330292847,6.02648362,0.705882353,0.772529732,0.333761209 diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/forecasting_script.py b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/forecasting_script.py deleted file mode 100644 index 40724de54..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/forecasting_script.py +++ /dev/null @@ -1,61 +0,0 @@ -""" -This is the script that is executed on the compute instance. It relies -on the model.pkl file which is uploaded along with this script to the -compute instance. -""" - -import argparse -from azureml.core import Dataset, Run -from sklearn.externals import joblib -from pandas.tseries.frequencies import to_offset - -parser = argparse.ArgumentParser() -parser.add_argument( - "--target_column_name", - type=str, - dest="target_column_name", - help="Target Column Name", -) -parser.add_argument( - "--test_dataset", type=str, dest="test_dataset", help="Test Dataset" -) - -args = parser.parse_args() -target_column_name = args.target_column_name -test_dataset_id = args.test_dataset - -run = Run.get_context() -ws = run.experiment.workspace - -# get the input dataset by id -test_dataset = Dataset.get_by_id(ws, id=test_dataset_id) - -X_test = test_dataset.to_pandas_dataframe().reset_index(drop=True) -y_test = X_test.pop(target_column_name).values - -# generate forecast -fitted_model = joblib.load("model.pkl") -# We have default quantiles values set as below(95th percentile) -quantiles = [0.025, 0.5, 0.975] -predicted_column_name = "predicted" -PI = "prediction_interval" -fitted_model.quantiles = quantiles -pred_quantiles = fitted_model.forecast_quantiles(X_test) -pred_quantiles[PI] = pred_quantiles[[min(quantiles), max(quantiles)]].apply( - lambda x: "[{}, {}]".format(x[0], x[1]), axis=1 -) -X_test[target_column_name] = y_test -X_test[PI] = pred_quantiles[PI] -X_test[predicted_column_name] = pred_quantiles[0.5] -# drop rows where prediction or actuals are nan -# happens because of missing actuals -# or at edges of time due to lags/rolling windows -clean = X_test[ - X_test[[target_column_name, predicted_column_name]].notnull().all(axis=1) -] - -file_name = "outputs/predictions.csv" -export_csv = clean.to_csv(file_name, header=True, index=False) # added Index - -# Upload the predictions into artifacts -run.upload_file(name=file_name, path_or_stream=file_name) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/run_forecast.py b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/run_forecast.py deleted file mode 100644 index cb1d9d886..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/run_forecast.py +++ /dev/null @@ -1,49 +0,0 @@ -import os -import shutil -from azureml.core import ScriptRunConfig - - -def run_remote_inference( - test_experiment, - compute_target, - train_run, - test_dataset, - target_column_name, - inference_folder="./forecast", -): - # Create local directory to copy the model.pkl and forecsting_script.py files into. - # These files will be uploaded to and executed on the compute instance. - os.makedirs(inference_folder, exist_ok=True) - shutil.copy("forecasting_script.py", inference_folder) - - train_run.download_file( - "outputs/model.pkl", os.path.join(inference_folder, "model.pkl") - ) - - inference_env = train_run.get_environment() - - config = ScriptRunConfig( - source_directory=inference_folder, - script="forecasting_script.py", - arguments=[ - "--target_column_name", - target_column_name, - "--test_dataset", - test_dataset.as_named_input(test_dataset.name), - ], - compute_target=compute_target, - environment=inference_env, - ) - - run = test_experiment.submit( - config, - tags={ - "training_run_id": train_run.id, - "run_algorithm": train_run.properties["run_algorithm"], - "valid_score": train_run.properties["score"], - "primary_metric": train_run.properties["primary_metric"], - }, - ) - - run.log("run_algorithm", run.tags["run_algorithm"]) - return run diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb deleted file mode 100644 index 2e773fbdf..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb +++ /dev/null @@ -1,494 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/1_determine_experiment_settings.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook we will explore the univaraite time-series data to determine the settings for an automated ML experiment. We will follow the thought process depicted in the following diagram:
\n", - "![Forecasting after training](figures/univariate_settings_map_20210408.jpg)\n", - "\n", - "The objective is to answer the following questions:\n", - "\n", - "
    \n", - "
  1. Is there a seasonal pattern in the data?
    2. \n", - "
      \n", - "
    • Importance: If we are able to detect regular seasonal patterns, the forecast accuracy may be improved by extracting these patterns and including them as features into the model.
      • \n", - "
    \n", - "
  2. Is the data stationary?
    3. \n", - "
      \n", - "
    • Importance: In the absense of features that capture trend behavior, ML models (regression and tree based) are not well equiped to predict stochastic trends. Working with stationary data solves this problem.
      • \n", - "
    \n", - "
  3. Is there a detectable auto-regressive pattern in the stationary data?
    4. \n", - "
      \n", - "
    • Importance: The accuracy of ML models can be improved if serial correlation is modeled by including lags of the dependent/target varaible as features. Including target lags in every experiment by default will result in a regression in accuracy scores if such setting is not warranted.
      • \n", - "
    \n", - "
\n", - "\n", - "The answers to these questions will help determine the appropriate settings for the automated ML experiment.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "import pandas as pd\n", - "\n", - "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", - "import matplotlib.pyplot as plt\n", - "from pandas.plotting import register_matplotlib_converters\n", - "\n", - "register_matplotlib_converters() # fixes the future warning issue\n", - "\n", - "from helper_functions import unit_root_test_wrapper\n", - "from statsmodels.tools.sm_exceptions import InterpolationWarning\n", - "\n", - "warnings.simplefilter(\"ignore\", InterpolationWarning)\n", - "\n", - "\n", - "# set printing options\n", - "pd.set_option(\"display.max_columns\", 500)\n", - "pd.set_option(\"display.width\", 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# load data\n", - "main_data_loc = \"data\"\n", - "train_file_name = \"S4248SM144SCEN.csv\"\n", - "\n", - "TARGET_COLNAME = \"S4248SM144SCEN\"\n", - "TIME_COLNAME = \"observation_date\"\n", - "COVID_PERIOD_START = \"2020-03-01\"\n", - "\n", - "df = pd.read_csv(os.path.join(main_data_loc, train_file_name))\n", - "df[TIME_COLNAME] = pd.to_datetime(df[TIME_COLNAME], format=\"%Y-%m-%d\")\n", - "df.sort_values(by=TIME_COLNAME, inplace=True)\n", - "df.set_index(TIME_COLNAME, inplace=True)\n", - "df.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot the entire dataset\n", - "fig, ax = plt.subplots(figsize=(6, 2), dpi=180)\n", - "ax.plot(df)\n", - "ax.title.set_text(\"Original Data Series\")\n", - "locs, labels = plt.xticks()\n", - "plt.xticks(rotation=45)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The graph plots the alcohol sales in the United States. Because the data is trending, it can be difficult to see cycles, seasonality or other interestng behaviors due to the scaling issues. For example, if there is a seasonal pattern, which we will discuss later, we cannot see them on the trending data. In such case, it is worth plotting the same data in first differences." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot the entire dataset in first differences\n", - "fig, ax = plt.subplots(figsize=(6, 2), dpi=180)\n", - "ax.plot(df.diff().dropna())\n", - "ax.title.set_text(\"Data in first differences\")\n", - "locs, labels = plt.xticks()\n", - "plt.xticks(rotation=45)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the previous plot we observe that the data is more volatile towards the end of the series. This period coincides with the Covid-19 period, so we will exclude it from our experiment. Since in this example there are no user-provided features it is hard to make an argument that a model trained on the less volatile pre-covid data will be able to accurately predict the covid period." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Seasonality\n", - "\n", - "#### Questions that need to be answered in this section:\n", - "1. Is there a seasonality?\n", - "2. If it's seasonal, does the data exhibit a trend (up or down)?\n", - "\n", - "It is hard to visually detect seasonality when the data is trending. The reason being is scale of seasonal fluctuations is dwarfed by the range of the trend in the data. One way to deal with this is to de-trend the data by taking the first differences. We will discuss this in more detail in the next section." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot the entire dataset in first differences\n", - "fig, ax = plt.subplots(figsize=(6, 2), dpi=180)\n", - "ax.plot(df.diff().dropna())\n", - "ax.title.set_text(\"Data in first differences\")\n", - "locs, labels = plt.xticks()\n", - "plt.xticks(rotation=45)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the next plot, we will exclude the Covid period again. We will also shorten the length of data because plotting a very long time series may prevent us from seeing seasonal patterns, if there are any, because the plot may look like a random walk." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# remove COVID period\n", - "df = df[:COVID_PERIOD_START]\n", - "\n", - "# plot the entire dataset in first differences\n", - "fig, ax = plt.subplots(figsize=(6, 2), dpi=180)\n", - "ax.plot(df[\"2015-01-01\":].diff().dropna())\n", - "ax.title.set_text(\"Data in first differences\")\n", - "locs, labels = plt.xticks()\n", - "plt.xticks(rotation=45)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Conclusion

\n", - "\n", - "Visual examination does not suggest clear seasonal patterns. We will set the STL_TYPE = None, and we will move to the next section that examines stationarity. \n", - "\n", - "\n", - "Say, we are working with a different data set that shows clear patterns of seasonality, we have several options for setting the settings:is hard to say which option will work best in your case, hence you will need to run both options to see which one results in more accurate forecasts. \n", - "
    \n", - "
  1. If the data does not appear to be trending, set DIFFERENCE_SERIES=False, TARGET_LAGS=None and STL_TYPE = \"season\"
    2. \n", - "
  2. If the data appears to be trending, consider one of the following two settings:\n", - "
      \n", - "
        \n", - "
      1. DIFFERENCE_SERIES=True, TARGET_LAGS=None and STL_TYPE = \"season\", or
        2. \n", - "
      2. DIFFERENCE_SERIES=False, TARGET_LAGS=None and STL_TYPE = \"trend_season\"
        3. \n", - "
      \n", - "
    • In the first case, by taking first differences we are removing stochastic trend, but we do not remove seasonal patterns. In the second case, we do not remove the stochastic trend and it can be captured by the trend component of the STL decomposition. It is hard to say which option will work best in your case, hence you will need to run both options to see which one results in more accurate forecasts.
      • \n", - "
    \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Stationarity\n", - "If the data does not exhibit seasonal patterns, we would like to see if the data is non-stationary. Particularly, we want to see if there is a clear trending behavior. If such behavior is observed, we would like to first difference the data and examine the plot of an auto-correlation function (ACF) known as correlogram. If the data is seasonal, differencing it will not get rid off the seasonality and this will be shown on the correlogram as well.\n", - "\n", - "
    \n", - "
  • Question: What is stationarity and how to we detect it?
    • \n", - "
      \n", - "
    • This is a fairly complex topic. Please read the following link for a high level discussion on this subject.
      • \n", - "
    • Simply put, we are looking for scenario when examining the time series plots the mean of the series is roughly the same, regardless which time interval you pick to compute it. Thus, trending and seasonal data are examples of non-stationary series.
      • \n", - "
    \n", - "
\n", - "\n", - "\n", - "
    \n", - "
  • Question: Why do want to work with stationary data?
    • \n", - "
      \n", - "
    • In the absence of features that capture stochastic trends, the ML models that use (deterministic) time based features (hour of the day, day of the week, month of the year, etc) cannot capture such trends, and will over or under predict depending on the behavior of the time series. By working with stationary data, we eliminate the need to predict such trends, which improves the forecast accuracy. Classical time series models such as Arima and Exponential Smoothing handle non-stationary series by design and do not need such transformations. By differencing the data we are still able to run the same family of models.
      • \n", - "
    \n", - "
\n", - "\n", - "#### Questions that need to be answered in this section:\n", - "
    \n", - "
  1. Is the data stationary?
    2. \n", - "
  2. Does the stationarized data (either the original or the differenced series) exhibit a clear auto-regressive pattern?
    3. \n", - "
\n", - "\n", - "To answer the first question, we run a series of tests (we call them unit root tests)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# unit root tests\n", - "test = unit_root_test_wrapper(df[TARGET_COLNAME])\n", - "print(\"---------------\", \"\\n\")\n", - "print(\"Summary table\", \"\\n\", test[\"summary\"], \"\\n\")\n", - "print(\"Is the {} series stationary?: {}\".format(TARGET_COLNAME, test[\"stationary\"]))\n", - "print(\"---------------\", \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the previous cell, we ran a series of unit root tests. The summary table contains the following columns:\n", - "
    \n", - "
  • test_name is the name of the test.\n", - "
      \n", - "
    • ADF: Augmented Dickey-Fuller test
      • \n", - "
    • KPSS: Kwiatkowski-Phillips\u00e2\u20ac\u201cSchmidt\u00e2\u20ac\u201cShin test
      • \n", - "
    • PP: Phillips-Perron test\n", - "
    • ADF GLS: Augmented Dickey-Fuller using generalized least squares method
      • \n", - "
    • AZ: Andrews-Zivot test
      • \n", - "
    \n", - "
  • statistic: test statistic
    • \n", - "
  • crit_val: critical value of the test statistic
    • \n", - "
  • p_val: p-value of the test statistic. If the p-val is less than 0.05, the null hypothesis is rejected.
    • \n", - "
  • stationary: is the series stationary based on the test result?
    • \n", - "
  • Null hypothesis: what is being tested. Notice, some test such as ADF and PP assume the process has a unit root and looks for evidence to reject this hypothesis. Other tests, ex.g: KPSS, assumes the process is stationary and looks for evidence to reject such claim.\n", - "
\n", - "\n", - "Each of the tests shows that the original time series is non-stationary. The final decision is based on the majority rule. If, there is a split decision, the algorithm will claim it is stationary. We run a series of tests because each test by itself may not be accurate. In many cases when there are conflicting test results, the user needs to make determination if the series is stationary or not.\n", - "\n", - "Since we found the series to be non-stationary, we will difference it and then test if the differenced series is stationary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# unit root tests\n", - "test = unit_root_test_wrapper(df[TARGET_COLNAME].diff().dropna())\n", - "print(\"---------------\", \"\\n\")\n", - "print(\"Summary table\", \"\\n\", test[\"summary\"], \"\\n\")\n", - "print(\"Is the {} series stationary?: {}\".format(TARGET_COLNAME, test[\"stationary\"]))\n", - "print(\"---------------\", \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Four out of five tests show that the series in first differences is stationary. Notice that this decision is not unanimous. Next, let's plot the original series in first-differences to illustrate the difference between non-stationary (unit root) process vs the stationary one." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot original and stationary data\n", - "fig = plt.figure(figsize=(10, 10))\n", - "ax1 = fig.add_subplot(211)\n", - "ax1.plot(df[TARGET_COLNAME], \"-b\")\n", - "ax2 = fig.add_subplot(212)\n", - "ax2.plot(df[TARGET_COLNAME].diff().dropna(), \"-b\")\n", - "ax1.title.set_text(\"Original data\")\n", - "ax2.title.set_text(\"Data in first differences\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you were asked a question \"What is the mean of the series before and after 2008?\", for the series titled \"Original data\" the mean values will be significantly different. This implies that the first moment of the series (in this case, it is the mean) is time dependent, i.e., mean changes depending on the interval one is looking at. Thus, the series is deemed to be non-stationary. On the other hand, for the series titled \"Data in first differences\" the means for both periods are roughly the same. Hence, the first moment is time invariant; meaning it does not depend on the interval of time one is looking at. In this example it is easy to visually distinguish between stationary and non-stationary data. Often this distinction is not easy to make, therefore we rely on the statistical tests described above to help us make an informed decision. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Conclusion

\n", - "Since we found the original process to be non-stationary (contains unit root), we will have to model the data in first differences. As a result, we will set the DIFFERENCE_SERIES parameter to True." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3 Check if there is a clear autoregressive pattern\n", - "We need to determine if we should include lags of the target variable as features in order to improve forecast accuracy. To do this, we will examine the ACF and partial ACF (PACF) plots of the stationary series. In our case, it is a series in first diffrences.\n", - "\n", - "
    \n", - "
  • Question: What is an Auto-regressive pattern? What are we looking for?
    • \n", - "
      \n", - "
    • We are looking for a classical profiles for an AR(p) process such as an exponential decay of an ACF and a the first $p$ significant lags of the PACF. For a more detailed explanation of ACF and PACF please refer to the appendix at the end of this notebook. For illustration purposes, let's examine the ACF/PACF profiles of the simulated data that follows a second order auto-regressive process, abbreviated as an AR(2).
    • \n", - "
    • \n", - "
      \n", - " The lag order is on the x-axis while the auto- and partial-correlation coefficients are on the y-axis. Vertical lines that are outside the shaded area represent statistically significant lags. Notice, the ACF function decays to zero and the PACF shows 2 significant spikes (we ignore the first spike for lag 0 in both plots since the linear relationship of any series with itself is always 1).
    • \n", - "
    \n", - "
      " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
        \n", - "
      • Question: What do I do if I observe an auto-regressive behavior?
        • \n", - "
          \n", - "
        • If such behavior is observed, we might improve the forecast accuracy by enabling the target lags feature in AutoML. There are a few options of doing this
          • \n", - "
            \n", - "
          1. Set the target lags parameter to 'auto', or
            2. \n", - "
          2. Specify the list of lags you want to include. Ex.g: target_lags = [1,2,5]
            3. \n", - "
          \n", - "
        \n", - "
        \n", - "
      • Next, let's examine the ACF and PACF plots of the stationary target variable (depicted below). Here, we do not see a decay in the ACF, instead we see a decay in PACF. It is hard to make an argument the the target variable exhibits auto-regressive behavior.
        • \n", - "
      " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the ACF/PACF for the series in differences\n", - "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n", - "plot_acf(df[TARGET_COLNAME].diff().dropna().values.squeeze(), ax=ax[0])\n", - "plot_pacf(df[TARGET_COLNAME].diff().dropna().values.squeeze(), ax=ax[1])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

      Conclusion

      \n", - "Since we do not see a clear indication of an AR(p) process, we will not be using target lags and will set the TARGET_LAGS parameter to None." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

      AutoML Experiment Settings

      \n", - "Based on the analysis performed, we should try the following settings for the AutoML experiment and use them in the \"2_run_experiment\" notebook.\n", - "
        \n", - "
      • STL_TYPE=None
        • \n", - "
      • DIFFERENCE_SERIES=True
        • \n", - "
      • TARGET_LAGS=None
        • \n", - "
      " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Appendix: ACF, PACF and Lag Selection\n", - "To do this, we will examine the ACF and partial ACF (PACF) plots of the differenced series. \n", - "\n", - "
        \n", - "
      • Question: What is the ACF?
        • \n", - "
          \n", - "
        • To understand the ACF, first let's look at the correlation coefficient $\\rho_{xz}$\n", - " \\begin{equation}\n", - " \\rho_{xz} = \\frac{\\sigma_{xz}}{\\sigma_{x} \\sigma_{zy}}\n", - " \\end{equation}\n", - "
          • \n", - " where $\\sigma_{xzy}$ is the covariance between two random variables $X$ and $Z$; $\\sigma_x$ and $\\sigma_z$ is the variance for $X$ and $Z$, respectively. The correlation coefficient measures the strength of linear relationship between two random variables. This metric can take any value from -1 to 1.
        • \n", - "
          \n", - "
        • The auto-correlation coefficient $\\rho_{Y_{t} Y_{t-k}}$ is the time series equivalent of the correlation coefficient, except instead of measuring linear association between two random variables $X$ and $Z$, it measures the strength of a linear relationship between a random variable $Y_t$ and its lag $Y_{t-k}$ for any positive interger value of $k$.
          • \n", - "
          \n", - "
        • To visualize the ACF for a particular lag, say lag 2, plot the second lag of a series $y_{t-2}$ on the x-axis, and plot the series itself $y_t$ on the y-axis. The autocorrelation coefficient is the slope of the best fitted regression line and can be interpreted as follows. A one unit increase in the lag of a variable one period ago leads to a $\\rho_{Y_{t} Y_{t-2}}$ units change in the variable in the current period. This interpreation can be applied to any lag.
          • \n", - "
          \n", - "
        • In the interpretation posted above we need to be careful not to confuse the word \"leads\" with \"causes\" since these are not the same thing. We do not know the lagged value of the varaible causes it to change. Afterall, there are probably many other features that may explain the movement in $Y_t$. All we are trying to do in this section is to identify situations when the variable contains the strong auto-regressive components that needs to be included in the model to improve forecast accuracy.
          • \n", - "
        \n", - "
      " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
        \n", - "
      • Question: What is the PACF?
        • \n", - "
          \n", - "
        • When describing the ACF we essentially running a regression between a partigular lag of a series, say, lag 4, and the series itself. What this implies is the regression coefficient for lag 4 captures the impact of everything that happens in lags 1, 2 and 3. In other words, if lag 1 is the most important lag and we exclude it from the regression, naturally, the regression model will assign the importance of the 1st lag to the 4th one. Partial auto-correlation function fixes this problem since it measures the contribution of each lag accounting for the information added by the intermediary lags. If we were to illustrate ACF and PACF for the fourth lag using the regression analogy, the difference is a follows: \n", - " \\begin{align}\n", - " Y_{t} &= a_{0} + a_{4} Y_{t-4} + e_{t} \\\\\n", - " Y_{t} &= b_{0} + b_{1} Y_{t-1} + b_{2} Y_{t-2} + b_{3} Y_{t-3} + b_{4} Y_{t-4} + \\varepsilon_{t} \\\\\n", - " \\end{align}\n", - "
          • \n", - "
          \n", - "
        • \n", - " Here, you can think of $a_4$ and $b_{4}$ as the auto- and partial auto-correlation coefficients for lag 4. Notice, in the second equation we explicitely accounting for the intermediate lags by adding them as regrerssors.\n", - "
          • \n", - "
        \n", - "
      " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
        \n", - "
      • Question: Auto-regressive pattern? What are we looking for?
        • \n", - "
          \n", - "
        • We are looking for a classical profiles for an AR(p) process such as an exponential decay of an ACF and a the first $p$ significant lags of the PACF. Let's examine the ACF/PACF profiles of the same simulated AR(2) shown in Section 3, and check if the ACF/PACF explanation are refelcted in these plots.
        • \n", - "
        • \n", - "
        • The autocorrelation coefficient for the 3rd lag is 0.6, which can be interpreted that a one unit increase in the value of the target varaible three periods ago leads to 0.6 units increase in the current period. However, the PACF plot shows that the partial autocorrealtion coefficient is zero (from a statistical point of view since it lies within the shaded region). This is happening because the 1st and 2nd lags are good predictors of the target variable. Ommiting these two lags from the regression results in the misleading conclusion that the third lag is a good prediciton.
        • \n", - "
          \n", - "
        • This is why it is important to examine both the ACF and the PACF plots when tring to determine the auto regressive order for the variable in question.
        • \n", - "
        \n", - "
      " - ] - } - ], - "metadata": { - "authors": [ - { - "name": "vlbejan" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.yml b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.yml deleted file mode 100644 index 419cde192..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-univariate-recipe-experiment-settings -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb deleted file mode 100644 index ba2c57855..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb +++ /dev/null @@ -1,593 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/2_run_experiment.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Running AutoML experiments\n", - "\n", - "See the `auto-ml-forecasting-univariate-recipe-experiment-settings` notebook on how to determine settings for seasonal features, target lags and whether the series needs to be differenced or not. To make experimentation user-friendly, the user has to specify several parameters: DIFFERENCE_SERIES, TARGET_LAGS and STL_TYPE. Once these parameters are set, the notebook will generate correct transformations and settings to run experiments, generate forecasts, compute inference set metrics and plot forecast vs actuals. It will also convert the forecast from first differences to levels (original units of measurement) if the DIFFERENCE_SERIES parameter is set to True before calculating inference set metrics.\n", - "\n", - "
      \n", - "\n", - "The output generated by this notebook is saved in the `experiment_output`folder." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import logging\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "import azureml.automl.runtime\n", - "from azureml.core.compute import AmlCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "import matplotlib.pyplot as plt\n", - "from helper_functions import ts_train_test_split, compute_metrics\n", - "\n", - "import azureml.core\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.train.automl import AutoMLConfig\n", - "\n", - "\n", - "# set printing options\n", - "np.set_printoptions(precision=4, suppress=True, linewidth=100)\n", - "pd.set_option(\"display.max_columns\", 500)\n", - "pd.set_option(\"display.width\", 1000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As part of the setup you have already created a **Workspace**. You will also need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "amlcompute_cluster_name = \"recipe-cluster\"\n", - "\n", - "found = False\n", - "# Check if this compute target already exists in the workspace.\n", - "cts = ws.compute_targets\n", - "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == \"AmlCompute\":\n", - " found = True\n", - " print(\"Found existing compute target.\")\n", - " compute_target = cts[amlcompute_cluster_name]\n", - "\n", - "if not found:\n", - " print(\"Creating a new compute target...\")\n", - " provisioning_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_D2_V2\", max_nodes=6\n", - " )\n", - "\n", - " # Create the cluster.\\n\",\n", - " compute_target = ComputeTarget.create(\n", - " ws, amlcompute_cluster_name, provisioning_config\n", - " )\n", - "\n", - "print(\"Checking cluster status...\")\n", - "# Can poll for a minimum number of nodes and for a specific timeout.\n", - "# If no min_node_count is provided, it will use the scale settings for the cluster.\n", - "compute_target.wait_for_completion(\n", - " show_output=True, min_node_count=None, timeout_in_minutes=20\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data\n", - "\n", - "Here, we will load the data from the csv file and drop the Covid period." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "main_data_loc = \"data\"\n", - "train_file_name = \"S4248SM144SCEN.csv\"\n", - "\n", - "TARGET_COLNAME = \"S4248SM144SCEN\"\n", - "TIME_COLNAME = \"observation_date\"\n", - "COVID_PERIOD_START = (\n", - " \"2020-03-01\" # start of the covid period. To be excluded from evaluation.\n", - ")\n", - "\n", - "# load data\n", - "df = pd.read_csv(os.path.join(main_data_loc, train_file_name))\n", - "df[TIME_COLNAME] = pd.to_datetime(df[TIME_COLNAME], format=\"%Y-%m-%d\")\n", - "df.sort_values(by=TIME_COLNAME, inplace=True)\n", - "\n", - "# remove the Covid period\n", - "df = df.query('{} <= \"{}\"'.format(TIME_COLNAME, COVID_PERIOD_START))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set parameters\n", - "\n", - "The first set of parameters is based on the analysis performed in the `auto-ml-forecasting-univariate-recipe-experiment-settings` notebook. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# set parameters based on the settings notebook analysis\n", - "DIFFERENCE_SERIES = True\n", - "TARGET_LAGS = None\n", - "STL_TYPE = None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, define additional parameters to be used in the AutoML config class.\n", - "\n", - "
        \n", - "
      • FORECAST_HORIZON: The forecast horizon is the number of periods into the future that the model should predict. Here, we set the horizon to 12 periods (i.e. 12 quarters). For more discussion of forecast horizons and guiding principles for setting them, please see the energy demand notebook . \n", - "
        • \n", - "
      • TIME_SERIES_ID_COLNAMES: The names of columns used to group a timeseries. It can be used to create multiple series. If time series identifier is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting. Since we are working with a single series, this list is empty.\n", - "
        • \n", - "
      • BLOCKED_MODELS: Optional list of models to be blocked from consideration during model selection stage. At this point we want to consider all ML and Time Series models.\n", - "
          \n", - "
        • See the following link for a list of supported Forecasting models
          • \n", - "
        \n", - "
        • \n", - "
      \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# set other parameters\n", - "FORECAST_HORIZON = 12\n", - "TIME_SERIES_ID_COLNAMES = []\n", - "BLOCKED_MODELS = []" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To run AutoML, you also need to create an **Experiment**. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# choose a name for the run history container in the workspace\n", - "if isinstance(TARGET_LAGS, list):\n", - " TARGET_LAGS_STR = (\n", - " \"-\".join(map(str, TARGET_LAGS)) if (len(TARGET_LAGS) > 0) else None\n", - " )\n", - "else:\n", - " TARGET_LAGS_STR = TARGET_LAGS\n", - "\n", - "experiment_desc = \"diff-{}_lags-{}_STL-{}\".format(\n", - " DIFFERENCE_SERIES, TARGET_LAGS_STR, STL_TYPE\n", - ")\n", - "experiment_name = \"alcohol_{}\".format(experiment_desc)\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"SDK version\"] = azureml.core.VERSION\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"SKU\"] = ws.sku\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Run History Name\"] = experiment_name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "print(outputDf.T)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# create output directory\n", - "output_dir = \"experiment_output/{}\".format(experiment_desc)\n", - "if not os.path.exists(output_dir):\n", - " os.makedirs(output_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# difference data and test for unit root\n", - "if DIFFERENCE_SERIES:\n", - " df_delta = df.copy()\n", - " df_delta[TARGET_COLNAME] = df[TARGET_COLNAME].diff()\n", - " df_delta.dropna(axis=0, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# split the data into train and test set\n", - "if DIFFERENCE_SERIES:\n", - " # generate train/inference sets using data in first differences\n", - " df_train, df_test = ts_train_test_split(\n", - " df_input=df_delta,\n", - " n=FORECAST_HORIZON,\n", - " time_colname=TIME_COLNAME,\n", - " ts_id_colnames=TIME_SERIES_ID_COLNAMES,\n", - " )\n", - "else:\n", - " df_train, df_test = ts_train_test_split(\n", - " df_input=df,\n", - " n=FORECAST_HORIZON,\n", - " time_colname=TIME_COLNAME,\n", - " ts_id_colnames=TIME_SERIES_ID_COLNAMES,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Upload files to the Datastore\n", - "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace) is paired with the storage account, which contains the default data store. We will use it to upload the bike share data and create [tabular dataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_train.to_csv(\"train.csv\", index=False)\n", - "df_test.to_csv(\"test.csv\", index=False)\n", - "\n", - "datastore = ws.get_default_datastore()\n", - "datastore.upload_files(\n", - " files=[\"./train.csv\"],\n", - " target_path=\"uni-recipe-dataset/tabular/\",\n", - " overwrite=True,\n", - " show_progress=True,\n", - ")\n", - "datastore.upload_files(\n", - " files=[\"./test.csv\"],\n", - " target_path=\"uni-recipe-dataset/tabular/\",\n", - " overwrite=True,\n", - " show_progress=True,\n", - ")\n", - "\n", - "from azureml.core import Dataset\n", - "\n", - "train_dataset = Dataset.Tabular.from_delimited_files(\n", - " path=[(datastore, \"uni-recipe-dataset/tabular/train.csv\")]\n", - ")\n", - "test_dataset = Dataset.Tabular.from_delimited_files(\n", - " path=[(datastore, \"uni-recipe-dataset/tabular/test.csv\")]\n", - ")\n", - "\n", - "# print the first 5 rows of the Dataset\n", - "train_dataset.to_pandas_dataframe().reset_index(drop=True).head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Config AutoML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "time_series_settings = {\n", - " \"time_column_name\": TIME_COLNAME,\n", - " \"forecast_horizon\": FORECAST_HORIZON,\n", - " \"target_lags\": TARGET_LAGS,\n", - " \"use_stl\": STL_TYPE,\n", - " \"blocked_models\": BLOCKED_MODELS,\n", - " \"time_series_id_column_names\": TIME_SERIES_ID_COLNAMES,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"forecasting\",\n", - " debug_log=\"sample_experiment.log\",\n", - " primary_metric=\"normalized_root_mean_squared_error\",\n", - " experiment_timeout_minutes=20,\n", - " iteration_timeout_minutes=5,\n", - " enable_early_stopping=True,\n", - " training_data=train_dataset,\n", - " label_column_name=TARGET_COLNAME,\n", - " n_cross_validations=5,\n", - " verbosity=logging.INFO,\n", - " max_cores_per_iteration=-1,\n", - " compute_target=compute_target,\n", - " **time_series_settings,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now run the experiment, you can go to Azure ML portal to view the run details." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=False)\n", - "remote_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve the Best Run details\n", - "Below we retrieve the best Run object from among all the runs in the experiment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = remote_run.get_best_child()\n", - "best_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Inference\n", - "\n", - "We now use the best fitted model from the AutoML Run to make forecasts for the test set. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n", - "\n", - "The inference will run on a remote compute. In this example, it will re-use the training compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_experiment = Experiment(ws, experiment_name + \"_inference\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retreiving forecasts from the model\n", - "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from run_forecast import run_remote_inference\n", - "\n", - "remote_run = run_remote_inference(\n", - " test_experiment=test_experiment,\n", - " compute_target=compute_target,\n", - " train_run=best_run,\n", - " test_dataset=test_dataset,\n", - " target_column_name=TARGET_COLNAME,\n", - ")\n", - "remote_run.wait_for_completion(show_output=False)\n", - "\n", - "remote_run.download_file(\"outputs/predictions.csv\", f\"{output_dir}/predictions.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download the prediction result for metrics calcuation\n", - "The test data with predictions are saved in artifact `outputs/predictions.csv`. We will use it to calculate accuracy metrics and vizualize predictions versus actuals." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_trans = pd.read_csv(f\"{output_dir}/predictions.csv\", parse_dates=[TIME_COLNAME])\n", - "X_trans.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# convert forecast in differences to levels\n", - "def convert_fcst_diff_to_levels(fcst, yt, df_orig):\n", - " \"\"\"Convert forecast from first differences to levels.\"\"\"\n", - " fcst = fcst.reset_index(drop=False, inplace=False)\n", - " fcst[\"predicted_level\"] = fcst[\"predicted\"].cumsum()\n", - " fcst[\"predicted_level\"] = fcst[\"predicted_level\"].astype(float) + float(yt)\n", - " # merge actuals\n", - " out = pd.merge(\n", - " fcst, df_orig[[TIME_COLNAME, TARGET_COLNAME]], on=[TIME_COLNAME], how=\"inner\"\n", - " )\n", - " out.rename(columns={TARGET_COLNAME: \"actual_level\"}, inplace=True)\n", - " return out" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if DIFFERENCE_SERIES:\n", - " # convert forecast in differences to the levels\n", - " INFORMATION_SET_DATE = max(df_train[TIME_COLNAME])\n", - " YT = df.query(\"{} == @INFORMATION_SET_DATE\".format(TIME_COLNAME))[TARGET_COLNAME]\n", - "\n", - " fcst_df = convert_fcst_diff_to_levels(fcst=X_trans, yt=YT, df_orig=df)\n", - "else:\n", - " fcst_df = X_trans.copy()\n", - " fcst_df[\"actual_level\"] = y_test\n", - " fcst_df[\"predicted_level\"] = y_predictions\n", - "\n", - "del X_trans" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate metrics and save output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# compute metrics\n", - "metrics_df = compute_metrics(fcst_df=fcst_df, metric_name=None, ts_id_colnames=None)\n", - "# save output\n", - "metrics_file_name = \"{}_metrics.csv\".format(experiment_name)\n", - "fcst_file_name = \"{}_forecst.csv\".format(experiment_name)\n", - "plot_file_name = \"{}_plot.pdf\".format(experiment_name)\n", - "\n", - "metrics_df.to_csv(os.path.join(output_dir, metrics_file_name), index=True)\n", - "fcst_df.to_csv(os.path.join(output_dir, fcst_file_name), index=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate and save visuals" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_df = df.query('{} > \"2010-01-01\"'.format(TIME_COLNAME))\n", - "plot_df.set_index(TIME_COLNAME, inplace=True)\n", - "fcst_df.set_index(TIME_COLNAME, inplace=True)\n", - "\n", - "# generate and save plots\n", - "fig, ax = plt.subplots(dpi=180)\n", - "ax.plot(plot_df[TARGET_COLNAME], \"-g\", label=\"Historical\")\n", - "ax.plot(fcst_df[\"actual_level\"], \"-b\", label=\"Actual\")\n", - "ax.plot(fcst_df[\"predicted_level\"], \"-r\", label=\"Forecast\")\n", - "ax.legend()\n", - "ax.set_title(\"Forecast vs Actuals\")\n", - "ax.set_xlabel(TIME_COLNAME)\n", - "ax.set_ylabel(TARGET_COLNAME)\n", - "locs, labels = plt.xticks()\n", - "\n", - "plt.setp(labels, rotation=45)\n", - "plt.savefig(os.path.join(output_dir, plot_file_name))" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "vlbejan" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.yml b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.yml deleted file mode 100644 index a370fe3a9..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-forecasting-univariate-recipe-run-experiment -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/data/S4248SM144SCEN.csv b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/data/S4248SM144SCEN.csv deleted file mode 100644 index 62881c299..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/data/S4248SM144SCEN.csv +++ /dev/null @@ -1,350 +0,0 @@ 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-2020-06-01,13982 -2020-07-01,15384 -2020-08-01,15701 -2020-09-01,15006 -2020-10-01,15741 -2020-11-01,14934 -2020-12-01,13061 -2021-01-01,15743 diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/figures/ACF_PACF_for_AR2.png b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/figures/ACF_PACF_for_AR2.png deleted file mode 100644 index 5163d2799..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/figures/ACF_PACF_for_AR2.png and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/figures/univariate_settings_map_20210408.jpg b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/figures/univariate_settings_map_20210408.jpg deleted file mode 100644 index 2fffa5d3a..000000000 Binary files a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/figures/univariate_settings_map_20210408.jpg and /dev/null differ diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/forecasting_script.py b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/forecasting_script.py deleted file mode 100644 index ca8f2f19d..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/forecasting_script.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -This is the script that is executed on the compute instance. It relies -on the model.pkl file which is uploaded along with this script to the -compute instance. -""" - -import argparse -from azureml.core import Dataset, Run -from azureml.automl.core.shared.constants import TimeSeriesInternal -from sklearn.externals import joblib - -parser = argparse.ArgumentParser() -parser.add_argument( - "--target_column_name", - type=str, - dest="target_column_name", - help="Target Column Name", -) -parser.add_argument( - "--test_dataset", type=str, dest="test_dataset", help="Test Dataset" -) - -args = parser.parse_args() -target_column_name = args.target_column_name -test_dataset_id = args.test_dataset - -run = Run.get_context() -ws = run.experiment.workspace - -# get the input dataset by id -test_dataset = Dataset.get_by_id(ws, id=test_dataset_id) - -X_test = ( - test_dataset.drop_columns(columns=[target_column_name]) - .to_pandas_dataframe() - .reset_index(drop=True) -) -y_test_df = ( - test_dataset.with_timestamp_columns(None) - .keep_columns(columns=[target_column_name]) - .to_pandas_dataframe() -) - -# generate forecast -fitted_model = joblib.load("model.pkl") -# We have default quantiles values set as below(95th percentile) -quantiles = [0.025, 0.5, 0.975] -predicted_column_name = "predicted" -PI = "prediction_interval" -fitted_model.quantiles = quantiles -pred_quantiles = fitted_model.forecast_quantiles(X_test) -pred_quantiles[PI] = pred_quantiles[[min(quantiles), max(quantiles)]].apply( - lambda x: "[{}, {}]".format(x[0], x[1]), axis=1 -) -X_test[target_column_name] = y_test_df[target_column_name] -X_test[PI] = pred_quantiles[PI] -X_test[predicted_column_name] = pred_quantiles[0.5] -# drop rows where prediction or actuals are nan -# happens because of missing actuals -# or at edges of time due to lags/rolling windows -clean = X_test[ - X_test[[target_column_name, predicted_column_name]].notnull().all(axis=1) -] -clean.rename(columns={target_column_name: "actual"}, inplace=True) - -file_name = "outputs/predictions.csv" -export_csv = clean.to_csv(file_name, header=True, index=False) # added Index - -# Upload the predictions into artifacts -run.upload_file(name=file_name, path_or_stream=file_name) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/helper_functions.py b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/helper_functions.py deleted file mode 100644 index 682ebf51a..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/helper_functions.py +++ /dev/null @@ -1,263 +0,0 @@ -""" -Helper functions to determine AutoML experiment settings for forecasting. -""" -import pandas as pd -import statsmodels.tsa.stattools as stattools -from arch import unitroot -from azureml.automl.core.shared import constants -from azureml.automl.runtime.shared.score import scoring - - -def adf_test(series, **kw): - """ - Wrapper for the augmented Dickey-Fuller test. Allows users to set the lag order. - - :param series: series to test - :return: dictionary of results - """ - if "lags" in kw.keys(): - msg = "Lag order of {} detected. Running the ADF test...".format( - str(kw["lags"]) - ) - print(msg) - statistic, pval, critval, resstore = stattools.adfuller( - series, maxlag=kw["lags"], autolag=kw["autolag"], store=kw["store"] - ) - else: - statistic, pval, critval, resstore = stattools.adfuller( - series, autolag=kw["IC"], store=kw["store"] - ) - - output = { - "statistic": statistic, - "pval": pval, - "critical": critval, - "resstore": resstore, - } - return output - - -def kpss_test(series, **kw): - """ - Wrapper for the KPSS test. Allows users to set the lag order. - - :param series: series to test - :return: dictionary of results - """ - if kw["store"]: - statistic, p_value, critical_values, rstore = stattools.kpss( - series, regression=kw["reg_type"], nlags=kw["lags"], store=kw["store"] - ) - else: - statistic, p_value, lags, critical_values = stattools.kpss( - series, regression=kw["reg_type"], nlags=kw["lags"] - ) - output = { - "statistic": statistic, - "pval": p_value, - "critical": critical_values, - "lags": rstore.lags if kw["store"] else lags, - } - - if kw["store"]: - output.update({"resstore": rstore}) - return output - - -def format_test_output(test_name, test_res, H0_unit_root=True): - """ - Helper function to format output. Return a dictionary with specific keys. Will be used to - construct the summary data frame for all unit root tests. - - TODO: Add functionality of choosing based on the max lag order specified by user. - - :param test_name: name of the test - :param test_res: object that contains corresponding test information. Can be None if test failed. - :param H0_unit_root: does the null hypothesis of the test assume a unit root process? Some tests do (ADF), - some don't (KPSS). - :return: dictionary of summary table for all tests and final decision on stationary vs non-stationary. - If test failed (test_res is None), return empty dictionary. - """ - # Check if the test failed by trying to extract the test statistic - if test_name in ("ADF", "KPSS"): - try: - test_res["statistic"] - except BaseException: - test_res = None - else: - try: - test_res.stat - except BaseException: - test_res = None - - if test_res is None: - return {} - - # extract necessary information - if test_name in ("ADF", "KPSS"): - statistic = test_res["statistic"] - crit_val = test_res["critical"]["5%"] - p_val = test_res["pval"] - lags = test_res["resstore"].usedlag if test_name == "ADF" else test_res["lags"] - else: - statistic = test_res.stat - crit_val = test_res.critical_values["5%"] - p_val = test_res.pvalue - lags = test_res.lags - - if H0_unit_root: - H0 = "The process is non-stationary" - stationary = "yes" if p_val < 0.05 else "not" - else: - H0 = "The process is stationary" - stationary = "yes" if p_val > 0.05 else "not" - - out = { - "test_name": test_name, - "statistic": statistic, - "crit_val": crit_val, - "p_val": p_val, - "lags": int(lags), - "stationary": stationary, - "Null Hypothesis": H0, - } - return out - - -def unit_root_test_wrapper(series, lags=None): - """ - Main function to run multiple stationarity tests. Runs five tests and returns a summary table + decision - based on the majority rule. If the number of tests that determine a series is stationary equals to the - number of tests that deem it non-stationary, we assume the series is non-stationary. - * Augmented Dickey-Fuller (ADF), - * KPSS, - * ADF using GLS, - * Phillips-Perron (PP), - * Zivot-Andrews (ZA) - - :param lags: (optional) parameter that allows user to run a series of tests for a specific lag value. - :param series: series to test - :return: dictionary of summary table for all tests and final decision on stationary vs nonstaionary - """ - # setting for ADF and KPSS tests - adf_settings = {"IC": "AIC", "store": True} - - kpss_settings = {"reg_type": "c", "lags": "auto", "store": True} - - arch_test_settings = {} # settings for PP, ADF GLS and ZA tests - if lags is not None: - adf_settings.update({"lags": lags, "autolag": None}) - kpss_settings.update({"lags:": lags}) - arch_test_settings = {"lags": lags} - # Run individual tests - adf = adf_test(series, **adf_settings) # ADF test - kpss = kpss_test(series, **kpss_settings) # KPSS test - pp = unitroot.PhillipsPerron(series, **arch_test_settings) # Phillips-Perron test - adfgls = unitroot.DFGLS(series, **arch_test_settings) # ADF using GLS test - za = unitroot.ZivotAndrews(series, **arch_test_settings) # Zivot-Andrews test - - # generate output table - adf_dict = format_test_output(test_name="ADF", test_res=adf, H0_unit_root=True) - kpss_dict = format_test_output(test_name="KPSS", test_res=kpss, H0_unit_root=False) - pp_dict = format_test_output( - test_name="Philips Perron", test_res=pp, H0_unit_root=True - ) - adfgls_dict = format_test_output( - test_name="ADF GLS", test_res=adfgls, H0_unit_root=True - ) - za_dict = format_test_output( - test_name="Zivot-Andrews", test_res=za, H0_unit_root=True - ) - - test_dict = { - "ADF": adf_dict, - "KPSS": kpss_dict, - "PP": pp_dict, - "ADF GLS": adfgls_dict, - "ZA": za_dict, - } - test_sum = pd.DataFrame.from_dict(test_dict, orient="index").reset_index(drop=True) - - # decision based on the majority rule - if test_sum.shape[0] > 0: - ratio = test_sum[test_sum["stationary"] == "yes"].shape[0] / test_sum.shape[0] - else: - ratio = 1 # all tests fail, assume the series is stationary - - # Majority rule. If the ratio is exactly 0.5, assume the series in non-stationary. - stationary = "YES" if (ratio > 0.5) else "NO" - - out = {"summary": test_sum, "stationary": stationary} - return out - - -def ts_train_test_split(df_input, n, time_colname, ts_id_colnames=None): - """ - Group data frame by time series ID and split on last n rows for each group. - - :param df_input: input data frame - :param n: number of observations in the test set - :param time_colname: time column - :param ts_id_colnames: (optional) list of grain column names - :return train and test data frames - """ - if ts_id_colnames is None: - ts_id_colnames = [] - ts_id_colnames_original = ts_id_colnames.copy() - if len(ts_id_colnames) == 0: - ts_id_colnames = ["Grain"] - df_input[ts_id_colnames[0]] = "dummy" - # Sort by ascending time - df_grouped = df_input.sort_values(time_colname).groupby( - ts_id_colnames, group_keys=False - ) - df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n]) - df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:]) - # drop group column name if it was not originally provided - if len(ts_id_colnames_original) == 0: - df_head.drop(ts_id_colnames, axis=1, inplace=True) - df_tail.drop(ts_id_colnames, axis=1, inplace=True) - return df_head, df_tail - - -def compute_metrics(fcst_df, metric_name=None, ts_id_colnames=None): - """ - Calculate metrics per grain. - - :param fcst_df: forecast data frame. Must contain 2 columns: 'actual_level' and 'predicted_level' - :param metric_name: (optional) name of the metric to return - :param ts_id_colnames: (optional) list of grain column names - :return: dictionary of summary table for all tests and final decision on stationary vs nonstaionary - """ - if ts_id_colnames is None: - ts_id_colnames = [] - if len(ts_id_colnames) == 0: - ts_id_colnames = ["TS_ID"] - fcst_df[ts_id_colnames[0]] = "dummy" - metrics_list = [] - for grain, df in fcst_df.groupby(ts_id_colnames): - try: - scores = scoring.score_regression( - y_test=df["actual_level"], - y_pred=df["predicted_level"], - metrics=list(constants.Metric.SCALAR_REGRESSION_SET), - ) - except BaseException: - msg = "{}: metrics calculation failed.".format(grain) - print(msg) - scores = {} - one_grain_metrics_df = pd.DataFrame( - list(scores.items()), columns=["metric_name", "metric"] - ).sort_values(["metric_name"]) - one_grain_metrics_df.reset_index(inplace=True, drop=True) - if len(ts_id_colnames) < 2: - one_grain_metrics_df["grain"] = ts_id_colnames[0] - else: - one_grain_metrics_df["grain"] = "|".join(list(grain)) - - metrics_list.append(one_grain_metrics_df) - # collect into a data frame - grain_metrics = pd.concat(metrics_list) - if metric_name is not None: - grain_metrics = grain_metrics.query("metric_name == @metric_name") - return grain_metrics diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/run_forecast.py b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/run_forecast.py deleted file mode 100644 index cb1d9d886..000000000 --- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/run_forecast.py +++ /dev/null @@ -1,49 +0,0 @@ -import os -import shutil -from azureml.core import ScriptRunConfig - - -def run_remote_inference( - test_experiment, - compute_target, - train_run, - test_dataset, - target_column_name, - inference_folder="./forecast", -): - # Create local directory to copy the model.pkl and forecsting_script.py files into. - # These files will be uploaded to and executed on the compute instance. - os.makedirs(inference_folder, exist_ok=True) - shutil.copy("forecasting_script.py", inference_folder) - - train_run.download_file( - "outputs/model.pkl", os.path.join(inference_folder, "model.pkl") - ) - - inference_env = train_run.get_environment() - - config = ScriptRunConfig( - source_directory=inference_folder, - script="forecasting_script.py", - arguments=[ - "--target_column_name", - target_column_name, - "--test_dataset", - test_dataset.as_named_input(test_dataset.name), - ], - compute_target=compute_target, - environment=inference_env, - ) - - run = test_experiment.submit( - config, - tags={ - "training_run_id": train_run.id, - "run_algorithm": train_run.properties["run_algorithm"], - "valid_score": train_run.properties["score"], - "primary_metric": train_run.properties["primary_metric"], - }, - ) - - run.log("run_algorithm", run.tags["run_algorithm"]) - return run diff --git a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb b/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb deleted file mode 100644 index b532dc178..000000000 --- a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb +++ /dev/null @@ -1,901 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Classification of credit card fraudulent transactions with local run **_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Train](#Train)\n", - "1. [Results](#Results)\n", - "1. [Test](#Tests)\n", - "1. [Explanation](#Explanation)\n", - "1. [Acknowledgements](#Acknowledgements)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this example we use the associated credit card dataset to showcase how you can use AutoML for a simple classification problem. The goal is to predict if a credit card transaction is considered a fraudulent charge.\n", - "\n", - "This notebook is using the local machine compute to train the model.\n", - "\n", - "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create an experiment using an existing workspace.\n", - "2. Configure AutoML using `AutoMLConfig`.\n", - "3. Train the model.\n", - "4. Explore the results.\n", - "5. Test the fitted model.\n", - "6. Explore any model's explanation and explore feature importance in azure portal.\n", - "7. Create an AKS cluster, deploy the webservice of AutoML scoring model and the explainer model to the AKS and consume the web service." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "from matplotlib import pyplot as plt\n", - "import pandas as pd\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.train.automl import AutoMLConfig\n", - "from azureml.interpret import ExplanationClient" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# choose a name for experiment\n", - "experiment_name = \"automl-classification-ccard-local\"\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Experiment Name\"] = experiment.name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load Data\n", - "\n", - "Load the credit card dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n", - "dataset = Dataset.Tabular.from_delimited_files(data)\n", - "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n", - "label_column_name = \"Class\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "\n", - "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification or regression|\n", - "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics:
      accuracy
      AUC_weighted
      average_precision_score_weighted
      norm_macro_recall
      precision_score_weighted|\n", - "|**enable_early_stopping**|Stop the run if the metric score is not showing improvement.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|Input dataset, containing both features and label column.|\n", - "|**label_column_name**|The name of the label column.|\n", - "\n", - "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "name": "enable-ensemble" - }, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"n_cross_validations\": 3,\n", - " \"primary_metric\": \"average_precision_score_weighted\",\n", - " \"experiment_timeout_hours\": 0.25, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ability to find the best model possible\n", - " \"verbosity\": logging.INFO,\n", - " \"enable_stack_ensemble\": False,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"classification\",\n", - " debug_log=\"automl_errors.log\",\n", - " training_data=training_data,\n", - " label_column_name=label_column_name,\n", - " **automl_settings,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while.\n", - "In this example, we specify `show_output = True` to print currently running iterations to the console." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_run = experiment.submit(automl_config, show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If you need to retrieve a run that already started, use the following code\n", - "# from azureml.train.automl.run import AutoMLRun\n", - "# local_run = AutoMLRun(experiment = experiment, run_id = '')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Widget for Monitoring Runs\n", - "\n", - "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n", - "\n", - "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(local_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Analyze results\n", - "\n", - "#### Retrieve the Best Model\n", - "\n", - "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run, fitted_model = local_run.get_output()\n", - "fitted_model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Print the properties of the model\n", - "The fitted_model is a python object and you can read the different properties of the object.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tests\n", - "\n", - "Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# convert the test data to dataframe\n", - "X_test_df = validation_data.drop_columns(\n", - " columns=[label_column_name]\n", - ").to_pandas_dataframe()\n", - "y_test_df = validation_data.keep_columns(\n", - " columns=[label_column_name], validate=True\n", - ").to_pandas_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# call the predict functions on the model\n", - "y_pred = fitted_model.predict(X_test_df)\n", - "y_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate metrics for the prediction\n", - "\n", - "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n", - "from the trained model that was returned." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "import numpy as np\n", - "import itertools\n", - "\n", - "cf = confusion_matrix(y_test_df.values, y_pred)\n", - "plt.imshow(cf, cmap=plt.cm.Blues, interpolation=\"nearest\")\n", - "plt.colorbar()\n", - "plt.title(\"Confusion Matrix\")\n", - "plt.xlabel(\"Predicted\")\n", - "plt.ylabel(\"Actual\")\n", - "class_labels = [\"False\", \"True\"]\n", - "tick_marks = np.arange(len(class_labels))\n", - "plt.xticks(tick_marks, class_labels)\n", - "plt.yticks([-0.5, 0, 1, 1.5], [\"\", \"False\", \"True\", \"\"])\n", - "# plotting text value inside cells\n", - "thresh = cf.max() / 2.0\n", - "for i, j in itertools.product(range(cf.shape[0]), range(cf.shape[1])):\n", - " plt.text(\n", - " j,\n", - " i,\n", - " format(cf[i, j], \"d\"),\n", - " horizontalalignment=\"center\",\n", - " color=\"white\" if cf[i, j] > thresh else \"black\",\n", - " )\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explanation\n", - "In this section, we will show how to compute model explanations and visualize the explanations using azureml-interpret package. We will also show how to run the automl model and the explainer model through deploying an AKS web service.\n", - "\n", - "Besides retrieving an existing model explanation for an AutoML model, you can also explain your AutoML model with different test data. The following steps will allow you to compute and visualize engineered feature importance based on your test data.\n", - "\n", - "### Run the explanation\n", - "#### Download the engineered feature importance from artifact store\n", - "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client = ExplanationClient.from_run(best_run)\n", - "engineered_explanations = client.download_model_explanation(raw=False)\n", - "print(engineered_explanations.get_feature_importance_dict())\n", - "print(\n", - " \"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n", - " + best_run.get_portal_url()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download the raw feature importance from artifact store\n", - "You can use ExplanationClient to download the raw feature explanations from the artifact store of the best_run. You can also use azure portal url to view the dash board visualization of the feature importance values of the raw features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "raw_explanations = client.download_model_explanation(raw=True)\n", - "print(raw_explanations.get_feature_importance_dict())\n", - "print(\n", - " \"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n", - " + best_run.get_portal_url()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Retrieve any other AutoML model from training" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_run, fitted_model = local_run.get_output(metric=\"accuracy\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setup the model explanations for AutoML models\n", - "The fitted_model can generate the following which will be used for getting the engineered explanations using automl_setup_model_explanations:-\n", - "\n", - "1. Featurized data from train samples/test samples\n", - "2. Gather engineered name lists\n", - "3. Find the classes in your labeled column in classification scenarios\n", - "\n", - "The automl_explainer_setup_obj contains all the structures from above list." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_train = training_data.drop_columns(columns=[label_column_name])\n", - "y_train = training_data.keep_columns(columns=[label_column_name], validate=True)\n", - "X_test = validation_data.drop_columns(columns=[label_column_name])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.automl.runtime.automl_explain_utilities import (\n", - " automl_setup_model_explanations,\n", - ")\n", - "\n", - "automl_explainer_setup_obj = automl_setup_model_explanations(\n", - " fitted_model,\n", - " X=X_train,\n", - " X_test=X_test,\n", - " y=y_train,\n", - " task=\"classification\",\n", - " automl_run=automl_run,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize the Mimic Explainer for feature importance\n", - "For explaining the AutoML models, use the MimicWrapper from azureml-interpret package. The MimicWrapper can be initialized with fields in automl_explainer_setup_obj, your workspace and a surrogate model to explain the AutoML model (fitted_model here). The MimicWrapper also takes the automl_run object where engineered explanations will be uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.interpret.mimic_wrapper import MimicWrapper\n", - "\n", - "explainer = MimicWrapper(\n", - " ws,\n", - " automl_explainer_setup_obj.automl_estimator,\n", - " explainable_model=automl_explainer_setup_obj.surrogate_model,\n", - " init_dataset=automl_explainer_setup_obj.X_transform,\n", - " run=automl_explainer_setup_obj.automl_run,\n", - " features=automl_explainer_setup_obj.engineered_feature_names,\n", - " feature_maps=[automl_explainer_setup_obj.feature_map],\n", - " classes=automl_explainer_setup_obj.classes,\n", - " explainer_kwargs=automl_explainer_setup_obj.surrogate_model_params,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n", - "The explain() method in MimicWrapper can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute the engineered explanations\n", - "engineered_explanations = explainer.explain(\n", - " [\"local\", \"global\"], eval_dataset=automl_explainer_setup_obj.X_test_transform\n", - ")\n", - "print(engineered_explanations.get_feature_importance_dict())\n", - "print(\n", - " \"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n", - " + automl_run.get_portal_url()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Use Mimic Explainer for computing and visualizing raw feature importance\n", - "The explain() method in MimicWrapper can be called with the transformed test samples to get the feature importance for the original features in your data. You can also use azure portal url to view the dash board visualization of the feature importance values of the original/raw features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute the raw explanations\n", - "raw_explanations = explainer.explain(\n", - " [\"local\", \"global\"],\n", - " get_raw=True,\n", - " raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n", - " eval_dataset=automl_explainer_setup_obj.X_test_transform,\n", - " raw_eval_dataset=automl_explainer_setup_obj.X_test_raw,\n", - ")\n", - "print(raw_explanations.get_feature_importance_dict())\n", - "print(\n", - " \"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n", - " + automl_run.get_portal_url()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize the scoring Explainer, save and upload it for later use in scoring explanation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer\n", - "import joblib\n", - "\n", - "# Initialize the ScoringExplainer\n", - "scoring_explainer = TreeScoringExplainer(\n", - " explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map]\n", - ")\n", - "\n", - "# Pickle scoring explainer locally to './scoring_explainer.pkl'\n", - "scoring_explainer_file_name = \"scoring_explainer.pkl\"\n", - "with open(scoring_explainer_file_name, \"wb\") as stream:\n", - " joblib.dump(scoring_explainer, stream)\n", - "\n", - "# Upload the scoring explainer to the automl run\n", - "automl_run.upload_file(\"outputs/scoring_explainer.pkl\", scoring_explainer_file_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploying the scoring and explainer models to a web service to Azure Kubernetes Service (AKS)\n", - "\n", - "We use the TreeScoringExplainer from azureml.interpret package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Register trained automl model present in the 'outputs' folder in the artifacts\n", - "original_model = automl_run.register_model(\n", - " model_name=\"automl_model\", model_path=\"outputs/model.pkl\"\n", - ")\n", - "scoring_explainer_model = automl_run.register_model(\n", - " model_name=\"scoring_explainer\", model_path=\"outputs/scoring_explainer.pkl\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the conda dependencies for setting up the service\n", - "\n", - "We need to download the conda dependencies using the automl_run object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.shared import constants\n", - "from azureml.core.environment import Environment\n", - "\n", - "automl_run.download_file(constants.CONDA_ENV_FILE_PATH, \"myenv.yml\")\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "myenv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Write the Entry Script\n", - "Write the script that will be used to predict on your model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import joblib\n", - "import pandas as pd\n", - "from azureml.core.model import Model\n", - "from azureml.train.automl.runtime.automl_explain_utilities import (\n", - " automl_setup_model_explanations,\n", - ")\n", - "\n", - "\n", - "def init():\n", - " global automl_model\n", - " global scoring_explainer\n", - "\n", - " # Retrieve the path to the model file using the model name\n", - " # Assume original model is named original_prediction_model\n", - " automl_model_path = Model.get_model_path(\"automl_model\")\n", - " scoring_explainer_path = Model.get_model_path(\"scoring_explainer\")\n", - "\n", - " automl_model = joblib.load(automl_model_path)\n", - " scoring_explainer = joblib.load(scoring_explainer_path)\n", - "\n", - "\n", - "def run(raw_data):\n", - " data = pd.read_json(raw_data, orient=\"records\")\n", - " # Make prediction\n", - " predictions = automl_model.predict(data)\n", - " # Setup for inferencing explanations\n", - " automl_explainer_setup_obj = automl_setup_model_explanations(\n", - " automl_model, X_test=data, task=\"classification\"\n", - " )\n", - " # Retrieve model explanations for engineered explanations\n", - " engineered_local_importance_values = scoring_explainer.explain(\n", - " automl_explainer_setup_obj.X_test_transform\n", - " )\n", - " # Retrieve model explanations for raw explanations\n", - " raw_local_importance_values = scoring_explainer.explain(\n", - " automl_explainer_setup_obj.X_test_transform, get_raw=True\n", - " )\n", - " # You can return any data type as long as it is JSON-serializable\n", - " return {\n", - " \"predictions\": predictions.tolist(),\n", - " \"engineered_local_importance_values\": engineered_local_importance_values,\n", - " \"raw_local_importance_values\": raw_local_importance_values,\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the InferenceConfig \n", - "Create the inference config that will be used when deploying the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "\n", - "inf_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Provision the AKS Cluster\n", - "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AksCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your cluster.\n", - "aks_name = \"scoring-explain\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " aks_target = ComputeTarget(workspace=ws, name=aks_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " prov_config = AksCompute.provisioning_configuration(vm_size=\"STANDARD_D3_V2\")\n", - " aks_target = ComputeTarget.create(\n", - " workspace=ws, name=aks_name, provisioning_configuration=prov_config\n", - " )\n", - "aks_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Deploy web service to AKS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the web service configuration (using default here)\n", - "from azureml.core.webservice import AksWebservice\n", - "from azureml.core.model import Model\n", - "\n", - "aks_config = AksWebservice.deploy_configuration()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aks_service_name = \"model-scoring-local-aks\"\n", - "\n", - "aks_service = Model.deploy(\n", - " workspace=ws,\n", - " name=aks_service_name,\n", - " models=[scoring_explainer_model, original_model],\n", - " inference_config=inf_config,\n", - " deployment_config=aks_config,\n", - " deployment_target=aks_target,\n", - ")\n", - "\n", - "aks_service.wait_for_deployment(show_output=True)\n", - "print(aks_service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View the service logs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aks_service.get_logs()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Consume the web service using run method to do the scoring and explanation of scoring.\n", - "We test the web sevice by passing data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Serialize the first row of the test data into json\n", - "X_test_json = X_test_df[:1].to_json(orient=\"records\")\n", - "print(X_test_json)\n", - "\n", - "# Call the service to get the predictions and the engineered and raw explanations\n", - "output = aks_service.run(X_test_json)\n", - "\n", - "# Print the predicted value\n", - "print(\"predictions:\\n{}\\n\".format(output[\"predictions\"]))\n", - "# Print the engineered feature importances for the predicted value\n", - "print(\n", - " \"engineered_local_importance_values:\\n{}\\n\".format(\n", - " output[\"engineered_local_importance_values\"]\n", - " )\n", - ")\n", - "# Print the raw feature importances for the predicted value\n", - "print(\n", - " \"raw_local_importance_values:\\n{}\\n\".format(output[\"raw_local_importance_values\"])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Clean up\n", - "Delete the service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aks_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Acknowledgements" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n", - "\n", - "\n", - "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u0192\u00c2\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n", - "Please cite the following works: \n", - "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n", - "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n", - "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n", - "o\tDal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n", - "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n", - "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "ratanase" - } - ], - "category": "tutorial", - "compute": [ - "Local" - ], - "datasets": [ - "creditcard" - ], - "deployment": [ - "None" - ], - "exclude_from_index": true, - "file_extension": ".py", - "framework": [ - "None" - ], - "friendly_name": "Classification of credit card fraudulent transactions using Automated ML", - "index_order": 5, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "tags": [ - "local_run", - "AutomatedML" - ], - "task": "Classification", - "version": "3.6.7" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml b/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml deleted file mode 100644 index 6c817042c..000000000 --- a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-classification-credit-card-fraud-local -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb deleted file mode 100644 index f6dd8f18f..000000000 --- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb +++ /dev/null @@ -1,947 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Regression with Aml Compute**_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Data](#Data)\n", - "1. [Train](#Train)\n", - "1. [Results](#Results)\n", - "1. [Test](#Test)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. The Regression goal is to predict the performance of certain combinations of hardware parts.\n", - "After training AutoML models for this regression data set, we show how you can compute model explanations on your remote compute using a sample explainer script.\n", - "\n", - "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create an `Experiment` in an existing `Workspace`.\n", - "2. Instantiating AutoMLConfig with FeaturizationConfig for customization\n", - "3. Train the model using remote compute.\n", - "4. Explore the results and featurization transparency options\n", - "5. Setup remote compute for computing the model explanations for a given AutoML model.\n", - "6. Start an AzureML experiment on your remote compute to compute explanations for an AutoML model.\n", - "7. Download the feature importance for engineered features and visualize the explanations for engineered features on azure portal. \n", - "8. Download the feature importance for raw features and visualize the explanations for raw features on azure portal. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import logging\n", - "\n", - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "\n", - "from azureml.automl.core.featurization import FeaturizationConfig\n", - "from azureml.train.automl import AutoMLConfig\n", - "from azureml.core.dataset import Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# Choose a name for the experiment.\n", - "experiment_name = \"automl-regression-hardware-explain\"\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace Name\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Experiment Name\"] = experiment.name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your cluster.\n", - "amlcompute_cluster_name = \"hardware-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n", - " )\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup Training and Test Data for AutoML experiment\n", - "\n", - "Load the hardware dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model. We also register the datasets in your workspace using a name so that these datasets may be accessed from the remote compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n", - "\n", - "dataset = Dataset.Tabular.from_delimited_files(data)\n", - "\n", - "# Split the dataset into train and test datasets\n", - "train_data, test_data = dataset.random_split(percentage=0.8, seed=223)\n", - "\n", - "\n", - "# Register the train dataset with your workspace\n", - "train_data.register(\n", - " workspace=ws,\n", - " name=\"machineData_train_dataset\",\n", - " description=\"hardware performance training data\",\n", - " create_new_version=True,\n", - ")\n", - "\n", - "# Register the test dataset with your workspace\n", - "test_data.register(\n", - " workspace=ws,\n", - " name=\"machineData_test_dataset\",\n", - " description=\"hardware performance test data\",\n", - " create_new_version=True,\n", - ")\n", - "\n", - "label = \"ERP\"\n", - "\n", - "train_data.to_pandas_dataframe().head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "\n", - "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification, regression or forecasting|\n", - "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics:
      spearman_correlation
      normalized_root_mean_squared_error
      r2_score
      normalized_mean_absolute_error|\n", - "|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n", - "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n", - "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*. Note: If the input data is sparse, featurization cannot be turned on.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n", - "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Customization\n", - "\n", - "Supported customization includes:\n", - "\n", - "1. Column purpose update: Override feature type for the specified column.\n", - "2. Transformer parameter update: Update parameters for the specified transformer. Currently supports Imputer and HashOneHotEncoder.\n", - "3. Drop columns: Columns to drop from being featurized.\n", - "4. Block transformers: Allow/Block transformers to be used on featurization process." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create FeaturizationConfig object using API calls" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-featurizationconfig-remarks2" - ] - }, - "outputs": [], - "source": [ - "featurization_config = FeaturizationConfig()\n", - "featurization_config.blocked_transformers = [\"LabelEncoder\"]\n", - "# featurization_config.drop_columns = ['MMIN']\n", - "featurization_config.add_column_purpose(\"MYCT\", \"Numeric\")\n", - "featurization_config.add_column_purpose(\"VendorName\", \"CategoricalHash\")\n", - "# default strategy mean, add transformer param for for 3 columns\n", - "featurization_config.add_transformer_params(\"Imputer\", [\"CACH\"], {\"strategy\": \"median\"})\n", - "featurization_config.add_transformer_params(\n", - " \"Imputer\", [\"CHMIN\"], {\"strategy\": \"median\"}\n", - ")\n", - "featurization_config.add_transformer_params(\n", - " \"Imputer\", [\"PRP\"], {\"strategy\": \"most_frequent\"}\n", - ")\n", - "# featurization_config.add_transformer_params('HashOneHotEncoder', [], {\"number_of_bits\": 3})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-featurizationconfig-remarks3" - ] - }, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"enable_early_stopping\": True,\n", - " \"experiment_timeout_hours\": 0.25,\n", - " \"max_concurrent_iterations\": 4,\n", - " \"max_cores_per_iteration\": -1,\n", - " \"n_cross_validations\": 5,\n", - " \"primary_metric\": \"normalized_root_mean_squared_error\",\n", - " \"verbosity\": logging.INFO,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"regression\",\n", - " debug_log=\"automl_errors.log\",\n", - " compute_target=compute_target,\n", - " featurization=featurization_config,\n", - " training_data=train_data,\n", - " label_column_name=label,\n", - " **automl_settings,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n", - "In this example, we specify `show_output = True` to print currently running iterations to the console." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the following cell to access previous runs. Uncomment the cell below and update the run_id." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from azureml.train.automl.run import AutoMLRun\n", - "# remote_run = AutoMLRun(experiment=experiment, run_id='>\", automl_run.experiment.name\n", - ") # your experiment name.\n", - "content = content.replace(\n", - " \"<>\", automl_run.id\n", - ") # Run-id of the AutoML run for which you want to explain the model.\n", - "content = content.replace(\"<>\", \"ERP\") # Your target column name\n", - "content = content.replace(\"<>\", \"regression\") # Training task type\n", - "# Name of your training dataset register with your workspace\n", - "content = content.replace(\"<>\", \"machineData_train_dataset\")\n", - "# Name of your test dataset register with your workspace\n", - "content = content.replace(\"<>\", \"machineData_test_dataset\")\n", - "\n", - "# Write sample file into your script folder.\n", - "with open(script_file_name, \"w\") as cefw:\n", - " cefw.write(content)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create conda configuration for model explanations experiment from automl_run object" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "import pkg_resources\n", - "\n", - "# create a new RunConfig object\n", - "conda_run_config = RunConfiguration(framework=\"python\")\n", - "\n", - "# Set compute target to AmlCompute\n", - "conda_run_config.target = compute_target\n", - "conda_run_config.environment.docker.enabled = True\n", - "\n", - "# specify CondaDependencies obj\n", - "conda_run_config.environment.python.conda_dependencies = (\n", - " automl_run.get_environment().python.conda_dependencies\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Submit the experiment for model explanations\n", - "Submit the experiment with the above `run_config` and the sample script for computing explanations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Now submit a run on AmlCompute for model explanations\n", - "from azureml.core.script_run_config import ScriptRunConfig\n", - "\n", - "script_run_config = ScriptRunConfig(\n", - " source_directory=script_folder,\n", - " script=\"train_explainer.py\",\n", - " run_config=conda_run_config,\n", - ")\n", - "\n", - "run = experiment.submit(script_run_config)\n", - "\n", - "# Show run details\n", - "run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "# Shows output of the run on stdout.\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature importance and visualizing explanation dashboard\n", - "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model on the azure portal." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download engineered feature importance from artifact store\n", - "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *automl_run*. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.interpret import ExplanationClient\n", - "\n", - "client = ExplanationClient.from_run(automl_run)\n", - "engineered_explanations = client.download_model_explanation(\n", - " raw=False, comment=\"engineered explanations\"\n", - ")\n", - "print(engineered_explanations.get_feature_importance_dict())\n", - "print(\n", - " \"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n", - " + automl_run.get_portal_url()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download raw feature importance from artifact store\n", - "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *automl_run*. You can also use azure portal url to view the dash board visualization of the feature importance values of the raw features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "raw_explanations = client.download_model_explanation(\n", - " raw=True, comment=\"raw explanations\"\n", - ")\n", - "print(raw_explanations.get_feature_importance_dict())\n", - "print(\n", - " \"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\"\n", - " + automl_run.get_portal_url()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Operationalize\n", - "In this section we will show how you can operationalize an AutoML model and the explainer which was used to compute the explanations in the previous section.\n", - "\n", - "### Register the AutoML model and the scoring explainer\n", - "We use the *TreeScoringExplainer* from *azureml-interpret* package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. \n", - "In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Register trained automl model present in the 'outputs' folder in the artifacts\n", - "original_model = automl_run.register_model(\n", - " model_name=\"automl_model\", model_path=\"outputs/model.pkl\"\n", - ")\n", - "scoring_explainer_model = automl_run.register_model(\n", - " model_name=\"scoring_explainer\", model_path=\"outputs/scoring_explainer.pkl\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the conda dependencies for setting up the service\n", - "We need to create the conda dependencies comprising of the *azureml* packages using the training environment from the *automl_run*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "conda_dep = automl_run.get_environment().python.conda_dependencies\n", - "\n", - "with open(\"myenv.yml\", \"w\") as f:\n", - " f.write(conda_dep.serialize_to_string())\n", - "with open(\"myenv.yml\", \"r\") as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View your scoring file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"score_explain.py\", \"r\") as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the service\n", - "In the cell below, we deploy the service using the conda file and the scoring file from the previous steps. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import Webservice\n", - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.webservice import AciWebservice\n", - "from azureml.core.model import Model\n", - "from azureml.core.environment import Environment\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(\n", - " cpu_cores=2,\n", - " memory_gb=2,\n", - " tags={\"data\": \"Machine Data\", \"method\": \"local_explanation\"},\n", - " description=\"Get local explanations for Machine test data\",\n", - ")\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score_explain.py\", environment=myenv)\n", - "\n", - "# Use configs and models generated above\n", - "service = Model.deploy(\n", - " ws,\n", - " \"model-scoring\",\n", - " [scoring_explainer_model, original_model],\n", - " inference_config,\n", - " aciconfig,\n", - ")\n", - "service.wait_for_deployment(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View the service logs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.get_logs()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Inference using some test data\n", - "Inference using some test data to see the predicted value from autml model, view the engineered feature importance for the predicted value and raw feature importance for the predicted value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if service.state == \"Healthy\":\n", - " X_test = test_data.drop_columns([label]).to_pandas_dataframe()\n", - " # Serialize the first row of the test data into json\n", - " X_test_json = X_test[:1].to_json(orient=\"records\")\n", - " print(X_test_json)\n", - " # Call the service to get the predictions and the engineered and raw explanations\n", - " output = service.run(X_test_json)\n", - " # Print the predicted value\n", - " print(output[\"predictions\"])\n", - " # Print the engineered feature importances for the predicted value\n", - " print(output[\"engineered_local_importance_values\"])\n", - " # Print the raw feature importances for the predicted value\n", - " print(output[\"raw_local_importance_values\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Delete the service\n", - "Delete the service once you have finished inferencing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# preview the first 3 rows of the dataset\n", - "\n", - "test_data = test_data.to_pandas_dataframe()\n", - "y_test = test_data[\"ERP\"].fillna(0)\n", - "test_data = test_data.drop(\"ERP\", 1)\n", - "test_data = test_data.fillna(0)\n", - "\n", - "\n", - "train_data = train_data.to_pandas_dataframe()\n", - "y_train = train_data[\"ERP\"].fillna(0)\n", - "train_data = train_data.drop(\"ERP\", 1)\n", - "train_data = train_data.fillna(0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred_train = fitted_model.predict(train_data)\n", - "y_residual_train = y_train - y_pred_train\n", - "\n", - "y_pred_test = fitted_model.predict(test_data)\n", - "y_residual_test = y_test - y_pred_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from sklearn.metrics import mean_squared_error, r2_score\n", - "\n", - "# Set up a multi-plot chart.\n", - "f, (a0, a1) = plt.subplots(\n", - " 1, 2, gridspec_kw={\"width_ratios\": [1, 1], \"wspace\": 0, \"hspace\": 0}\n", - ")\n", - "f.suptitle(\"Regression Residual Values\", fontsize=18)\n", - "f.set_figheight(6)\n", - "f.set_figwidth(16)\n", - "\n", - "# Plot residual values of training set.\n", - "a0.axis([0, 360, -100, 100])\n", - "a0.plot(y_residual_train, \"bo\", alpha=0.5)\n", - "a0.plot([-10, 360], [0, 0], \"r-\", lw=3)\n", - "a0.text(\n", - " 16,\n", - " 170,\n", - " \"RMSE = {0:.2f}\".format(np.sqrt(mean_squared_error(y_train, y_pred_train))),\n", - " fontsize=12,\n", - ")\n", - "a0.text(\n", - " 16, 140, \"R2 score = {0:.2f}\".format(r2_score(y_train, y_pred_train)), fontsize=12\n", - ")\n", - "a0.set_xlabel(\"Training samples\", fontsize=12)\n", - "a0.set_ylabel(\"Residual Values\", fontsize=12)\n", - "\n", - "# Plot residual values of test set.\n", - "a1.axis([0, 90, -100, 100])\n", - "a1.plot(y_residual_test, \"bo\", alpha=0.5)\n", - "a1.plot([-10, 360], [0, 0], \"r-\", lw=3)\n", - "a1.text(\n", - " 5,\n", - " 170,\n", - " \"RMSE = {0:.2f}\".format(np.sqrt(mean_squared_error(y_test, y_pred_test))),\n", - " fontsize=12,\n", - ")\n", - "a1.text(5, 140, \"R2 score = {0:.2f}\".format(r2_score(y_test, y_pred_test)), fontsize=12)\n", - "a1.set_xlabel(\"Test samples\", fontsize=12)\n", - "a1.set_yticklabels([])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "test_pred = plt.scatter(y_test, y_pred_test, color=\"\")\n", - "test_test = plt.scatter(y_test, y_test, color=\"g\")\n", - "plt.legend(\n", - " (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n", - ")\n", - "plt.show()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "anshirga" - } - ], - "categories": [ - "how-to-use-azureml", - "automated-machine-learning" - ], - "category": "tutorial", - "compute": [ - "AML" - ], - "datasets": [ - "MachineData" - ], - "deployment": [ - "ACI" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Automated ML run with featurization and model explainability.", - "index_order": 5, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "tags": [ - "featurization", - "explainability", - "remote_run", - "AutomatedML" - ], - "task": "Regression" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.yml b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.yml deleted file mode 100644 index 9db24f2b5..000000000 --- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-regression-explanation-featurization -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/score_explain.py b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/score_explain.py deleted file mode 100644 index 226828a88..000000000 --- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/score_explain.py +++ /dev/null @@ -1,68 +0,0 @@ -import pandas as pd -import joblib -from azureml.core.model import Model -from azureml.train.automl.runtime.automl_explain_utilities import ( - automl_setup_model_explanations, -) -import scipy as sp - - -def init(): - - global automl_model - global scoring_explainer - - # Retrieve the path to the model file using the model name - # Assume original model is named original_prediction_model - automl_model_path = Model.get_model_path("automl_model") - scoring_explainer_path = Model.get_model_path("scoring_explainer") - - automl_model = joblib.load(automl_model_path) - scoring_explainer = joblib.load(scoring_explainer_path) - - -def is_multi_dimensional(matrix): - if hasattr(matrix, "ndim") and matrix.ndim > 1: - return True - if hasattr(matrix, "shape") and matrix.shape[1]: - return True - return False - - -def convert_matrix(matrix): - if sp.sparse.issparse(matrix): - matrix = matrix.todense() - if is_multi_dimensional(matrix): - matrix = matrix.tolist() - return matrix - - -def run(raw_data): - # Get predictions and explanations for each data point - data = pd.read_json(raw_data, orient="records") - # Make prediction - predictions = automl_model.predict(data) - # Setup for inferencing explanations - automl_explainer_setup_obj = automl_setup_model_explanations( - automl_model, X_test=data, task="regression" - ) - # Retrieve model explanations for engineered explanations - engineered_local_importance_values = scoring_explainer.explain( - automl_explainer_setup_obj.X_test_transform - ) - engineered_local_importance_values = convert_matrix( - engineered_local_importance_values - ) - - # Retrieve model explanations for raw explanations - raw_local_importance_values = scoring_explainer.explain( - automl_explainer_setup_obj.X_test_transform, get_raw=True - ) - raw_local_importance_values = convert_matrix(raw_local_importance_values) - - # You can return any data type as long as it is JSON-serializable - return { - "predictions": predictions.tolist(), - "engineered_local_importance_values": engineered_local_importance_values, - "raw_local_importance_values": raw_local_importance_values, - } diff --git a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py deleted file mode 100644 index 9750ee658..000000000 --- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. -# Licensed under the MIT license. -import os -import joblib - -from interpret.ext.glassbox import LGBMExplainableModel -from azureml.automl.core.shared.constants import MODEL_PATH -from azureml.core.experiment import Experiment -from azureml.core.dataset import Dataset -from azureml.core.run import Run -from azureml.interpret.mimic_wrapper import MimicWrapper -from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer -from azureml.train.automl.runtime.automl_explain_utilities import ( - automl_setup_model_explanations, - automl_check_model_if_explainable, -) - - -OUTPUT_DIR = "./outputs/" -os.makedirs(OUTPUT_DIR, exist_ok=True) - -# Get workspace from the run context -run = Run.get_context() -ws = run.experiment.workspace - -# Get the AutoML run object from the experiment name and the workspace -experiment = Experiment(ws, "<>") -automl_run = Run(experiment=experiment, run_id="<>") - -# Check if this AutoML model is explainable -if not automl_check_model_if_explainable(automl_run): - raise Exception( - "Model explanations are currently not supported for " - + automl_run.get_properties().get("run_algorithm") - ) - -# Download the best model from the artifact store -automl_run.download_file(name=MODEL_PATH, output_file_path="model.pkl") - -# Load the AutoML model into memory -fitted_model = joblib.load("model.pkl") - -# Get the train dataset from the workspace -train_dataset = Dataset.get_by_name(workspace=ws, name="<>") -# Drop the labeled column to get the training set. -X_train = train_dataset.drop_columns(columns=["<>"]) -y_train = train_dataset.keep_columns(columns=["<>"], validate=True) - -# Get the test dataset from the workspace -test_dataset = Dataset.get_by_name(workspace=ws, name="<>") -# Drop the labeled column to get the testing set. -X_test = test_dataset.drop_columns(columns=["<>"]) - -# Setup the class for explaining the AutoML models -automl_explainer_setup_obj = automl_setup_model_explanations( - fitted_model, "<>", X=X_train, X_test=X_test, y=y_train, automl_run=automl_run -) - -# Initialize the Mimic Explainer -explainer = MimicWrapper( - ws, - automl_explainer_setup_obj.automl_estimator, - LGBMExplainableModel, - init_dataset=automl_explainer_setup_obj.X_transform, - run=automl_explainer_setup_obj.automl_run, - features=automl_explainer_setup_obj.engineered_feature_names, - feature_maps=[automl_explainer_setup_obj.feature_map], - classes=automl_explainer_setup_obj.classes, -) - -# Compute the engineered explanations -engineered_explanations = explainer.explain( - ["local", "global"], - tag="engineered explanations", - eval_dataset=automl_explainer_setup_obj.X_test_transform, -) - -# Compute the raw explanations -raw_explanations = explainer.explain( - ["local", "global"], - get_raw=True, - tag="raw explanations", - raw_feature_names=automl_explainer_setup_obj.raw_feature_names, - eval_dataset=automl_explainer_setup_obj.X_test_transform, - raw_eval_dataset=automl_explainer_setup_obj.X_test_raw, -) - -print("Engineered and raw explanations computed successfully") - -# Initialize the ScoringExplainer -scoring_explainer = TreeScoringExplainer( - explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map] -) - -# Pickle scoring explainer locally -with open("scoring_explainer.pkl", "wb") as stream: - joblib.dump(scoring_explainer, stream) - -# Upload the scoring explainer to the automl run -automl_run.upload_file("outputs/scoring_explainer.pkl", "scoring_explainer.pkl") diff --git a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb deleted file mode 100644 index 968032963..000000000 --- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb +++ /dev/null @@ -1,470 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Regression with Aml Compute**_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Data](#Data)\n", - "1. [Train](#Train)\n", - "1. [Results](#Results)\n", - "1. [Test](#Test)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. The Regression goal is to predict the performance of certain combinations of hardware parts.\n", - "\n", - "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create an `Experiment` in an existing `Workspace`.\n", - "2. Configure AutoML using `AutoMLConfig`.\n", - "3. Train the model using local compute.\n", - "4. Explore the results.\n", - "5. Test the best fitted model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.train.automl import AutoMLConfig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# Choose a name for the experiment.\n", - "experiment_name = \"automl-regression\"\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output[\"Subscription ID\"] = ws.subscription_id\n", - "output[\"Workspace\"] = ws.name\n", - "output[\"Resource Group\"] = ws.resource_group\n", - "output[\"Location\"] = ws.location\n", - "output[\"Run History Name\"] = experiment_name\n", - "pd.set_option(\"display.max_colwidth\", None)\n", - "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "cpu_cluster_name = \"reg-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print(\"Found existing cluster, use it.\")\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(\n", - " vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n", - " )\n", - " compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load Data\n", - "Load the hardware dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n", - "dataset = Dataset.Tabular.from_delimited_files(data)\n", - "\n", - "# Split the dataset into train and test datasets\n", - "train_data, test_data = dataset.random_split(percentage=0.8, seed=223)\n", - "\n", - "label = \"ERP\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "\n", - "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification, regression or forecasting|\n", - "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics:
      spearman_correlation
      normalized_root_mean_squared_error
      r2_score
      normalized_mean_absolute_error|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n", - "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|\n", - "\n", - "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "automlconfig-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"n_cross_validations\": 3,\n", - " \"primary_metric\": \"r2_score\",\n", - " \"enable_early_stopping\": True,\n", - " \"experiment_timeout_hours\": 0.3, # for real scenarios we reccommend a timeout of at least one hour\n", - " \"max_concurrent_iterations\": 4,\n", - " \"max_cores_per_iteration\": -1,\n", - " \"verbosity\": logging.INFO,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"regression\",\n", - " compute_target=compute_target,\n", - " training_data=train_data,\n", - " label_column_name=label,\n", - " **automl_settings,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. Execution of remote runs is asynchronous. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If you need to retrieve a run that already started, use the following code\n", - "# from azureml.train.automl.run import AutoMLRun\n", - "# remote_run = AutoMLRun(experiment = experiment, run_id = '')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Widget for Monitoring Runs\n", - "\n", - "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n", - "\n", - "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(remote_run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve the Best Model\n", - "\n", - "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run, fitted_model = remote_run.get_output()\n", - "print(best_run)\n", - "print(fitted_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Best Model Based on Any Other Metric\n", - "Show the run and the model that has the smallest `root_mean_squared_error` value (which turned out to be the same as the one with largest `spearman_correlation` value):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lookup_metric = \"root_mean_squared_error\"\n", - "best_run, fitted_model = remote_run.get_output(metric=lookup_metric)\n", - "print(best_run)\n", - "print(fitted_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Model from a Specific Iteration\n", - "Show the run and the model from the third iteration:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "iteration = 3\n", - "third_run, third_model = remote_run.get_output(iteration=iteration)\n", - "print(third_run)\n", - "print(third_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_test = test_data.keep_columns(\"ERP\").to_pandas_dataframe()\n", - "test_data = test_data.drop_columns(\"ERP\").to_pandas_dataframe()\n", - "\n", - "\n", - "y_train = train_data.keep_columns(\"ERP\").to_pandas_dataframe()\n", - "train_data = train_data.drop_columns(\"ERP\").to_pandas_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred_train = fitted_model.predict(train_data)\n", - "y_residual_train = y_train.values - y_pred_train\n", - "\n", - "y_pred_test = fitted_model.predict(test_data)\n", - "y_residual_test = y_test.values - y_pred_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from sklearn.metrics import mean_squared_error, r2_score\n", - "\n", - "# Set up a multi-plot chart.\n", - "f, (a0, a1) = plt.subplots(\n", - " 1, 2, gridspec_kw={\"width_ratios\": [1, 1], \"wspace\": 0, \"hspace\": 0}\n", - ")\n", - "f.suptitle(\"Regression Residual Values\", fontsize=18)\n", - "f.set_figheight(6)\n", - "f.set_figwidth(16)\n", - "\n", - "# Plot residual values of training set.\n", - "a0.axis([0, 360, -100, 100])\n", - "a0.plot(y_residual_train, \"bo\", alpha=0.5)\n", - "a0.plot([-10, 360], [0, 0], \"r-\", lw=3)\n", - "a0.text(\n", - " 16,\n", - " 170,\n", - " \"RMSE = {0:.2f}\".format(np.sqrt(mean_squared_error(y_train, y_pred_train))),\n", - " fontsize=12,\n", - ")\n", - "a0.text(\n", - " 16, 140, \"R2 score = {0:.2f}\".format(r2_score(y_train, y_pred_train)), fontsize=12\n", - ")\n", - "a0.set_xlabel(\"Training samples\", fontsize=12)\n", - "a0.set_ylabel(\"Residual Values\", fontsize=12)\n", - "\n", - "# Plot residual values of test set.\n", - "a1.axis([0, 90, -100, 100])\n", - "a1.plot(y_residual_test, \"bo\", alpha=0.5)\n", - "a1.plot([-10, 360], [0, 0], \"r-\", lw=3)\n", - "a1.text(\n", - " 5,\n", - " 170,\n", - " \"RMSE = {0:.2f}\".format(np.sqrt(mean_squared_error(y_test, y_pred_test))),\n", - " fontsize=12,\n", - ")\n", - "a1.text(5, 140, \"R2 score = {0:.2f}\".format(r2_score(y_test, y_pred_test)), fontsize=12)\n", - "a1.set_xlabel(\"Test samples\", fontsize=12)\n", - "a1.set_yticklabels([])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "test_pred = plt.scatter(y_test, y_pred_test, color=\"\")\n", - "test_test = plt.scatter(y_test, y_test, color=\"g\")\n", - "plt.legend(\n", - " (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "ratanase" - } - ], - "categories": [ - "how-to-use-azureml", - "automated-machine-learning" - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml deleted file mode 100644 index 4e84e13ad..000000000 --- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: auto-ml-regression -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/sql-server/README.md b/how-to-use-azureml/automated-machine-learning/sql-server/README.md deleted file mode 100644 index 6db22e412..000000000 --- a/how-to-use-azureml/automated-machine-learning/sql-server/README.md +++ /dev/null @@ -1,113 +0,0 @@ -# Table of Contents -1. [Introduction](#introduction) -1. [Setup using Azure Data Studio](#azuredatastudiosetup) -1. [Energy demand example using Azure Data Studio](#azuredatastudioenergydemand) -1. [Set using SQL Server Management Studio for SQL Server 2017 on Windows](#ssms2017) -1. [Set using SQL Server Management Studio for SQL Server 2019 on Linux](#ssms2019) -1. [Energy demand example using SQL Server Management Studio](#ssmsenergydemand) - - - -# Introduction -SQL Server 2017 or 2019 can call Azure ML automated machine learning to create models trained on data from SQL Server. -This uses the sp_execute_external_script stored procedure, which can call Python scripts. -SQL Server 2017 and SQL Server 2019 can both run on Windows or Linux. -However, this integration is not available for SQL Server 2017 on Linux. - -This folder shows how to setup the integration and has a sample that uses the integration to train and predict based on an energy demand dataset. - -This integration is part of SQL Server and so can be used from any SQL client. -These instructions show using it from Azure Data Studio or SQL Server Managment Studio. - - -## Setup using Azure Data Studio - -These step show setting up the integration using Azure Data Studio. - -1. If you don't already have SQL Server, you can install it from [https://www.microsoft.com/en-us/sql-server/sql-server-downloads](https://www.microsoft.com/en-us/sql-server/sql-server-downloads) -1. Install Azure Data Studio from [https://docs.microsoft.com/en-us/sql/azure-data-studio/download?view=sql-server-2017](https://docs.microsoft.com/en-us/sql/azure-data-studio/download?view=sql-server-2017) -1. Start Azure Data Studio and connect to SQL Server. [https://docs.microsoft.com/en-us/sql/azure-data-studio/sql-notebooks?view=sql-server-2017](https://docs.microsoft.com/en-us/sql/azure-data-studio/sql-notebooks?view=sql-server-2017) -1. Create a database named "automl". -1. Open the notebook how-to-use-azureml\automated-machine-learning\sql-server\setup\auto-ml-sql-setup.ipynb and follow the instructions in it. - - -## Energy demand example using Azure Data Studio - -Once you have completed the setup, you can try the energy demand sample in the notebook energy-demand\auto-ml-sql-energy-demand.ipynb. -This has cells to train a model, predict based on the model and show metrics for each pipeline run in training the model. - - -## Setup using SQL Server Management Studio for SQL Server 2017 on Windows - -These instruction setup the integration for SQL Server 2017 on Windows. - -1. If you don't already have SQL Server, you can install it from [https://www.microsoft.com/en-us/sql-server/sql-server-downloads](https://www.microsoft.com/en-us/sql-server/sql-server-downloads) -2. Enable external scripts with the following commands: -```sh - sp_configure 'external scripts enabled',1 - reconfigure with override -``` -3. Stop SQL Server. -4. Install the automated machine learning libraries using the following commands from Administrator command prompt (If you are using a non-default SQL Server instance name, replace MSSQLSERVER in the second command with the instance name) -```sh - cd "C:\Program Files\Microsoft SQL Server" - cd "MSSQL14.MSSQLSERVER\PYTHON_SERVICES" - python.exe -m pip install azureml-sdk[automl] - python.exe -m pip install --upgrade numpy - python.exe -m pip install --upgrade sklearn -``` -5. Start SQL Server and the service "SQL Server Launchpad service". -6. In Windows Firewall, click on advanced settings and in Outbound Rules, disable "Block network access for R local user accounts in SQL Server instance xxxx". -7. Execute the files in the setup folder in SQL Server Management Studio: aml_model.sql, aml_connection.sql, AutoMLGetMetrics.sql, AutoMLPredict.sql and AutoMLTrain.sql -8. Create an Azure Machine Learning Workspace. You can use the instructions at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace ](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace) -9. Create a config.json file file using the subscription id, resource group name and workspace name that you used to create the workspace. The file is described at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace) -10. Create an Azure service principal. You can do this with the commands: -```sh - az login - az account set --subscription subscriptionid - az ad sp create-for-rbac --name principlename --password password -``` -11. Insert the values \, \ and \ returned by create-for-rbac above into the aml_connection table. Set \ as the absolute path to your config.json file. Set the name to “Default”. - - -## Setup using SQL Server Management Studio for SQL Server 2019 on Linux -1. Install SQL Server 2019 from: [https://www.microsoft.com/en-us/sql-server/sql-server-downloads](https://www.microsoft.com/en-us/sql-server/sql-server-downloads) -2. Install machine learning support from: [https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-machine-learning?view=sqlallproducts-allversions#ubuntu](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-machine-learning?view=sqlallproducts-allversions#ubuntu) -3. Then install SQL Server management Studio from [https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017](https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017) -4. Enable external scripts with the following commands: -```sh - sp_configure 'external scripts enabled',1 - reconfigure with override -``` -5. Stop SQL Server. -6. Install the automated machine learning libraries using the following commands from Administrator command (If you are using a non-default SQL Server instance name, replace MSSQLSERVER in the second command with the instance name): -```sh - sudo /opt/mssql/mlservices/bin/python/python -m pip install azureml-sdk[automl] - sudo /opt/mssql/mlservices/bin/python/python -m pip install --upgrade numpy - sudo /opt/mssql/mlservices/bin/python/python -m pip install --upgrade sklearn -``` -7. Start SQL Server. -8. Execute the files aml_model.sql, aml_connection.sql, AutoMLGetMetrics.sql, AutoMLPredict.sql, AutoMLForecast.sql and AutoMLTrain.sql in SQL Server Management Studio. -9. Create an Azure Machine Learning Workspace. You can use the instructions at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace) -10. Create a config.json file file using the subscription id, resource group name and workspace name that you use to create the workspace. The file is described at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace) -11. Create an Azure service principal. You can do this with the commands: -```sh - az login - az account set --subscription subscriptionid - az ad sp create-for-rbac --name principlename --password password -``` -12. Insert the values \, \ and \ returned by create-for-rbac above into the aml_connection table. Set \ as the absolute path to your config.json file. Set the name to “Default”. - - -## Energy demand example using SQL Server Management Studio - -Once you have completed the setup, you can try the energy demand sample queries. -First you need to load the sample data in the database. -1. In SQL Server Management Studio, you can right-click the database, select Tasks, then Import Flat file. -1. Select the file MachineLearningNotebooks\notebooks\how-to-use-azureml\automated-machine-learning\forecasting-energy-demand\nyc_energy.csv. -1. When you get to the column definition page, allow nulls for all columns. - -You can then run the queries in the energy-demand folder: -* TrainEnergyDemand.sql runs AutoML, trains multiple models on data and selects the best model. -* ForecastEnergyDemand.sql forecasts based on the most recent training run. -* GetMetrics.sql returns all the metrics for each model in the most recent training run. diff --git a/how-to-use-azureml/azure-databricks/automl/README.md b/how-to-use-azureml/azure-databricks/automl/README.md deleted file mode 100644 index 9a8bc7451..000000000 --- a/how-to-use-azureml/azure-databricks/automl/README.md +++ /dev/null @@ -1,70 +0,0 @@ -# Automated ML introduction -Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions. - - -If you are new to Data Science, automated ML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use. - -If you are an experienced data scientist, automated ML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. Automated ML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire. - -# Install Instructions using Azure Databricks : - -#### For Databricks non ML runtime 7.1(scala 2.21, spark 3.0.0) and up, Install Automated Machine Learning sdk by adding and running the following command as the first cell of your notebook. This will install AutoML dependencies specific for your notebook. - -%pip install --upgrade --force-reinstall -r https://aka.ms/automl_linux_requirements.txt - - -#### For Databricks non ML runtime 7.0 and lower, Install Automated Machine Learning sdk using init script as shown below before running the notebook.** - -**Create the Azure Databricks cluster-scoped init script 'azureml-cluster-init.sh' as below - -1. Create the base directory you want to store the init script in if it does not exist. - ``` - dbutils.fs.mkdirs("dbfs:/databricks/init/") - ``` - -2. Create the script azureml-cluster-init.sh - ``` - dbutils.fs.put("/databricks/init/azureml-cluster-init.sh",""" - #!/bin/bash - set -ex - /databricks/python/bin/pip install --upgrade --force-reinstall -r https://aka.ms/automl_linux_requirements.txt - """, True) - ``` - -3. Check that the script exists. - ``` - display(dbutils.fs.ls("dbfs:/databricks/init/azureml-cluster-init.sh")) - ``` - -**Install libraries to cluster using init script 'azureml-cluster-init.sh' created in previous step - -1. Configure the cluster to run the script. - * Using the cluster configuration page - 1. On the cluster configuration page, click the Advanced Options toggle. - 1. At the bottom of the page, click the Init Scripts tab. - 1. In the Destination drop-down, select a destination type. Example: 'DBFS' - 1. Specify a path to the init script. - ``` - dbfs:/databricks/init/azureml-cluster-init.sh - ``` - 1. Click Add - - * Using the API. - ``` - curl -n -X POST -H 'Content-Type: application/json' -d '{ - "cluster_id": "", - "num_workers": , - "spark_version": "", - "node_type_id": "", - "cluster_log_conf": { - "dbfs" : { - "destination": "dbfs:/cluster-logs" - } - }, - "init_scripts": [ { - "dbfs": { - "destination": "dbfs:/databricks/init/azureml-cluster-init.sh" - } - } ] - }' https:///api/2.0/clusters/edit - ``` diff --git a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb deleted file mode 100644 index 10ccdecbb..000000000 --- a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb +++ /dev/null @@ -1,453 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## AutoML Installation\n", - "\n", - "**For Databricks non ML runtime 7.1(scala 2.21, spark 3.0.0) and up, Install AML sdk by running the following command in the first cell of the notebook.**\n", - "\n", - "%pip install --upgrade --force-reinstall -r https://aka.ms/automl_linux_requirements.txt\n", - "\n", - "**For Databricks non ML runtime 7.0 and lower, Install AML sdk using init script as shown in [readme](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/azure-databricks/automl/README.md) before running this notebook.**\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AutoML : Classification with Local Compute on Azure DataBricks\n", - "\n", - "In this example we use the scikit-learn's to showcase how you can use AutoML for a simple classification problem.\n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n", - "2. Create an `Experiment` in an existing `Workspace`.\n", - "3. Configure AutoML using `AutoMLConfig`.\n", - "4. Train the model using AzureDataBricks.\n", - "5. Explore the results.\n", - "6. Test the best fitted model.\n", - "\n", - "Prerequisites:\n", - "Before running this notebook, please follow the readme for installing necessary libraries to your cluster." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register Machine Learning Services Resource Provider\n", - "Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n", - "Start the Azure portal.\n", - "Select your All services and then Subscription.\n", - "Select the subscription that you want to use.\n", - "Click on Resource providers\n", - "Click the Register link next to Microsoft.MachineLearningServices" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Check the Azure ML Core SDK Version to Validate Your Installation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "\n", - "print(\"SDK Version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize an Azure ML Workspace\n", - "### What is an Azure ML Workspace and Why Do I Need One?\n", - "\n", - "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows. In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n", - "\n", - "\n", - "### What do I Need?\n", - "\n", - "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n", - "* A name for your workspace. You can choose one.\n", - "* Your subscription id. Use the `id` value from the `az account show` command output above.\n", - "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n", - "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subscription_id = \"\" #you should be owner or contributor\n", - "resource_group = \"\" #you should be owner or contributor\n", - "workspace_name = \"\" #your workspace name\n", - "workspace_region = \"\" #your region" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating a Workspace\n", - "If you already have access to an Azure ML workspace you want to use, you can skip this cell. Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n", - "\n", - "This will fail when:\n", - "1. The workspace already exists.\n", - "2. You do not have permission to create a workspace in the resource group.\n", - "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n", - "\n", - "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n", - "\n", - "**Note:** Creation of a new workspace can take several minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Workspace class and check the Azure ML SDK version.\n", - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.create(name = workspace_name,\n", - " subscription_id = subscription_id,\n", - " resource_group = resource_group, \n", - " location = workspace_region, \n", - " exist_ok=True)\n", - "ws.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuring Your Local Environment\n", - "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace(workspace_name = workspace_name,\n", - " subscription_id = subscription_id,\n", - " resource_group = resource_group)\n", - "\n", - "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n", - "ws.write_config()\n", - "write_config(path=\"/databricks/driver/aml_config/\",file_name=)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an Experiment\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import os\n", - "import random\n", - "import time\n", - "import json\n", - "\n", - "from matplotlib import pyplot as plt\n", - "from matplotlib.pyplot import imshow\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.train.automl import AutoMLConfig\n", - "from azureml.train.automl.run import AutoMLRun" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a name for the experiment and specify the project folder.\n", - "experiment_name = 'automl-local-classification'\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output['SDK version'] = azureml.core.VERSION\n", - "output['Subscription ID'] = ws.subscription_id\n", - "output['Workspace Name'] = ws.name\n", - "output['Resource Group'] = ws.resource_group\n", - "output['Location'] = ws.location\n", - "output['Experiment Name'] = experiment.name\n", - "pd.set_option('display.max_colwidth', -1)\n", - "pd.DataFrame(data = output, index = ['']).T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Training Data Using Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Automated ML takes a `TabularDataset` as input.\n", - "\n", - "You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n", - "from azureml.core.dataset import Dataset\n", - "\n", - "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n", - "dataset = Dataset.Tabular.from_delimited_files(example_data)\n", - "dataset.take(5).to_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Review the TabularDataset\n", - "You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only j records for all the steps in the TabularDataset, which makes it fast even against large datasets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_data = dataset.drop_columns(columns=['FBI Code'])\n", - "label = 'Primary Type'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure AutoML\n", - "\n", - "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification or regression|\n", - "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics:
      accuracy
      AUC_weighted
      average_precision_score_weighted
      norm_macro_recall
      precision_score_weighted|\n", - "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics:
      spearman_correlation
      normalized_root_mean_squared_error
      r2_score
      normalized_mean_absolute_error|\n", - "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n", - "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n", - "|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n", - "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|Input dataset, containing both features and label column.|\n", - "|**label_column_name**|The name of the label column.|" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_config = AutoMLConfig(task = 'classification',\n", - " debug_log = 'automl_errors.log',\n", - " primary_metric = 'AUC_weighted',\n", - " iteration_timeout_minutes = 10,\n", - " iterations = 5,\n", - " n_cross_validations = 10,\n", - " max_concurrent_iterations = 2, #change it based on number of worker nodes\n", - " verbosity = logging.INFO,\n", - " spark_context=sc, #databricks/spark related\n", - " training_data=training_data,\n", - " label_column_name=label)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the Models\n", - "\n", - "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_run = experiment.submit(automl_config, show_output = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explore the Results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Portal URL for Monitoring Runs\n", - "\n", - "The following will provide a link to the web interface to explore individual run details and status. In the future we might support output displayed in the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "displayHTML(\"Azure Portal: {}\".format(local_run.get_portal_url(), local_run.id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy\n", - "\n", - "### Retrieve the Best Model\n", - "\n", - "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run, fitted_model = local_run.get_output()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the Best Fitted Model\n", - "\n", - "#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n", - "\n", - "df_test = dataset_test.to_pandas_dataframe()\n", - "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n", - "\n", - "y_test = df_test[['Primary Type']]\n", - "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Testing Our Best Fitted Model\n", - "We will try to predict digits and see how our model works. This is just an example to show you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fitted_model.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.png)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "savitam" - }, - { - "name": "sasum" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - }, - "name": "auto-ml-classification-local-adb", - "notebookId": 1275190406842063 - }, - "nbformat": 4, - "nbformat_minor": 1 -} \ No newline at end of file diff --git a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb deleted file mode 100644 index 5865afc15..000000000 --- a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb +++ /dev/null @@ -1,581 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## AutoML Installation\n", - "\n", - "**For Databricks non ML runtime 7.1(scala 2.21, spark 3.0.0) and up, Install AML sdk by running the following command in the first cell of the notebook.**\n", - "\n", - "%pip install --upgrade --force-reinstall -r https://aka.ms/automl_linux_requirements.txt\n", - "\n", - "**For Databricks non ML runtime 7.0 and lower, Install AML sdk using init script as shown in [readme](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/azure-databricks/automl/README.md) before running this notebook.**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AutoML : Classification with Local Compute on Azure DataBricks with deployment to ACI\n", - "\n", - "In this example we use the scikit-learn's to showcase how you can use AutoML for a simple classification problem.\n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n", - "2. Create an `Experiment` in an existing `Workspace`.\n", - "3. Configure AutoML using `AutoMLConfig`.\n", - "4. Train the model using AzureDataBricks.\n", - "5. Explore the results.\n", - "6. Register the model.\n", - "7. Deploy the model.\n", - "8. Test the best fitted model.\n", - "\n", - "Prerequisites:\n", - "Before running this notebook, please follow the readme for installing necessary libraries to your cluster." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register Machine Learning Services Resource Provider\n", - "Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n", - "Start the Azure portal.\n", - "Select your All services and then Subscription.\n", - "Select the subscription that you want to use.\n", - "Click on Resource providers\n", - "Click the Register link next to Microsoft.MachineLearningServices" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Check the Azure ML Core SDK Version to Validate Your Installation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "\n", - "print(\"SDK Version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize an Azure ML Workspace\n", - "### What is an Azure ML Workspace and Why Do I Need One?\n", - "\n", - "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows. In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n", - "\n", - "\n", - "### What do I Need?\n", - "\n", - "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n", - "* A name for your workspace. You can choose one.\n", - "* Your subscription id. Use the `id` value from the `az account show` command output above.\n", - "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n", - "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subscription_id = \"\" #you should be owner or contributor\n", - "resource_group = \"\" #you should be owner or contributor\n", - "workspace_name = \"\" #your workspace name\n", - "workspace_region = \"\" #your region" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating a Workspace\n", - "If you already have access to an Azure ML workspace you want to use, you can skip this cell. Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n", - "\n", - "This will fail when:\n", - "1. The workspace already exists.\n", - "2. You do not have permission to create a workspace in the resource group.\n", - "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n", - "\n", - "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n", - "\n", - "**Note:** Creation of a new workspace can take several minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the Workspace class and check the Azure ML SDK version.\n", - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.create(name = workspace_name,\n", - " subscription_id = subscription_id,\n", - " resource_group = resource_group, \n", - " location = workspace_region, \n", - " exist_ok=True)\n", - "ws.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuring Your Local Environment\n", - "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace(workspace_name = workspace_name,\n", - " subscription_id = subscription_id,\n", - " resource_group = resource_group)\n", - "\n", - "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n", - "ws.write_config()\n", - "write_config(path=\"/databricks/driver/aml_config/\",file_name=)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an Experiment\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import os\n", - "import random\n", - "import time\n", - "import json\n", - "\n", - "from matplotlib import pyplot as plt\n", - "from matplotlib.pyplot import imshow\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.train.automl import AutoMLConfig\n", - "from azureml.train.automl.run import AutoMLRun" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a name for the experiment and specify the project folder.\n", - "experiment_name = 'automl-local-classification'\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output['SDK version'] = azureml.core.VERSION\n", - "output['Subscription ID'] = ws.subscription_id\n", - "output['Workspace Name'] = ws.name\n", - "output['Resource Group'] = ws.resource_group\n", - "output['Location'] = ws.location\n", - "output['Experiment Name'] = experiment.name\n", - "pd.set_option('display.max_colwidth', -1)\n", - "pd.DataFrame(data = output, index = ['']).T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Training Data Using Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Automated ML takes a `TabularDataset` as input.\n", - "\n", - "You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n", - "from azureml.core.dataset import Dataset\n", - "\n", - "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n", - "dataset = Dataset.Tabular.from_delimited_files(example_data)\n", - "dataset.take(5).to_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Review the TabularDataset\n", - "You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only j records for all the steps in the TabularDataset, which makes it fast even against large datasets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_data = dataset.drop_columns(columns=['FBI Code'])\n", - "label = 'Primary Type'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure AutoML\n", - "\n", - "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification or regression|\n", - "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics:
      accuracy
      AUC_weighted
      average_precision_score_weighted
      norm_macro_recall
      precision_score_weighted|\n", - "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics:
      spearman_correlation
      normalized_root_mean_squared_error
      r2_score
      normalized_mean_absolute_error|\n", - "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n", - "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n", - "|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n", - "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|Input dataset, containing both features and label column.|\n", - "|**label_column_name**|The name of the label column.|" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_config = AutoMLConfig(task = 'classification',\n", - " debug_log = 'automl_errors.log',\n", - " primary_metric = 'AUC_weighted',\n", - " iteration_timeout_minutes = 10,\n", - " iterations = 5,\n", - " n_cross_validations = 10,\n", - " max_concurrent_iterations = 2, #change it based on number of worker nodes\n", - " verbosity = logging.INFO,\n", - " spark_context=sc, #databricks/spark related\n", - " training_data=training_data,\n", - " label_column_name=label)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the Models\n", - "\n", - "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_run = experiment.submit(automl_config, show_output = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explore the Results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Portal URL for Monitoring Runs\n", - "\n", - "The following will provide a link to the web interface to explore individual run details and status. In the future we might support output displayed in the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "displayHTML(\"Azure Portal: {}\".format(local_run.get_portal_url(), local_run.id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy\n", - "\n", - "### Retrieve the Best Model\n", - "\n", - "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run, fitted_model = local_run.get_output()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download the conda environment file\n", - "From the *best_run* download the conda environment file that was used to train the AutoML model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.shared import constants\n", - "conda_env_file_name = 'conda_env.yml'\n", - "best_run.download_file(name=\"outputs/conda_env_v_1_0_0.yml\", output_file_path=conda_env_file_name)\n", - "with open(conda_env_file_name, \"r\") as conda_file:\n", - " conda_file_contents = conda_file.read()\n", - " print(conda_file_contents)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download the model scoring file\n", - "From the *best_run* download the scoring file to get the predictions from the AutoML model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.automl.core.shared import constants\n", - "script_file_name = 'scoring_file.py'\n", - "best_run.download_file(name=\"outputs/scoring_file_v_1_0_0.py\", output_file_path=script_file_name)\n", - "with open(script_file_name, \"r\") as scoring_file:\n", - " scoring_file_contents = scoring_file.read()\n", - " print(scoring_file_contents)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register the Fitted Model for Deployment\n", - "If neither metric nor iteration are specified in the register_model call, the iteration with the best primary metric is registered." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "description = 'AutoML Model'\n", - "tags = None\n", - "model = local_run.register_model(description = description, tags = tags)\n", - "local_run.model_id # This will be written to the scoring script file later in the notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the model as a Web Service on Azure Container Instance\n", - "\n", - "Create the configuration needed for deploying the model as a web service service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.webservice import AciWebservice\n", - "from azureml.core.environment import Environment\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=conda_env_file_name)\n", - "inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", - " memory_gb = 1, \n", - " tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n", - " description = 'sample service for Automl Classification')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import Webservice\n", - "from azureml.core.model import Model\n", - "\n", - "aci_service_name = 'automl-databricks-local'\n", - "print(aci_service_name)\n", - "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", - "aci_service.wait_for_deployment(True)\n", - "print(aci_service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the Best Fitted Model\n", - "\n", - "#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n", - "\n", - "df_test = dataset_test.to_pandas_dataframe()\n", - "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n", - "\n", - "y_test = df_test[['Primary Type']]\n", - "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Testing Our Best Fitted Model\n", - "We will try to predict digits and see how our model works. This is just an example to show you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fitted_model.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "### Delete the service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aci_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.png)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "savitam" - }, - { - "name": "sasum" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - }, - "name": "auto-ml-classification-local-adb", - "notebookId": 3772036807853791 - }, - "nbformat": 4, - "nbformat_minor": 1 -} \ No newline at end of file diff --git a/how-to-use-azureml/azure-synapse/README.md b/how-to-use-azureml/azure-synapse/README.md deleted file mode 100644 index 1398865e3..000000000 --- a/how-to-use-azureml/azure-synapse/README.md +++ /dev/null @@ -1,84 +0,0 @@ -Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources—at scale. Azure Synapse brings these worlds together with a unified experience to ingest, explore, prepare, manage, and serve data for immediate BI and machine learning needs. A core offering within Azure Synapse Analytics are serverless Apache Spark pools enhanced for big data workloads.  - -Synapse in Aml integration is for customers who want to use Apache Spark in Azure Synapse Analytics to prepare data at scale in Azure ML before training their ML model. This will allow customers to work on their end-to-end ML lifecycle including large-scale data preparation, model training and deployment within Azure ML workspace without having to use suboptimal tools for machine learning or switch between multiple tools for data preparation and model training. The ability to perform all ML tasks within Azure ML will reduce time required for customers to iterate on a machine learning project which typically includes multiple rounds of data preparation and training. - -In the public preview, the capabilities are provided: - -- Link Azure Synapse Analytics workspace to Azure Machine Learning workspace (via ARM, UI or SDK) -- Attach Apache Spark pools powered by Azure Synapse Analytics as Azure Machine Learning compute targets (via ARM, UI or SDK) -- Launch Apache Spark sessions in notebooks and perform interactive data exploration and preparation. This interactive experience leverages Apache Spark magic and customers will have session-level Conda support to install packages. -- Productionize ML pipelines by leveraging Apache Spark pools to pre-process big data - -# Using Synapse in Azure machine learning - -## Create synapse resources - -Follow up the documents to create Synapse workspace and resource-setup.sh is available for you to create the resources. - -- Create from [Portal](https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-workspace) -- Create from [Cli](https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-workspace-cli) - -Follow up the documents to create Synapse spark pool - -- Create from [Portal](https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-apache-spark-pool-portal) -- Create from [Cli](https://docs.microsoft.com/en-us/cli/azure/ext/synapse/synapse/spark/pool?view=azure-cli-latest) - -## Link Synapse Workspace - -Make sure you are the owner of synapse workspace so that you can link synapse workspace into AML. -You can run resource-setup.py to link the synapse workspace and attach compute - -```python -from azureml.core import Workspace -ws = Workspace.from_config() - -from azureml.core import LinkedService, SynapseWorkspaceLinkedServiceConfiguration -synapse_link_config = SynapseWorkspaceLinkedServiceConfiguration( - subscription_id="", - resource_group="> - -*Project Gutenberg is proud to cooperate with The World Library* -in the presentation of The Complete Works of William Shakespeare -for your reading for education and entertainment. 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All rights reserved. \n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/spark_job_on_synapse_spark_pool.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Synapse Spark Pool as a Compute Target from Azure Machine Learning Remote Run\n", - "1. To use Synapse Spark Pool as a compute target from Experiment Run, [ScriptRunConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.script_run_config.scriptrunconfig?view=azure-ml-py) is used, the same as other Experiment Runs. This notebook demonstrates how to leverage ScriptRunConfig to submit an experiment run to an attached Synapse Spark cluster.\n", - "2. To use Synapse Spark Pool as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [SynapseSparkStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.synapse_spark_step.synapsesparkstep?view=azure-ml-py) is used. This notebook demonstrates how to leverage SynapseSparkStep in Azure Machine Learning Pipeline.\n", - "\n", - "## Before you begin:\n", - "1. **Create an Azure Synapse workspace**, check [this] (https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-workspace) for more information.\n", - "2. **Create Spark Pool in Synapse workspace**: check [this] (https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-apache-spark-pool-portal) for more information." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Azure Machine Learning and Pipeline SDK-specific imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import azureml.core\n", - "from azureml.core import Workspace, Experiment\n", - "from azureml.core import LinkedService, SynapseWorkspaceLinkedServiceConfiguration\n", - "from azureml.core.compute import ComputeTarget, AmlCompute, SynapseCompute\n", - "from azureml.exceptions import ComputeTargetException\n", - "from azureml.data import HDFSOutputDatasetConfig\n", - "from azureml.core.datastore import Datastore\n", - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.pipeline.core import Pipeline\n", - "from azureml.pipeline.steps import PythonScriptStep, SynapseSparkStep\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Link Synapse workspace to AML \n", - "You have to be an \"Owner\" of Synapse workspace resource to perform linking. You can check your role in the Azure resource management portal, if you don't have an \"Owner\" role, you can contact an \"Owner\" to link the workspaces for you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Replace with your resource info before running.\n", - "\n", - "synapse_subscription_id=os.getenv(\"SYNAPSE_SUBSCRIPTION_ID\", \"\")\n", - "synapse_resource_group=os.getenv(\"SYNAPSE_RESOURCE_GROUP\", \"\")\n", - "synapse_workspace_name=os.getenv(\"SYNAPSE_WORKSPACE_NAME\", \"\")\n", - "synapse_linked_service_name=os.getenv(\"SYNAPSE_LINKED_SERVICE_NAME\", \"\")\n", - "\n", - "synapse_link_config = SynapseWorkspaceLinkedServiceConfiguration(\n", - " subscription_id=synapse_subscription_id,\n", - " resource_group=synapse_resource_group,\n", - " name=synapse_workspace_name\n", - ")\n", - "\n", - "linked_service = LinkedService.register(\n", - " workspace=ws,\n", - " name=synapse_linked_service_name,\n", - " linked_service_config=synapse_link_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Linked service property\n", - "\n", - "A MSI (system_assigned_identity_principal_id) will be generated for each linked service, for example:\n", - "\n", - "name=synapselink,

      \n", - "type=Synapse,

      \n", - "linked_service_resource_id=/subscriptions/4faaaf21-663f-4391-96fd-47197c630979/resourceGroups/static_resources_synapse_test/providers/Microsoft.Synapse/workspaces/synapsetest2,

      \n", - "system_assigned_identity_principal_id=eb355d52-3806-4c5a-aec9-91447e8cfc2e

      \n", - "\n", - "#### Make sure you grant \"Synapse Apache Spark Administrator\" role of the synapse workspace to the generated workspace linking MSI in Synapse studio portal before you submit job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linked_service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "LinkedService.list(ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Attach Synapse spark pool as AML compute target" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "synapse_spark_pool_name=os.getenv(\"SYNAPSE_SPARK_POOL_NAME\", \"\")\n", - "synapse_compute_name=os.getenv(\"SYNAPSE_COMPUTE_NAME\", \"\")\n", - "\n", - "attach_config = SynapseCompute.attach_configuration(\n", - " linked_service,\n", - " type=\"SynapseSpark\",\n", - " pool_name=synapse_spark_pool_name)\n", - "\n", - "synapse_compute=ComputeTarget.attach(\n", - " workspace=ws,\n", - " name=synapse_compute_name,\n", - " attach_configuration=attach_config)\n", - "\n", - "synapse_compute.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Start an experiment run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use the default blob storage\n", - "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n", - "print('Datastore {} will be used'.format(def_blob_store.name))\n", - "\n", - "# We are uploading a sample file in the local directory to be used as a datasource\n", - "file_name = \"Titanic.csv\"\n", - "def_blob_store.upload_files(files=[\"./{}\".format(file_name)], overwrite=False)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tabular dataset as input" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "titanic_tabular_dataset = Dataset.Tabular.from_delimited_files(path=[(def_blob_store, file_name)])\n", - "input1 = titanic_tabular_dataset.as_named_input(\"tabular_input\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## File dataset as input" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "titanic_file_dataset = Dataset.File.from_files(path=[(def_blob_store, file_name)])\n", - "input2 = titanic_file_dataset.as_named_input(\"file_input\").as_hdfs()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Output config: the output will be registered as a File dataset\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.data import HDFSOutputDatasetConfig\n", - "output = HDFSOutputDatasetConfig(destination=(def_blob_store,\"test\")).register_on_complete(name=\"registered_dataset\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataprep script" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "os.makedirs(\"code\", exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile code/dataprep.py\n", - "import os\n", - "import sys\n", - "import azureml.core\n", - "from pyspark.sql import SparkSession\n", - "from azureml.core import Run, Dataset\n", - "\n", - "print(azureml.core.VERSION)\n", - "print(os.environ)\n", - "\n", - "import argparse\n", - "parser = argparse.ArgumentParser()\n", - "parser.add_argument(\"--tabular_input\")\n", - "parser.add_argument(\"--file_input\")\n", - "parser.add_argument(\"--output_dir\")\n", - "args = parser.parse_args()\n", - "\n", - "# use dataset sdk to read tabular dataset\n", - "run_context = Run.get_context()\n", - "dataset = Dataset.get_by_id(run_context.experiment.workspace,id=args.tabular_input)\n", - "sdf = dataset.to_spark_dataframe()\n", - "sdf.show()\n", - "\n", - "# use hdfs path to read file dataset\n", - "spark= SparkSession.builder.getOrCreate()\n", - "sdf = spark.read.option(\"header\", \"true\").csv(args.file_input)\n", - "sdf.show()\n", - "\n", - "sdf.coalesce(1).write\\\n", - ".option(\"header\", \"true\")\\\n", - ".mode(\"append\")\\\n", - ".csv(args.output_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up Conda dependency for the following Script Run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.environment import CondaDependencies\n", - "conda_dep = CondaDependencies()\n", - "conda_dep.add_pip_package(\"azureml-core==1.20.0\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## How to leverage ScriptRunConfig to submit an experiment run to an attached Synapse Spark cluster" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import RunConfiguration\n", - "from azureml.core import ScriptRunConfig \n", - "from azureml.core import Experiment\n", - "\n", - "run_config = RunConfiguration(framework=\"pyspark\")\n", - "run_config.target = synapse_compute_name\n", - "\n", - "run_config.spark.configuration[\"spark.driver.memory\"] = \"1g\" \n", - "run_config.spark.configuration[\"spark.driver.cores\"] = 2 \n", - "run_config.spark.configuration[\"spark.executor.memory\"] = \"1g\" \n", - "run_config.spark.configuration[\"spark.executor.cores\"] = 1 \n", - "run_config.spark.configuration[\"spark.executor.instances\"] = 1 \n", - "\n", - "run_config.environment.python.conda_dependencies = conda_dep\n", - "\n", - "script_run_config = ScriptRunConfig(source_directory = './code',\n", - " script= 'dataprep.py',\n", - " arguments = [\"--tabular_input\", input1, \n", - " \"--file_input\", input2,\n", - " \"--output_dir\", output],\n", - " run_config = run_config) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment \n", - "exp = Experiment(workspace=ws, name=\"synapse-spark\") \n", - "run = exp.submit(config=script_run_config) \n", - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## How to leverage SynapseSparkStep in an AML pipeline to orchestrate data prep step on Synapse Spark and training step on AzureML compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a name for your CPU cluster\n", - "cpu_cluster_name = \"cpucluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", - " max_nodes=1)\n", - " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - "\n", - "cpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile code/train.py\n", - "import glob\n", - "import os\n", - "import sys\n", - "from os import listdir\n", - "from os.path import isfile, join\n", - "\n", - "mypath = os.environ[\"step2_input\"]\n", - "files = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n", - "for file in files:\n", - " with open(join(mypath,file)) as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "titanic_tabular_dataset = Dataset.Tabular.from_delimited_files(path=[(def_blob_store, file_name)])\n", - "titanic_file_dataset = Dataset.File.from_files(path=[(def_blob_store, file_name)])\n", - "\n", - "step1_input1 = titanic_tabular_dataset.as_named_input(\"tabular_input\")\n", - "step1_input2 = titanic_file_dataset.as_named_input(\"file_input\").as_hdfs()\n", - "step1_output = HDFSOutputDatasetConfig(destination=(def_blob_store,\"test\")).register_on_complete(name=\"registered_dataset\")\n", - "\n", - "step2_input = step1_output.as_input(\"step2_input\").as_download()\n", - "\n", - "\n", - "from azureml.core.environment import Environment\n", - "env = Environment(name=\"myenv\")\n", - "env.python.conda_dependencies.add_pip_package(\"azureml-core==1.20.0\")\n", - "\n", - "step_1 = SynapseSparkStep(name = 'synapse-spark',\n", - " file = 'dataprep.py',\n", - " source_directory=\"./code\", \n", - " inputs=[step1_input1, step1_input2],\n", - " outputs=[step1_output],\n", - " arguments = [\"--tabular_input\", step1_input1, \n", - " \"--file_input\", step1_input2,\n", - " \"--output_dir\", step1_output],\n", - " compute_target = synapse_compute_name,\n", - " driver_memory = \"7g\",\n", - " driver_cores = 4,\n", - " executor_memory = \"7g\",\n", - " executor_cores = 2,\n", - " num_executors = 1,\n", - " environment = env)\n", - "\n", - "step_2 = PythonScriptStep(script_name=\"train.py\",\n", - " arguments=[step2_input],\n", - " inputs=[step2_input],\n", - " compute_target=cpu_cluster_name,\n", - " source_directory=\"./code\",\n", - " allow_reuse=False)\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[step_1, step_2])\n", - "pipeline_run = pipeline.submit('synapse-pipeline', regenerate_outputs=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "yunzhan" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "nteract": { - "version": "0.28.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb b/how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb deleted file mode 100644 index 1eebb8a45..000000000 --- a/how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb +++ /dev/null @@ -1,327 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/spark_session_on_synapse_spark_pool.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Interactive Spark Session on Synapse Spark Pool" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install package" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install -U \"azureml-synapse\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For JupyterLab, please additionally run:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!jupyter lab build --minimize=False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## PLEASE restart kernel and then refresh web page before starting spark session." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 0. How to leverage Spark Magic for interactive Spark experience" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2020-06-05T03:22:14.965395Z", - "iopub.status.busy": "2020-06-05T03:22:14.965395Z", - "iopub.status.idle": "2020-06-05T03:22:14.970398Z", - "shell.execute_reply": "2020-06-05T03:22:14.969397Z", - "shell.execute_reply.started": "2020-06-05T03:22:14.965395Z" - } - }, - "outputs": [], - "source": [ - "# show help\n", - "%synapse ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Start Synapse Session" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "synapse_compute_name=os.getenv(\"SYNAPSE_COMPUTE_NAME\", \"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# use Synapse compute linked to the Compute Instance's workspace with an aml envrionment.\n", - "# conda dependencies specified in the environment will be installed before the spark session started.\n", - "\n", - "%synapse start -c $synapse_compute_name -e AzureML-Minimal" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# use Synapse compute from anther workspace via its config file\n", - "\n", - "# %synapse start -c -f config.json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# use Synapse compute from anther workspace via subscription_id, resource_group and workspace_name\n", - "\n", - "# %synapse start -c -s -r -w " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# start a spark session with an AML environment, \n", - "# %synapse start -c -s -r -w -e AzureML-Minimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Data prepration\n", - "\n", - "Three types of datastore are supported in synapse spark, and you have two ways to load the data.\n", - "\n", - "\n", - "| Datastore Type | Data Acess |\n", - "|--------------------|-------------------------------|\n", - "| Blob | Credential |\n", - "| Adlsgen1 | Credential & Credential-less |\n", - "| Adlsgen2 | Credential & Credential-less |" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example 1: Data loading by HDFS path" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Read data from Blob**\n", - "\n", - "```python\n", - "# setup access key or sas token\n", - "\n", - "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.key..blob.core.windows.net\", \"\")\n", - "sc._jsc.hadoopConfiguration().set(\"fs.azure.sas...blob.core.windows.net\", \"sas token\")\n", - "\n", - "df = spark.read.parquet(\"wasbs://@.blob.core.windows.net/\")\n", - "```\n", - "\n", - "**Read data from Adlsgen1**\n", - "\n", - "```python\n", - "# setup service pricinpal which has access of the data\n", - "# If no data Credential is setup, the user identity will be used to do access control\n", - "\n", - "sc._jsc.hadoopConfiguration().set(\"fs.adl.account..oauth2.access.token.provider.type\",\"ClientCredential\")\n", - "sc._jsc.hadoopConfiguration().set(\"fs.adl.account..oauth2.client.id\", \"\")\n", - "sc._jsc.hadoopConfiguration().set(\"fs.adl.account..oauth2.credential\", \"\")\n", - "sc._jsc.hadoopConfiguration().set(\"fs.adl.account..oauth2.refresh.url\", \"https://login.microsoftonline.com//oauth2/token\")\n", - "\n", - "df = spark.read.csv(\"adl://.azuredatalakestore.net/\")\n", - "```\n", - "\n", - "**Read data from Adlsgen2**\n", - "\n", - "```python\n", - "# setup service pricinpal which has access of the data\n", - "# If no data Credential is setup, the user identity will be used to do access control\n", - "\n", - "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.auth.type..dfs.core.windows.net\",\"OAuth\")\n", - "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth.provider.type..dfs.core.windows.net\", \"org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider\")\n", - "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth2.client.id..dfs.core.windows.net\", \"\")\n", - "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth2.client.secret..dfs.core.windows.net\", \"\")\n", - "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth2.client.endpoint..dfs.core.windows.net\", \"https://login.microsoftonline.com//oauth2/token\")\n", - "\n", - "df = spark.read.csv(\"abfss://@.dfs.core.windows.net/\")\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2020-06-04T08:11:18.812276Z", - "iopub.status.busy": "2020-06-04T08:11:18.812276Z", - "iopub.status.idle": "2020-06-04T08:11:23.854526Z", - "shell.execute_reply": "2020-06-04T08:11:23.853525Z", - "shell.execute_reply.started": "2020-06-04T08:11:18.812276Z" - } - }, - "outputs": [], - "source": [ - "%%synapse\n", - "\n", - "from pyspark.sql.functions import col, desc\n", - "\n", - "df = spark.read.option(\"header\", \"true\").csv(\"wasbs://demo@dprepdata.blob.core.windows.net/Titanic.csv\")\n", - "df.filter(col('Survived') == 1).groupBy('Age').count().orderBy(desc('count')).show(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example 2: Data loading by AML Dataset\n", - "\n", - "You can create tabular data by following the [guidance](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets) and use to_spark_dataframe() to load the data.\n", - "\n", - "```text\n", - "%%synapse\n", - "\n", - "import azureml.core\n", - "print(azureml.core.VERSION)\n", - "\n", - "from azureml.core import Workspace, Dataset\n", - "ws = Workspace.get(name='', subscription_id='', resource_group='')\n", - "ds = Dataset.get_by_name(ws, \"\")\n", - "df = ds.to_spark_dataframe()\n", - "\n", - "# You can do more data transformation on spark dataframe\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Session Metadata\n", - "After session started, you can check the session's metadata, find the links to Synapse portal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%synapse meta" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Stop Session\n", - "When current session reach the status timeout, dead or any failure, you must explicitly stop it before start new one. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%synapse stop" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "yunzhan" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "nteract": { - "version": "0.28.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/azure-synapse/start_script.py b/how-to-use-azureml/azure-synapse/start_script.py deleted file mode 100644 index da5aa13e1..000000000 --- a/how-to-use-azureml/azure-synapse/start_script.py +++ /dev/null @@ -1,18 +0,0 @@ -from pyspark.sql import SparkSession - -import argparse -parser = argparse.ArgumentParser() -parser.add_argument("--input", default="") -parser.add_argument("--output", default="") - -args, unparsed = parser.parse_known_args() - -spark = SparkSession.builder.getOrCreate() -sc = spark.sparkContext - -arr = sc._gateway.new_array(sc._jvm.java.lang.String, 2) -arr[0] = args.input -arr[1] = args.output - -obj = sc._jvm.WordCount -obj.main(arr) diff --git a/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb b/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb index 6a8b15146..803450d9b 100644 --- a/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb +++ b/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb @@ -1,395 +1,395 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deploy Multiple Models as Webservice\n", - "\n", - "This example shows how to deploy a Webservice with multiple models in step-by-step fashion:\n", - "\n", - " 1. Register Models\n", - " 2. Deploy Models as Webservice" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register Models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, we will be using and registering two models. \n", - "\n", - "First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import joblib\n", - "import sklearn\n", - "\n", - "from sklearn.datasets import load_diabetes\n", - "from sklearn.linear_model import BayesianRidge, Ridge\n", - "\n", - "x, y = load_diabetes(return_X_y=True)\n", - "\n", - "first_model = Ridge().fit(x, y)\n", - "second_model = BayesianRidge().fit(x, y)\n", - "\n", - "joblib.dump(first_model, \"first_model.pkl\")\n", - "joblib.dump(second_model, \"second_model.pkl\")\n", - "\n", - "print(\"Trained models using scikit-learn {}.\".format(sklearn.__version__))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have our trained models locally, we will register them as Models with the names `my_first_model` and `my_second_model` in the workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "register model from file" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "my_model_1 = Model.register(model_path=\"first_model.pkl\",\n", - " model_name=\"my_first_model\",\n", - " workspace=ws)\n", - "\n", - "my_model_2 = Model.register(model_path=\"second_model.pkl\",\n", - " model_name=\"my_second_model\",\n", - " workspace=ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Write the Entry Script\n", - "Write the script that will be used to predict on your models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model.get_model_path()\n", - "\n", - "To get the paths of your models, use `Model.get_model_path(model_name, version=None, _workspace=None)` method. This method will find the path to a model using the name of the model registered under the workspace.\n", - "\n", - "In this example, we do not use the optional arguments `version` and `_workspace`.\n", - "\n", - "#### Using environment variable AZUREML_MODEL_DIR\n", - "\n", - "In other [examples](../deploy-to-cloud/score.py) with a single model deployment, we use the environment variable `AZUREML_MODEL_DIR` and model file name to get the model path. \n", - "\n", - "For single model deployments, this environment variable is the path to the model folder (`./azureml-models/$MODEL_NAME/$VERSION`). When we deploy multiple models, the environment variable is set to the folder containing all models (./azureml-models).\n", - "\n", - "If you're using multiple models and you know the versions of the models you deploy, you can use this method to get the model path:\n", - "\n", - "```python\n", - "# Construct the model path using the registered model name, version, and model file name\n", - "model_1_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'my_first_model', '1', 'first_model.pkl')\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import joblib\n", - "import json\n", - "import numpy as np\n", - "\n", - "from azureml.core.model import Model\n", - "\n", - "def init():\n", - " global model_1, model_2\n", - " # Here \"my_first_model\" is the name of the model registered under the workspace.\n", - " # This call will return the path to the .pkl file on the local disk.\n", - " model_1_path = Model.get_model_path(model_name='my_first_model')\n", - " model_2_path = Model.get_model_path(model_name='my_second_model')\n", - " \n", - " # Deserialize the model files back into scikit-learn models.\n", - " model_1 = joblib.load(model_1_path)\n", - " model_2 = joblib.load(model_2_path)\n", - "\n", - "# Note you can pass in multiple rows for scoring.\n", - "def run(raw_data):\n", - " try:\n", - " data = json.loads(raw_data)['data']\n", - " data = np.array(data)\n", - " \n", - " # Call predict() on each model\n", - " result_1 = model_1.predict(data)\n", - " result_2 = model_2.predict(data)\n", - "\n", - " # You can return any JSON-serializable value.\n", - " return {\"prediction1\": result_1.tolist(), \"prediction2\": result_2.tolist()}\n", - " except Exception as e:\n", - " result = str(e)\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Environment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment. Please note that your environment must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service.\n", - "\n", - "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "env = Environment(\"deploytocloudenv\")\n", - "env.python.conda_dependencies.add_pip_package(\"joblib\")\n", - "env.python.conda_dependencies.add_pip_package(\"numpy\")\n", - "env.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Inference Configuration\n", - "\n", - "There is now support for a source directory, you can upload an entire folder from your local machine as dependencies for the Webservice.\n", - "Note: in that case, environments's entry_script and file_path are relative paths to the source_directory path; myenv.docker.base_dockerfile is a string containing extra docker steps or contents of the docker file.\n", - "\n", - "Sample code for using a source directory:\n", - "\n", - "```python\n", - "from azureml.core.environment import Environment\n", - "from azureml.core.model import InferenceConfig\n", - "\n", - "myenv = Environment.from_conda_specification(name='myenv', file_path='env/myenv.yml')\n", - "\n", - "# explicitly set base_image to None when setting base_dockerfile\n", - "myenv.docker.base_image = None\n", - "# add extra docker commends to execute\n", - "myenv.docker.base_dockerfile = \"FROM ubuntu\\n RUN echo \\\"hello\\\"\"\n", - "\n", - "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n", - " entry_script=\"x/y/score.py\",\n", - " environment=myenv)\n", - "```\n", - "\n", - " - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n", - " - env.docker.base_dockerfile: any extra steps you want to inject into docker file\n", - " - source_directory: holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n", - " - entry_script: contains logic specific to initializing your model and running predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create image" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy Model as Webservice on Azure Container Instance\n", - "\n", - "Note that the service creation can take few minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "azuremlexception-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "\n", - "aci_service_name = \"aciservice-multimodel\"\n", - "\n", - "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n", - "\n", - "service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config, overwrite=True)\n", - "service.wait_for_deployment(True)\n", - "\n", - "print(service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Test web service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "test_sample = json.dumps({'data': x[0:2].tolist()})\n", - "\n", - "prediction = service.run(test_sample)\n", - "\n", - "print(prediction)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Delete ACI to clean up" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "deploy service", - "aci" - ] - }, - "outputs": [], - "source": [ - "service.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploy Multiple Models as Webservice\n", + "\n", + "This example shows how to deploy a Webservice with multiple models in step-by-step fashion:\n", + "\n", + " 1. Register Models\n", + " 2. Deploy Models as Webservice" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register Models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we will be using and registering two models. \n", + "\n", + "First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "import sklearn\n", + "\n", + "from sklearn.datasets import load_diabetes\n", + "from sklearn.linear_model import BayesianRidge, Ridge\n", + "\n", + "x, y = load_diabetes(return_X_y=True)\n", + "\n", + "first_model = Ridge().fit(x, y)\n", + "second_model = BayesianRidge().fit(x, y)\n", + "\n", + "joblib.dump(first_model, \"first_model.pkl\")\n", + "joblib.dump(second_model, \"second_model.pkl\")\n", + "\n", + "print(\"Trained models using scikit-learn {}.\".format(sklearn.__version__))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have our trained models locally, we will register them as Models with the names `my_first_model` and `my_second_model` in the workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "register model from file" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "my_model_1 = Model.register(model_path=\"first_model.pkl\",\n", + " model_name=\"my_first_model\",\n", + " workspace=ws)\n", + "\n", + "my_model_2 = Model.register(model_path=\"second_model.pkl\",\n", + " model_name=\"my_second_model\",\n", + " workspace=ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Write the Entry Script\n", + "Write the script that will be used to predict on your models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model.get_model_path()\n", + "\n", + "To get the paths of your models, use `Model.get_model_path(model_name, version=None, _workspace=None)` method. This method will find the path to a model using the name of the model registered under the workspace.\n", + "\n", + "In this example, we do not use the optional arguments `version` and `_workspace`.\n", + "\n", + "#### Using environment variable AZUREML_MODEL_DIR\n", + "\n", + "In other [examples](../deploy-to-cloud/score.py) with a single model deployment, we use the environment variable `AZUREML_MODEL_DIR` and model file name to get the model path. \n", + "\n", + "For single model deployments, this environment variable is the path to the model folder (`./azureml-models/$MODEL_NAME/$VERSION`). When we deploy multiple models, the environment variable is set to the folder containing all models (./azureml-models).\n", + "\n", + "If you're using multiple models and you know the versions of the models you deploy, you can use this method to get the model path:\n", + "\n", + "```python\n", + "# Construct the model path using the registered model name, version, and model file name\n", + "model_1_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'my_first_model', '1', 'first_model.pkl')\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import joblib\n", + "import json\n", + "import numpy as np\n", + "\n", + "from azureml.core.model import Model\n", + "\n", + "def init():\n", + " global model_1, model_2\n", + " # Here \"my_first_model\" is the name of the model registered under the workspace.\n", + " # This call will return the path to the .pkl file on the local disk.\n", + " model_1_path = Model.get_model_path(model_name='my_first_model')\n", + " model_2_path = Model.get_model_path(model_name='my_second_model')\n", + " \n", + " # Deserialize the model files back into scikit-learn models.\n", + " model_1 = joblib.load(model_1_path)\n", + " model_2 = joblib.load(model_2_path)\n", + "\n", + "# Note you can pass in multiple rows for scoring.\n", + "def run(raw_data):\n", + " try:\n", + " data = json.loads(raw_data)['data']\n", + " data = np.array(data)\n", + " \n", + " # Call predict() on each model\n", + " result_1 = model_1.predict(data)\n", + " result_2 = model_2.predict(data)\n", + "\n", + " # You can return any JSON-serializable value.\n", + " return {\"prediction1\": result_1.tolist(), \"prediction2\": result_2.tolist()}\n", + " except Exception as e:\n", + " result = str(e)\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Environment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment. Please note that your environment must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service.\n", + "\n", + "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "\n", + "env = Environment(\"deploytocloudenv\")\n", + "env.python.conda_dependencies.add_pip_package(\"joblib\")\n", + "env.python.conda_dependencies.add_pip_package(\"numpy\")\n", + "env.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Inference Configuration\n", + "\n", + "There is now support for a source directory, you can upload an entire folder from your local machine as dependencies for the Webservice.\n", + "Note: in that case, environments's entry_script and file_path are relative paths to the source_directory path; myenv.docker.base_dockerfile is a string containing extra docker steps or contents of the docker file.\n", + "\n", + "Sample code for using a source directory:\n", + "\n", + "```python\n", + "from azureml.core.environment import Environment\n", + "from azureml.core.model import InferenceConfig\n", + "\n", + "myenv = Environment.from_conda_specification(name='myenv', file_path='env/myenv.yml')\n", + "\n", + "# explicitly set base_image to None when setting base_dockerfile\n", + "myenv.docker.base_image = None\n", + "# add extra docker commends to execute\n", + "myenv.docker.base_dockerfile = \"FROM ubuntu\\n RUN echo \\\"hello\\\"\"\n", + "\n", + "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n", + " entry_script=\"x/y/score.py\",\n", + " environment=myenv)\n", + "```\n", + "\n", + " - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n", + " - env.docker.base_dockerfile: any extra steps you want to inject into docker file\n", + " - source_directory: holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n", + " - entry_script: contains logic specific to initializing your model and running predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create image" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "\n", + "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy Model as Webservice on Azure Container Instance\n", + "\n", + "Note that the service creation can take few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "azuremlexception-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.webservice import AciWebservice\n", + "\n", + "aci_service_name = \"aciservice-multimodel\"\n", + "\n", + "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n", + "\n", + "service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config, overwrite=True)\n", + "service.wait_for_deployment(True)\n", + "\n", + "print(service.state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Test web service" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "test_sample = json.dumps({'data': x[0:2].tolist()})\n", + "\n", + "prediction = service.run(test_sample)\n", + "\n", + "print(prediction)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Delete ACI to clean up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "deploy service", + "aci" + ] + }, + "outputs": [], + "source": [ + "service.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "jenns" + } ], - "metadata": { - "authors": [ - { - "name": "jenns" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb index 10380475e..955af3b51 100644 --- a/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb +++ b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb @@ -1,593 +1,593 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Register model and deploy as webservice in ACI\n", - "\n", - "Following this notebook, you will:\n", - "\n", - " - Learn how to register a model in your Azure Machine Learning Workspace.\n", - " - Deploy your model as a web service in an Azure Container Instance." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create a workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "\n", - "\n", - "# Check core SDK version number.\n", - "print('SDK version:', azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "\n", - "Create a [Workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create trained model\n", - "\n", - "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import joblib\n", - "\n", - "from sklearn.datasets import load_diabetes\n", - "from sklearn.linear_model import Ridge\n", - "\n", - "\n", - "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n", - "\n", - "model = Ridge().fit(dataset_x, dataset_y)\n", - "\n", - "joblib.dump(model, 'sklearn_regression_model.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register input and output datasets\n", - "\n", - "Here, you will register the data used to create the model in your workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "from azureml.core import Dataset\n", - "\n", - "\n", - "np.savetxt('features.csv', dataset_x, delimiter=',')\n", - "np.savetxt('labels.csv', dataset_y, delimiter=',')\n", - "\n", - "datastore = ws.get_default_datastore()\n", - "datastore.upload_files(files=['./features.csv', './labels.csv'],\n", - " target_path='sklearn_regression/',\n", - " overwrite=True)\n", - "\n", - "input_dataset = Dataset.Tabular.from_delimited_files(path=[(datastore, 'sklearn_regression/features.csv')])\n", - "output_dataset = Dataset.Tabular.from_delimited_files(path=[(datastore, 'sklearn_regression/labels.csv')])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register model\n", - "\n", - "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-).\n", - "\n", - "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace. Also, marking this model with the scikit-learn framework will simplify deploying it as a web service, as we'll see later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "register model from file", - "sample-model-register" - ] - }, - "outputs": [], - "source": [ - "import sklearn\n", - "\n", - "from azureml.core import Model\n", - "from azureml.core.resource_configuration import ResourceConfiguration\n", - "\n", - "\n", - "model = Model.register(workspace=ws,\n", - " model_name='my-sklearn-model', # Name of the registered model in your workspace.\n", - " model_path='./sklearn_regression_model.pkl', # Local file to upload and register as a model.\n", - " model_framework=Model.Framework.SCIKITLEARN, # Framework used to create the model.\n", - " model_framework_version=sklearn.__version__, # Version of scikit-learn used to create the model.\n", - " sample_input_dataset=input_dataset,\n", - " sample_output_dataset=output_dataset,\n", - " resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),\n", - " description='Ridge regression model to predict diabetes progression.',\n", - " tags={'area': 'diabetes', 'type': 'regression'})\n", - "\n", - "print('Name:', model.name)\n", - "print('Version:', model.version)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy model\n", - "\n", - "Deploy your model as a web service using [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). Web services take one or more models, load them in an environment, and run them on one of several supported deployment targets. For more information on all your options when deploying models, see the [next steps](#Next-steps) section at the end of this notebook.\n", - "\n", - "For this example, we will deploy your scikit-learn model to an Azure Container Instance (ACI)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use a default environment (for supported models)\n", - "\n", - "The Azure Machine Learning service provides a default environment for supported model frameworks, including scikit-learn, based on the metadata you provided when registering your model. This is the easiest way to deploy your model.\n", - "\n", - "Even when you deploy your model to ACI with a default environment you can still customize the deploy configuration (i.e. the number of cores and amount of memory made available for the deployment) using the [AciWebservice.deploy_configuration()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--). Look at the \"Use a custom environment\" section of this notebook for more information on deploy configuration.\n", - "\n", - "**Note**: This step can take several minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service_name = 'my-sklearn-service'\n", - "\n", - "service = Model.deploy(ws, service_name, [model], overwrite=True)\n", - "service.wait_for_deployment(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After your model is deployed, perform a call to the web service using [service.run()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#run-input-)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "\n", - "input_payload = json.dumps({\n", - " 'data': dataset_x[0:2].tolist(),\n", - " 'method': 'predict' # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n", - "})\n", - "\n", - "output = service.run(input_payload)\n", - "\n", - "print(output)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you are finished testing your service, clean up the deployment with [service.delete()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#delete--)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use a custom environment\n", - "\n", - "If you want more control over how your model is run, if it uses another framework, or if it has special runtime requirements, you can instead specify your own environment and scoring method. Custom environments can be used for any model you want to deploy.\n", - "\n", - "Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "\n", - "environment = Environment('my-sklearn-environment')\n", - "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", - " 'azureml-defaults',\n", - " 'inference-schema[numpy-support]',\n", - " 'joblib',\n", - " 'numpy',\n", - " 'scikit-learn=={}'.format(sklearn.__version__)\n", - "])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When using a custom environment, you must also provide Python code for initializing and running your model. An example script is included with this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open('score.py') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Deploy your model in the custom environment by providing an [InferenceConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py) object to [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). In this case we are also using the [AciWebservice.deploy_configuration()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--) method to generate a custom deploy configuration.\n", - "\n", - "**Note**: This step can take several minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "azuremlexception-remarks-sample", - "sample-aciwebservice-deploy-config" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.webservice import AciWebservice\n", - "\n", - "\n", - "service_name = 'my-custom-env-service'\n", - "\n", - "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", - "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n", - "\n", - "service = Model.deploy(workspace=ws,\n", - " name=service_name,\n", - " models=[model],\n", - " inference_config=inference_config,\n", - " deployment_config=aci_config,\n", - " overwrite=True)\n", - "service.wait_for_deployment(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After your model is deployed, make a call to the web service using [service.run()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#run-input-)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "input_payload = json.dumps({\n", - " 'data': dataset_x[0:2].tolist()\n", - "})\n", - "\n", - "output = service.run(input_payload)\n", - "\n", - "print(output)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you are finished testing your service, clean up the deployment with [service.delete()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#delete--)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model Profiling\n", - "\n", - "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n", - "\n", - "In order to profile your model you will need:\n", - "- a registered model\n", - "- an entry script\n", - "- an inference configuration\n", - "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n", - "\n", - "Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n", - "\n", - "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n", - "\n", - "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n", - "You can try get the dataset first to see if it's already registered." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Datastore\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.data import dataset_type_definitions\n", - "\n", - "dataset_name='diabetes_sample_request_data'\n", - "\n", - "dataset_registered = False\n", - "try:\n", - " sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n", - " dataset_registered = True\n", - "except:\n", - " print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n", - "\n", - "if not dataset_registered:\n", - " # create a string that can be utf-8 encoded and\n", - " # put in the body of the request\n", - " serialized_input_json = json.dumps({\n", - " 'data': [\n", - " [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n", - " -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n", - " ]\n", - " })\n", - " dataset_content = []\n", - " for i in range(100):\n", - " dataset_content.append(serialized_input_json)\n", - " dataset_content = '\\n'.join(dataset_content)\n", - " file_name = \"{}.txt\".format(dataset_name)\n", - " f = open(file_name, 'w')\n", - " f.write(dataset_content)\n", - " f.close()\n", - "\n", - " # upload the txt file created above to the Datastore and create a dataset from it\n", - " data_store = Datastore.get_default(ws)\n", - " data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n", - " datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n", - " sample_request_data = Dataset.Tabular.from_delimited_files(\n", - " datastore_path,\n", - " separator='\\n',\n", - " infer_column_types=True,\n", - " header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n", - " sample_request_data = sample_request_data.register(workspace=ws,\n", - " name=dataset_name,\n", - " create_new_version=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "\n", - "environment = Environment('my-sklearn-environment')\n", - "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", - " 'azureml-defaults',\n", - " 'inference-schema[numpy-support]',\n", - " 'joblib',\n", - " 'numpy',\n", - " 'scikit-learn=={}'.format(sklearn.__version__)\n", - "])\n", - "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", - "# if cpu and memory_in_gb parameters are not provided\n", - "# the model will be profiled on default configuration of\n", - "# 3.5CPU and 15GB memory\n", - "profile = Model.profile(ws,\n", - " 'rgrsn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n", - " [model],\n", - " inference_config,\n", - " input_dataset=sample_request_data,\n", - " cpu=1.0,\n", - " memory_in_gb=0.5)\n", - "\n", - "# profiling is a long running operation and may take up to 25 min\n", - "profile.wait_for_completion(True)\n", - "details = profile.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model packaging\n", - "\n", - "If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n", - "\n", - "You must include an Environment object in your inference configuration to use `Model.package()`.\n", - "\n", - "```python\n", - "package = Model.package(ws, [model], inference_config)\n", - "package.wait_for_creation(show_output=True) # Or show_output=False to hide the Docker build logs.\n", - "package.pull()\n", - "```\n", - "\n", - "Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n", - "\n", - "```python\n", - "package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n", - "package.wait_for_creation(show_output=True)\n", - "package.save(\"./local_context_dir\")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - " - To run a production-ready web service, see the [notebook on deployment to Azure Kubernetes Service](../production-deploy-to-aks/production-deploy-to-aks.ipynb).\n", - " - To run a local web service, see the [notebook on deployment to a local Docker container](../deploy-to-local/register-model-deploy-local.ipynb).\n", - " - For more information on datasets, see the [notebook on training with datasets](../../work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb).\n", - " - For more information on environments, see the [notebook on using environments](../../training/using-environments/using-environments.ipynb).\n", - " - For information on all the available deployment targets, see [“How and where to deploy models”](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#choose-a-compute-target)." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "vaidyas" - } - ], - "category": "deployment", - "compute": [ - "None" - ], - "datasets": [ - "Diabetes" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "Scikit-learn" - ], - "friendly_name": "Register model and deploy as webservice", - "index_order": 3, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" - }, - "star_tag": [ - "featured" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Register model and deploy as webservice in ACI\n", + "\n", + "Following this notebook, you will:\n", + "\n", + " - Learn how to register a model in your Azure Machine Learning Workspace.\n", + " - Deploy your model as a web service in an Azure Container Instance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create a workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "\n", + "\n", + "# Check core SDK version number.\n", + "print('SDK version:', azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize workspace\n", + "\n", + "Create a [Workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create trained model\n", + "\n", + "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "\n", + "from sklearn.datasets import load_diabetes\n", + "from sklearn.linear_model import Ridge\n", + "\n", + "\n", + "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n", + "\n", + "model = Ridge().fit(dataset_x, dataset_y)\n", + "\n", + "joblib.dump(model, 'sklearn_regression_model.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register input and output datasets\n", + "\n", + "Here, you will register the data used to create the model in your workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from azureml.core import Dataset\n", + "\n", + "\n", + "np.savetxt('features.csv', dataset_x, delimiter=',')\n", + "np.savetxt('labels.csv', dataset_y, delimiter=',')\n", + "\n", + "datastore = ws.get_default_datastore()\n", + "datastore.upload_files(files=['./features.csv', './labels.csv'],\n", + " target_path='sklearn_regression/',\n", + " overwrite=True)\n", + "\n", + "input_dataset = Dataset.Tabular.from_delimited_files(path=[(datastore, 'sklearn_regression/features.csv')])\n", + "output_dataset = Dataset.Tabular.from_delimited_files(path=[(datastore, 'sklearn_regression/labels.csv')])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register model\n", + "\n", + "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-).\n", + "\n", + "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace. Also, marking this model with the scikit-learn framework will simplify deploying it as a web service, as we'll see later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Deploy a model with Azure Machine Learning" + "register model from file", + "sample-model-register" + ] + }, + "outputs": [], + "source": [ + "import sklearn\n", + "\n", + "from azureml.core import Model\n", + "from azureml.core.resource_configuration import ResourceConfiguration\n", + "\n", + "\n", + "model = Model.register(workspace=ws,\n", + " model_name='my-sklearn-model', # Name of the registered model in your workspace.\n", + " model_path='./sklearn_regression_model.pkl', # Local file to upload and register as a model.\n", + " model_framework=Model.Framework.SCIKITLEARN, # Framework used to create the model.\n", + " model_framework_version=sklearn.__version__, # Version of scikit-learn used to create the model.\n", + " sample_input_dataset=input_dataset,\n", + " sample_output_dataset=output_dataset,\n", + " resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),\n", + " description='Ridge regression model to predict diabetes progression.',\n", + " tags={'area': 'diabetes', 'type': 'regression'})\n", + "\n", + "print('Name:', model.name)\n", + "print('Version:', model.version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy model\n", + "\n", + "Deploy your model as a web service using [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). Web services take one or more models, load them in an environment, and run them on one of several supported deployment targets. For more information on all your options when deploying models, see the [next steps](#Next-steps) section at the end of this notebook.\n", + "\n", + "For this example, we will deploy your scikit-learn model to an Azure Container Instance (ACI)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use a default environment (for supported models)\n", + "\n", + "The Azure Machine Learning service provides a default environment for supported model frameworks, including scikit-learn, based on the metadata you provided when registering your model. This is the easiest way to deploy your model.\n", + "\n", + "Even when you deploy your model to ACI with a default environment you can still customize the deploy configuration (i.e. the number of cores and amount of memory made available for the deployment) using the [AciWebservice.deploy_configuration()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--). Look at the \"Use a custom environment\" section of this notebook for more information on deploy configuration.\n", + "\n", + "**Note**: This step can take several minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "service_name = 'my-sklearn-service'\n", + "\n", + "service = Model.deploy(ws, service_name, [model], overwrite=True)\n", + "service.wait_for_deployment(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After your model is deployed, perform a call to the web service using [service.run()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#run-input-)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "\n", + "input_payload = json.dumps({\n", + " 'data': dataset_x[0:2].tolist(),\n", + " 'method': 'predict' # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n", + "})\n", + "\n", + "output = service.run(input_payload)\n", + "\n", + "print(output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you are finished testing your service, clean up the deployment with [service.delete()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#delete--)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use a custom environment\n", + "\n", + "If you want more control over how your model is run, if it uses another framework, or if it has special runtime requirements, you can instead specify your own environment and scoring method. Custom environments can be used for any model you want to deploy.\n", + "\n", + "Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "\n", + "environment = Environment('my-sklearn-environment')\n", + "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", + " 'azureml-defaults',\n", + " 'inference-schema[numpy-support]',\n", + " 'joblib',\n", + " 'numpy',\n", + " 'scikit-learn=={}'.format(sklearn.__version__)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When using a custom environment, you must also provide Python code for initializing and running your model. An example script is included with this notebook." + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('score.py') as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Deploy your model in the custom environment by providing an [InferenceConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py) object to [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). In this case we are also using the [AciWebservice.deploy_configuration()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--) method to generate a custom deploy configuration.\n", + "\n", + "**Note**: This step can take several minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "azuremlexception-remarks-sample", + "sample-aciwebservice-deploy-config" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "from azureml.core.webservice import AciWebservice\n", + "\n", + "\n", + "service_name = 'my-custom-env-service'\n", + "\n", + "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", + "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n", + "\n", + "service = Model.deploy(workspace=ws,\n", + " name=service_name,\n", + " models=[model],\n", + " inference_config=inference_config,\n", + " deployment_config=aci_config,\n", + " overwrite=True)\n", + "service.wait_for_deployment(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After your model is deployed, make a call to the web service using [service.run()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#run-input-)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_payload = json.dumps({\n", + " 'data': dataset_x[0:2].tolist()\n", + "})\n", + "\n", + "output = service.run(input_payload)\n", + "\n", + "print(output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you are finished testing your service, clean up the deployment with [service.delete()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#delete--)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Profiling\n", + "\n", + "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n", + "\n", + "In order to profile your model you will need:\n", + "- a registered model\n", + "- an entry script\n", + "- an inference configuration\n", + "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n", + "\n", + "Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n", + "\n", + "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n", + "\n", + "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n", + "You can try get the dataset first to see if it's already registered." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Datastore\n", + "from azureml.core.dataset import Dataset\n", + "from azureml.data import dataset_type_definitions\n", + "\n", + "dataset_name='diabetes_sample_request_data'\n", + "\n", + "dataset_registered = False\n", + "try:\n", + " sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n", + " dataset_registered = True\n", + "except:\n", + " print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n", + "\n", + "if not dataset_registered:\n", + " # create a string that can be utf-8 encoded and\n", + " # put in the body of the request\n", + " serialized_input_json = json.dumps({\n", + " 'data': [\n", + " [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n", + " -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n", + " ]\n", + " })\n", + " dataset_content = []\n", + " for i in range(100):\n", + " dataset_content.append(serialized_input_json)\n", + " dataset_content = '\\n'.join(dataset_content)\n", + " file_name = \"{}.txt\".format(dataset_name)\n", + " f = open(file_name, 'w')\n", + " f.write(dataset_content)\n", + " f.close()\n", + "\n", + " # upload the txt file created above to the Datastore and create a dataset from it\n", + " data_store = Datastore.get_default(ws)\n", + " data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n", + " datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n", + " sample_request_data = Dataset.Tabular.from_delimited_files(\n", + " datastore_path,\n", + " separator='\\n',\n", + " infer_column_types=True,\n", + " header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n", + " sample_request_data = sample_request_data.register(workspace=ws,\n", + " name=dataset_name,\n", + " create_new_version=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "\n", + "environment = Environment('my-sklearn-environment')\n", + "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", + " 'azureml-defaults',\n", + " 'inference-schema[numpy-support]',\n", + " 'joblib',\n", + " 'numpy',\n", + " 'scikit-learn=={}'.format(sklearn.__version__)\n", + "])\n", + "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", + "# if cpu and memory_in_gb parameters are not provided\n", + "# the model will be profiled on default configuration of\n", + "# 3.5CPU and 15GB memory\n", + "profile = Model.profile(ws,\n", + " 'rgrsn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n", + " [model],\n", + " inference_config,\n", + " input_dataset=sample_request_data,\n", + " cpu=1.0,\n", + " memory_in_gb=0.5)\n", + "\n", + "# profiling is a long running operation and may take up to 25 min\n", + "profile.wait_for_completion(True)\n", + "details = profile.get_details()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model packaging\n", + "\n", + "If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n", + "\n", + "You must include an Environment object in your inference configuration to use `Model.package()`.\n", + "\n", + "```python\n", + "package = Model.package(ws, [model], inference_config)\n", + "package.wait_for_creation(show_output=True) # Or show_output=False to hide the Docker build logs.\n", + "package.pull()\n", + "```\n", + "\n", + "Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n", + "\n", + "```python\n", + "package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n", + "package.wait_for_creation(show_output=True)\n", + "package.save(\"./local_context_dir\")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + " - To run a production-ready web service, see the [notebook on deployment to Azure Kubernetes Service](../production-deploy-to-aks/production-deploy-to-aks.ipynb).\n", + " - To run a local web service, see the [notebook on deployment to a local Docker container](../deploy-to-local/register-model-deploy-local.ipynb).\n", + " - For more information on datasets, see the [notebook on training with datasets](../../work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb).\n", + " - For more information on environments, see the [notebook on using environments](../../training/using-environments/using-environments.ipynb).\n", + " - For information on all the available deployment targets, see [“How and where to deploy models”](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#choose-a-compute-target)." + ] + } + ], + "metadata": { + "authors": [ + { + "name": "vaidyas" + } + ], + "category": "deployment", + "compute": [ + "None" + ], + "datasets": [ + "Diabetes" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "Scikit-learn" + ], + "friendly_name": "Register model and deploy as webservice", + "index_order": 3, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + }, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Deploy a model with Azure Machine Learning" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb index 9095363a7..28969b8f0 100644 --- a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb +++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb @@ -1,495 +1,495 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Register model and deploy locally with advanced usages\n", - "\n", - "This example shows how to deploy a web service in step-by-step fashion:\n", - "\n", - " 1. Register model\n", - " 2. Deploy the image as a web service in a local Docker container.\n", - " 3. Quickly test changes to your entry script by reloading the local service.\n", - " 4. Optionally, you can also make changes to model, conda or extra_docker_file_steps and update local service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create trained model\n", - "\n", - "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import joblib\n", - "\n", - "from sklearn.datasets import load_diabetes\n", - "from sklearn.linear_model import Ridge\n", - "\n", - "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n", - "\n", - "sk_model = Ridge().fit(dataset_x, dataset_y)\n", - "\n", - "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n", - "\n", - "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "register model from file", - "sample-model-register" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n", - " model_name=\"sklearn_regression_model\",\n", - " tags={'area': \"diabetes\", 'type': \"regression\"},\n", - " description=\"Ridge regression model to predict diabetes\",\n", - " workspace=ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Manage your dependencies in a folder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "source_directory = \"source_directory\"\n", - "\n", - "os.makedirs(source_directory, exist_ok=True)\n", - "os.makedirs(os.path.join(source_directory, \"x/y\"), exist_ok=True)\n", - "os.makedirs(os.path.join(source_directory, \"env\"), exist_ok=True)\n", - "os.makedirs(os.path.join(source_directory, \"dockerstep\"), exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Show `score.py`. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile source_directory/x/y/score.py\n", - "import joblib\n", - "import json\n", - "import numpy as np\n", - "import os\n", - "\n", - "from inference_schema.schema_decorators import input_schema, output_schema\n", - "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", - "\n", - "def init():\n", - " global model\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment. Join this path with the filename of the model file.\n", - " # It holds the path to the directory that contains the deployed model (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # If there are multiple models, this value is the path to the directory containing all deployed models (./azureml-models)\n", - " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", - " # Deserialize the model file back into a sklearn model.\n", - " model = joblib.load(model_path)\n", - "\n", - " global name\n", - " # Note here, the entire source directory from inference config gets added into image.\n", - " # Below is an example of how you can use any extra files in image.\n", - " with open('./source_directory/extradata.json') as json_file:\n", - " data = json.load(json_file)\n", - " name = data[\"people\"][0][\"name\"]\n", - "\n", - "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", - "output_sample = np.array([3726.995])\n", - "\n", - "@input_schema('data', NumpyParameterType(input_sample))\n", - "@output_schema(NumpyParameterType(output_sample))\n", - "def run(data):\n", - " try:\n", - " result = model.predict(data)\n", - " # You can return any JSON-serializable object.\n", - " return \"Hello \" + name + \" here is your result = \" + str(result)\n", - " except Exception as e:\n", - " error = str(e)\n", - " return error" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile source_directory/extradata.json\n", - "{\n", - " \"people\": [\n", - " {\n", - " \"website\": \"microsoft.com\", \n", - " \"from\": \"Seattle\", \n", - " \"name\": \"Mrudula\"\n", - " }\n", - " ]\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Inference Configuration\n", - "\n", - " - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n", - " - env.docker.base_dockerfile: any extra steps you want to inject into docker file\n", - " - source_directory: holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n", - " - entry_script: contains logic specific to initializing your model and running predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sklearn\n", - "\n", - "from azureml.core.environment import Environment\n", - "from azureml.core.model import InferenceConfig\n", - "\n", - "\n", - "myenv = Environment('myenv')\n", - "myenv.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n", - "myenv.python.conda_dependencies.add_pip_package(\"joblib\")\n", - "myenv.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))\n", - "\n", - "# explicitly set base_image to None when setting base_dockerfile\n", - "myenv.docker.base_image = None\n", - "myenv.docker.base_dockerfile = \"FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\\nRUN echo \\\"this is test\\\"\"\n", - "myenv.inferencing_stack_version = \"latest\"\n", - "\n", - "inference_config = InferenceConfig(source_directory=source_directory,\n", - " entry_script=\"x/y/score.py\",\n", - " environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy Model as a Local Docker Web Service\n", - "\n", - "*Make sure you have Docker installed and running.*\n", - "\n", - "Note that the service creation can take few minutes.\n", - "\n", - "NOTE:\n", - "\n", - "The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n", - "\n", - " # PowerShell command to switch to Linux engine\n", - " & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "deploy service", - "aci" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.webservice import LocalWebservice\n", - "\n", - "# This is optional, if not provided Docker will choose a random unused port.\n", - "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n", - "\n", - "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n", - "\n", - "local_service.wait_for_deployment()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Local service port: {}'.format(local_service.port))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check Status and Get Container Logs\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(local_service.get_logs())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test Web Service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the web service with some input data to get a prediction." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "sample_input = json.dumps({\n", - " 'data': dataset_x[0:2].tolist()\n", - "})\n", - "\n", - "print(local_service.run(sample_input))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reload Service\n", - "\n", - "You can update your score.py file and then call `reload()` to quickly restart the service. This will only reload your execution script and dependency files, it will not rebuild the underlying Docker image. As a result, `reload()` is fast, but if you do need to rebuild the image -- to add a new Conda or pip package, for instance -- you will have to call `update()`, instead (see below)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile source_directory/x/y/score.py\n", - "import joblib\n", - "import json\n", - "import numpy as np\n", - "import os\n", - "\n", - "from inference_schema.schema_decorators import input_schema, output_schema\n", - "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", - "\n", - "def init():\n", - " global model\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", - " # Deserialize the model file back into a sklearn model.\n", - " model = joblib.load(model_path)\n", - "\n", - " global name, from_location\n", - " # Note here, the entire source directory from inference config gets added into image.\n", - " # Below is an example of how you can use any extra files in image.\n", - " with open('source_directory/extradata.json') as json_file: \n", - " data = json.load(json_file)\n", - " name = data[\"people\"][0][\"name\"]\n", - " from_location = data[\"people\"][0][\"from\"]\n", - "\n", - "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", - "output_sample = np.array([3726.995])\n", - "\n", - "@input_schema('data', NumpyParameterType(input_sample))\n", - "@output_schema(NumpyParameterType(output_sample))\n", - "def run(data):\n", - " try:\n", - " result = model.predict(data)\n", - " # You can return any JSON-serializable object.\n", - " return \"Hello \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n", - " except Exception as e:\n", - " error = str(e)\n", - " return error" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_service.reload()\n", - "print(\"--------------------------------------------------------------\")\n", - "\n", - "# After calling reload(), run() will return the updated message.\n", - "local_service.run(sample_input)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Update Service\n", - "\n", - "If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n", - "\n", - "```python\n", - "\n", - "local_service.update(models=[SomeOtherModelObject],\n", - " deployment_config=local_config,\n", - " inference_config=inference_config)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete Service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_service.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Register model and deploy locally with advanced usages\n", + "\n", + "This example shows how to deploy a web service in step-by-step fashion:\n", + "\n", + " 1. Register model\n", + " 2. Deploy the image as a web service in a local Docker container.\n", + " 3. Quickly test changes to your entry script by reloading the local service.\n", + " 4. Optionally, you can also make changes to model, conda or extra_docker_file_steps and update local service" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create trained model\n", + "\n", + "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "\n", + "from sklearn.datasets import load_diabetes\n", + "from sklearn.linear_model import Ridge\n", + "\n", + "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n", + "\n", + "sk_model = Ridge().fit(dataset_x, dataset_y)\n", + "\n", + "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n", + "\n", + "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "register model from file", + "sample-model-register" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n", + " model_name=\"sklearn_regression_model\",\n", + " tags={'area': \"diabetes\", 'type': \"regression\"},\n", + " description=\"Ridge regression model to predict diabetes\",\n", + " workspace=ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Manage your dependencies in a folder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "source_directory = \"source_directory\"\n", + "\n", + "os.makedirs(source_directory, exist_ok=True)\n", + "os.makedirs(os.path.join(source_directory, \"x/y\"), exist_ok=True)\n", + "os.makedirs(os.path.join(source_directory, \"env\"), exist_ok=True)\n", + "os.makedirs(os.path.join(source_directory, \"dockerstep\"), exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show `score.py`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile source_directory/x/y/score.py\n", + "import joblib\n", + "import json\n", + "import numpy as np\n", + "import os\n", + "\n", + "from inference_schema.schema_decorators import input_schema, output_schema\n", + "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", + "\n", + "def init():\n", + " global model\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment. Join this path with the filename of the model file.\n", + " # It holds the path to the directory that contains the deployed model (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # If there are multiple models, this value is the path to the directory containing all deployed models (./azureml-models)\n", + " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", + " # Deserialize the model file back into a sklearn model.\n", + " model = joblib.load(model_path)\n", + "\n", + " global name\n", + " # Note here, the entire source directory from inference config gets added into image.\n", + " # Below is an example of how you can use any extra files in image.\n", + " with open('./source_directory/extradata.json') as json_file:\n", + " data = json.load(json_file)\n", + " name = data[\"people\"][0][\"name\"]\n", + "\n", + "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", + "output_sample = np.array([3726.995])\n", + "\n", + "@input_schema('data', NumpyParameterType(input_sample))\n", + "@output_schema(NumpyParameterType(output_sample))\n", + "def run(data):\n", + " try:\n", + " result = model.predict(data)\n", + " # You can return any JSON-serializable object.\n", + " return \"Hello \" + name + \" here is your result = \" + str(result)\n", + " except Exception as e:\n", + " error = str(e)\n", + " return error" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile source_directory/extradata.json\n", + "{\n", + " \"people\": [\n", + " {\n", + " \"website\": \"microsoft.com\", \n", + " \"from\": \"Seattle\", \n", + " \"name\": \"Mrudula\"\n", + " }\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Inference Configuration\n", + "\n", + " - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n", + " - env.docker.base_dockerfile: any extra steps you want to inject into docker file\n", + " - source_directory: holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n", + " - entry_script: contains logic specific to initializing your model and running predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn\n", + "\n", + "from azureml.core.environment import Environment\n", + "from azureml.core.model import InferenceConfig\n", + "\n", + "\n", + "myenv = Environment('myenv')\n", + "myenv.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n", + "myenv.python.conda_dependencies.add_pip_package(\"joblib\")\n", + "myenv.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))\n", + "\n", + "# explicitly set base_image to None when setting base_dockerfile\n", + "myenv.docker.base_image = None\n", + "myenv.docker.base_dockerfile = \"FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\\nRUN echo \\\"this is test\\\"\"\n", + "myenv.inferencing_stack_version = \"latest\"\n", + "\n", + "inference_config = InferenceConfig(source_directory=source_directory,\n", + " entry_script=\"x/y/score.py\",\n", + " environment=myenv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy Model as a Local Docker Web Service\n", + "\n", + "*Make sure you have Docker installed and running.*\n", + "\n", + "Note that the service creation can take few minutes.\n", + "\n", + "NOTE:\n", + "\n", + "The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n", + "\n", + " # PowerShell command to switch to Linux engine\n", + " & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "deploy service", + "aci" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.webservice import LocalWebservice\n", + "\n", + "# This is optional, if not provided Docker will choose a random unused port.\n", + "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n", + "\n", + "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n", + "\n", + "local_service.wait_for_deployment()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Local service port: {}'.format(local_service.port))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check Status and Get Container Logs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(local_service.get_logs())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test Web Service" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the web service with some input data to get a prediction." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "sample_input = json.dumps({\n", + " 'data': dataset_x[0:2].tolist()\n", + "})\n", + "\n", + "print(local_service.run(sample_input))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reload Service\n", + "\n", + "You can update your score.py file and then call `reload()` to quickly restart the service. This will only reload your execution script and dependency files, it will not rebuild the underlying Docker image. As a result, `reload()` is fast, but if you do need to rebuild the image -- to add a new Conda or pip package, for instance -- you will have to call `update()`, instead (see below)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile source_directory/x/y/score.py\n", + "import joblib\n", + "import json\n", + "import numpy as np\n", + "import os\n", + "\n", + "from inference_schema.schema_decorators import input_schema, output_schema\n", + "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", + "\n", + "def init():\n", + " global model\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", + " # Deserialize the model file back into a sklearn model.\n", + " model = joblib.load(model_path)\n", + "\n", + " global name, from_location\n", + " # Note here, the entire source directory from inference config gets added into image.\n", + " # Below is an example of how you can use any extra files in image.\n", + " with open('source_directory/extradata.json') as json_file: \n", + " data = json.load(json_file)\n", + " name = data[\"people\"][0][\"name\"]\n", + " from_location = data[\"people\"][0][\"from\"]\n", + "\n", + "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", + "output_sample = np.array([3726.995])\n", + "\n", + "@input_schema('data', NumpyParameterType(input_sample))\n", + "@output_schema(NumpyParameterType(output_sample))\n", + "def run(data):\n", + " try:\n", + " result = model.predict(data)\n", + " # You can return any JSON-serializable object.\n", + " return \"Hello \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n", + " except Exception as e:\n", + " error = str(e)\n", + " return error" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "local_service.reload()\n", + "print(\"--------------------------------------------------------------\")\n", + "\n", + "# After calling reload(), run() will return the updated message.\n", + "local_service.run(sample_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Update Service\n", + "\n", + "If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n", + "\n", + "```python\n", + "\n", + "local_service.update(models=[SomeOtherModelObject],\n", + " deployment_config=local_config,\n", + " inference_config=inference_config)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Delete Service" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "local_service.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "keriehm" + } ], - "metadata": { - "authors": [ - { - "name": "keriehm" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb index 33e5eadcf..08ceb498c 100644 --- a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb +++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb @@ -1,556 +1,556 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Register model and deploy locally\n", - "\n", - "This example shows how to deploy a web service in step-by-step fashion:\n", - "\n", - " 1. Register model\n", - " 2. Deploy the image as a web service in a local Docker container.\n", - " 3. Quickly test changes to your entry script by reloading the local service.\n", - " 4. Optionally, you can also make changes to model, conda or extra_docker_file_steps and update local service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create trained model\n", - "\n", - "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import joblib\n", - "\n", - "from sklearn.datasets import load_diabetes\n", - "from sklearn.linear_model import Ridge\n", - "\n", - "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n", - "\n", - "sk_model = Ridge().fit(dataset_x, dataset_y)\n", - "\n", - "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we are registering the serialized file `sklearn_regression_model.pkl` in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n", - "\n", - "You can add tags and descriptions to your models. Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "register model from file" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n", - " model_name=\"sklearn_regression_model\",\n", - " tags={'area': \"diabetes\", 'type': \"regression\"},\n", - " description=\"Ridge regression model to predict diabetes\",\n", - " workspace=ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Environment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sklearn\n", - "\n", - "from azureml.core.environment import Environment\n", - "\n", - "environment = Environment(\"LocalDeploy\")\n", - "environment.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n", - "environment.python.conda_dependencies.add_pip_package(\"joblib\")\n", - "environment.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Provide the Scoring Script\n", - "\n", - "This Python script handles the model execution inside the service container. The `init()` method loads the model file, and `run(data)` is called for every input to the service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import joblib\n", - "import json\n", - "import numpy as np\n", - "import os\n", - "\n", - "from inference_schema.schema_decorators import input_schema, output_schema\n", - "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", - "\n", - "def init():\n", - " global model\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", - " # Deserialize the model file back into a sklearn model.\n", - " model = joblib.load(model_path)\n", - "\n", - "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", - "output_sample = np.array([3726.995])\n", - "\n", - "@input_schema('data', NumpyParameterType(input_sample))\n", - "@output_schema(NumpyParameterType(output_sample))\n", - "def run(data):\n", - " try:\n", - " result = model.predict(data)\n", - " # You can return any JSON-serializable object.\n", - " return result.tolist()\n", - " except Exception as e:\n", - " error = str(e)\n", - " return error" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Inference Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "\n", - "inference_config = InferenceConfig(entry_script=\"score.py\",\n", - " environment=environment)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy Model as a Local Docker Web Service\n", - "\n", - "*Make sure you have Docker installed and running.*\n", - "\n", - "Note that the service creation can take few minutes.\n", - "\n", - "NOTE:\n", - "\n", - "The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n", - "\n", - " # PowerShell command to switch to Linux engine\n", - " & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-localwebservice-deploy" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.webservice import LocalWebservice\n", - "\n", - "# This is optional, if not provided Docker will choose a random unused port.\n", - "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n", - "\n", - "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n", - "\n", - "local_service.wait_for_deployment()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Local service port: {}'.format(local_service.port))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check Status and Get Container Logs\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(local_service.get_logs())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test Web Service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the web service with some input data to get a prediction." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "sample_input = json.dumps({\n", - " 'data': dataset_x[0:2].tolist()\n", - "})\n", - "\n", - "local_service.run(sample_input)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reload Service\n", - "\n", - "You can update your score.py file and then call `reload()` to quickly restart the service. This will only reload your execution script and dependency files, it will not rebuild the underlying Docker image. As a result, `reload()` is fast, but if you do need to rebuild the image -- to add a new Conda or pip package, for instance -- you will have to call `update()`, instead (see below)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import joblib\n", - "import json\n", - "import numpy as np\n", - "import os\n", - "\n", - "from inference_schema.schema_decorators import input_schema, output_schema\n", - "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", - "\n", - "def init():\n", - " global model\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", - " # Deserialize the model file back into a sklearn model.\n", - " model = joblib.load(model_path)\n", - "\n", - "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", - "output_sample = np.array([3726.995])\n", - "\n", - "@input_schema('data', NumpyParameterType(input_sample))\n", - "@output_schema(NumpyParameterType(output_sample))\n", - "def run(data):\n", - " try:\n", - " result = model.predict(data)\n", - " # You can return any JSON-serializable object.\n", - " return 'Hello from the updated score.py: ' + str(result.tolist())\n", - " except Exception as e:\n", - " error = str(e)\n", - " return error" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_service.reload()\n", - "print(\"--------------------------------------------------------------\")\n", - "\n", - "# After calling reload(), run() will return the updated message.\n", - "local_service.run(sample_input)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Update Service\n", - "\n", - "If you want to change your model(s), Conda dependencies or deployment configuration, call `update()` to rebuild the Docker image.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_service.update(models=[model],\n", - " inference_config=inference_config,\n", - " deployment_config=deployment_config)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy model to AKS cluster based on the LocalWebservice's configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This is a one time setup for AKS Cluster. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n", - "from azureml.core.compute import AksCompute, ComputeTarget\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your AKS cluster\n", - "aks_name = 'my-aks-9' \n", - "\n", - "# Verify the cluster does not exist already\n", - "try:\n", - " aks_target = ComputeTarget(workspace=ws, name=aks_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " # Use the default configuration (can also provide parameters to customize)\n", - " prov_config = AksCompute.provisioning_configuration()\n", - "\n", - " # Create the cluster\n", - " aks_target = ComputeTarget.create(workspace = ws, \n", - " name = aks_name, \n", - " provisioning_configuration = prov_config)\n", - "\n", - "if aks_target.get_status() != \"Succeeded\":\n", - " aks_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AksWebservice\n", - "# Set the web service configuration (using default here)\n", - "aks_config = AksWebservice.deploy_configuration()\n", - "\n", - "# # Enable token auth and disable (key) auth on the webservice\n", - "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "aks_service_name ='aks-service-1'\n", - "\n", - "aks_service = local_service.deploy_to_cloud(name=aks_service_name,\n", - " deployment_config=aks_config,\n", - " deployment_target=aks_target)\n", - "\n", - "aks_service.wait_for_deployment(show_output = True)\n", - "print(aks_service.state)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Test aks service\n", - "\n", - "sample_input = json.dumps({\n", - " 'data': dataset_x[0:2].tolist()\n", - "})\n", - "\n", - "aks_service.run(sample_input)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Delete the service if not needed.\n", - "aks_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete Service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_service.delete()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "keriehm" - } - ], - "category": "tutorial", - "compute": [ - "Local" - ], - "datasets": [ - "None" - ], - "deployment": [ - "Local" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Register a model and deploy locally", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - }, - "star_tag": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Register model and deploy locally\n", + "\n", + "This example shows how to deploy a web service in step-by-step fashion:\n", + "\n", + " 1. Register model\n", + " 2. Deploy the image as a web service in a local Docker container.\n", + " 3. Quickly test changes to your entry script by reloading the local service.\n", + " 4. Optionally, you can also make changes to model, conda or extra_docker_file_steps and update local service" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create trained model\n", + "\n", + "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "\n", + "from sklearn.datasets import load_diabetes\n", + "from sklearn.linear_model import Ridge\n", + "\n", + "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n", + "\n", + "sk_model = Ridge().fit(dataset_x, dataset_y)\n", + "\n", + "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we are registering the serialized file `sklearn_regression_model.pkl` in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n", + "\n", + "You can add tags and descriptions to your models. Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Deployment" + "register model from file" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n", + " model_name=\"sklearn_regression_model\",\n", + " tags={'area': \"diabetes\", 'type': \"regression\"},\n", + " description=\"Ridge regression model to predict diabetes\",\n", + " workspace=ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn\n", + "\n", + "from azureml.core.environment import Environment\n", + "\n", + "environment = Environment(\"LocalDeploy\")\n", + "environment.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n", + "environment.python.conda_dependencies.add_pip_package(\"joblib\")\n", + "environment.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Provide the Scoring Script\n", + "\n", + "This Python script handles the model execution inside the service container. The `init()` method loads the model file, and `run(data)` is called for every input to the service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import joblib\n", + "import json\n", + "import numpy as np\n", + "import os\n", + "\n", + "from inference_schema.schema_decorators import input_schema, output_schema\n", + "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", + "\n", + "def init():\n", + " global model\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", + " # Deserialize the model file back into a sklearn model.\n", + " model = joblib.load(model_path)\n", + "\n", + "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", + "output_sample = np.array([3726.995])\n", + "\n", + "@input_schema('data', NumpyParameterType(input_sample))\n", + "@output_schema(NumpyParameterType(output_sample))\n", + "def run(data):\n", + " try:\n", + " result = model.predict(data)\n", + " # You can return any JSON-serializable object.\n", + " return result.tolist()\n", + " except Exception as e:\n", + " error = str(e)\n", + " return error" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Inference Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "\n", + "inference_config = InferenceConfig(entry_script=\"score.py\",\n", + " environment=environment)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy Model as a Local Docker Web Service\n", + "\n", + "*Make sure you have Docker installed and running.*\n", + "\n", + "Note that the service creation can take few minutes.\n", + "\n", + "NOTE:\n", + "\n", + "The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n", + "\n", + " # PowerShell command to switch to Linux engine\n", + " & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "sample-localwebservice-deploy" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.webservice import LocalWebservice\n", + "\n", + "# This is optional, if not provided Docker will choose a random unused port.\n", + "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n", + "\n", + "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n", + "\n", + "local_service.wait_for_deployment()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Local service port: {}'.format(local_service.port))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check Status and Get Container Logs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(local_service.get_logs())" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test Web Service" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the web service with some input data to get a prediction." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "sample_input = json.dumps({\n", + " 'data': dataset_x[0:2].tolist()\n", + "})\n", + "\n", + "local_service.run(sample_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reload Service\n", + "\n", + "You can update your score.py file and then call `reload()` to quickly restart the service. This will only reload your execution script and dependency files, it will not rebuild the underlying Docker image. As a result, `reload()` is fast, but if you do need to rebuild the image -- to add a new Conda or pip package, for instance -- you will have to call `update()`, instead (see below)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import joblib\n", + "import json\n", + "import numpy as np\n", + "import os\n", + "\n", + "from inference_schema.schema_decorators import input_schema, output_schema\n", + "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n", + "\n", + "def init():\n", + " global model\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", + " # Deserialize the model file back into a sklearn model.\n", + " model = joblib.load(model_path)\n", + "\n", + "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n", + "output_sample = np.array([3726.995])\n", + "\n", + "@input_schema('data', NumpyParameterType(input_sample))\n", + "@output_schema(NumpyParameterType(output_sample))\n", + "def run(data):\n", + " try:\n", + " result = model.predict(data)\n", + " # You can return any JSON-serializable object.\n", + " return 'Hello from the updated score.py: ' + str(result.tolist())\n", + " except Exception as e:\n", + " error = str(e)\n", + " return error" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "local_service.reload()\n", + "print(\"--------------------------------------------------------------\")\n", + "\n", + "# After calling reload(), run() will return the updated message.\n", + "local_service.run(sample_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Update Service\n", + "\n", + "If you want to change your model(s), Conda dependencies or deployment configuration, call `update()` to rebuild the Docker image.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "local_service.update(models=[model],\n", + " inference_config=inference_config,\n", + " deployment_config=deployment_config)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy model to AKS cluster based on the LocalWebservice's configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This is a one time setup for AKS Cluster. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n", + "from azureml.core.compute import AksCompute, ComputeTarget\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your AKS cluster\n", + "aks_name = 'my-aks-9' \n", + "\n", + "# Verify the cluster does not exist already\n", + "try:\n", + " aks_target = ComputeTarget(workspace=ws, name=aks_name)\n", + " print('Found existing cluster, use it.')\n", + "except ComputeTargetException:\n", + " # Use the default configuration (can also provide parameters to customize)\n", + " prov_config = AksCompute.provisioning_configuration()\n", + "\n", + " # Create the cluster\n", + " aks_target = ComputeTarget.create(workspace = ws, \n", + " name = aks_name, \n", + " provisioning_configuration = prov_config)\n", + "\n", + "if aks_target.get_status() != \"Succeeded\":\n", + " aks_target.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.webservice import AksWebservice\n", + "# Set the web service configuration (using default here)\n", + "aks_config = AksWebservice.deploy_configuration()\n", + "\n", + "# # Enable token auth and disable (key) auth on the webservice\n", + "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "aks_service_name ='aks-service-1'\n", + "\n", + "aks_service = local_service.deploy_to_cloud(name=aks_service_name,\n", + " deployment_config=aks_config,\n", + " deployment_target=aks_target)\n", + "\n", + "aks_service.wait_for_deployment(show_output = True)\n", + "print(aks_service.state)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test aks service\n", + "\n", + "sample_input = json.dumps({\n", + " 'data': dataset_x[0:2].tolist()\n", + "})\n", + "\n", + "aks_service.run(sample_input)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the service if not needed.\n", + "aks_service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Delete Service" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "local_service.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "keriehm" + } + ], + "category": "tutorial", + "compute": [ + "Local" + ], + "datasets": [ + "None" + ], + "deployment": [ + "Local" + ], + "exclude_from_index": false, + "framework": [ + "None" + ], + "friendly_name": "Register a model and deploy locally", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + }, + "star_tag": [], + "tags": [ + "None" + ], + "task": "Deployment" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb b/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb index 25002c44d..6f97ddd75 100644 --- a/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb +++ b/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb @@ -1,371 +1,371 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deploy models to Azure Kubernetes Service (AKS) using controlled roll out\n", - "This notebook will show you how to deploy mulitple AKS webservices with the same scoring endpoint and how to roll out your models in a controlled manner by configuring % of scoring traffic going to each webservice. If you are using a Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create an Azure ML Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check for latest version\n", - "import azureml.core\n", - "print(azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Create a [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register the model\n", - "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-).\n", - "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Model\n", - "\n", - "model = Model.register(workspace=ws,\n", - " model_name='sklearn_regression_model.pkl', # Name of the registered model in your workspace.\n", - " model_path='./sklearn_regression_model.pkl', # Local file to upload and register as a model.\n", - " model_framework=Model.Framework.SCIKITLEARN, # Framework used to create the model.\n", - " model_framework_version='0.19.1', # Version of scikit-learn used to create the model.\n", - " description='Ridge regression model to predict diabetes progression.',\n", - " tags={'area': 'diabetes', 'type': 'regression'})\n", - "\n", - "print('Name:', model.name)\n", - "print('Version:', model.version)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register an environment (for all models)\n", - "\n", - "If you control over how your model is run, or if it has special runtime requirements, you can specify your own environment and scoring method.\n", - "\n", - "Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "environment=Environment('my-sklearn-environment')\n", - "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", - " 'azureml-defaults',\n", - " 'inference-schema[numpy-support]',\n", - " 'numpy',\n", - " 'scikit-learn==0.19.1',\n", - " 'scipy'\n", - "])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When using a custom environment, you must also provide Python code for initializing and running your model. An example script is included with this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open('score.py') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the InferenceConfig\n", - "Create the inference configuration to reference your environment and entry script during deployment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "\n", - "inference_config = InferenceConfig(entry_script='score.py', \n", - " source_directory='.',\n", - " environment=environment)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Provision the AKS Cluster\n", - "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import AksCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "# Use the default configuration (can also provide parameters to customize)\n", - "prov_config = AksCompute.provisioning_configuration()\n", - "\n", - "aks_name = 'my-aks' \n", - "# Create the cluster\n", - "aks_target = ComputeTarget.create(workspace = ws, \n", - " name = aks_name, \n", - " provisioning_configuration = prov_config) \n", - "aks_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an Endpoint and add a version (AKS service)\n", - "This creates a new endpoint and adds a version behind it. By default the first version added is the default version. You can specify the traffic percentile a version takes behind an endpoint. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# deploying the model and create a new endpoint\n", - "from azureml.core.webservice import AksEndpoint\n", - "# from azureml.core.compute import ComputeTarget\n", - "\n", - "#select a created compute\n", - "compute = ComputeTarget(ws, 'my-aks')\n", - "namespace_name=\"endpointnamespace\"\n", - "# define the endpoint name\n", - "endpoint_name = \"myendpoint1\"\n", - "# define the service name\n", - "version_name= \"versiona\"\n", - "\n", - "endpoint_deployment_config = AksEndpoint.deploy_configuration(tags = {'modelVersion':'firstversion', 'department':'finance'}, \n", - " description = \"my first version\", namespace = namespace_name, \n", - " version_name = version_name, traffic_percentile = 40)\n", - "\n", - "endpoint = Model.deploy(ws, endpoint_name, [model], inference_config, endpoint_deployment_config, compute)\n", - "endpoint.wait_for_deployment(True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint.get_logs()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add another version of the service to an existing endpoint\n", - "This adds another version behind an existing endpoint. You can specify the traffic percentile the new version takes. If no traffic_percentile is specified then it defaults to 0. All the unspecified traffic percentile (in this example 50) across all versions goes to default version." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding a new version to an existing Endpoint.\n", - "version_name_add=\"versionb\" \n", - "\n", - "endpoint.create_version(version_name = version_name_add, inference_config=inference_config, models=[model], tags = {'modelVersion':'secondversion', 'department':'finance'}, \n", - " description = \"my second version\", traffic_percentile = 10)\n", - "endpoint.wait_for_deployment(True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Update an existing version in an endpoint\n", - "There are two types of versions: control and treatment. An endpoint contains one or more treatment versions but only one control version. This categorization helps compare the different versions against the defined control version." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint.update_version(version_name=endpoint.versions[version_name_add].name, description=\"my second version update\", traffic_percentile=40, is_default=True, is_control_version_type=True)\n", - "endpoint.wait_for_deployment(True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the web service using run method\n", - "Test the web sevice by passing in data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Scoring on endpoint\n", - "import json\n", - "test_sample = json.dumps({'data': [\n", - " [1,2,3,4,5,6,7,8,9,10], \n", - " [10,9,8,7,6,5,4,3,2,1]\n", - "]})\n", - "\n", - "test_sample_encoded = bytes(test_sample, encoding='utf8')\n", - "prediction = endpoint.run(input_data=test_sample_encoded)\n", - "print(prediction)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete Resources" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# deleting a version in an endpoint\n", - "endpoint.delete_version(version_name=version_name)\n", - "endpoint.wait_for_deployment(True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# deleting an endpoint, this will delete all versions in the endpoint and the endpoint itself\n", - "endpoint.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploy models to Azure Kubernetes Service (AKS) using controlled roll out\n", + "This notebook will show you how to deploy mulitple AKS webservices with the same scoring endpoint and how to roll out your models in a controlled manner by configuring % of scoring traffic going to each webservice. If you are using a Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create an Azure ML Workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check for latest version\n", + "import azureml.core\n", + "print(azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize workspace\n", + "Create a [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.workspace import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register the model\n", + "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-).\n", + "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Model\n", + "\n", + "model = Model.register(workspace=ws,\n", + " model_name='sklearn_regression_model.pkl', # Name of the registered model in your workspace.\n", + " model_path='./sklearn_regression_model.pkl', # Local file to upload and register as a model.\n", + " model_framework=Model.Framework.SCIKITLEARN, # Framework used to create the model.\n", + " model_framework_version='0.19.1', # Version of scikit-learn used to create the model.\n", + " description='Ridge regression model to predict diabetes progression.',\n", + " tags={'area': 'diabetes', 'type': 'regression'})\n", + "\n", + "print('Name:', model.name)\n", + "print('Version:', model.version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register an environment (for all models)\n", + "\n", + "If you control over how your model is run, or if it has special runtime requirements, you can specify your own environment and scoring method.\n", + "\n", + "Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "environment=Environment('my-sklearn-environment')\n", + "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", + " 'azureml-defaults',\n", + " 'inference-schema[numpy-support]',\n", + " 'numpy',\n", + " 'scikit-learn==0.19.1',\n", + " 'scipy'\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When using a custom environment, you must also provide Python code for initializing and running your model. An example script is included with this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('score.py') as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the InferenceConfig\n", + "Create the inference configuration to reference your environment and entry script during deployment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "\n", + "inference_config = InferenceConfig(entry_script='score.py', \n", + " source_directory='.',\n", + " environment=environment)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Provision the AKS Cluster\n", + "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import AksCompute\n", + "from azureml.core.compute import ComputeTarget\n", + "# Use the default configuration (can also provide parameters to customize)\n", + "prov_config = AksCompute.provisioning_configuration()\n", + "\n", + "aks_name = 'my-aks' \n", + "# Create the cluster\n", + "aks_target = ComputeTarget.create(workspace = ws, \n", + " name = aks_name, \n", + " provisioning_configuration = prov_config) \n", + "aks_target.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an Endpoint and add a version (AKS service)\n", + "This creates a new endpoint and adds a version behind it. By default the first version added is the default version. You can specify the traffic percentile a version takes behind an endpoint. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# deploying the model and create a new endpoint\n", + "from azureml.core.webservice import AksEndpoint\n", + "# from azureml.core.compute import ComputeTarget\n", + "\n", + "#select a created compute\n", + "compute = ComputeTarget(ws, 'my-aks')\n", + "namespace_name=\"endpointnamespace\"\n", + "# define the endpoint name\n", + "endpoint_name = \"myendpoint1\"\n", + "# define the service name\n", + "version_name= \"versiona\"\n", + "\n", + "endpoint_deployment_config = AksEndpoint.deploy_configuration(tags = {'modelVersion':'firstversion', 'department':'finance'}, \n", + " description = \"my first version\", namespace = namespace_name, \n", + " version_name = version_name, traffic_percentile = 40)\n", + "\n", + "endpoint = Model.deploy(ws, endpoint_name, [model], inference_config, endpoint_deployment_config, compute)\n", + "endpoint.wait_for_deployment(True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "endpoint.get_logs()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add another version of the service to an existing endpoint\n", + "This adds another version behind an existing endpoint. You can specify the traffic percentile the new version takes. If no traffic_percentile is specified then it defaults to 0. All the unspecified traffic percentile (in this example 50) across all versions goes to default version." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding a new version to an existing Endpoint.\n", + "version_name_add=\"versionb\" \n", + "\n", + "endpoint.create_version(version_name = version_name_add, inference_config=inference_config, models=[model], tags = {'modelVersion':'secondversion', 'department':'finance'}, \n", + " description = \"my second version\", traffic_percentile = 10)\n", + "endpoint.wait_for_deployment(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Update an existing version in an endpoint\n", + "There are two types of versions: control and treatment. An endpoint contains one or more treatment versions but only one control version. This categorization helps compare the different versions against the defined control version." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "endpoint.update_version(version_name=endpoint.versions[version_name_add].name, description=\"my second version update\", traffic_percentile=40, is_default=True, is_control_version_type=True)\n", + "endpoint.wait_for_deployment(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test the web service using run method\n", + "Test the web sevice by passing in data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scoring on endpoint\n", + "import json\n", + "test_sample = json.dumps({'data': [\n", + " [1,2,3,4,5,6,7,8,9,10], \n", + " [10,9,8,7,6,5,4,3,2,1]\n", + "]})\n", + "\n", + "test_sample_encoded = bytes(test_sample, encoding='utf8')\n", + "prediction = endpoint.run(input_data=test_sample_encoded)\n", + "print(prediction)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Delete Resources" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# deleting a version in an endpoint\n", + "endpoint.delete_version(version_name=version_name)\n", + "endpoint.wait_for_deployment(True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# deleting an endpoint, this will delete all versions in the endpoint and the endpoint itself\n", + "endpoint.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "shipatel" + } + ], + "category": "deployment", + "compute": [ + "None" + ], + "datasets": [ + "Diabetes" + ], + "deployment": [ + "Azure Kubernetes Service" ], - "metadata": { - "authors": [ - { - "name": "shipatel" - } - ], - "category": "deployment", - "compute": [ - "None" - ], - "datasets": [ - "Diabetes" - ], - "deployment": [ - "Azure Kubernetes Service" - ], - "exclude_from_index": false, - "framework": [ - "Scikit-learn" - ], - "friendly_name": "Deploy models to AKS using controlled roll out", - "index_order": 3, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - }, - "star_tag": [ - "featured" - ], - "tags": [ - "None" - ], - "task": "Deploy a model with Azure Machine Learning" + "exclude_from_index": false, + "framework": [ + "Scikit-learn" + ], + "friendly_name": "Deploy models to AKS using controlled roll out", + "index_order": 3, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Deploy a model with Azure Machine Learning" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb index 9a06114f0..82b857e58 100644 --- a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb +++ b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb @@ -1,498 +1,498 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Enabling App Insights for Services in Production\n", - "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model.\n", - "\n", - "\n", - "## What does Application Insights monitor?\n", - "It monitors request rates, response times, failure rates, etc. For more information visit [App Insights docs.](https://docs.microsoft.com/en-us/azure/application-insights/app-insights-overview)\n", - "\n", - "\n", - "## What is different compared to standard production deployment process?\n", - "If you want to enable generic App Insights for a service run:\n", - "```python\n", - "aks_service= Webservice(ws, \"aks-w-dc2\")\n", - "aks_service.update(enable_app_insights=True)```\n", - "Where \"aks-w-dc2\" is your service name. You can also do this from the Azure Portal under your Workspace--> deployments--> Select deployment--> Edit--> Advanced Settings--> Select \"Enable AppInsights diagnostics\"\n", - "\n", - "If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n", - "1. Update scoring file.\n", - "2. Update aks configuration.\n", - "3. Deploy the model with this new configuration. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Import your dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "import json\n", - "\n", - "from azureml.core import Workspace\n", - "from azureml.core.compute import AksCompute, ComputeTarget\n", - "from azureml.core.webservice import AksWebservice\n", - "\n", - "print(azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Set up your configuration and create a workspace\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Register Model\n", - "Register an existing trained model, add descirption and tags." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Model\n", - "\n", - "model = Model.register(model_path=\"sklearn_regression_model.pkl\", # This points to a local file.\n", - " model_name=\"sklearn_regression_model.pkl\", # This is the name the model is registered as.\n", - " tags={'area': \"diabetes\", 'type': \"regression\"},\n", - " description=\"Ridge regression model to predict diabetes\",\n", - " workspace=ws)\n", - "\n", - "print(model.name, model.description, model.version)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. *Update your scoring file with custom print statements*\n", - "Here is an example:\n", - "### a. In your init function add:\n", - "```python\n", - "print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))```\n", - "\n", - "### b. In your run function add:\n", - "```python\n", - "print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import os\n", - "import pickle\n", - "import json\n", - "import numpy\n", - "from sklearn.externals import joblib\n", - "from sklearn.linear_model import Ridge\n", - "import time\n", - "\n", - "def init():\n", - " global model\n", - " #Print statement for appinsights custom traces:\n", - " print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n", - "\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", - "\n", - " # deserialize the model file back into a sklearn model\n", - " model = joblib.load(model_path)\n", - "\n", - "\n", - "# note you can pass in multiple rows for scoring\n", - "def run(raw_data):\n", - " try:\n", - " data = json.loads(raw_data)['data']\n", - " data = numpy.array(data)\n", - " result = model.predict(data)\n", - " print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))\n", - " # you can return any datatype as long as it is JSON-serializable\n", - " return result.tolist()\n", - " except Exception as e:\n", - " error = str(e)\n", - " print (error + time.strftime(\"%H:%M:%S\"))\n", - " return error" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. *Create myenv.yml file*\n", - "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.20.3'],\n", - " pip_packages=['azureml-defaults'])\n", - "\n", - "with open(\"myenv.yml\",\"w\") as f:\n", - " f.write(myenv.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Create Inference Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.environment import Environment\n", - "from azureml.core.model import InferenceConfig\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy to ACI (Optional)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "\n", - "aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores=1,\n", - " memory_gb=1,\n", - " tags={'area': \"diabetes\", 'type': \"regression\"},\n", - " description=\"Predict diabetes using regression model\",\n", - " enable_app_insights=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aci_service_name = \"aci-service-appinsights\"\n", - "\n", - "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config, overwrite=True)\n", - "aci_service.wait_for_deployment(show_output=True)\n", - "\n", - "print(aci_service.state)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if aci_service.state == \"Healthy\":\n", - " test_sample = json.dumps({\n", - " \"data\": [\n", - " [1,28,13,45,54,6,57,8,8,10],\n", - " [101,9,8,37,6,45,4,3,2,41]\n", - " ]\n", - " })\n", - "\n", - " prediction = aci_service.run(test_sample)\n", - "\n", - " print(prediction)\n", - "else:\n", - " raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aci_service.error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Deploy to AKS service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create AKS compute if you haven't done so.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AksCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "aks_name = \"my-aks-insights\"\n", - "\n", - "creating_compute = False\n", - "try:\n", - " aks_target = ComputeTarget(ws, aks_name)\n", - " print(\"Using existing AKS compute target {}.\".format(aks_name))\n", - "except ComputeTargetException:\n", - " print(\"Creating a new AKS compute target {}.\".format(aks_name))\n", - "\n", - " # Use the default configuration (can also provide parameters to customize).\n", - " prov_config = AksCompute.provisioning_configuration()\n", - " aks_target = ComputeTarget.create(workspace=ws,\n", - " name=aks_name,\n", - " provisioning_configuration=prov_config)\n", - " creating_compute = True" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "if creating_compute and aks_target.provisioning_state != \"Succeeded\":\n", - " aks_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(aks_target.provisioning_state)\n", - "print(aks_target.provisioning_errors)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you already have a cluster you can attach the service to it:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "%%time\n", - "resource_id = '/subscriptions//resourcegroups//providers/Microsoft.ContainerService/managedClusters/'\n", - "create_name= 'myaks4'\n", - "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n", - "aks_target = ComputeTarget.attach(workspace=ws,\n", - " name=create_name,\n", - " attach_configuration=attach_config)\n", - "## Wait for the operation to complete\n", - "aks_target.wait_for_provisioning(True)```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### a. *Activate App Insights through updating AKS Webservice configuration*\n", - "In order to enable App Insights in your service you will need to update your AKS configuration file:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the web service configuration.\n", - "aks_deployment_config = AksWebservice.deploy_configuration(enable_app_insights=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### b. Deploy your service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if aks_target.provisioning_state == \"Succeeded\":\n", - " aks_service_name = \"aks-service-appinsights\"\n", - " aks_service = Model.deploy(ws,\n", - " aks_service_name,\n", - " [model],\n", - " inference_config,\n", - " aks_deployment_config,\n", - " deployment_target=aks_target,\n", - " overwrite=True)\n", - " aks_service.wait_for_deployment(show_output=True)\n", - " print(aks_service.state)\n", - "else:\n", - " raise ValueError(\"AKS cluster provisioning failed. Error: \", aks_target.provisioning_errors)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 8. Test your service " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "\n", - "if aks_service.state == \"Healthy\":\n", - " test_sample = json.dumps({\n", - " \"data\": [\n", - " [1,28,13,45,54,6,57,8,8,10],\n", - " [101,9,8,37,6,45,4,3,2,41]\n", - " ]\n", - " })\n", - "\n", - " prediction = aks_service.run(input_data=test_sample)\n", - " print(prediction)\n", - "else:\n", - " raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aks_service.error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9. See your service telemetry in App Insights\n", - "1. Go to the [Azure Portal](https://portal.azure.com/)\n", - "2. All resources--> Select the subscription/resource group where you created your Workspace--> Select the App Insights type\n", - "3. Click on the AppInsights resource. You'll see a highlevel dashboard with information on Requests, Server response time and availability.\n", - "4. Click on the top banner \"Analytics\"\n", - "5. In the \"Schema\" section select \"traces\" and run your query.\n", - "6. Voila! All your custom traces should be there." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Disable App Insights" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aks_service.update(enable_app_insights=False)\n", - "aks_service.wait_for_deployment(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "aks_service.delete()\n", - "aci_service.delete()\n", - "model.delete()\n", - "if creating_compute:\n", - " aks_target.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Enabling App Insights for Services in Production\n", + "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model.\n", + "\n", + "\n", + "## What does Application Insights monitor?\n", + "It monitors request rates, response times, failure rates, etc. For more information visit [App Insights docs.](https://docs.microsoft.com/en-us/azure/application-insights/app-insights-overview)\n", + "\n", + "\n", + "## What is different compared to standard production deployment process?\n", + "If you want to enable generic App Insights for a service run:\n", + "```python\n", + "aks_service= Webservice(ws, \"aks-w-dc2\")\n", + "aks_service.update(enable_app_insights=True)```\n", + "Where \"aks-w-dc2\" is your service name. You can also do this from the Azure Portal under your Workspace--> deployments--> Select deployment--> Edit--> Advanced Settings--> Select \"Enable AppInsights diagnostics\"\n", + "\n", + "If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n", + "1. Update scoring file.\n", + "2. Update aks configuration.\n", + "3. Deploy the model with this new configuration. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Import your dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "import json\n", + "\n", + "from azureml.core import Workspace\n", + "from azureml.core.compute import AksCompute, ComputeTarget\n", + "from azureml.core.webservice import AksWebservice\n", + "\n", + "print(azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Set up your configuration and create a workspace\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Register Model\n", + "Register an existing trained model, add descirption and tags." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Model\n", + "\n", + "model = Model.register(model_path=\"sklearn_regression_model.pkl\", # This points to a local file.\n", + " model_name=\"sklearn_regression_model.pkl\", # This is the name the model is registered as.\n", + " tags={'area': \"diabetes\", 'type': \"regression\"},\n", + " description=\"Ridge regression model to predict diabetes\",\n", + " workspace=ws)\n", + "\n", + "print(model.name, model.description, model.version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. *Update your scoring file with custom print statements*\n", + "Here is an example:\n", + "### a. In your init function add:\n", + "```python\n", + "print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))```\n", + "\n", + "### b. In your run function add:\n", + "```python\n", + "print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import os\n", + "import pickle\n", + "import json\n", + "import numpy\n", + "from sklearn.externals import joblib\n", + "from sklearn.linear_model import Ridge\n", + "import time\n", + "\n", + "def init():\n", + " global model\n", + " #Print statement for appinsights custom traces:\n", + " print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n", + "\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", + "\n", + " # deserialize the model file back into a sklearn model\n", + " model = joblib.load(model_path)\n", + "\n", + "\n", + "# note you can pass in multiple rows for scoring\n", + "def run(raw_data):\n", + " try:\n", + " data = json.loads(raw_data)['data']\n", + " data = numpy.array(data)\n", + " result = model.predict(data)\n", + " print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))\n", + " # you can return any datatype as long as it is JSON-serializable\n", + " return result.tolist()\n", + " except Exception as e:\n", + " error = str(e)\n", + " print (error + time.strftime(\"%H:%M:%S\"))\n", + " return error" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. *Create myenv.yml file*\n", + "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.20.3'],\n", + " pip_packages=['azureml-defaults'])\n", + "\n", + "with open(\"myenv.yml\",\"w\") as f:\n", + " f.write(myenv.serialize_to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Create Inference Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.environment import Environment\n", + "from azureml.core.model import InferenceConfig\n", + "\n", + "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", + "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy to ACI (Optional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.webservice import AciWebservice\n", + "\n", + "aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores=1,\n", + " memory_gb=1,\n", + " tags={'area': \"diabetes\", 'type': \"regression\"},\n", + " description=\"Predict diabetes using regression model\",\n", + " enable_app_insights=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aci_service_name = \"aci-service-appinsights\"\n", + "\n", + "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config, overwrite=True)\n", + "aci_service.wait_for_deployment(show_output=True)\n", + "\n", + "print(aci_service.state)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if aci_service.state == \"Healthy\":\n", + " test_sample = json.dumps({\n", + " \"data\": [\n", + " [1,28,13,45,54,6,57,8,8,10],\n", + " [101,9,8,37,6,45,4,3,2,41]\n", + " ]\n", + " })\n", + "\n", + " prediction = aci_service.run(test_sample)\n", + "\n", + " print(prediction)\n", + "else:\n", + " raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aci_service.error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Deploy to AKS service" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create AKS compute if you haven't done so.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AksCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "aks_name = \"my-aks-insights\"\n", + "\n", + "creating_compute = False\n", + "try:\n", + " aks_target = ComputeTarget(ws, aks_name)\n", + " print(\"Using existing AKS compute target {}.\".format(aks_name))\n", + "except ComputeTargetException:\n", + " print(\"Creating a new AKS compute target {}.\".format(aks_name))\n", + "\n", + " # Use the default configuration (can also provide parameters to customize).\n", + " prov_config = AksCompute.provisioning_configuration()\n", + " aks_target = ComputeTarget.create(workspace=ws,\n", + " name=aks_name,\n", + " provisioning_configuration=prov_config)\n", + " creating_compute = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "if creating_compute and aks_target.provisioning_state != \"Succeeded\":\n", + " aks_target.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(aks_target.provisioning_state)\n", + "print(aks_target.provisioning_errors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you already have a cluster you can attach the service to it:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "%%time\n", + "resource_id = '/subscriptions//resourcegroups//providers/Microsoft.ContainerService/managedClusters/'\n", + "create_name= 'myaks4'\n", + "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n", + "aks_target = ComputeTarget.attach(workspace=ws,\n", + " name=create_name,\n", + " attach_configuration=attach_config)\n", + "## Wait for the operation to complete\n", + "aks_target.wait_for_provisioning(True)```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### a. *Activate App Insights through updating AKS Webservice configuration*\n", + "In order to enable App Insights in your service you will need to update your AKS configuration file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the web service configuration.\n", + "aks_deployment_config = AksWebservice.deploy_configuration(enable_app_insights=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### b. Deploy your service" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if aks_target.provisioning_state == \"Succeeded\":\n", + " aks_service_name = \"aks-service-appinsights\"\n", + " aks_service = Model.deploy(ws,\n", + " aks_service_name,\n", + " [model],\n", + " inference_config,\n", + " aks_deployment_config,\n", + " deployment_target=aks_target,\n", + " overwrite=True)\n", + " aks_service.wait_for_deployment(show_output=True)\n", + " print(aks_service.state)\n", + "else:\n", + " raise ValueError(\"AKS cluster provisioning failed. Error: \", aks_target.provisioning_errors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Test your service " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "if aks_service.state == \"Healthy\":\n", + " test_sample = json.dumps({\n", + " \"data\": [\n", + " [1,28,13,45,54,6,57,8,8,10],\n", + " [101,9,8,37,6,45,4,3,2,41]\n", + " ]\n", + " })\n", + "\n", + " prediction = aks_service.run(input_data=test_sample)\n", + " print(prediction)\n", + "else:\n", + " raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aks_service.error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. See your service telemetry in App Insights\n", + "1. Go to the [Azure Portal](https://portal.azure.com/)\n", + "2. All resources--> Select the subscription/resource group where you created your Workspace--> Select the App Insights type\n", + "3. Click on the AppInsights resource. You'll see a highlevel dashboard with information on Requests, Server response time and availability.\n", + "4. Click on the top banner \"Analytics\"\n", + "5. In the \"Schema\" section select \"traces\" and run your query.\n", + "6. Voila! All your custom traces should be there." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Disable App Insights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aks_service.update(enable_app_insights=False)\n", + "aks_service.wait_for_deployment(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clean up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "aks_service.delete()\n", + "aci_service.delete()\n", + "model.delete()\n", + "if creating_compute:\n", + " aks_target.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "gopalv" + } ], - "metadata": { - "authors": [ - { - "name": "gopalv" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb index bcf507019..562f119ab 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb @@ -1,434 +1,434 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# YOLO Real-time Object Detection using ONNX on AzureML\n", - "\n", - "This example shows how to convert the TinyYOLO model from CoreML to ONNX and operationalize it as a web service using Azure Machine Learning services and the ONNX Runtime.\n", - "\n", - "## What is ONNX\n", - "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n", - "\n", - "## YOLO Details\n", - "You Only Look Once (YOLO) is a state-of-the-art, real-time object detection system. For more information about YOLO, please visit the [YOLO website](https://pjreddie.com/darknet/yolo/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "To make the best use of your time, make sure you have done the following:\n", - "\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (config.json)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Install necessary packages\n", - "\n", - "You'll need to run the following commands to use this tutorial:\n", - "\n", - "```sh\n", - "pip install onnxmltools\n", - "pip install coremltools # use this on Linux and Mac\n", - "pip install git+https://github.com/apple/coremltools # use this on Windows\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert model to ONNX\n", - "\n", - "First we download the CoreML model. We use the CoreML model from [Matthijs Hollemans's tutorial](https://github.com/hollance/YOLO-CoreML-MPSNNGraph). This may take a few minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib.request\n", - "\n", - "coreml_model_url = \"https://github.com/hollance/YOLO-CoreML-MPSNNGraph/raw/master/TinyYOLO-CoreML/TinyYOLO-CoreML/TinyYOLO.mlmodel\"\n", - "urllib.request.urlretrieve(coreml_model_url, filename=\"TinyYOLO.mlmodel\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we use ONNXMLTools to convert the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import onnxmltools\n", - "import coremltools\n", - "\n", - "# Load a CoreML model\n", - "coreml_model = coremltools.utils.load_spec('TinyYOLO.mlmodel')\n", - "\n", - "# Convert from CoreML into ONNX\n", - "onnx_model = onnxmltools.convert_coreml(coreml_model, 'TinyYOLOv2')\n", - "\n", - "# Fix the preprocessor bias in the ImageScaler\n", - "for init in onnx_model.graph.initializer:\n", - " if init.name == 'scalerPreprocessor_bias':\n", - " init.dims[1] = 1\n", - "\n", - "# Save ONNX model\n", - "onnxmltools.utils.save_model(onnx_model, 'tinyyolov2.onnx')\n", - "\n", - "import os\n", - "print(os.path.getsize('tinyyolov2.onnx'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploying as a web service with Azure ML\n", - "\n", - "### Load Azure ML workspace\n", - "\n", - "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.location, ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Registering your model with Azure ML\n", - "\n", - "Now we upload the model and register it in the workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "model = Model.register(model_path = \"tinyyolov2.onnx\",\n", - " model_name = \"tinyyolov2\",\n", - " tags = {\"onnx\": \"demo\"},\n", - " description = \"TinyYOLO\",\n", - " workspace = ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Displaying your registered models\n", - "\n", - "You can optionally list out all the models that you have registered in this workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "models = ws.models\n", - "for name, m in models.items():\n", - " print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write scoring file\n", - "\n", - "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import json\n", - "import time\n", - "import sys\n", - "import os\n", - "from azureml.core.model import Model\n", - "import numpy as np # we're going to use numpy to process input and output data\n", - "import onnxruntime # to inference ONNX models, we use the ONNX Runtime\n", - "\n", - "def init():\n", - " global session\n", - " model = Model.get_model_path(model_name = 'tinyyolov2')\n", - " session = onnxruntime.InferenceSession(model)\n", - "\n", - "def preprocess(input_data_json):\n", - " # convert the JSON data into the tensor input\n", - " return np.array(json.loads(input_data_json)['data']).astype('float32')\n", - "\n", - "def postprocess(result):\n", - " return np.array(result).tolist()\n", - "\n", - "def run(input_data_json):\n", - " try:\n", - " start = time.time() # start timer\n", - " input_data = preprocess(input_data_json)\n", - " input_name = session.get_inputs()[0].name # get the id of the first input of the model \n", - " result = session.run([], {input_name: input_data})\n", - " end = time.time() # stop timer\n", - " return {\"result\": postprocess(result),\n", - " \"time\": end - start}\n", - " except Exception as e:\n", - " result = str(e)\n", - " return {\"error\": result}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting up inference configuration\n", - "First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.conda_dependencies import CondaDependencies \n", - "\n", - "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n", - "\n", - "with open(\"myenv.yml\",\"w\") as f:\n", - " f.write(myenv.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we create the inference configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.environment import Environment\n", - "\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", - " memory_gb = 1, \n", - " tags = {'demo': 'onnx'}, \n", - " description = 'web service for TinyYOLO ONNX model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cell will take a few minutes to run as the model gets packaged up and deployed to ACI." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aci_service_name = 'my-aci-service-tiny-yolo'\n", - "print(\"Service\", aci_service_name)\n", - "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", - "aci_service.wait_for_deployment(True)\n", - "print(aci_service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if aci_service.state != 'Healthy':\n", - " # run this command for debugging.\n", - " print(aci_service.get_logs())\n", - " aci_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Success!\n", - "\n", - "If you've made it this far, you've deployed a working web service that does object detection using an ONNX model. You can get the URL for the webservice with the code below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(aci_service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you are eventually done using the web service, remember to delete it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aci_service.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# YOLO Real-time Object Detection using ONNX on AzureML\n", + "\n", + "This example shows how to convert the TinyYOLO model from CoreML to ONNX and operationalize it as a web service using Azure Machine Learning services and the ONNX Runtime.\n", + "\n", + "## What is ONNX\n", + "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n", + "\n", + "## YOLO Details\n", + "You Only Look Once (YOLO) is a state-of-the-art, real-time object detection system. For more information about YOLO, please visit the [YOLO website](https://pjreddie.com/darknet/yolo/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "To make the best use of your time, make sure you have done the following:\n", + "\n", + "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", + "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook to:\n", + " * install the AML SDK\n", + " * create a workspace and its configuration file (config.json)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Install necessary packages\n", + "\n", + "You'll need to run the following commands to use this tutorial:\n", + "\n", + "```sh\n", + "pip install onnxmltools\n", + "pip install coremltools # use this on Linux and Mac\n", + "pip install git+https://github.com/apple/coremltools # use this on Windows\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert model to ONNX\n", + "\n", + "First we download the CoreML model. We use the CoreML model from [Matthijs Hollemans's tutorial](https://github.com/hollance/YOLO-CoreML-MPSNNGraph). This may take a few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import urllib.request\n", + "\n", + "coreml_model_url = \"https://github.com/hollance/YOLO-CoreML-MPSNNGraph/raw/master/TinyYOLO-CoreML/TinyYOLO-CoreML/TinyYOLO.mlmodel\"\n", + "urllib.request.urlretrieve(coreml_model_url, filename=\"TinyYOLO.mlmodel\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we use ONNXMLTools to convert the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import onnxmltools\n", + "import coremltools\n", + "\n", + "# Load a CoreML model\n", + "coreml_model = coremltools.utils.load_spec('TinyYOLO.mlmodel')\n", + "\n", + "# Convert from CoreML into ONNX\n", + "onnx_model = onnxmltools.convert_coreml(coreml_model, 'TinyYOLOv2')\n", + "\n", + "# Fix the preprocessor bias in the ImageScaler\n", + "for init in onnx_model.graph.initializer:\n", + " if init.name == 'scalerPreprocessor_bias':\n", + " init.dims[1] = 1\n", + "\n", + "# Save ONNX model\n", + "onnxmltools.utils.save_model(onnx_model, 'tinyyolov2.onnx')\n", + "\n", + "import os\n", + "print(os.path.getsize('tinyyolov2.onnx'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploying as a web service with Azure ML\n", + "\n", + "### Load Azure ML workspace\n", + "\n", + "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.location, ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Registering your model with Azure ML\n", + "\n", + "Now we upload the model and register it in the workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "model = Model.register(model_path = \"tinyyolov2.onnx\",\n", + " model_name = \"tinyyolov2\",\n", + " tags = {\"onnx\": \"demo\"},\n", + " description = \"TinyYOLO\",\n", + " workspace = ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Displaying your registered models\n", + "\n", + "You can optionally list out all the models that you have registered in this workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "models = ws.models\n", + "for name, m in models.items():\n", + " print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write scoring file\n", + "\n", + "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import json\n", + "import time\n", + "import sys\n", + "import os\n", + "from azureml.core.model import Model\n", + "import numpy as np # we're going to use numpy to process input and output data\n", + "import onnxruntime # to inference ONNX models, we use the ONNX Runtime\n", + "\n", + "def init():\n", + " global session\n", + " model = Model.get_model_path(model_name = 'tinyyolov2')\n", + " session = onnxruntime.InferenceSession(model)\n", + "\n", + "def preprocess(input_data_json):\n", + " # convert the JSON data into the tensor input\n", + " return np.array(json.loads(input_data_json)['data']).astype('float32')\n", + "\n", + "def postprocess(result):\n", + " return np.array(result).tolist()\n", + "\n", + "def run(input_data_json):\n", + " try:\n", + " start = time.time() # start timer\n", + " input_data = preprocess(input_data_json)\n", + " input_name = session.get_inputs()[0].name # get the id of the first input of the model \n", + " result = session.run([], {input_name: input_data})\n", + " end = time.time() # stop timer\n", + " return {\"result\": postprocess(result),\n", + " \"time\": end - start}\n", + " except Exception as e:\n", + " result = str(e)\n", + " return {\"error\": result}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting up inference configuration\n", + "First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.conda_dependencies import CondaDependencies \n", + "\n", + "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n", + "\n", + "with open(\"myenv.yml\",\"w\") as f:\n", + " f.write(myenv.serialize_to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we create the inference configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "from azureml.core.environment import Environment\n", + "\n", + "\n", + "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", + "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.webservice import AciWebservice\n", + "\n", + "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", + " memory_gb = 1, \n", + " tags = {'demo': 'onnx'}, \n", + " description = 'web service for TinyYOLO ONNX model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell will take a few minutes to run as the model gets packaged up and deployed to ACI." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aci_service_name = 'my-aci-service-tiny-yolo'\n", + "print(\"Service\", aci_service_name)\n", + "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", + "aci_service.wait_for_deployment(True)\n", + "print(aci_service.state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if aci_service.state != 'Healthy':\n", + " # run this command for debugging.\n", + " print(aci_service.get_logs())\n", + " aci_service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Success!\n", + "\n", + "If you've made it this far, you've deployed a working web service that does object detection using an ONNX model. You can get the URL for the webservice with the code below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(aci_service.scoring_uri)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you are eventually done using the web service, remember to delete it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aci_service.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "viswamy" + } + ], + "category": "deployment", + "compute": [ + "local" + ], + "datasets": [ + "PASCAL VOC" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "ONNX" + ], + "friendly_name": "Convert and deploy TinyYolo with ONNX Runtime", + "index_order": 5, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + }, + "star_tag": [ + "featured" + ], + "tags": [ + "ONNX Converter" ], - "metadata": { - "authors": [ - { - "name": "viswamy" - } - ], - "category": "deployment", - "compute": [ - "local" - ], - "datasets": [ - "PASCAL VOC" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "ONNX" - ], - "friendly_name": "Convert and deploy TinyYolo with ONNX Runtime", - "index_order": 5, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "star_tag": [ - "featured" - ], - "tags": [ - "ONNX Converter" - ], - "task": "Object Detection" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "task": "Object Detection" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb index 3b37c55ec..ee0970f84 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb @@ -1,800 +1,800 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Facial Expression Recognition (FER+) using ONNX Runtime on Azure ML\n", - "\n", - "This example shows how to deploy an image classification neural network using the Facial Expression Recognition ([FER](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. This tutorial will show you how to deploy a FER+ model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n", - "\n", - "Throughout this tutorial, we will be referring to ONNX, a neural network exchange format used to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools (CNTK, PyTorch, Caffe, MXNet, TensorFlow) and choose the combination that is best for them. ONNX is developed and supported by a community of partners including Microsoft AI, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai) and [open source files](https://github.com/onnx).\n", - "\n", - "[ONNX Runtime](https://aka.ms/onnxruntime-python) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization. We use the CPU version of ONNX Runtime in this tutorial, but will soon be releasing an additional tutorial for deploying this model using ONNX Runtime GPU.\n", - "\n", - "#### Tutorial Objectives:\n", - "\n", - "1. Describe the FER+ dataset and pretrained Convolutional Neural Net ONNX model for Emotion Recognition, stored in the ONNX model zoo.\n", - "2. Deploy and run the pretrained FER+ ONNX model on an Azure Machine Learning instance\n", - "3. Predict labels for test set data points in the cloud using ONNX Runtime and Azure ML" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "### 1. Install Azure ML SDK and create a new workspace\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n", - "\n", - "### 2. Install additional packages needed for this Notebook\n", - "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n", - "\n", - "```sh\n", - "(myenv) $ pip install matplotlib onnx opencv-python\n", - "```\n", - "\n", - "**Debugging tip**: Make sure that to activate your virtual environment (myenv) before you re-launch this notebook using the `jupyter notebook` comand. Choose the respective Python kernel for your new virtual environment using the `Kernel > Change Kernel` menu above. If you have completed the steps correctly, the upper right corner of your screen should state `Python [conda env:myenv]` instead of `Python [default]`.\n", - "\n", - "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n", - "\n", - "In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# urllib is a built-in Python library to download files from URLs\n", - "\n", - "# Objective: retrieve the latest version of the ONNX Emotion FER+ model files from the\n", - "# ONNX Model Zoo and save it in the same folder as this tutorial\n", - "\n", - "import urllib.request\n", - "\n", - "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n", - "\n", - "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion-ferplus-7.tar.gz\")\n", - "\n", - "# the ! magic command tells our jupyter notebook kernel to run the following line of \n", - "# code from the command line instead of the notebook kernel\n", - "\n", - "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n", - "\n", - "!tar xvzf emotion-ferplus-7.tar.gz" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy a VM with your ONNX model in the Cloud\n", - "\n", - "### Load Azure ML workspace\n", - "\n", - "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.location, ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Registering your model with Azure ML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_dir = \"emotion_ferplus\" # replace this with the location of your model files\n", - "\n", - "# leave as is if it's in the same folder as this notebook" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "model = Model.register(model_path = model_dir + \"/\" + \"model.onnx\",\n", - " model_name = \"onnx_emotion\",\n", - " tags = {\"onnx\": \"demo\"},\n", - " description = \"FER+ emotion recognition CNN from ONNX Model Zoo\",\n", - " workspace = ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optional: Displaying your registered models\n", - "\n", - "This step is not required, so feel free to skip it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "models = ws.models\n", - "for name, m in models.items():\n", - " print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ONNX FER+ Model Methodology\n", - "\n", - "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) in the ONNX model zoo.\n", - "\n", - "The original Facial Emotion Recognition (FER) Dataset was released in 2013 by Pierre-Luc Carrier and Aaron Courville as part of a [Kaggle Competition](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data), but some of the labels are not entirely appropriate for the expression. In the FER+ Dataset, each photo was evaluated by at least 10 croud sourced reviewers, creating a more accurate basis for ground truth. \n", - "\n", - "You can see the difference of label quality in the sample model input below. The FER labels are the first word below each image, and the FER+ labels are the second word below each image.\n", - "\n", - "![](https://raw.githubusercontent.com/Microsoft/FERPlus/master/FER+vsFER.png)\n", - "\n", - "***Input: Photos of cropped faces from FER+ Dataset***\n", - "\n", - "***Task: Classify each facial image into its appropriate emotions in the emotion table***\n", - "\n", - "``` emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, 'anger':4, 'disgust':5, 'fear':6, 'contempt':7} ```\n", - "\n", - "***Output: Emotion prediction for input image***\n", - "\n", - "\n", - "Remember, once the application is deployed in Azure ML, you can use your own images as input for the model to classify." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# for images and plots in this notebook\n", - "import matplotlib.pyplot as plt \n", - "\n", - "# display images inline\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model Description\n", - "\n", - "The FER+ model from the ONNX Model Zoo is summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image from Barsoum et. al's paper [\"Training Deep Networks for Facial Expression Recognition\n", - "with Crowd-Sourced Label Distribution\"](https://arxiv.org/pdf/1608.01041.pdf), with our (64 x 64) input images and our output probabilities for each of the labels." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](https://raw.githubusercontent.com/vinitra/FERPlus/master/emotion_model_img.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Specify our Score and Environment Files" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file. You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n", - "\n", - "### Write Score File\n", - "\n", - "A score file is what tells our Azure cloud service what to do. After initializing our model using azureml.core.model, we start an ONNX Runtime inference session to evaluate the data passed in on our function calls." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import json\n", - "import numpy as np\n", - "import onnxruntime\n", - "import sys\n", - "import os\n", - "import time\n", - "\n", - "def init():\n", - " global session, input_name, output_name\n", - " model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.onnx')\n", - " session = onnxruntime.InferenceSession(model, None)\n", - " input_name = session.get_inputs()[0].name\n", - " output_name = session.get_outputs()[0].name \n", - " \n", - "def run(input_data):\n", - " '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n", - " We will call the run function later from our Jupyter Notebook \n", - " so our azure service can evaluate our model input in the cloud. '''\n", - "\n", - " try:\n", - " # load in our data, convert to readable format\n", - " data = np.array(json.loads(input_data)['data']).astype('float32')\n", - " \n", - " start = time.time()\n", - " r = session.run([output_name], {input_name : data})\n", - " end = time.time()\n", - " \n", - " result = emotion_map(postprocess(r[0]))\n", - " \n", - " result_dict = {\"result\": result,\n", - " \"time_in_sec\": [end - start]}\n", - " except Exception as e:\n", - " result_dict = {\"error\": str(e)}\n", - " \n", - " return json.dumps(result_dict)\n", - "\n", - "def emotion_map(classes, N=1):\n", - " \"\"\"Take the most probable labels (output of postprocess) and returns the \n", - " top N emotional labels that fit the picture.\"\"\"\n", - " \n", - " emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, \n", - " 'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n", - " \n", - " emotion_keys = list(emotion_table.keys())\n", - " emotions = []\n", - " for i in range(N):\n", - " emotions.append(emotion_keys[classes[i]])\n", - " return emotions\n", - "\n", - "def softmax(x):\n", - " \"\"\"Compute softmax values (probabilities from 0 to 1) for each possible label.\"\"\"\n", - " x = x.reshape(-1)\n", - " e_x = np.exp(x - np.max(x))\n", - " return e_x / e_x.sum(axis=0)\n", - "\n", - "def postprocess(scores):\n", - " \"\"\"This function takes the scores generated by the network and \n", - " returns the class IDs in decreasing order of probability.\"\"\"\n", - " prob = softmax(scores)\n", - " prob = np.squeeze(prob)\n", - " classes = np.argsort(prob)[::-1]\n", - " return classes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write Environment File\n", - "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.conda_dependencies import CondaDependencies \n", - "\n", - "\n", - "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n", - "\n", - "with open(\"myenv.yml\",\"w\") as f:\n", - " f.write(myenv.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup inference configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.environment import Environment\n", - "\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", - " memory_gb = 1, \n", - " tags = {'demo': 'onnx'}, \n", - " description = 'ONNX for emotion recognition model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cell will likely take a few minutes to run as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aci_service_name = 'onnx-demo-emotion'\n", - "print(\"Service\", aci_service_name)\n", - "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", - "aci_service.wait_for_deployment(True)\n", - "print(aci_service.state)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if aci_service.state != 'Healthy':\n", - " # run this command for debugging.\n", - " print(aci_service.get_logs())\n", - "\n", - " # If your deployment fails, make sure to delete your aci_service before trying again!\n", - " # aci_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Success!\n", - "\n", - "If you've made it this far, you've deployed a working VM with a facial emotion recognition model running in the cloud using Azure ML. Congratulations!\n", - "\n", - "Let's see how well our model deals with our test images." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Testing and Evaluation\n", - "\n", - "### Useful Helper Functions\n", - "\n", - "We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def emotion_map(classes, N=1):\n", - " \"\"\"Take the most probable labels (output of postprocess) and returns the \n", - " top N emotional labels that fit the picture.\"\"\"\n", - " \n", - " emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, \n", - " 'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n", - " \n", - " emotion_keys = list(emotion_table.keys())\n", - " emotions = []\n", - " for c in range(N):\n", - " emotions.append(emotion_keys[classes[c]])\n", - " return emotions\n", - "\n", - "def softmax(x):\n", - " \"\"\"Compute softmax values (probabilities from 0 to 1) for each possible label.\"\"\"\n", - " x = x.reshape(-1)\n", - " e_x = np.exp(x - np.max(x))\n", - " return e_x / e_x.sum(axis=0)\n", - "\n", - "def postprocess(scores):\n", - " \"\"\"This function takes the scores generated by the network and \n", - " returns the class IDs in decreasing order of probability.\"\"\"\n", - " prob = softmax(scores)\n", - " prob = np.squeeze(prob)\n", - " classes = np.argsort(prob)[::-1]\n", - " return classes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load Test Data\n", - "\n", - "These are already in your directory from your ONNX model download (from the model zoo).\n", - "\n", - "Notice that our Model Zoo files have a .pb extension. This is because they are [protobuf files (Protocol Buffers)](https://developers.google.com/protocol-buffers/docs/pythontutorial), so we need to read in our data through our ONNX TensorProto reader into a format we can work with, like numerical arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# to manipulate our arrays\n", - "import numpy as np \n", - "\n", - "# read in test data protobuf files included with the model\n", - "import onnx\n", - "from onnx import numpy_helper\n", - "\n", - "# to use parsers to read in our model/data\n", - "import json\n", - "import os\n", - "\n", - "test_inputs = []\n", - "test_outputs = []\n", - "\n", - "# read in 3 testing images from .pb files\n", - "test_data_size = 3\n", - "\n", - "for num in np.arange(test_data_size):\n", - " input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(num), 'input_0.pb')\n", - " output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(num), 'output_0.pb')\n", - " \n", - " # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n", - " tensor = onnx.TensorProto()\n", - " with open(input_test_data, 'rb') as f:\n", - " tensor.ParseFromString(f.read())\n", - " \n", - " input_data = numpy_helper.to_array(tensor)\n", - " test_inputs.append(input_data)\n", - " \n", - " with open(output_test_data, 'rb') as f:\n", - " tensor.ParseFromString(f.read())\n", - " \n", - " output_data = numpy_helper.to_array(tensor)\n", - " output_processed = emotion_map(postprocess(output_data[0]))[0]\n", - " test_outputs.append(output_processed)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea" - } - }, - "source": [ - "### Show some sample images\n", - "We use `matplotlib` to plot 3 test images from the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "396d478b-34aa-4afa-9898-cdce8222a516" - } - }, - "outputs": [], - "source": [ - "plt.figure(figsize = (20, 20))\n", - "for test_image in np.arange(3):\n", - " test_inputs[test_image].reshape(1, 64, 64)\n", - " plt.subplot(1, 8, test_image+1)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.text(x = 10, y = -10, s = test_outputs[test_image], fontsize = 18)\n", - " plt.imshow(test_inputs[test_image].reshape(64, 64), cmap = plt.cm.gray)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run evaluation / prediction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize = (16, 6))\n", - "plt.subplot(1, 8, 1)\n", - "\n", - "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n", - "plt.text(x = 0, y = -20, s = \"Result: \", fontsize = 13, color = 'black')\n", - "plt.text(x = 0, y = -10, s = \"Inference Time: \", fontsize = 13, color = 'black')\n", - "plt.text(x = 3, y = 14, s = \"Model Input\", fontsize = 12, color = 'black')\n", - "plt.text(x = 6, y = 18, s = \"(64 x 64)\", fontsize = 12, color = 'black')\n", - "plt.imshow(np.ones((28,28)), cmap=plt.cm.Greys) \n", - "\n", - "\n", - "for i in np.arange(test_data_size):\n", - " \n", - " input_data = json.dumps({'data': test_inputs[i].tolist()})\n", - "\n", - " # predict using the deployed model\n", - " r = json.loads(aci_service.run(input_data))\n", - " \n", - " if \"error\" in r:\n", - " print(r['error'])\n", - " break\n", - " \n", - " result = r['result'][0]\n", - " time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n", - " \n", - " ground_truth = test_outputs[i]\n", - " \n", - " # compare actual value vs. the predicted values:\n", - " plt.subplot(1, 8, i+2)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - "\n", - " # use different color for misclassified sample\n", - " font_color = 'red' if ground_truth != result else 'black'\n", - " clr_map = plt.cm.Greys if ground_truth != result else plt.cm.gray\n", - "\n", - " # ground truth labels are in blue\n", - " plt.text(x = 10, y = -70, s = ground_truth, fontsize = 18, color = 'blue')\n", - " \n", - " # predictions are in black if correct, red if incorrect\n", - " plt.text(x = 10, y = -45, s = result, fontsize = 18, color = font_color)\n", - " plt.text(x = 5, y = -22, s = str(time_ms) + ' ms', fontsize = 14, color = font_color)\n", - "\n", - " \n", - " plt.imshow(test_inputs[i].reshape(64, 64), cmap = clr_map)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Try classifying your own images!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Preprocessing functions take your image and format it so it can be passed\n", - "# as input into our ONNX model\n", - "\n", - "import cv2\n", - "\n", - "def rgb2gray(rgb):\n", - " \"\"\"Convert the input image into grayscale\"\"\"\n", - " return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n", - "\n", - "def resize_img(img_to_resize):\n", - " \"\"\"Resize image to FER+ model input dimensions\"\"\"\n", - " r_img = cv2.resize(img_to_resize, dsize=(64, 64), interpolation=cv2.INTER_AREA)\n", - " r_img.resize((1, 1, 64, 64))\n", - " return r_img\n", - "\n", - "def preprocess(img_to_preprocess):\n", - " \"\"\"Resize input images and convert them to grayscale.\"\"\"\n", - " if img_to_preprocess.shape == (64, 64):\n", - " img_to_preprocess.resize((1, 1, 64, 64))\n", - " return img_to_preprocess\n", - " \n", - " grayscale = rgb2gray(img_to_preprocess)\n", - " processed_img = resize_img(grayscale)\n", - " return processed_img" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Replace the following string with your own path/test image\n", - "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n", - "\n", - "# Any PNG or JPG image file should work\n", - "# Make sure to include the entire path with // instead of /\n", - "\n", - "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/face.png\"\n", - "\n", - "your_test_image = \"\"\n", - "\n", - "import matplotlib.image as mpimg\n", - "\n", - "if your_test_image != \"\":\n", - " img = mpimg.imread(your_test_image)\n", - " plt.subplot(1,3,1)\n", - " plt.imshow(img, cmap = plt.cm.Greys)\n", - " print(\"Old Dimensions: \", img.shape)\n", - " img = preprocess(img)\n", - " print(\"New Dimensions: \", img.shape)\n", - "else:\n", - " img = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if img is None:\n", - " print(\"Add the path for your image data.\")\n", - "else:\n", - " input_data = json.dumps({'data': img.tolist()})\n", - "\n", - " try:\n", - " r = json.loads(aci_service.run(input_data))\n", - " result = r['result'][0]\n", - " time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n", - " except KeyError as e:\n", - " print(str(e))\n", - "\n", - " plt.figure(figsize = (16, 6))\n", - " plt.subplot(1,8,1)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.text(x = -10, y = -40, s = \"Model prediction: \", fontsize = 14)\n", - " plt.text(x = -10, y = -25, s = \"Inference time: \", fontsize = 14)\n", - " plt.text(x = 100, y = -40, s = str(result), fontsize = 14)\n", - " plt.text(x = 100, y = -25, s = str(time_ms) + \" ms\", fontsize = 14)\n", - " plt.text(x = -10, y = -10, s = \"Model Input image: \", fontsize = 14)\n", - " plt.imshow(img.reshape((64, 64)), cmap = plt.cm.gray) \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# remember to delete your service after you are done using it!\n", - "\n", - "aci_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "Congratulations!\n", - "\n", - "In this tutorial, you have:\n", - "- familiarized yourself with ONNX Runtime inference and the pretrained models in the ONNX model zoo\n", - "- understood a state-of-the-art convolutional neural net image classification model (FER+ in ONNX) and deployed it in the Azure ML cloud\n", - "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n", - "\n", - "Next steps:\n", - "- If you have not already, check out another interesting ONNX/AML application that lets you set up a state-of-the-art [handwritten image classification model (MNIST)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model for handwritten digit classification in an Azure ML virtual machine.\n", - "- Keep an eye out for an updated version of this tutorial that uses ONNX Runtime GPU.\n", - "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Facial Expression Recognition (FER+) using ONNX Runtime on Azure ML\n", + "\n", + "This example shows how to deploy an image classification neural network using the Facial Expression Recognition ([FER](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. This tutorial will show you how to deploy a FER+ model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n", + "\n", + "Throughout this tutorial, we will be referring to ONNX, a neural network exchange format used to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools (CNTK, PyTorch, Caffe, MXNet, TensorFlow) and choose the combination that is best for them. ONNX is developed and supported by a community of partners including Microsoft AI, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai) and [open source files](https://github.com/onnx).\n", + "\n", + "[ONNX Runtime](https://aka.ms/onnxruntime-python) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization. We use the CPU version of ONNX Runtime in this tutorial, but will soon be releasing an additional tutorial for deploying this model using ONNX Runtime GPU.\n", + "\n", + "#### Tutorial Objectives:\n", + "\n", + "1. Describe the FER+ dataset and pretrained Convolutional Neural Net ONNX model for Emotion Recognition, stored in the ONNX model zoo.\n", + "2. Deploy and run the pretrained FER+ ONNX model on an Azure Machine Learning instance\n", + "3. Predict labels for test set data points in the cloud using ONNX Runtime and Azure ML" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "### 1. Install Azure ML SDK and create a new workspace\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n", + "\n", + "### 2. Install additional packages needed for this Notebook\n", + "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n", + "\n", + "```sh\n", + "(myenv) $ pip install matplotlib onnx opencv-python\n", + "```\n", + "\n", + "**Debugging tip**: Make sure that to activate your virtual environment (myenv) before you re-launch this notebook using the `jupyter notebook` comand. Choose the respective Python kernel for your new virtual environment using the `Kernel > Change Kernel` menu above. If you have completed the steps correctly, the upper right corner of your screen should state `Python [conda env:myenv]` instead of `Python [default]`.\n", + "\n", + "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n", + "\n", + "In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# urllib is a built-in Python library to download files from URLs\n", + "\n", + "# Objective: retrieve the latest version of the ONNX Emotion FER+ model files from the\n", + "# ONNX Model Zoo and save it in the same folder as this tutorial\n", + "\n", + "import urllib.request\n", + "\n", + "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n", + "\n", + "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion-ferplus-7.tar.gz\")\n", + "\n", + "# the ! magic command tells our jupyter notebook kernel to run the following line of \n", + "# code from the command line instead of the notebook kernel\n", + "\n", + "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n", + "\n", + "!tar xvzf emotion-ferplus-7.tar.gz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy a VM with your ONNX model in the Cloud\n", + "\n", + "### Load Azure ML workspace\n", + "\n", + "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.location, ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Registering your model with Azure ML" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_dir = \"emotion_ferplus\" # replace this with the location of your model files\n", + "\n", + "# leave as is if it's in the same folder as this notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "model = Model.register(model_path = model_dir + \"/\" + \"model.onnx\",\n", + " model_name = \"onnx_emotion\",\n", + " tags = {\"onnx\": \"demo\"},\n", + " description = \"FER+ emotion recognition CNN from ONNX Model Zoo\",\n", + " workspace = ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional: Displaying your registered models\n", + "\n", + "This step is not required, so feel free to skip it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "models = ws.models\n", + "for name, m in models.items():\n", + " print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ONNX FER+ Model Methodology\n", + "\n", + "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) in the ONNX model zoo.\n", + "\n", + "The original Facial Emotion Recognition (FER) Dataset was released in 2013 by Pierre-Luc Carrier and Aaron Courville as part of a [Kaggle Competition](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data), but some of the labels are not entirely appropriate for the expression. In the FER+ Dataset, each photo was evaluated by at least 10 croud sourced reviewers, creating a more accurate basis for ground truth. \n", + "\n", + "You can see the difference of label quality in the sample model input below. The FER labels are the first word below each image, and the FER+ labels are the second word below each image.\n", + "\n", + "![](https://raw.githubusercontent.com/Microsoft/FERPlus/master/FER+vsFER.png)\n", + "\n", + "***Input: Photos of cropped faces from FER+ Dataset***\n", + "\n", + "***Task: Classify each facial image into its appropriate emotions in the emotion table***\n", + "\n", + "``` emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, 'anger':4, 'disgust':5, 'fear':6, 'contempt':7} ```\n", + "\n", + "***Output: Emotion prediction for input image***\n", + "\n", + "\n", + "Remember, once the application is deployed in Azure ML, you can use your own images as input for the model to classify." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# for images and plots in this notebook\n", + "import matplotlib.pyplot as plt \n", + "\n", + "# display images inline\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Description\n", + "\n", + "The FER+ model from the ONNX Model Zoo is summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image from Barsoum et. al's paper [\"Training Deep Networks for Facial Expression Recognition\n", + "with Crowd-Sourced Label Distribution\"](https://arxiv.org/pdf/1608.01041.pdf), with our (64 x 64) input images and our output probabilities for each of the labels." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](https://raw.githubusercontent.com/vinitra/FERPlus/master/emotion_model_img.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specify our Score and Environment Files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file. You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n", + "\n", + "### Write Score File\n", + "\n", + "A score file is what tells our Azure cloud service what to do. After initializing our model using azureml.core.model, we start an ONNX Runtime inference session to evaluate the data passed in on our function calls." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import json\n", + "import numpy as np\n", + "import onnxruntime\n", + "import sys\n", + "import os\n", + "import time\n", + "\n", + "def init():\n", + " global session, input_name, output_name\n", + " model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.onnx')\n", + " session = onnxruntime.InferenceSession(model, None)\n", + " input_name = session.get_inputs()[0].name\n", + " output_name = session.get_outputs()[0].name \n", + " \n", + "def run(input_data):\n", + " '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n", + " We will call the run function later from our Jupyter Notebook \n", + " so our azure service can evaluate our model input in the cloud. '''\n", + "\n", + " try:\n", + " # load in our data, convert to readable format\n", + " data = np.array(json.loads(input_data)['data']).astype('float32')\n", + " \n", + " start = time.time()\n", + " r = session.run([output_name], {input_name : data})\n", + " end = time.time()\n", + " \n", + " result = emotion_map(postprocess(r[0]))\n", + " \n", + " result_dict = {\"result\": result,\n", + " \"time_in_sec\": [end - start]}\n", + " except Exception as e:\n", + " result_dict = {\"error\": str(e)}\n", + " \n", + " return json.dumps(result_dict)\n", + "\n", + "def emotion_map(classes, N=1):\n", + " \"\"\"Take the most probable labels (output of postprocess) and returns the \n", + " top N emotional labels that fit the picture.\"\"\"\n", + " \n", + " emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, \n", + " 'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n", + " \n", + " emotion_keys = list(emotion_table.keys())\n", + " emotions = []\n", + " for i in range(N):\n", + " emotions.append(emotion_keys[classes[i]])\n", + " return emotions\n", + "\n", + "def softmax(x):\n", + " \"\"\"Compute softmax values (probabilities from 0 to 1) for each possible label.\"\"\"\n", + " x = x.reshape(-1)\n", + " e_x = np.exp(x - np.max(x))\n", + " return e_x / e_x.sum(axis=0)\n", + "\n", + "def postprocess(scores):\n", + " \"\"\"This function takes the scores generated by the network and \n", + " returns the class IDs in decreasing order of probability.\"\"\"\n", + " prob = softmax(scores)\n", + " prob = np.squeeze(prob)\n", + " classes = np.argsort(prob)[::-1]\n", + " return classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write Environment File\n", + "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.conda_dependencies import CondaDependencies \n", + "\n", + "\n", + "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n", + "\n", + "with open(\"myenv.yml\",\"w\") as f:\n", + " f.write(myenv.serialize_to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup inference configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "from azureml.core.environment import Environment\n", + "\n", + "\n", + "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", + "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.webservice import AciWebservice\n", + "\n", + "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", + " memory_gb = 1, \n", + " tags = {'demo': 'onnx'}, \n", + " description = 'ONNX for emotion recognition model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell will likely take a few minutes to run as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aci_service_name = 'onnx-demo-emotion'\n", + "print(\"Service\", aci_service_name)\n", + "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", + "aci_service.wait_for_deployment(True)\n", + "print(aci_service.state)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if aci_service.state != 'Healthy':\n", + " # run this command for debugging.\n", + " print(aci_service.get_logs())\n", + "\n", + " # If your deployment fails, make sure to delete your aci_service before trying again!\n", + " # aci_service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Success!\n", + "\n", + "If you've made it this far, you've deployed a working VM with a facial emotion recognition model running in the cloud using Azure ML. Congratulations!\n", + "\n", + "Let's see how well our model deals with our test images." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing and Evaluation\n", + "\n", + "### Useful Helper Functions\n", + "\n", + "We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def emotion_map(classes, N=1):\n", + " \"\"\"Take the most probable labels (output of postprocess) and returns the \n", + " top N emotional labels that fit the picture.\"\"\"\n", + " \n", + " emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, \n", + " 'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n", + " \n", + " emotion_keys = list(emotion_table.keys())\n", + " emotions = []\n", + " for c in range(N):\n", + " emotions.append(emotion_keys[classes[c]])\n", + " return emotions\n", + "\n", + "def softmax(x):\n", + " \"\"\"Compute softmax values (probabilities from 0 to 1) for each possible label.\"\"\"\n", + " x = x.reshape(-1)\n", + " e_x = np.exp(x - np.max(x))\n", + " return e_x / e_x.sum(axis=0)\n", + "\n", + "def postprocess(scores):\n", + " \"\"\"This function takes the scores generated by the network and \n", + " returns the class IDs in decreasing order of probability.\"\"\"\n", + " prob = softmax(scores)\n", + " prob = np.squeeze(prob)\n", + " classes = np.argsort(prob)[::-1]\n", + " return classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Test Data\n", + "\n", + "These are already in your directory from your ONNX model download (from the model zoo).\n", + "\n", + "Notice that our Model Zoo files have a .pb extension. This is because they are [protobuf files (Protocol Buffers)](https://developers.google.com/protocol-buffers/docs/pythontutorial), so we need to read in our data through our ONNX TensorProto reader into a format we can work with, like numerical arrays." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# to manipulate our arrays\n", + "import numpy as np \n", + "\n", + "# read in test data protobuf files included with the model\n", + "import onnx\n", + "from onnx import numpy_helper\n", + "\n", + "# to use parsers to read in our model/data\n", + "import json\n", + "import os\n", + "\n", + "test_inputs = []\n", + "test_outputs = []\n", + "\n", + "# read in 3 testing images from .pb files\n", + "test_data_size = 3\n", + "\n", + "for num in np.arange(test_data_size):\n", + " input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(num), 'input_0.pb')\n", + " output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(num), 'output_0.pb')\n", + " \n", + " # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n", + " tensor = onnx.TensorProto()\n", + " with open(input_test_data, 'rb') as f:\n", + " tensor.ParseFromString(f.read())\n", + " \n", + " input_data = numpy_helper.to_array(tensor)\n", + " test_inputs.append(input_data)\n", + " \n", + " with open(output_test_data, 'rb') as f:\n", + " tensor.ParseFromString(f.read())\n", + " \n", + " output_data = numpy_helper.to_array(tensor)\n", + " output_processed = emotion_map(postprocess(output_data[0]))[0]\n", + " test_outputs.append(output_processed)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nbpresent": { + "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea" + } + }, + "source": [ + "### Show some sample images\n", + "We use `matplotlib` to plot 3 test images from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "nbpresent": { + "id": "396d478b-34aa-4afa-9898-cdce8222a516" } + }, + "outputs": [], + "source": [ + "plt.figure(figsize = (20, 20))\n", + "for test_image in np.arange(3):\n", + " test_inputs[test_image].reshape(1, 64, 64)\n", + " plt.subplot(1, 8, test_image+1)\n", + " plt.axhline('')\n", + " plt.axvline('')\n", + " plt.text(x = 10, y = -10, s = test_outputs[test_image], fontsize = 18)\n", + " plt.imshow(test_inputs[test_image].reshape(64, 64), cmap = plt.cm.gray)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run evaluation / prediction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (16, 6))\n", + "plt.subplot(1, 8, 1)\n", + "\n", + "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n", + "plt.text(x = 0, y = -20, s = \"Result: \", fontsize = 13, color = 'black')\n", + "plt.text(x = 0, y = -10, s = \"Inference Time: \", fontsize = 13, color = 'black')\n", + "plt.text(x = 3, y = 14, s = \"Model Input\", fontsize = 12, color = 'black')\n", + "plt.text(x = 6, y = 18, s = \"(64 x 64)\", fontsize = 12, color = 'black')\n", + "plt.imshow(np.ones((28,28)), cmap=plt.cm.Greys) \n", + "\n", + "\n", + "for i in np.arange(test_data_size):\n", + " \n", + " input_data = json.dumps({'data': test_inputs[i].tolist()})\n", + "\n", + " # predict using the deployed model\n", + " r = json.loads(aci_service.run(input_data))\n", + " \n", + " if \"error\" in r:\n", + " print(r['error'])\n", + " break\n", + " \n", + " result = r['result'][0]\n", + " time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n", + " \n", + " ground_truth = test_outputs[i]\n", + " \n", + " # compare actual value vs. the predicted values:\n", + " plt.subplot(1, 8, i+2)\n", + " plt.axhline('')\n", + " plt.axvline('')\n", + "\n", + " # use different color for misclassified sample\n", + " font_color = 'red' if ground_truth != result else 'black'\n", + " clr_map = plt.cm.Greys if ground_truth != result else plt.cm.gray\n", + "\n", + " # ground truth labels are in blue\n", + " plt.text(x = 10, y = -70, s = ground_truth, fontsize = 18, color = 'blue')\n", + " \n", + " # predictions are in black if correct, red if incorrect\n", + " plt.text(x = 10, y = -45, s = result, fontsize = 18, color = font_color)\n", + " plt.text(x = 5, y = -22, s = str(time_ms) + ' ms', fontsize = 14, color = font_color)\n", + "\n", + " \n", + " plt.imshow(test_inputs[i].reshape(64, 64), cmap = clr_map)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Try classifying your own images!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Preprocessing functions take your image and format it so it can be passed\n", + "# as input into our ONNX model\n", + "\n", + "import cv2\n", + "\n", + "def rgb2gray(rgb):\n", + " \"\"\"Convert the input image into grayscale\"\"\"\n", + " return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n", + "\n", + "def resize_img(img_to_resize):\n", + " \"\"\"Resize image to FER+ model input dimensions\"\"\"\n", + " r_img = cv2.resize(img_to_resize, dsize=(64, 64), interpolation=cv2.INTER_AREA)\n", + " r_img.resize((1, 1, 64, 64))\n", + " return r_img\n", + "\n", + "def preprocess(img_to_preprocess):\n", + " \"\"\"Resize input images and convert them to grayscale.\"\"\"\n", + " if img_to_preprocess.shape == (64, 64):\n", + " img_to_preprocess.resize((1, 1, 64, 64))\n", + " return img_to_preprocess\n", + " \n", + " grayscale = rgb2gray(img_to_preprocess)\n", + " processed_img = resize_img(grayscale)\n", + " return processed_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Replace the following string with your own path/test image\n", + "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n", + "\n", + "# Any PNG or JPG image file should work\n", + "# Make sure to include the entire path with // instead of /\n", + "\n", + "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/face.png\"\n", + "\n", + "your_test_image = \"\"\n", + "\n", + "import matplotlib.image as mpimg\n", + "\n", + "if your_test_image != \"\":\n", + " img = mpimg.imread(your_test_image)\n", + " plt.subplot(1,3,1)\n", + " plt.imshow(img, cmap = plt.cm.Greys)\n", + " print(\"Old Dimensions: \", img.shape)\n", + " img = preprocess(img)\n", + " print(\"New Dimensions: \", img.shape)\n", + "else:\n", + " img = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if img is None:\n", + " print(\"Add the path for your image data.\")\n", + "else:\n", + " input_data = json.dumps({'data': img.tolist()})\n", + "\n", + " try:\n", + " r = json.loads(aci_service.run(input_data))\n", + " result = r['result'][0]\n", + " time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n", + " except KeyError as e:\n", + " print(str(e))\n", + "\n", + " plt.figure(figsize = (16, 6))\n", + " plt.subplot(1,8,1)\n", + " plt.axhline('')\n", + " plt.axvline('')\n", + " plt.text(x = -10, y = -40, s = \"Model prediction: \", fontsize = 14)\n", + " plt.text(x = -10, y = -25, s = \"Inference time: \", fontsize = 14)\n", + " plt.text(x = 100, y = -40, s = str(result), fontsize = 14)\n", + " plt.text(x = 100, y = -25, s = str(time_ms) + \" ms\", fontsize = 14)\n", + " plt.text(x = -10, y = -10, s = \"Model Input image: \", fontsize = 14)\n", + " plt.imshow(img.reshape((64, 64)), cmap = plt.cm.gray) \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# remember to delete your service after you are done using it!\n", + "\n", + "aci_service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "Congratulations!\n", + "\n", + "In this tutorial, you have:\n", + "- familiarized yourself with ONNX Runtime inference and the pretrained models in the ONNX model zoo\n", + "- understood a state-of-the-art convolutional neural net image classification model (FER+ in ONNX) and deployed it in the Azure ML cloud\n", + "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n", + "\n", + "Next steps:\n", + "- If you have not already, check out another interesting ONNX/AML application that lets you set up a state-of-the-art [handwritten image classification model (MNIST)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model for handwritten digit classification in an Azure ML virtual machine.\n", + "- Keep an eye out for an updated version of this tutorial that uses ONNX Runtime GPU.\n", + "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "viswamy" + } + ], + "category": "deployment", + "compute": [ + "Local" + ], + "datasets": [ + "Emotion FER" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "ONNX" + ], + "friendly_name": "Deploy Facial Expression Recognition (FER+) with ONNX Runtime", + "index_order": 2, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + }, + "msauthor": "vinitra.swamy", + "star_tag": [], + "tags": [ + "ONNX Model Zoo" ], - "metadata": { - "authors": [ - { - "name": "viswamy" - } - ], - "category": "deployment", - "compute": [ - "Local" - ], - "datasets": [ - "Emotion FER" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "ONNX" - ], - "friendly_name": "Deploy Facial Expression Recognition (FER+) with ONNX Runtime", - "index_order": 2, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "msauthor": "vinitra.swamy", - "star_tag": [], - "tags": [ - "ONNX Model Zoo" - ], - "task": "Facial Expression Recognition" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "task": "Facial Expression Recognition" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb index 7d481129a..81935efe1 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb @@ -1,777 +1,777 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Handwritten Digit Classification (MNIST) using ONNX Runtime on Azure ML\n", - "\n", - "This example shows how to deploy an image classification neural network using the Modified National Institute of Standards and Technology ([MNIST](http://yann.lecun.com/exdb/mnist/)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing number from 0 to 9. This tutorial will show you how to deploy a MNIST model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n", - "\n", - "Throughout this tutorial, we will be referring to ONNX, a neural network exchange format used to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools (CNTK, PyTorch, Caffe, MXNet, TensorFlow) and choose the combination that is best for them. ONNX is developed and supported by a community of partners including Microsoft AI, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai) and [open source files](https://github.com/onnx).\n", - "\n", - "[ONNX Runtime](https://aka.ms/onnxruntime-python) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization.\n", - "\n", - "#### Tutorial Objectives:\n", - "\n", - "- Describe the MNIST dataset and pretrained Convolutional Neural Net ONNX model, stored in the ONNX model zoo.\n", - "- Deploy and run the pretrained MNIST ONNX model on an Azure Machine Learning instance\n", - "- Predict labels for test set data points in the cloud using ONNX Runtime and Azure ML" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "### 1. Install Azure ML SDK and create a new workspace\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n", - "\n", - "### 2. Install additional packages needed for this tutorial notebook\n", - "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed. \n", - "\n", - "```sh\n", - "(myenv) $ pip install matplotlib onnx opencv-python\n", - "```\n", - "\n", - "**Debugging tip**: Make sure that you run the \"jupyter notebook\" command to launch this notebook after activating your virtual environment. Choose the respective Python kernel for your new virtual environment using the `Kernel > Change Kernel` menu above. If you have completed the steps correctly, the upper right corner of your screen should state `Python [conda env:myenv]` instead of `Python [default]`.\n", - "\n", - "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n", - "\n", - "In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/vision/classification/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# urllib is a built-in Python library to download files from URLs\n", - "\n", - "# Objective: retrieve the latest version of the ONNX MNIST model files from the\n", - "# ONNX Model Zoo and save it in the same folder as this tutorial\n", - "\n", - "import urllib.request\n", - "\n", - "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n", - "\n", - "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# the ! magic command tells our jupyter notebook kernel to run the following line of \n", - "# code from the command line instead of the notebook kernel\n", - "\n", - "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n", - "\n", - "!tar xvzf mnist-7.tar.gz" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy a VM with your ONNX model in the Cloud\n", - "\n", - "### Load Azure ML workspace\n", - "\n", - "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Registering your model with Azure ML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_dir = \"mnist\" # replace this with the location of your model files\n", - "\n", - "# leave as is if it's in the same folder as this notebook" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "model = Model.register(workspace = ws,\n", - " model_path = model_dir + \"/\" + \"model.onnx\",\n", - " model_name = \"mnist_1\",\n", - " tags = {\"onnx\": \"demo\"},\n", - " description = \"MNIST image classification CNN from ONNX Model Zoo\",)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optional: Displaying your registered models\n", - "\n", - "This step is not required, so feel free to skip it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "models = ws.models\n", - "for name, m in models.items():\n", - " print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea" - } - }, - "source": [ - "### ONNX MNIST Model Methodology\n", - "\n", - "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/vision/classification/mnist) in the ONNX model zoo.\n", - "\n", - "***Input: Handwritten Images from MNIST Dataset***\n", - "\n", - "***Task: Classify each MNIST image into an appropriate digit***\n", - "\n", - "***Output: Digit prediction for input image***\n", - "\n", - "Run the cell below to look at some of the sample images from the MNIST dataset that we used to train this ONNX model. Remember, once the application is deployed in Azure ML, you can use your own images as input for the model to classify!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# for images and plots in this notebook\n", - "import matplotlib.pyplot as plt \n", - "from IPython.display import Image\n", - "\n", - "# display images inline\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Image(url=\"http://3.bp.blogspot.com/_UpN7DfJA0j4/TJtUBWPk0SI/AAAAAAAAABY/oWPMtmqJn3k/s1600/mnist_originals.png\", width=200, height=200)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Specify our Score and Environment Files" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file. You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n", - "\n", - "### Write Score File\n", - "\n", - "A score file is what tells our Azure cloud service what to do. After initializing our model using azureml.core.model, we start an ONNX Runtime inference session to evaluate the data passed in on our function calls." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import json\n", - "import numpy as np\n", - "import onnxruntime\n", - "import sys\n", - "import os\n", - "import time\n", - "\n", - "\n", - "def init():\n", - " global session, input_name, output_name\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.onnx')\n", - " session = onnxruntime.InferenceSession(model, None)\n", - " input_name = session.get_inputs()[0].name\n", - " output_name = session.get_outputs()[0].name \n", - " \n", - "\n", - "def preprocess(input_data_json):\n", - " # convert the JSON data into the tensor input\n", - " return np.array(json.loads(input_data_json)['data']).astype('float32')\n", - "\n", - "def postprocess(result):\n", - " # We use argmax to pick the highest confidence label\n", - " return int(np.argmax(np.array(result).squeeze(), axis=0))\n", - " \n", - "def run(input_data):\n", - "\n", - " try:\n", - " # load in our data, convert to readable format\n", - " data = preprocess(input_data)\n", - " \n", - " # start timer\n", - " start = time.time()\n", - " \n", - " r = session.run([output_name], {input_name: data})\n", - " \n", - " #end timer\n", - " end = time.time()\n", - " \n", - " result = postprocess(r)\n", - " result_dict = {\"result\": result,\n", - " \"time_in_sec\": end - start}\n", - " except Exception as e:\n", - " result_dict = {\"error\": str(e)}\n", - " \n", - " return result_dict\n", - "\n", - "def choose_class(result_prob):\n", - " \"\"\"We use argmax to determine the right label to choose from our output\"\"\"\n", - " return int(np.argmax(result_prob, axis=0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write Environment File\n", - "\n", - "This step creates a YAML environment file that specifies which dependencies we would like to see in our Linux Virtual Machine. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.conda_dependencies import CondaDependencies \n", - "\n", - "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n", - "\n", - "with open(\"myenv.yml\",\"w\") as f:\n", - " f.write(myenv.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Inference Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.environment import Environment\n", - "\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", - " memory_gb = 1, \n", - " tags = {'demo': 'onnx'}, \n", - " description = 'ONNX for mnist model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cell will likely take a few minutes to run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aci_service_name = 'onnx-demo-mnist'\n", - "print(\"Service\", aci_service_name)\n", - "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", - "aci_service.wait_for_deployment(True)\n", - "print(aci_service.state)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if aci_service.state != 'Healthy':\n", - " # run this command for debugging.\n", - " print(aci_service.get_logs())\n", - "\n", - " # If your deployment fails, make sure to delete your aci_service or rename your service before trying again!\n", - " # aci_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Success!\n", - "\n", - "If you've made it this far, you've deployed a working VM with a handwritten digit classifier running in the cloud using Azure ML. Congratulations!\n", - "\n", - "You can get the URL for the webservice with the code below. Let's now see how well our model deals with our test images." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(aci_service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Testing and Evaluation\n", - "\n", - "### Load Test Data\n", - "\n", - "These are already in your directory from your ONNX model download (from the model zoo).\n", - "\n", - "Notice that our Model Zoo files have a .pb extension. This is because they are [protobuf files (Protocol Buffers)](https://developers.google.com/protocol-buffers/docs/pythontutorial), so we need to read in our data through our ONNX TensorProto reader into a format we can work with, like numerical arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# to manipulate our arrays\n", - "import numpy as np \n", - "\n", - "# read in test data protobuf files included with the model\n", - "import onnx\n", - "from onnx import numpy_helper\n", - "\n", - "# to use parsers to read in our model/data\n", - "import json\n", - "import os\n", - "\n", - "test_inputs = []\n", - "test_outputs = []\n", - "\n", - "# read in 3 testing images from .pb files\n", - "test_data_size = 3\n", - "\n", - "for i in np.arange(test_data_size):\n", - " input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'input_0.pb')\n", - " output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'output_0.pb')\n", - " \n", - " # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n", - " tensor = onnx.TensorProto()\n", - " with open(input_test_data, 'rb') as f:\n", - " tensor.ParseFromString(f.read())\n", - " \n", - " input_data = numpy_helper.to_array(tensor)\n", - " test_inputs.append(input_data)\n", - " \n", - " with open(output_test_data, 'rb') as f:\n", - " tensor.ParseFromString(f.read())\n", - " \n", - " output_data = numpy_helper.to_array(tensor)\n", - " test_outputs.append(output_data)\n", - " \n", - "if len(test_inputs) == test_data_size:\n", - " print('Test data loaded successfully.')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea" - } - }, - "source": [ - "### Show some sample images\n", - "We use `matplotlib` to plot 3 test images from the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "396d478b-34aa-4afa-9898-cdce8222a516" - } - }, - "outputs": [], - "source": [ - "plt.figure(figsize = (16, 6))\n", - "for test_image in np.arange(3):\n", - " plt.subplot(1, 15, test_image+1)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.imshow(test_inputs[test_image].reshape(28, 28), cmap = plt.cm.Greys)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run evaluation / prediction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize = (16, 6))\n", - "plt.subplot(1, 8, 1)\n", - "\n", - "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n", - "plt.text(x = 0, y = -20, s = \"Result: \", fontsize = 13, color = 'black')\n", - "plt.text(x = 0, y = -10, s = \"Inference Time: \", fontsize = 13, color = 'black')\n", - "plt.text(x = 3, y = 14, s = \"Model Input\", fontsize = 12, color = 'black')\n", - "plt.text(x = 6, y = 18, s = \"(28 x 28)\", fontsize = 12, color = 'black')\n", - "plt.imshow(np.ones((28,28)), cmap=plt.cm.Greys) \n", - "\n", - "\n", - "for i in np.arange(test_data_size):\n", - " \n", - " input_data = json.dumps({'data': test_inputs[i].tolist()})\n", - " \n", - " # predict using the deployed model\n", - " r = aci_service.run(input_data)\n", - " \n", - " if \"error\" in r:\n", - " print(r['error'])\n", - " break\n", - " \n", - " result = r['result']\n", - " time_ms = np.round(r['time_in_sec'] * 1000, 2)\n", - " \n", - " ground_truth = int(np.argmax(test_outputs[i]))\n", - " \n", - " # compare actual value vs. the predicted values:\n", - " plt.subplot(1, 8, i+2)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - "\n", - " # use different color for misclassified sample\n", - " font_color = 'red' if ground_truth != result else 'black'\n", - " clr_map = plt.cm.gray if ground_truth != result else plt.cm.Greys\n", - "\n", - " # ground truth labels are in blue\n", - " plt.text(x = 10, y = -30, s = ground_truth, fontsize = 18, color = 'blue')\n", - " \n", - " # predictions are in black if correct, red if incorrect\n", - " plt.text(x = 10, y = -20, s = result, fontsize = 18, color = font_color)\n", - " plt.text(x = 5, y = -10, s = str(time_ms) + ' ms', fontsize = 14, color = font_color)\n", - "\n", - " \n", - " plt.imshow(test_inputs[i].reshape(28, 28), cmap = clr_map)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Try classifying your own images!\n", - "\n", - "Create your own handwritten image and pass it into the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Preprocessing functions take your image and format it so it can be passed\n", - "# as input into our ONNX model\n", - "\n", - "import cv2\n", - "\n", - "def rgb2gray(rgb):\n", - " \"\"\"Convert the input image into grayscale\"\"\"\n", - " return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n", - "\n", - "def resize_img(img_to_resize):\n", - " \"\"\"Resize image to MNIST model input dimensions\"\"\"\n", - " r_img = cv2.resize(img_to_resize, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n", - " r_img.resize((1, 1, 28, 28))\n", - " return r_img\n", - "\n", - "def preprocess(img_to_preprocess):\n", - " \"\"\"Resize input images and convert them to grayscale.\"\"\"\n", - " if img_to_preprocess.shape == (28, 28):\n", - " img_to_preprocess.resize((1, 1, 28, 28))\n", - " return img_to_preprocess\n", - " \n", - " grayscale = rgb2gray(img_to_preprocess)\n", - " processed_img = resize_img(grayscale)\n", - " return processed_img" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Replace this string with your own path/test image\n", - "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n", - "\n", - "# Any PNG or JPG image file should work\n", - "\n", - "your_test_image = \"\"\n", - "\n", - "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/handwritten_digit.png\"\n", - "\n", - "import matplotlib.image as mpimg\n", - "\n", - "if your_test_image != \"\":\n", - " img = mpimg.imread(your_test_image)\n", - " plt.subplot(1,3,1)\n", - " plt.imshow(img, cmap = plt.cm.Greys)\n", - " print(\"Old Dimensions: \", img.shape)\n", - " img = preprocess(img)\n", - " print(\"New Dimensions: \", img.shape)\n", - "else:\n", - " img = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if img is None:\n", - " print(\"Add the path for your image data.\")\n", - "else:\n", - " input_data = json.dumps({'data': img.tolist()})\n", - "\n", - " try:\n", - " r = aci_service.run(input_data)\n", - " result = r['result']\n", - " time_ms = np.round(r['time_in_sec'] * 1000, 2)\n", - " except KeyError as e:\n", - " print(str(e))\n", - "\n", - " plt.figure(figsize = (16, 6))\n", - " plt.subplot(1, 15,1)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.text(x = -100, y = -20, s = \"Model prediction: \", fontsize = 14)\n", - " plt.text(x = -100, y = -10, s = \"Inference time: \", fontsize = 14)\n", - " plt.text(x = 0, y = -20, s = str(result), fontsize = 14)\n", - " plt.text(x = 0, y = -10, s = str(time_ms) + \" ms\", fontsize = 14)\n", - " plt.text(x = -100, y = 14, s = \"Input image: \", fontsize = 14)\n", - " plt.imshow(img.reshape(28, 28), cmap = plt.cm.gray) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optional: How does our ONNX MNIST model work? \n", - "#### A brief explanation of Convolutional Neural Networks\n", - "\n", - "A [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) (CNN, or ConvNet) is a type of [feed-forward](https://en.wikipedia.org/wiki/Feedforward_neural_network) artificial neural network made up of neurons that have learnable weights and biases. The CNNs take advantage of the spatial nature of the data. In nature, we perceive different objects by their shapes, size and colors. For example, objects in a natural scene are typically edges, corners/vertices (defined by two of more edges), color patches etc. These primitives are often identified using different detectors (e.g., edge detection, color detector) or combination of detectors interacting to facilitate image interpretation (object classification, region of interest detection, scene description etc.) in real world vision related tasks. These detectors are also known as filters. Convolution is a mathematical operator that takes an image and a filter as input and produces a filtered output (representing say edges, corners, or colors in the input image). \n", - "\n", - "Historically, these filters are a set of weights that were often hand crafted or modeled with mathematical functions (e.g., [Gaussian](https://en.wikipedia.org/wiki/Gaussian_filter) / [Laplacian](http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm) / [Canny](https://en.wikipedia.org/wiki/Canny_edge_detector) filter). The filter outputs are mapped through non-linear activation functions mimicking human brain cells called [neurons](https://en.wikipedia.org/wiki/Neuron). Popular deep CNNs or ConvNets (such as [AlexNet](https://en.wikipedia.org/wiki/AlexNet), [VGG](https://arxiv.org/abs/1409.1556), [Inception](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf), [ResNet](https://arxiv.org/pdf/1512.03385v1.pdf)) that are used for various [computer vision](https://en.wikipedia.org/wiki/Computer_vision) tasks have many of these architectural primitives (inspired from biology). \n", - "\n", - "### Convolution Layer\n", - "\n", - "A convolution layer is a set of filters. Each filter is defined by a weight (**W**) matrix, and bias ($b$).\n", - "\n", - "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model Description\n", - "\n", - "The MNIST model from the ONNX Model Zoo uses maxpooling to update the weights in its convolutions, summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image, with our input images and our output probabilities of each of our 10 labels. If you're interested in exploring the logic behind creating a Deep Learning model further, please look at the [training tutorial for our ONNX MNIST Convolutional Neural Network](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# remember to delete your service after you are done using it!\n", - "\n", - "aci_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "Congratulations!\n", - "\n", - "In this tutorial, you have:\n", - "- familiarized yourself with ONNX Runtime inference and the pretrained models in the ONNX model zoo\n", - "- understood a state-of-the-art convolutional neural net image classification model (MNIST in ONNX) and deployed it in Azure ML cloud\n", - "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n", - "\n", - "Next steps:\n", - "- Check out another interesting application based on a Microsoft Research computer vision paper that lets you set up a [facial emotion recognition model](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model in an Azure ML virtual machine.\n", - "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Handwritten Digit Classification (MNIST) using ONNX Runtime on Azure ML\n", + "\n", + "This example shows how to deploy an image classification neural network using the Modified National Institute of Standards and Technology ([MNIST](http://yann.lecun.com/exdb/mnist/)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing number from 0 to 9. This tutorial will show you how to deploy a MNIST model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n", + "\n", + "Throughout this tutorial, we will be referring to ONNX, a neural network exchange format used to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools (CNTK, PyTorch, Caffe, MXNet, TensorFlow) and choose the combination that is best for them. ONNX is developed and supported by a community of partners including Microsoft AI, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai) and [open source files](https://github.com/onnx).\n", + "\n", + "[ONNX Runtime](https://aka.ms/onnxruntime-python) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization.\n", + "\n", + "#### Tutorial Objectives:\n", + "\n", + "- Describe the MNIST dataset and pretrained Convolutional Neural Net ONNX model, stored in the ONNX model zoo.\n", + "- Deploy and run the pretrained MNIST ONNX model on an Azure Machine Learning instance\n", + "- Predict labels for test set data points in the cloud using ONNX Runtime and Azure ML" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "### 1. Install Azure ML SDK and create a new workspace\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n", + "\n", + "### 2. Install additional packages needed for this tutorial notebook\n", + "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed. \n", + "\n", + "```sh\n", + "(myenv) $ pip install matplotlib onnx opencv-python\n", + "```\n", + "\n", + "**Debugging tip**: Make sure that you run the \"jupyter notebook\" command to launch this notebook after activating your virtual environment. Choose the respective Python kernel for your new virtual environment using the `Kernel > Change Kernel` menu above. If you have completed the steps correctly, the upper right corner of your screen should state `Python [conda env:myenv]` instead of `Python [default]`.\n", + "\n", + "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n", + "\n", + "In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/vision/classification/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# urllib is a built-in Python library to download files from URLs\n", + "\n", + "# Objective: retrieve the latest version of the ONNX MNIST model files from the\n", + "# ONNX Model Zoo and save it in the same folder as this tutorial\n", + "\n", + "import urllib.request\n", + "\n", + "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n", + "\n", + "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# the ! magic command tells our jupyter notebook kernel to run the following line of \n", + "# code from the command line instead of the notebook kernel\n", + "\n", + "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n", + "\n", + "!tar xvzf mnist-7.tar.gz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy a VM with your ONNX model in the Cloud\n", + "\n", + "### Load Azure ML workspace\n", + "\n", + "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Registering your model with Azure ML" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_dir = \"mnist\" # replace this with the location of your model files\n", + "\n", + "# leave as is if it's in the same folder as this notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "model = Model.register(workspace = ws,\n", + " model_path = model_dir + \"/\" + \"model.onnx\",\n", + " model_name = \"mnist_1\",\n", + " tags = {\"onnx\": \"demo\"},\n", + " description = \"MNIST image classification CNN from ONNX Model Zoo\",)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional: Displaying your registered models\n", + "\n", + "This step is not required, so feel free to skip it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "models = ws.models\n", + "for name, m in models.items():\n", + " print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nbpresent": { + "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea" } + }, + "source": [ + "### ONNX MNIST Model Methodology\n", + "\n", + "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/vision/classification/mnist) in the ONNX model zoo.\n", + "\n", + "***Input: Handwritten Images from MNIST Dataset***\n", + "\n", + "***Task: Classify each MNIST image into an appropriate digit***\n", + "\n", + "***Output: Digit prediction for input image***\n", + "\n", + "Run the cell below to look at some of the sample images from the MNIST dataset that we used to train this ONNX model. Remember, once the application is deployed in Azure ML, you can use your own images as input for the model to classify!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# for images and plots in this notebook\n", + "import matplotlib.pyplot as plt \n", + "from IPython.display import Image\n", + "\n", + "# display images inline\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Image(url=\"http://3.bp.blogspot.com/_UpN7DfJA0j4/TJtUBWPk0SI/AAAAAAAAABY/oWPMtmqJn3k/s1600/mnist_originals.png\", width=200, height=200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specify our Score and Environment Files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file. You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n", + "\n", + "### Write Score File\n", + "\n", + "A score file is what tells our Azure cloud service what to do. After initializing our model using azureml.core.model, we start an ONNX Runtime inference session to evaluate the data passed in on our function calls." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import json\n", + "import numpy as np\n", + "import onnxruntime\n", + "import sys\n", + "import os\n", + "import time\n", + "\n", + "\n", + "def init():\n", + " global session, input_name, output_name\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.onnx')\n", + " session = onnxruntime.InferenceSession(model, None)\n", + " input_name = session.get_inputs()[0].name\n", + " output_name = session.get_outputs()[0].name \n", + " \n", + "\n", + "def preprocess(input_data_json):\n", + " # convert the JSON data into the tensor input\n", + " return np.array(json.loads(input_data_json)['data']).astype('float32')\n", + "\n", + "def postprocess(result):\n", + " # We use argmax to pick the highest confidence label\n", + " return int(np.argmax(np.array(result).squeeze(), axis=0))\n", + " \n", + "def run(input_data):\n", + "\n", + " try:\n", + " # load in our data, convert to readable format\n", + " data = preprocess(input_data)\n", + " \n", + " # start timer\n", + " start = time.time()\n", + " \n", + " r = session.run([output_name], {input_name: data})\n", + " \n", + " #end timer\n", + " end = time.time()\n", + " \n", + " result = postprocess(r)\n", + " result_dict = {\"result\": result,\n", + " \"time_in_sec\": end - start}\n", + " except Exception as e:\n", + " result_dict = {\"error\": str(e)}\n", + " \n", + " return result_dict\n", + "\n", + "def choose_class(result_prob):\n", + " \"\"\"We use argmax to determine the right label to choose from our output\"\"\"\n", + " return int(np.argmax(result_prob, axis=0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write Environment File\n", + "\n", + "This step creates a YAML environment file that specifies which dependencies we would like to see in our Linux Virtual Machine. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.conda_dependencies import CondaDependencies \n", + "\n", + "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n", + "\n", + "with open(\"myenv.yml\",\"w\") as f:\n", + " f.write(myenv.serialize_to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Inference Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "from azureml.core.environment import Environment\n", + "\n", + "\n", + "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", + "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.webservice import AciWebservice\n", + "\n", + "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", + " memory_gb = 1, \n", + " tags = {'demo': 'onnx'}, \n", + " description = 'ONNX for mnist model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell will likely take a few minutes to run." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aci_service_name = 'onnx-demo-mnist'\n", + "print(\"Service\", aci_service_name)\n", + "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", + "aci_service.wait_for_deployment(True)\n", + "print(aci_service.state)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if aci_service.state != 'Healthy':\n", + " # run this command for debugging.\n", + " print(aci_service.get_logs())\n", + "\n", + " # If your deployment fails, make sure to delete your aci_service or rename your service before trying again!\n", + " # aci_service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Success!\n", + "\n", + "If you've made it this far, you've deployed a working VM with a handwritten digit classifier running in the cloud using Azure ML. Congratulations!\n", + "\n", + "You can get the URL for the webservice with the code below. Let's now see how well our model deals with our test images." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(aci_service.scoring_uri)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing and Evaluation\n", + "\n", + "### Load Test Data\n", + "\n", + "These are already in your directory from your ONNX model download (from the model zoo).\n", + "\n", + "Notice that our Model Zoo files have a .pb extension. This is because they are [protobuf files (Protocol Buffers)](https://developers.google.com/protocol-buffers/docs/pythontutorial), so we need to read in our data through our ONNX TensorProto reader into a format we can work with, like numerical arrays." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# to manipulate our arrays\n", + "import numpy as np \n", + "\n", + "# read in test data protobuf files included with the model\n", + "import onnx\n", + "from onnx import numpy_helper\n", + "\n", + "# to use parsers to read in our model/data\n", + "import json\n", + "import os\n", + "\n", + "test_inputs = []\n", + "test_outputs = []\n", + "\n", + "# read in 3 testing images from .pb files\n", + "test_data_size = 3\n", + "\n", + "for i in np.arange(test_data_size):\n", + " input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'input_0.pb')\n", + " output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'output_0.pb')\n", + " \n", + " # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n", + " tensor = onnx.TensorProto()\n", + " with open(input_test_data, 'rb') as f:\n", + " tensor.ParseFromString(f.read())\n", + " \n", + " input_data = numpy_helper.to_array(tensor)\n", + " test_inputs.append(input_data)\n", + " \n", + " with open(output_test_data, 'rb') as f:\n", + " tensor.ParseFromString(f.read())\n", + " \n", + " output_data = numpy_helper.to_array(tensor)\n", + " test_outputs.append(output_data)\n", + " \n", + "if len(test_inputs) == test_data_size:\n", + " print('Test data loaded successfully.')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nbpresent": { + "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea" + } + }, + "source": [ + "### Show some sample images\n", + "We use `matplotlib` to plot 3 test images from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "nbpresent": { + "id": "396d478b-34aa-4afa-9898-cdce8222a516" + } + }, + "outputs": [], + "source": [ + "plt.figure(figsize = (16, 6))\n", + "for test_image in np.arange(3):\n", + " plt.subplot(1, 15, test_image+1)\n", + " plt.axhline('')\n", + " plt.axvline('')\n", + " plt.imshow(test_inputs[test_image].reshape(28, 28), cmap = plt.cm.Greys)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run evaluation / prediction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (16, 6))\n", + "plt.subplot(1, 8, 1)\n", + "\n", + "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n", + "plt.text(x = 0, y = -20, s = \"Result: \", fontsize = 13, color = 'black')\n", + "plt.text(x = 0, y = -10, s = \"Inference Time: \", fontsize = 13, color = 'black')\n", + "plt.text(x = 3, y = 14, s = \"Model Input\", fontsize = 12, color = 'black')\n", + "plt.text(x = 6, y = 18, s = \"(28 x 28)\", fontsize = 12, color = 'black')\n", + "plt.imshow(np.ones((28,28)), cmap=plt.cm.Greys) \n", + "\n", + "\n", + "for i in np.arange(test_data_size):\n", + " \n", + " input_data = json.dumps({'data': test_inputs[i].tolist()})\n", + " \n", + " # predict using the deployed model\n", + " r = aci_service.run(input_data)\n", + " \n", + " if \"error\" in r:\n", + " print(r['error'])\n", + " break\n", + " \n", + " result = r['result']\n", + " time_ms = np.round(r['time_in_sec'] * 1000, 2)\n", + " \n", + " ground_truth = int(np.argmax(test_outputs[i]))\n", + " \n", + " # compare actual value vs. the predicted values:\n", + " plt.subplot(1, 8, i+2)\n", + " plt.axhline('')\n", + " plt.axvline('')\n", + "\n", + " # use different color for misclassified sample\n", + " font_color = 'red' if ground_truth != result else 'black'\n", + " clr_map = plt.cm.gray if ground_truth != result else plt.cm.Greys\n", + "\n", + " # ground truth labels are in blue\n", + " plt.text(x = 10, y = -30, s = ground_truth, fontsize = 18, color = 'blue')\n", + " \n", + " # predictions are in black if correct, red if incorrect\n", + " plt.text(x = 10, y = -20, s = result, fontsize = 18, color = font_color)\n", + " plt.text(x = 5, y = -10, s = str(time_ms) + ' ms', fontsize = 14, color = font_color)\n", + "\n", + " \n", + " plt.imshow(test_inputs[i].reshape(28, 28), cmap = clr_map)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Try classifying your own images!\n", + "\n", + "Create your own handwritten image and pass it into the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Preprocessing functions take your image and format it so it can be passed\n", + "# as input into our ONNX model\n", + "\n", + "import cv2\n", + "\n", + "def rgb2gray(rgb):\n", + " \"\"\"Convert the input image into grayscale\"\"\"\n", + " return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n", + "\n", + "def resize_img(img_to_resize):\n", + " \"\"\"Resize image to MNIST model input dimensions\"\"\"\n", + " r_img = cv2.resize(img_to_resize, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n", + " r_img.resize((1, 1, 28, 28))\n", + " return r_img\n", + "\n", + "def preprocess(img_to_preprocess):\n", + " \"\"\"Resize input images and convert them to grayscale.\"\"\"\n", + " if img_to_preprocess.shape == (28, 28):\n", + " img_to_preprocess.resize((1, 1, 28, 28))\n", + " return img_to_preprocess\n", + " \n", + " grayscale = rgb2gray(img_to_preprocess)\n", + " processed_img = resize_img(grayscale)\n", + " return processed_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Replace this string with your own path/test image\n", + "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n", + "\n", + "# Any PNG or JPG image file should work\n", + "\n", + "your_test_image = \"\"\n", + "\n", + "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/handwritten_digit.png\"\n", + "\n", + "import matplotlib.image as mpimg\n", + "\n", + "if your_test_image != \"\":\n", + " img = mpimg.imread(your_test_image)\n", + " plt.subplot(1,3,1)\n", + " plt.imshow(img, cmap = plt.cm.Greys)\n", + " print(\"Old Dimensions: \", img.shape)\n", + " img = preprocess(img)\n", + " print(\"New Dimensions: \", img.shape)\n", + "else:\n", + " img = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if img is None:\n", + " print(\"Add the path for your image data.\")\n", + "else:\n", + " input_data = json.dumps({'data': img.tolist()})\n", + "\n", + " try:\n", + " r = aci_service.run(input_data)\n", + " result = r['result']\n", + " time_ms = np.round(r['time_in_sec'] * 1000, 2)\n", + " except KeyError as e:\n", + " print(str(e))\n", + "\n", + " plt.figure(figsize = (16, 6))\n", + " plt.subplot(1, 15,1)\n", + " plt.axhline('')\n", + " plt.axvline('')\n", + " plt.text(x = -100, y = -20, s = \"Model prediction: \", fontsize = 14)\n", + " plt.text(x = -100, y = -10, s = \"Inference time: \", fontsize = 14)\n", + " plt.text(x = 0, y = -20, s = str(result), fontsize = 14)\n", + " plt.text(x = 0, y = -10, s = str(time_ms) + \" ms\", fontsize = 14)\n", + " plt.text(x = -100, y = 14, s = \"Input image: \", fontsize = 14)\n", + " plt.imshow(img.reshape(28, 28), cmap = plt.cm.gray) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional: How does our ONNX MNIST model work? \n", + "#### A brief explanation of Convolutional Neural Networks\n", + "\n", + "A [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) (CNN, or ConvNet) is a type of [feed-forward](https://en.wikipedia.org/wiki/Feedforward_neural_network) artificial neural network made up of neurons that have learnable weights and biases. The CNNs take advantage of the spatial nature of the data. In nature, we perceive different objects by their shapes, size and colors. For example, objects in a natural scene are typically edges, corners/vertices (defined by two of more edges), color patches etc. These primitives are often identified using different detectors (e.g., edge detection, color detector) or combination of detectors interacting to facilitate image interpretation (object classification, region of interest detection, scene description etc.) in real world vision related tasks. These detectors are also known as filters. Convolution is a mathematical operator that takes an image and a filter as input and produces a filtered output (representing say edges, corners, or colors in the input image). \n", + "\n", + "Historically, these filters are a set of weights that were often hand crafted or modeled with mathematical functions (e.g., [Gaussian](https://en.wikipedia.org/wiki/Gaussian_filter) / [Laplacian](http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm) / [Canny](https://en.wikipedia.org/wiki/Canny_edge_detector) filter). The filter outputs are mapped through non-linear activation functions mimicking human brain cells called [neurons](https://en.wikipedia.org/wiki/Neuron). Popular deep CNNs or ConvNets (such as [AlexNet](https://en.wikipedia.org/wiki/AlexNet), [VGG](https://arxiv.org/abs/1409.1556), [Inception](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf), [ResNet](https://arxiv.org/pdf/1512.03385v1.pdf)) that are used for various [computer vision](https://en.wikipedia.org/wiki/Computer_vision) tasks have many of these architectural primitives (inspired from biology). \n", + "\n", + "### Convolution Layer\n", + "\n", + "A convolution layer is a set of filters. Each filter is defined by a weight (**W**) matrix, and bias ($b$).\n", + "\n", + "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Description\n", + "\n", + "The MNIST model from the ONNX Model Zoo uses maxpooling to update the weights in its convolutions, summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image, with our input images and our output probabilities of each of our 10 labels. If you're interested in exploring the logic behind creating a Deep Learning model further, please look at the [training tutorial for our ONNX MNIST Convolutional Neural Network](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# remember to delete your service after you are done using it!\n", + "\n", + "aci_service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "Congratulations!\n", + "\n", + "In this tutorial, you have:\n", + "- familiarized yourself with ONNX Runtime inference and the pretrained models in the ONNX model zoo\n", + "- understood a state-of-the-art convolutional neural net image classification model (MNIST in ONNX) and deployed it in Azure ML cloud\n", + "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n", + "\n", + "Next steps:\n", + "- Check out another interesting application based on a Microsoft Research computer vision paper that lets you set up a [facial emotion recognition model](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model in an Azure ML virtual machine.\n", + "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "viswamy" + } + ], + "category": "deployment", + "compute": [ + "Local" + ], + "datasets": [ + "MNIST" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "ONNX" + ], + "friendly_name": "Deploy MNIST digit recognition with ONNX Runtime", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + }, + "msauthor": "vinitra.swamy", + "star_tag": [], + "tags": [ + "ONNX Model Zoo" ], - "metadata": { - "authors": [ - { - "name": "viswamy" - } - ], - "category": "deployment", - "compute": [ - "Local" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "ONNX" - ], - "friendly_name": "Deploy MNIST digit recognition with ONNX Runtime", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "msauthor": "vinitra.swamy", - "star_tag": [], - "tags": [ - "ONNX Model Zoo" - ], - "task": "Image Classification" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "task": "Image Classification" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb index 42fdb6d8a..8b94e597d 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb @@ -1,228 +1,228 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Register ONNX model and deploy as webservice\n", - "\n", - "Following this notebook, you will:\n", - "\n", - " - Learn how to register an ONNX in your Azure Machine Learning Workspace.\n", - " - Deploy your model as a web service in an Azure Container Instance." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create a workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "\n", - "# Check core SDK version number.\n", - "print('SDK version:', azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "\n", - "Create a [Workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register model\n", - "\n", - "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-). For this example, we have provided a trained ONNX MNIST model(`mnist-model.onnx` in the notebook's directory).\n", - "\n", - "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace. Also, marking this model with the scikit-learn framework will simplify deploying it as a web service, as we'll see later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "register model from file" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Model\n", - "\n", - "model = Model.register(workspace=ws,\n", - " model_name='mnist-sample', # Name of the registered model in your workspace.\n", - " model_path='mnist-model.onnx', # Local ONNX model to upload and register as a model.\n", - " model_framework=Model.Framework.ONNX , # Framework used to create the model.\n", - " model_framework_version='1.3', # Version of ONNX used to create the model.\n", - " description='Onnx MNIST model')\n", - "\n", - "print('Name:', model.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy model\n", - "\n", - "Deploy your model as a web service using [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). Web services take one or more models, load them in an environment, and run them on one of several supported deployment targets.\n", - "\n", - "For this example, we will deploy the ONNX model to an Azure Container Instance (ACI)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use a default environment (for supported models)\n", - "\n", - "The Azure Machine Learning service provides a default environment for supported model frameworks, including ONNX, based on the metadata you provided when registering your model. This is the easiest way to deploy your model.\n", - "\n", - "**Note**: This step can take several minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Webservice\n", - "from azureml.exceptions import WebserviceException\n", - "\n", - "service_name = 'onnx-mnist-service'\n", - "\n", - "# Remove any existing service under the same name.\n", - "try:\n", - " Webservice(ws, service_name).delete()\n", - "except WebserviceException:\n", - " pass\n", - "\n", - "service = Model.deploy(ws, service_name, [model])\n", - "service.wait_for_deployment(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After your model is deployed, perform a call to the web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "\n", - "headers = {'Content-Type': 'application/json', 'Accept': 'application/json'}\n", - "\n", - "if service.auth_enabled:\n", - " headers['Authorization'] = 'Bearer '+ service.get_keys()[0]\n", - "elif service.token_auth_enabled:\n", - " headers['Authorization'] = 'Bearer '+ service.get_token()[0]\n", - "\n", - "scoring_uri = service.scoring_uri\n", - "print(scoring_uri)\n", - "with open('onnx-mnist-predict-input.json', 'rb') as data_file:\n", - " response = requests.post(\n", - " scoring_uri, data=data_file, headers=headers)\n", - "print(response.status_code)\n", - "print(response.elapsed)\n", - "print(response.json())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you are finished testing your service, clean up the deployment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Register ONNX model and deploy as webservice\n", + "\n", + "Following this notebook, you will:\n", + "\n", + " - Learn how to register an ONNX in your Azure Machine Learning Workspace.\n", + " - Deploy your model as a web service in an Azure Container Instance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create a workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "\n", + "# Check core SDK version number.\n", + "print('SDK version:', azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize workspace\n", + "\n", + "Create a [Workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register model\n", + "\n", + "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-). For this example, we have provided a trained ONNX MNIST model(`mnist-model.onnx` in the notebook's directory).\n", + "\n", + "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace. Also, marking this model with the scikit-learn framework will simplify deploying it as a web service, as we'll see later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "register model from file" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Model\n", + "\n", + "model = Model.register(workspace=ws,\n", + " model_name='mnist-sample', # Name of the registered model in your workspace.\n", + " model_path='mnist-model.onnx', # Local ONNX model to upload and register as a model.\n", + " model_framework=Model.Framework.ONNX , # Framework used to create the model.\n", + " model_framework_version='1.3', # Version of ONNX used to create the model.\n", + " description='Onnx MNIST model')\n", + "\n", + "print('Name:', model.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy model\n", + "\n", + "Deploy your model as a web service using [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). Web services take one or more models, load them in an environment, and run them on one of several supported deployment targets.\n", + "\n", + "For this example, we will deploy the ONNX model to an Azure Container Instance (ACI)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use a default environment (for supported models)\n", + "\n", + "The Azure Machine Learning service provides a default environment for supported model frameworks, including ONNX, based on the metadata you provided when registering your model. This is the easiest way to deploy your model.\n", + "\n", + "**Note**: This step can take several minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Webservice\n", + "from azureml.exceptions import WebserviceException\n", + "\n", + "service_name = 'onnx-mnist-service'\n", + "\n", + "# Remove any existing service under the same name.\n", + "try:\n", + " Webservice(ws, service_name).delete()\n", + "except WebserviceException:\n", + " pass\n", + "\n", + "service = Model.deploy(ws, service_name, [model])\n", + "service.wait_for_deployment(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After your model is deployed, perform a call to the web service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "headers = {'Content-Type': 'application/json', 'Accept': 'application/json'}\n", + "\n", + "if service.auth_enabled:\n", + " headers['Authorization'] = 'Bearer '+ service.get_keys()[0]\n", + "elif service.token_auth_enabled:\n", + " headers['Authorization'] = 'Bearer '+ service.get_token()[0]\n", + "\n", + "scoring_uri = service.scoring_uri\n", + "print(scoring_uri)\n", + "with open('onnx-mnist-predict-input.json', 'rb') as data_file:\n", + " response = requests.post(\n", + " scoring_uri, data=data_file, headers=headers)\n", + "print(response.status_code)\n", + "print(response.elapsed)\n", + "print(response.json())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you are finished testing your service, clean up the deployment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "service.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "vaidyas" + } ], - "metadata": { - "authors": [ - { - "name": "vaidyas" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb b/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb index fb408032d..e4b5c7c80 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb @@ -1,416 +1,416 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ResNet50 Image Classification using ONNX and AzureML\n", - "\n", - "This example shows how to deploy the ResNet50 ONNX model as a web service using Azure Machine Learning services and the ONNX Runtime.\n", - "\n", - "## What is ONNX\n", - "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n", - "\n", - "## ResNet50 Details\n", - "ResNet classifies the major object in an input image into a set of 1000 pre-defined classes. For more information about the ResNet50 model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/resnet). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "To make the best use of your time, make sure you have done the following:\n", - "\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (config.json)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download pre-trained ONNX model from ONNX Model Zoo.\n", - "\n", - "Download the [ResNet50v2 model and test data](https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz) and extract it in the same folder as this tutorial notebook.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib.request\n", - "\n", - "onnx_model_url = \"https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz\"\n", - "urllib.request.urlretrieve(onnx_model_url, filename=\"resnet50v2.tar.gz\")\n", - "\n", - "!tar xvzf resnet50v2.tar.gz" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploying as a web service with Azure ML" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load your Azure ML workspace\n", - "\n", - "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.location, ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Register your model with Azure ML\n", - "\n", - "Now we upload the model and register it in the workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "model = Model.register(model_path = \"resnet50v2/resnet50v2.onnx\",\n", - " model_name = \"resnet50v2\",\n", - " tags = {\"onnx\": \"demo\"},\n", - " description = \"ResNet50v2 from ONNX Model Zoo\",\n", - " workspace = ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Displaying your registered models\n", - "\n", - "You can optionally list out all the models that you have registered in this workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "models = ws.models\n", - "for name, m in models.items():\n", - " print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write scoring file\n", - "\n", - "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import json\n", - "import time\n", - "import sys\n", - "import os\n", - "import numpy as np # we're going to use numpy to process input and output data\n", - "import onnxruntime # to inference ONNX models, we use the ONNX Runtime\n", - "\n", - "def softmax(x):\n", - " x = x.reshape(-1)\n", - " e_x = np.exp(x - np.max(x))\n", - " return e_x / e_x.sum(axis=0)\n", - "\n", - "def init():\n", - " global session\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50v2.onnx')\n", - " session = onnxruntime.InferenceSession(model, None)\n", - "\n", - "def preprocess(input_data_json):\n", - " # convert the JSON data into the tensor input\n", - " img_data = np.array(json.loads(input_data_json)['data']).astype('float32')\n", - " \n", - " #normalize\n", - " mean_vec = np.array([0.485, 0.456, 0.406])\n", - " stddev_vec = np.array([0.229, 0.224, 0.225])\n", - " norm_img_data = np.zeros(img_data.shape).astype('float32')\n", - " for i in range(img_data.shape[0]):\n", - " norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]\n", - "\n", - " return norm_img_data\n", - "\n", - "def postprocess(result):\n", - " return softmax(np.array(result)).tolist()\n", - "\n", - "def run(input_data_json):\n", - " try:\n", - " start = time.time()\n", - " # load in our data which is expected as NCHW 224x224 image\n", - " input_data = preprocess(input_data_json)\n", - " input_name = session.get_inputs()[0].name # get the id of the first input of the model \n", - " result = session.run([], {input_name: input_data})\n", - " end = time.time() # stop timer\n", - " return {\"result\": postprocess(result),\n", - " \"time\": end - start}\n", - " except Exception as e:\n", - " result = str(e)\n", - " return {\"error\": result}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create inference configuration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we create a YAML file that specifies which dependencies we would like to see in our container." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.conda_dependencies import CondaDependencies \n", - "\n", - "\n", - "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n", - "\n", - "with open(\"myenv.yml\",\"w\") as f:\n", - " f.write(myenv.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the inference configuration object. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.environment import Environment\n", - "\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", - " memory_gb = 1, \n", - " tags = {'demo': 'onnx'}, \n", - " description = 'web service for ResNet50 ONNX model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cell will likely take a few minutes to run as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from random import randint\n", - "\n", - "aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n", - "print(\"Service\", aci_service_name)\n", - "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", - "aci_service.wait_for_deployment(True)\n", - "print(aci_service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if aci_service.state != 'Healthy':\n", - " # run this command for debugging.\n", - " print(aci_service.get_logs())\n", - " aci_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Success!\n", - "\n", - "If you've made it this far, you've deployed a working web service that does image classification using an ONNX model. You can get the URL for the webservice with the code below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(aci_service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you are eventually done using the web service, remember to delete it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aci_service.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ResNet50 Image Classification using ONNX and AzureML\n", + "\n", + "This example shows how to deploy the ResNet50 ONNX model as a web service using Azure Machine Learning services and the ONNX Runtime.\n", + "\n", + "## What is ONNX\n", + "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n", + "\n", + "## ResNet50 Details\n", + "ResNet classifies the major object in an input image into a set of 1000 pre-defined classes. For more information about the ResNet50 model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/resnet). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "To make the best use of your time, make sure you have done the following:\n", + "\n", + "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", + "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n", + " * install the AML SDK\n", + " * create a workspace and its configuration file (config.json)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Download pre-trained ONNX model from ONNX Model Zoo.\n", + "\n", + "Download the [ResNet50v2 model and test data](https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz) and extract it in the same folder as this tutorial notebook.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import urllib.request\n", + "\n", + "onnx_model_url = \"https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz\"\n", + "urllib.request.urlretrieve(onnx_model_url, filename=\"resnet50v2.tar.gz\")\n", + "\n", + "!tar xvzf resnet50v2.tar.gz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploying as a web service with Azure ML" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load your Azure ML workspace\n", + "\n", + "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.location, ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register your model with Azure ML\n", + "\n", + "Now we upload the model and register it in the workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "model = Model.register(model_path = \"resnet50v2/resnet50v2.onnx\",\n", + " model_name = \"resnet50v2\",\n", + " tags = {\"onnx\": \"demo\"},\n", + " description = \"ResNet50v2 from ONNX Model Zoo\",\n", + " workspace = ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Displaying your registered models\n", + "\n", + "You can optionally list out all the models that you have registered in this workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "models = ws.models\n", + "for name, m in models.items():\n", + " print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write scoring file\n", + "\n", + "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import json\n", + "import time\n", + "import sys\n", + "import os\n", + "import numpy as np # we're going to use numpy to process input and output data\n", + "import onnxruntime # to inference ONNX models, we use the ONNX Runtime\n", + "\n", + "def softmax(x):\n", + " x = x.reshape(-1)\n", + " e_x = np.exp(x - np.max(x))\n", + " return e_x / e_x.sum(axis=0)\n", + "\n", + "def init():\n", + " global session\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50v2.onnx')\n", + " session = onnxruntime.InferenceSession(model, None)\n", + "\n", + "def preprocess(input_data_json):\n", + " # convert the JSON data into the tensor input\n", + " img_data = np.array(json.loads(input_data_json)['data']).astype('float32')\n", + " \n", + " #normalize\n", + " mean_vec = np.array([0.485, 0.456, 0.406])\n", + " stddev_vec = np.array([0.229, 0.224, 0.225])\n", + " norm_img_data = np.zeros(img_data.shape).astype('float32')\n", + " for i in range(img_data.shape[0]):\n", + " norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]\n", + "\n", + " return norm_img_data\n", + "\n", + "def postprocess(result):\n", + " return softmax(np.array(result)).tolist()\n", + "\n", + "def run(input_data_json):\n", + " try:\n", + " start = time.time()\n", + " # load in our data which is expected as NCHW 224x224 image\n", + " input_data = preprocess(input_data_json)\n", + " input_name = session.get_inputs()[0].name # get the id of the first input of the model \n", + " result = session.run([], {input_name: input_data})\n", + " end = time.time() # stop timer\n", + " return {\"result\": postprocess(result),\n", + " \"time\": end - start}\n", + " except Exception as e:\n", + " result = str(e)\n", + " return {\"error\": result}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create inference configuration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we create a YAML file that specifies which dependencies we would like to see in our container." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.conda_dependencies import CondaDependencies \n", + "\n", + "\n", + "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n", + "\n", + "with open(\"myenv.yml\",\"w\") as f:\n", + " f.write(myenv.serialize_to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the inference configuration object. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "from azureml.core.environment import Environment\n", + "\n", + "\n", + "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", + "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.webservice import AciWebservice\n", + "\n", + "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", + " memory_gb = 1, \n", + " tags = {'demo': 'onnx'}, \n", + " description = 'web service for ResNet50 ONNX model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell will likely take a few minutes to run as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from random import randint\n", + "\n", + "aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n", + "print(\"Service\", aci_service_name)\n", + "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", + "aci_service.wait_for_deployment(True)\n", + "print(aci_service.state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if aci_service.state != 'Healthy':\n", + " # run this command for debugging.\n", + " print(aci_service.get_logs())\n", + " aci_service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Success!\n", + "\n", + "If you've made it this far, you've deployed a working web service that does image classification using an ONNX model. You can get the URL for the webservice with the code below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(aci_service.scoring_uri)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you are eventually done using the web service, remember to delete it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aci_service.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "viswamy" + } + ], + "category": "deployment", + "compute": [ + "Local" + ], + "datasets": [ + "ImageNet" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "ONNX" + ], + "friendly_name": "Deploy ResNet50 with ONNX Runtime", + "index_order": 4, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + }, + "star_tag": [], + "tags": [ + "ONNX Model Zoo" ], - "metadata": { - "authors": [ - { - "name": "viswamy" - } - ], - "category": "deployment", - "compute": [ - "Local" - ], - "datasets": [ - "ImageNet" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "ONNX" - ], - "friendly_name": "Deploy ResNet50 with ONNX Runtime", - "index_order": 4, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "star_tag": [], - "tags": [ - "ONNX Model Zoo" - ], - "task": "Image Classification" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "task": "Image Classification" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb b/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb index 92d8ef5ef..7ec5f73b2 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb @@ -1,663 +1,663 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MNIST Handwritten Digit Classification using ONNX and AzureML\n", - "\n", - "This example shows how to train a model on the MNIST data using PyTorch, save it as an ONNX model, and deploy it as a web service using Azure Machine Learning services and the ONNX Runtime.\n", - "\n", - "## What is ONNX\n", - "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n", - "\n", - "## MNIST Details\n", - "The Modified National Institute of Standards and Technology (MNIST) dataset consists of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing numbers from 0 to 9. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/). For more information about the MNIST model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/mnist). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (`config.json`)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model\n", - "\n", - "### Create a remote compute target\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n", - "\n", - "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"gpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target.')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n", - " max_nodes=6)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)\n", - "\n", - "# Use the 'status' property to get a detailed status for the current cluster. \n", - "print(compute_target.status.serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project directory\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "project_folder = './pytorch-mnist'\n", - "os.makedirs(project_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copy the training script [`mnist.py`](mnist.py) into your project directory. Make sure the training script has the following code to create an ONNX file:\n", - "```python\n", - "dummy_input = torch.randn(1, 1, 28, 28, device=device)\n", - "model_path = os.path.join(output_dir, 'mnist.onnx')\n", - "torch.onnx.export(model, dummy_input, model_path)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "shutil.copy('mnist.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment\n", - "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this transfer learning PyTorch tutorial. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = 'pytorch1-mnist'\n", - "experiment = Experiment(ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a PyTorch estimator\n", - "The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch). The following code will define a single-node PyTorch job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.dnn import PyTorch\n", - "\n", - "estimator = PyTorch(source_directory=project_folder, \n", - " script_params={'--output-dir': './outputs'},\n", - " compute_target=compute_target,\n", - " entry_script='mnist.py',\n", - " use_gpu=True)\n", - "\n", - "# upgrade to PyTorch 1.0 Preview, which has better support for ONNX\n", - "estimator.conda_dependencies.remove_conda_package('pytorch=0.4.0')\n", - "estimator.conda_dependencies.add_conda_package('pytorch-nightly')\n", - "estimator.conda_dependencies.add_channel('pytorch')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `script_params` parameter is a dictionary containing the command-line arguments to your training script `entry_script`. Please note the following:\n", - "- We specified the output directory as `./outputs`. The `outputs` directory is specially treated by AML in that all the content in this directory gets uploaded to your workspace as part of your run history. The files written to this directory are therefore accessible even once your remote run is over. In this tutorial, we will save our trained model to this output directory.\n", - "\n", - "To leverage the Azure VM's GPU for training, we set `use_gpu=True`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job\n", - "Run your experiment by submitting your estimator object. Note that this call is asynchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = experiment.submit(estimator)\n", - "print(run.get_details())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor your run\n", - "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can block until the script has completed training before running more code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download the model (optional)\n", - "\n", - "Once the run completes, you can choose to download the ONNX model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# list all the files from the run\n", - "run.get_file_names()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_path = os.path.join('outputs', 'mnist.onnx')\n", - "run.download_file(model_path, output_file_path=model_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Register the model\n", - "You can also register the model from your run to your workspace. The `model_path` parameter takes in the relative path on the remote VM to the model file in your `outputs` directory. You can then deploy this registered model as a web service through the AML SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = run.register_model(model_name='mnist', model_path=model_path)\n", - "print(model.name, model.id, model.version, sep = '\\t')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Displaying your registered models (optional)\n", - "\n", - "You can optionally list out all the models that you have registered in this workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "models = ws.models\n", - "for name, m in models.items():\n", - " print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploying as a web service\n", - "\n", - "### Write scoring file\n", - "\n", - "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import json\n", - "import time\n", - "import sys\n", - "import os\n", - "from azureml.core.model import Model\n", - "import numpy as np # we're going to use numpy to process input and output data\n", - "import onnxruntime # to inference ONNX models, we use the ONNX Runtime\n", - "\n", - "def init():\n", - " global session\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'mnist.onnx')\n", - " session = onnxruntime.InferenceSession(model)\n", - "\n", - "def preprocess(input_data_json):\n", - " # convert the JSON data into the tensor input\n", - " return np.array(json.loads(input_data_json)['data']).astype('float32')\n", - "\n", - "def postprocess(result):\n", - " # We use argmax to pick the highest confidence label\n", - " return int(np.argmax(np.array(result).squeeze(), axis=0))\n", - "\n", - "def run(input_data_json):\n", - " try:\n", - " start = time.time() # start timer\n", - " input_data = preprocess(input_data_json)\n", - " input_name = session.get_inputs()[0].name # get the id of the first input of the model \n", - " result = session.run([], {input_name: input_data})\n", - " end = time.time() # stop timer\n", - " return {\"result\": postprocess(result),\n", - " \"time\": end - start}\n", - " except Exception as e:\n", - " result = str(e)\n", - " return {\"error\": result}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create inference configuration\n", - "First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.conda_dependencies import CondaDependencies \n", - "\n", - "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\", \"azureml-defaults\"])\n", - "\n", - "with open(\"myenv.yml\",\"w\") as f:\n", - " f.write(myenv.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we setup the inference configuration " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.environment import Environment\n", - "\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", - " memory_gb = 1, \n", - " tags = {'demo': 'onnx'}, \n", - " description = 'web service for MNIST ONNX model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cell will likely take a few minutes to run as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "from random import randint\n", - "\n", - "aci_service_name = 'onnx-demo-mnist'+str(randint(0,100))\n", - "print(\"Service\", aci_service_name)\n", - "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", - "aci_service.wait_for_deployment(True)\n", - "print(aci_service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if aci_service.state != 'Healthy':\n", - " # run this command for debugging.\n", - " print(aci_service.get_logs())\n", - " aci_service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Success!\n", - "\n", - "If you've made it this far, you've deployed a working web service that does handwritten digit classification using an ONNX model. You can get the URL for the webservice with the code below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(aci_service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you are eventually done using the web service, remember to delete it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aci_service.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MNIST Handwritten Digit Classification using ONNX and AzureML\n", + "\n", + "This example shows how to train a model on the MNIST data using PyTorch, save it as an ONNX model, and deploy it as a web service using Azure Machine Learning services and the ONNX Runtime.\n", + "\n", + "## What is ONNX\n", + "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n", + "\n", + "## MNIST Details\n", + "The Modified National Institute of Standards and Technology (MNIST) dataset consists of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing numbers from 0 to 9. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/). For more information about the MNIST model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/mnist). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", + "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n", + " * install the AML SDK\n", + " * create a workspace and its configuration file (`config.json`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize workspace\n", + "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.workspace import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print('Workspace name: ' + ws.name, \n", + " 'Azure region: ' + ws.location, \n", + " 'Resource group: ' + ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train model\n", + "\n", + "### Create a remote compute target\n", + "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n", + "\n", + "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# choose a name for your cluster\n", + "cluster_name = \"gpu-cluster\"\n", + "\n", + "try:\n", + " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", + " print('Found existing compute target.')\n", + "except ComputeTargetException:\n", + " print('Creating a new compute target...')\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n", + " max_nodes=6)\n", + "\n", + " # create the cluster\n", + " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", + "\n", + "compute_target.wait_for_completion(show_output=True)\n", + "\n", + "# Use the 'status' property to get a detailed status for the current cluster. \n", + "print(compute_target.status.serialize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a project directory\n", + "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "project_folder = './pytorch-mnist'\n", + "os.makedirs(project_folder, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copy the training script [`mnist.py`](mnist.py) into your project directory. Make sure the training script has the following code to create an ONNX file:\n", + "```python\n", + "dummy_input = torch.randn(1, 1, 28, 28, device=device)\n", + "model_path = os.path.join(output_dir, 'mnist.onnx')\n", + "torch.onnx.export(model, dummy_input, model_path)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "shutil.copy('mnist.py', project_folder)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create an experiment\n", + "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this transfer learning PyTorch tutorial. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "\n", + "experiment_name = 'pytorch1-mnist'\n", + "experiment = Experiment(ws, name=experiment_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a PyTorch estimator\n", + "The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch). The following code will define a single-node PyTorch job." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.train.dnn import PyTorch\n", + "\n", + "estimator = PyTorch(source_directory=project_folder, \n", + " script_params={'--output-dir': './outputs'},\n", + " compute_target=compute_target,\n", + " entry_script='mnist.py',\n", + " use_gpu=True)\n", + "\n", + "# upgrade to PyTorch 1.0 Preview, which has better support for ONNX\n", + "estimator.conda_dependencies.remove_conda_package('pytorch=0.4.0')\n", + "estimator.conda_dependencies.add_conda_package('pytorch-nightly')\n", + "estimator.conda_dependencies.add_channel('pytorch')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `script_params` parameter is a dictionary containing the command-line arguments to your training script `entry_script`. Please note the following:\n", + "- We specified the output directory as `./outputs`. The `outputs` directory is specially treated by AML in that all the content in this directory gets uploaded to your workspace as part of your run history. The files written to this directory are therefore accessible even once your remote run is over. In this tutorial, we will save our trained model to this output directory.\n", + "\n", + "To leverage the Azure VM's GPU for training, we set `use_gpu=True`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Submit job\n", + "Run your experiment by submitting your estimator object. Note that this call is asynchronous." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run = experiment.submit(estimator)\n", + "print(run.get_details())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Monitor your run\n", + "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can block until the script has completed training before running more code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download the model (optional)\n", + "\n", + "Once the run completes, you can choose to download the ONNX model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# list all the files from the run\n", + "run.get_file_names()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_path = os.path.join('outputs', 'mnist.onnx')\n", + "run.download_file(model_path, output_file_path=model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register the model\n", + "You can also register the model from your run to your workspace. The `model_path` parameter takes in the relative path on the remote VM to the model file in your `outputs` directory. You can then deploy this registered model as a web service through the AML SDK." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = run.register_model(model_name='mnist', model_path=model_path)\n", + "print(model.name, model.id, model.version, sep = '\\t')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Displaying your registered models (optional)\n", + "\n", + "You can optionally list out all the models that you have registered in this workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "models = ws.models\n", + "for name, m in models.items():\n", + " print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploying as a web service\n", + "\n", + "### Write scoring file\n", + "\n", + "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import json\n", + "import time\n", + "import sys\n", + "import os\n", + "from azureml.core.model import Model\n", + "import numpy as np # we're going to use numpy to process input and output data\n", + "import onnxruntime # to inference ONNX models, we use the ONNX Runtime\n", + "\n", + "def init():\n", + " global session\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'mnist.onnx')\n", + " session = onnxruntime.InferenceSession(model)\n", + "\n", + "def preprocess(input_data_json):\n", + " # convert the JSON data into the tensor input\n", + " return np.array(json.loads(input_data_json)['data']).astype('float32')\n", + "\n", + "def postprocess(result):\n", + " # We use argmax to pick the highest confidence label\n", + " return int(np.argmax(np.array(result).squeeze(), axis=0))\n", + "\n", + "def run(input_data_json):\n", + " try:\n", + " start = time.time() # start timer\n", + " input_data = preprocess(input_data_json)\n", + " input_name = session.get_inputs()[0].name # get the id of the first input of the model \n", + " result = session.run([], {input_name: input_data})\n", + " end = time.time() # stop timer\n", + " return {\"result\": postprocess(result),\n", + " \"time\": end - start}\n", + " except Exception as e:\n", + " result = str(e)\n", + " return {\"error\": result}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create inference configuration\n", + "First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.conda_dependencies import CondaDependencies \n", + "\n", + "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\", \"azureml-defaults\"])\n", + "\n", + "with open(\"myenv.yml\",\"w\") as f:\n", + " f.write(myenv.serialize_to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we setup the inference configuration " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "from azureml.core.environment import Environment\n", + "\n", + "\n", + "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", + "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.webservice import AciWebservice\n", + "\n", + "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", + " memory_gb = 1, \n", + " tags = {'demo': 'onnx'}, \n", + " description = 'web service for MNIST ONNX model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell will likely take a few minutes to run as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "from random import randint\n", + "\n", + "aci_service_name = 'onnx-demo-mnist'+str(randint(0,100))\n", + "print(\"Service\", aci_service_name)\n", + "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", + "aci_service.wait_for_deployment(True)\n", + "print(aci_service.state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if aci_service.state != 'Healthy':\n", + " # run this command for debugging.\n", + " print(aci_service.get_logs())\n", + " aci_service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Success!\n", + "\n", + "If you've made it this far, you've deployed a working web service that does handwritten digit classification using an ONNX model. You can get the URL for the webservice with the code below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(aci_service.scoring_uri)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you are eventually done using the web service, remember to delete it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aci_service.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "viswamy" + } ], - "metadata": { - "authors": [ - { - "name": "viswamy" - } - ], - "category": "deployment", - "compute": [ - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "ONNX" - ], - "friendly_name": "Train MNIST in PyTorch, convert, and deploy with ONNX Runtime", - "index_order": 3, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "star_tag": [], - "tags": [ - "ONNX Converter" - ], - "task": "Image Classification", - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": { - "c899ddfc2b134ca9b89a4f278ac7c997": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.1.0", - "model_name": "LayoutModel", - "state": {} - }, - "d146cbdbd4e04710b3eebc15a66957ce": { - "model_module": "azureml_widgets", - "model_module_version": "1.0.0", - "model_name": "ShowRunDetailsModel", - "state": { - "child_runs_metrics": {}, - "compute_target_status": { - "current_node_count": 1, - "node_state_counts": { - "idleNodeCount": 1, - "leavingNodeCount": 0, - "preparingNodeCount": 0, - "runningNodeCount": 0, - "unusableNodeCount": 0 - }, - "provisioning_errors": null, - "provisioning_state": "Succeeded", - "requested_node_count": 1, - "scale_settings": { - "autoScale": { - "initialNodeCount": 0, - "maximumNodeCount": 4, - "minimumNodeCount": 0 - }, - "manual": null - }, - "vm_size": "STANDARD_NC6" - }, - "error": "", - "layout": "IPY_MODEL_c899ddfc2b134ca9b89a4f278ac7c997", - "run_id": "pytorch1-mnist_1537876563990", - "run_logs": "Uploading experiment status to history service.\nAdding run profile attachment azureml-logs/60_control_log.txt\nUploading experiment status to history service.\nAdding run profile attachment azureml-logs/80_driver_log.txt\nScript process exited with code 0\nUploading driver log...\nFinalizing run...\n\nDownloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\nDownloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\nDownloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\nDownloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\nProcessing...\nDone!\nTrain Epoch: 1 [0/60000 (0%)]\tLoss: 2.365850\nTrain Epoch: 1 [640/60000 (1%)]\tLoss: 2.305295\nTrain Epoch: 1 [1280/60000 (2%)]\tLoss: 2.301407\nTrain Epoch: 1 [1920/60000 (3%)]\tLoss: 2.316538\nTrain Epoch: 1 [2560/60000 (4%)]\tLoss: 2.255810\nTrain Epoch: 1 [3200/60000 (5%)]\tLoss: 2.224511\nTrain Epoch: 1 [3840/60000 (6%)]\tLoss: 2.216569\nTrain Epoch: 1 [4480/60000 (7%)]\tLoss: 2.181396\nTrain Epoch: 1 [5120/60000 (9%)]\tLoss: 2.116898\nTrain Epoch: 1 [5760/60000 (10%)]\tLoss: 2.045963\nTrain Epoch: 1 [6400/60000 (11%)]\tLoss: 1.973494\nTrain Epoch: 1 [7040/60000 (12%)]\tLoss: 1.968609\nTrain Epoch: 1 [7680/60000 (13%)]\tLoss: 1.787280\nTrain Epoch: 1 [8320/60000 (14%)]\tLoss: 1.735044\nTrain Epoch: 1 [8960/60000 (15%)]\tLoss: 1.680426\nTrain Epoch: 1 [9600/60000 (16%)]\tLoss: 1.486279\nTrain Epoch: 1 [10240/60000 (17%)]\tLoss: 1.545747\nTrain Epoch: 1 [10880/60000 (18%)]\tLoss: 1.193543\nTrain Epoch: 1 [11520/60000 (19%)]\tLoss: 1.652350\nTrain Epoch: 1 [12160/60000 (20%)]\tLoss: 0.982182\nTrain Epoch: 1 [12800/60000 (21%)]\tLoss: 1.331902\nTrain Epoch: 1 [13440/60000 (22%)]\tLoss: 1.089598\nTrain Epoch: 1 [14080/60000 (23%)]\tLoss: 0.998703\nTrain Epoch: 1 [14720/60000 (25%)]\tLoss: 0.992036\nTrain Epoch: 1 [15360/60000 (26%)]\tLoss: 0.979473\nTrain Epoch: 1 [16000/60000 (27%)]\tLoss: 1.141276\nTrain Epoch: 1 [16640/60000 (28%)]\tLoss: 0.836921\nTrain Epoch: 1 [17280/60000 (29%)]\tLoss: 0.764657\nTrain Epoch: 1 [17920/60000 (30%)]\tLoss: 0.826818\nTrain Epoch: 1 [18560/60000 (31%)]\tLoss: 0.837834\nTrain Epoch: 1 [19200/60000 (32%)]\tLoss: 0.899033\nTrain Epoch: 1 [19840/60000 (33%)]\tLoss: 0.868245\nTrain Epoch: 1 [20480/60000 (34%)]\tLoss: 0.930491\nTrain Epoch: 1 [21120/60000 (35%)]\tLoss: 0.795202\nTrain Epoch: 1 [21760/60000 (36%)]\tLoss: 0.575117\nTrain Epoch: 1 [22400/60000 (37%)]\tLoss: 0.577884\nTrain Epoch: 1 [23040/60000 (38%)]\tLoss: 0.708801\nTrain Epoch: 1 [23680/60000 (39%)]\tLoss: 0.927512\nTrain Epoch: 1 [24320/60000 (41%)]\tLoss: 0.598836\nTrain Epoch: 1 [24960/60000 (42%)]\tLoss: 0.944021\nTrain Epoch: 1 [25600/60000 (43%)]\tLoss: 0.811654\nTrain Epoch: 1 [26240/60000 (44%)]\tLoss: 0.590322\nTrain Epoch: 1 [26880/60000 (45%)]\tLoss: 0.555104\nTrain Epoch: 1 [27520/60000 (46%)]\tLoss: 0.795565\nTrain Epoch: 1 [28160/60000 (47%)]\tLoss: 0.603378\nTrain Epoch: 1 [28800/60000 (48%)]\tLoss: 0.552437\nTrain Epoch: 1 [29440/60000 (49%)]\tLoss: 0.662064\nTrain Epoch: 1 [30080/60000 (50%)]\tLoss: 0.682541\nTrain Epoch: 1 [30720/60000 (51%)]\tLoss: 0.659051\nTrain Epoch: 1 [31360/60000 (52%)]\tLoss: 0.781052\nTrain Epoch: 1 [32000/60000 (53%)]\tLoss: 0.595491\nTrain Epoch: 1 [32640/60000 (54%)]\tLoss: 0.367289\nTrain Epoch: 1 [33280/60000 (55%)]\tLoss: 0.459428\nTrain Epoch: 1 [33920/60000 (57%)]\tLoss: 0.819237\nTrain Epoch: 1 [34560/60000 (58%)]\tLoss: 0.773166\nTrain Epoch: 1 [35200/60000 (59%)]\tLoss: 0.557691\nTrain Epoch: 1 [35840/60000 (60%)]\tLoss: 0.854719\nTrain Epoch: 1 [36480/60000 (61%)]\tLoss: 0.497524\nTrain Epoch: 1 [37120/60000 (62%)]\tLoss: 0.582861\nTrain Epoch: 1 [37760/60000 (63%)]\tLoss: 0.839674\nTrain Epoch: 1 [38400/60000 (64%)]\tLoss: 0.557275\nTrain Epoch: 1 [39040/60000 (65%)]\tLoss: 0.419819\nTrain Epoch: 1 [39680/60000 (66%)]\tLoss: 0.694659\nTrain Epoch: 1 [40320/60000 (67%)]\tLoss: 0.678524\nTrain Epoch: 1 [40960/60000 (68%)]\tLoss: 0.514364\nTrain Epoch: 1 [41600/60000 (69%)]\tLoss: 0.400510\nTrain Epoch: 1 [42240/60000 (70%)]\tLoss: 0.526099\nTrain Epoch: 1 [42880/60000 (71%)]\tLoss: 0.387087\nTrain Epoch: 1 [43520/60000 (72%)]\tLoss: 0.730123\nTrain Epoch: 1 [44160/60000 (74%)]\tLoss: 0.678924\nTrain Epoch: 1 [44800/60000 (75%)]\tLoss: 0.425195\nTrain Epoch: 1 [45440/60000 (76%)]\tLoss: 0.656437\nTrain Epoch: 1 [46080/60000 (77%)]\tLoss: 0.348130\nTrain Epoch: 1 [46720/60000 (78%)]\tLoss: 0.487442\nTrain Epoch: 1 [47360/60000 (79%)]\tLoss: 0.649533\nTrain Epoch: 1 [48000/60000 (80%)]\tLoss: 0.541395\nTrain Epoch: 1 [48640/60000 (81%)]\tLoss: 0.464202\nTrain Epoch: 1 [49280/60000 (82%)]\tLoss: 0.750336\nTrain Epoch: 1 [49920/60000 (83%)]\tLoss: 0.548484\nTrain Epoch: 1 [50560/60000 (84%)]\tLoss: 0.421382\nTrain Epoch: 1 [51200/60000 (85%)]\tLoss: 0.680766\nTrain Epoch: 1 [51840/60000 (86%)]\tLoss: 0.483003\nTrain Epoch: 1 [52480/60000 (87%)]\tLoss: 0.610840\nTrain Epoch: 1 [53120/60000 (88%)]\tLoss: 0.483278\nTrain Epoch: 1 [53760/60000 (90%)]\tLoss: 0.553161\nTrain Epoch: 1 [54400/60000 (91%)]\tLoss: 0.465237\nTrain Epoch: 1 [55040/60000 (92%)]\tLoss: 0.558884\nTrain Epoch: 1 [55680/60000 (93%)]\tLoss: 0.528969\nTrain Epoch: 1 [56320/60000 (94%)]\tLoss: 0.370189\nTrain Epoch: 1 [56960/60000 (95%)]\tLoss: 0.379404\nTrain Epoch: 1 [57600/60000 (96%)]\tLoss: 0.263894\nTrain Epoch: 1 [58240/60000 (97%)]\tLoss: 0.432745\nTrain Epoch: 1 [58880/60000 (98%)]\tLoss: 0.455681\nTrain Epoch: 1 [59520/60000 (99%)]\tLoss: 0.483901\n/azureml-envs/azureml_de892a6d0f01a442356c3959dd42e13b/lib/python3.6/site-packages/torch/nn/functional.py:54: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n warnings.warn(warning.format(ret))\n\nTest set: Average loss: 0.2073, Accuracy: 9384/10000 (94%)\n\nTrain Epoch: 2 [0/60000 (0%)]\tLoss: 0.390797\nTrain Epoch: 2 [640/60000 (1%)]\tLoss: 0.214512\nTrain Epoch: 2 [1280/60000 (2%)]\tLoss: 0.226415\nTrain Epoch: 2 [1920/60000 (3%)]\tLoss: 0.491764\nTrain Epoch: 2 [2560/60000 (4%)]\tLoss: 0.333604\nTrain Epoch: 2 [3200/60000 (5%)]\tLoss: 0.514239\nTrain Epoch: 2 [3840/60000 (6%)]\tLoss: 0.430618\nTrain Epoch: 2 [4480/60000 (7%)]\tLoss: 0.579474\nTrain Epoch: 2 [5120/60000 (9%)]\tLoss: 0.259456\nTrain Epoch: 2 [5760/60000 (10%)]\tLoss: 0.651198\nTrain Epoch: 2 [6400/60000 (11%)]\tLoss: 0.338269\nTrain Epoch: 2 [7040/60000 (12%)]\tLoss: 0.335233\nTrain Epoch: 2 [7680/60000 (13%)]\tLoss: 0.518132\nTrain Epoch: 2 [8320/60000 (14%)]\tLoss: 0.363488\nTrain Epoch: 2 [8960/60000 (15%)]\tLoss: 0.437092\nTrain Epoch: 2 [9600/60000 (16%)]\tLoss: 0.362660\nTrain Epoch: 2 [10240/60000 (17%)]\tLoss: 0.432337\nTrain Epoch: 2 [10880/60000 (18%)]\tLoss: 0.360611\nTrain Epoch: 2 [11520/60000 (19%)]\tLoss: 0.305427\nTrain Epoch: 2 [12160/60000 (20%)]\tLoss: 0.347859\nTrain Epoch: 2 [12800/60000 (21%)]\tLoss: 0.408770\nTrain Epoch: 2 [13440/60000 (22%)]\tLoss: 0.469975\nTrain Epoch: 2 [14080/60000 (23%)]\tLoss: 0.673716\nTrain Epoch: 2 [14720/60000 (25%)]\tLoss: 0.388876\nTrain Epoch: 2 [15360/60000 (26%)]\tLoss: 0.462371\nTrain Epoch: 2 [16000/60000 (27%)]\tLoss: 0.530107\nTrain Epoch: 2 [16640/60000 (28%)]\tLoss: 0.448767\nTrain Epoch: 2 [17280/60000 (29%)]\tLoss: 0.412764\nTrain Epoch: 2 [17920/60000 (30%)]\tLoss: 0.301494\nTrain Epoch: 2 [18560/60000 (31%)]\tLoss: 0.465599\nTrain Epoch: 2 [19200/60000 (32%)]\tLoss: 0.434249\nTrain Epoch: 2 [19840/60000 (33%)]\tLoss: 0.324006\nTrain Epoch: 2 [20480/60000 (34%)]\tLoss: 0.447446\nTrain Epoch: 2 [21120/60000 (35%)]\tLoss: 0.291222\nTrain Epoch: 2 [21760/60000 (36%)]\tLoss: 0.557065\nTrain Epoch: 2 [22400/60000 (37%)]\tLoss: 0.552659\nTrain Epoch: 2 [23040/60000 (38%)]\tLoss: 0.378901\nTrain Epoch: 2 [23680/60000 (39%)]\tLoss: 0.360550\nTrain Epoch: 2 [24320/60000 (41%)]\tLoss: 0.283795\nTrain Epoch: 2 [24960/60000 (42%)]\tLoss: 0.475816\nTrain Epoch: 2 [25600/60000 (43%)]\tLoss: 0.283652\nTrain Epoch: 2 [26240/60000 (44%)]\tLoss: 0.276265\nTrain Epoch: 2 [26880/60000 (45%)]\tLoss: 0.527902\nTrain Epoch: 2 [27520/60000 (46%)]\tLoss: 0.437130\nTrain Epoch: 2 [28160/60000 (47%)]\tLoss: 0.277132\nTrain Epoch: 2 [28800/60000 (48%)]\tLoss: 0.471580\nTrain Epoch: 2 [29440/60000 (49%)]\tLoss: 0.380154\nTrain Epoch: 2 [30080/60000 (50%)]\tLoss: 0.232072\nTrain Epoch: 2 [30720/60000 (51%)]\tLoss: 0.366567\nTrain Epoch: 2 [31360/60000 (52%)]\tLoss: 0.469628\nTrain Epoch: 2 [32000/60000 (53%)]\tLoss: 0.440017\nTrain Epoch: 2 [32640/60000 (54%)]\tLoss: 0.421814\nTrain Epoch: 2 [33280/60000 (55%)]\tLoss: 0.367687\nTrain Epoch: 2 [33920/60000 (57%)]\tLoss: 0.448384\nTrain Epoch: 2 [34560/60000 (58%)]\tLoss: 0.550283\nTrain Epoch: 2 [35200/60000 (59%)]\tLoss: 0.609798\nTrain Epoch: 2 [35840/60000 (60%)]\tLoss: 0.461334\nTrain Epoch: 2 [36480/60000 (61%)]\tLoss: 0.443838\nTrain Epoch: 2 [37120/60000 (62%)]\tLoss: 0.306666\nTrain Epoch: 2 [37760/60000 (63%)]\tLoss: 0.432083\nTrain Epoch: 2 [38400/60000 (64%)]\tLoss: 0.277025\nTrain Epoch: 2 [39040/60000 (65%)]\tLoss: 0.298752\nTrain Epoch: 2 [39680/60000 (66%)]\tLoss: 0.427435\nTrain Epoch: 2 [40320/60000 (67%)]\tLoss: 0.374736\nTrain Epoch: 2 [40960/60000 (68%)]\tLoss: 0.246496\nTrain Epoch: 2 [41600/60000 (69%)]\tLoss: 0.662259\nTrain Epoch: 2 [42240/60000 (70%)]\tLoss: 0.497635\nTrain Epoch: 2 [42880/60000 (71%)]\tLoss: 0.237556\nTrain Epoch: 2 [43520/60000 (72%)]\tLoss: 0.194535\nTrain Epoch: 2 [44160/60000 (74%)]\tLoss: 0.258943\nTrain Epoch: 2 [44800/60000 (75%)]\tLoss: 0.437360\nTrain Epoch: 2 [45440/60000 (76%)]\tLoss: 0.355489\nTrain Epoch: 2 [46080/60000 (77%)]\tLoss: 0.335020\nTrain Epoch: 2 [46720/60000 (78%)]\tLoss: 0.565189\nTrain Epoch: 2 [47360/60000 (79%)]\tLoss: 0.430366\nTrain Epoch: 2 [48000/60000 (80%)]\tLoss: 0.266303\nTrain Epoch: 2 [48640/60000 (81%)]\tLoss: 0.172954\nTrain Epoch: 2 [49280/60000 (82%)]\tLoss: 0.245803\nTrain Epoch: 2 [49920/60000 (83%)]\tLoss: 0.426530\nTrain Epoch: 2 [50560/60000 (84%)]\tLoss: 0.468984\nTrain Epoch: 2 [51200/60000 (85%)]\tLoss: 0.370892\nTrain Epoch: 2 [51840/60000 (86%)]\tLoss: 0.300021\nTrain Epoch: 2 [52480/60000 (87%)]\tLoss: 0.392199\nTrain Epoch: 2 [53120/60000 (88%)]\tLoss: 0.510658\nTrain Epoch: 2 [53760/60000 (90%)]\tLoss: 0.376290\nTrain Epoch: 2 [54400/60000 (91%)]\tLoss: 0.273752\nTrain Epoch: 2 [55040/60000 (92%)]\tLoss: 0.234505\nTrain Epoch: 2 [55680/60000 (93%)]\tLoss: 0.610978\nTrain Epoch: 2 [56320/60000 (94%)]\tLoss: 0.154850\nTrain Epoch: 2 [56960/60000 (95%)]\tLoss: 0.374254\nTrain Epoch: 2 [57600/60000 (96%)]\tLoss: 0.292167\nTrain Epoch: 2 [58240/60000 (97%)]\tLoss: 0.478376\nTrain Epoch: 2 [58880/60000 (98%)]\tLoss: 0.303128\nTrain Epoch: 2 [59520/60000 (99%)]\tLoss: 0.376779\n\nTest set: Average loss: 0.1297, Accuracy: 9597/10000 (96%)\n\nTrain Epoch: 3 [0/60000 (0%)]\tLoss: 0.450588\nTrain Epoch: 3 [640/60000 (1%)]\tLoss: 0.361118\nTrain Epoch: 3 [1280/60000 (2%)]\tLoss: 0.374497\nTrain Epoch: 3 [1920/60000 (3%)]\tLoss: 0.312127\nTrain Epoch: 3 [2560/60000 (4%)]\tLoss: 0.353896\nTrain Epoch: 3 [3200/60000 (5%)]\tLoss: 0.320840\nTrain Epoch: 3 [3840/60000 (6%)]\tLoss: 0.218477\nTrain Epoch: 3 [4480/60000 (7%)]\tLoss: 0.295629\nTrain Epoch: 3 [5120/60000 (9%)]\tLoss: 0.339400\nTrain Epoch: 3 [5760/60000 (10%)]\tLoss: 0.170357\nTrain Epoch: 3 [6400/60000 (11%)]\tLoss: 0.416447\nTrain Epoch: 3 [7040/60000 (12%)]\tLoss: 0.320326\nTrain Epoch: 3 [7680/60000 (13%)]\tLoss: 0.318410\nTrain Epoch: 3 [8320/60000 (14%)]\tLoss: 0.384793\nTrain Epoch: 3 [8960/60000 (15%)]\tLoss: 0.343415\nTrain Epoch: 3 [9600/60000 (16%)]\tLoss: 0.284627\nTrain Epoch: 3 [10240/60000 (17%)]\tLoss: 0.151805\nTrain Epoch: 3 [10880/60000 (18%)]\tLoss: 0.401332\nTrain Epoch: 3 [11520/60000 (19%)]\tLoss: 0.253159\nTrain Epoch: 3 [12160/60000 (20%)]\tLoss: 0.339563\nTrain Epoch: 3 [12800/60000 (21%)]\tLoss: 0.237430\nTrain Epoch: 3 [13440/60000 (22%)]\tLoss: 0.311402\nTrain Epoch: 3 [14080/60000 (23%)]\tLoss: 0.241667\nTrain Epoch: 3 [14720/60000 (25%)]\tLoss: 0.265347\nTrain Epoch: 3 [15360/60000 (26%)]\tLoss: 0.367453\nTrain Epoch: 3 [16000/60000 (27%)]\tLoss: 0.190671\nTrain Epoch: 3 [16640/60000 (28%)]\tLoss: 0.313052\nTrain Epoch: 3 [17280/60000 (29%)]\tLoss: 0.368028\nTrain Epoch: 3 [17920/60000 (30%)]\tLoss: 0.268639\nTrain Epoch: 3 [18560/60000 (31%)]\tLoss: 0.341066\nTrain Epoch: 3 [19200/60000 (32%)]\tLoss: 0.457961\nTrain Epoch: 3 [19840/60000 (33%)]\tLoss: 0.732400\nTrain Epoch: 3 [20480/60000 (34%)]\tLoss: 0.330679\nTrain Epoch: 3 [21120/60000 (35%)]\tLoss: 0.279778\nTrain Epoch: 3 [21760/60000 (36%)]\tLoss: 0.305972\nTrain Epoch: 3 [22400/60000 (37%)]\tLoss: 0.402131\nTrain Epoch: 3 [23040/60000 (38%)]\tLoss: 0.345302\nTrain Epoch: 3 [23680/60000 (39%)]\tLoss: 0.251726\nTrain Epoch: 3 [24320/60000 (41%)]\tLoss: 0.152062\nTrain Epoch: 3 [24960/60000 (42%)]\tLoss: 0.149305\nTrain Epoch: 3 [25600/60000 (43%)]\tLoss: 0.364678\nTrain Epoch: 3 [26240/60000 (44%)]\tLoss: 0.067165\nTrain Epoch: 3 [26880/60000 (45%)]\tLoss: 0.229927\nTrain Epoch: 3 [27520/60000 (46%)]\tLoss: 0.236894\nTrain Epoch: 3 [28160/60000 (47%)]\tLoss: 0.486373\nTrain Epoch: 3 [28800/60000 (48%)]\tLoss: 0.453053\nTrain Epoch: 3 [29440/60000 (49%)]\tLoss: 0.283823\nTrain Epoch: 3 [30080/60000 (50%)]\tLoss: 0.185119\nTrain Epoch: 3 [30720/60000 (51%)]\tLoss: 0.381274\nTrain Epoch: 3 [31360/60000 (52%)]\tLoss: 0.394533\nTrain Epoch: 3 [32000/60000 (53%)]\tLoss: 0.392791\nTrain Epoch: 3 [32640/60000 (54%)]\tLoss: 0.230672\nTrain Epoch: 3 [33280/60000 (55%)]\tLoss: 0.393846\nTrain Epoch: 3 [33920/60000 (57%)]\tLoss: 0.676802\nTrain Epoch: 3 [34560/60000 (58%)]\tLoss: 0.160434\nTrain Epoch: 3 [35200/60000 (59%)]\tLoss: 0.211318\nTrain Epoch: 3 [35840/60000 (60%)]\tLoss: 0.245763\nTrain Epoch: 3 [36480/60000 (61%)]\tLoss: 0.198454\nTrain Epoch: 3 [37120/60000 (62%)]\tLoss: 0.243536\nTrain Epoch: 3 [37760/60000 (63%)]\tLoss: 0.151804\nTrain Epoch: 3 [38400/60000 (64%)]\tLoss: 0.176093\nTrain Epoch: 3 [39040/60000 (65%)]\tLoss: 0.237228\nTrain Epoch: 3 [39680/60000 (66%)]\tLoss: 0.146441\nTrain Epoch: 3 [40320/60000 (67%)]\tLoss: 0.345162\nTrain Epoch: 3 [40960/60000 (68%)]\tLoss: 0.400378\nTrain Epoch: 3 [41600/60000 (69%)]\tLoss: 0.259152\nTrain Epoch: 3 [42240/60000 (70%)]\tLoss: 0.569659\nTrain Epoch: 3 [42880/60000 (71%)]\tLoss: 0.166401\nTrain Epoch: 3 [43520/60000 (72%)]\tLoss: 0.220592\nTrain Epoch: 3 [44160/60000 (74%)]\tLoss: 0.303227\nTrain Epoch: 3 [44800/60000 (75%)]\tLoss: 0.193691\nTrain Epoch: 3 [45440/60000 (76%)]\tLoss: 0.257408\nTrain Epoch: 3 [46080/60000 (77%)]\tLoss: 0.391211\nTrain Epoch: 3 [46720/60000 (78%)]\tLoss: 0.419841\nTrain Epoch: 3 [47360/60000 (79%)]\tLoss: 0.121861\nTrain Epoch: 3 [48000/60000 (80%)]\tLoss: 0.176442\nTrain Epoch: 3 [48640/60000 (81%)]\tLoss: 0.534631\nTrain Epoch: 3 [49280/60000 (82%)]\tLoss: 0.296596\nTrain Epoch: 3 [49920/60000 (83%)]\tLoss: 0.190096\nTrain Epoch: 3 [50560/60000 (84%)]\tLoss: 0.360826\nTrain Epoch: 3 [51200/60000 (85%)]\tLoss: 0.427482\nTrain Epoch: 3 [51840/60000 (86%)]\tLoss: 0.251076\nTrain Epoch: 3 [52480/60000 (87%)]\tLoss: 0.319904\nTrain Epoch: 3 [53120/60000 (88%)]\tLoss: 0.228778\nTrain Epoch: 3 [53760/60000 (90%)]\tLoss: 0.180340\nTrain Epoch: 3 [54400/60000 (91%)]\tLoss: 0.236512\nTrain Epoch: 3 [55040/60000 (92%)]\tLoss: 0.206779\nTrain Epoch: 3 [55680/60000 (93%)]\tLoss: 0.323677\nTrain Epoch: 3 [56320/60000 (94%)]\tLoss: 0.406382\nTrain Epoch: 3 [56960/60000 (95%)]\tLoss: 0.426768\nTrain Epoch: 3 [57600/60000 (96%)]\tLoss: 0.595419\nTrain Epoch: 3 [58240/60000 (97%)]\tLoss: 0.175457\nTrain Epoch: 3 [58880/60000 (98%)]\tLoss: 0.301019\nTrain Epoch: 3 [59520/60000 (99%)]\tLoss: 0.419139\n\nTest set: Average loss: 0.1049, Accuracy: 9686/10000 (97%)\n\nTrain Epoch: 4 [0/60000 (0%)]\tLoss: 0.352631\nTrain Epoch: 4 [640/60000 (1%)]\tLoss: 0.343671\nTrain Epoch: 4 [1280/60000 (2%)]\tLoss: 0.170439\nTrain Epoch: 4 [1920/60000 (3%)]\tLoss: 0.289486\nTrain Epoch: 4 [2560/60000 (4%)]\tLoss: 0.096597\nTrain Epoch: 4 [3200/60000 (5%)]\tLoss: 0.263759\nTrain Epoch: 4 [3840/60000 (6%)]\tLoss: 0.369941\nTrain Epoch: 4 [4480/60000 (7%)]\tLoss: 0.326594\nTrain Epoch: 4 [5120/60000 (9%)]\tLoss: 0.174094\nTrain Epoch: 4 [5760/60000 (10%)]\tLoss: 0.442069\nTrain Epoch: 4 [6400/60000 (11%)]\tLoss: 0.179002\nTrain Epoch: 4 [7040/60000 (12%)]\tLoss: 0.292742\nTrain Epoch: 4 [7680/60000 (13%)]\tLoss: 0.209898\nTrain Epoch: 4 [8320/60000 (14%)]\tLoss: 0.401671\nTrain Epoch: 4 [8960/60000 (15%)]\tLoss: 0.205146\nTrain Epoch: 4 [9600/60000 (16%)]\tLoss: 0.250836\nTrain Epoch: 4 [10240/60000 (17%)]\tLoss: 0.156622\nTrain Epoch: 4 [10880/60000 (18%)]\tLoss: 0.214578\nTrain Epoch: 4 [11520/60000 (19%)]\tLoss: 0.155916\nTrain Epoch: 4 [12160/60000 (20%)]\tLoss: 0.416294\nTrain Epoch: 4 [12800/60000 (21%)]\tLoss: 0.197429\nTrain Epoch: 4 [13440/60000 (22%)]\tLoss: 0.154103\nTrain Epoch: 4 [14080/60000 (23%)]\tLoss: 0.377950\nTrain Epoch: 4 [14720/60000 (25%)]\tLoss: 0.338084\nTrain Epoch: 4 [15360/60000 (26%)]\tLoss: 0.242834\nTrain Epoch: 4 [16000/60000 (27%)]\tLoss: 0.139219\nTrain Epoch: 4 [16640/60000 (28%)]\tLoss: 0.242067\nTrain Epoch: 4 [17280/60000 (29%)]\tLoss: 0.189929\nTrain Epoch: 4 [17920/60000 (30%)]\tLoss: 0.358215\nTrain Epoch: 4 [18560/60000 (31%)]\tLoss: 0.354969\nTrain Epoch: 4 [19200/60000 (32%)]\tLoss: 0.303644\nTrain Epoch: 4 [19840/60000 (33%)]\tLoss: 0.322343\nTrain Epoch: 4 [20480/60000 (34%)]\tLoss: 0.225422\nTrain Epoch: 4 [21120/60000 (35%)]\tLoss: 0.614347\nTrain Epoch: 4 [21760/60000 (36%)]\tLoss: 0.448674\nTrain Epoch: 4 [22400/60000 (37%)]\tLoss: 0.362976\nTrain Epoch: 4 [23040/60000 (38%)]\tLoss: 0.100357\nTrain Epoch: 4 [23680/60000 (39%)]\tLoss: 0.289331\nTrain Epoch: 4 [24320/60000 (41%)]\tLoss: 0.405818\nTrain Epoch: 4 [24960/60000 (42%)]\tLoss: 0.212617\nTrain Epoch: 4 [25600/60000 (43%)]\tLoss: 0.348597\nTrain Epoch: 4 [26240/60000 (44%)]\tLoss: 0.351009\nTrain Epoch: 4 [26880/60000 (45%)]\tLoss: 0.341456\nTrain Epoch: 4 [27520/60000 (46%)]\tLoss: 0.297527\nTrain Epoch: 4 [28160/60000 (47%)]\tLoss: 0.281190\nTrain Epoch: 4 [28800/60000 (48%)]\tLoss: 0.187359\nTrain Epoch: 4 [29440/60000 (49%)]\tLoss: 0.178844\nTrain Epoch: 4 [30080/60000 (50%)]\tLoss: 0.201243\nTrain Epoch: 4 [30720/60000 (51%)]\tLoss: 0.305701\nTrain Epoch: 4 [31360/60000 (52%)]\tLoss: 0.370592\nTrain Epoch: 4 [32000/60000 (53%)]\tLoss: 0.241955\nTrain Epoch: 4 [32640/60000 (54%)]\tLoss: 0.278765\nTrain Epoch: 4 [33280/60000 (55%)]\tLoss: 0.284302\nTrain Epoch: 4 [33920/60000 (57%)]\tLoss: 0.337426\nTrain Epoch: 4 [34560/60000 (58%)]\tLoss: 0.277304\nTrain Epoch: 4 [35200/60000 (59%)]\tLoss: 0.221228\nTrain Epoch: 4 [35840/60000 (60%)]\tLoss: 0.150985\nTrain Epoch: 4 [36480/60000 (61%)]\tLoss: 0.312087\nTrain Epoch: 4 [37120/60000 (62%)]\tLoss: 0.170111\nTrain Epoch: 4 [37760/60000 (63%)]\tLoss: 0.291135\nTrain Epoch: 4 [38400/60000 (64%)]\tLoss: 0.160971\nTrain Epoch: 4 [39040/60000 (65%)]\tLoss: 0.390679\nTrain Epoch: 4 [39680/60000 (66%)]\tLoss: 0.434802\nTrain Epoch: 4 [40320/60000 (67%)]\tLoss: 0.281539\nTrain Epoch: 4 [40960/60000 (68%)]\tLoss: 0.172577\nTrain Epoch: 4 [41600/60000 (69%)]\tLoss: 0.348624\nTrain Epoch: 4 [42240/60000 (70%)]\tLoss: 0.380416\nTrain Epoch: 4 [42880/60000 (71%)]\tLoss: 0.483520\nTrain Epoch: 4 [43520/60000 (72%)]\tLoss: 0.216825\nTrain Epoch: 4 [44160/60000 (74%)]\tLoss: 0.320874\nTrain Epoch: 4 [44800/60000 (75%)]\tLoss: 0.213358\nTrain Epoch: 4 [45440/60000 (76%)]\tLoss: 0.218650\nTrain Epoch: 4 [46080/60000 (77%)]\tLoss: 0.221090\nTrain Epoch: 4 [46720/60000 (78%)]\tLoss: 0.325981\nTrain Epoch: 4 [47360/60000 (79%)]\tLoss: 0.283184\nTrain Epoch: 4 [48000/60000 (80%)]\tLoss: 0.072845\nTrain Epoch: 4 [48640/60000 (81%)]\tLoss: 0.206940\nTrain Epoch: 4 [49280/60000 (82%)]\tLoss: 0.423454\nTrain Epoch: 4 [49920/60000 (83%)]\tLoss: 0.475285\nTrain Epoch: 4 [50560/60000 (84%)]\tLoss: 0.128978\nTrain Epoch: 4 [51200/60000 (85%)]\tLoss: 0.195609\nTrain Epoch: 4 [51840/60000 (86%)]\tLoss: 0.125730\nTrain Epoch: 4 [52480/60000 (87%)]\tLoss: 0.137783\nTrain Epoch: 4 [53120/60000 (88%)]\tLoss: 0.375247\nTrain Epoch: 4 [53760/60000 (90%)]\tLoss: 0.243497\nTrain Epoch: 4 [54400/60000 (91%)]\tLoss: 0.236100\nTrain Epoch: 4 [55040/60000 (92%)]\tLoss: 0.266795\nTrain Epoch: 4 [55680/60000 (93%)]\tLoss: 0.229095\nTrain Epoch: 4 [56320/60000 (94%)]\tLoss: 0.167610\nTrain Epoch: 4 [56960/60000 (95%)]\tLoss: 0.240640\nTrain Epoch: 4 [57600/60000 (96%)]\tLoss: 0.153999\nTrain Epoch: 4 [58240/60000 (97%)]\tLoss: 0.753790\nTrain Epoch: 4 [58880/60000 (98%)]\tLoss: 0.143998\nTrain Epoch: 4 [59520/60000 (99%)]\tLoss: 0.310583\n\nTest set: Average loss: 0.0843, Accuracy: 9739/10000 (97%)\n\nTrain Epoch: 5 [0/60000 (0%)]\tLoss: 0.227892\nTrain Epoch: 5 [640/60000 (1%)]\tLoss: 0.162702\nTrain Epoch: 5 [1280/60000 (2%)]\tLoss: 0.227571\nTrain Epoch: 5 [1920/60000 (3%)]\tLoss: 0.148511\nTrain Epoch: 5 [2560/60000 (4%)]\tLoss: 0.187414\nTrain Epoch: 5 [3200/60000 (5%)]\tLoss: 0.194418\nTrain Epoch: 5 [3840/60000 (6%)]\tLoss: 0.276495\nTrain Epoch: 5 [4480/60000 (7%)]\tLoss: 0.268769\nTrain Epoch: 5 [5120/60000 (9%)]\tLoss: 0.163968\nTrain Epoch: 5 [5760/60000 (10%)]\tLoss: 0.349296\nTrain Epoch: 5 [6400/60000 (11%)]\tLoss: 0.217248\nTrain Epoch: 5 [7040/60000 (12%)]\tLoss: 0.195263\nTrain Epoch: 5 [7680/60000 (13%)]\tLoss: 0.339447\nTrain Epoch: 5 [8320/60000 (14%)]\tLoss: 0.224461\nTrain Epoch: 5 [8960/60000 (15%)]\tLoss: 0.095605\nTrain Epoch: 5 [9600/60000 (16%)]\tLoss: 0.196891\nTrain Epoch: 5 [10240/60000 (17%)]\tLoss: 0.218742\nTrain Epoch: 5 [10880/60000 (18%)]\tLoss: 0.071347\nTrain Epoch: 5 [11520/60000 (19%)]\tLoss: 0.403286\nTrain Epoch: 5 [12160/60000 (20%)]\tLoss: 0.149740\nTrain Epoch: 5 [12800/60000 (21%)]\tLoss: 0.160939\nTrain Epoch: 5 [13440/60000 (22%)]\tLoss: 0.236512\nTrain Epoch: 5 [14080/60000 (23%)]\tLoss: 0.348727\nTrain Epoch: 5 [14720/60000 (25%)]\tLoss: 0.190054\nTrain Epoch: 5 [15360/60000 (26%)]\tLoss: 0.272029\nTrain Epoch: 5 [16000/60000 (27%)]\tLoss: 0.427739\nTrain Epoch: 5 [16640/60000 (28%)]\tLoss: 0.322332\nTrain Epoch: 5 [17280/60000 (29%)]\tLoss: 0.141410\nTrain Epoch: 5 [17920/60000 (30%)]\tLoss: 0.098900\nTrain Epoch: 5 [18560/60000 (31%)]\tLoss: 0.252387\nTrain Epoch: 5 [19200/60000 (32%)]\tLoss: 0.182150\nTrain Epoch: 5 [19840/60000 (33%)]\tLoss: 0.133239\nTrain Epoch: 5 [20480/60000 (34%)]\tLoss: 0.126683\nTrain Epoch: 5 [21120/60000 (35%)]\tLoss: 0.370189\nTrain Epoch: 5 [21760/60000 (36%)]\tLoss: 0.162514\nTrain Epoch: 5 [22400/60000 (37%)]\tLoss: 0.272352\nTrain Epoch: 5 [23040/60000 (38%)]\tLoss: 0.298543\nTrain Epoch: 5 [23680/60000 (39%)]\tLoss: 0.235891\nTrain Epoch: 5 [24320/60000 (41%)]\tLoss: 0.187710\nTrain Epoch: 5 [24960/60000 (42%)]\tLoss: 0.185363\nTrain Epoch: 5 [25600/60000 (43%)]\tLoss: 0.193369\nTrain Epoch: 5 [26240/60000 (44%)]\tLoss: 0.155984\nTrain Epoch: 5 [26880/60000 (45%)]\tLoss: 0.388923\nTrain Epoch: 5 [27520/60000 (46%)]\tLoss: 0.192868\nTrain Epoch: 5 [28160/60000 (47%)]\tLoss: 0.535787\nTrain Epoch: 5 [28800/60000 (48%)]\tLoss: 0.161020\nTrain Epoch: 5 [29440/60000 (49%)]\tLoss: 0.242179\nTrain Epoch: 5 [30080/60000 (50%)]\tLoss: 0.136554\nTrain Epoch: 5 [30720/60000 (51%)]\tLoss: 0.190672\nTrain Epoch: 5 [31360/60000 (52%)]\tLoss: 0.118027\nTrain Epoch: 5 [32000/60000 (53%)]\tLoss: 0.278750\nTrain Epoch: 5 [32640/60000 (54%)]\tLoss: 0.418058\nTrain Epoch: 5 [33280/60000 (55%)]\tLoss: 0.287063\nTrain Epoch: 5 [33920/60000 (57%)]\tLoss: 0.279596\nTrain Epoch: 5 [34560/60000 (58%)]\tLoss: 0.181579\nTrain Epoch: 5 [35200/60000 (59%)]\tLoss: 0.443592\nTrain Epoch: 5 [35840/60000 (60%)]\tLoss: 0.095470\nTrain Epoch: 5 [36480/60000 (61%)]\tLoss: 0.277385\nTrain Epoch: 5 [37120/60000 (62%)]\tLoss: 0.263358\nTrain Epoch: 5 [37760/60000 (63%)]\tLoss: 0.190867\nTrain Epoch: 5 [38400/60000 (64%)]\tLoss: 0.176580\nTrain Epoch: 5 [39040/60000 (65%)]\tLoss: 0.360235\nTrain Epoch: 5 [39680/60000 (66%)]\tLoss: 0.172416\nTrain Epoch: 5 [40320/60000 (67%)]\tLoss: 0.174126\nTrain Epoch: 5 [40960/60000 (68%)]\tLoss: 0.202162\nTrain Epoch: 5 [41600/60000 (69%)]\tLoss: 0.196991\nTrain Epoch: 5 [42240/60000 (70%)]\tLoss: 0.224622\nTrain Epoch: 5 [42880/60000 (71%)]\tLoss: 0.180406\nTrain Epoch: 5 [43520/60000 (72%)]\tLoss: 0.060447\nTrain Epoch: 5 [44160/60000 (74%)]\tLoss: 0.322497\nTrain Epoch: 5 [44800/60000 (75%)]\tLoss: 0.239324\nTrain Epoch: 5 [45440/60000 (76%)]\tLoss: 0.348920\nTrain Epoch: 5 [46080/60000 (77%)]\tLoss: 0.240017\nTrain Epoch: 5 [46720/60000 (78%)]\tLoss: 0.237575\nTrain Epoch: 5 [47360/60000 (79%)]\tLoss: 0.142648\nTrain Epoch: 5 [48000/60000 (80%)]\tLoss: 0.227562\nTrain Epoch: 5 [48640/60000 (81%)]\tLoss: 0.254358\nTrain Epoch: 5 [49280/60000 (82%)]\tLoss: 0.135818\nTrain Epoch: 5 [49920/60000 (83%)]\tLoss: 0.386120\nTrain Epoch: 5 [50560/60000 (84%)]\tLoss: 0.328150\nTrain Epoch: 5 [51200/60000 (85%)]\tLoss: 0.276833\nTrain Epoch: 5 [51840/60000 (86%)]\tLoss: 0.308869\nTrain Epoch: 5 [52480/60000 (87%)]\tLoss: 0.246442\nTrain Epoch: 5 [53120/60000 (88%)]\tLoss: 0.240874\nTrain Epoch: 5 [53760/60000 (90%)]\tLoss: 0.114337\nTrain Epoch: 5 [54400/60000 (91%)]\tLoss: 0.217325\nTrain Epoch: 5 [55040/60000 (92%)]\tLoss: 0.223010\nTrain Epoch: 5 [55680/60000 (93%)]\tLoss: 0.138459\nTrain Epoch: 5 [56320/60000 (94%)]\tLoss: 0.283678\nTrain Epoch: 5 [56960/60000 (95%)]\tLoss: 0.158834\nTrain Epoch: 5 [57600/60000 (96%)]\tLoss: 0.164267\nTrain Epoch: 5 [58240/60000 (97%)]\tLoss: 0.290795\nTrain Epoch: 5 [58880/60000 (98%)]\tLoss: 0.451639\nTrain Epoch: 5 [59520/60000 (99%)]\tLoss: 0.349018\n\nTest set: Average loss: 0.0797, Accuracy: 9758/10000 (98%)\n\nTrain Epoch: 6 [0/60000 (0%)]\tLoss: 0.311334\nTrain Epoch: 6 [640/60000 (1%)]\tLoss: 0.129143\nTrain Epoch: 6 [1280/60000 (2%)]\tLoss: 0.227222\nTrain Epoch: 6 [1920/60000 (3%)]\tLoss: 0.157591\nTrain Epoch: 6 [2560/60000 (4%)]\tLoss: 0.205490\nTrain Epoch: 6 [3200/60000 (5%)]\tLoss: 0.421089\nTrain Epoch: 6 [3840/60000 (6%)]\tLoss: 0.157544\nTrain Epoch: 6 [4480/60000 (7%)]\tLoss: 0.087023\nTrain Epoch: 6 [5120/60000 (9%)]\tLoss: 0.130669\nTrain Epoch: 6 [5760/60000 (10%)]\tLoss: 0.059450\nTrain Epoch: 6 [6400/60000 (11%)]\tLoss: 0.121786\nTrain Epoch: 6 [7040/60000 (12%)]\tLoss: 0.177859\nTrain Epoch: 6 [7680/60000 (13%)]\tLoss: 0.217464\nTrain Epoch: 6 [8320/60000 (14%)]\tLoss: 0.183426\nTrain Epoch: 6 [8960/60000 (15%)]\tLoss: 0.237282\nTrain Epoch: 6 [9600/60000 (16%)]\tLoss: 0.210031\nTrain Epoch: 6 [10240/60000 (17%)]\tLoss: 0.256110\nTrain Epoch: 6 [10880/60000 (18%)]\tLoss: 0.155481\nTrain Epoch: 6 [11520/60000 (19%)]\tLoss: 0.166967\nTrain Epoch: 6 [12160/60000 (20%)]\tLoss: 0.144590\nTrain Epoch: 6 [12800/60000 (21%)]\tLoss: 0.229593\nTrain Epoch: 6 [13440/60000 (22%)]\tLoss: 0.092102\nTrain Epoch: 6 [14080/60000 (23%)]\tLoss: 0.144247\nTrain Epoch: 6 [14720/60000 (25%)]\tLoss: 0.459083\nTrain Epoch: 6 [15360/60000 (26%)]\tLoss: 0.174974\nTrain Epoch: 6 [16000/60000 (27%)]\tLoss: 0.146433\nTrain Epoch: 6 [16640/60000 (28%)]\tLoss: 0.291392\nTrain Epoch: 6 [17280/60000 (29%)]\tLoss: 0.203127\nTrain Epoch: 6 [17920/60000 (30%)]\tLoss: 0.255063\nTrain Epoch: 6 [18560/60000 (31%)]\tLoss: 0.167576\nTrain Epoch: 6 [19200/60000 (32%)]\tLoss: 0.171914\nTrain Epoch: 6 [19840/60000 (33%)]\tLoss: 0.215950\nTrain Epoch: 6 [20480/60000 (34%)]\tLoss: 0.246624\nTrain Epoch: 6 [21120/60000 (35%)]\tLoss: 0.242730\nTrain Epoch: 6 [21760/60000 (36%)]\tLoss: 0.345666\nTrain Epoch: 6 [22400/60000 (37%)]\tLoss: 0.229078\nTrain Epoch: 6 [23040/60000 (38%)]\tLoss: 0.283169\nTrain Epoch: 6 [23680/60000 (39%)]\tLoss: 0.246430\nTrain Epoch: 6 [24320/60000 (41%)]\tLoss: 0.217211\nTrain Epoch: 6 [24960/60000 (42%)]\tLoss: 0.168141\nTrain Epoch: 6 [25600/60000 (43%)]\tLoss: 0.297715\nTrain Epoch: 6 [26240/60000 (44%)]\tLoss: 0.200130\nTrain Epoch: 6 [26880/60000 (45%)]\tLoss: 0.344390\nTrain Epoch: 6 [27520/60000 (46%)]\tLoss: 0.246202\nTrain Epoch: 6 [28160/60000 (47%)]\tLoss: 0.272422\nTrain Epoch: 6 [28800/60000 (48%)]\tLoss: 0.117001\nTrain Epoch: 6 [29440/60000 (49%)]\tLoss: 0.246031\nTrain Epoch: 6 [30080/60000 (50%)]\tLoss: 0.138119\nTrain Epoch: 6 [30720/60000 (51%)]\tLoss: 0.214345\nTrain Epoch: 6 [31360/60000 (52%)]\tLoss: 0.134483\nTrain Epoch: 6 [32000/60000 (53%)]\tLoss: 0.201771\nTrain Epoch: 6 [32640/60000 (54%)]\tLoss: 0.201668\nTrain Epoch: 6 [33280/60000 (55%)]\tLoss: 0.111183\nTrain Epoch: 6 [33920/60000 (57%)]\tLoss: 0.093289\nTrain Epoch: 6 [34560/60000 (58%)]\tLoss: 0.171475\nTrain Epoch: 6 [35200/60000 (59%)]\tLoss: 0.178729\nTrain Epoch: 6 [35840/60000 (60%)]\tLoss: 0.144986\nTrain Epoch: 6 [36480/60000 (61%)]\tLoss: 0.302206\nTrain Epoch: 6 [37120/60000 (62%)]\tLoss: 0.389723\nTrain Epoch: 6 [37760/60000 (63%)]\tLoss: 0.268302\nTrain Epoch: 6 [38400/60000 (64%)]\tLoss: 0.358240\nTrain Epoch: 6 [39040/60000 (65%)]\tLoss: 0.241359\nTrain Epoch: 6 [39680/60000 (66%)]\tLoss: 0.282464\nTrain Epoch: 6 [40320/60000 (67%)]\tLoss: 0.205064\nTrain Epoch: 6 [40960/60000 (68%)]\tLoss: 0.106739\nTrain Epoch: 6 [41600/60000 (69%)]\tLoss: 0.076333\nTrain Epoch: 6 [42240/60000 (70%)]\tLoss: 0.157558\nTrain Epoch: 6 [42880/60000 (71%)]\tLoss: 0.217494\nTrain Epoch: 6 [43520/60000 (72%)]\tLoss: 0.183687\nTrain Epoch: 6 [44160/60000 (74%)]\tLoss: 0.217155\nTrain Epoch: 6 [44800/60000 (75%)]\tLoss: 0.108482\nTrain Epoch: 6 [45440/60000 (76%)]\tLoss: 0.324247\nTrain Epoch: 6 [46080/60000 (77%)]\tLoss: 0.352494\nTrain Epoch: 6 [46720/60000 (78%)]\tLoss: 0.163462\nTrain Epoch: 6 [47360/60000 (79%)]\tLoss: 0.154820\nTrain Epoch: 6 [48000/60000 (80%)]\tLoss: 0.174164\nTrain Epoch: 6 [48640/60000 (81%)]\tLoss: 0.196258\nTrain Epoch: 6 [49280/60000 (82%)]\tLoss: 0.226030\nTrain Epoch: 6 [49920/60000 (83%)]\tLoss: 0.306971\nTrain Epoch: 6 [50560/60000 (84%)]\tLoss: 0.387282\nTrain Epoch: 6 [51200/60000 (85%)]\tLoss: 0.213550\nTrain Epoch: 6 [51840/60000 (86%)]\tLoss: 0.133755\nTrain Epoch: 6 [52480/60000 (87%)]\tLoss: 0.176044\nTrain Epoch: 6 [53120/60000 (88%)]\tLoss: 0.282900\nTrain Epoch: 6 [53760/60000 (90%)]\tLoss: 0.154157\nTrain Epoch: 6 [54400/60000 (91%)]\tLoss: 0.138895\nTrain Epoch: 6 [55040/60000 (92%)]\tLoss: 0.254137\nTrain Epoch: 6 [55680/60000 (93%)]\tLoss: 0.107765\nTrain Epoch: 6 [56320/60000 (94%)]\tLoss: 0.118788\nTrain Epoch: 6 [56960/60000 (95%)]\tLoss: 0.142051\nTrain Epoch: 6 [57600/60000 (96%)]\tLoss: 0.176375\nTrain Epoch: 6 [58240/60000 (97%)]\tLoss: 0.131573\nTrain Epoch: 6 [58880/60000 (98%)]\tLoss: 0.347166\nTrain Epoch: 6 [59520/60000 (99%)]\tLoss: 0.217951\n\nTest set: Average loss: 0.0690, Accuracy: 9776/10000 (98%)\n\nTrain Epoch: 7 [0/60000 (0%)]\tLoss: 0.142441\nTrain Epoch: 7 [640/60000 (1%)]\tLoss: 0.078599\nTrain Epoch: 7 [1280/60000 (2%)]\tLoss: 0.121731\nTrain Epoch: 7 [1920/60000 (3%)]\tLoss: 0.070044\nTrain Epoch: 7 [2560/60000 (4%)]\tLoss: 0.224216\nTrain Epoch: 7 [3200/60000 (5%)]\tLoss: 0.104122\nTrain Epoch: 7 [3840/60000 (6%)]\tLoss: 0.228575\nTrain Epoch: 7 [4480/60000 (7%)]\tLoss: 0.377044\nTrain Epoch: 7 [5120/60000 (9%)]\tLoss: 0.296184\nTrain Epoch: 7 [5760/60000 (10%)]\tLoss: 0.099891\nTrain Epoch: 7 [6400/60000 (11%)]\tLoss: 0.269691\nTrain Epoch: 7 [7040/60000 (12%)]\tLoss: 0.240640\nTrain Epoch: 7 [7680/60000 (13%)]\tLoss: 0.171192\nTrain Epoch: 7 [8320/60000 (14%)]\tLoss: 0.306889\nTrain Epoch: 7 [8960/60000 (15%)]\tLoss: 0.238503\nTrain Epoch: 7 [9600/60000 (16%)]\tLoss: 0.286252\nTrain Epoch: 7 [10240/60000 (17%)]\tLoss: 0.171058\nTrain Epoch: 7 [10880/60000 (18%)]\tLoss: 0.208866\nTrain Epoch: 7 [11520/60000 (19%)]\tLoss: 0.418091\nTrain Epoch: 7 [12160/60000 (20%)]\tLoss: 0.115058\nTrain Epoch: 7 [12800/60000 (21%)]\tLoss: 0.159557\nTrain Epoch: 7 [13440/60000 (22%)]\tLoss: 0.085076\nTrain Epoch: 7 [14080/60000 (23%)]\tLoss: 0.244673\nTrain Epoch: 7 [14720/60000 (25%)]\tLoss: 0.316326\nTrain Epoch: 7 [15360/60000 (26%)]\tLoss: 0.370775\nTrain Epoch: 7 [16000/60000 (27%)]\tLoss: 0.235262\nTrain Epoch: 7 [16640/60000 (28%)]\tLoss: 0.296188\nTrain Epoch: 7 [17280/60000 (29%)]\tLoss: 0.224960\nTrain Epoch: 7 [17920/60000 (30%)]\tLoss: 0.162341\nTrain Epoch: 7 [18560/60000 (31%)]\tLoss: 0.136551\nTrain Epoch: 7 [19200/60000 (32%)]\tLoss: 0.111435\nTrain Epoch: 7 [19840/60000 (33%)]\tLoss: 0.173483\nTrain Epoch: 7 [20480/60000 (34%)]\tLoss: 0.170351\nTrain Epoch: 7 [21120/60000 (35%)]\tLoss: 0.109828\nTrain Epoch: 7 [21760/60000 (36%)]\tLoss: 0.219692\nTrain Epoch: 7 [22400/60000 (37%)]\tLoss: 0.085780\nTrain Epoch: 7 [23040/60000 (38%)]\tLoss: 0.076800\nTrain Epoch: 7 [23680/60000 (39%)]\tLoss: 0.163377\nTrain Epoch: 7 [24320/60000 (41%)]\tLoss: 0.178391\nTrain Epoch: 7 [24960/60000 (42%)]\tLoss: 0.311988\nTrain Epoch: 7 [25600/60000 (43%)]\tLoss: 0.215559\nTrain Epoch: 7 [26240/60000 (44%)]\tLoss: 0.199207\nTrain Epoch: 7 [26880/60000 (45%)]\tLoss: 0.201917\nTrain Epoch: 7 [27520/60000 (46%)]\tLoss: 0.163283\nTrain Epoch: 7 [28160/60000 (47%)]\tLoss: 0.107533\nTrain Epoch: 7 [28800/60000 (48%)]\tLoss: 0.046209\nTrain Epoch: 7 [29440/60000 (49%)]\tLoss: 0.173062\nTrain Epoch: 7 [30080/60000 (50%)]\tLoss: 0.088925\nTrain Epoch: 7 [30720/60000 (51%)]\tLoss: 0.068962\nTrain Epoch: 7 [31360/60000 (52%)]\tLoss: 0.223214\nTrain Epoch: 7 [32000/60000 (53%)]\tLoss: 0.096083\nTrain Epoch: 7 [32640/60000 (54%)]\tLoss: 0.327635\nTrain Epoch: 7 [33280/60000 (55%)]\tLoss: 0.278620\nTrain Epoch: 7 [33920/60000 (57%)]\tLoss: 0.223806\nTrain Epoch: 7 [34560/60000 (58%)]\tLoss: 0.121638\nTrain Epoch: 7 [35200/60000 (59%)]\tLoss: 0.182739\nTrain Epoch: 7 [35840/60000 (60%)]\tLoss: 0.172866\nTrain Epoch: 7 [36480/60000 (61%)]\tLoss: 0.180873\nTrain Epoch: 7 [37120/60000 (62%)]\tLoss: 0.298984\nTrain Epoch: 7 [37760/60000 (63%)]\tLoss: 0.251939\nTrain Epoch: 7 [38400/60000 (64%)]\tLoss: 0.105321\nTrain Epoch: 7 [39040/60000 (65%)]\tLoss: 0.200500\nTrain Epoch: 7 [39680/60000 (66%)]\tLoss: 0.309791\nTrain Epoch: 7 [40320/60000 (67%)]\tLoss: 0.114949\nTrain Epoch: 7 [40960/60000 (68%)]\tLoss: 0.066153\nTrain Epoch: 7 [41600/60000 (69%)]\tLoss: 0.327437\nTrain Epoch: 7 [42240/60000 (70%)]\tLoss: 0.179023\nTrain Epoch: 7 [42880/60000 (71%)]\tLoss: 0.089861\nTrain Epoch: 7 [43520/60000 (72%)]\tLoss: 0.111230\nTrain Epoch: 7 [44160/60000 (74%)]\tLoss: 0.108233\nTrain Epoch: 7 [44800/60000 (75%)]\tLoss: 0.145669\nTrain Epoch: 7 [45440/60000 (76%)]\tLoss: 0.122024\nTrain Epoch: 7 [46080/60000 (77%)]\tLoss: 0.083490\nTrain Epoch: 7 [46720/60000 (78%)]\tLoss: 0.116002\nTrain Epoch: 7 [47360/60000 (79%)]\tLoss: 0.200240\nTrain Epoch: 7 [48000/60000 (80%)]\tLoss: 0.363707\nTrain Epoch: 7 [48640/60000 (81%)]\tLoss: 0.294594\nTrain Epoch: 7 [49280/60000 (82%)]\tLoss: 0.127643\nTrain Epoch: 7 [49920/60000 (83%)]\tLoss: 0.202008\nTrain Epoch: 7 [50560/60000 (84%)]\tLoss: 0.159551\nTrain Epoch: 7 [51200/60000 (85%)]\tLoss: 0.221197\nTrain Epoch: 7 [51840/60000 (86%)]\tLoss: 0.266463\nTrain Epoch: 7 [52480/60000 (87%)]\tLoss: 0.073967\nTrain Epoch: 7 [53120/60000 (88%)]\tLoss: 0.350092\nTrain Epoch: 7 [53760/60000 (90%)]\tLoss: 0.106500\nTrain Epoch: 7 [54400/60000 (91%)]\tLoss: 0.208859\nTrain Epoch: 7 [55040/60000 (92%)]\tLoss: 0.209937\nTrain Epoch: 7 [55680/60000 (93%)]\tLoss: 0.215286\nTrain Epoch: 7 [56320/60000 (94%)]\tLoss: 0.117026\nTrain Epoch: 7 [56960/60000 (95%)]\tLoss: 0.132321\nTrain Epoch: 7 [57600/60000 (96%)]\tLoss: 0.286004\nTrain Epoch: 7 [58240/60000 (97%)]\tLoss: 0.170485\nTrain Epoch: 7 [58880/60000 (98%)]\tLoss: 0.196613\nTrain Epoch: 7 [59520/60000 (99%)]\tLoss: 0.293870\n\nTest set: Average loss: 0.0657, Accuracy: 9801/10000 (98%)\n\nTrain Epoch: 8 [0/60000 (0%)]\tLoss: 0.315451\nTrain Epoch: 8 [640/60000 (1%)]\tLoss: 0.114413\nTrain Epoch: 8 [1280/60000 (2%)]\tLoss: 0.129036\nTrain Epoch: 8 [1920/60000 (3%)]\tLoss: 0.141999\nTrain Epoch: 8 [2560/60000 (4%)]\tLoss: 0.118697\nTrain Epoch: 8 [3200/60000 (5%)]\tLoss: 0.126823\nTrain Epoch: 8 [3840/60000 (6%)]\tLoss: 0.053924\nTrain Epoch: 8 [4480/60000 (7%)]\tLoss: 0.296224\nTrain Epoch: 8 [5120/60000 (9%)]\tLoss: 0.121338\nTrain Epoch: 8 [5760/60000 (10%)]\tLoss: 0.255161\nTrain Epoch: 8 [6400/60000 (11%)]\tLoss: 0.170684\nTrain Epoch: 8 [7040/60000 (12%)]\tLoss: 0.092008\nTrain Epoch: 8 [7680/60000 (13%)]\tLoss: 0.283091\nTrain Epoch: 8 [8320/60000 (14%)]\tLoss: 0.027133\nTrain Epoch: 8 [8960/60000 (15%)]\tLoss: 0.195686\nTrain Epoch: 8 [9600/60000 (16%)]\tLoss: 0.343612\nTrain Epoch: 8 [10240/60000 (17%)]\tLoss: 0.108563\nTrain Epoch: 8 [10880/60000 (18%)]\tLoss: 0.223832\nTrain Epoch: 8 [11520/60000 (19%)]\tLoss: 0.175617\nTrain Epoch: 8 [12160/60000 (20%)]\tLoss: 0.145828\nTrain Epoch: 8 [12800/60000 (21%)]\tLoss: 0.178722\nTrain Epoch: 8 [13440/60000 (22%)]\tLoss: 0.151158\nTrain Epoch: 8 [14080/60000 (23%)]\tLoss: 0.183155\nTrain Epoch: 8 [14720/60000 (25%)]\tLoss: 0.110281\nTrain Epoch: 8 [15360/60000 (26%)]\tLoss: 0.282224\nTrain Epoch: 8 [16000/60000 (27%)]\tLoss: 0.097411\nTrain Epoch: 8 [16640/60000 (28%)]\tLoss: 0.264533\nTrain Epoch: 8 [17280/60000 (29%)]\tLoss: 0.194778\nTrain Epoch: 8 [17920/60000 (30%)]\tLoss: 0.235924\nTrain Epoch: 8 [18560/60000 (31%)]\tLoss: 0.236801\nTrain Epoch: 8 [19200/60000 (32%)]\tLoss: 0.178174\nTrain Epoch: 8 [19840/60000 (33%)]\tLoss: 0.218752\nTrain Epoch: 8 [20480/60000 (34%)]\tLoss: 0.208353\nTrain Epoch: 8 [21120/60000 (35%)]\tLoss: 0.193034\nTrain Epoch: 8 [21760/60000 (36%)]\tLoss: 0.138453\nTrain Epoch: 8 [22400/60000 (37%)]\tLoss: 0.175271\nTrain Epoch: 8 [23040/60000 (38%)]\tLoss: 0.157295\nTrain Epoch: 8 [23680/60000 (39%)]\tLoss: 0.156248\nTrain Epoch: 8 [24320/60000 (41%)]\tLoss: 0.153413\nTrain Epoch: 8 [24960/60000 (42%)]\tLoss: 0.084870\nTrain Epoch: 8 [25600/60000 (43%)]\tLoss: 0.150966\nTrain Epoch: 8 [26240/60000 (44%)]\tLoss: 0.160973\nTrain Epoch: 8 [26880/60000 (45%)]\tLoss: 0.231433\nTrain Epoch: 8 [27520/60000 (46%)]\tLoss: 0.144396\nTrain Epoch: 8 [28160/60000 (47%)]\tLoss: 0.200417\nTrain Epoch: 8 [28800/60000 (48%)]\tLoss: 0.152939\nTrain Epoch: 8 [29440/60000 (49%)]\tLoss: 0.109962\nTrain Epoch: 8 [30080/60000 (50%)]\tLoss: 0.134907\nTrain Epoch: 8 [30720/60000 (51%)]\tLoss: 0.088782\nTrain Epoch: 8 [31360/60000 (52%)]\tLoss: 0.129031\nTrain Epoch: 8 [32000/60000 (53%)]\tLoss: 0.184744\nTrain Epoch: 8 [32640/60000 (54%)]\tLoss: 0.155463\nTrain Epoch: 8 [33280/60000 (55%)]\tLoss: 0.174192\nTrain Epoch: 8 [33920/60000 (57%)]\tLoss: 0.172103\nTrain Epoch: 8 [34560/60000 (58%)]\tLoss: 0.201503\nTrain Epoch: 8 [35200/60000 (59%)]\tLoss: 0.287885\nTrain Epoch: 8 [35840/60000 (60%)]\tLoss: 0.133675\nTrain Epoch: 8 [36480/60000 (61%)]\tLoss: 0.243534\nTrain Epoch: 8 [37120/60000 (62%)]\tLoss: 0.196020\nTrain Epoch: 8 [37760/60000 (63%)]\tLoss: 0.101380\nTrain Epoch: 8 [38400/60000 (64%)]\tLoss: 0.108299\nTrain Epoch: 8 [39040/60000 (65%)]\tLoss: 0.159048\nTrain Epoch: 8 [39680/60000 (66%)]\tLoss: 0.204734\nTrain Epoch: 8 [40320/60000 (67%)]\tLoss: 0.238383\nTrain Epoch: 8 [40960/60000 (68%)]\tLoss: 0.592663\nTrain Epoch: 8 [41600/60000 (69%)]\tLoss: 0.116080\nTrain Epoch: 8 [42240/60000 (70%)]\tLoss: 0.039719\nTrain Epoch: 8 [42880/60000 (71%)]\tLoss: 0.148190\nTrain Epoch: 8 [43520/60000 (72%)]\tLoss: 0.241765\nTrain Epoch: 8 [44160/60000 (74%)]\tLoss: 0.235942\nTrain Epoch: 8 [44800/60000 (75%)]\tLoss: 0.175277\nTrain Epoch: 8 [45440/60000 (76%)]\tLoss: 0.143608\nTrain Epoch: 8 [46080/60000 (77%)]\tLoss: 0.114853\nTrain Epoch: 8 [46720/60000 (78%)]\tLoss: 0.232284\nTrain Epoch: 8 [47360/60000 (79%)]\tLoss: 0.321072\nTrain Epoch: 8 [48000/60000 (80%)]\tLoss: 0.310765\nTrain Epoch: 8 [48640/60000 (81%)]\tLoss: 0.102070\nTrain Epoch: 8 [49280/60000 (82%)]\tLoss: 0.372137\nTrain Epoch: 8 [49920/60000 (83%)]\tLoss: 0.109344\nTrain Epoch: 8 [50560/60000 (84%)]\tLoss: 0.382866\nTrain Epoch: 8 [51200/60000 (85%)]\tLoss: 0.270467\nTrain Epoch: 8 [51840/60000 (86%)]\tLoss: 0.061211\nTrain Epoch: 8 [52480/60000 (87%)]\tLoss: 0.233812\nTrain Epoch: 8 [53120/60000 (88%)]\tLoss: 0.176510\nTrain Epoch: 8 [53760/60000 (90%)]\tLoss: 0.120536\nTrain Epoch: 8 [54400/60000 (91%)]\tLoss: 0.241959\nTrain Epoch: 8 [55040/60000 (92%)]\tLoss: 0.183966\nTrain Epoch: 8 [55680/60000 (93%)]\tLoss: 0.125279\nTrain Epoch: 8 [56320/60000 (94%)]\tLoss: 0.152849\nTrain Epoch: 8 [56960/60000 (95%)]\tLoss: 0.219788\nTrain Epoch: 8 [57600/60000 (96%)]\tLoss: 0.077843\nTrain Epoch: 8 [58240/60000 (97%)]\tLoss: 0.304191\nTrain Epoch: 8 [58880/60000 (98%)]\tLoss: 0.363550\nTrain Epoch: 8 [59520/60000 (99%)]\tLoss: 0.326421\n\nTest set: Average loss: 0.0632, Accuracy: 9807/10000 (98%)\n\nTrain Epoch: 9 [0/60000 (0%)]\tLoss: 0.140965\nTrain Epoch: 9 [640/60000 (1%)]\tLoss: 0.206063\nTrain Epoch: 9 [1280/60000 (2%)]\tLoss: 0.189364\nTrain Epoch: 9 [1920/60000 (3%)]\tLoss: 0.367962\nTrain Epoch: 9 [2560/60000 (4%)]\tLoss: 0.108362\nTrain Epoch: 9 [3200/60000 (5%)]\tLoss: 0.109142\nTrain Epoch: 9 [3840/60000 (6%)]\tLoss: 0.270022\nTrain Epoch: 9 [4480/60000 (7%)]\tLoss: 0.200647\nTrain Epoch: 9 [5120/60000 (9%)]\tLoss: 0.162118\nTrain Epoch: 9 [5760/60000 (10%)]\tLoss: 0.167245\nTrain Epoch: 9 [6400/60000 (11%)]\tLoss: 0.188903\nTrain Epoch: 9 [7040/60000 (12%)]\tLoss: 0.280550\nTrain Epoch: 9 [7680/60000 (13%)]\tLoss: 0.116265\nTrain Epoch: 9 [8320/60000 (14%)]\tLoss: 0.602693\nTrain Epoch: 9 [8960/60000 (15%)]\tLoss: 0.148682\nTrain Epoch: 9 [9600/60000 (16%)]\tLoss: 0.225477\nTrain Epoch: 9 [10240/60000 (17%)]\tLoss: 0.133642\nTrain Epoch: 9 [10880/60000 (18%)]\tLoss: 0.116083\nTrain Epoch: 9 [11520/60000 (19%)]\tLoss: 0.348113\nTrain Epoch: 9 [12160/60000 (20%)]\tLoss: 0.219562\nTrain Epoch: 9 [12800/60000 (21%)]\tLoss: 0.117716\nTrain Epoch: 9 [13440/60000 (22%)]\tLoss: 0.218508\nTrain Epoch: 9 [14080/60000 (23%)]\tLoss: 0.323755\nTrain Epoch: 9 [14720/60000 (25%)]\tLoss: 0.211174\nTrain Epoch: 9 [15360/60000 (26%)]\tLoss: 0.451853\nTrain Epoch: 9 [16000/60000 (27%)]\tLoss: 0.155174\nTrain Epoch: 9 [16640/60000 (28%)]\tLoss: 0.134905\nTrain Epoch: 9 [17280/60000 (29%)]\tLoss: 0.172428\nTrain Epoch: 9 [17920/60000 (30%)]\tLoss: 0.306172\nTrain Epoch: 9 [18560/60000 (31%)]\tLoss: 0.133085\nTrain Epoch: 9 [19200/60000 (32%)]\tLoss: 0.449040\nTrain Epoch: 9 [19840/60000 (33%)]\tLoss: 0.084722\nTrain Epoch: 9 [20480/60000 (34%)]\tLoss: 0.188086\nTrain Epoch: 9 [21120/60000 (35%)]\tLoss: 0.222472\nTrain Epoch: 9 [21760/60000 (36%)]\tLoss: 0.275132\nTrain Epoch: 9 [22400/60000 (37%)]\tLoss: 0.287421\nTrain Epoch: 9 [23040/60000 (38%)]\tLoss: 0.105733\nTrain Epoch: 9 [23680/60000 (39%)]\tLoss: 0.157949\nTrain Epoch: 9 [24320/60000 (41%)]\tLoss: 0.073462\nTrain Epoch: 9 [24960/60000 (42%)]\tLoss: 0.240201\nTrain Epoch: 9 [25600/60000 (43%)]\tLoss: 0.060848\nTrain Epoch: 9 [26240/60000 (44%)]\tLoss: 0.173801\nTrain Epoch: 9 [26880/60000 (45%)]\tLoss: 0.148143\nTrain Epoch: 9 [27520/60000 (46%)]\tLoss: 0.180779\nTrain Epoch: 9 [28160/60000 (47%)]\tLoss: 0.393192\nTrain Epoch: 9 [28800/60000 (48%)]\tLoss: 0.239243\nTrain Epoch: 9 [29440/60000 (49%)]\tLoss: 0.064345\nTrain Epoch: 9 [30080/60000 (50%)]\tLoss: 0.315658\nTrain Epoch: 9 [30720/60000 (51%)]\tLoss: 0.105739\nTrain Epoch: 9 [31360/60000 (52%)]\tLoss: 0.246439\nTrain Epoch: 9 [32000/60000 (53%)]\tLoss: 0.145221\nTrain Epoch: 9 [32640/60000 (54%)]\tLoss: 0.287615\nTrain Epoch: 9 [33280/60000 (55%)]\tLoss: 0.310717\nTrain Epoch: 9 [33920/60000 (57%)]\tLoss: 0.322760\nTrain Epoch: 9 [34560/60000 (58%)]\tLoss: 0.294462\nTrain Epoch: 9 [35200/60000 (59%)]\tLoss: 0.168697\nTrain Epoch: 9 [35840/60000 (60%)]\tLoss: 0.153495\nTrain Epoch: 9 [36480/60000 (61%)]\tLoss: 0.146843\nTrain Epoch: 9 [37120/60000 (62%)]\tLoss: 0.176622\nTrain Epoch: 9 [37760/60000 (63%)]\tLoss: 0.400825\nTrain Epoch: 9 [38400/60000 (64%)]\tLoss: 0.197533\nTrain Epoch: 9 [39040/60000 (65%)]\tLoss: 0.109741\nTrain Epoch: 9 [39680/60000 (66%)]\tLoss: 0.049689\nTrain Epoch: 9 [40320/60000 (67%)]\tLoss: 0.253087\nTrain Epoch: 9 [40960/60000 (68%)]\tLoss: 0.222971\nTrain Epoch: 9 [41600/60000 (69%)]\tLoss: 0.095467\nTrain Epoch: 9 [42240/60000 (70%)]\tLoss: 0.043052\nTrain Epoch: 9 [42880/60000 (71%)]\tLoss: 0.105347\nTrain Epoch: 9 [43520/60000 (72%)]\tLoss: 0.133342\nTrain Epoch: 9 [44160/60000 (74%)]\tLoss: 0.266375\nTrain Epoch: 9 [44800/60000 (75%)]\tLoss: 0.156081\nTrain Epoch: 9 [45440/60000 (76%)]\tLoss: 0.206747\nTrain Epoch: 9 [46080/60000 (77%)]\tLoss: 0.158561\nTrain Epoch: 9 [46720/60000 (78%)]\tLoss: 0.416148\nTrain Epoch: 9 [47360/60000 (79%)]\tLoss: 0.147991\nTrain Epoch: 9 [48000/60000 (80%)]\tLoss: 0.112567\nTrain Epoch: 9 [48640/60000 (81%)]\tLoss: 0.100846\nTrain Epoch: 9 [49280/60000 (82%)]\tLoss: 0.103345\nTrain Epoch: 9 [49920/60000 (83%)]\tLoss: 0.205922\nTrain Epoch: 9 [50560/60000 (84%)]\tLoss: 0.097610\nTrain Epoch: 9 [51200/60000 (85%)]\tLoss: 0.071967\nTrain Epoch: 9 [51840/60000 (86%)]\tLoss: 0.068125\nTrain Epoch: 9 [52480/60000 (87%)]\tLoss: 0.057313\nTrain Epoch: 9 [53120/60000 (88%)]\tLoss: 0.162428\nTrain Epoch: 9 [53760/60000 (90%)]\tLoss: 0.097614\nTrain Epoch: 9 [54400/60000 (91%)]\tLoss: 0.075174\nTrain Epoch: 9 [55040/60000 (92%)]\tLoss: 0.095530\nTrain Epoch: 9 [55680/60000 (93%)]\tLoss: 0.142529\nTrain Epoch: 9 [56320/60000 (94%)]\tLoss: 0.132163\nTrain Epoch: 9 [56960/60000 (95%)]\tLoss: 0.201932\nTrain Epoch: 9 [57600/60000 (96%)]\tLoss: 0.238939\nTrain Epoch: 9 [58240/60000 (97%)]\tLoss: 0.037396\nTrain Epoch: 9 [58880/60000 (98%)]\tLoss: 0.077772\nTrain Epoch: 9 [59520/60000 (99%)]\tLoss: 0.177759\n\nTest set: Average loss: 0.0559, Accuracy: 9813/10000 (98%)\n\nTrain Epoch: 10 [0/60000 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9837/10000 (98%)\n\n\n\nThe experiment completed successfully. 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0.483003\nTrain Epoch: 1 [52480/60000 (87%)]\tLoss: 0.610840\nTrain Epoch: 1 [53120/60000 (88%)]\tLoss: 0.483278\nTrain Epoch: 1 [53760/60000 (90%)]\tLoss: 0.553161\nTrain Epoch: 1 [54400/60000 (91%)]\tLoss: 0.465237\nTrain Epoch: 1 [55040/60000 (92%)]\tLoss: 0.558884\nTrain Epoch: 1 [55680/60000 (93%)]\tLoss: 0.528969\nTrain Epoch: 1 [56320/60000 (94%)]\tLoss: 0.370189\nTrain Epoch: 1 [56960/60000 (95%)]\tLoss: 0.379404\nTrain Epoch: 1 [57600/60000 (96%)]\tLoss: 0.263894\nTrain Epoch: 1 [58240/60000 (97%)]\tLoss: 0.432745\nTrain Epoch: 1 [58880/60000 (98%)]\tLoss: 0.455681\nTrain Epoch: 1 [59520/60000 (99%)]\tLoss: 0.483901\n/azureml-envs/azureml_de892a6d0f01a442356c3959dd42e13b/lib/python3.6/site-packages/torch/nn/functional.py:54: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n warnings.warn(warning.format(ret))\n\nTest set: Average loss: 0.2073, Accuracy: 9384/10000 (94%)\n\nTrain Epoch: 2 [0/60000 (0%)]\tLoss: 0.390797\nTrain Epoch: 2 [640/60000 (1%)]\tLoss: 0.214512\nTrain Epoch: 2 [1280/60000 (2%)]\tLoss: 0.226415\nTrain Epoch: 2 [1920/60000 (3%)]\tLoss: 0.491764\nTrain Epoch: 2 [2560/60000 (4%)]\tLoss: 0.333604\nTrain Epoch: 2 [3200/60000 (5%)]\tLoss: 0.514239\nTrain Epoch: 2 [3840/60000 (6%)]\tLoss: 0.430618\nTrain Epoch: 2 [4480/60000 (7%)]\tLoss: 0.579474\nTrain Epoch: 2 [5120/60000 (9%)]\tLoss: 0.259456\nTrain Epoch: 2 [5760/60000 (10%)]\tLoss: 0.651198\nTrain Epoch: 2 [6400/60000 (11%)]\tLoss: 0.338269\nTrain Epoch: 2 [7040/60000 (12%)]\tLoss: 0.335233\nTrain Epoch: 2 [7680/60000 (13%)]\tLoss: 0.518132\nTrain Epoch: 2 [8320/60000 (14%)]\tLoss: 0.363488\nTrain Epoch: 2 [8960/60000 (15%)]\tLoss: 0.437092\nTrain Epoch: 2 [9600/60000 (16%)]\tLoss: 0.362660\nTrain Epoch: 2 [10240/60000 (17%)]\tLoss: 0.432337\nTrain Epoch: 2 [10880/60000 (18%)]\tLoss: 0.360611\nTrain Epoch: 2 [11520/60000 (19%)]\tLoss: 0.305427\nTrain Epoch: 2 [12160/60000 (20%)]\tLoss: 0.347859\nTrain Epoch: 2 [12800/60000 (21%)]\tLoss: 0.408770\nTrain Epoch: 2 [13440/60000 (22%)]\tLoss: 0.469975\nTrain Epoch: 2 [14080/60000 (23%)]\tLoss: 0.673716\nTrain Epoch: 2 [14720/60000 (25%)]\tLoss: 0.388876\nTrain Epoch: 2 [15360/60000 (26%)]\tLoss: 0.462371\nTrain Epoch: 2 [16000/60000 (27%)]\tLoss: 0.530107\nTrain Epoch: 2 [16640/60000 (28%)]\tLoss: 0.448767\nTrain Epoch: 2 [17280/60000 (29%)]\tLoss: 0.412764\nTrain Epoch: 2 [17920/60000 (30%)]\tLoss: 0.301494\nTrain Epoch: 2 [18560/60000 (31%)]\tLoss: 0.465599\nTrain Epoch: 2 [19200/60000 (32%)]\tLoss: 0.434249\nTrain Epoch: 2 [19840/60000 (33%)]\tLoss: 0.324006\nTrain Epoch: 2 [20480/60000 (34%)]\tLoss: 0.447446\nTrain Epoch: 2 [21120/60000 (35%)]\tLoss: 0.291222\nTrain Epoch: 2 [21760/60000 (36%)]\tLoss: 0.557065\nTrain Epoch: 2 [22400/60000 (37%)]\tLoss: 0.552659\nTrain Epoch: 2 [23040/60000 (38%)]\tLoss: 0.378901\nTrain Epoch: 2 [23680/60000 (39%)]\tLoss: 0.360550\nTrain Epoch: 2 [24320/60000 (41%)]\tLoss: 0.283795\nTrain Epoch: 2 [24960/60000 (42%)]\tLoss: 0.475816\nTrain Epoch: 2 [25600/60000 (43%)]\tLoss: 0.283652\nTrain Epoch: 2 [26240/60000 (44%)]\tLoss: 0.276265\nTrain Epoch: 2 [26880/60000 (45%)]\tLoss: 0.527902\nTrain Epoch: 2 [27520/60000 (46%)]\tLoss: 0.437130\nTrain Epoch: 2 [28160/60000 (47%)]\tLoss: 0.277132\nTrain Epoch: 2 [28800/60000 (48%)]\tLoss: 0.471580\nTrain Epoch: 2 [29440/60000 (49%)]\tLoss: 0.380154\nTrain Epoch: 2 [30080/60000 (50%)]\tLoss: 0.232072\nTrain Epoch: 2 [30720/60000 (51%)]\tLoss: 0.366567\nTrain Epoch: 2 [31360/60000 (52%)]\tLoss: 0.469628\nTrain Epoch: 2 [32000/60000 (53%)]\tLoss: 0.440017\nTrain Epoch: 2 [32640/60000 (54%)]\tLoss: 0.421814\nTrain Epoch: 2 [33280/60000 (55%)]\tLoss: 0.367687\nTrain Epoch: 2 [33920/60000 (57%)]\tLoss: 0.448384\nTrain Epoch: 2 [34560/60000 (58%)]\tLoss: 0.550283\nTrain Epoch: 2 [35200/60000 (59%)]\tLoss: 0.609798\nTrain Epoch: 2 [35840/60000 (60%)]\tLoss: 0.461334\nTrain Epoch: 2 [36480/60000 (61%)]\tLoss: 0.443838\nTrain Epoch: 2 [37120/60000 (62%)]\tLoss: 0.306666\nTrain Epoch: 2 [37760/60000 (63%)]\tLoss: 0.432083\nTrain Epoch: 2 [38400/60000 (64%)]\tLoss: 0.277025\nTrain Epoch: 2 [39040/60000 (65%)]\tLoss: 0.298752\nTrain Epoch: 2 [39680/60000 (66%)]\tLoss: 0.427435\nTrain Epoch: 2 [40320/60000 (67%)]\tLoss: 0.374736\nTrain Epoch: 2 [40960/60000 (68%)]\tLoss: 0.246496\nTrain Epoch: 2 [41600/60000 (69%)]\tLoss: 0.662259\nTrain Epoch: 2 [42240/60000 (70%)]\tLoss: 0.497635\nTrain Epoch: 2 [42880/60000 (71%)]\tLoss: 0.237556\nTrain Epoch: 2 [43520/60000 (72%)]\tLoss: 0.194535\nTrain Epoch: 2 [44160/60000 (74%)]\tLoss: 0.258943\nTrain Epoch: 2 [44800/60000 (75%)]\tLoss: 0.437360\nTrain Epoch: 2 [45440/60000 (76%)]\tLoss: 0.355489\nTrain Epoch: 2 [46080/60000 (77%)]\tLoss: 0.335020\nTrain Epoch: 2 [46720/60000 (78%)]\tLoss: 0.565189\nTrain Epoch: 2 [47360/60000 (79%)]\tLoss: 0.430366\nTrain Epoch: 2 [48000/60000 (80%)]\tLoss: 0.266303\nTrain Epoch: 2 [48640/60000 (81%)]\tLoss: 0.172954\nTrain Epoch: 2 [49280/60000 (82%)]\tLoss: 0.245803\nTrain Epoch: 2 [49920/60000 (83%)]\tLoss: 0.426530\nTrain Epoch: 2 [50560/60000 (84%)]\tLoss: 0.468984\nTrain Epoch: 2 [51200/60000 (85%)]\tLoss: 0.370892\nTrain Epoch: 2 [51840/60000 (86%)]\tLoss: 0.300021\nTrain Epoch: 2 [52480/60000 (87%)]\tLoss: 0.392199\nTrain Epoch: 2 [53120/60000 (88%)]\tLoss: 0.510658\nTrain Epoch: 2 [53760/60000 (90%)]\tLoss: 0.376290\nTrain Epoch: 2 [54400/60000 (91%)]\tLoss: 0.273752\nTrain Epoch: 2 [55040/60000 (92%)]\tLoss: 0.234505\nTrain Epoch: 2 [55680/60000 (93%)]\tLoss: 0.610978\nTrain Epoch: 2 [56320/60000 (94%)]\tLoss: 0.154850\nTrain Epoch: 2 [56960/60000 (95%)]\tLoss: 0.374254\nTrain Epoch: 2 [57600/60000 (96%)]\tLoss: 0.292167\nTrain Epoch: 2 [58240/60000 (97%)]\tLoss: 0.478376\nTrain Epoch: 2 [58880/60000 (98%)]\tLoss: 0.303128\nTrain Epoch: 2 [59520/60000 (99%)]\tLoss: 0.376779\n\nTest set: Average loss: 0.1297, Accuracy: 9597/10000 (96%)\n\nTrain Epoch: 3 [0/60000 (0%)]\tLoss: 0.450588\nTrain Epoch: 3 [640/60000 (1%)]\tLoss: 0.361118\nTrain Epoch: 3 [1280/60000 (2%)]\tLoss: 0.374497\nTrain Epoch: 3 [1920/60000 (3%)]\tLoss: 0.312127\nTrain Epoch: 3 [2560/60000 (4%)]\tLoss: 0.353896\nTrain Epoch: 3 [3200/60000 (5%)]\tLoss: 0.320840\nTrain Epoch: 3 [3840/60000 (6%)]\tLoss: 0.218477\nTrain Epoch: 3 [4480/60000 (7%)]\tLoss: 0.295629\nTrain Epoch: 3 [5120/60000 (9%)]\tLoss: 0.339400\nTrain Epoch: 3 [5760/60000 (10%)]\tLoss: 0.170357\nTrain Epoch: 3 [6400/60000 (11%)]\tLoss: 0.416447\nTrain Epoch: 3 [7040/60000 (12%)]\tLoss: 0.320326\nTrain Epoch: 3 [7680/60000 (13%)]\tLoss: 0.318410\nTrain Epoch: 3 [8320/60000 (14%)]\tLoss: 0.384793\nTrain Epoch: 3 [8960/60000 (15%)]\tLoss: 0.343415\nTrain Epoch: 3 [9600/60000 (16%)]\tLoss: 0.284627\nTrain Epoch: 3 [10240/60000 (17%)]\tLoss: 0.151805\nTrain Epoch: 3 [10880/60000 (18%)]\tLoss: 0.401332\nTrain Epoch: 3 [11520/60000 (19%)]\tLoss: 0.253159\nTrain Epoch: 3 [12160/60000 (20%)]\tLoss: 0.339563\nTrain Epoch: 3 [12800/60000 (21%)]\tLoss: 0.237430\nTrain Epoch: 3 [13440/60000 (22%)]\tLoss: 0.311402\nTrain Epoch: 3 [14080/60000 (23%)]\tLoss: 0.241667\nTrain Epoch: 3 [14720/60000 (25%)]\tLoss: 0.265347\nTrain Epoch: 3 [15360/60000 (26%)]\tLoss: 0.367453\nTrain Epoch: 3 [16000/60000 (27%)]\tLoss: 0.190671\nTrain Epoch: 3 [16640/60000 (28%)]\tLoss: 0.313052\nTrain Epoch: 3 [17280/60000 (29%)]\tLoss: 0.368028\nTrain Epoch: 3 [17920/60000 (30%)]\tLoss: 0.268639\nTrain Epoch: 3 [18560/60000 (31%)]\tLoss: 0.341066\nTrain Epoch: 3 [19200/60000 (32%)]\tLoss: 0.457961\nTrain Epoch: 3 [19840/60000 (33%)]\tLoss: 0.732400\nTrain Epoch: 3 [20480/60000 (34%)]\tLoss: 0.330679\nTrain Epoch: 3 [21120/60000 (35%)]\tLoss: 0.279778\nTrain Epoch: 3 [21760/60000 (36%)]\tLoss: 0.305972\nTrain Epoch: 3 [22400/60000 (37%)]\tLoss: 0.402131\nTrain Epoch: 3 [23040/60000 (38%)]\tLoss: 0.345302\nTrain Epoch: 3 [23680/60000 (39%)]\tLoss: 0.251726\nTrain Epoch: 3 [24320/60000 (41%)]\tLoss: 0.152062\nTrain Epoch: 3 [24960/60000 (42%)]\tLoss: 0.149305\nTrain Epoch: 3 [25600/60000 (43%)]\tLoss: 0.364678\nTrain Epoch: 3 [26240/60000 (44%)]\tLoss: 0.067165\nTrain Epoch: 3 [26880/60000 (45%)]\tLoss: 0.229927\nTrain Epoch: 3 [27520/60000 (46%)]\tLoss: 0.236894\nTrain Epoch: 3 [28160/60000 (47%)]\tLoss: 0.486373\nTrain Epoch: 3 [28800/60000 (48%)]\tLoss: 0.453053\nTrain Epoch: 3 [29440/60000 (49%)]\tLoss: 0.283823\nTrain Epoch: 3 [30080/60000 (50%)]\tLoss: 0.185119\nTrain Epoch: 3 [30720/60000 (51%)]\tLoss: 0.381274\nTrain Epoch: 3 [31360/60000 (52%)]\tLoss: 0.394533\nTrain Epoch: 3 [32000/60000 (53%)]\tLoss: 0.392791\nTrain Epoch: 3 [32640/60000 (54%)]\tLoss: 0.230672\nTrain Epoch: 3 [33280/60000 (55%)]\tLoss: 0.393846\nTrain Epoch: 3 [33920/60000 (57%)]\tLoss: 0.676802\nTrain Epoch: 3 [34560/60000 (58%)]\tLoss: 0.160434\nTrain Epoch: 3 [35200/60000 (59%)]\tLoss: 0.211318\nTrain Epoch: 3 [35840/60000 (60%)]\tLoss: 0.245763\nTrain Epoch: 3 [36480/60000 (61%)]\tLoss: 0.198454\nTrain Epoch: 3 [37120/60000 (62%)]\tLoss: 0.243536\nTrain Epoch: 3 [37760/60000 (63%)]\tLoss: 0.151804\nTrain Epoch: 3 [38400/60000 (64%)]\tLoss: 0.176093\nTrain Epoch: 3 [39040/60000 (65%)]\tLoss: 0.237228\nTrain Epoch: 3 [39680/60000 (66%)]\tLoss: 0.146441\nTrain Epoch: 3 [40320/60000 (67%)]\tLoss: 0.345162\nTrain Epoch: 3 [40960/60000 (68%)]\tLoss: 0.400378\nTrain Epoch: 3 [41600/60000 (69%)]\tLoss: 0.259152\nTrain Epoch: 3 [42240/60000 (70%)]\tLoss: 0.569659\nTrain Epoch: 3 [42880/60000 (71%)]\tLoss: 0.166401\nTrain Epoch: 3 [43520/60000 (72%)]\tLoss: 0.220592\nTrain Epoch: 3 [44160/60000 (74%)]\tLoss: 0.303227\nTrain Epoch: 3 [44800/60000 (75%)]\tLoss: 0.193691\nTrain Epoch: 3 [45440/60000 (76%)]\tLoss: 0.257408\nTrain Epoch: 3 [46080/60000 (77%)]\tLoss: 0.391211\nTrain Epoch: 3 [46720/60000 (78%)]\tLoss: 0.419841\nTrain Epoch: 3 [47360/60000 (79%)]\tLoss: 0.121861\nTrain Epoch: 3 [48000/60000 (80%)]\tLoss: 0.176442\nTrain Epoch: 3 [48640/60000 (81%)]\tLoss: 0.534631\nTrain Epoch: 3 [49280/60000 (82%)]\tLoss: 0.296596\nTrain Epoch: 3 [49920/60000 (83%)]\tLoss: 0.190096\nTrain Epoch: 3 [50560/60000 (84%)]\tLoss: 0.360826\nTrain Epoch: 3 [51200/60000 (85%)]\tLoss: 0.427482\nTrain Epoch: 3 [51840/60000 (86%)]\tLoss: 0.251076\nTrain Epoch: 3 [52480/60000 (87%)]\tLoss: 0.319904\nTrain Epoch: 3 [53120/60000 (88%)]\tLoss: 0.228778\nTrain Epoch: 3 [53760/60000 (90%)]\tLoss: 0.180340\nTrain Epoch: 3 [54400/60000 (91%)]\tLoss: 0.236512\nTrain Epoch: 3 [55040/60000 (92%)]\tLoss: 0.206779\nTrain Epoch: 3 [55680/60000 (93%)]\tLoss: 0.323677\nTrain Epoch: 3 [56320/60000 (94%)]\tLoss: 0.406382\nTrain Epoch: 3 [56960/60000 (95%)]\tLoss: 0.426768\nTrain Epoch: 3 [57600/60000 (96%)]\tLoss: 0.595419\nTrain Epoch: 3 [58240/60000 (97%)]\tLoss: 0.175457\nTrain Epoch: 3 [58880/60000 (98%)]\tLoss: 0.301019\nTrain Epoch: 3 [59520/60000 (99%)]\tLoss: 0.419139\n\nTest set: Average loss: 0.1049, Accuracy: 9686/10000 (97%)\n\nTrain Epoch: 4 [0/60000 (0%)]\tLoss: 0.352631\nTrain Epoch: 4 [640/60000 (1%)]\tLoss: 0.343671\nTrain Epoch: 4 [1280/60000 (2%)]\tLoss: 0.170439\nTrain Epoch: 4 [1920/60000 (3%)]\tLoss: 0.289486\nTrain Epoch: 4 [2560/60000 (4%)]\tLoss: 0.096597\nTrain Epoch: 4 [3200/60000 (5%)]\tLoss: 0.263759\nTrain Epoch: 4 [3840/60000 (6%)]\tLoss: 0.369941\nTrain Epoch: 4 [4480/60000 (7%)]\tLoss: 0.326594\nTrain Epoch: 4 [5120/60000 (9%)]\tLoss: 0.174094\nTrain Epoch: 4 [5760/60000 (10%)]\tLoss: 0.442069\nTrain Epoch: 4 [6400/60000 (11%)]\tLoss: 0.179002\nTrain Epoch: 4 [7040/60000 (12%)]\tLoss: 0.292742\nTrain Epoch: 4 [7680/60000 (13%)]\tLoss: 0.209898\nTrain Epoch: 4 [8320/60000 (14%)]\tLoss: 0.401671\nTrain Epoch: 4 [8960/60000 (15%)]\tLoss: 0.205146\nTrain Epoch: 4 [9600/60000 (16%)]\tLoss: 0.250836\nTrain Epoch: 4 [10240/60000 (17%)]\tLoss: 0.156622\nTrain Epoch: 4 [10880/60000 (18%)]\tLoss: 0.214578\nTrain Epoch: 4 [11520/60000 (19%)]\tLoss: 0.155916\nTrain Epoch: 4 [12160/60000 (20%)]\tLoss: 0.416294\nTrain Epoch: 4 [12800/60000 (21%)]\tLoss: 0.197429\nTrain Epoch: 4 [13440/60000 (22%)]\tLoss: 0.154103\nTrain Epoch: 4 [14080/60000 (23%)]\tLoss: 0.377950\nTrain Epoch: 4 [14720/60000 (25%)]\tLoss: 0.338084\nTrain Epoch: 4 [15360/60000 (26%)]\tLoss: 0.242834\nTrain Epoch: 4 [16000/60000 (27%)]\tLoss: 0.139219\nTrain Epoch: 4 [16640/60000 (28%)]\tLoss: 0.242067\nTrain Epoch: 4 [17280/60000 (29%)]\tLoss: 0.189929\nTrain Epoch: 4 [17920/60000 (30%)]\tLoss: 0.358215\nTrain Epoch: 4 [18560/60000 (31%)]\tLoss: 0.354969\nTrain Epoch: 4 [19200/60000 (32%)]\tLoss: 0.303644\nTrain Epoch: 4 [19840/60000 (33%)]\tLoss: 0.322343\nTrain Epoch: 4 [20480/60000 (34%)]\tLoss: 0.225422\nTrain Epoch: 4 [21120/60000 (35%)]\tLoss: 0.614347\nTrain Epoch: 4 [21760/60000 (36%)]\tLoss: 0.448674\nTrain Epoch: 4 [22400/60000 (37%)]\tLoss: 0.362976\nTrain Epoch: 4 [23040/60000 (38%)]\tLoss: 0.100357\nTrain Epoch: 4 [23680/60000 (39%)]\tLoss: 0.289331\nTrain Epoch: 4 [24320/60000 (41%)]\tLoss: 0.405818\nTrain Epoch: 4 [24960/60000 (42%)]\tLoss: 0.212617\nTrain Epoch: 4 [25600/60000 (43%)]\tLoss: 0.348597\nTrain Epoch: 4 [26240/60000 (44%)]\tLoss: 0.351009\nTrain Epoch: 4 [26880/60000 (45%)]\tLoss: 0.341456\nTrain Epoch: 4 [27520/60000 (46%)]\tLoss: 0.297527\nTrain Epoch: 4 [28160/60000 (47%)]\tLoss: 0.281190\nTrain Epoch: 4 [28800/60000 (48%)]\tLoss: 0.187359\nTrain Epoch: 4 [29440/60000 (49%)]\tLoss: 0.178844\nTrain Epoch: 4 [30080/60000 (50%)]\tLoss: 0.201243\nTrain Epoch: 4 [30720/60000 (51%)]\tLoss: 0.305701\nTrain Epoch: 4 [31360/60000 (52%)]\tLoss: 0.370592\nTrain Epoch: 4 [32000/60000 (53%)]\tLoss: 0.241955\nTrain Epoch: 4 [32640/60000 (54%)]\tLoss: 0.278765\nTrain Epoch: 4 [33280/60000 (55%)]\tLoss: 0.284302\nTrain Epoch: 4 [33920/60000 (57%)]\tLoss: 0.337426\nTrain Epoch: 4 [34560/60000 (58%)]\tLoss: 0.277304\nTrain Epoch: 4 [35200/60000 (59%)]\tLoss: 0.221228\nTrain Epoch: 4 [35840/60000 (60%)]\tLoss: 0.150985\nTrain Epoch: 4 [36480/60000 (61%)]\tLoss: 0.312087\nTrain Epoch: 4 [37120/60000 (62%)]\tLoss: 0.170111\nTrain Epoch: 4 [37760/60000 (63%)]\tLoss: 0.291135\nTrain Epoch: 4 [38400/60000 (64%)]\tLoss: 0.160971\nTrain Epoch: 4 [39040/60000 (65%)]\tLoss: 0.390679\nTrain Epoch: 4 [39680/60000 (66%)]\tLoss: 0.434802\nTrain Epoch: 4 [40320/60000 (67%)]\tLoss: 0.281539\nTrain Epoch: 4 [40960/60000 (68%)]\tLoss: 0.172577\nTrain Epoch: 4 [41600/60000 (69%)]\tLoss: 0.348624\nTrain Epoch: 4 [42240/60000 (70%)]\tLoss: 0.380416\nTrain Epoch: 4 [42880/60000 (71%)]\tLoss: 0.483520\nTrain Epoch: 4 [43520/60000 (72%)]\tLoss: 0.216825\nTrain Epoch: 4 [44160/60000 (74%)]\tLoss: 0.320874\nTrain Epoch: 4 [44800/60000 (75%)]\tLoss: 0.213358\nTrain Epoch: 4 [45440/60000 (76%)]\tLoss: 0.218650\nTrain Epoch: 4 [46080/60000 (77%)]\tLoss: 0.221090\nTrain Epoch: 4 [46720/60000 (78%)]\tLoss: 0.325981\nTrain Epoch: 4 [47360/60000 (79%)]\tLoss: 0.283184\nTrain Epoch: 4 [48000/60000 (80%)]\tLoss: 0.072845\nTrain Epoch: 4 [48640/60000 (81%)]\tLoss: 0.206940\nTrain Epoch: 4 [49280/60000 (82%)]\tLoss: 0.423454\nTrain Epoch: 4 [49920/60000 (83%)]\tLoss: 0.475285\nTrain Epoch: 4 [50560/60000 (84%)]\tLoss: 0.128978\nTrain Epoch: 4 [51200/60000 (85%)]\tLoss: 0.195609\nTrain Epoch: 4 [51840/60000 (86%)]\tLoss: 0.125730\nTrain Epoch: 4 [52480/60000 (87%)]\tLoss: 0.137783\nTrain Epoch: 4 [53120/60000 (88%)]\tLoss: 0.375247\nTrain Epoch: 4 [53760/60000 (90%)]\tLoss: 0.243497\nTrain Epoch: 4 [54400/60000 (91%)]\tLoss: 0.236100\nTrain Epoch: 4 [55040/60000 (92%)]\tLoss: 0.266795\nTrain Epoch: 4 [55680/60000 (93%)]\tLoss: 0.229095\nTrain Epoch: 4 [56320/60000 (94%)]\tLoss: 0.167610\nTrain Epoch: 4 [56960/60000 (95%)]\tLoss: 0.240640\nTrain Epoch: 4 [57600/60000 (96%)]\tLoss: 0.153999\nTrain Epoch: 4 [58240/60000 (97%)]\tLoss: 0.753790\nTrain Epoch: 4 [58880/60000 (98%)]\tLoss: 0.143998\nTrain Epoch: 4 [59520/60000 (99%)]\tLoss: 0.310583\n\nTest set: Average loss: 0.0843, Accuracy: 9739/10000 (97%)\n\nTrain Epoch: 5 [0/60000 (0%)]\tLoss: 0.227892\nTrain Epoch: 5 [640/60000 (1%)]\tLoss: 0.162702\nTrain Epoch: 5 [1280/60000 (2%)]\tLoss: 0.227571\nTrain Epoch: 5 [1920/60000 (3%)]\tLoss: 0.148511\nTrain Epoch: 5 [2560/60000 (4%)]\tLoss: 0.187414\nTrain Epoch: 5 [3200/60000 (5%)]\tLoss: 0.194418\nTrain Epoch: 5 [3840/60000 (6%)]\tLoss: 0.276495\nTrain Epoch: 5 [4480/60000 (7%)]\tLoss: 0.268769\nTrain Epoch: 5 [5120/60000 (9%)]\tLoss: 0.163968\nTrain Epoch: 5 [5760/60000 (10%)]\tLoss: 0.349296\nTrain Epoch: 5 [6400/60000 (11%)]\tLoss: 0.217248\nTrain Epoch: 5 [7040/60000 (12%)]\tLoss: 0.195263\nTrain Epoch: 5 [7680/60000 (13%)]\tLoss: 0.339447\nTrain Epoch: 5 [8320/60000 (14%)]\tLoss: 0.224461\nTrain Epoch: 5 [8960/60000 (15%)]\tLoss: 0.095605\nTrain Epoch: 5 [9600/60000 (16%)]\tLoss: 0.196891\nTrain Epoch: 5 [10240/60000 (17%)]\tLoss: 0.218742\nTrain Epoch: 5 [10880/60000 (18%)]\tLoss: 0.071347\nTrain Epoch: 5 [11520/60000 (19%)]\tLoss: 0.403286\nTrain Epoch: 5 [12160/60000 (20%)]\tLoss: 0.149740\nTrain Epoch: 5 [12800/60000 (21%)]\tLoss: 0.160939\nTrain Epoch: 5 [13440/60000 (22%)]\tLoss: 0.236512\nTrain Epoch: 5 [14080/60000 (23%)]\tLoss: 0.348727\nTrain Epoch: 5 [14720/60000 (25%)]\tLoss: 0.190054\nTrain Epoch: 5 [15360/60000 (26%)]\tLoss: 0.272029\nTrain Epoch: 5 [16000/60000 (27%)]\tLoss: 0.427739\nTrain Epoch: 5 [16640/60000 (28%)]\tLoss: 0.322332\nTrain Epoch: 5 [17280/60000 (29%)]\tLoss: 0.141410\nTrain Epoch: 5 [17920/60000 (30%)]\tLoss: 0.098900\nTrain Epoch: 5 [18560/60000 (31%)]\tLoss: 0.252387\nTrain Epoch: 5 [19200/60000 (32%)]\tLoss: 0.182150\nTrain Epoch: 5 [19840/60000 (33%)]\tLoss: 0.133239\nTrain Epoch: 5 [20480/60000 (34%)]\tLoss: 0.126683\nTrain Epoch: 5 [21120/60000 (35%)]\tLoss: 0.370189\nTrain Epoch: 5 [21760/60000 (36%)]\tLoss: 0.162514\nTrain Epoch: 5 [22400/60000 (37%)]\tLoss: 0.272352\nTrain Epoch: 5 [23040/60000 (38%)]\tLoss: 0.298543\nTrain Epoch: 5 [23680/60000 (39%)]\tLoss: 0.235891\nTrain Epoch: 5 [24320/60000 (41%)]\tLoss: 0.187710\nTrain Epoch: 5 [24960/60000 (42%)]\tLoss: 0.185363\nTrain Epoch: 5 [25600/60000 (43%)]\tLoss: 0.193369\nTrain Epoch: 5 [26240/60000 (44%)]\tLoss: 0.155984\nTrain Epoch: 5 [26880/60000 (45%)]\tLoss: 0.388923\nTrain Epoch: 5 [27520/60000 (46%)]\tLoss: 0.192868\nTrain Epoch: 5 [28160/60000 (47%)]\tLoss: 0.535787\nTrain Epoch: 5 [28800/60000 (48%)]\tLoss: 0.161020\nTrain Epoch: 5 [29440/60000 (49%)]\tLoss: 0.242179\nTrain Epoch: 5 [30080/60000 (50%)]\tLoss: 0.136554\nTrain Epoch: 5 [30720/60000 (51%)]\tLoss: 0.190672\nTrain Epoch: 5 [31360/60000 (52%)]\tLoss: 0.118027\nTrain Epoch: 5 [32000/60000 (53%)]\tLoss: 0.278750\nTrain Epoch: 5 [32640/60000 (54%)]\tLoss: 0.418058\nTrain Epoch: 5 [33280/60000 (55%)]\tLoss: 0.287063\nTrain Epoch: 5 [33920/60000 (57%)]\tLoss: 0.279596\nTrain Epoch: 5 [34560/60000 (58%)]\tLoss: 0.181579\nTrain Epoch: 5 [35200/60000 (59%)]\tLoss: 0.443592\nTrain Epoch: 5 [35840/60000 (60%)]\tLoss: 0.095470\nTrain Epoch: 5 [36480/60000 (61%)]\tLoss: 0.277385\nTrain Epoch: 5 [37120/60000 (62%)]\tLoss: 0.263358\nTrain Epoch: 5 [37760/60000 (63%)]\tLoss: 0.190867\nTrain Epoch: 5 [38400/60000 (64%)]\tLoss: 0.176580\nTrain Epoch: 5 [39040/60000 (65%)]\tLoss: 0.360235\nTrain Epoch: 5 [39680/60000 (66%)]\tLoss: 0.172416\nTrain Epoch: 5 [40320/60000 (67%)]\tLoss: 0.174126\nTrain Epoch: 5 [40960/60000 (68%)]\tLoss: 0.202162\nTrain Epoch: 5 [41600/60000 (69%)]\tLoss: 0.196991\nTrain Epoch: 5 [42240/60000 (70%)]\tLoss: 0.224622\nTrain Epoch: 5 [42880/60000 (71%)]\tLoss: 0.180406\nTrain Epoch: 5 [43520/60000 (72%)]\tLoss: 0.060447\nTrain Epoch: 5 [44160/60000 (74%)]\tLoss: 0.322497\nTrain Epoch: 5 [44800/60000 (75%)]\tLoss: 0.239324\nTrain Epoch: 5 [45440/60000 (76%)]\tLoss: 0.348920\nTrain Epoch: 5 [46080/60000 (77%)]\tLoss: 0.240017\nTrain Epoch: 5 [46720/60000 (78%)]\tLoss: 0.237575\nTrain Epoch: 5 [47360/60000 (79%)]\tLoss: 0.142648\nTrain Epoch: 5 [48000/60000 (80%)]\tLoss: 0.227562\nTrain Epoch: 5 [48640/60000 (81%)]\tLoss: 0.254358\nTrain Epoch: 5 [49280/60000 (82%)]\tLoss: 0.135818\nTrain Epoch: 5 [49920/60000 (83%)]\tLoss: 0.386120\nTrain Epoch: 5 [50560/60000 (84%)]\tLoss: 0.328150\nTrain Epoch: 5 [51200/60000 (85%)]\tLoss: 0.276833\nTrain Epoch: 5 [51840/60000 (86%)]\tLoss: 0.308869\nTrain Epoch: 5 [52480/60000 (87%)]\tLoss: 0.246442\nTrain Epoch: 5 [53120/60000 (88%)]\tLoss: 0.240874\nTrain Epoch: 5 [53760/60000 (90%)]\tLoss: 0.114337\nTrain Epoch: 5 [54400/60000 (91%)]\tLoss: 0.217325\nTrain Epoch: 5 [55040/60000 (92%)]\tLoss: 0.223010\nTrain Epoch: 5 [55680/60000 (93%)]\tLoss: 0.138459\nTrain Epoch: 5 [56320/60000 (94%)]\tLoss: 0.283678\nTrain Epoch: 5 [56960/60000 (95%)]\tLoss: 0.158834\nTrain Epoch: 5 [57600/60000 (96%)]\tLoss: 0.164267\nTrain Epoch: 5 [58240/60000 (97%)]\tLoss: 0.290795\nTrain Epoch: 5 [58880/60000 (98%)]\tLoss: 0.451639\nTrain Epoch: 5 [59520/60000 (99%)]\tLoss: 0.349018\n\nTest set: Average loss: 0.0797, Accuracy: 9758/10000 (98%)\n\nTrain Epoch: 6 [0/60000 (0%)]\tLoss: 0.311334\nTrain Epoch: 6 [640/60000 (1%)]\tLoss: 0.129143\nTrain Epoch: 6 [1280/60000 (2%)]\tLoss: 0.227222\nTrain Epoch: 6 [1920/60000 (3%)]\tLoss: 0.157591\nTrain Epoch: 6 [2560/60000 (4%)]\tLoss: 0.205490\nTrain Epoch: 6 [3200/60000 (5%)]\tLoss: 0.421089\nTrain Epoch: 6 [3840/60000 (6%)]\tLoss: 0.157544\nTrain Epoch: 6 [4480/60000 (7%)]\tLoss: 0.087023\nTrain Epoch: 6 [5120/60000 (9%)]\tLoss: 0.130669\nTrain Epoch: 6 [5760/60000 (10%)]\tLoss: 0.059450\nTrain Epoch: 6 [6400/60000 (11%)]\tLoss: 0.121786\nTrain Epoch: 6 [7040/60000 (12%)]\tLoss: 0.177859\nTrain Epoch: 6 [7680/60000 (13%)]\tLoss: 0.217464\nTrain Epoch: 6 [8320/60000 (14%)]\tLoss: 0.183426\nTrain Epoch: 6 [8960/60000 (15%)]\tLoss: 0.237282\nTrain Epoch: 6 [9600/60000 (16%)]\tLoss: 0.210031\nTrain Epoch: 6 [10240/60000 (17%)]\tLoss: 0.256110\nTrain Epoch: 6 [10880/60000 (18%)]\tLoss: 0.155481\nTrain Epoch: 6 [11520/60000 (19%)]\tLoss: 0.166967\nTrain Epoch: 6 [12160/60000 (20%)]\tLoss: 0.144590\nTrain Epoch: 6 [12800/60000 (21%)]\tLoss: 0.229593\nTrain Epoch: 6 [13440/60000 (22%)]\tLoss: 0.092102\nTrain Epoch: 6 [14080/60000 (23%)]\tLoss: 0.144247\nTrain Epoch: 6 [14720/60000 (25%)]\tLoss: 0.459083\nTrain Epoch: 6 [15360/60000 (26%)]\tLoss: 0.174974\nTrain Epoch: 6 [16000/60000 (27%)]\tLoss: 0.146433\nTrain Epoch: 6 [16640/60000 (28%)]\tLoss: 0.291392\nTrain Epoch: 6 [17280/60000 (29%)]\tLoss: 0.203127\nTrain Epoch: 6 [17920/60000 (30%)]\tLoss: 0.255063\nTrain Epoch: 6 [18560/60000 (31%)]\tLoss: 0.167576\nTrain Epoch: 6 [19200/60000 (32%)]\tLoss: 0.171914\nTrain Epoch: 6 [19840/60000 (33%)]\tLoss: 0.215950\nTrain Epoch: 6 [20480/60000 (34%)]\tLoss: 0.246624\nTrain Epoch: 6 [21120/60000 (35%)]\tLoss: 0.242730\nTrain Epoch: 6 [21760/60000 (36%)]\tLoss: 0.345666\nTrain Epoch: 6 [22400/60000 (37%)]\tLoss: 0.229078\nTrain Epoch: 6 [23040/60000 (38%)]\tLoss: 0.283169\nTrain Epoch: 6 [23680/60000 (39%)]\tLoss: 0.246430\nTrain Epoch: 6 [24320/60000 (41%)]\tLoss: 0.217211\nTrain Epoch: 6 [24960/60000 (42%)]\tLoss: 0.168141\nTrain Epoch: 6 [25600/60000 (43%)]\tLoss: 0.297715\nTrain Epoch: 6 [26240/60000 (44%)]\tLoss: 0.200130\nTrain Epoch: 6 [26880/60000 (45%)]\tLoss: 0.344390\nTrain Epoch: 6 [27520/60000 (46%)]\tLoss: 0.246202\nTrain Epoch: 6 [28160/60000 (47%)]\tLoss: 0.272422\nTrain Epoch: 6 [28800/60000 (48%)]\tLoss: 0.117001\nTrain Epoch: 6 [29440/60000 (49%)]\tLoss: 0.246031\nTrain Epoch: 6 [30080/60000 (50%)]\tLoss: 0.138119\nTrain Epoch: 6 [30720/60000 (51%)]\tLoss: 0.214345\nTrain Epoch: 6 [31360/60000 (52%)]\tLoss: 0.134483\nTrain Epoch: 6 [32000/60000 (53%)]\tLoss: 0.201771\nTrain Epoch: 6 [32640/60000 (54%)]\tLoss: 0.201668\nTrain Epoch: 6 [33280/60000 (55%)]\tLoss: 0.111183\nTrain Epoch: 6 [33920/60000 (57%)]\tLoss: 0.093289\nTrain Epoch: 6 [34560/60000 (58%)]\tLoss: 0.171475\nTrain Epoch: 6 [35200/60000 (59%)]\tLoss: 0.178729\nTrain Epoch: 6 [35840/60000 (60%)]\tLoss: 0.144986\nTrain Epoch: 6 [36480/60000 (61%)]\tLoss: 0.302206\nTrain Epoch: 6 [37120/60000 (62%)]\tLoss: 0.389723\nTrain Epoch: 6 [37760/60000 (63%)]\tLoss: 0.268302\nTrain Epoch: 6 [38400/60000 (64%)]\tLoss: 0.358240\nTrain Epoch: 6 [39040/60000 (65%)]\tLoss: 0.241359\nTrain Epoch: 6 [39680/60000 (66%)]\tLoss: 0.282464\nTrain Epoch: 6 [40320/60000 (67%)]\tLoss: 0.205064\nTrain Epoch: 6 [40960/60000 (68%)]\tLoss: 0.106739\nTrain Epoch: 6 [41600/60000 (69%)]\tLoss: 0.076333\nTrain Epoch: 6 [42240/60000 (70%)]\tLoss: 0.157558\nTrain Epoch: 6 [42880/60000 (71%)]\tLoss: 0.217494\nTrain Epoch: 6 [43520/60000 (72%)]\tLoss: 0.183687\nTrain Epoch: 6 [44160/60000 (74%)]\tLoss: 0.217155\nTrain Epoch: 6 [44800/60000 (75%)]\tLoss: 0.108482\nTrain Epoch: 6 [45440/60000 (76%)]\tLoss: 0.324247\nTrain Epoch: 6 [46080/60000 (77%)]\tLoss: 0.352494\nTrain Epoch: 6 [46720/60000 (78%)]\tLoss: 0.163462\nTrain Epoch: 6 [47360/60000 (79%)]\tLoss: 0.154820\nTrain Epoch: 6 [48000/60000 (80%)]\tLoss: 0.174164\nTrain Epoch: 6 [48640/60000 (81%)]\tLoss: 0.196258\nTrain Epoch: 6 [49280/60000 (82%)]\tLoss: 0.226030\nTrain Epoch: 6 [49920/60000 (83%)]\tLoss: 0.306971\nTrain Epoch: 6 [50560/60000 (84%)]\tLoss: 0.387282\nTrain Epoch: 6 [51200/60000 (85%)]\tLoss: 0.213550\nTrain Epoch: 6 [51840/60000 (86%)]\tLoss: 0.133755\nTrain Epoch: 6 [52480/60000 (87%)]\tLoss: 0.176044\nTrain Epoch: 6 [53120/60000 (88%)]\tLoss: 0.282900\nTrain Epoch: 6 [53760/60000 (90%)]\tLoss: 0.154157\nTrain Epoch: 6 [54400/60000 (91%)]\tLoss: 0.138895\nTrain Epoch: 6 [55040/60000 (92%)]\tLoss: 0.254137\nTrain Epoch: 6 [55680/60000 (93%)]\tLoss: 0.107765\nTrain Epoch: 6 [56320/60000 (94%)]\tLoss: 0.118788\nTrain Epoch: 6 [56960/60000 (95%)]\tLoss: 0.142051\nTrain Epoch: 6 [57600/60000 (96%)]\tLoss: 0.176375\nTrain Epoch: 6 [58240/60000 (97%)]\tLoss: 0.131573\nTrain Epoch: 6 [58880/60000 (98%)]\tLoss: 0.347166\nTrain Epoch: 6 [59520/60000 (99%)]\tLoss: 0.217951\n\nTest set: Average loss: 0.0690, Accuracy: 9776/10000 (98%)\n\nTrain Epoch: 7 [0/60000 (0%)]\tLoss: 0.142441\nTrain Epoch: 7 [640/60000 (1%)]\tLoss: 0.078599\nTrain Epoch: 7 [1280/60000 (2%)]\tLoss: 0.121731\nTrain Epoch: 7 [1920/60000 (3%)]\tLoss: 0.070044\nTrain Epoch: 7 [2560/60000 (4%)]\tLoss: 0.224216\nTrain Epoch: 7 [3200/60000 (5%)]\tLoss: 0.104122\nTrain Epoch: 7 [3840/60000 (6%)]\tLoss: 0.228575\nTrain Epoch: 7 [4480/60000 (7%)]\tLoss: 0.377044\nTrain Epoch: 7 [5120/60000 (9%)]\tLoss: 0.296184\nTrain Epoch: 7 [5760/60000 (10%)]\tLoss: 0.099891\nTrain Epoch: 7 [6400/60000 (11%)]\tLoss: 0.269691\nTrain Epoch: 7 [7040/60000 (12%)]\tLoss: 0.240640\nTrain Epoch: 7 [7680/60000 (13%)]\tLoss: 0.171192\nTrain Epoch: 7 [8320/60000 (14%)]\tLoss: 0.306889\nTrain Epoch: 7 [8960/60000 (15%)]\tLoss: 0.238503\nTrain Epoch: 7 [9600/60000 (16%)]\tLoss: 0.286252\nTrain Epoch: 7 [10240/60000 (17%)]\tLoss: 0.171058\nTrain Epoch: 7 [10880/60000 (18%)]\tLoss: 0.208866\nTrain Epoch: 7 [11520/60000 (19%)]\tLoss: 0.418091\nTrain Epoch: 7 [12160/60000 (20%)]\tLoss: 0.115058\nTrain Epoch: 7 [12800/60000 (21%)]\tLoss: 0.159557\nTrain Epoch: 7 [13440/60000 (22%)]\tLoss: 0.085076\nTrain Epoch: 7 [14080/60000 (23%)]\tLoss: 0.244673\nTrain Epoch: 7 [14720/60000 (25%)]\tLoss: 0.316326\nTrain Epoch: 7 [15360/60000 (26%)]\tLoss: 0.370775\nTrain Epoch: 7 [16000/60000 (27%)]\tLoss: 0.235262\nTrain Epoch: 7 [16640/60000 (28%)]\tLoss: 0.296188\nTrain Epoch: 7 [17280/60000 (29%)]\tLoss: 0.224960\nTrain Epoch: 7 [17920/60000 (30%)]\tLoss: 0.162341\nTrain Epoch: 7 [18560/60000 (31%)]\tLoss: 0.136551\nTrain Epoch: 7 [19200/60000 (32%)]\tLoss: 0.111435\nTrain Epoch: 7 [19840/60000 (33%)]\tLoss: 0.173483\nTrain Epoch: 7 [20480/60000 (34%)]\tLoss: 0.170351\nTrain Epoch: 7 [21120/60000 (35%)]\tLoss: 0.109828\nTrain Epoch: 7 [21760/60000 (36%)]\tLoss: 0.219692\nTrain Epoch: 7 [22400/60000 (37%)]\tLoss: 0.085780\nTrain Epoch: 7 [23040/60000 (38%)]\tLoss: 0.076800\nTrain Epoch: 7 [23680/60000 (39%)]\tLoss: 0.163377\nTrain Epoch: 7 [24320/60000 (41%)]\tLoss: 0.178391\nTrain Epoch: 7 [24960/60000 (42%)]\tLoss: 0.311988\nTrain Epoch: 7 [25600/60000 (43%)]\tLoss: 0.215559\nTrain Epoch: 7 [26240/60000 (44%)]\tLoss: 0.199207\nTrain Epoch: 7 [26880/60000 (45%)]\tLoss: 0.201917\nTrain Epoch: 7 [27520/60000 (46%)]\tLoss: 0.163283\nTrain Epoch: 7 [28160/60000 (47%)]\tLoss: 0.107533\nTrain Epoch: 7 [28800/60000 (48%)]\tLoss: 0.046209\nTrain Epoch: 7 [29440/60000 (49%)]\tLoss: 0.173062\nTrain Epoch: 7 [30080/60000 (50%)]\tLoss: 0.088925\nTrain Epoch: 7 [30720/60000 (51%)]\tLoss: 0.068962\nTrain Epoch: 7 [31360/60000 (52%)]\tLoss: 0.223214\nTrain Epoch: 7 [32000/60000 (53%)]\tLoss: 0.096083\nTrain Epoch: 7 [32640/60000 (54%)]\tLoss: 0.327635\nTrain Epoch: 7 [33280/60000 (55%)]\tLoss: 0.278620\nTrain Epoch: 7 [33920/60000 (57%)]\tLoss: 0.223806\nTrain Epoch: 7 [34560/60000 (58%)]\tLoss: 0.121638\nTrain Epoch: 7 [35200/60000 (59%)]\tLoss: 0.182739\nTrain Epoch: 7 [35840/60000 (60%)]\tLoss: 0.172866\nTrain Epoch: 7 [36480/60000 (61%)]\tLoss: 0.180873\nTrain Epoch: 7 [37120/60000 (62%)]\tLoss: 0.298984\nTrain Epoch: 7 [37760/60000 (63%)]\tLoss: 0.251939\nTrain Epoch: 7 [38400/60000 (64%)]\tLoss: 0.105321\nTrain Epoch: 7 [39040/60000 (65%)]\tLoss: 0.200500\nTrain Epoch: 7 [39680/60000 (66%)]\tLoss: 0.309791\nTrain Epoch: 7 [40320/60000 (67%)]\tLoss: 0.114949\nTrain Epoch: 7 [40960/60000 (68%)]\tLoss: 0.066153\nTrain Epoch: 7 [41600/60000 (69%)]\tLoss: 0.327437\nTrain Epoch: 7 [42240/60000 (70%)]\tLoss: 0.179023\nTrain Epoch: 7 [42880/60000 (71%)]\tLoss: 0.089861\nTrain Epoch: 7 [43520/60000 (72%)]\tLoss: 0.111230\nTrain Epoch: 7 [44160/60000 (74%)]\tLoss: 0.108233\nTrain Epoch: 7 [44800/60000 (75%)]\tLoss: 0.145669\nTrain Epoch: 7 [45440/60000 (76%)]\tLoss: 0.122024\nTrain Epoch: 7 [46080/60000 (77%)]\tLoss: 0.083490\nTrain Epoch: 7 [46720/60000 (78%)]\tLoss: 0.116002\nTrain Epoch: 7 [47360/60000 (79%)]\tLoss: 0.200240\nTrain Epoch: 7 [48000/60000 (80%)]\tLoss: 0.363707\nTrain Epoch: 7 [48640/60000 (81%)]\tLoss: 0.294594\nTrain Epoch: 7 [49280/60000 (82%)]\tLoss: 0.127643\nTrain Epoch: 7 [49920/60000 (83%)]\tLoss: 0.202008\nTrain Epoch: 7 [50560/60000 (84%)]\tLoss: 0.159551\nTrain Epoch: 7 [51200/60000 (85%)]\tLoss: 0.221197\nTrain Epoch: 7 [51840/60000 (86%)]\tLoss: 0.266463\nTrain Epoch: 7 [52480/60000 (87%)]\tLoss: 0.073967\nTrain Epoch: 7 [53120/60000 (88%)]\tLoss: 0.350092\nTrain Epoch: 7 [53760/60000 (90%)]\tLoss: 0.106500\nTrain Epoch: 7 [54400/60000 (91%)]\tLoss: 0.208859\nTrain Epoch: 7 [55040/60000 (92%)]\tLoss: 0.209937\nTrain Epoch: 7 [55680/60000 (93%)]\tLoss: 0.215286\nTrain Epoch: 7 [56320/60000 (94%)]\tLoss: 0.117026\nTrain Epoch: 7 [56960/60000 (95%)]\tLoss: 0.132321\nTrain Epoch: 7 [57600/60000 (96%)]\tLoss: 0.286004\nTrain Epoch: 7 [58240/60000 (97%)]\tLoss: 0.170485\nTrain Epoch: 7 [58880/60000 (98%)]\tLoss: 0.196613\nTrain Epoch: 7 [59520/60000 (99%)]\tLoss: 0.293870\n\nTest set: Average loss: 0.0657, Accuracy: 9801/10000 (98%)\n\nTrain Epoch: 8 [0/60000 (0%)]\tLoss: 0.315451\nTrain Epoch: 8 [640/60000 (1%)]\tLoss: 0.114413\nTrain Epoch: 8 [1280/60000 (2%)]\tLoss: 0.129036\nTrain Epoch: 8 [1920/60000 (3%)]\tLoss: 0.141999\nTrain Epoch: 8 [2560/60000 (4%)]\tLoss: 0.118697\nTrain Epoch: 8 [3200/60000 (5%)]\tLoss: 0.126823\nTrain Epoch: 8 [3840/60000 (6%)]\tLoss: 0.053924\nTrain Epoch: 8 [4480/60000 (7%)]\tLoss: 0.296224\nTrain Epoch: 8 [5120/60000 (9%)]\tLoss: 0.121338\nTrain Epoch: 8 [5760/60000 (10%)]\tLoss: 0.255161\nTrain Epoch: 8 [6400/60000 (11%)]\tLoss: 0.170684\nTrain Epoch: 8 [7040/60000 (12%)]\tLoss: 0.092008\nTrain Epoch: 8 [7680/60000 (13%)]\tLoss: 0.283091\nTrain Epoch: 8 [8320/60000 (14%)]\tLoss: 0.027133\nTrain Epoch: 8 [8960/60000 (15%)]\tLoss: 0.195686\nTrain Epoch: 8 [9600/60000 (16%)]\tLoss: 0.343612\nTrain Epoch: 8 [10240/60000 (17%)]\tLoss: 0.108563\nTrain Epoch: 8 [10880/60000 (18%)]\tLoss: 0.223832\nTrain Epoch: 8 [11520/60000 (19%)]\tLoss: 0.175617\nTrain Epoch: 8 [12160/60000 (20%)]\tLoss: 0.145828\nTrain Epoch: 8 [12800/60000 (21%)]\tLoss: 0.178722\nTrain Epoch: 8 [13440/60000 (22%)]\tLoss: 0.151158\nTrain Epoch: 8 [14080/60000 (23%)]\tLoss: 0.183155\nTrain Epoch: 8 [14720/60000 (25%)]\tLoss: 0.110281\nTrain Epoch: 8 [15360/60000 (26%)]\tLoss: 0.282224\nTrain Epoch: 8 [16000/60000 (27%)]\tLoss: 0.097411\nTrain Epoch: 8 [16640/60000 (28%)]\tLoss: 0.264533\nTrain Epoch: 8 [17280/60000 (29%)]\tLoss: 0.194778\nTrain Epoch: 8 [17920/60000 (30%)]\tLoss: 0.235924\nTrain Epoch: 8 [18560/60000 (31%)]\tLoss: 0.236801\nTrain Epoch: 8 [19200/60000 (32%)]\tLoss: 0.178174\nTrain Epoch: 8 [19840/60000 (33%)]\tLoss: 0.218752\nTrain Epoch: 8 [20480/60000 (34%)]\tLoss: 0.208353\nTrain Epoch: 8 [21120/60000 (35%)]\tLoss: 0.193034\nTrain Epoch: 8 [21760/60000 (36%)]\tLoss: 0.138453\nTrain Epoch: 8 [22400/60000 (37%)]\tLoss: 0.175271\nTrain Epoch: 8 [23040/60000 (38%)]\tLoss: 0.157295\nTrain Epoch: 8 [23680/60000 (39%)]\tLoss: 0.156248\nTrain Epoch: 8 [24320/60000 (41%)]\tLoss: 0.153413\nTrain Epoch: 8 [24960/60000 (42%)]\tLoss: 0.084870\nTrain Epoch: 8 [25600/60000 (43%)]\tLoss: 0.150966\nTrain Epoch: 8 [26240/60000 (44%)]\tLoss: 0.160973\nTrain Epoch: 8 [26880/60000 (45%)]\tLoss: 0.231433\nTrain Epoch: 8 [27520/60000 (46%)]\tLoss: 0.144396\nTrain Epoch: 8 [28160/60000 (47%)]\tLoss: 0.200417\nTrain Epoch: 8 [28800/60000 (48%)]\tLoss: 0.152939\nTrain Epoch: 8 [29440/60000 (49%)]\tLoss: 0.109962\nTrain Epoch: 8 [30080/60000 (50%)]\tLoss: 0.134907\nTrain Epoch: 8 [30720/60000 (51%)]\tLoss: 0.088782\nTrain Epoch: 8 [31360/60000 (52%)]\tLoss: 0.129031\nTrain Epoch: 8 [32000/60000 (53%)]\tLoss: 0.184744\nTrain Epoch: 8 [32640/60000 (54%)]\tLoss: 0.155463\nTrain Epoch: 8 [33280/60000 (55%)]\tLoss: 0.174192\nTrain Epoch: 8 [33920/60000 (57%)]\tLoss: 0.172103\nTrain Epoch: 8 [34560/60000 (58%)]\tLoss: 0.201503\nTrain Epoch: 8 [35200/60000 (59%)]\tLoss: 0.287885\nTrain Epoch: 8 [35840/60000 (60%)]\tLoss: 0.133675\nTrain Epoch: 8 [36480/60000 (61%)]\tLoss: 0.243534\nTrain Epoch: 8 [37120/60000 (62%)]\tLoss: 0.196020\nTrain Epoch: 8 [37760/60000 (63%)]\tLoss: 0.101380\nTrain Epoch: 8 [38400/60000 (64%)]\tLoss: 0.108299\nTrain Epoch: 8 [39040/60000 (65%)]\tLoss: 0.159048\nTrain Epoch: 8 [39680/60000 (66%)]\tLoss: 0.204734\nTrain Epoch: 8 [40320/60000 (67%)]\tLoss: 0.238383\nTrain Epoch: 8 [40960/60000 (68%)]\tLoss: 0.592663\nTrain Epoch: 8 [41600/60000 (69%)]\tLoss: 0.116080\nTrain Epoch: 8 [42240/60000 (70%)]\tLoss: 0.039719\nTrain Epoch: 8 [42880/60000 (71%)]\tLoss: 0.148190\nTrain Epoch: 8 [43520/60000 (72%)]\tLoss: 0.241765\nTrain Epoch: 8 [44160/60000 (74%)]\tLoss: 0.235942\nTrain Epoch: 8 [44800/60000 (75%)]\tLoss: 0.175277\nTrain Epoch: 8 [45440/60000 (76%)]\tLoss: 0.143608\nTrain Epoch: 8 [46080/60000 (77%)]\tLoss: 0.114853\nTrain Epoch: 8 [46720/60000 (78%)]\tLoss: 0.232284\nTrain Epoch: 8 [47360/60000 (79%)]\tLoss: 0.321072\nTrain Epoch: 8 [48000/60000 (80%)]\tLoss: 0.310765\nTrain Epoch: 8 [48640/60000 (81%)]\tLoss: 0.102070\nTrain Epoch: 8 [49280/60000 (82%)]\tLoss: 0.372137\nTrain Epoch: 8 [49920/60000 (83%)]\tLoss: 0.109344\nTrain Epoch: 8 [50560/60000 (84%)]\tLoss: 0.382866\nTrain Epoch: 8 [51200/60000 (85%)]\tLoss: 0.270467\nTrain Epoch: 8 [51840/60000 (86%)]\tLoss: 0.061211\nTrain Epoch: 8 [52480/60000 (87%)]\tLoss: 0.233812\nTrain Epoch: 8 [53120/60000 (88%)]\tLoss: 0.176510\nTrain Epoch: 8 [53760/60000 (90%)]\tLoss: 0.120536\nTrain Epoch: 8 [54400/60000 (91%)]\tLoss: 0.241959\nTrain Epoch: 8 [55040/60000 (92%)]\tLoss: 0.183966\nTrain Epoch: 8 [55680/60000 (93%)]\tLoss: 0.125279\nTrain Epoch: 8 [56320/60000 (94%)]\tLoss: 0.152849\nTrain Epoch: 8 [56960/60000 (95%)]\tLoss: 0.219788\nTrain Epoch: 8 [57600/60000 (96%)]\tLoss: 0.077843\nTrain Epoch: 8 [58240/60000 (97%)]\tLoss: 0.304191\nTrain Epoch: 8 [58880/60000 (98%)]\tLoss: 0.363550\nTrain Epoch: 8 [59520/60000 (99%)]\tLoss: 0.326421\n\nTest set: Average loss: 0.0632, Accuracy: 9807/10000 (98%)\n\nTrain Epoch: 9 [0/60000 (0%)]\tLoss: 0.140965\nTrain Epoch: 9 [640/60000 (1%)]\tLoss: 0.206063\nTrain Epoch: 9 [1280/60000 (2%)]\tLoss: 0.189364\nTrain Epoch: 9 [1920/60000 (3%)]\tLoss: 0.367962\nTrain Epoch: 9 [2560/60000 (4%)]\tLoss: 0.108362\nTrain Epoch: 9 [3200/60000 (5%)]\tLoss: 0.109142\nTrain Epoch: 9 [3840/60000 (6%)]\tLoss: 0.270022\nTrain Epoch: 9 [4480/60000 (7%)]\tLoss: 0.200647\nTrain Epoch: 9 [5120/60000 (9%)]\tLoss: 0.162118\nTrain Epoch: 9 [5760/60000 (10%)]\tLoss: 0.167245\nTrain Epoch: 9 [6400/60000 (11%)]\tLoss: 0.188903\nTrain Epoch: 9 [7040/60000 (12%)]\tLoss: 0.280550\nTrain Epoch: 9 [7680/60000 (13%)]\tLoss: 0.116265\nTrain Epoch: 9 [8320/60000 (14%)]\tLoss: 0.602693\nTrain Epoch: 9 [8960/60000 (15%)]\tLoss: 0.148682\nTrain Epoch: 9 [9600/60000 (16%)]\tLoss: 0.225477\nTrain Epoch: 9 [10240/60000 (17%)]\tLoss: 0.133642\nTrain Epoch: 9 [10880/60000 (18%)]\tLoss: 0.116083\nTrain Epoch: 9 [11520/60000 (19%)]\tLoss: 0.348113\nTrain Epoch: 9 [12160/60000 (20%)]\tLoss: 0.219562\nTrain Epoch: 9 [12800/60000 (21%)]\tLoss: 0.117716\nTrain Epoch: 9 [13440/60000 (22%)]\tLoss: 0.218508\nTrain Epoch: 9 [14080/60000 (23%)]\tLoss: 0.323755\nTrain Epoch: 9 [14720/60000 (25%)]\tLoss: 0.211174\nTrain Epoch: 9 [15360/60000 (26%)]\tLoss: 0.451853\nTrain Epoch: 9 [16000/60000 (27%)]\tLoss: 0.155174\nTrain Epoch: 9 [16640/60000 (28%)]\tLoss: 0.134905\nTrain Epoch: 9 [17280/60000 (29%)]\tLoss: 0.172428\nTrain Epoch: 9 [17920/60000 (30%)]\tLoss: 0.306172\nTrain Epoch: 9 [18560/60000 (31%)]\tLoss: 0.133085\nTrain Epoch: 9 [19200/60000 (32%)]\tLoss: 0.449040\nTrain Epoch: 9 [19840/60000 (33%)]\tLoss: 0.084722\nTrain Epoch: 9 [20480/60000 (34%)]\tLoss: 0.188086\nTrain Epoch: 9 [21120/60000 (35%)]\tLoss: 0.222472\nTrain Epoch: 9 [21760/60000 (36%)]\tLoss: 0.275132\nTrain Epoch: 9 [22400/60000 (37%)]\tLoss: 0.287421\nTrain Epoch: 9 [23040/60000 (38%)]\tLoss: 0.105733\nTrain Epoch: 9 [23680/60000 (39%)]\tLoss: 0.157949\nTrain Epoch: 9 [24320/60000 (41%)]\tLoss: 0.073462\nTrain Epoch: 9 [24960/60000 (42%)]\tLoss: 0.240201\nTrain Epoch: 9 [25600/60000 (43%)]\tLoss: 0.060848\nTrain Epoch: 9 [26240/60000 (44%)]\tLoss: 0.173801\nTrain Epoch: 9 [26880/60000 (45%)]\tLoss: 0.148143\nTrain Epoch: 9 [27520/60000 (46%)]\tLoss: 0.180779\nTrain Epoch: 9 [28160/60000 (47%)]\tLoss: 0.393192\nTrain Epoch: 9 [28800/60000 (48%)]\tLoss: 0.239243\nTrain Epoch: 9 [29440/60000 (49%)]\tLoss: 0.064345\nTrain Epoch: 9 [30080/60000 (50%)]\tLoss: 0.315658\nTrain Epoch: 9 [30720/60000 (51%)]\tLoss: 0.105739\nTrain Epoch: 9 [31360/60000 (52%)]\tLoss: 0.246439\nTrain Epoch: 9 [32000/60000 (53%)]\tLoss: 0.145221\nTrain Epoch: 9 [32640/60000 (54%)]\tLoss: 0.287615\nTrain Epoch: 9 [33280/60000 (55%)]\tLoss: 0.310717\nTrain Epoch: 9 [33920/60000 (57%)]\tLoss: 0.322760\nTrain Epoch: 9 [34560/60000 (58%)]\tLoss: 0.294462\nTrain Epoch: 9 [35200/60000 (59%)]\tLoss: 0.168697\nTrain Epoch: 9 [35840/60000 (60%)]\tLoss: 0.153495\nTrain Epoch: 9 [36480/60000 (61%)]\tLoss: 0.146843\nTrain Epoch: 9 [37120/60000 (62%)]\tLoss: 0.176622\nTrain Epoch: 9 [37760/60000 (63%)]\tLoss: 0.400825\nTrain Epoch: 9 [38400/60000 (64%)]\tLoss: 0.197533\nTrain Epoch: 9 [39040/60000 (65%)]\tLoss: 0.109741\nTrain Epoch: 9 [39680/60000 (66%)]\tLoss: 0.049689\nTrain Epoch: 9 [40320/60000 (67%)]\tLoss: 0.253087\nTrain Epoch: 9 [40960/60000 (68%)]\tLoss: 0.222971\nTrain Epoch: 9 [41600/60000 (69%)]\tLoss: 0.095467\nTrain Epoch: 9 [42240/60000 (70%)]\tLoss: 0.043052\nTrain Epoch: 9 [42880/60000 (71%)]\tLoss: 0.105347\nTrain Epoch: 9 [43520/60000 (72%)]\tLoss: 0.133342\nTrain Epoch: 9 [44160/60000 (74%)]\tLoss: 0.266375\nTrain Epoch: 9 [44800/60000 (75%)]\tLoss: 0.156081\nTrain Epoch: 9 [45440/60000 (76%)]\tLoss: 0.206747\nTrain Epoch: 9 [46080/60000 (77%)]\tLoss: 0.158561\nTrain Epoch: 9 [46720/60000 (78%)]\tLoss: 0.416148\nTrain Epoch: 9 [47360/60000 (79%)]\tLoss: 0.147991\nTrain Epoch: 9 [48000/60000 (80%)]\tLoss: 0.112567\nTrain Epoch: 9 [48640/60000 (81%)]\tLoss: 0.100846\nTrain Epoch: 9 [49280/60000 (82%)]\tLoss: 0.103345\nTrain Epoch: 9 [49920/60000 (83%)]\tLoss: 0.205922\nTrain Epoch: 9 [50560/60000 (84%)]\tLoss: 0.097610\nTrain Epoch: 9 [51200/60000 (85%)]\tLoss: 0.071967\nTrain Epoch: 9 [51840/60000 (86%)]\tLoss: 0.068125\nTrain Epoch: 9 [52480/60000 (87%)]\tLoss: 0.057313\nTrain Epoch: 9 [53120/60000 (88%)]\tLoss: 0.162428\nTrain Epoch: 9 [53760/60000 (90%)]\tLoss: 0.097614\nTrain Epoch: 9 [54400/60000 (91%)]\tLoss: 0.075174\nTrain Epoch: 9 [55040/60000 (92%)]\tLoss: 0.095530\nTrain Epoch: 9 [55680/60000 (93%)]\tLoss: 0.142529\nTrain Epoch: 9 [56320/60000 (94%)]\tLoss: 0.132163\nTrain Epoch: 9 [56960/60000 (95%)]\tLoss: 0.201932\nTrain Epoch: 9 [57600/60000 (96%)]\tLoss: 0.238939\nTrain Epoch: 9 [58240/60000 (97%)]\tLoss: 0.037396\nTrain Epoch: 9 [58880/60000 (98%)]\tLoss: 0.077772\nTrain Epoch: 9 [59520/60000 (99%)]\tLoss: 0.177759\n\nTest set: Average loss: 0.0559, Accuracy: 9813/10000 (98%)\n\nTrain Epoch: 10 [0/60000 (0%)]\tLoss: 0.112115\nTrain Epoch: 10 [640/60000 (1%)]\tLoss: 0.089035\nTrain Epoch: 10 [1280/60000 (2%)]\tLoss: 0.177925\nTrain Epoch: 10 [1920/60000 (3%)]\tLoss: 0.147350\nTrain Epoch: 10 [2560/60000 (4%)]\tLoss: 0.170561\nTrain Epoch: 10 [3200/60000 (5%)]\tLoss: 0.207891\nTrain Epoch: 10 [3840/60000 (6%)]\tLoss: 0.340160\nTrain Epoch: 10 [4480/60000 (7%)]\tLoss: 0.229032\nTrain Epoch: 10 [5120/60000 (9%)]\tLoss: 0.335419\nTrain Epoch: 10 [5760/60000 (10%)]\tLoss: 0.101219\nTrain Epoch: 10 [6400/60000 (11%)]\tLoss: 0.085085\nTrain Epoch: 10 [7040/60000 (12%)]\tLoss: 0.053658\nTrain Epoch: 10 [7680/60000 (13%)]\tLoss: 0.106224\nTrain Epoch: 10 [8320/60000 (14%)]\tLoss: 0.146947\nTrain Epoch: 10 [8960/60000 (15%)]\tLoss: 0.210157\nTrain Epoch: 10 [9600/60000 (16%)]\tLoss: 0.167598\nTrain Epoch: 10 [10240/60000 (17%)]\tLoss: 0.184822\nTrain Epoch: 10 [10880/60000 (18%)]\tLoss: 0.149518\nTrain Epoch: 10 [11520/60000 (19%)]\tLoss: 0.091374\nTrain Epoch: 10 [12160/60000 (20%)]\tLoss: 0.331635\nTrain Epoch: 10 [12800/60000 (21%)]\tLoss: 0.345818\nTrain Epoch: 10 [13440/60000 (22%)]\tLoss: 0.057789\nTrain Epoch: 10 [14080/60000 (23%)]\tLoss: 0.189208\nTrain Epoch: 10 [14720/60000 (25%)]\tLoss: 0.116747\nTrain Epoch: 10 [15360/60000 (26%)]\tLoss: 0.101344\nTrain Epoch: 10 [16000/60000 (27%)]\tLoss: 0.116675\nTrain Epoch: 10 [16640/60000 (28%)]\tLoss: 0.158562\nTrain Epoch: 10 [17280/60000 (29%)]\tLoss: 0.173697\nTrain Epoch: 10 [17920/60000 (30%)]\tLoss: 0.167972\nTrain Epoch: 10 [18560/60000 (31%)]\tLoss: 0.125186\nTrain Epoch: 10 [19200/60000 (32%)]\tLoss: 0.116458\nTrain Epoch: 10 [19840/60000 (33%)]\tLoss: 0.107688\nTrain Epoch: 10 [20480/60000 (34%)]\tLoss: 0.131942\nTrain Epoch: 10 [21120/60000 (35%)]\tLoss: 0.189690\nTrain Epoch: 10 [21760/60000 (36%)]\tLoss: 0.106075\nTrain Epoch: 10 [22400/60000 (37%)]\tLoss: 0.100791\nTrain Epoch: 10 [23040/60000 (38%)]\tLoss: 0.151750\nTrain Epoch: 10 [23680/60000 (39%)]\tLoss: 0.242852\nTrain Epoch: 10 [24320/60000 (41%)]\tLoss: 0.367772\nTrain Epoch: 10 [24960/60000 (42%)]\tLoss: 0.160668\nTrain Epoch: 10 [25600/60000 (43%)]\tLoss: 0.209858\nTrain Epoch: 10 [26240/60000 (44%)]\tLoss: 0.267443\nTrain Epoch: 10 [26880/60000 (45%)]\tLoss: 0.134159\nTrain Epoch: 10 [27520/60000 (46%)]\tLoss: 0.176844\nTrain Epoch: 10 [28160/60000 (47%)]\tLoss: 0.083609\nTrain Epoch: 10 [28800/60000 (48%)]\tLoss: 0.093472\nTrain Epoch: 10 [29440/60000 (49%)]\tLoss: 0.133502\nTrain Epoch: 10 [30080/60000 (50%)]\tLoss: 0.207314\nTrain Epoch: 10 [30720/60000 (51%)]\tLoss: 0.095819\nTrain Epoch: 10 [31360/60000 (52%)]\tLoss: 0.165338\nTrain Epoch: 10 [32000/60000 (53%)]\tLoss: 0.172792\nTrain Epoch: 10 [32640/60000 (54%)]\tLoss: 0.200346\nTrain Epoch: 10 [33280/60000 (55%)]\tLoss: 0.188566\nTrain Epoch: 10 [33920/60000 (57%)]\tLoss: 0.063107\nTrain Epoch: 10 [34560/60000 (58%)]\tLoss: 0.208076\nTrain Epoch: 10 [35200/60000 (59%)]\tLoss: 0.336500\nTrain Epoch: 10 [35840/60000 (60%)]\tLoss: 0.098523\nTrain Epoch: 10 [36480/60000 (61%)]\tLoss: 0.239501\nTrain Epoch: 10 [37120/60000 (62%)]\tLoss: 0.108441\nTrain Epoch: 10 [37760/60000 (63%)]\tLoss: 0.161891\nTrain Epoch: 10 [38400/60000 (64%)]\tLoss: 0.232178\nTrain Epoch: 10 [39040/60000 (65%)]\tLoss: 0.281599\nTrain Epoch: 10 [39680/60000 (66%)]\tLoss: 0.202701\nTrain Epoch: 10 [40320/60000 (67%)]\tLoss: 0.313276\nTrain Epoch: 10 [40960/60000 (68%)]\tLoss: 0.149932\nTrain Epoch: 10 [41600/60000 (69%)]\tLoss: 0.078690\nTrain Epoch: 10 [42240/60000 (70%)]\tLoss: 0.068174\nTrain Epoch: 10 [42880/60000 (71%)]\tLoss: 0.114682\nTrain Epoch: 10 [43520/60000 (72%)]\tLoss: 0.278032\nTrain Epoch: 10 [44160/60000 (74%)]\tLoss: 0.207701\nTrain Epoch: 10 [44800/60000 (75%)]\tLoss: 0.149129\nTrain Epoch: 10 [45440/60000 (76%)]\tLoss: 0.209997\nTrain Epoch: 10 [46080/60000 (77%)]\tLoss: 0.181944\nTrain Epoch: 10 [46720/60000 (78%)]\tLoss: 0.071149\nTrain Epoch: 10 [47360/60000 (79%)]\tLoss: 0.088598\nTrain Epoch: 10 [48000/60000 (80%)]\tLoss: 0.196593\nTrain Epoch: 10 [48640/60000 (81%)]\tLoss: 0.195960\nTrain Epoch: 10 [49280/60000 (82%)]\tLoss: 0.227564\nTrain Epoch: 10 [49920/60000 (83%)]\tLoss: 0.051203\nTrain Epoch: 10 [50560/60000 (84%)]\tLoss: 0.105916\nTrain Epoch: 10 [51200/60000 (85%)]\tLoss: 0.176384\nTrain Epoch: 10 [51840/60000 (86%)]\tLoss: 0.054657\nTrain Epoch: 10 [52480/60000 (87%)]\tLoss: 0.107465\nTrain Epoch: 10 [53120/60000 (88%)]\tLoss: 0.072626\nTrain Epoch: 10 [53760/60000 (90%)]\tLoss: 0.187904\nTrain Epoch: 10 [54400/60000 (91%)]\tLoss: 0.104509\nTrain Epoch: 10 [55040/60000 (92%)]\tLoss: 0.174006\nTrain Epoch: 10 [55680/60000 (93%)]\tLoss: 0.122760\nTrain Epoch: 10 [56320/60000 (94%)]\tLoss: 0.150131\nTrain Epoch: 10 [56960/60000 (95%)]\tLoss: 0.076365\nTrain Epoch: 10 [57600/60000 (96%)]\tLoss: 0.127536\nTrain Epoch: 10 [58240/60000 (97%)]\tLoss: 0.233154\nTrain Epoch: 10 [58880/60000 (98%)]\tLoss: 0.113188\nTrain Epoch: 10 [59520/60000 (99%)]\tLoss: 0.282389\n\nTest set: Average loss: 0.0531, Accuracy: 9837/10000 (98%)\n\n\n\nThe experiment completed successfully. Finalizing run...\nLogging experiment finalizing status in history service\n\n\nRun is completed.", + "run_properties": { + "SendToClient": "1", + "arguments": "--output-dir ./outputs", + "created_utc": "2018-09-25T11:56:04.832205Z", + "distributed_processes": [], + "end_time_utc": "2018-09-25T12:15:57.841467Z", + "log_files": { + "azureml-logs/55_batchai_execution.txt": "https://onnxamlistorageekgyifen.blob.core.windows.net/azureml/ExperimentRun/pytorch1-mnist_1537876563990/azureml-logs/55_batchai_execution.txt?sv=2017-04-17&sr=b&sig=NNkIC62xdG1h6156XtjtgwTJ1ScXlfxhBiBicNNoExE%3D&st=2018-09-25T12%3A06%3A00Z&se=2018-09-25T20%3A16%3A00Z&sp=r", + "azureml-logs/60_control_log.txt": "https://onnxamlistorageekgyifen.blob.core.windows.net/azureml/ExperimentRun/pytorch1-mnist_1537876563990/azureml-logs/60_control_log.txt?sv=2017-04-17&sr=b&sig=i2mtPt6w5xHkEjpkyfl%2BSD1GPpIdpzIbY6sVUQ62QMo%3D&st=2018-09-25T12%3A06%3A00Z&se=2018-09-25T20%3A16%3A00Z&sp=r", + "azureml-logs/80_driver_log.txt": "https://onnxamlistorageekgyifen.blob.core.windows.net/azureml/ExperimentRun/pytorch1-mnist_1537876563990/azureml-logs/80_driver_log.txt?sv=2017-04-17&sr=b&sig=CvqNHP18huWuXWdi%2BeiPcnztgJfI1iQQ6fV6Li25z1Y%3D&st=2018-09-25T12%3A06%3A00Z&se=2018-09-25T20%3A16%3A00Z&sp=r", + "azureml-logs/azureml.log": "https://onnxamlistorageekgyifen.blob.core.windows.net/azureml/ExperimentRun/pytorch1-mnist_1537876563990/azureml-logs/azureml.log?sv=2017-04-17&sr=b&sig=UTaxvUU4Ua%2FpsXPwQnSIV%2FbKK1zERtclIIjcTfbcSzQ%3D&st=2018-09-25T12%3A06%3A00Z&se=2018-09-25T20%3A16%3A00Z&sp=r" + }, + "properties": { + "ContentSnapshotId": "727976ee-33bf-44c7-af65-ef1a1cbd2980", + "azureml.runsource": "experiment" + }, + "run_duration": "0:19:53", + "run_id": "pytorch1-mnist_1537876563990", + "script_name": "mnist.py", + "status": "Completed", + "tags": {} + }, + "widget_settings": {}, + "workbench_uri": "https://mlworkspace.azure.ai/portal/subscriptions/75f78a03-482f-4fd8-8c71-5ddc08f92726/resourceGroups/onnxdemos/providers/Microsoft.MachineLearningServices/workspaces/onnx-aml-ignite-demo/experiment/pytorch1-mnist/run/pytorch1-mnist_1537876563990" } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + } + }, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb index d78603b2d..0b1a50851 100644 --- a/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb +++ b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb @@ -1,350 +1,350 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deploying a web service to Azure Kubernetes Service (AKS)\n", - "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n", - "We then test and delete the service, image and model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "print(azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Get workspace\n", - "Load existing workspace from the config file info." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download the model\n", - "\n", - "Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. This cell will download a [pretrained resnet50](http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz) and unpack it to that directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import requests\n", - "import shutil\n", - "import tarfile\n", - "import tempfile\n", - "\n", - "from io import BytesIO\n", - "\n", - "model_url = \"http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz\"\n", - "\n", - "archive_prefix = \"./resnet_v1_fp32_savedmodel_NCHW_jpg/1538686758/\"\n", - "target_folder = \"resnet50\"\n", - "\n", - "if not os.path.exists(target_folder):\n", - " response = requests.get(model_url)\n", - " archive = tarfile.open(fileobj=BytesIO(response.content))\n", - " with tempfile.TemporaryDirectory() as temp_folder:\n", - " archive.extractall(temp_folder)\n", - " shutil.copytree(os.path.join(temp_folder, archive_prefix), target_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Register the model\n", - "Register an existing trained model, add description and tags." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "model = Model.register(model_path=\"resnet50\", # This points to the local directory to upload.\n", - " model_name=\"resnet50\", # This is the name the model is registered as.\n", - " tags={'area': \"Image classification\", 'type': \"classification\"},\n", - " description=\"Image classification trained on Imagenet Dataset\",\n", - " workspace=ws)\n", - "\n", - "print(model.name, model.description, model.version)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Provision the AKS Cluster\n", - "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AksCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your GPU cluster\n", - "gpu_cluster_name = \"aks-gpu-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n", - " print(\"Found existing gpu cluster\")\n", - "except ComputeTargetException:\n", - " print(\"Creating new gpu-cluster\")\n", - " \n", - " # Specify the configuration for the new cluster\n", - " compute_config = AksCompute.provisioning_configuration(cluster_purpose=AksCompute.ClusterPurpose.DEV_TEST,\n", - " agent_count=1,\n", - " vm_size=\"Standard_NV6\")\n", - " # Create the cluster with the specified name and configuration\n", - " gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n", - "\n", - " # Wait for the cluster to complete, show the output log\n", - " gpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deploy the model as a web service to AKS\n", - "\n", - "First create a scoring script" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import tensorflow as tf\n", - "import numpy as np\n", - "import json\n", - "import os\n", - "from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n", - "from azureml.contrib.services.aml_response import AMLResponse\n", - "\n", - "def init():\n", - " global session\n", - " global input_name\n", - " global output_name\n", - " \n", - " session = tf.Session()\n", - "\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50')\n", - " model = tf.saved_model.loader.load(session, ['serve'], model_path)\n", - " if len(model.signature_def['serving_default'].inputs) > 1:\n", - " raise ValueError(\"This score.py only supports one input\")\n", - " input_name = [tensor.name for tensor in model.signature_def['serving_default'].inputs.values()][0]\n", - " output_name = [tensor.name for tensor in model.signature_def['serving_default'].outputs.values()]\n", - " \n", - "\n", - "@rawhttp\n", - "def run(request):\n", - " if request.method == 'POST':\n", - " reqBody = request.get_data(False)\n", - " resp = score(reqBody)\n", - " return AMLResponse(resp, 200)\n", - " if request.method == 'GET':\n", - " respBody = str.encode(\"GET is not supported\")\n", - " return AMLResponse(respBody, 405)\n", - " return AMLResponse(\"bad request\", 500)\n", - "\n", - "def score(data):\n", - " result = session.run(output_name, {input_name: [data]})\n", - " return json.dumps(result[1].tolist())\n", - "\n", - "if __name__ == \"__main__\":\n", - " init()\n", - " with open(\"test_image.jpg\", 'rb') as f:\n", - " content = f.read()\n", - " print(score(content))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now create the deployment configuration objects and deploy the model as a webservice." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the web service configuration (using default here)\n", - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.webservice import AksWebservice\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.environment import Environment, DEFAULT_GPU_IMAGE\n", - "\n", - "env = Environment('deploytocloudenv')\n", - "# Please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers#featured-tags)\n", - "# for open-sourced GPU base images.\n", - "env.docker.base_image = DEFAULT_GPU_IMAGE\n", - "env.python.conda_dependencies = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy'],\n", - " pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n", - "\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n", - "aks_config = AksWebservice.deploy_configuration()\n", - "\n", - "# # Enable token auth and disable (key) auth on the webservice\n", - "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "aks_service_name ='gpu-rn50'\n", - "\n", - "aks_service = Model.deploy(workspace=ws,\n", - " name=aks_service_name,\n", - " models=[model],\n", - " inference_config=inference_config,\n", - " deployment_config=aks_config,\n", - " deployment_target=gpu_cluster)\n", - "\n", - "aks_service.wait_for_deployment(show_output = True)\n", - "print(aks_service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test the web service\n", - "We test the web sevice by passing the test images content." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "import requests\n", - "\n", - "# if (key) auth is enabled, fetch keys and include in the request\n", - "key1, key2 = aks_service.get_keys()\n", - "\n", - "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n", - "\n", - "# # if token auth is enabled, fetch token and include in the request\n", - "# access_token, fetch_after = aks_service.get_token()\n", - "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n", - "\n", - "test_sample = open('snowleopardgaze.jpg', 'rb').read()\n", - "resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Clean up\n", - "Delete the service, image, model and compute target" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "aks_service.delete()\n", - "model.delete()\n", - "gpu_cluster.delete()\n" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploying a web service to Azure Kubernetes Service (AKS)\n", + "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n", + "We then test and delete the service, image and model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "print(azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get workspace\n", + "Load existing workspace from the config file info." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.workspace import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download the model\n", + "\n", + "Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. This cell will download a [pretrained resnet50](http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz) and unpack it to that directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "import shutil\n", + "import tarfile\n", + "import tempfile\n", + "\n", + "from io import BytesIO\n", + "\n", + "model_url = \"http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz\"\n", + "\n", + "archive_prefix = \"./resnet_v1_fp32_savedmodel_NCHW_jpg/1538686758/\"\n", + "target_folder = \"resnet50\"\n", + "\n", + "if not os.path.exists(target_folder):\n", + " response = requests.get(model_url)\n", + " archive = tarfile.open(fileobj=BytesIO(response.content))\n", + " with tempfile.TemporaryDirectory() as temp_folder:\n", + " archive.extractall(temp_folder)\n", + " shutil.copytree(os.path.join(temp_folder, archive_prefix), target_folder)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Register the model\n", + "Register an existing trained model, add description and tags." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "model = Model.register(model_path=\"resnet50\", # This points to the local directory to upload.\n", + " model_name=\"resnet50\", # This is the name the model is registered as.\n", + " tags={'area': \"Image classification\", 'type': \"classification\"},\n", + " description=\"Image classification trained on Imagenet Dataset\",\n", + " workspace=ws)\n", + "\n", + "print(model.name, model.description, model.version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Provision the AKS Cluster\n", + "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AksCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your GPU cluster\n", + "gpu_cluster_name = \"aks-gpu-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n", + " print(\"Found existing gpu cluster\")\n", + "except ComputeTargetException:\n", + " print(\"Creating new gpu-cluster\")\n", + " \n", + " # Specify the configuration for the new cluster\n", + " compute_config = AksCompute.provisioning_configuration(cluster_purpose=AksCompute.ClusterPurpose.DEV_TEST,\n", + " agent_count=1,\n", + " vm_size=\"Standard_NV6\")\n", + " # Create the cluster with the specified name and configuration\n", + " gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n", + "\n", + " # Wait for the cluster to complete, show the output log\n", + " gpu_cluster.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploy the model as a web service to AKS\n", + "\n", + "First create a scoring script" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import json\n", + "import os\n", + "from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n", + "from azureml.contrib.services.aml_response import AMLResponse\n", + "\n", + "def init():\n", + " global session\n", + " global input_name\n", + " global output_name\n", + " \n", + " session = tf.Session()\n", + "\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50')\n", + " model = tf.saved_model.loader.load(session, ['serve'], model_path)\n", + " if len(model.signature_def['serving_default'].inputs) > 1:\n", + " raise ValueError(\"This score.py only supports one input\")\n", + " input_name = [tensor.name for tensor in model.signature_def['serving_default'].inputs.values()][0]\n", + " output_name = [tensor.name for tensor in model.signature_def['serving_default'].outputs.values()]\n", + " \n", + "\n", + "@rawhttp\n", + "def run(request):\n", + " if request.method == 'POST':\n", + " reqBody = request.get_data(False)\n", + " resp = score(reqBody)\n", + " return AMLResponse(resp, 200)\n", + " if request.method == 'GET':\n", + " respBody = str.encode(\"GET is not supported\")\n", + " return AMLResponse(respBody, 405)\n", + " return AMLResponse(\"bad request\", 500)\n", + "\n", + "def score(data):\n", + " result = session.run(output_name, {input_name: [data]})\n", + " return json.dumps(result[1].tolist())\n", + "\n", + "if __name__ == \"__main__\":\n", + " init()\n", + " with open(\"test_image.jpg\", 'rb') as f:\n", + " content = f.read()\n", + " print(score(content))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create the deployment configuration objects and deploy the model as a webservice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the web service configuration (using default here)\n", + "from azureml.core.model import InferenceConfig\n", + "from azureml.core.webservice import AksWebservice\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "from azureml.core.environment import Environment, DEFAULT_GPU_IMAGE\n", + "\n", + "env = Environment('deploytocloudenv')\n", + "# Please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers#featured-tags)\n", + "# for open-sourced GPU base images.\n", + "env.docker.base_image = DEFAULT_GPU_IMAGE\n", + "env.python.conda_dependencies = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy'],\n", + " pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n", + "\n", + "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n", + "aks_config = AksWebservice.deploy_configuration()\n", + "\n", + "# # Enable token auth and disable (key) auth on the webservice\n", + "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "aks_service_name ='gpu-rn50'\n", + "\n", + "aks_service = Model.deploy(workspace=ws,\n", + " name=aks_service_name,\n", + " models=[model],\n", + " inference_config=inference_config,\n", + " deployment_config=aks_config,\n", + " deployment_target=gpu_cluster)\n", + "\n", + "aks_service.wait_for_deployment(show_output = True)\n", + "print(aks_service.state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test the web service\n", + "We test the web sevice by passing the test images content." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "import requests\n", + "\n", + "# if (key) auth is enabled, fetch keys and include in the request\n", + "key1, key2 = aks_service.get_keys()\n", + "\n", + "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n", + "\n", + "# # if token auth is enabled, fetch token and include in the request\n", + "# access_token, fetch_after = aks_service.get_token()\n", + "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n", + "\n", + "test_sample = open('snowleopardgaze.jpg', 'rb').read()\n", + "resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clean up\n", + "Delete the service, image, model and compute target" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "aks_service.delete()\n", + "model.delete()\n", + "gpu_cluster.delete()\n" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "vaidyas" + } ], - "metadata": { - "authors": [ - { - "name": "vaidyas" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb index 99a4ecd9d..236fca47a 100644 --- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb +++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb @@ -1,356 +1,356 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deploying a web service to Azure Kubernetes Service (AKS)\n", - "This notebook shows the steps for deploying a service: registering a model, provisioning a cluster with ssl (one time action), and deploying a service to it. \n", - "We then test and delete the service, image and model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "from azureml.core.compute import AksCompute, ComputeTarget\n", - "from azureml.core.webservice import Webservice, AksWebservice\n", - "from azureml.core.model import Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "print(azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Get workspace\n", - "Load existing workspace from the config file info." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Register the model\n", - "Register an existing trained model, add descirption and tags." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Register the model\n", - "from azureml.core.model import Model\n", - "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n", - " model_name = \"sklearn_model\", # this is the name the model is registered as\n", - " tags = {'area': \"diabetes\", 'type': \"regression\"},\n", - " description = \"Ridge regression model to predict diabetes\",\n", - " workspace = ws)\n", - "\n", - "print(model.name, model.description, model.version)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create the Environment\n", - "Create an environment that the model will be deployed with" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies \n", - "\n", - "conda_deps = CondaDependencies.create(conda_packages=['numpy', 'scikit-learn==0.19.1', 'scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n", - "myenv = Environment(name='myenv')\n", - "myenv.python.conda_dependencies = conda_deps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Use a custom Docker image\n", - "\n", - "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n", - "\n", - "Only supported with `python` runtime.\n", - "```python\n", - "# use an image available in public Container Registry without authentication\n", - "myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n", - "\n", - "# or, use an image available in a private Container Registry\n", - "myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n", - "myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n", - "myenv.docker.base_image_registry.username = \"username\"\n", - "myenv.docker.base_image_registry.password = \"password\"\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Write the Entry Script\n", - "Write the script that will be used to predict on your model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score_ssl.py\n", - "import os\n", - "import pickle\n", - "import json\n", - "import numpy\n", - "from sklearn.externals import joblib\n", - "from sklearn.linear_model import Ridge\n", - "from inference_schema.schema_decorators import input_schema, output_schema\n", - "from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType\n", - "\n", - "def init():\n", - " global model\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", - " # deserialize the model file back into a sklearn model\n", - " model = joblib.load(model_path)\n", - "\n", - "\n", - "standard_sample_input = {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }\n", - "standard_sample_output = {'outcome': 1}\n", - "\n", - "@input_schema('param', StandardPythonParameterType(standard_sample_input))\n", - "@output_schema(StandardPythonParameterType(standard_sample_output))\n", - "def run(param):\n", - " try:\n", - " raw_data = [param['a'], param['b'], param['c'], param['d'], param['e'], param['f'], param['g'], param['h'], param['i'], param['j']]\n", - " data = numpy.array([raw_data])\n", - " result = model.predict(data)\n", - " return { 'outcome' : result[0] }\n", - " except Exception as e:\n", - " error = str(e)\n", - " return error" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create the InferenceConfig\n", - "Create the inference config that will be used when deploying the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "\n", - "inf_config = InferenceConfig(entry_script='score_ssl.py', environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Provision the AKS Cluster with SSL\n", - "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use the default configuration (can also provide parameters to customize)\n", - "\n", - "provisioning_config = AksCompute.provisioning_configuration()\n", - "# Leaf domain label generates a name using the formula\n", - "# \"######..cloudapp.azure.net\"\n", - "# where \"######\" is a random series of characters\n", - "provisioning_config.enable_ssl(leaf_domain_label = \"contoso\", overwrite_existing_domain = True)\n", - "\n", - "aks_name = 'my-aks-ssl-1' \n", - "# Create the cluster\n", - "aks_target = ComputeTarget.create(workspace = ws, \n", - " name = aks_name, \n", - " provisioning_configuration = provisioning_config)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "aks_target.wait_for_completion(show_output = True)\n", - "print(aks_target.provisioning_state)\n", - "print(aks_target.provisioning_errors)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deploy web service to AKS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-deploy-to-aks" - ] - }, - "outputs": [], - "source": [ - "%%time\n", - "\n", - "aks_config = AksWebservice.deploy_configuration()\n", - "\n", - "aks_service_name ='aks-service-ssl-1'\n", - "\n", - "aks_service = Model.deploy(workspace=ws,\n", - " name=aks_service_name,\n", - " models=[model],\n", - " inference_config=inf_config,\n", - " deployment_config=aks_config,\n", - " deployment_target=aks_target,\n", - " overwrite=True)\n", - "\n", - "aks_service.wait_for_deployment(show_output = True)\n", - "print(aks_service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test the web service using run method\n", - "We test the web sevice by passing data.\n", - "Run() method retrieves API keys behind the scenes to make sure that call is authenticated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "import json\n", - "\n", - "standard_sample_input = json.dumps({'param': {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }})\n", - "\n", - "aks_service.run(input_data=standard_sample_input)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Clean up\n", - "Delete the service, image and model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "aks_service.delete()\n", - "model.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploying a web service to Azure Kubernetes Service (AKS)\n", + "This notebook shows the steps for deploying a service: registering a model, provisioning a cluster with ssl (one time action), and deploying a service to it. \n", + "We then test and delete the service, image and model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "from azureml.core.compute import AksCompute, ComputeTarget\n", + "from azureml.core.webservice import Webservice, AksWebservice\n", + "from azureml.core.model import Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "print(azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get workspace\n", + "Load existing workspace from the config file info." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.workspace import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Register the model\n", + "Register an existing trained model, add descirption and tags." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Register the model\n", + "from azureml.core.model import Model\n", + "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n", + " model_name = \"sklearn_model\", # this is the name the model is registered as\n", + " tags = {'area': \"diabetes\", 'type': \"regression\"},\n", + " description = \"Ridge regression model to predict diabetes\",\n", + " workspace = ws)\n", + "\n", + "print(model.name, model.description, model.version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create the Environment\n", + "Create an environment that the model will be deployed with" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.conda_dependencies import CondaDependencies \n", + "\n", + "conda_deps = CondaDependencies.create(conda_packages=['numpy', 'scikit-learn==0.19.1', 'scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n", + "myenv = Environment(name='myenv')\n", + "myenv.python.conda_dependencies = conda_deps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use a custom Docker image\n", + "\n", + "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n", + "\n", + "Only supported with `python` runtime.\n", + "```python\n", + "# use an image available in public Container Registry without authentication\n", + "myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n", + "\n", + "# or, use an image available in a private Container Registry\n", + "myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n", + "myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n", + "myenv.docker.base_image_registry.username = \"username\"\n", + "myenv.docker.base_image_registry.password = \"password\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write the Entry Script\n", + "Write the script that will be used to predict on your model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score_ssl.py\n", + "import os\n", + "import pickle\n", + "import json\n", + "import numpy\n", + "from sklearn.externals import joblib\n", + "from sklearn.linear_model import Ridge\n", + "from inference_schema.schema_decorators import input_schema, output_schema\n", + "from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType\n", + "\n", + "def init():\n", + " global model\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", + " # deserialize the model file back into a sklearn model\n", + " model = joblib.load(model_path)\n", + "\n", + "\n", + "standard_sample_input = {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }\n", + "standard_sample_output = {'outcome': 1}\n", + "\n", + "@input_schema('param', StandardPythonParameterType(standard_sample_input))\n", + "@output_schema(StandardPythonParameterType(standard_sample_output))\n", + "def run(param):\n", + " try:\n", + " raw_data = [param['a'], param['b'], param['c'], param['d'], param['e'], param['f'], param['g'], param['h'], param['i'], param['j']]\n", + " data = numpy.array([raw_data])\n", + " result = model.predict(data)\n", + " return { 'outcome' : result[0] }\n", + " except Exception as e:\n", + " error = str(e)\n", + " return error" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create the InferenceConfig\n", + "Create the inference config that will be used when deploying the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "\n", + "inf_config = InferenceConfig(entry_script='score_ssl.py', environment=myenv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Provision the AKS Cluster with SSL\n", + "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\n", + "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use the default configuration (can also provide parameters to customize)\n", + "\n", + "provisioning_config = AksCompute.provisioning_configuration()\n", + "# Leaf domain label generates a name using the formula\n", + "# \"######..cloudapp.azure.net\"\n", + "# where \"######\" is a random series of characters\n", + "provisioning_config.enable_ssl(leaf_domain_label = \"contoso\", overwrite_existing_domain = True)\n", + "\n", + "aks_name = 'my-aks-ssl-1' \n", + "# Create the cluster\n", + "aks_target = ComputeTarget.create(workspace = ws, \n", + " name = aks_name, \n", + " provisioning_configuration = provisioning_config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "aks_target.wait_for_completion(show_output = True)\n", + "print(aks_target.provisioning_state)\n", + "print(aks_target.provisioning_errors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploy web service to AKS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "sample-deploy-to-aks" + ] + }, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "aks_config = AksWebservice.deploy_configuration()\n", + "\n", + "aks_service_name ='aks-service-ssl-1'\n", + "\n", + "aks_service = Model.deploy(workspace=ws,\n", + " name=aks_service_name,\n", + " models=[model],\n", + " inference_config=inf_config,\n", + " deployment_config=aks_config,\n", + " deployment_target=aks_target,\n", + " overwrite=True)\n", + "\n", + "aks_service.wait_for_deployment(show_output = True)\n", + "print(aks_service.state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test the web service using run method\n", + "We test the web sevice by passing data.\n", + "Run() method retrieves API keys behind the scenes to make sure that call is authenticated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "import json\n", + "\n", + "standard_sample_input = json.dumps({'param': {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }})\n", + "\n", + "aks_service.run(input_data=standard_sample_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clean up\n", + "Delete the service, image and model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "aks_service.delete()\n", + "model.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "vaidyas" + } ], - "metadata": { - "authors": [ - { - "name": "vaidyas" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb index 05580b7db..af5daab56 100644 --- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb +++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb @@ -1,623 +1,623 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deploying a web service to Azure Kubernetes Service (AKS)\n", - "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n", - "We then test and delete the service, image and model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "from azureml.core.compute import AksCompute, ComputeTarget\n", - "from azureml.core.webservice import Webservice, AksWebservice\n", - "from azureml.core.model import Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "print(azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Get workspace\n", - "Load existing workspace from the config file info." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Register the model\n", - "Register an existing trained model, add descirption and tags." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Register the model\n", - "from azureml.core.model import Model\n", - "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n", - " model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n", - " tags = {'area': \"diabetes\", 'type': \"regression\"},\n", - " description = \"Ridge regression model to predict diabetes\",\n", - " workspace = ws)\n", - "\n", - "print(model.name, model.description, model.version)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create the Environment\n", - "Create an environment that the model will be deployed with" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies \n", - "\n", - "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.19.1','scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n", - "myenv = Environment(name='myenv')\n", - "myenv.python.conda_dependencies = conda_deps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Use a custom Docker image\n", - "\n", - "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n", - "\n", - "Only supported with `python` runtime.\n", - "```python\n", - "# use an image available in public Container Registry without authentication\n", - "myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n", - "\n", - "# or, use an image available in a private Container Registry\n", - "myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n", - "myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n", - "myenv.docker.base_image_registry.username = \"username\"\n", - "myenv.docker.base_image_registry.password = \"password\"\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Write the Entry Script\n", - "Write the script that will be used to predict on your model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import os\n", - "import pickle\n", - "import json\n", - "import numpy\n", - "from sklearn.externals import joblib\n", - "from sklearn.linear_model import Ridge\n", - "\n", - "def init():\n", - " global model\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", - " # deserialize the model file back into a sklearn model\n", - " model = joblib.load(model_path)\n", - "\n", - "# note you can pass in multiple rows for scoring\n", - "def run(raw_data):\n", - " try:\n", - " data = json.loads(raw_data)['data']\n", - " data = numpy.array(data)\n", - " result = model.predict(data)\n", - " # you can return any data type as long as it is JSON-serializable\n", - " return result.tolist()\n", - " except Exception as e:\n", - " error = str(e)\n", - " return error" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create the InferenceConfig\n", - "Create the inference config that will be used when deploying the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "\n", - "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Profiling\n", - "\n", - "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n", - "\n", - "In order to profile your model you will need:\n", - "- a registered model\n", - "- an entry script\n", - "- an inference configuration\n", - "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n", - "\n", - "Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n", - "\n", - "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n", - "\n", - "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n", - "You can try get the dataset first to see if it's already registered." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azureml.core import Datastore\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.data import dataset_type_definitions\n", - "\n", - "dataset_name='sample_request_data'\n", - "\n", - "dataset_registered = False\n", - "try:\n", - " sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n", - " dataset_registered = True\n", - "except:\n", - " print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n", - "\n", - "if not dataset_registered:\n", - " input_json = {'data': [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n", - " [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\n", - " # create a string that can be put in the body of the request\n", - " serialized_input_json = json.dumps(input_json)\n", - " dataset_content = []\n", - " for i in range(100):\n", - " dataset_content.append(serialized_input_json)\n", - " sample_request_data = '\\n'.join(dataset_content)\n", - " file_name = \"{}.txt\".format(dataset_name)\n", - " f = open(file_name, 'w')\n", - " f.write(sample_request_data)\n", - " f.close()\n", - "\n", - " # upload the txt file created above to the Datastore and create a dataset from it\n", - " data_store = Datastore.get_default(ws)\n", - " data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n", - " datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n", - " sample_request_data = Dataset.Tabular.from_delimited_files(\n", - " datastore_path,\n", - " separator='\\n',\n", - " infer_column_types=True,\n", - " header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n", - " sample_request_data = sample_request_data.register(workspace=ws,\n", - " name=dataset_name,\n", - " create_new_version=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "from azureml.core import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.model import Model, InferenceConfig\n", - "\n", - "\n", - "environment = Environment('my-sklearn-environment')\n", - "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", - " 'azureml-defaults',\n", - " 'inference-schema[numpy-support]',\n", - " 'joblib',\n", - " 'numpy',\n", - " 'scikit-learn==0.19.1',\n", - " 'scipy'\n", - "])\n", - "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", - "# if cpu and memory_in_gb parameters are not provided\n", - "# the model will be profiled on default configuration of\n", - "# 3.5CPU and 15GB memory\n", - "profile = Model.profile(ws,\n", - " 'sklearn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n", - " [model],\n", - " inference_config,\n", - " input_dataset=sample_request_data,\n", - " cpu=1.0,\n", - " memory_in_gb=0.5)\n", - "\n", - "# profiling is a long running operation and may take up to 25 min\n", - "profile.wait_for_completion(True)\n", - "details = profile.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Provision the AKS Cluster\n", - "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your AKS cluster\n", - "aks_name = 'my-aks-9' \n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " aks_target = ComputeTarget(workspace=ws, name=aks_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " # Use the default configuration (can also provide parameters to customize)\n", - " prov_config = AksCompute.provisioning_configuration()\n", - "\n", - " # Create the cluster\n", - " aks_target = ComputeTarget.create(workspace = ws, \n", - " name = aks_name, \n", - " provisioning_configuration = prov_config)\n", - "\n", - "if aks_target.get_status() != \"Succeeded\":\n", - " aks_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create AKS Cluster in an existing virtual network (optional)\n", - "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-enable-virtual-network#use-azure-kubernetes-service) for more details." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from azureml.core.compute import ComputeTarget, AksCompute\n", - "\n", - "# # Create the compute configuration and set virtual network information\n", - "# config = AksCompute.provisioning_configuration(location=\"eastus2\")\n", - "# config.vnet_resourcegroup_name = \"mygroup\"\n", - "# config.vnet_name = \"mynetwork\"\n", - "# config.subnet_name = \"default\"\n", - "# config.service_cidr = \"10.0.0.0/16\"\n", - "# config.dns_service_ip = \"10.0.0.10\"\n", - "# config.docker_bridge_cidr = \"172.17.0.1/16\"\n", - "\n", - "# # Create the compute target\n", - "# aks_target = ComputeTarget.create(workspace = ws,\n", - "# name = \"myaks\",\n", - "# provisioning_configuration = config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Enable SSL on the AKS Cluster (optional)\n", - "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# provisioning_config = AksCompute.provisioning_configuration(ssl_cert_pem_file=\"cert.pem\", ssl_key_pem_file=\"key.pem\", ssl_cname=\"www.contoso.com\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "aks_target.wait_for_completion(show_output = True)\n", - "print(aks_target.provisioning_state)\n", - "print(aks_target.provisioning_errors)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optional step: Attach existing AKS cluster\n", - "\n", - "If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# # Use the default configuration (can also provide parameters to customize)\n", - "# resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n", - "\n", - "# create_name='my-existing-aks' \n", - "# # Create the cluster\n", - "# attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n", - "# aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n", - "# # Wait for the operation to complete\n", - "# aks_target.wait_for_completion(True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deploy web service to AKS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-deploy-to-aks" - ] - }, - "outputs": [], - "source": [ - "# Set the web service configuration (using default here)\n", - "aks_config = AksWebservice.deploy_configuration()\n", - "\n", - "# # Enable token auth and disable (key) auth on the webservice\n", - "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-deploy-to-aks" - ] - }, - "outputs": [], - "source": [ - "%%time\n", - "aks_service_name ='aks-service-1'\n", - "\n", - "aks_service = Model.deploy(workspace=ws,\n", - " name=aks_service_name,\n", - " models=[model],\n", - " inference_config=inf_config,\n", - " deployment_config=aks_config,\n", - " deployment_target=aks_target)\n", - "\n", - "aks_service.wait_for_deployment(show_output = True)\n", - "print(aks_service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test the web service using run method\n", - "We test the web sevice by passing data.\n", - "Run() method retrieves API keys behind the scenes to make sure that call is authenticated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "import json\n", - "\n", - "test_sample = json.dumps({'data': [\n", - " [1,2,3,4,5,6,7,8,9,10], \n", - " [10,9,8,7,6,5,4,3,2,1]\n", - "]})\n", - "test_sample = bytes(test_sample,encoding = 'utf8')\n", - "\n", - "prediction = aks_service.run(input_data = test_sample)\n", - "print(prediction)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test the web service using raw HTTP request (optional)\n", - "Alternatively you can construct a raw HTTP request and send it to the service. In this case you need to explicitly pass the HTTP header. This process is shown in the next 2 cells." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# # if (key) auth is enabled, retrieve the API keys. AML generates two keys.\n", - "# key1, Key2 = aks_service.get_keys()\n", - "# print(key1)\n", - "\n", - "# # if token auth is enabled, retrieve the token.\n", - "# access_token, refresh_after = aks_service.get_token()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# construct raw HTTP request and send to the service\n", - "# %%time\n", - "\n", - "# import requests\n", - "\n", - "# import json\n", - "\n", - "# test_sample = json.dumps({'data': [\n", - "# [1,2,3,4,5,6,7,8,9,10], \n", - "# [10,9,8,7,6,5,4,3,2,1]\n", - "# ]})\n", - "# test_sample = bytes(test_sample,encoding = 'utf8')\n", - "\n", - "# # If (key) auth is enabled, don't forget to add key to the HTTP header.\n", - "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n", - "\n", - "# # If token auth is enabled, don't forget to add token to the HTTP header.\n", - "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n", - "\n", - "# resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)\n", - "\n", - "\n", - "# print(\"prediction:\", resp.text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Clean up\n", - "Delete the service, image and model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "aks_service.delete()\n", - "model.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploying a web service to Azure Kubernetes Service (AKS)\n", + "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n", + "We then test and delete the service, image and model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "from azureml.core.compute import AksCompute, ComputeTarget\n", + "from azureml.core.webservice import Webservice, AksWebservice\n", + "from azureml.core.model import Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "print(azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get workspace\n", + "Load existing workspace from the config file info." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.workspace import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Register the model\n", + "Register an existing trained model, add descirption and tags." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Register the model\n", + "from azureml.core.model import Model\n", + "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n", + " model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n", + " tags = {'area': \"diabetes\", 'type': \"regression\"},\n", + " description = \"Ridge regression model to predict diabetes\",\n", + " workspace = ws)\n", + "\n", + "print(model.name, model.description, model.version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create the Environment\n", + "Create an environment that the model will be deployed with" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.conda_dependencies import CondaDependencies \n", + "\n", + "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.19.1','scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n", + "myenv = Environment(name='myenv')\n", + "myenv.python.conda_dependencies = conda_deps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use a custom Docker image\n", + "\n", + "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n", + "\n", + "Only supported with `python` runtime.\n", + "```python\n", + "# use an image available in public Container Registry without authentication\n", + "myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n", + "\n", + "# or, use an image available in a private Container Registry\n", + "myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n", + "myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n", + "myenv.docker.base_image_registry.username = \"username\"\n", + "myenv.docker.base_image_registry.password = \"password\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write the Entry Script\n", + "Write the script that will be used to predict on your model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile score.py\n", + "import os\n", + "import pickle\n", + "import json\n", + "import numpy\n", + "from sklearn.externals import joblib\n", + "from sklearn.linear_model import Ridge\n", + "\n", + "def init():\n", + " global model\n", + " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", + " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", + " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", + " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n", + " # deserialize the model file back into a sklearn model\n", + " model = joblib.load(model_path)\n", + "\n", + "# note you can pass in multiple rows for scoring\n", + "def run(raw_data):\n", + " try:\n", + " data = json.loads(raw_data)['data']\n", + " data = numpy.array(data)\n", + " result = model.predict(data)\n", + " # you can return any data type as long as it is JSON-serializable\n", + " return result.tolist()\n", + " except Exception as e:\n", + " error = str(e)\n", + " return error" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create the InferenceConfig\n", + "Create the inference config that will be used when deploying the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "\n", + "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Profiling\n", + "\n", + "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n", + "\n", + "In order to profile your model you will need:\n", + "- a registered model\n", + "- an entry script\n", + "- an inference configuration\n", + "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n", + "\n", + "Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n", + "\n", + "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n", + "\n", + "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n", + "You can try get the dataset first to see if it's already registered." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from azureml.core import Datastore\n", + "from azureml.core.dataset import Dataset\n", + "from azureml.data import dataset_type_definitions\n", + "\n", + "dataset_name='sample_request_data'\n", + "\n", + "dataset_registered = False\n", + "try:\n", + " sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n", + " dataset_registered = True\n", + "except:\n", + " print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n", + "\n", + "if not dataset_registered:\n", + " input_json = {'data': [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n", + " [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\n", + " # create a string that can be put in the body of the request\n", + " serialized_input_json = json.dumps(input_json)\n", + " dataset_content = []\n", + " for i in range(100):\n", + " dataset_content.append(serialized_input_json)\n", + " sample_request_data = '\\n'.join(dataset_content)\n", + " file_name = \"{}.txt\".format(dataset_name)\n", + " f = open(file_name, 'w')\n", + " f.write(sample_request_data)\n", + " f.close()\n", + "\n", + " # upload the txt file created above to the Datastore and create a dataset from it\n", + " data_store = Datastore.get_default(ws)\n", + " data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n", + " datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n", + " sample_request_data = Dataset.Tabular.from_delimited_files(\n", + " datastore_path,\n", + " separator='\\n',\n", + " infer_column_types=True,\n", + " header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n", + " sample_request_data = sample_request_data.register(workspace=ws,\n", + " name=dataset_name,\n", + " create_new_version=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "from azureml.core import Environment\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "from azureml.core.model import Model, InferenceConfig\n", + "\n", + "\n", + "environment = Environment('my-sklearn-environment')\n", + "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", + " 'azureml-defaults',\n", + " 'inference-schema[numpy-support]',\n", + " 'joblib',\n", + " 'numpy',\n", + " 'scikit-learn==0.19.1',\n", + " 'scipy'\n", + "])\n", + "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", + "# if cpu and memory_in_gb parameters are not provided\n", + "# the model will be profiled on default configuration of\n", + "# 3.5CPU and 15GB memory\n", + "profile = Model.profile(ws,\n", + " 'sklearn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n", + " [model],\n", + " inference_config,\n", + " input_dataset=sample_request_data,\n", + " cpu=1.0,\n", + " memory_in_gb=0.5)\n", + "\n", + "# profiling is a long running operation and may take up to 25 min\n", + "profile.wait_for_completion(True)\n", + "details = profile.get_details()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Provision the AKS Cluster\n", + "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your AKS cluster\n", + "aks_name = 'my-aks-9' \n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " aks_target = ComputeTarget(workspace=ws, name=aks_name)\n", + " print('Found existing cluster, use it.')\n", + "except ComputeTargetException:\n", + " # Use the default configuration (can also provide parameters to customize)\n", + " prov_config = AksCompute.provisioning_configuration()\n", + "\n", + " # Create the cluster\n", + " aks_target = ComputeTarget.create(workspace = ws, \n", + " name = aks_name, \n", + " provisioning_configuration = prov_config)\n", + "\n", + "if aks_target.get_status() != \"Succeeded\":\n", + " aks_target.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create AKS Cluster in an existing virtual network (optional)\n", + "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-enable-virtual-network#use-azure-kubernetes-service) for more details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# from azureml.core.compute import ComputeTarget, AksCompute\n", + "\n", + "# # Create the compute configuration and set virtual network information\n", + "# config = AksCompute.provisioning_configuration(location=\"eastus2\")\n", + "# config.vnet_resourcegroup_name = \"mygroup\"\n", + "# config.vnet_name = \"mynetwork\"\n", + "# config.subnet_name = \"default\"\n", + "# config.service_cidr = \"10.0.0.0/16\"\n", + "# config.dns_service_ip = \"10.0.0.10\"\n", + "# config.docker_bridge_cidr = \"172.17.0.1/16\"\n", + "\n", + "# # Create the compute target\n", + "# aks_target = ComputeTarget.create(workspace = ws,\n", + "# name = \"myaks\",\n", + "# provisioning_configuration = config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Enable SSL on the AKS Cluster (optional)\n", + "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# provisioning_config = AksCompute.provisioning_configuration(ssl_cert_pem_file=\"cert.pem\", ssl_key_pem_file=\"key.pem\", ssl_cname=\"www.contoso.com\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "aks_target.wait_for_completion(show_output = True)\n", + "print(aks_target.provisioning_state)\n", + "print(aks_target.provisioning_errors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional step: Attach existing AKS cluster\n", + "\n", + "If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # Use the default configuration (can also provide parameters to customize)\n", + "# resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n", + "\n", + "# create_name='my-existing-aks' \n", + "# # Create the cluster\n", + "# attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n", + "# aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n", + "# # Wait for the operation to complete\n", + "# aks_target.wait_for_completion(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploy web service to AKS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "sample-deploy-to-aks" + ] + }, + "outputs": [], + "source": [ + "# Set the web service configuration (using default here)\n", + "aks_config = AksWebservice.deploy_configuration()\n", + "\n", + "# # Enable token auth and disable (key) auth on the webservice\n", + "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "sample-deploy-to-aks" + ] + }, + "outputs": [], + "source": [ + "%%time\n", + "aks_service_name ='aks-service-1'\n", + "\n", + "aks_service = Model.deploy(workspace=ws,\n", + " name=aks_service_name,\n", + " models=[model],\n", + " inference_config=inf_config,\n", + " deployment_config=aks_config,\n", + " deployment_target=aks_target)\n", + "\n", + "aks_service.wait_for_deployment(show_output = True)\n", + "print(aks_service.state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test the web service using run method\n", + "We test the web sevice by passing data.\n", + "Run() method retrieves API keys behind the scenes to make sure that call is authenticated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "import json\n", + "\n", + "test_sample = json.dumps({'data': [\n", + " [1,2,3,4,5,6,7,8,9,10], \n", + " [10,9,8,7,6,5,4,3,2,1]\n", + "]})\n", + "test_sample = bytes(test_sample,encoding = 'utf8')\n", + "\n", + "prediction = aks_service.run(input_data = test_sample)\n", + "print(prediction)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test the web service using raw HTTP request (optional)\n", + "Alternatively you can construct a raw HTTP request and send it to the service. In this case you need to explicitly pass the HTTP header. This process is shown in the next 2 cells." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # if (key) auth is enabled, retrieve the API keys. AML generates two keys.\n", + "# key1, Key2 = aks_service.get_keys()\n", + "# print(key1)\n", + "\n", + "# # if token auth is enabled, retrieve the token.\n", + "# access_token, refresh_after = aks_service.get_token()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# construct raw HTTP request and send to the service\n", + "# %%time\n", + "\n", + "# import requests\n", + "\n", + "# import json\n", + "\n", + "# test_sample = json.dumps({'data': [\n", + "# [1,2,3,4,5,6,7,8,9,10], \n", + "# [10,9,8,7,6,5,4,3,2,1]\n", + "# ]})\n", + "# test_sample = bytes(test_sample,encoding = 'utf8')\n", + "\n", + "# # If (key) auth is enabled, don't forget to add key to the HTTP header.\n", + "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n", + "\n", + "# # If token auth is enabled, don't forget to add token to the HTTP header.\n", + "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n", + "\n", + "# resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)\n", + "\n", + "\n", + "# print(\"prediction:\", resp.text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clean up\n", + "Delete the service, image and model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "aks_service.delete()\n", + "model.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "vaidyas" + } ], - "metadata": { - "authors": [ - { - "name": "vaidyas" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb b/how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb index fd2f73b8f..31eda1a24 100644 --- a/how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb +++ b/how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb @@ -1,349 +1,349 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Register Spark Model and deploy as Webservice\n", - "\n", - "This example shows how to deploy a Webservice in step-by-step fashion:\n", - "\n", - " 1. Register Spark Model\n", - " 2. Deploy Spark Model as Webservice" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Register Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can add tags and descriptions to your Models. Note you need to have a `iris.model` file in the current directory. This model file is generated using [train in spark](../training/train-in-spark/train-in-spark.ipynb) notebook. The below call registers that file as a Model with the same name `iris.model` in the workspace.\n", - "\n", - "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "register model from file" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "model = Model.register(model_path=\"iris.model\",\n", - " model_name=\"iris.model\",\n", - " tags={'type': \"regression\"},\n", - " description=\"Logistic regression model to predict iris species\",\n", - " workspace=ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fetch Environment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment.\n", - "\n", - "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\r\n", - "from azureml.core.environment import SparkPackage\r\n", - "from azureml.core.conda_dependencies import CondaDependencies\r\n", - "\r\n", - "myenv = Environment('my-pyspark-environment')\r\n", - "myenv.docker.base_image = \"mcr.microsoft.com/mmlspark/release:0.15\"\r\n", - "myenv.inferencing_stack_version = \"latest\"\r\n", - "myenv.python.conda_dependencies = CondaDependencies.create(pip_packages=[\"azureml-core\",\"azureml-defaults\",\"azureml-telemetry\",\"azureml-train-restclients-hyperdrive\",\"azureml-train-core\"], python_version=\"3.6.2\")\r\n", - "myenv.python.conda_dependencies.add_channel(\"conda-forge\")\r\n", - "myenv.spark.packages = [SparkPackage(\"com.microsoft.ml.spark\", \"mmlspark_2.11\", \"0.15\"), SparkPackage(\"com.microsoft.azure\", \"azure-storage\", \"2.0.0\"), SparkPackage(\"org.apache.hadoop\", \"hadoop-azure\", \"2.7.0\")]\r\n", - "myenv.spark.repositories = [\"https://mmlspark.azureedge.net/maven\"]\r\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Inference Configuration\n", - "\n", - "There is now support for a source directory, you can upload an entire folder from your local machine as dependencies for the Webservice.\n", - "Note: in that case, your entry_script is relative path to the source_directory path.\n", - "\n", - "Sample code for using a source directory:\n", - "\n", - "```python\n", - "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n", - " entry_script=\"x/y/score.py\",\n", - " environment=environment)\n", - "```\n", - "\n", - " - source_directory = holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n", - " - entry_script = contains logic specific to initializing your model and running predictions\n", - " - environment = An environment object to use for the deployment. Doesn't have to be registered" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create image" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy Model as Webservice on Azure Container Instance\n", - "\n", - "Note that the service creation can take few minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "azuremlexception-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice, Webservice\n", - "from azureml.exceptions import WebserviceException\n", - "\n", - "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n", - "aci_service_name = 'aciservice1'\n", - "\n", - "try:\n", - " # if you want to get existing service below is the command\n", - " # since aci name needs to be unique in subscription deleting existing aci if any\n", - " # we use aci_service_name to create azure aci\n", - " service = Webservice(ws, name=aci_service_name)\n", - " if service:\n", - " service.delete()\n", - "except WebserviceException as e:\n", - " print()\n", - "\n", - "service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\n", - "\n", - "service.wait_for_deployment(True)\n", - "print(service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Test web service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "test_sample = json.dumps({'features':{'type':1,'values':[4.3,3.0,1.1,0.1]},'label':2.0})\n", - "\n", - "test_sample_encoded = bytes(test_sample, encoding='utf8')\n", - "prediction = service.run(input_data=test_sample_encoded)\n", - "print(prediction)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Delete ACI to clean up" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "deploy service", - "aci" - ] - }, - "outputs": [], - "source": [ - "service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model Profiling\n", - "\n", - "You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n", - "\n", - "```python\n", - "profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n", - "profile.wait_for_profiling(True)\n", - "profiling_results = profile.get_results()\n", - "print(profiling_results)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model Packaging\n", - "\n", - "If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n", - "\n", - "You must include an Environment object in your inference configuration to use `Model.package()`.\n", - "\n", - "```python\n", - "package = Model.package(ws, [model], inference_config)\n", - "package.wait_for_creation(show_output=True) # Or show_output=False to hide the Docker build logs.\n", - "package.pull()\n", - "```\n", - "\n", - "Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n", - "\n", - "```python\n", - "package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n", - "package.wait_for_creation(show_output=True)\n", - "package.save(\"./local_context_dir\")\n", - "```" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Register Spark Model and deploy as Webservice\n", + "\n", + "This example shows how to deploy a Webservice in step-by-step fashion:\n", + "\n", + " 1. Register Spark Model\n", + " 2. Deploy Spark Model as Webservice" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can add tags and descriptions to your Models. Note you need to have a `iris.model` file in the current directory. This model file is generated using [train in spark](../training/train-in-spark/train-in-spark.ipynb) notebook. The below call registers that file as a Model with the same name `iris.model` in the workspace.\n", + "\n", + "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "register model from file" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "model = Model.register(model_path=\"iris.model\",\n", + " model_name=\"iris.model\",\n", + " tags={'type': \"regression\"},\n", + " description=\"Logistic regression model to predict iris species\",\n", + " workspace=ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fetch Environment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment.\n", + "\n", + "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\r\n", + "from azureml.core.environment import SparkPackage\r\n", + "from azureml.core.conda_dependencies import CondaDependencies\r\n", + "\r\n", + "myenv = Environment('my-pyspark-environment')\r\n", + "myenv.docker.base_image = \"mcr.microsoft.com/mmlspark/release:0.15\"\r\n", + "myenv.inferencing_stack_version = \"latest\"\r\n", + "myenv.python.conda_dependencies = CondaDependencies.create(pip_packages=[\"azureml-core\",\"azureml-defaults\",\"azureml-telemetry\",\"azureml-train-restclients-hyperdrive\",\"azureml-train-core\"], python_version=\"3.6.2\")\r\n", + "myenv.python.conda_dependencies.add_channel(\"conda-forge\")\r\n", + "myenv.spark.packages = [SparkPackage(\"com.microsoft.ml.spark\", \"mmlspark_2.11\", \"0.15\"), SparkPackage(\"com.microsoft.azure\", \"azure-storage\", \"2.0.0\"), SparkPackage(\"org.apache.hadoop\", \"hadoop-azure\", \"2.7.0\")]\r\n", + "myenv.spark.repositories = [\"https://mmlspark.azureedge.net/maven\"]\r\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Inference Configuration\n", + "\n", + "There is now support for a source directory, you can upload an entire folder from your local machine as dependencies for the Webservice.\n", + "Note: in that case, your entry_script is relative path to the source_directory path.\n", + "\n", + "Sample code for using a source directory:\n", + "\n", + "```python\n", + "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n", + " entry_script=\"x/y/score.py\",\n", + " environment=environment)\n", + "```\n", + "\n", + " - source_directory = holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n", + " - entry_script = contains logic specific to initializing your model and running predictions\n", + " - environment = An environment object to use for the deployment. Doesn't have to be registered" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create image" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "\n", + "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy Model as Webservice on Azure Container Instance\n", + "\n", + "Note that the service creation can take few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "azuremlexception-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.webservice import AciWebservice, Webservice\n", + "from azureml.exceptions import WebserviceException\n", + "\n", + "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n", + "aci_service_name = 'aciservice1'\n", + "\n", + "try:\n", + " # if you want to get existing service below is the command\n", + " # since aci name needs to be unique in subscription deleting existing aci if any\n", + " # we use aci_service_name to create azure aci\n", + " service = Webservice(ws, name=aci_service_name)\n", + " if service:\n", + " service.delete()\n", + "except WebserviceException as e:\n", + " print()\n", + "\n", + "service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\n", + "\n", + "service.wait_for_deployment(True)\n", + "print(service.state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Test web service" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "test_sample = json.dumps({'features':{'type':1,'values':[4.3,3.0,1.1,0.1]},'label':2.0})\n", + "\n", + "test_sample_encoded = bytes(test_sample, encoding='utf8')\n", + "prediction = service.run(input_data=test_sample_encoded)\n", + "print(prediction)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Delete ACI to clean up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "deploy service", + "aci" + ] + }, + "outputs": [], + "source": [ + "service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Profiling\n", + "\n", + "You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n", + "\n", + "```python\n", + "profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n", + "profile.wait_for_profiling(True)\n", + "profiling_results = profile.get_results()\n", + "print(profiling_results)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Packaging\n", + "\n", + "If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n", + "\n", + "You must include an Environment object in your inference configuration to use `Model.package()`.\n", + "\n", + "```python\n", + "package = Model.package(ws, [model], inference_config)\n", + "package.wait_for_creation(show_output=True) # Or show_output=False to hide the Docker build logs.\n", + "package.pull()\n", + "```\n", + "\n", + "Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n", + "\n", + "```python\n", + "package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n", + "package.wait_for_creation(show_output=True)\n", + "package.save(\"./local_context_dir\")\n", + "```" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "vaidyas" + } + ], + "category": "deployment", + "compute": [ + "None" + ], + "datasets": [ + "Iris" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "PySpark" ], - "metadata": { - "authors": [ - { - "name": "vaidyas" - } - ], - "category": "deployment", - "compute": [ - "None" - ], - "datasets": [ - "Iris" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "PySpark" - ], - "friendly_name": "Register Spark model and deploy as webservice", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } + "friendly_name": "Register Spark model and deploy as webservice", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb b/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb index df3ec7c8b..f3f40243e 100644 --- a/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb @@ -1,297 +1,297 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Explain tree-based models on GPU using GPUTreeExplainer\n", - "\n", - "\n", - "_**This notebook illustrates how to use shap's GPUTreeExplainer on an Azure GPU machine.**_\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Problem: Train a tree-based model and explain the model on an Azure GPU machine using the GPUTreeExplainer.\n", - "\n", - "---\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Run model explainer locally at training time](#Explain)\n", - " 1. Apply feature transformations\n", - " 1. Train a binary classification model\n", - " 1. Explain the model on raw features\n", - " 1. Generate global explanations\n", - " 1. Generate local explanations\n", - "1. [Visualize explanations](#Visualize)\n", - "1. [Deploy model and scoring explainer](#Deploy)\n", - "1. [Next steps](#Next)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "This notebook demonstrates how to use the GPUTreeExplainer on some simple datasets. Like the TreeExplainer, the GPUTreeExplainer is specifically designed for tree-based machine learning models, but it is designed to accelerate the computations using NVIDIA GPUs.\n", - "\n", - "\n", - "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n", - "\n", - "Notebook synopsis:\n", - "\n", - "1. Creating an Experiment in an existing Workspace\n", - "2. Configuration and remote run with a GPU machine" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import os\n", - "import shutil\n", - "\n", - "import pandas as pd\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.core.compute import AmlCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "from azureml.core.run import Run\n", - "from azureml.core.model import Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n", - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As part of the setup you have already created a Workspace. To run the script, you also need to create an Experiment. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# Choose an experiment name.\n", - "experiment_name = 'gpu-tree-explainer'\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output['Subscription ID'] = ws.subscription_id\n", - "output['Workspace Name'] = ws.name\n", - "output['Resource Group'] = ws.resource_group\n", - "output['Location'] = ws.location\n", - "output['Experiment Name'] = experiment.name\n", - "pd.set_option('display.max_colwidth', -1)\n", - "outputDf = pd.DataFrame(data = output, index = [''])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create project directory\n", - "\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import shutil\n", - "\n", - "project_folder = './azureml-shap-gpu-tree-explainer'\n", - "os.makedirs(project_folder, exist_ok=True)\n", - "shutil.copy('gpu_tree_explainer.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up a compute cluster\n", - "This section uses a user-provided compute cluster (named \"gpu-shap-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "num_nodes = 1\n", - "\n", - "# Choose a name for your cluster.\n", - "amlcompute_cluster_name = \"gpu-shap-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n", - " # To use GPUTreeExplainer, select a GPU such as \"STANDARD_NC6\" \n", - " # or similar GPU option\n", - " # available in your workspace\n", - " max_nodes = num_nodes)\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure & Run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "# Create a new RunConfig object\n", - "run_config = RunConfiguration(framework=\"python\")\n", - "\n", - "# Set compute target to AmlCompute target created in previous step\n", - "run_config.target = amlcompute_cluster_name\n", - "\n", - "from azureml.core import Environment\n", - "\n", - "environment_name = \"shap-gpu-tree\"\n", - "\n", - "env = Environment(environment_name)\n", - "\n", - "env.docker.enabled = True\n", - "env.docker.base_image = None\n", - "env.docker.base_dockerfile = \"\"\"\n", - "FROM rapidsai/rapidsai:cuda10.0-devel-ubuntu18.04\n", - "RUN apt-get update && \\\n", - "apt-get install -y fuse && \\\n", - "apt-get install -y build-essential && \\\n", - "apt-get install -y python3-dev && \\\n", - "source activate rapids && \\\n", - "apt-get install -y g++ && \\\n", - "printenv && \\\n", - "echo \"which nvcc: \" && \\\n", - "which nvcc && \\\n", - "pip install azureml-defaults && \\\n", - "pip install azureml-telemetry && \\\n", - "cd /usr/local/src && \\\n", - "git clone https://github.com/slundberg/shap && \\\n", - "cd shap && \\\n", - "mkdir build && \\\n", - "python setup.py install --user && \\\n", - "pip uninstall -y xgboost && \\\n", - "rm /conda/envs/rapids/lib/libxgboost.so && \\\n", - "pip install xgboost==1.4.2\n", - "\"\"\"\n", - "\n", - "env.python.user_managed_dependencies = True\n", - "\n", - "from azureml.core import Run\n", - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=project_folder, \n", - " script='gpu_tree_explainer.py', \n", - " compute_target=amlcompute_cluster_name,\n", - " environment=env) \n", - "run = experiment.submit(config=src)\n", - "run" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explain tree-based models on GPU using GPUTreeExplainer\n", + "\n", + "\n", + "_**This notebook illustrates how to use shap's GPUTreeExplainer on an Azure GPU machine.**_\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Problem: Train a tree-based model and explain the model on an Azure GPU machine using the GPUTreeExplainer.\n", + "\n", + "---\n", + "\n", + "## Table of Contents\n", + "\n", + "1. [Introduction](#Introduction)\n", + "1. [Setup](#Setup)\n", + "1. [Run model explainer locally at training time](#Explain)\n", + " 1. Apply feature transformations\n", + " 1. Train a binary classification model\n", + " 1. Explain the model on raw features\n", + " 1. Generate global explanations\n", + " 1. Generate local explanations\n", + "1. [Visualize explanations](#Visualize)\n", + "1. [Deploy model and scoring explainer](#Deploy)\n", + "1. [Next steps](#Next)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "This notebook demonstrates how to use the GPUTreeExplainer on some simple datasets. Like the TreeExplainer, the GPUTreeExplainer is specifically designed for tree-based machine learning models, but it is designed to accelerate the computations using NVIDIA GPUs.\n", + "\n", + "\n", + "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n", + "\n", + "Notebook synopsis:\n", + "\n", + "1. Creating an Experiment in an existing Workspace\n", + "2. Configuration and remote run with a GPU machine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import os\n", + "import shutil\n", + "\n", + "import pandas as pd\n", + "\n", + "import azureml.core\n", + "from azureml.core.experiment import Experiment\n", + "from azureml.core.workspace import Workspace\n", + "from azureml.core.dataset import Dataset\n", + "from azureml.core.compute import AmlCompute\n", + "from azureml.core.compute import ComputeTarget\n", + "from azureml.core.run import Run\n", + "from azureml.core.model import Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"This notebook was created using version AZUREML-SDK-VERSION of the Azure ML SDK\")\n", + "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As part of the setup you have already created a Workspace. To run the script, you also need to create an Experiment. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "\n", + "# Choose an experiment name.\n", + "experiment_name = 'gpu-tree-explainer'\n", + "\n", + "experiment = Experiment(ws, experiment_name)\n", + "\n", + "output = {}\n", + "output['Subscription ID'] = ws.subscription_id\n", + "output['Workspace Name'] = ws.name\n", + "output['Resource Group'] = ws.resource_group\n", + "output['Location'] = ws.location\n", + "output['Experiment Name'] = experiment.name\n", + "pd.set_option('display.max_colwidth', -1)\n", + "outputDf = pd.DataFrame(data = output, index = [''])\n", + "outputDf.T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create project directory\n", + "\n", + "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "\n", + "project_folder = './azureml-shap-gpu-tree-explainer'\n", + "os.makedirs(project_folder, exist_ok=True)\n", + "shutil.copy('gpu_tree_explainer.py', project_folder)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up a compute cluster\n", + "This section uses a user-provided compute cluster (named \"gpu-shap-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "num_nodes = 1\n", + "\n", + "# Choose a name for your cluster.\n", + "amlcompute_cluster_name = \"gpu-shap-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", + " print('Found existing cluster, use it.')\n", + "except ComputeTargetException:\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n", + " # To use GPUTreeExplainer, select a GPU such as \"STANDARD_NC6\" \n", + " # or similar GPU option\n", + " # available in your workspace\n", + " max_nodes = num_nodes)\n", + " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", + "\n", + "compute_target.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure & Run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.runconfig import RunConfiguration\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "# Create a new RunConfig object\n", + "run_config = RunConfiguration(framework=\"python\")\n", + "\n", + "# Set compute target to AmlCompute target created in previous step\n", + "run_config.target = amlcompute_cluster_name\n", + "\n", + "from azureml.core import Environment\n", + "\n", + "environment_name = \"shap-gpu-tree\"\n", + "\n", + "env = Environment(environment_name)\n", + "\n", + "env.docker.enabled = True\n", + "env.docker.base_image = None\n", + "env.docker.base_dockerfile = \"\"\"\n", + "FROM rapidsai/rapidsai:cuda10.0-devel-ubuntu18.04\n", + "RUN apt-get update && \\\n", + "apt-get install -y fuse && \\\n", + "apt-get install -y build-essential && \\\n", + "apt-get install -y python3-dev && \\\n", + "source activate rapids && \\\n", + "apt-get install -y g++ && \\\n", + "printenv && \\\n", + "echo \"which nvcc: \" && \\\n", + "which nvcc && \\\n", + "pip install azureml-defaults && \\\n", + "pip install azureml-telemetry && \\\n", + "cd /usr/local/src && \\\n", + "git clone https://github.com/slundberg/shap && \\\n", + "cd shap && \\\n", + "mkdir build && \\\n", + "python setup.py install --user && \\\n", + "pip uninstall -y xgboost && \\\n", + "rm /conda/envs/rapids/lib/libxgboost.so && \\\n", + "pip install xgboost==1.4.2\n", + "\"\"\"\n", + "\n", + "env.python.user_managed_dependencies = True\n", + "\n", + "from azureml.core import Run\n", + "from azureml.core import ScriptRunConfig\n", + "\n", + "src = ScriptRunConfig(source_directory=project_folder, \n", + " script='gpu_tree_explainer.py', \n", + " compute_target=amlcompute_cluster_name,\n", + " environment=env) \n", + "run = experiment.submit(config=src)\n", + "run" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "ilmat" + } ], - "metadata": { - "authors": [ - { - "name": "ilmat" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb index 79e12f87d..7fe71e7c2 100644 --- a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb @@ -1,512 +1,512 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train and explain models remotely via Azure Machine Learning Compute\n", - "\n", - "\n", - "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to train and explain a regression model remotely on an Azure Machine Learning Compute Target (AMLCompute).**_\n", - "\n", - "\n", - "\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - " 1. Initialize a Workspace\n", - " 1. Create an Experiment\n", - " 1. Introduction to AmlCompute\n", - " 1. Submit an AmlCompute run\n", - "1. Additional operations to perform on AmlCompute\n", - "1. [Download model explanations from Azure Machine Learning Run History](#Download)\n", - "1. [Visualize explanations](#Visualize)\n", - "1. [Next steps](#Next)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "This notebook showcases how to train and explain a regression model remotely via Azure Machine Learning Compute (AMLCompute), and download the calculated explanations locally for visualization.\n", - "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations.\n", - "\n", - "We will showcase one of the tabular data explainers: TabularExplainer (SHAP).\n", - "\n", - "Problem: Boston Housing Price Prediction with scikit-learn (train a model and run an explainer remotely via AMLCompute, and download and visualize the remotely-calculated explanations.)\n", - "\n", - "| ![explanations-run-history](./img/explanations-run-history.png) |\n", - "|:--:|\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't.\n", - "\n", - "\n", - "If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n", - "If you are using Jupyter Labs run the following command:\n", - "```\n", - "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize a Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create An Experiment\n", - "\n", - "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "experiment_name = 'explainer-remote-run-on-amlcompute'\n", - "experiment = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction to AmlCompute\n", - "\n", - "Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n", - "\n", - "Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n", - "\n", - "For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n", - "\n", - "If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n", - "\n", - "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n", - "\n", - "\n", - "The training script `train_explain.py` is already created for you. Let's have a look." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit an AmlCompute run\n", - "\n", - "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n", - "\n", - "You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "\n", - "AmlCompute.supported_vmsizes(workspace=ws)\n", - "# AmlCompute.supported_vmsizes(workspace=ws, location='southcentralus')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create project directory\n", - "\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import shutil\n", - "\n", - "project_folder = './explainer-remote-run-on-amlcompute'\n", - "os.makedirs(project_folder, exist_ok=True)\n", - "shutil.copy('train_explain.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Provision a compute target\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n", - "\n", - "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n", - "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "cpu_cluster_name = \"cpu-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", - " max_nodes=4)\n", - " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - "\n", - "cpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure & Run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "import sys\n", - "\n", - "# Create a new RunConfig object\n", - "run_config = RunConfiguration(framework=\"python\")\n", - "\n", - "# Set compute target to AmlCompute target created in previous step\n", - "run_config.target = cpu_cluster.name\n", - "\n", - "azureml_pip_packages = [\n", - " 'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n", - "]\n", - "\n", - "python_version = '{0}.{1}'.format(sys.version_info[0], sys.version_info[1])\n", - "\n", - "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n", - "# In production scenario user would choose their dependencies\n", - "import pkg_resources\n", - "available_packages = pkg_resources.working_set\n", - "sklearn_ver = None\n", - "pandas_ver = None\n", - "for dist in list(available_packages):\n", - " if dist.key == 'scikit-learn':\n", - " sklearn_ver = dist.version\n", - " elif dist.key == 'pandas':\n", - " pandas_ver = dist.version\n", - "sklearn_dep = 'scikit-learn'\n", - "pandas_dep = 'pandas'\n", - "if sklearn_ver:\n", - " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", - "if pandas_ver:\n", - " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", - "# Specify CondaDependencies obj\n", - "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", - "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", - "# environment, otherwise if a model is trained or deployed in a different environment this can\n", - "# cause errors. Please take extra care when specifying your dependencies in a production environment.\n", - "azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n", - "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages, python_version=python_version)\n", - "\n", - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=project_folder, \n", - " script='train_explain.py', \n", - " run_config=run_config) \n", - "run = experiment.submit(config=src)\n", - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "# Shows output of the run on stdout.\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_metrics()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download \n", - "1. Download model explanation data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.interpret import ExplanationClient\n", - "\n", - "# Get model explanation data\n", - "client = ExplanationClient.from_run(run)\n", - "global_explanation = client.download_model_explanation()\n", - "local_importance_values = global_explanation.local_importance_values\n", - "expected_values = global_explanation.expected_values\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Or you can use the saved run.id to retrive the feature importance values\n", - "client = ExplanationClient.from_run_id(ws, experiment_name, run.id)\n", - "global_explanation = client.download_model_explanation()\n", - "local_importance_values = global_explanation.local_importance_values\n", - "expected_values = global_explanation.expected_values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the top k (e.g., 4) most important features with their importance values\n", - "global_explanation_topk = client.download_model_explanation(top_k=4)\n", - "global_importance_values = global_explanation_topk.get_ranked_global_values()\n", - "global_importance_names = global_explanation_topk.get_ranked_global_names()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('global importance values: {}'.format(global_importance_values))\n", - "print('global importance names: {}'.format(global_importance_names))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. Download model file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve model for visualization and deployment\n", - "from azureml.core.model import Model\n", - "import joblib\n", - "original_model = Model(ws, 'model_explain_model_on_amlcomp')\n", - "model_path = original_model.download(exist_ok=True)\n", - "original_model = joblib.load(model_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3. Download test dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve x_test for visualization\n", - "x_test_path = './x_test_boston_housing.pkl'\n", - "run.download_file('x_test_boston_housing.pkl', output_file_path=x_test_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_test = joblib.load('x_test_boston_housing.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize\n", - "Load the visualization dashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from raiwidgets import ExplanationDashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ExplanationDashboard(global_explanation, original_model, dataset=x_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next\n", - "Learn about other use cases of the explain package on a:\n", - "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb) \n", - "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n", - "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n", - "1. Explain models with engineered features:\n", - " 1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n", - " 1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n", - "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n", - "1. Inferencing time: deploy a classification model and explainer:\n", - " 1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n", - " 1. [Deploy a locally-trained keras model and explainer](../scoring-time/train-explain-model-keras-locally-and-deploy.ipynb)\n", - " 1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train and explain models remotely via Azure Machine Learning Compute\n", + "\n", + "\n", + "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to train and explain a regression model remotely on an Azure Machine Learning Compute Target (AMLCompute).**_\n", + "\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "\n", + "1. [Introduction](#Introduction)\n", + "1. [Setup](#Setup)\n", + " 1. Initialize a Workspace\n", + " 1. Create an Experiment\n", + " 1. Introduction to AmlCompute\n", + " 1. Submit an AmlCompute run\n", + "1. Additional operations to perform on AmlCompute\n", + "1. [Download model explanations from Azure Machine Learning Run History](#Download)\n", + "1. [Visualize explanations](#Visualize)\n", + "1. [Next steps](#Next)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This notebook showcases how to train and explain a regression model remotely via Azure Machine Learning Compute (AMLCompute), and download the calculated explanations locally for visualization.\n", + "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations.\n", + "\n", + "We will showcase one of the tabular data explainers: TabularExplainer (SHAP).\n", + "\n", + "Problem: Boston Housing Price Prediction with scikit-learn (train a model and run an explainer remotely via AMLCompute, and download and visualize the remotely-calculated explanations.)\n", + "\n", + "| ![explanations-run-history](./img/explanations-run-history.png) |\n", + "|:--:|\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't.\n", + "\n", + "\n", + "If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n", + "If you are using Jupyter Labs run the following command:\n", + "```\n", + "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize a Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create An Experiment\n", + "\n", + "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "experiment_name = 'explainer-remote-run-on-amlcompute'\n", + "experiment = Experiment(workspace=ws, name=experiment_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to AmlCompute\n", + "\n", + "Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n", + "\n", + "Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n", + "\n", + "For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n", + "\n", + "If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n", + "\n", + "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n", + "\n", + "\n", + "The training script `train_explain.py` is already created for you. Let's have a look." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submit an AmlCompute run\n", + "\n", + "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n", + "\n", + "You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "\n", + "AmlCompute.supported_vmsizes(workspace=ws)\n", + "# AmlCompute.supported_vmsizes(workspace=ws, location='southcentralus')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create project directory\n", + "\n", + "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "\n", + "project_folder = './explainer-remote-run-on-amlcompute'\n", + "os.makedirs(project_folder, exist_ok=True)\n", + "shutil.copy('train_explain.py', project_folder)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Provision a compute target\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\n", + "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n", + "\n", + "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n", + "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your CPU cluster\n", + "cpu_cluster_name = \"cpu-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", + " print('Found existing cluster, use it.')\n", + "except ComputeTargetException:\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", + " max_nodes=4)\n", + " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", + "\n", + "cpu_cluster.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure & Run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.runconfig import RunConfiguration\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "import sys\n", + "\n", + "# Create a new RunConfig object\n", + "run_config = RunConfiguration(framework=\"python\")\n", + "\n", + "# Set compute target to AmlCompute target created in previous step\n", + "run_config.target = cpu_cluster.name\n", + "\n", + "azureml_pip_packages = [\n", + " 'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n", + "]\n", + "\n", + "python_version = '{0}.{1}'.format(sys.version_info[0], sys.version_info[1])\n", + "\n", + "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n", + "# In production scenario user would choose their dependencies\n", + "import pkg_resources\n", + "available_packages = pkg_resources.working_set\n", + "sklearn_ver = None\n", + "pandas_ver = None\n", + "for dist in list(available_packages):\n", + " if dist.key == 'scikit-learn':\n", + " sklearn_ver = dist.version\n", + " elif dist.key == 'pandas':\n", + " pandas_ver = dist.version\n", + "sklearn_dep = 'scikit-learn'\n", + "pandas_dep = 'pandas'\n", + "if sklearn_ver:\n", + " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", + "if pandas_ver:\n", + " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", + "# Specify CondaDependencies obj\n", + "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", + "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", + "# environment, otherwise if a model is trained or deployed in a different environment this can\n", + "# cause errors. Please take extra care when specifying your dependencies in a production environment.\n", + "azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n", + "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages, python_version=python_version)\n", + "\n", + "from azureml.core import ScriptRunConfig\n", + "\n", + "src = ScriptRunConfig(source_directory=project_folder, \n", + " script='train_explain.py', \n", + " run_config=run_config) \n", + "run = experiment.submit(config=src)\n", + "run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# Shows output of the run on stdout.\n", + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.get_metrics()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download \n", + "1. Download model explanation data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.interpret import ExplanationClient\n", + "\n", + "# Get model explanation data\n", + "client = ExplanationClient.from_run(run)\n", + "global_explanation = client.download_model_explanation()\n", + "local_importance_values = global_explanation.local_importance_values\n", + "expected_values = global_explanation.expected_values\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Or you can use the saved run.id to retrive the feature importance values\n", + "client = ExplanationClient.from_run_id(ws, experiment_name, run.id)\n", + "global_explanation = client.download_model_explanation()\n", + "local_importance_values = global_explanation.local_importance_values\n", + "expected_values = global_explanation.expected_values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the top k (e.g., 4) most important features with their importance values\n", + "global_explanation_topk = client.download_model_explanation(top_k=4)\n", + "global_importance_values = global_explanation_topk.get_ranked_global_values()\n", + "global_importance_names = global_explanation_topk.get_ranked_global_names()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('global importance values: {}'.format(global_importance_values))\n", + "print('global importance names: {}'.format(global_importance_names))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Download model file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve model for visualization and deployment\n", + "from azureml.core.model import Model\n", + "import joblib\n", + "original_model = Model(ws, 'model_explain_model_on_amlcomp')\n", + "model_path = original_model.download(exist_ok=True)\n", + "original_model = joblib.load(model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Download test dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve x_test for visualization\n", + "x_test_path = './x_test_boston_housing.pkl'\n", + "run.download_file('x_test_boston_housing.pkl', output_file_path=x_test_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_test = joblib.load('x_test_boston_housing.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize\n", + "Load the visualization dashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from raiwidgets import ExplanationDashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ExplanationDashboard(global_explanation, original_model, dataset=x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next\n", + "Learn about other use cases of the explain package on a:\n", + "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb) \n", + "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n", + "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n", + "1. Explain models with engineered features:\n", + " 1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n", + " 1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n", + "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n", + "1. Inferencing time: deploy a classification model and explainer:\n", + " 1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n", + " 1. [Deploy a locally-trained keras model and explainer](../scoring-time/train-explain-model-keras-locally-and-deploy.ipynb)\n", + " 1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "authors": [ + { + "name": "mesameki" + } ], - "metadata": { - "authors": [ - { - "name": "mesameki" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb index b9cb9cb2e..eb8fb734a 100644 --- a/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb @@ -1,617 +1,617 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Save and retrieve explanations via Azure Machine Learning Run History\n", - "\n", - "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to save and retrieve classification model explanations to/from Azure Machine Learning Run History.**_\n", - "\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Run model explainer locally at training time](#Explain)\n", - " 1. Apply feature transformations\n", - " 1. Train a binary classification model\n", - " 1. Explain the model on raw features\n", - " 1. Generate global explanations\n", - " 1. Generate local explanations\n", - "1. [Upload model explanations to Azure Machine Learning Run History](#Upload)\n", - "1. [Download model explanations from Azure Machine Learning Run History](#Download)\n", - "1. [Visualize explanations](#Visualize)\n", - "1. [Next steps](#Next)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "This notebook showcases how to explain a classification model predictions locally at training time, upload explanations to the Azure Machine Learning's run history, and download previously-uploaded explanations from the Run History.\n", - "It demonstrates the API calls that you need to make to upload/download the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and downloaded explanations.\n", - "\n", - "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n", - "\n", - "\n", - "\n", - "Problem: IBM employee attrition classification with scikit-learn (run model explainer locally and upload explanation to the Azure Machine Learning Run History)\n", - "\n", - "1. Train a SVM classification model using Scikit-learn\n", - "2. Run 'explain-model-sample' with AML Run History, which leverages run history service to store and manage the explanation data\n", - "---\n", - "\n", - "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n", - "If you are using Jupyter Labs run the following command:\n", - "```\n", - "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explain\n", - "\n", - "### Run model explainer locally at training time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.pipeline import Pipeline\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", - "from sklearn.svm import SVC\n", - "import pandas as pd\n", - "\n", - "# Explainers:\n", - "# 1. SHAP Tabular Explainer\n", - "from interpret.ext.blackbox import TabularExplainer\n", - "\n", - "# OR\n", - "\n", - "# 2. Mimic Explainer\n", - "from interpret.ext.blackbox import MimicExplainer\n", - "# You can use one of the following four interpretable models as a global surrogate to the black box model\n", - "from interpret.ext.glassbox import LGBMExplainableModel\n", - "from interpret.ext.glassbox import LinearExplainableModel\n", - "from interpret.ext.glassbox import SGDExplainableModel\n", - "from interpret.ext.glassbox import DecisionTreeExplainableModel\n", - "\n", - "# OR\n", - "\n", - "# 3. PFI Explainer\n", - "from interpret.ext.blackbox import PFIExplainer " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the IBM employee attrition data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the IBM employee attrition dataset\n", - "outdirname = 'dataset.6.21.19'\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "import zipfile\n", - "zipfilename = outdirname + '.zip'\n", - "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n", - "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n", - " unzip.extractall('.')\n", - "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')\n", - "\n", - "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n", - "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n", - "# Dropping Employee Number since it is merely an identifier\n", - "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n", - "\n", - "attritionData = attritionData.drop(['Over18'], axis=1)\n", - "\n", - "# Since all values are 80\n", - "attritionData = attritionData.drop(['StandardHours'], axis=1)\n", - "\n", - "# Converting target variables from string to numerical values\n", - "target_map = {'Yes': 1, 'No': 0}\n", - "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n", - "target = attritionData[\"Attrition_numerical\"]\n", - "\n", - "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Split data into train and test\n", - "from sklearn.model_selection import train_test_split\n", - "x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n", - " target, \n", - " test_size=0.2,\n", - " random_state=0,\n", - " stratify=target)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Creating dummy columns for each categorical feature\n", - "categorical = []\n", - "for col, value in attritionXData.iteritems():\n", - " if value.dtype == 'object':\n", - " categorical.append(col)\n", - " \n", - "# Store the numerical columns in a list numerical\n", - "numerical = attritionXData.columns.difference(categorical) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Transform raw features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.compose import ColumnTransformer\n", - "\n", - "# We create the preprocessing pipelines for both numeric and categorical data.\n", - "numeric_transformer = Pipeline(steps=[\n", - " ('imputer', SimpleImputer(strategy='median')),\n", - " ('scaler', StandardScaler())])\n", - "\n", - "categorical_transformer = Pipeline(steps=[\n", - " ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n", - " ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n", - "\n", - "transformations = ColumnTransformer(\n", - " transformers=[\n", - " ('num', numeric_transformer, numerical),\n", - " ('cat', categorical_transformer, categorical)])\n", - "\n", - "# Append classifier to preprocessing pipeline.\n", - "# Now we have a full prediction pipeline.\n", - "clf = Pipeline(steps=[('preprocessor', transformations),\n", - " ('classifier', SVC(C=1.0, probability=True))])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train a SVM classification model, which you want to explain" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = clf.fit(x_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Explain predictions on your local machine" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 1. Using SHAP TabularExplainer\n", - "# clf.steps[-1][1] returns the trained classification model\n", - "explainer = TabularExplainer(clf.steps[-1][1], \n", - " initialization_examples=x_train, \n", - " features=attritionXData.columns, \n", - " classes=[\"Not leaving\", \"leaving\"], \n", - " transformations=transformations)\n", - "\n", - "\n", - "\n", - "\n", - "# 2. Using MimicExplainer\n", - "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model. Useful for high-dimensional data where the number of rows is less than the number of columns. \n", - "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n", - "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n", - "# explainer = MimicExplainer(clf.steps[-1][1], \n", - "# x_train, \n", - "# LGBMExplainableModel, \n", - "# augment_data=True, \n", - "# max_num_of_augmentations=10, \n", - "# features=attritionXData.columns, \n", - "# classes=[\"Not leaving\", \"leaving\"], \n", - "# transformations=transformations)\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "# 3. Using PFIExplainer\n", - "\n", - "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n", - "# Note that if a metric function is provided a higher value must be better.\n", - "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n", - "# Default metrics: \n", - "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n", - "# Mean absolute error for regression\n", - "\n", - "# explainer = PFIExplainer(clf.steps[-1][1], \n", - "# features=x_train.columns, \n", - "# transformations=transformations,\n", - "# classes=[\"Not leaving\", \"leaving\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate global explanations\n", - "Explain overall model predictions (global explanation)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n", - "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n", - "global_explanation = explainer.explain_global(x_test)\n", - "\n", - "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n", - "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sorted SHAP values\n", - "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n", - "# Corresponding feature names\n", - "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n", - "# Feature ranks (based on original order of features)\n", - "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n", - "\n", - "# Note: PFIExplainer does not support per class explanations\n", - "# Per class feature names\n", - "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n", - "# Per class feature importance values\n", - "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print out a dictionary that holds the sorted feature importance names and values\n", - "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Explain overall model predictions as a collection of local (instance-level) explanations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Feature shap values for all features and all data points in the training data\n", - "print('local importance values: {}'.format(global_explanation.local_importance_values))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate local explanations\n", - "Explain local data points (individual instances)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Note: PFIExplainer does not support local explanations\n", - "# You can pass a specific data point or a group of data points to the explain_local function\n", - "\n", - "# E.g., Explain the first data point in the test set\n", - "instance_num = 1\n", - "local_explanation = explainer.explain_local(x_test[:instance_num])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the prediction for the first member of the test set and explain why model made that prediction\n", - "prediction_value = clf.predict(x_test)[instance_num]\n", - "\n", - "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n", - "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n", - "\n", - "print('local importance values: {}'.format(sorted_local_importance_values))\n", - "print('local importance names: {}'.format(sorted_local_importance_names))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Upload\n", - "Upload explanations to Azure Machine Learning Run History" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Experiment\n", - "from azureml.interpret import ExplanationClient\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_name = 'explain-model-sample'\n", - "experiment = Experiment(ws, experiment_name)\n", - "run = experiment.start_logging()\n", - "client = ExplanationClient.from_run(run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uploading model explanation data for storage or visualization in webUX\n", - "# The explanation can then be downloaded on any compute\n", - "# Multiple explanations can be uploaded\n", - "client.upload_model_explanation(global_explanation, comment='global explanation: all features')\n", - "# Or you can only upload the explanation object with the top k feature info\n", - "#client.upload_model_explanation(global_explanation, top_k=2, comment='global explanation: Only top 2 features')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uploading model explanation data for storage or visualization in webUX\n", - "# The explanation can then be downloaded on any compute\n", - "# Multiple explanations can be uploaded\n", - "client.upload_model_explanation(local_explanation, comment='local explanation for test point 1: all features')\n", - "\n", - "# Alterntively, you can only upload the local explanation object with the top k feature info\n", - "#client.upload_model_explanation(local_explanation, top_k=2, comment='local explanation: top 2 features')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download\n", - "Download explanations from Azure Machine Learning Run History" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# List uploaded explanations\n", - "client.list_model_explanations()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for explanation in client.list_model_explanations():\n", - " \n", - " if explanation['comment'] == 'local explanation for test point 1: all features':\n", - " downloaded_local_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n", - " # You can pass a k value to only download the top k feature importance values\n", - " downloaded_local_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n", - " \n", - " \n", - " elif explanation['comment'] == 'global explanation: all features':\n", - " downloaded_global_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n", - " # You can pass a k value to only download the top k feature importance values\n", - " downloaded_global_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize\n", - "Load the visualization dashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from raiwidgets import ExplanationDashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ExplanationDashboard(downloaded_global_explanation, model, dataset=x_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## End\n", - "Complete the run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.complete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next\n", - "Learn about other use cases of the explain package on a:\n", - "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb) \n", - "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n", - "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n", - "1. Explain models with engineered features:\n", - " 1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n", - " 1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n", - "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n", - "1. Inferencing time: deploy a classification model and explainer:\n", - " 1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n", - " 1. [Deploy a locally-trained keras model and explainer](../scoring-time/train-explain-model-keras-locally-and-deploy.ipynb)\n", - " 1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save and retrieve explanations via Azure Machine Learning Run History\n", + "\n", + "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to save and retrieve classification model explanations to/from Azure Machine Learning Run History.**_\n", + "\n", + "\n", + "## Table of Contents\n", + "\n", + "1. [Introduction](#Introduction)\n", + "1. [Setup](#Setup)\n", + "1. [Run model explainer locally at training time](#Explain)\n", + " 1. Apply feature transformations\n", + " 1. Train a binary classification model\n", + " 1. Explain the model on raw features\n", + " 1. Generate global explanations\n", + " 1. Generate local explanations\n", + "1. [Upload model explanations to Azure Machine Learning Run History](#Upload)\n", + "1. [Download model explanations from Azure Machine Learning Run History](#Download)\n", + "1. [Visualize explanations](#Visualize)\n", + "1. [Next steps](#Next)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This notebook showcases how to explain a classification model predictions locally at training time, upload explanations to the Azure Machine Learning's run history, and download previously-uploaded explanations from the Run History.\n", + "It demonstrates the API calls that you need to make to upload/download the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and downloaded explanations.\n", + "\n", + "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n", + "\n", + "\n", + "\n", + "Problem: IBM employee attrition classification with scikit-learn (run model explainer locally and upload explanation to the Azure Machine Learning Run History)\n", + "\n", + "1. Train a SVM classification model using Scikit-learn\n", + "2. Run 'explain-model-sample' with AML Run History, which leverages run history service to store and manage the explanation data\n", + "---\n", + "\n", + "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n", + "If you are using Jupyter Labs run the following command:\n", + "```\n", + "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explain\n", + "\n", + "### Run model explainer locally at training time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", + "from sklearn.svm import SVC\n", + "import pandas as pd\n", + "\n", + "# Explainers:\n", + "# 1. SHAP Tabular Explainer\n", + "from interpret.ext.blackbox import TabularExplainer\n", + "\n", + "# OR\n", + "\n", + "# 2. Mimic Explainer\n", + "from interpret.ext.blackbox import MimicExplainer\n", + "# You can use one of the following four interpretable models as a global surrogate to the black box model\n", + "from interpret.ext.glassbox import LGBMExplainableModel\n", + "from interpret.ext.glassbox import LinearExplainableModel\n", + "from interpret.ext.glassbox import SGDExplainableModel\n", + "from interpret.ext.glassbox import DecisionTreeExplainableModel\n", + "\n", + "# OR\n", + "\n", + "# 3. PFI Explainer\n", + "from interpret.ext.blackbox import PFIExplainer " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the IBM employee attrition data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the IBM employee attrition dataset\n", + "outdirname = 'dataset.6.21.19'\n", + "try:\n", + " from urllib import urlretrieve\n", + "except ImportError:\n", + " from urllib.request import urlretrieve\n", + "import zipfile\n", + "zipfilename = outdirname + '.zip'\n", + "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n", + "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n", + " unzip.extractall('.')\n", + "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')\n", + "\n", + "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n", + "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n", + "# Dropping Employee Number since it is merely an identifier\n", + "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n", + "\n", + "attritionData = attritionData.drop(['Over18'], axis=1)\n", + "\n", + "# Since all values are 80\n", + "attritionData = attritionData.drop(['StandardHours'], axis=1)\n", + "\n", + "# Converting target variables from string to numerical values\n", + "target_map = {'Yes': 1, 'No': 0}\n", + "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n", + "target = attritionData[\"Attrition_numerical\"]\n", + "\n", + "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Split data into train and test\n", + "from sklearn.model_selection import train_test_split\n", + "x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n", + " target, \n", + " test_size=0.2,\n", + " random_state=0,\n", + " stratify=target)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating dummy columns for each categorical feature\n", + "categorical = []\n", + "for col, value in attritionXData.iteritems():\n", + " if value.dtype == 'object':\n", + " categorical.append(col)\n", + " \n", + "# Store the numerical columns in a list numerical\n", + "numerical = attritionXData.columns.difference(categorical) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transform raw features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.compose import ColumnTransformer\n", + "\n", + "# We create the preprocessing pipelines for both numeric and categorical data.\n", + "numeric_transformer = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='median')),\n", + " ('scaler', StandardScaler())])\n", + "\n", + "categorical_transformer = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n", + " ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n", + "\n", + "transformations = ColumnTransformer(\n", + " transformers=[\n", + " ('num', numeric_transformer, numerical),\n", + " ('cat', categorical_transformer, categorical)])\n", + "\n", + "# Append classifier to preprocessing pipeline.\n", + "# Now we have a full prediction pipeline.\n", + "clf = Pipeline(steps=[('preprocessor', transformations),\n", + " ('classifier', SVC(C=1.0, probability=True))])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train a SVM classification model, which you want to explain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = clf.fit(x_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explain predictions on your local machine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 1. Using SHAP TabularExplainer\n", + "# clf.steps[-1][1] returns the trained classification model\n", + "explainer = TabularExplainer(clf.steps[-1][1], \n", + " initialization_examples=x_train, \n", + " features=attritionXData.columns, \n", + " classes=[\"Not leaving\", \"leaving\"], \n", + " transformations=transformations)\n", + "\n", + "\n", + "\n", + "\n", + "# 2. Using MimicExplainer\n", + "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model. Useful for high-dimensional data where the number of rows is less than the number of columns. \n", + "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n", + "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n", + "# explainer = MimicExplainer(clf.steps[-1][1], \n", + "# x_train, \n", + "# LGBMExplainableModel, \n", + "# augment_data=True, \n", + "# max_num_of_augmentations=10, \n", + "# features=attritionXData.columns, \n", + "# classes=[\"Not leaving\", \"leaving\"], \n", + "# transformations=transformations)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# 3. Using PFIExplainer\n", + "\n", + "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n", + "# Note that if a metric function is provided a higher value must be better.\n", + "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n", + "# Default metrics: \n", + "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n", + "# Mean absolute error for regression\n", + "\n", + "# explainer = PFIExplainer(clf.steps[-1][1], \n", + "# features=x_train.columns, \n", + "# transformations=transformations,\n", + "# classes=[\"Not leaving\", \"leaving\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate global explanations\n", + "Explain overall model predictions (global explanation)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n", + "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n", + "global_explanation = explainer.explain_global(x_test)\n", + "\n", + "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n", + "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sorted SHAP values\n", + "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n", + "# Corresponding feature names\n", + "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n", + "# Feature ranks (based on original order of features)\n", + "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n", + "\n", + "# Note: PFIExplainer does not support per class explanations\n", + "# Per class feature names\n", + "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n", + "# Per class feature importance values\n", + "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Print out a dictionary that holds the sorted feature importance names and values\n", + "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explain overall model predictions as a collection of local (instance-level) explanations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Feature shap values for all features and all data points in the training data\n", + "print('local importance values: {}'.format(global_explanation.local_importance_values))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate local explanations\n", + "Explain local data points (individual instances)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Note: PFIExplainer does not support local explanations\n", + "# You can pass a specific data point or a group of data points to the explain_local function\n", + "\n", + "# E.g., Explain the first data point in the test set\n", + "instance_num = 1\n", + "local_explanation = explainer.explain_local(x_test[:instance_num])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the prediction for the first member of the test set and explain why model made that prediction\n", + "prediction_value = clf.predict(x_test)[instance_num]\n", + "\n", + "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n", + "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n", + "\n", + "print('local importance values: {}'.format(sorted_local_importance_values))\n", + "print('local importance names: {}'.format(sorted_local_importance_names))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Upload\n", + "Upload explanations to Azure Machine Learning Run History" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "from azureml.core import Workspace, Experiment\n", + "from azureml.interpret import ExplanationClient\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print('Workspace name: ' + ws.name, \n", + " 'Azure region: ' + ws.location, \n", + " 'Subscription id: ' + ws.subscription_id, \n", + " 'Resource group: ' + ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "experiment_name = 'explain-model-sample'\n", + "experiment = Experiment(ws, experiment_name)\n", + "run = experiment.start_logging()\n", + "client = ExplanationClient.from_run(run)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uploading model explanation data for storage or visualization in webUX\n", + "# The explanation can then be downloaded on any compute\n", + "# Multiple explanations can be uploaded\n", + "client.upload_model_explanation(global_explanation, comment='global explanation: all features')\n", + "# Or you can only upload the explanation object with the top k feature info\n", + "#client.upload_model_explanation(global_explanation, top_k=2, comment='global explanation: Only top 2 features')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uploading model explanation data for storage or visualization in webUX\n", + "# The explanation can then be downloaded on any compute\n", + "# Multiple explanations can be uploaded\n", + "client.upload_model_explanation(local_explanation, comment='local explanation for test point 1: all features')\n", + "\n", + "# Alterntively, you can only upload the local explanation object with the top k feature info\n", + "#client.upload_model_explanation(local_explanation, top_k=2, comment='local explanation: top 2 features')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download\n", + "Download explanations from Azure Machine Learning Run History" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# List uploaded explanations\n", + "client.list_model_explanations()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for explanation in client.list_model_explanations():\n", + " \n", + " if explanation['comment'] == 'local explanation for test point 1: all features':\n", + " downloaded_local_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n", + " # You can pass a k value to only download the top k feature importance values\n", + " downloaded_local_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n", + " \n", + " \n", + " elif explanation['comment'] == 'global explanation: all features':\n", + " downloaded_global_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n", + " # You can pass a k value to only download the top k feature importance values\n", + " downloaded_global_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize\n", + "Load the visualization dashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from raiwidgets import ExplanationDashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ExplanationDashboard(downloaded_global_explanation, model, dataset=x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## End\n", + "Complete the run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.complete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next\n", + "Learn about other use cases of the explain package on a:\n", + "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb) \n", + "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n", + "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n", + "1. Explain models with engineered features:\n", + " 1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n", + " 1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n", + "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n", + "1. Inferencing time: deploy a classification model and explainer:\n", + " 1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n", + " 1. [Deploy a locally-trained keras model and explainer](../scoring-time/train-explain-model-keras-locally-and-deploy.ipynb)\n", + " 1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "authors": [ + { + "name": "mesameki" + } ], - "metadata": { - "authors": [ - { - "name": "mesameki" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb index 121e3fe69..ea9232948 100644 --- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb @@ -1,525 +1,525 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train and explain models locally and deploy model and scoring explainer\n", - "\n", - "\n", - "_**This notebook illustrates how to use the Azure Machine Learning Interpretability SDK to deploy a locally-trained model and its corresponding scoring explainer to Azure Container Instances (ACI) as a web service.**_\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Problem: IBM employee attrition classification with scikit-learn (train and explain a model locally and use Azure Container Instances (ACI) for deploying your model and its corresponding scoring explainer as a web service.)\n", - "\n", - "---\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Run model explainer locally at training time](#Explain)\n", - " 1. Apply feature transformations\n", - " 1. Train a binary classification model\n", - " 1. Explain the model on raw features\n", - " 1. Generate global explanations\n", - " 1. Generate local explanations\n", - "1. [Visualize explanations](#Visualize)\n", - "1. [Deploy model and scoring explainer](#Deploy)\n", - "1. [Next steps](#Next)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "\n", - "This notebook showcases how to train and explain a classification model locally, and deploy the trained model and its corresponding explainer to Azure Container Instances (ACI).\n", - "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations. It also demonstrates how to use Azure Machine Learning MLOps capabilities to deploy your model and its corresponding explainer.\n", - "\n", - "We will showcase one of the tabular data explainers: TabularExplainer (SHAP) and follow these steps:\n", - "1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n", - "2.\tRun the script locally.\n", - "3.\tUse the interpretability toolkit\u00e2\u20ac\u2122s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n", - "5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n", - "6.\tDevelop a scoring script.\n", - "7.\tCreate an image and register it in the image registry.\n", - "8.\tDeploy the image as a web service in Azure.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "Make sure you go through the [configuration notebook](../../../../configuration.ipynb) first if you haven't." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize a Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explain\n", - "Create An Experiment: **Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "experiment_name = 'explain_model_at_scoring_time'\n", - "experiment = Experiment(workspace=ws, name=experiment_name)\n", - "run = experiment.start_logging()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get IBM attrition data\n", - "import os\n", - "import pandas as pd\n", - "\n", - "outdirname = 'dataset.6.21.19'\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "import zipfile\n", - "zipfilename = outdirname + '.zip'\n", - "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n", - "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n", - " unzip.extractall('.')\n", - "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "import joblib\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "from interpret.ext.blackbox import TabularExplainer\n", - "\n", - "os.makedirs('./outputs', exist_ok=True)\n", - "\n", - "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n", - "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n", - "# Dropping Employee Number since it is merely an identifier\n", - "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n", - "attritionData = attritionData.drop(['Over18'], axis=1)\n", - "# Since all values are 80\n", - "attritionData = attritionData.drop(['StandardHours'], axis=1)\n", - "\n", - "# Converting target variables from string to numerical values\n", - "target_map = {'Yes': 1, 'No': 0}\n", - "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n", - "target = attritionData[\"Attrition_numerical\"]\n", - "\n", - "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)\n", - "\n", - "# Creating dummy columns for each categorical feature\n", - "categorical = []\n", - "for col, value in attritionXData.iteritems():\n", - " if value.dtype == 'object':\n", - " categorical.append(col)\n", - "\n", - "# Store the numerical columns in a list numerical\n", - "numerical = attritionXData.columns.difference(categorical)\n", - "\n", - "# We create the preprocessing pipelines for both numeric and categorical data.\n", - "numeric_transformer = Pipeline(steps=[\n", - " ('imputer', SimpleImputer(strategy='median')),\n", - " ('scaler', StandardScaler())])\n", - "\n", - "categorical_transformer = Pipeline(steps=[\n", - " ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n", - " ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n", - "\n", - "transformations = ColumnTransformer(\n", - " transformers=[\n", - " ('num', numeric_transformer, numerical),\n", - " ('cat', categorical_transformer, categorical)])\n", - "\n", - "# Append classifier to preprocessing pipeline.\n", - "# Now we have a full prediction pipeline.\n", - "clf = Pipeline(steps=[('preprocessor', transformations),\n", - " ('classifier', RandomForestClassifier())])\n", - "\n", - "# Split data into train and test\n", - "x_train, x_test, y_train, y_test = train_test_split(attritionXData,\n", - " target,\n", - " test_size=0.2,\n", - " random_state=0,\n", - " stratify=target)\n", - "\n", - "# Preprocess the data and fit the classification model\n", - "clf.fit(x_train, y_train)\n", - "model = clf.steps[-1][1]\n", - "\n", - "model_file_name = 'log_reg.pkl'\n", - "\n", - "# Save model in the outputs folder so it automatically get uploaded\n", - "with open(model_file_name, 'wb') as file:\n", - " joblib.dump(value=clf, filename=os.path.join('./outputs/',\n", - " model_file_name))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Explain predictions on your local machine\n", - "tabular_explainer = TabularExplainer(model, \n", - " initialization_examples=x_train, \n", - " features=attritionXData.columns, \n", - " classes=[\"Not leaving\", \"leaving\"], \n", - " transformations=transformations)\n", - "\n", - "# Explain overall model predictions (global explanation)\n", - "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n", - "# x_train can be passed as well, but with more examples explanations it will\n", - "# take longer although they may be more accurate\n", - "global_explanation = tabular_explainer.explain_global(x_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer, save\n", - "# ScoringExplainer\n", - "scoring_explainer = TreeScoringExplainer(tabular_explainer)\n", - "# Pickle scoring explainer locally\n", - "save(scoring_explainer, exist_ok=True)\n", - "\n", - "# Register original model\n", - "run.upload_file('original_model.pkl', os.path.join('./outputs/', model_file_name))\n", - "original_model = run.register_model(model_name='local_deploy_model', \n", - " model_path='original_model.pkl')\n", - "\n", - "# Register scoring explainer\n", - "run.upload_file('IBM_attrition_explainer.pkl', 'scoring_explainer.pkl')\n", - "scoring_explainer_model = run.register_model(model_name='IBM_attrition_explainer', model_path='IBM_attrition_explainer.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize\n", - "Visualize the explanations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from raiwidgets import ExplanationDashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ExplanationDashboard(global_explanation, clf, dataset=x_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy \n", - "\n", - "Deploy Model and ScoringExplainer.\n", - "\n", - "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.conda_dependencies import CondaDependencies \n", - "import sys\n", - "\n", - "# azureml-defaults is required to host the model as a web service.\n", - "azureml_pip_packages = [\n", - " 'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n", - " 'azureml-interpret'\n", - "]\n", - "\n", - "python_version = '{0}.{1}'.format(sys.version_info[0], sys.version_info[1])\n", - "\n", - "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n", - "# In production scenario user would choose their dependencies\n", - "import pkg_resources\n", - "available_packages = pkg_resources.working_set\n", - "sklearn_ver = None\n", - "pandas_ver = None\n", - "for dist in available_packages:\n", - " if dist.key == 'scikit-learn':\n", - " sklearn_ver = dist.version\n", - " elif dist.key == 'pandas':\n", - " pandas_ver = dist.version\n", - "sklearn_dep = 'scikit-learn'\n", - "pandas_dep = 'pandas'\n", - "if sklearn_ver:\n", - " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", - "if pandas_ver:\n", - " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", - "# Specify CondaDependencies obj\n", - "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", - "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", - "# environment, otherwise if a model is trained or deployed in a different environment this can\n", - "# cause errors. Please take extra care when specifying your dependencies in a production environment.\n", - "myenv = CondaDependencies.create(\n", - " python_version=python_version,\n", - " pip_packages=['pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages)\n", - "\n", - "with open(\"myenv.yml\",\"w\") as f:\n", - " f.write(myenv.serialize_to_string())\n", - "\n", - "with open(\"myenv.yml\",\"r\") as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "# Retrieve scoring explainer for deployment\n", - "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.webservice import AciWebservice\n", - "from azureml.core.environment import Environment\n", - "from azureml.exceptions import WebserviceException\n", - "\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n", - " memory_gb=2, \n", - " tags={\"data\": \"IBM_Attrition\", \n", - " \"method\" : \"local_explanation\"}, \n", - " description='Get local explanations for IBM Employee Attrition data')\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score_local_explain.py\", environment=myenv)\n", - "\n", - "# Use configs and models generated above\n", - "service = Model.deploy(ws, 'model-scoring-deploy-local', [scoring_explainer_model, original_model], inference_config, aciconfig)\n", - "try:\n", - " service.wait_for_deployment(show_output=True)\n", - "except WebserviceException as e:\n", - " print(e.message)\n", - " print(service.get_logs())\n", - " raise" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from raiutils.webservice import post_with_retries\n", - "\n", - "\n", - "# Create data to test service with\n", - "sample_data = '{\"Age\":{\"899\":49},\"BusinessTravel\":{\"899\":\"Travel_Rarely\"},\"DailyRate\":{\"899\":1098},\"Department\":{\"899\":\"Research & Development\"},\"DistanceFromHome\":{\"899\":4},\"Education\":{\"899\":2},\"EducationField\":{\"899\":\"Medical\"},\"EnvironmentSatisfaction\":{\"899\":1},\"Gender\":{\"899\":\"Male\"},\"HourlyRate\":{\"899\":85},\"JobInvolvement\":{\"899\":2},\"JobLevel\":{\"899\":5},\"JobRole\":{\"899\":\"Manager\"},\"JobSatisfaction\":{\"899\":3},\"MaritalStatus\":{\"899\":\"Married\"},\"MonthlyIncome\":{\"899\":18711},\"MonthlyRate\":{\"899\":12124},\"NumCompaniesWorked\":{\"899\":2},\"OverTime\":{\"899\":\"No\"},\"PercentSalaryHike\":{\"899\":13},\"PerformanceRating\":{\"899\":3},\"RelationshipSatisfaction\":{\"899\":3},\"StockOptionLevel\":{\"899\":1},\"TotalWorkingYears\":{\"899\":23},\"TrainingTimesLastYear\":{\"899\":2},\"WorkLifeBalance\":{\"899\":4},\"YearsAtCompany\":{\"899\":1},\"YearsInCurrentRole\":{\"899\":0},\"YearsSinceLastPromotion\":{\"899\":0},\"YearsWithCurrManager\":{\"899\":0}}'\n", - "\n", - "\n", - "\n", - "headers = {'Content-Type':'application/json'}\n", - "\n", - "# Send request to service\n", - "print(\"POST to url\", service.scoring_uri)\n", - "resp = post_with_retries(service.scoring_uri, sample_data, headers)\n", - "\n", - "# Can covert back to Python objects from json string if desired\n", - "print(\"prediction:\", resp.text)\n", - "result = json.loads(resp.text)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the feature importance for the prediction\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt; plt.rcdefaults()\n", - "\n", - "labels = json.loads(sample_data)\n", - "labels = labels.keys()\n", - "objects = labels\n", - "y_pos = np.arange(len(objects))\n", - "performance = result[\"local_importance_values\"][0][0]\n", - "\n", - "plt.bar(y_pos, performance, align='center', alpha=0.5)\n", - "plt.xticks(y_pos, objects)\n", - "locs, labels = plt.xticks()\n", - "plt.setp(labels, rotation=90)\n", - "plt.ylabel('Feature impact - leaving vs not leaving')\n", - "plt.title('Local feature importance for prediction')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next\n", - "Learn about other use cases of the explain package on a:\n", - "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb) \n", - "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n", - "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n", - "1. Explain models with engineered features:\n", - " 1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n", - " 1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n", - "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n", - "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n", - "1. [Inferencing time: deploy a remotely-trained model and explainer](./train-explain-model-on-amlcompute-and-deploy.ipynb)\n", - "1. [Inferencing time: deploy a locally-trained keras model and explainer](./train-explain-model-keras-locally-and-deploy.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train and explain models locally and deploy model and scoring explainer\n", + "\n", + "\n", + "_**This notebook illustrates how to use the Azure Machine Learning Interpretability SDK to deploy a locally-trained model and its corresponding scoring explainer to Azure Container Instances (ACI) as a web service.**_\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Problem: IBM employee attrition classification with scikit-learn (train and explain a model locally and use Azure Container Instances (ACI) for deploying your model and its corresponding scoring explainer as a web service.)\n", + "\n", + "---\n", + "\n", + "## Table of Contents\n", + "\n", + "1. [Introduction](#Introduction)\n", + "1. [Setup](#Setup)\n", + "1. [Run model explainer locally at training time](#Explain)\n", + " 1. Apply feature transformations\n", + " 1. Train a binary classification model\n", + " 1. Explain the model on raw features\n", + " 1. Generate global explanations\n", + " 1. Generate local explanations\n", + "1. [Visualize explanations](#Visualize)\n", + "1. [Deploy model and scoring explainer](#Deploy)\n", + "1. [Next steps](#Next)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "\n", + "This notebook showcases how to train and explain a classification model locally, and deploy the trained model and its corresponding explainer to Azure Container Instances (ACI).\n", + "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations. It also demonstrates how to use Azure Machine Learning MLOps capabilities to deploy your model and its corresponding explainer.\n", + "\n", + "We will showcase one of the tabular data explainers: TabularExplainer (SHAP) and follow these steps:\n", + "1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n", + "2.\tRun the script locally.\n", + "3.\tUse the interpretability toolkit’s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n", + "5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n", + "6.\tDevelop a scoring script.\n", + "7.\tCreate an image and register it in the image registry.\n", + "8.\tDeploy the image as a web service in Azure.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "Make sure you go through the [configuration notebook](../../../../configuration.ipynb) first if you haven't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize a Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explain\n", + "Create An Experiment: **Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "experiment_name = 'explain_model_at_scoring_time'\n", + "experiment = Experiment(workspace=ws, name=experiment_name)\n", + "run = experiment.start_logging()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get IBM attrition data\n", + "import os\n", + "import pandas as pd\n", + "\n", + "outdirname = 'dataset.6.21.19'\n", + "try:\n", + " from urllib import urlretrieve\n", + "except ImportError:\n", + " from urllib.request import urlretrieve\n", + "import zipfile\n", + "zipfilename = outdirname + '.zip'\n", + "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n", + "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n", + " unzip.extractall('.')\n", + "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "import joblib\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "from interpret.ext.blackbox import TabularExplainer\n", + "\n", + "os.makedirs('./outputs', exist_ok=True)\n", + "\n", + "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n", + "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n", + "# Dropping Employee Number since it is merely an identifier\n", + "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n", + "attritionData = attritionData.drop(['Over18'], axis=1)\n", + "# Since all values are 80\n", + "attritionData = attritionData.drop(['StandardHours'], axis=1)\n", + "\n", + "# Converting target variables from string to numerical values\n", + "target_map = {'Yes': 1, 'No': 0}\n", + "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n", + "target = attritionData[\"Attrition_numerical\"]\n", + "\n", + "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)\n", + "\n", + "# Creating dummy columns for each categorical feature\n", + "categorical = []\n", + "for col, value in attritionXData.iteritems():\n", + " if value.dtype == 'object':\n", + " categorical.append(col)\n", + "\n", + "# Store the numerical columns in a list numerical\n", + "numerical = attritionXData.columns.difference(categorical)\n", + "\n", + "# We create the preprocessing pipelines for both numeric and categorical data.\n", + "numeric_transformer = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='median')),\n", + " ('scaler', StandardScaler())])\n", + "\n", + "categorical_transformer = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n", + " ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n", + "\n", + "transformations = ColumnTransformer(\n", + " transformers=[\n", + " ('num', numeric_transformer, numerical),\n", + " ('cat', categorical_transformer, categorical)])\n", + "\n", + "# Append classifier to preprocessing pipeline.\n", + "# Now we have a full prediction pipeline.\n", + "clf = Pipeline(steps=[('preprocessor', transformations),\n", + " ('classifier', RandomForestClassifier())])\n", + "\n", + "# Split data into train and test\n", + "x_train, x_test, y_train, y_test = train_test_split(attritionXData,\n", + " target,\n", + " test_size=0.2,\n", + " random_state=0,\n", + " stratify=target)\n", + "\n", + "# Preprocess the data and fit the classification model\n", + "clf.fit(x_train, y_train)\n", + "model = clf.steps[-1][1]\n", + "\n", + "model_file_name = 'log_reg.pkl'\n", + "\n", + "# Save model in the outputs folder so it automatically get uploaded\n", + "with open(model_file_name, 'wb') as file:\n", + " joblib.dump(value=clf, filename=os.path.join('./outputs/',\n", + " model_file_name))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Explain predictions on your local machine\n", + "tabular_explainer = TabularExplainer(model, \n", + " initialization_examples=x_train, \n", + " features=attritionXData.columns, \n", + " classes=[\"Not leaving\", \"leaving\"], \n", + " transformations=transformations)\n", + "\n", + "# Explain overall model predictions (global explanation)\n", + "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n", + "# x_train can be passed as well, but with more examples explanations it will\n", + "# take longer although they may be more accurate\n", + "global_explanation = tabular_explainer.explain_global(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer, save\n", + "# ScoringExplainer\n", + "scoring_explainer = TreeScoringExplainer(tabular_explainer)\n", + "# Pickle scoring explainer locally\n", + "save(scoring_explainer, exist_ok=True)\n", + "\n", + "# Register original model\n", + "run.upload_file('original_model.pkl', os.path.join('./outputs/', model_file_name))\n", + "original_model = run.register_model(model_name='local_deploy_model', \n", + " model_path='original_model.pkl')\n", + "\n", + "# Register scoring explainer\n", + "run.upload_file('IBM_attrition_explainer.pkl', 'scoring_explainer.pkl')\n", + "scoring_explainer_model = run.register_model(model_name='IBM_attrition_explainer', model_path='IBM_attrition_explainer.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize\n", + "Visualize the explanations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from raiwidgets import ExplanationDashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ExplanationDashboard(global_explanation, clf, dataset=x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy \n", + "\n", + "Deploy Model and ScoringExplainer.\n", + "\n", + "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.conda_dependencies import CondaDependencies \n", + "import sys\n", + "\n", + "# azureml-defaults is required to host the model as a web service.\n", + "azureml_pip_packages = [\n", + " 'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n", + " 'azureml-interpret'\n", + "]\n", + "\n", + "python_version = '{0}.{1}'.format(sys.version_info[0], sys.version_info[1])\n", + "\n", + "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n", + "# In production scenario user would choose their dependencies\n", + "import pkg_resources\n", + "available_packages = pkg_resources.working_set\n", + "sklearn_ver = None\n", + "pandas_ver = None\n", + "for dist in available_packages:\n", + " if dist.key == 'scikit-learn':\n", + " sklearn_ver = dist.version\n", + " elif dist.key == 'pandas':\n", + " pandas_ver = dist.version\n", + "sklearn_dep = 'scikit-learn'\n", + "pandas_dep = 'pandas'\n", + "if sklearn_ver:\n", + " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", + "if pandas_ver:\n", + " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", + "# Specify CondaDependencies obj\n", + "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", + "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", + "# environment, otherwise if a model is trained or deployed in a different environment this can\n", + "# cause errors. Please take extra care when specifying your dependencies in a production environment.\n", + "myenv = CondaDependencies.create(\n", + " python_version=python_version,\n", + " pip_packages=['pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages)\n", + "\n", + "with open(\"myenv.yml\",\"w\") as f:\n", + " f.write(myenv.serialize_to_string())\n", + "\n", + "with open(\"myenv.yml\",\"r\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "# Retrieve scoring explainer for deployment\n", + "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "from azureml.core.webservice import AciWebservice\n", + "from azureml.core.environment import Environment\n", + "from azureml.exceptions import WebserviceException\n", + "\n", + "\n", + "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n", + " memory_gb=2, \n", + " tags={\"data\": \"IBM_Attrition\", \n", + " \"method\" : \"local_explanation\"}, \n", + " description='Get local explanations for IBM Employee Attrition data')\n", + "\n", + "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", + "inference_config = InferenceConfig(entry_script=\"score_local_explain.py\", environment=myenv)\n", + "\n", + "# Use configs and models generated above\n", + "service = Model.deploy(ws, 'model-scoring-deploy-local', [scoring_explainer_model, original_model], inference_config, aciconfig)\n", + "try:\n", + " service.wait_for_deployment(show_output=True)\n", + "except WebserviceException as e:\n", + " print(e.message)\n", + " print(service.get_logs())\n", + " raise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from raiutils.webservice import post_with_retries\n", + "\n", + "\n", + "# Create data to test service with\n", + "sample_data = '{\"Age\":{\"899\":49},\"BusinessTravel\":{\"899\":\"Travel_Rarely\"},\"DailyRate\":{\"899\":1098},\"Department\":{\"899\":\"Research & Development\"},\"DistanceFromHome\":{\"899\":4},\"Education\":{\"899\":2},\"EducationField\":{\"899\":\"Medical\"},\"EnvironmentSatisfaction\":{\"899\":1},\"Gender\":{\"899\":\"Male\"},\"HourlyRate\":{\"899\":85},\"JobInvolvement\":{\"899\":2},\"JobLevel\":{\"899\":5},\"JobRole\":{\"899\":\"Manager\"},\"JobSatisfaction\":{\"899\":3},\"MaritalStatus\":{\"899\":\"Married\"},\"MonthlyIncome\":{\"899\":18711},\"MonthlyRate\":{\"899\":12124},\"NumCompaniesWorked\":{\"899\":2},\"OverTime\":{\"899\":\"No\"},\"PercentSalaryHike\":{\"899\":13},\"PerformanceRating\":{\"899\":3},\"RelationshipSatisfaction\":{\"899\":3},\"StockOptionLevel\":{\"899\":1},\"TotalWorkingYears\":{\"899\":23},\"TrainingTimesLastYear\":{\"899\":2},\"WorkLifeBalance\":{\"899\":4},\"YearsAtCompany\":{\"899\":1},\"YearsInCurrentRole\":{\"899\":0},\"YearsSinceLastPromotion\":{\"899\":0},\"YearsWithCurrManager\":{\"899\":0}}'\n", + "\n", + "\n", + "\n", + "headers = {'Content-Type':'application/json'}\n", + "\n", + "# Send request to service\n", + "print(\"POST to url\", service.scoring_uri)\n", + "resp = post_with_retries(service.scoring_uri, sample_data, headers)\n", + "\n", + "# Can covert back to Python objects from json string if desired\n", + "print(\"prediction:\", resp.text)\n", + "result = json.loads(resp.text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the feature importance for the prediction\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt; plt.rcdefaults()\n", + "\n", + "labels = json.loads(sample_data)\n", + "labels = labels.keys()\n", + "objects = labels\n", + "y_pos = np.arange(len(objects))\n", + "performance = result[\"local_importance_values\"][0][0]\n", + "\n", + "plt.bar(y_pos, performance, align='center', alpha=0.5)\n", + "plt.xticks(y_pos, objects)\n", + "locs, labels = plt.xticks()\n", + "plt.setp(labels, rotation=90)\n", + "plt.ylabel('Feature impact - leaving vs not leaving')\n", + "plt.title('Local feature importance for prediction')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next\n", + "Learn about other use cases of the explain package on a:\n", + "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb) \n", + "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n", + "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n", + "1. Explain models with engineered features:\n", + " 1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n", + " 1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n", + "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n", + "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n", + "1. [Inferencing time: deploy a remotely-trained model and explainer](./train-explain-model-on-amlcompute-and-deploy.ipynb)\n", + "1. [Inferencing time: deploy a locally-trained keras model and explainer](./train-explain-model-keras-locally-and-deploy.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "authors": [ + { + "name": "mesameki" + } ], - "metadata": { - "authors": [ - { - "name": "mesameki" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb index 37c1a0345..42b0edd66 100644 --- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb @@ -1,586 +1,586 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train and explain models remotely via Azure Machine Learning Compute and deploy model and scoring explainer\n", - "\n", - "\n", - "_**This notebook illustrates how to use the Azure Machine Learning Interpretability SDK to train and explain a classification model remotely on an Azure Machine Leanrning Compute Target (AMLCompute), and use Azure Container Instances (ACI) for deploying your model and its corresponding scoring explainer as a web service.**_\n", - "\n", - "Problem: IBM employee attrition classification with scikit-learn (train a model and run an explainer remotely via AMLCompute, and deploy model and its corresponding explainer.)\n", - "\n", - "---\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Run model explainer locally at training time](#Explain)\n", - " 1. Apply feature transformations\n", - " 1. Train a binary classification model\n", - " 1. Explain the model on raw features\n", - " 1. Generate global explanations\n", - " 1. Generate local explanations\n", - "1. [Visualize results](#Visualize)\n", - "1. [Deploy model and scoring explainer](#Deploy)\n", - "1. [Next steps](#Next)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "This notebook showcases how to train and explain a classification model remotely via Azure Machine Learning Compute (AMLCompute), download the calculated explanations locally for visualization and inspection, and deploy the final model and its corresponding explainer to Azure Container Instances (ACI).\n", - "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations, and using Azure Machine Learning MLOps capabilities to deploy your model and its corresponding explainer.\n", - "\n", - "We will showcase one of the tabular data explainers: TabularExplainer (SHAP) and follow these steps:\n", - "1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n", - "2.\tCreate and configure a compute target.\n", - "3.\tSubmit the scripts to the configured compute target to run in that environment. During training, the scripts can read from or write to datastore. And the records of execution (e.g., model, metrics, prediction explanations) are saved as runs in the workspace and grouped under experiments.\n", - "4.\tQuery the experiment for logged metrics and explanations from the current and past runs. Use the interpretability toolkit\u00e2\u20ac\u2122s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n", - "5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n", - "6.\tDevelop a scoring script.\n", - "7.\tCreate an image and register it in the image registry.\n", - "8.\tDeploy the image as a web service in Azure.\n", - "\n", - "| ![azure-machine-learning-cycle](./img/azure-machine-learning-cycle.png) |\n", - "|:--:|" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "Make sure you go through the [configuration notebook](../../../../configuration.ipynb) first if you haven't." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize a Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explain\n", - "\n", - "Create An Experiment: **Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "experiment_name = 'explainer-remote-run-on-amlcompute'\n", - "experiment = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction to AmlCompute\n", - "\n", - "Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n", - "\n", - "Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n", - "\n", - "For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n", - "\n", - "If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n", - "\n", - "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n", - "\n", - "\n", - "The training script `run_explainer.py` is already created for you. Let's have a look." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit an AmlCompute run\n", - "\n", - "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n", - "\n", - "You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "\n", - "AmlCompute.supported_vmsizes(workspace=ws)\n", - "# AmlCompute.supported_vmsizes(workspace=ws, location='southcentralus')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create project directory\n", - "\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import shutil\n", - "\n", - "project_folder = './explainer-remote-run-on-amlcompute'\n", - "os.makedirs(project_folder, exist_ok=True)\n", - "shutil.copy('train_explain.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Provision a compute target\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n", - "\n", - "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n", - "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "cpu_cluster_name = \"cpu-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", - " max_nodes=4)\n", - " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - "\n", - "cpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure & Run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n", - "import sys\n", - "\n", - "# Create a new runconfig object\n", - "run_config = RunConfiguration()\n", - "\n", - "# Set compute target to AmlCompute target created in previous step\n", - "run_config.target = cpu_cluster.name\n", - "\n", - "# Set Docker base image to the default CPU-based image\n", - "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n", - "\n", - "# Use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", - "run_config.environment.python.user_managed_dependencies = False\n", - "\n", - "azureml_pip_packages = [\n", - " 'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n", - "]\n", - " \n", - "python_version = '{0}.{1}'.format(sys.version_info[0], sys.version_info[1])\n", - "\n", - "# Note: this is to pin the scikit-learn version to be same as notebook.\n", - "# In production scenario user would choose their dependencies\n", - "import pkg_resources\n", - "available_packages = pkg_resources.working_set\n", - "sklearn_ver = None\n", - "pandas_ver = None\n", - "for dist in available_packages:\n", - " if dist.key == 'scikit-learn':\n", - " sklearn_ver = dist.version\n", - " elif dist.key == 'pandas':\n", - " pandas_ver = dist.version\n", - "sklearn_dep = 'scikit-learn'\n", - "pandas_dep = 'pandas'\n", - "if sklearn_ver:\n", - " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", - "if pandas_ver:\n", - " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", - "# Specify CondaDependencies obj\n", - "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", - "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", - "# environment, otherwise if a model is trained or deployed in a different environment this can\n", - "# cause errors. Please take extra care when specifying your dependencies in a production environment.\n", - "azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n", - "run_config.environment.python.conda_dependencies = CondaDependencies.create(\n", - " python_version=python_version,\n", - " pip_packages=azureml_pip_packages)\n", - "\n", - "# Now submit a run on AmlCompute\n", - "from azureml.core.script_run_config import ScriptRunConfig\n", - "\n", - "script_run_config = ScriptRunConfig(source_directory=project_folder,\n", - " script='train_explain.py',\n", - " run_config=run_config)\n", - "\n", - "run = experiment.submit(script_run_config)\n", - "\n", - "# Show run details\n", - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "# Shows output of the run on stdout.\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n", - "# 'cpucluster' in this case but use a different VM family for instance.\n", - "\n", - "# cpu_cluster.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download Model Explanation, Model, and Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve model for visualization and deployment\n", - "from azureml.core.model import Model\n", - "import joblib\n", - "original_model = Model(ws, 'amlcompute_deploy_model')\n", - "model_path = original_model.download(exist_ok=True)\n", - "original_svm_model = joblib.load(model_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve global explanation for visualization\n", - "from azureml.interpret import ExplanationClient\n", - "\n", - "# get model explanation data\n", - "client = ExplanationClient.from_run(run)\n", - "global_explanation = client.download_model_explanation()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve x_test for visualization\n", - "x_test_path = './x_test.pkl'\n", - "run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n", - "x_test = joblib.load(x_test_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize\n", - "Visualize the explanations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from raiwidgets import ExplanationDashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ExplanationDashboard(global_explanation, original_svm_model, dataset=x_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy\n", - "Deploy Model and ScoringExplainer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n", - "azureml_pip_packages = [\n", - " 'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n", - " 'azureml-interpret'\n", - "]\n", - " \n", - "\n", - "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n", - "# In production scenario user would choose their dependencies\n", - "available_packages = pkg_resources.working_set\n", - "sklearn_ver = None\n", - "pandas_ver = None\n", - "for dist in available_packages:\n", - " if dist.key == 'scikit-learn':\n", - " sklearn_ver = dist.version\n", - " elif dist.key == 'pandas':\n", - " pandas_ver = dist.version\n", - "sklearn_dep = 'scikit-learn'\n", - "pandas_dep = 'pandas'\n", - "if sklearn_ver:\n", - " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", - "if pandas_ver:\n", - " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", - "# Specify CondaDependencies obj\n", - "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", - "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", - "# environment, otherwise if a model is trained or deployed in a different environment this can\n", - "# cause errors. Please take extra care when specifying your dependencies in a production environment.\n", - "azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n", - "myenv = CondaDependencies.create(python_version=python_version, pip_packages=azureml_pip_packages)\n", - "\n", - "with open(\"myenv.yml\",\"w\") as f:\n", - " f.write(myenv.serialize_to_string())\n", - "\n", - "with open(\"myenv.yml\",\"r\") as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve scoring explainer for deployment\n", - "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.webservice import AciWebservice\n", - "from azureml.core.environment import Environment\n", - "from azureml.exceptions import WebserviceException\n", - "\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n", - " memory_gb=1, \n", - " tags={\"data\": \"IBM_Attrition\", \n", - " \"method\" : \"local_explanation\"}, \n", - " description='Get local explanations for IBM Employee Attrition data')\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score_remote_explain.py\", environment=myenv)\n", - "\n", - "# Use configs and models generated above\n", - "service = Model.deploy(ws, 'model-scoring-service', [scoring_explainer_model, original_model], inference_config, aciconfig)\n", - "try:\n", - " service.wait_for_deployment(show_output=True)\n", - "except WebserviceException as e:\n", - " print(e.message)\n", - " print(service.get_logs())\n", - " raise" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from raiutils.webservice import post_with_retries\n", - "\n", - "# Create data to test service with\n", - "examples = x_test[:4]\n", - "input_data = examples.to_json()\n", - "\n", - "headers = {'Content-Type':'application/json'}\n", - "\n", - "# Send request to service\n", - "print(\"POST to url\", service.scoring_uri)\n", - "resp = post_with_retries(service.scoring_uri, input_data, headers)\n", - "\n", - "# Can covert back to Python objects from json string if desired\n", - "print(\"prediction:\", resp.text)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next\n", - "Learn about other use cases of the explain package on a:\n", - "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb) \n", - "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n", - "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n", - "1. Explain models with engineered features:\n", - " 1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n", - " 1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n", - "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n", - "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n", - "1. [Inferencing time: deploy a locally-trained model and explainer](./train-explain-model-locally-and-deploy.ipynb)\n", - "1. [Inferencing time: deploy a locally-trained keras model and explainer](./train-explain-model-keras-locally-and-deploy.ipynb)" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train and explain models remotely via Azure Machine Learning Compute and deploy model and scoring explainer\n", + "\n", + "\n", + "_**This notebook illustrates how to use the Azure Machine Learning Interpretability SDK to train and explain a classification model remotely on an Azure Machine Leanrning Compute Target (AMLCompute), and use Azure Container Instances (ACI) for deploying your model and its corresponding scoring explainer as a web service.**_\n", + "\n", + "Problem: IBM employee attrition classification with scikit-learn (train a model and run an explainer remotely via AMLCompute, and deploy model and its corresponding explainer.)\n", + "\n", + "---\n", + "\n", + "## Table of Contents\n", + "\n", + "1. [Introduction](#Introduction)\n", + "1. [Setup](#Setup)\n", + "1. [Run model explainer locally at training time](#Explain)\n", + " 1. Apply feature transformations\n", + " 1. Train a binary classification model\n", + " 1. Explain the model on raw features\n", + " 1. Generate global explanations\n", + " 1. Generate local explanations\n", + "1. [Visualize results](#Visualize)\n", + "1. [Deploy model and scoring explainer](#Deploy)\n", + "1. [Next steps](#Next)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This notebook showcases how to train and explain a classification model remotely via Azure Machine Learning Compute (AMLCompute), download the calculated explanations locally for visualization and inspection, and deploy the final model and its corresponding explainer to Azure Container Instances (ACI).\n", + "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations, and using Azure Machine Learning MLOps capabilities to deploy your model and its corresponding explainer.\n", + "\n", + "We will showcase one of the tabular data explainers: TabularExplainer (SHAP) and follow these steps:\n", + "1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n", + "2.\tCreate and configure a compute target.\n", + "3.\tSubmit the scripts to the configured compute target to run in that environment. During training, the scripts can read from or write to datastore. And the records of execution (e.g., model, metrics, prediction explanations) are saved as runs in the workspace and grouped under experiments.\n", + "4.\tQuery the experiment for logged metrics and explanations from the current and past runs. Use the interpretability toolkit’s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n", + "5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n", + "6.\tDevelop a scoring script.\n", + "7.\tCreate an image and register it in the image registry.\n", + "8.\tDeploy the image as a web service in Azure.\n", + "\n", + "| ![azure-machine-learning-cycle](./img/azure-machine-learning-cycle.png) |\n", + "|:--:|" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "Make sure you go through the [configuration notebook](../../../../configuration.ipynb) first if you haven't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize a Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explain\n", + "\n", + "Create An Experiment: **Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "experiment_name = 'explainer-remote-run-on-amlcompute'\n", + "experiment = Experiment(workspace=ws, name=experiment_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to AmlCompute\n", + "\n", + "Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n", + "\n", + "Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n", + "\n", + "For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n", + "\n", + "If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n", + "\n", + "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n", + "\n", + "\n", + "The training script `run_explainer.py` is already created for you. Let's have a look." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submit an AmlCompute run\n", + "\n", + "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n", + "\n", + "You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "\n", + "AmlCompute.supported_vmsizes(workspace=ws)\n", + "# AmlCompute.supported_vmsizes(workspace=ws, location='southcentralus')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create project directory\n", + "\n", + "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "\n", + "project_folder = './explainer-remote-run-on-amlcompute'\n", + "os.makedirs(project_folder, exist_ok=True)\n", + "shutil.copy('train_explain.py', project_folder)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Provision a compute target\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\n", + "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n", + "\n", + "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n", + "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your CPU cluster\n", + "cpu_cluster_name = \"cpu-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", + " print('Found existing cluster, use it.')\n", + "except ComputeTargetException:\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", + " max_nodes=4)\n", + " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", + "\n", + "cpu_cluster.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure & Run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.runconfig import RunConfiguration\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n", + "import sys\n", + "\n", + "# Create a new runconfig object\n", + "run_config = RunConfiguration()\n", + "\n", + "# Set compute target to AmlCompute target created in previous step\n", + "run_config.target = cpu_cluster.name\n", + "\n", + "# Set Docker base image to the default CPU-based image\n", + "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n", + "\n", + "# Use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", + "run_config.environment.python.user_managed_dependencies = False\n", + "\n", + "azureml_pip_packages = [\n", + " 'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n", + "]\n", + " \n", + "python_version = '{0}.{1}'.format(sys.version_info[0], sys.version_info[1])\n", + "\n", + "# Note: this is to pin the scikit-learn version to be same as notebook.\n", + "# In production scenario user would choose their dependencies\n", + "import pkg_resources\n", + "available_packages = pkg_resources.working_set\n", + "sklearn_ver = None\n", + "pandas_ver = None\n", + "for dist in available_packages:\n", + " if dist.key == 'scikit-learn':\n", + " sklearn_ver = dist.version\n", + " elif dist.key == 'pandas':\n", + " pandas_ver = dist.version\n", + "sklearn_dep = 'scikit-learn'\n", + "pandas_dep = 'pandas'\n", + "if sklearn_ver:\n", + " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", + "if pandas_ver:\n", + " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", + "# Specify CondaDependencies obj\n", + "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", + "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", + "# environment, otherwise if a model is trained or deployed in a different environment this can\n", + "# cause errors. Please take extra care when specifying your dependencies in a production environment.\n", + "azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n", + "run_config.environment.python.conda_dependencies = CondaDependencies.create(\n", + " python_version=python_version,\n", + " pip_packages=azureml_pip_packages)\n", + "\n", + "# Now submit a run on AmlCompute\n", + "from azureml.core.script_run_config import ScriptRunConfig\n", + "\n", + "script_run_config = ScriptRunConfig(source_directory=project_folder,\n", + " script='train_explain.py',\n", + " run_config=run_config)\n", + "\n", + "run = experiment.submit(script_run_config)\n", + "\n", + "# Show run details\n", + "run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# Shows output of the run on stdout.\n", + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n", + "# 'cpucluster' in this case but use a different VM family for instance.\n", + "\n", + "# cpu_cluster.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download Model Explanation, Model, and Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve model for visualization and deployment\n", + "from azureml.core.model import Model\n", + "import joblib\n", + "original_model = Model(ws, 'amlcompute_deploy_model')\n", + "model_path = original_model.download(exist_ok=True)\n", + "original_svm_model = joblib.load(model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve global explanation for visualization\n", + "from azureml.interpret import ExplanationClient\n", + "\n", + "# get model explanation data\n", + "client = ExplanationClient.from_run(run)\n", + "global_explanation = client.download_model_explanation()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve x_test for visualization\n", + "x_test_path = './x_test.pkl'\n", + "run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n", + "x_test = joblib.load(x_test_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize\n", + "Visualize the explanations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from raiwidgets import ExplanationDashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ExplanationDashboard(global_explanation, original_svm_model, dataset=x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy\n", + "Deploy Model and ScoringExplainer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n", + "azureml_pip_packages = [\n", + " 'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n", + " 'azureml-interpret'\n", + "]\n", + " \n", + "\n", + "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n", + "# In production scenario user would choose their dependencies\n", + "available_packages = pkg_resources.working_set\n", + "sklearn_ver = None\n", + "pandas_ver = None\n", + "for dist in available_packages:\n", + " if dist.key == 'scikit-learn':\n", + " sklearn_ver = dist.version\n", + " elif dist.key == 'pandas':\n", + " pandas_ver = dist.version\n", + "sklearn_dep = 'scikit-learn'\n", + "pandas_dep = 'pandas'\n", + "if sklearn_ver:\n", + " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", + "if pandas_ver:\n", + " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", + "# Specify CondaDependencies obj\n", + "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", + "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", + "# environment, otherwise if a model is trained or deployed in a different environment this can\n", + "# cause errors. Please take extra care when specifying your dependencies in a production environment.\n", + "azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n", + "myenv = CondaDependencies.create(python_version=python_version, pip_packages=azureml_pip_packages)\n", + "\n", + "with open(\"myenv.yml\",\"w\") as f:\n", + " f.write(myenv.serialize_to_string())\n", + "\n", + "with open(\"myenv.yml\",\"r\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve scoring explainer for deployment\n", + "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import InferenceConfig\n", + "from azureml.core.webservice import AciWebservice\n", + "from azureml.core.environment import Environment\n", + "from azureml.exceptions import WebserviceException\n", + "\n", + "\n", + "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n", + " memory_gb=1, \n", + " tags={\"data\": \"IBM_Attrition\", \n", + " \"method\" : \"local_explanation\"}, \n", + " description='Get local explanations for IBM Employee Attrition data')\n", + "\n", + "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", + "inference_config = InferenceConfig(entry_script=\"score_remote_explain.py\", environment=myenv)\n", + "\n", + "# Use configs and models generated above\n", + "service = Model.deploy(ws, 'model-scoring-service', [scoring_explainer_model, original_model], inference_config, aciconfig)\n", + "try:\n", + " service.wait_for_deployment(show_output=True)\n", + "except WebserviceException as e:\n", + " print(e.message)\n", + " print(service.get_logs())\n", + " raise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from raiutils.webservice import post_with_retries\n", + "\n", + "# Create data to test service with\n", + "examples = x_test[:4]\n", + "input_data = examples.to_json()\n", + "\n", + "headers = {'Content-Type':'application/json'}\n", + "\n", + "# Send request to service\n", + "print(\"POST to url\", service.scoring_uri)\n", + "resp = post_with_retries(service.scoring_uri, input_data, headers)\n", + "\n", + "# Can covert back to Python objects from json string if desired\n", + "print(\"prediction:\", resp.text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "service.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next\n", + "Learn about other use cases of the explain package on a:\n", + "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb) \n", + "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n", + "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n", + "1. Explain models with engineered features:\n", + " 1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n", + " 1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n", + "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n", + "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n", + "1. [Inferencing time: deploy a locally-trained model and explainer](./train-explain-model-locally-and-deploy.ipynb)\n", + "1. [Inferencing time: deploy a locally-trained keras model and explainer](./train-explain-model-keras-locally-and-deploy.ipynb)" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "mesameki" + } ], - "metadata": { - "authors": [ - { - "name": "mesameki" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb index 8221f22de..38a5989a9 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb @@ -1,609 +1,616 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Azure Machine Learning Pipeline with DataTransferStep\n", - "This notebook is used to demonstrate the use of DataTransferStep in an Azure Machine Learning Pipeline.\n", - "\n", - "> **Note:** In Azure Machine Learning, you can write output data directly to Azure Blob Storage, Azure Data Lake Storage Gen 1, Azure Data Lake Storage Gen 2, Azure FileShare without going through extra DataTransferStep. Learn how to use [OutputFileDatasetConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.data.output_dataset_config.outputfiledatasetconfig?view=azure-ml-py) to achieve that with sample notebooks [here](https://aka.ms/pipeline-with-dataset).**\n", - "\n", - "In certain cases, you will need to transfer data from one data location to another. For example, your data may be in Azure SQL Database and you may want to move it to Azure Data Lake storage. Or, your data is in an ADLS account and you want to make it available in the Blob storage. The built-in **DataTransferStep** class helps you transfer data in these situations.\n", - "\n", - "The below examples show how to move data between different storage types supported in Azure Machine Learning.\n", - "\n", - "## Data transfer currently supports following storage types:\n", - "\n", - "| Data store | Supported as a source | Supported as a sink |\n", - "| --- | --- | --- |\n", - "| Azure Blob Storage | Yes | Yes |\n", - "| Azure Data Lake Storage Gen 1 | Yes | Yes |\n", - "| Azure Data Lake Storage Gen 2 | Yes | Yes |\n", - "| Azure SQL Database | Yes | Yes |\n", - "| Azure Database for PostgreSQL | Yes | Yes |\n", - "| Azure Database for MySQL | Yes | Yes |" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Azure Machine Learning and Pipeline SDK-specific imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import azureml.core\n", - "from azureml.core.compute import ComputeTarget, DataFactoryCompute\n", - "from azureml.exceptions import ComputeTargetException\n", - "from azureml.core import Workspace, Experiment\n", - "from azureml.pipeline.core import Pipeline\n", - "from azureml.core.datastore import Datastore\n", - "from azureml.data.data_reference import DataReference\n", - "from azureml.pipeline.steps import DataTransferStep\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json\n", - "\n", - "If you don't have a config.json file, please go through the [configuration Notebook](https://aka.ms/pl-config) first.\n", - "\n", - "This sets you up with a working config file that has information on your workspace, subscription id, etc. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register Datastores and create DataReferences\n", - "\n", - "For background on registering your data store, consult this article:\n", - "\n", - "https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data\n", - "\n", - "> Please make sure to update the following code examples with appropriate values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure Blob Storage\n", - "\n", - "> Since Blob Storage can contain a file and directory with the same name, you can use **source_reference_type** and **destination_reference_type** optional arguments in DataTransferStep constructor to explicitly specify whether you're referring to the file or the directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "datastore-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "from azureml.exceptions import UserErrorException\n", - "\n", - "blob_datastore_name='MyBlobDatastore'\n", - "account_name=os.getenv(\"BLOB_ACCOUNTNAME_62\", \"\") # Storage account name\n", - "container_name=os.getenv(\"BLOB_CONTAINER_62\", \"\") # Name of Azure blob container\n", - "account_key=os.getenv(\"BLOB_ACCOUNT_KEY_62\", \"\") # Storage account key\n", - "\n", - "try:\n", - " blob_datastore = Datastore.get(ws, blob_datastore_name)\n", - " print(\"Found Blob Datastore with name: %s\" % blob_datastore_name)\n", - "except UserErrorException:\n", - " blob_datastore = Datastore.register_azure_blob_container(\n", - " workspace=ws,\n", - " datastore_name=blob_datastore_name,\n", - " account_name=account_name, # Storage account name\n", - " container_name=container_name, # Name of Azure blob container\n", - " account_key=account_key) # Storage account key\n", - " print(\"Registered blob datastore with name: %s\" % blob_datastore_name)\n", - "\n", - "blob_data_ref = DataReference(\n", - " datastore=blob_datastore,\n", - " data_reference_name=\"blob_test_data\",\n", - " path_on_datastore=\"testdata\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure Data Lake Storage Gen1\n", - "\n", - "Please consult the following articles for detailed steps on setting up service principal authentication and assigning correct permissions to Data Lake Storage account:\n", - "\n", - "https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory\n", - "https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-store#use-service-principal-authentication" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore_name='MyAdlsDatastore'\n", - "subscription_id=os.getenv(\"ADL_SUBSCRIPTION_62\", \"\") # subscription id of ADLS account\n", - "resource_group=os.getenv(\"ADL_RESOURCE_GROUP_62\", \"\") # resource group of ADLS account\n", - "store_name=os.getenv(\"ADL_STORENAME_62\", \"\") # ADLS account name\n", - "tenant_id=os.getenv(\"ADL_TENANT_62\", \"\") # tenant id of service principal\n", - "client_id=os.getenv(\"ADL_CLIENTID_62\", \"\") # client id of service principal\n", - "client_secret=os.getenv(\"ADL_CLIENT_SECRET_62\", \"\") # the secret of service principal\n", - "\n", - "try:\n", - " adls_datastore = Datastore.get(ws, datastore_name)\n", - " print(\"Found datastore with name: %s\" % datastore_name)\n", - "except UserErrorException:\n", - " adls_datastore = Datastore.register_azure_data_lake(\n", - " workspace=ws,\n", - " datastore_name=datastore_name,\n", - " subscription_id=subscription_id, # subscription id of ADLS account\n", - " resource_group=resource_group, # resource group of ADLS account\n", - " store_name=store_name, # ADLS account name\n", - " tenant_id=tenant_id, # tenant id of service principal\n", - " client_id=client_id, # client id of service principal\n", - " client_secret=client_secret) # the secret of service principal\n", - " print(\"Registered datastore with name: %s\" % datastore_name)\n", - "\n", - "adls_data_ref = DataReference(\n", - " datastore=adls_datastore,\n", - " data_reference_name=\"adls_test_data\",\n", - " path_on_datastore=\"testdata\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure Data Lake Storage Gen2\n", - "\n", - "Please consult the following article for detailed steps on setting up service principal authentication and assigning correct permissions to Data lake Storage Gen2 account:\n", - "\n", - "https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-storage#service-principal-authentication" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "adlsgen2_datastore_name = 'myadlsgen2datastore'\n", - "account_name=os.getenv(\"ADLSGEN2_ACCOUNTNAME_62\", \"\") # ADLS Gen2 account name\n", - "tenant_id=os.getenv(\"ADLSGEN2_TENANT_62\", \"\") # tenant id of service principal\n", - "client_id=os.getenv(\"ADLSGEN2_CLIENTID_62\", \"\") # client id of service principal\n", - "client_secret=os.getenv(\"ADLSGEN2_CLIENT_SECRET_62\", \"\") # the secret of service principal\n", - "\n", - "try:\n", - " adlsgen2_datastore = Datastore.get(ws, adlsgen2_datastore_name)\n", - " print(\"Found ADLS Gen2 datastore with name: %s\" % adlsgen2_datastore_name)\n", - "except:\n", - " adlsgen2_datastore = Datastore.register_azure_data_lake_gen2(\n", - " workspace=ws,\n", - " datastore_name=adlsgen2_datastore_name,\n", - " filesystem='test', # Name of ADLS Gen2 filesystem\n", - " account_name=account_name, # ADLS Gen2 account name\n", - " tenant_id=tenant_id, # tenant id of service principal\n", - " client_id=client_id, # client id of service principal\n", - " client_secret=client_secret) # the secret of service principal\n", - " print(\"Registered datastore with name: %s\" % adlsgen2_datastore_name)\n", - "\n", - "adlsgen2_data_ref = DataReference(\n", - " datastore=adlsgen2_datastore,\n", - " data_reference_name='adlsgen2_test_data',\n", - " path_on_datastore='testdata')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure SQL Database\n", - "\n", - "For enabling service principal authentication for an Azure SQL Database, please follow this section in Azure Data Factory documentation: https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-sql-database#service-principal-authentication\n", - "\n", - "> Note: When copying data **to** an Azure SQL Database, data will be _appended_ to an existing table. We also expect the source file to have a header row and the names should exactly match with column names in destination table." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "sql_datastore_name=\"MySqlDatastore\"\n", - "server_name=os.getenv(\"SQL_SERVERNAME_62\", \"\") # Name of SQL server\n", - "database_name=os.getenv(\"SQL_DATBASENAME_62\", \"\") # Name of SQL database\n", - "client_id=os.getenv(\"SQL_CLIENTNAME_62\", \"\") # client id of service principal with permissions to access database\n", - "client_secret=os.getenv(\"SQL_CLIENTSECRET_62\", \"\") # the secret of service principal\n", - "tenant_id=os.getenv(\"SQL_TENANTID_62\", \"\") # tenant id of service principal\n", - "\n", - "try:\n", - " sql_datastore = Datastore.get(ws, sql_datastore_name)\n", - " print(\"Found sql database datastore with name: %s\" % sql_datastore_name)\n", - "except UserErrorException:\n", - " sql_datastore = Datastore.register_azure_sql_database(\n", - " workspace=ws,\n", - " datastore_name=sql_datastore_name,\n", - " server_name=server_name,\n", - " database_name=database_name,\n", - " client_id=client_id,\n", - " client_secret=client_secret,\n", - " tenant_id=tenant_id)\n", - " print(\"Registered sql databse datastore with name: %s\" % sql_datastore_name)\n", - "\n", - "from azureml.data.sql_data_reference import SqlDataReference\n", - "\n", - "sql_query_data_ref = SqlDataReference(\n", - " datastore=sql_datastore,\n", - " data_reference_name=\"sql_query_data_ref\",\n", - " sql_query=\"select top 1 * from TestData\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure Database for PostgreSQL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "psql_datastore_name=\"MyPostgreSqlDatastore\"\n", - "server_name=os.getenv(\"PSQL_SERVERNAME_62\", \"\") # Name of PostgreSQL server \n", - "database_name=os.getenv(\"PSQL_DATBASENAME_62\", \"\") # Name of PostgreSQL database\n", - "user_id=os.getenv(\"PSQL_USERID_62\", \"\") # user id\n", - "user_password=os.getenv(\"PSQL_USERPW_62\", \"\") # user password\n", - "\n", - "try:\n", - " psql_datastore = Datastore.get(ws, psql_datastore_name)\n", - " print(\"Found PostgreSQL database datastore with name: %s\" % psql_datastore_name)\n", - "except UserErrorException:\n", - " psql_datastore = Datastore.register_azure_postgre_sql(\n", - " workspace=ws,\n", - " datastore_name=psql_datastore_name,\n", - " server_name=server_name,\n", - " database_name=database_name,\n", - " user_id=user_id,\n", - " user_password=user_password)\n", - " print(\"Registered PostgreSQL databse datastore with name: %s\" % psql_datastore_name)\n", - "\n", - "from azureml.data.sql_data_reference import SqlDataReference\n", - "\n", - "psql_query_data_ref = SqlDataReference(\n", - " datastore=psql_datastore,\n", - " data_reference_name=\"psql_query_data_ref\",\n", - " sql_query=\"SELECT * FROM testtable\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure Database for MySQL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "mysql_datastore_name=\"MySqlDatastore\"\n", - "server_name=os.getenv(\"MYSQL_SERVERNAME_62\", \"\") # Name of MySQL server \n", - "database_name=os.getenv(\"MYSQL_DATBASENAME_62\", \"\") # Name of MySQL database\n", - "user_id=os.getenv(\"MYSQL_USERID_62\", \"\") # user id\n", - "user_password=os.getenv(\"MYSQL_USERPW_62\", \"\") # user password\n", - "\n", - "try:\n", - " mysql_datastore = Datastore.get(ws, mysql_datastore_name)\n", - " print(\"Found MySQL database datastore with name: %s\" % mysql_datastore_name)\n", - "except UserErrorException:\n", - " mysql_datastore = Datastore.register_azure_my_sql(\n", - " workspace=ws,\n", - " datastore_name=mysql_datastore_name,\n", - " server_name=server_name,\n", - " database_name=database_name,\n", - " user_id=user_id,\n", - " user_password=user_password)\n", - " print(\"Registered MySQL databse datastore with name: %s\" % mysql_datastore_name)\n", - "\n", - "from azureml.data.sql_data_reference import SqlDataReference\n", - "\n", - "mysql_query_data_ref = SqlDataReference(\n", - " datastore=mysql_datastore,\n", - " data_reference_name=\"mysql_query_data_ref\",\n", - " sql_query=\"SELECT * FROM testtable\")\n", - "\n", - "mysql_table_data_ref = SqlDataReference(\n", - " datastore=mysql_datastore,\n", - " data_reference_name=\"mysql_table_data_ref\",\n", - " sql_table=\"testtable\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup Data Factory Account" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_factory_name = 'adftest'\n", - "\n", - "def get_or_create_data_factory(workspace, factory_name):\n", - " try:\n", - " return DataFactoryCompute(workspace, factory_name)\n", - " except ComputeTargetException as e:\n", - " if 'ComputeTargetNotFound' in e.message:\n", - " print('Data factory not found, creating...')\n", - " provisioning_config = DataFactoryCompute.provisioning_configuration()\n", - " data_factory = ComputeTarget.create(workspace, factory_name, provisioning_config)\n", - " data_factory.wait_for_completion()\n", - " return data_factory\n", - " else:\n", - " raise e\n", - " \n", - "data_factory_compute = get_or_create_data_factory(ws, data_factory_name)\n", - "\n", - "print(\"Setup Azure Data Factory account complete\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a DataTransferStep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**DataTransferStep** is used to transfer data between Azure Blob, Azure Data Lake Store, and Azure SQL database.\n", - "\n", - "- **name:** Name of module\n", - "- **source_data_reference:** Input connection that serves as source of data transfer operation.\n", - "- **destination_data_reference:** Input connection that serves as destination of data transfer operation.\n", - "- **compute_target:** Azure Data Factory to use for transferring data.\n", - "- **allow_reuse:** Whether the step should reuse results of previous DataTransferStep when run with same inputs. Set as False to force data to be transferred again.\n", - "\n", - "Optional arguments to explicitly specify whether a path corresponds to a file or a directory. These are useful when storage contains both file and directory with the same name or when creating a new destination path.\n", - "\n", - "- **source_reference_type:** An optional string specifying the type of source_data_reference. Possible values include: 'file', 'directory'. When not specified, we use the type of existing path or directory if it's a new path.\n", - "- **destination_reference_type:** An optional string specifying the type of destination_data_reference. Possible values include: 'file', 'directory'. When not specified, we use the type of existing path or directory if it's a new path." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "transfer_adls_to_blob = DataTransferStep(\n", - " name=\"transfer_adls_to_blob\",\n", - " source_data_reference=adls_data_ref,\n", - " destination_data_reference=blob_data_ref,\n", - " compute_target=data_factory_compute)\n", - "\n", - "print(\"Data transfer step created\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "transfer_adlsgen2_to_blob = DataTransferStep(\n", - " name='transfer_adlsgen2_to_blob',\n", - " source_data_reference=adlsgen2_data_ref,\n", - " destination_data_reference=blob_data_ref,\n", - " compute_target=data_factory_compute)\n", - "\n", - "transfer_sql_to_blob = DataTransferStep(\n", - " name=\"transfer_sql_to_blob\",\n", - " source_data_reference=sql_query_data_ref,\n", - " destination_data_reference=blob_data_ref,\n", - " compute_target=data_factory_compute,\n", - " destination_reference_type='file')\n", - "\n", - "transfer_psql_to_blob = DataTransferStep(\n", - " name=\"transfer_psql_to_blob\",\n", - " source_data_reference=psql_query_data_ref,\n", - " destination_data_reference=blob_data_ref,\n", - " compute_target=data_factory_compute,\n", - " destination_reference_type='file')\n", - "\n", - "transfer_mysql_to_blob = DataTransferStep(\n", - " name=\"transfer_mysql_to_blob\",\n", - " source_data_reference=mysql_query_data_ref,\n", - " destination_data_reference=blob_data_ref,\n", - " compute_target=data_factory_compute)\n", - "print(\"Data transfer step created for Sql server, PostgreSQL and MySQL\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and Submit the Experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_01 = Pipeline(\n", - " description=\"data_transfer_01\",\n", - " workspace=ws,\n", - " steps=[transfer_adls_to_blob])\n", - "\n", - "pipeline_run_01 = Experiment(ws, \"Data_Transfer_example_01\").submit(pipeline_01)\n", - "pipeline_run_01.wait_for_completion()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_02 = Pipeline(\n", - " description=\"data_transfer_02\",\n", - " workspace=ws,\n", - " steps=[transfer_sql_to_blob,transfer_psql_to_blob, transfer_adlsgen2_to_blob])\n", - "\n", - "pipeline_run_02 = Experiment(ws, \"Data_Transfer_example_02\").submit(pipeline_02)\n", - "pipeline_run_02.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run_01).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run_02).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next: Databricks as a Compute Target\n", - "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. This [notebook](https://aka.ms/pl-databricks) demonstrates the use of a DatabricksStep in an Azure Machine Learning Pipeline." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "shbijlan" - } - ], - "category": "tutorial", - "compute": [ - "ADF" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Azure Machine Learning Pipeline with DataTranferStep", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - }, - "order_index": 4, - "star_tag": [ - "featured" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Azure Machine Learning Pipeline with DataTransferStep\n", + "This notebook is used to demonstrate the use of DataTransferStep in an Azure Machine Learning Pipeline.\n", + "\n", + "> **Note:** In Azure Machine Learning, you can write output data directly to Azure Blob Storage, Azure Data Lake Storage Gen 1, Azure Data Lake Storage Gen 2, Azure FileShare without going through extra DataTransferStep. Learn how to use [OutputFileDatasetConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.data.output_dataset_config.outputfiledatasetconfig?view=azure-ml-py) to achieve that with sample notebooks [here](https://aka.ms/pipeline-with-dataset).**\n", + "\n", + "In certain cases, you will need to transfer data from one data location to another. For example, your data may be in Azure SQL Database and you may want to move it to Azure Data Lake storage. Or, your data is in an ADLS account and you want to make it available in the Blob storage. The built-in **DataTransferStep** class helps you transfer data in these situations.\n", + "\n", + "The below examples show how to move data between different storage types supported in Azure Machine Learning.\n", + "\n", + "## Data transfer currently supports following storage types:\n", + "\n", + "| Data store | Supported as a source | Supported as a sink |\n", + "| --- | --- | --- |\n", + "| Azure Blob Storage | Yes | Yes |\n", + "| Azure Data Lake Storage Gen 1 | Yes | Yes |\n", + "| Azure Data Lake Storage Gen 2 | Yes | Yes |\n", + "| Azure SQL Database | Yes | Yes |\n", + "| Azure Database for PostgreSQL | Yes | Yes |\n", + "| Azure Database for MySQL | Yes | Yes |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Azure Machine Learning and Pipeline SDK-specific imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import azureml.core\n", + "from azureml.core.compute import ComputeTarget, DataFactoryCompute\n", + "from azureml.exceptions import ComputeTargetException\n", + "from azureml.core import Workspace, Experiment\n", + "from azureml.pipeline.core import Pipeline\n", + "from azureml.core.datastore import Datastore\n", + "from azureml.data.data_reference import DataReference\n", + "from azureml.pipeline.steps import DataTransferStep\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json\n", + "\n", + "If you don't have a config.json file, please go through the [configuration Notebook](https://aka.ms/pl-config) first.\n", + "\n", + "This sets you up with a working config file that has information on your workspace, subscription id, etc. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register Datastores and create DataReferences\n", + "\n", + "For background on registering your data store, consult this article:\n", + "\n", + "https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data\n", + "\n", + "> Please make sure to update the following code examples with appropriate values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Azure Blob Storage\n", + "\n", + "> Since Blob Storage can contain a file and directory with the same name, you can use **source_reference_type** and **destination_reference_type** optional arguments in DataTransferStep constructor to explicitly specify whether you're referring to the file or the directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of DataTranferStep" + "datastore-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "from azureml.exceptions import UserErrorException\n", + "\n", + "blob_datastore_name='MyBlobDatastore'\n", + "account_name=os.getenv(\"BLOB_ACCOUNTNAME_62\", \"\") # Storage account name\n", + "container_name=os.getenv(\"BLOB_CONTAINER_62\", \"\") # Name of Azure blob container\n", + "account_key=os.getenv(\"BLOB_ACCOUNT_KEY_62\", \"\") # Storage account key\n", + "\n", + "try:\n", + " blob_datastore = Datastore.get(ws, blob_datastore_name)\n", + " print(\"Found Blob Datastore with name: %s\" % blob_datastore_name)\n", + "except UserErrorException:\n", + " blob_datastore = Datastore.register_azure_blob_container(\n", + " workspace=ws,\n", + " datastore_name=blob_datastore_name,\n", + " account_name=account_name, # Storage account name\n", + " container_name=container_name, # Name of Azure blob container\n", + " account_key=account_key) # Storage account key\n", + " print(\"Registered blob datastore with name: %s\" % blob_datastore_name)\n", + "\n", + "blob_data_ref = DataReference(\n", + " datastore=blob_datastore,\n", + " data_reference_name=\"blob_test_data\",\n", + " path_on_datastore=\"testdata\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Azure Data Lake Storage Gen1\n", + "\n", + "Please consult the following articles for detailed steps on setting up service principal authentication and assigning correct permissions to Data Lake Storage account:\n", + "\n", + "https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory\n", + "https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-store#use-service-principal-authentication" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "datastore_name='MyAdlsDatastore'\n", + "subscription_id=os.getenv(\"ADL_SUBSCRIPTION_62\", \"\") # subscription id of ADLS account\n", + "resource_group=os.getenv(\"ADL_RESOURCE_GROUP_62\", \"\") # resource group of ADLS account\n", + "store_name=os.getenv(\"ADL_STORENAME_62\", \"\") # ADLS account name\n", + "tenant_id=os.getenv(\"ADL_TENANT_62\", \"\") # tenant id of service principal\n", + "client_id=os.getenv(\"ADL_CLIENTID_62\", \"\") # client id of service principal\n", + "client_secret=os.getenv(\"ADL_CLIENT_SECRET_62\", \"\") # the secret of service principal\n", + "\n", + "try:\n", + " adls_datastore = Datastore.get(ws, datastore_name)\n", + " print(\"Found datastore with name: %s\" % datastore_name)\n", + "except UserErrorException:\n", + " adls_datastore = Datastore.register_azure_data_lake(\n", + " workspace=ws,\n", + " datastore_name=datastore_name,\n", + " subscription_id=subscription_id, # subscription id of ADLS account\n", + " resource_group=resource_group, # resource group of ADLS account\n", + " store_name=store_name, # ADLS account name\n", + " tenant_id=tenant_id, # tenant id of service principal\n", + " client_id=client_id, # client id of service principal\n", + " client_secret=client_secret) # the secret of service principal\n", + " print(\"Registered datastore with name: %s\" % datastore_name)\n", + "\n", + "adls_data_ref = DataReference(\n", + " datastore=adls_datastore,\n", + " data_reference_name=\"adls_test_data\",\n", + " path_on_datastore=\"testdata\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Azure Data Lake Storage Gen2\n", + "\n", + "Please consult the following article for detailed steps on setting up service principal authentication and assigning correct permissions to Data lake Storage Gen2 account:\n", + "\n", + "https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-storage#service-principal-authentication" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#\n", + "#adlsgen2_datastore_name = 'myadlsgen2datastore'\n", + "#account_name=os.getenv(\"ADLSGEN2_ACCOUNTNAME_62\", \"\") # ADLS Gen2 account name\n", + "#tenant_id=os.getenv(\"ADLSGEN2_TENANT_62\", \"\") # tenant id of service principal\n", + "#client_id=os.getenv(\"ADLSGEN2_CLIENTID_62\", \"\") # client id of service principal\n", + "#client_secret=os.getenv(\"ADLSGEN2_CLIENT_SECRET_62\", \"\") # the secret of service principal\n", + "\n", + "#try:\n", + "# adlsgen2_datastore = Datastore.get(ws, adlsgen2_datastore_name)\n", + "# print(\"Found ADLS Gen2 datastore with name: %s\" % adlsgen2_datastore_name)\n", + "#except:\n", + "# adlsgen2_datastore = Datastore.register_azure_data_lake_gen2(\n", + "# workspace=ws,\n", + "# datastore_name=adlsgen2_datastore_name,\n", + "# filesystem='test', # Name of ADLS Gen2 filesystem\n", + "# account_name=account_name, # ADLS Gen2 account name\n", + "# tenant_id=tenant_id, # tenant id of service principal\n", + "# client_id=client_id, # client id of service principal\n", + "# client_secret=client_secret) # the secret of service principal\n", + "# print(\"Registered datastore with name: %s\" % adlsgen2_datastore_name)\n", + "#\n", + "#adlsgen2_data_ref = DataReference(\n", + "# datastore=adlsgen2_datastore,\n", + "# data_reference_name='adlsgen2_test_data',\n", + "# path_on_datastore='testdata')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Azure SQL Database\n", + "\n", + "For enabling service principal authentication for an Azure SQL Database, please follow this section in Azure Data Factory documentation: https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-sql-database#service-principal-authentication\n", + "\n", + "> Note: When copying data **to** an Azure SQL Database, data will be _appended_ to an existing table. We also expect the source file to have a header row and the names should exactly match with column names in destination table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#\n", + "#sql_datastore_name=\"MySqlDatastore\"\n", + "#server_name=os.getenv(\"SQL_SERVERNAME_62\", \"\") # Name of SQL server\n", + "#database_name=os.getenv(\"SQL_DATBASENAME_62\", \"\") # Name of SQL database\n", + "#client_id=os.getenv(\"SQL_CLIENTNAME_62\", \"\") # client id of service principal with permissions to access database\n", + "#client_secret=os.getenv(\"SQL_CLIENTSECRET_62\", \"\") # the secret of service principal\n", + "#tenant_id=os.getenv(\"SQL_TENANTID_62\", \"\") # tenant id of service principal\n", + "#\n", + "#try:\n", + "# sql_datastore = Datastore.get(ws, sql_datastore_name)\n", + "# print(\"Found sql database datastore with name: %s\" % sql_datastore_name)\n", + "#except UserErrorException:\n", + "# sql_datastore = Datastore.register_azure_sql_database(\n", + "# workspace=ws,\n", + "# datastore_name=sql_datastore_name,\n", + "# server_name=server_name,\n", + "# database_name=database_name,\n", + "# client_id=client_id,\n", + "# client_secret=client_secret,\n", + "# tenant_id=tenant_id)\n", + "# print(\"Registered sql databse datastore with name: %s\" % sql_datastore_name)\n", + "#\n", + "#from azureml.data.sql_data_reference import SqlDataReference\n", + "#\n", + "#sql_query_data_ref = SqlDataReference(\n", + "# datastore=sql_datastore,\n", + "# data_reference_name=\"sql_query_data_ref\",\n", + "# sql_query=\"select top 1 * from TestData\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Azure Database for PostgreSQL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#\n", + "#psql_datastore_name=\"MyPostgreSqlDatastore\"\n", + "#server_name=os.getenv(\"PSQL_SERVERNAME_62\", \"\") # Name of PostgreSQL server \n", + "#database_name=os.getenv(\"PSQL_DATBASENAME_62\", \"\") # Name of PostgreSQL database\n", + "#user_id=os.getenv(\"PSQL_USERID_62\", \"\") # user id\n", + "#user_password=os.getenv(\"PSQL_USERPW_62\", \"\") # user password\n", + "#\n", + "#try:\n", + "# psql_datastore = Datastore.get(ws, psql_datastore_name)\n", + "# print(\"Found PostgreSQL database datastore with name: %s\" % psql_datastore_name)\n", + "#except UserErrorException:\n", + "# psql_datastore = Datastore.register_azure_postgre_sql(\n", + "# workspace=ws,\n", + "# datastore_name=psql_datastore_name,\n", + "# server_name=server_name,\n", + "# database_name=database_name,\n", + "# user_id=user_id,\n", + "# user_password=user_password)\n", + "# print(\"Registered PostgreSQL databse datastore with name: %s\" % psql_datastore_name)\n", + "#\n", + "#from azureml.data.sql_data_reference import SqlDataReference\n", + "#\n", + "#psql_query_data_ref = SqlDataReference(\n", + "# datastore=psql_datastore,\n", + "# data_reference_name=\"psql_query_data_ref\",\n", + "# sql_query=\"SELECT * FROM testtable\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Azure Database for MySQL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#\n", + "#mysql_datastore_name=\"MySqlDatastore\"\n", + "#server_name=os.getenv(\"MYSQL_SERVERNAME_62\", \"\") # Name of MySQL server \n", + "#database_name=os.getenv(\"MYSQL_DATBASENAME_62\", \"\") # Name of MySQL database\n", + "#user_id=os.getenv(\"MYSQL_USERID_62\", \"\") # user id\n", + "#user_password=os.getenv(\"MYSQL_USERPW_62\", \"\") # user password\n", + "#\n", + "#try:\n", + "# mysql_datastore = Datastore.get(ws, mysql_datastore_name)\n", + "# print(\"Found MySQL database datastore with name: %s\" % mysql_datastore_name)\n", + "#except UserErrorException:\n", + "# mysql_datastore = Datastore.register_azure_my_sql(\n", + "# workspace=ws,\n", + "# datastore_name=mysql_datastore_name,\n", + "# server_name=server_name,\n", + "# database_name=database_name,\n", + "# user_id=user_id,\n", + "# user_password=user_password)\n", + "# print(\"Registered MySQL databse datastore with name: %s\" % mysql_datastore_name)\n", + "#\n", + "#from azureml.data.sql_data_reference import SqlDataReference\n", + "#\n", + "#mysql_query_data_ref = SqlDataReference(\n", + "# datastore=mysql_datastore,\n", + "# data_reference_name=\"mysql_query_data_ref\",\n", + "# sql_query=\"SELECT * FROM testtable\")\n", + "#\n", + "#mysql_table_data_ref = SqlDataReference(\n", + "# datastore=mysql_datastore,\n", + "# data_reference_name=\"mysql_table_data_ref\",\n", + "# sql_table=\"testtable\")" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup Data Factory Account" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_factory_name = 'adftest'\n", + "\n", + "def get_or_create_data_factory(workspace, factory_name):\n", + " try:\n", + " return DataFactoryCompute(workspace, factory_name)\n", + " except ComputeTargetException as e:\n", + " if 'ComputeTargetNotFound' in e.message:\n", + " print('Data factory not found, creating...')\n", + " provisioning_config = DataFactoryCompute.provisioning_configuration()\n", + " data_factory = ComputeTarget.create(workspace, factory_name, provisioning_config)\n", + " data_factory.wait_for_completion()\n", + " return data_factory\n", + " else:\n", + " raise e\n", + " \n", + "data_factory_compute = get_or_create_data_factory(ws, data_factory_name)\n", + "\n", + "print(\"Setup Azure Data Factory account complete\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a DataTransferStep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**DataTransferStep** is used to transfer data between Azure Blob, Azure Data Lake Store, and Azure SQL database.\n", + "\n", + "- **name:** Name of module\n", + "- **source_data_reference:** Input connection that serves as source of data transfer operation.\n", + "- **destination_data_reference:** Input connection that serves as destination of data transfer operation.\n", + "- **compute_target:** Azure Data Factory to use for transferring data.\n", + "- **allow_reuse:** Whether the step should reuse results of previous DataTransferStep when run with same inputs. Set as False to force data to be transferred again.\n", + "\n", + "Optional arguments to explicitly specify whether a path corresponds to a file or a directory. These are useful when storage contains both file and directory with the same name or when creating a new destination path.\n", + "\n", + "- **source_reference_type:** An optional string specifying the type of source_data_reference. Possible values include: 'file', 'directory'. When not specified, we use the type of existing path or directory if it's a new path.\n", + "- **destination_reference_type:** An optional string specifying the type of destination_data_reference. Possible values include: 'file', 'directory'. When not specified, we use the type of existing path or directory if it's a new path." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "transfer_adls_to_blob = DataTransferStep(\n", + " name=\"transfer_adls_to_blob\",\n", + " source_data_reference=adls_data_ref,\n", + " destination_data_reference=blob_data_ref,\n", + " compute_target=data_factory_compute)\n", + "\n", + "print(\"Data transfer step created\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#\n", + "#transfer_adlsgen2_to_blob = DataTransferStep(\n", + "# name='transfer_adlsgen2_to_blob',\n", + "# source_data_reference=adlsgen2_data_ref,\n", + "# destination_data_reference=blob_data_ref,\n", + "# compute_target=data_factory_compute)\n", + "#\n", + "#transfer_sql_to_blob = DataTransferStep(\n", + "# name=\"transfer_sql_to_blob\",\n", + "# source_data_reference=sql_query_data_ref,\n", + "# destination_data_reference=blob_data_ref,\n", + "# compute_target=data_factory_compute,\n", + "# destination_reference_type='file')\n", + "#\n", + "#transfer_psql_to_blob = DataTransferStep(\n", + "# name=\"transfer_psql_to_blob\",\n", + "# source_data_reference=psql_query_data_ref,\n", + "# destination_data_reference=blob_data_ref,\n", + "# compute_target=data_factory_compute,\n", + "# destination_reference_type='file')\n", + "#\n", + "#transfer_mysql_to_blob = DataTransferStep(\n", + "# name=\"transfer_mysql_to_blob\",\n", + "# source_data_reference=mysql_query_data_ref,\n", + "# destination_data_reference=blob_data_ref,\n", + "# compute_target=data_factory_compute)\n", + "#print(\"Data transfer step created for Sql server, PostgreSQL and MySQL\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and Submit the Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_01 = Pipeline(\n", + " description=\"data_transfer_01\",\n", + " workspace=ws,\n", + " steps=[transfer_adls_to_blob])\n", + "\n", + "pipeline_run_01 = Experiment(ws, \"Data_Transfer_example_01\").submit(pipeline_01)\n", + "pipeline_run_01.wait_for_completion()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#pipeline_02 = Pipeline(\n", + "# description=\"data_transfer_02\",\n", + "# workspace=ws,\n", + "# steps=[transfer_sql_to_blob,transfer_psql_to_blob, transfer_adlsgen2_to_blob])\n", + "#\n", + "#pipeline_run_02 = Experiment(ws, \"Data_Transfer_example_02\").submit(pipeline_02)\n", + "#pipeline_run_02.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(pipeline_run_01).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#from azureml.widgets import RunDetails\n", + "#RunDetails(pipeline_run_02).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next: Databricks as a Compute Target\n", + "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. This [notebook](https://aka.ms/pl-databricks) demonstrates the use of a DatabricksStep in an Azure Machine Learning Pipeline." + ] + } + ], + "metadata": { + "authors": [ + { + "name": "shbijlan" + } + ], + "category": "tutorial", + "compute": [ + "ADF" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "Azure Machine Learning Pipeline with DataTranferStep", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "order_index": 4, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of DataTranferStep" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb index 63233de15..8f70dd382 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb @@ -1,659 +1,659 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Azure Machine Learning Pipelines: Getting Started\n", - "\n", - "## Overview\n", - "\n", - "\n", - "A common scenario when using machine learning components is to have a data workflow that includes the following steps:\n", - "\n", - "- Preparing/preprocessing a given dataset for training, followed by\n", - "- Training a machine learning model on this data, and then\n", - "- Deploying this trained model in a separate environment, and finally\n", - "- Running a batch scoring task on another data set, using the trained model.\n", - "\n", - "Azure's Machine Learning pipelines give you a way to combine multiple steps like these into one configurable workflow, so that multiple agents/users can share and/or reuse this workflow. Machine learning pipelines thus provide a consistent, reproducible mechanism for building, evaluating, deploying, and running ML systems.\n", - "\n", - "To get more information about Azure machine learning pipelines, please read our [Azure Machine Learning Pipelines](https://aka.ms/pl-concept) overview, or the [readme article](https://aka.ms/pl-readme).\n", - "\n", - "In this notebook, we provide a gentle introduction to Azure machine learning pipelines. We build a pipeline that runs jobs unattended on different compute clusters; in this notebook, you'll see how to use the basic Azure ML SDK APIs for constructing this pipeline.\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites and Azure Machine Learning Basics\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure Machine Learning Imports\n", - "\n", - "In this first code cell, we import key Azure Machine Learning modules that we will use below. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import requests\n", - "import tempfile\n", - "import azureml.core\n", - "from azureml.core import Workspace, Experiment, Datastore\n", - "from azureml.widgets import RunDetails\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pipeline-specific SDK imports\n", - "\n", - "Here, we import key pipeline modules, whose use will be illustrated in the examples below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "\n", - "print(\"Pipeline SDK-specific imports completed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize Workspace\n", - "\n", - "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n", - "\n", - "# Default datastore\n", - "def_blob_store = ws.get_default_datastore() \n", - "# The following call GETS the Azure Blob Store associated with your workspace.\n", - "# Note that workspaceblobstore is **the name of this store and CANNOT BE CHANGED and must be used as is** \n", - "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n", - "print(\"Blobstore's name: {}\".format(def_blob_store.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Required data and script files for the the tutorial\n", - "Sample files required to finish this tutorial are already copied to the corresponding source_directory locations. Even though the .py provided in the samples does not have much \"ML work\" as a data scientist, you will work on this extensively as part of your work. To complete this tutorial, the contents of these files are not very important. The one-line files are for demostration purpose only." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Datastore concepts\n", - "A [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target. \n", - "\n", - "A Datastore can either be backed by an Azure File Storage (default) or by an Azure Blob Storage.\n", - "\n", - "In this next step, we will upload the training and test set into the workspace's default storage (File storage), and another piece of data to Azure Blob Storage. When to use [Azure Blobs](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), [Azure Files](https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction), or [Azure Disks](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/managed-disks-overview) is [detailed here](https://docs.microsoft.com/en-us/azure/storage/common/storage-decide-blobs-files-disks).\n", - "\n", - "**Please take good note of the concept of the datastore.**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Upload data to default datastore\n", - "Default datastore on workspace is the Azure File storage. The workspace has a Blob storage associated with it as well. Let's upload a file to each of these storages." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# download data file from remote\n", - "response = requests.get(\"https://dprepdata.blob.core.windows.net/demo/Titanic.csv\")\n", - "titanic_file = os.path.join(tempfile.mkdtemp(), \"Titanic.csv\")\n", - "with open(titanic_file, \"w\") as f:\n", - " f.write(response.content.decode(\"utf-8\"))\n", - "# get_default_datastore() gets the default Azure Blob Store associated with your workspace.\n", - "# Here we are reusing the def_blob_store object we obtained earlier\n", - "def_blob_store.upload_files([titanic_file], target_path=\"titanic\", overwrite=True)\n", - "print(\"Upload call completed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (Optional) See your files using Azure Portal\n", - "Once you successfully uploaded the files, you can browse to them (or upload more files) using [Azure Portal](https://portal.azure.com). At the portal, make sure you have selected your subscription (click *Resource Groups* and then select the subscription). Then look for your **Machine Learning Workspace** name. It has a link to your storage. Click on the storage link. It will take you to a page where you can see [Blobs](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), [Files](https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction), [Tables](https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-overview), and [Queues](https://docs.microsoft.com/en-us/azure/storage/queues/storage-queues-introduction). We have uploaded a file each to the Blob storage and to the File storage in the above step. You should be able to see both of these files in their respective locations. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Targets\n", - "A compute target specifies where to execute your program such as a remote Docker on a VM, or a cluster. A compute target needs to be addressable and accessible by you.\n", - "\n", - "**You need at least one compute target to send your payload to. We are planning to use Azure Machine Learning Compute exclusively for this tutorial for all steps. However in some cases you may require multiple compute targets as some steps may run in one compute target like Azure Machine Learning Compute, and some other steps in the same pipeline could run in a different compute target.**\n", - "\n", - "*The example belows show creating/retrieving/attaching to an Azure Machine Learning Compute instance.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### List of Compute Targets on the workspace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cts = ws.compute_targets\n", - "for ct in cts:\n", - " print(ct)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Retrieve or create a Azure Machine Learning compute\n", - "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. We will create an Azure Machine Learning Compute containing **STANDARD_D2_V2 CPU VMs**. This process is broken down into the following steps:\n", - "\n", - "1. Create the configuration\n", - "2. Create the Azure Machine Learning compute\n", - "\n", - "**This process will take about 3 minutes and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "aml_compute_target = \"cpu-cluster\"\n", - "try:\n", - " aml_compute = AmlCompute(ws, aml_compute_target)\n", - " print(\"found existing compute target.\")\n", - "except ComputeTargetException:\n", - " print(\"creating new compute target\")\n", - " \n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", - " min_nodes = 1, \n", - " max_nodes = 4) \n", - " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", - " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - "print(\"Azure Machine Learning Compute attached\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n", - "# example: un-comment the following line.\n", - "# print(aml_compute.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Wait for this call to finish before proceeding (you will see the asterisk turning to a number).**\n", - "\n", - "Now that you have created the compute target, let's see what the workspace's compute_targets() function returns. You should now see one entry named 'amlcompute' of type AmlCompute." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Now that we have completed learning the basics of Azure Machine Learning (AML), let's go ahead and start understanding the Pipeline concepts.**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating a Step in a Pipeline\n", - "A Step is a unit of execution. Step typically needs a target of execution (compute target), a script to execute, and may require script arguments and inputs, and can produce outputs. The step also could take a number of other parameters. Azure Machine Learning Pipelines provides the following built-in Steps:\n", - "\n", - "- [**PythonScriptStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py): Adds a step to run a Python script in a Pipeline.\n", - "- [**AdlaStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep?view=azure-ml-py): Adds a step to run U-SQL script using Azure Data Lake Analytics.\n", - "- [**DataTransferStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.data_transfer_step.datatransferstep?view=azure-ml-py): Transfers data between Azure Blob and Data Lake accounts.\n", - "- [**DatabricksStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py): Adds a DataBricks notebook as a step in a Pipeline.\n", - "- [**HyperDriveStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.hyper_drive_step.hyperdrivestep?view=azure-ml-py): Creates a Hyper Drive step for Hyper Parameter Tuning in a Pipeline.\n", - "- [**AzureBatchStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.azurebatch_step.azurebatchstep?view=azure-ml-py): Creates a step for submitting jobs to Azure Batch\n", - "- [**EstimatorStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep?view=azure-ml-py): Adds a step to run Estimator in a Pipeline.\n", - "- [**MpiStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.mpi_step.mpistep?view=azure-ml-py): Adds a step to run a MPI job in a Pipeline.\n", - "- [**AutoMLStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.automlstep?view=azure-ml-py): Creates a AutoML step in a Pipeline.\n", - "\n", - "The following code will create a PythonScriptStep to be executed in the Azure Machine Learning Compute we created above using train.py, one of the files already made available in the `source_directory`.\n", - "\n", - "A **PythonScriptStep** is a basic, built-in step to run a Python Script on a compute target. It takes a script name and optionally other parameters like arguments for the script, compute target, inputs and outputs. If no compute target is specified, default compute target for the workspace is used. You can also use a [**RunConfiguration**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) to specify requirements for the PythonScriptStep, such as conda dependencies and docker image.\n", - "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uses default values for PythonScriptStep construct.\n", - "\n", - "source_directory = './train'\n", - "print('Source directory for the step is {}.'.format(os.path.realpath(source_directory)))\n", - "\n", - "# Syntax\n", - "# PythonScriptStep(\n", - "# script_name, \n", - "# name=None, \n", - "# arguments=None, \n", - "# compute_target=None, \n", - "# runconfig=None, \n", - "# inputs=None, \n", - "# outputs=None, \n", - "# params=None, \n", - "# source_directory=None, \n", - "# allow_reuse=True, \n", - "# version=None, \n", - "# hash_paths=None)\n", - "# This returns a Step\n", - "step1 = PythonScriptStep(name=\"train_step\",\n", - " script_name=\"train.py\", \n", - " compute_target=aml_compute, \n", - " source_directory=source_directory,\n", - " allow_reuse=True)\n", - "print(\"Step1 created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:** In the above call to PythonScriptStep(), the flag *allow_reuse* determines whether the step should reuse previous results when run with the same settings/inputs. This flag's default value is *True*; the default is set to *True* because, when inputs and parameters have not changed, we typically do not want to re-run a given pipeline step. \n", - "\n", - "If *allow_reuse* is set to *False*, a new run will always be generated for this step during pipeline execution. The *allow_reuse* flag can come in handy in situations where you do *not* want to re-run a pipeline step." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running a few steps in parallel\n", - "Here we are looking at a simple scenario where we are running a few steps (all involving PythonScriptStep) in parallel. Running nodes in **parallel** is the default behavior for steps in a pipeline.\n", - "\n", - "We already have one step defined earlier. Let's define few more steps. For step3, we are using customized conda-dependency, and job might fail when \"azureml-defaults\" (or other meta package) is not in pip-package list. We need to be aware if we are not using any of the meta packages (azureml-sdk, azureml-defaults, azureml-core), and we recommend installing \"azureml-defaults\"." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For this step, we use a different source_directory\n", - "source_directory = './compare'\n", - "print('Source directory for the step is {}.'.format(os.path.realpath(source_directory)))\n", - "\n", - "# All steps use the same Azure Machine Learning compute target as well\n", - "step2 = PythonScriptStep(name=\"compare_step\",\n", - " script_name=\"compare.py\", \n", - " compute_target=aml_compute, \n", - " source_directory=source_directory)\n", - "\n", - "# Use a RunConfiguration to specify some additional requirements for this step.\n", - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n", - "\n", - "# create a new runconfig object\n", - "run_config = RunConfiguration()\n", - "\n", - "# enable Docker \n", - "run_config.environment.docker.enabled = True\n", - "\n", - "# set Docker base image to the default CPU-based image\n", - "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n", - "\n", - "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", - "run_config.environment.python.user_managed_dependencies = False\n", - "\n", - "# specify CondaDependencies obj\n", - "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n", - "\n", - "# For this step, we use yet another source_directory\n", - "source_directory = './extract'\n", - "print('Source directory for the step is {}.'.format(os.path.realpath(source_directory)))\n", - "\n", - "step3 = PythonScriptStep(name=\"extract_step\",\n", - " script_name=\"extract.py\", \n", - " compute_target=aml_compute, \n", - " source_directory=source_directory,\n", - " runconfig=run_config)\n", - "\n", - "# list of steps to run\n", - "steps = [step1, step2, step3]\n", - "print(\"Step lists created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build the pipeline\n", - "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py). By deafult, all these steps will run in **parallel** once we submit the pipeline for run.\n", - "\n", - "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment(class)?view=azure-ml-py#submit-config--tags-none----kwargs-). When submit is called, a [PipelineRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Syntax\n", - "# Pipeline(workspace, \n", - "# steps, \n", - "# description=None, \n", - "# default_datastore_name=None, \n", - "# default_source_directory=None, \n", - "# resolve_closure=True, \n", - "# _workflow_provider=None, \n", - "# _service_endpoint=None)\n", - "\n", - "pipeline1 = Pipeline(workspace=ws, steps=steps)\n", - "print (\"Pipeline is built\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Validate the pipeline\n", - "You have the option to [validate](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#validate--) the pipeline prior to submitting for run. The platform runs validation steps such as checking for circular dependencies and parameter checks etc. even if you do not explicitly call validate method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline1.validate()\n", - "print(\"Pipeline validation complete\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit the pipeline\n", - "[Submitting](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#submit) the pipeline involves creating an [Experiment](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment?view=azure-ml-py) object and providing the built pipeline for submission. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Submit syntax\n", - "# submit(experiment_name, \n", - "# pipeline_parameters=None, \n", - "# continue_on_step_failure=False, \n", - "# regenerate_outputs=False)\n", - "\n", - "pipeline_run1 = Experiment(ws, 'Hello_World1').submit(pipeline1, regenerate_outputs=False)\n", - "print(\"Pipeline is submitted for execution\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:** If regenerate_outputs is set to True, a new submit will always force generation of all step outputs, and disallow data reuse for any step of this run. Once this run is complete, however, subsequent runs may reuse the results of this run.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Examine the pipeline run\n", - "\n", - "#### Use RunDetails Widget\n", - "We are going to use the RunDetails widget to examine the run of the pipeline. You can click each row below to get more details on the step runs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(pipeline_run1).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Use Pipeline SDK objects\n", - "You can cycle through the node_run objects and examine job logs, stdout, and stderr of each of the steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "step_runs = pipeline_run1.get_children()\n", - "for step_run in step_runs:\n", - " status = step_run.get_status()\n", - " print('Script:', step_run.name, 'status:', status)\n", - " \n", - " # Change this if you want to see details even if the Step has succeeded.\n", - " if status == \"Failed\":\n", - " joblog = step_run.get_job_log()\n", - " print('job log:', joblog)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get additonal run details\n", - "If you wait until the pipeline_run is finished, you may be able to get additional details on the run. **Since this is a blocking call, the following code is commented out.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#pipeline_run1.wait_for_completion()\n", - "#for step_run in pipeline_run1.get_children():\n", - "# print(\"{}: {}\".format(step_run.name, step_run.get_metrics()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running a few steps in sequence\n", - "Now let's see how we run a few steps in sequence. We already have three steps defined earlier. Let's *reuse* those steps for this part.\n", - "\n", - "We will reuse step1, step2, step3, but build the pipeline in such a way that we chain step3 after step2 and step2 after step1. Note that there is no explicit data dependency between these steps, but still steps can be made dependent by using the [run_after](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep?view=azure-ml-py#run-after-step-) construct." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "step2.run_after(step1)\n", - "step3.run_after(step2)\n", - "\n", - "# Try a loop\n", - "#step2.run_after(step3)\n", - "\n", - "# Now, construct the pipeline using the steps.\n", - "\n", - "# We can specify the \"final step\" in the chain, \n", - "# Pipeline will take care of \"transitive closure\" and \n", - "# figure out the implicit or explicit dependencies\n", - "# https://www.geeksforgeeks.org/transitive-closure-of-a-graph/\n", - "pipeline2 = Pipeline(workspace=ws, steps=[step3])\n", - "print (\"Pipeline is built\")\n", - "\n", - "pipeline2.validate()\n", - "print(\"Simple validation complete\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run2 = Experiment(ws, 'Hello_World2').submit(pipeline2)\n", - "print(\"Pipeline is submitted for execution\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(pipeline_run2).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next: Pipelines with data dependency\n", - "The next [notebook](https://aka.ms/pl-data-dep) demostrates how to construct a pipeline with data dependency." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Getting Started with Azure Machine Learning Pipelines", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - }, - "order_index": 1, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Azure Machine Learning Pipelines: Getting Started\n", + "\n", + "## Overview\n", + "\n", + "\n", + "A common scenario when using machine learning components is to have a data workflow that includes the following steps:\n", + "\n", + "- Preparing/preprocessing a given dataset for training, followed by\n", + "- Training a machine learning model on this data, and then\n", + "- Deploying this trained model in a separate environment, and finally\n", + "- Running a batch scoring task on another data set, using the trained model.\n", + "\n", + "Azure's Machine Learning pipelines give you a way to combine multiple steps like these into one configurable workflow, so that multiple agents/users can share and/or reuse this workflow. Machine learning pipelines thus provide a consistent, reproducible mechanism for building, evaluating, deploying, and running ML systems.\n", + "\n", + "To get more information about Azure machine learning pipelines, please read our [Azure Machine Learning Pipelines](https://aka.ms/pl-concept) overview, or the [readme article](https://aka.ms/pl-readme).\n", + "\n", + "In this notebook, we provide a gentle introduction to Azure machine learning pipelines. We build a pipeline that runs jobs unattended on different compute clusters; in this notebook, you'll see how to use the basic Azure ML SDK APIs for constructing this pipeline.\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites and Azure Machine Learning Basics\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Azure Machine Learning Imports\n", + "\n", + "In this first code cell, we import key Azure Machine Learning modules that we will use below. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "import tempfile\n", + "import azureml.core\n", + "from azureml.core import Workspace, Experiment, Datastore\n", + "from azureml.widgets import RunDetails\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pipeline-specific SDK imports\n", + "\n", + "Here, we import key pipeline modules, whose use will be illustrated in the examples below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Pipeline\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "\n", + "print(\"Pipeline SDK-specific imports completed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize Workspace\n", + "\n", + "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Getting Started notebook for ANML Pipelines" + "create workspace" + ] + }, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n", + "\n", + "# Default datastore\n", + "def_blob_store = ws.get_default_datastore() \n", + "# The following call GETS the Azure Blob Store associated with your workspace.\n", + "# Note that workspaceblobstore is **the name of this store and CANNOT BE CHANGED and must be used as is** \n", + "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n", + "print(\"Blobstore's name: {}\".format(def_blob_store.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Required data and script files for the the tutorial\n", + "Sample files required to finish this tutorial are already copied to the corresponding source_directory locations. Even though the .py provided in the samples does not have much \"ML work\" as a data scientist, you will work on this extensively as part of your work. To complete this tutorial, the contents of these files are not very important. The one-line files are for demostration purpose only." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Datastore concepts\n", + "A [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target. \n", + "\n", + "A Datastore can either be backed by an Azure File Storage (default) or by an Azure Blob Storage.\n", + "\n", + "In this next step, we will upload the training and test set into the workspace's default storage (File storage), and another piece of data to Azure Blob Storage. When to use [Azure Blobs](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), [Azure Files](https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction), or [Azure Disks](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/managed-disks-overview) is [detailed here](https://docs.microsoft.com/en-us/azure/storage/common/storage-decide-blobs-files-disks).\n", + "\n", + "**Please take good note of the concept of the datastore.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Upload data to default datastore\n", + "Default datastore on workspace is the Azure File storage. The workspace has a Blob storage associated with it as well. Let's upload a file to each of these storages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# download data file from remote\n", + "response = requests.get(\"https://dprepdata.blob.core.windows.net/demo/Titanic.csv\")\n", + "titanic_file = os.path.join(tempfile.mkdtemp(), \"Titanic.csv\")\n", + "with open(titanic_file, \"w\") as f:\n", + " f.write(response.content.decode(\"utf-8\"))\n", + "# get_default_datastore() gets the default Azure Blob Store associated with your workspace.\n", + "# Here we are reusing the def_blob_store object we obtained earlier\n", + "def_blob_store.upload_files([titanic_file], target_path=\"titanic\", overwrite=True)\n", + "print(\"Upload call completed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (Optional) See your files using Azure Portal\n", + "Once you successfully uploaded the files, you can browse to them (or upload more files) using [Azure Portal](https://portal.azure.com). At the portal, make sure you have selected your subscription (click *Resource Groups* and then select the subscription). Then look for your **Machine Learning Workspace** name. It has a link to your storage. Click on the storage link. It will take you to a page where you can see [Blobs](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), [Files](https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction), [Tables](https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-overview), and [Queues](https://docs.microsoft.com/en-us/azure/storage/queues/storage-queues-introduction). We have uploaded a file each to the Blob storage and to the File storage in the above step. You should be able to see both of these files in their respective locations. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Targets\n", + "A compute target specifies where to execute your program such as a remote Docker on a VM, or a cluster. A compute target needs to be addressable and accessible by you.\n", + "\n", + "**You need at least one compute target to send your payload to. We are planning to use Azure Machine Learning Compute exclusively for this tutorial for all steps. However in some cases you may require multiple compute targets as some steps may run in one compute target like Azure Machine Learning Compute, and some other steps in the same pipeline could run in a different compute target.**\n", + "\n", + "*The example belows show creating/retrieving/attaching to an Azure Machine Learning Compute instance.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### List of Compute Targets on the workspace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cts = ws.compute_targets\n", + "for ct in cts:\n", + " print(ct)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Retrieve or create a Azure Machine Learning compute\n", + "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\n", + "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. We will create an Azure Machine Learning Compute containing **STANDARD_D2_V2 CPU VMs**. This process is broken down into the following steps:\n", + "\n", + "1. Create the configuration\n", + "2. Create the Azure Machine Learning compute\n", + "\n", + "**This process will take about 3 minutes and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "aml_compute_target = \"cpu-cluster\"\n", + "try:\n", + " aml_compute = AmlCompute(ws, aml_compute_target)\n", + " print(\"found existing compute target.\")\n", + "except ComputeTargetException:\n", + " print(\"creating new compute target\")\n", + " \n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", + " min_nodes = 1, \n", + " max_nodes = 4) \n", + " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", + " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", + " \n", + "print(\"Azure Machine Learning Compute attached\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n", + "# example: un-comment the following line.\n", + "# print(aml_compute.get_status().serialize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Wait for this call to finish before proceeding (you will see the asterisk turning to a number).**\n", + "\n", + "Now that you have created the compute target, let's see what the workspace's compute_targets() function returns. You should now see one entry named 'amlcompute' of type AmlCompute." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Now that we have completed learning the basics of Azure Machine Learning (AML), let's go ahead and start understanding the Pipeline concepts.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating a Step in a Pipeline\n", + "A Step is a unit of execution. Step typically needs a target of execution (compute target), a script to execute, and may require script arguments and inputs, and can produce outputs. The step also could take a number of other parameters. Azure Machine Learning Pipelines provides the following built-in Steps:\n", + "\n", + "- [**PythonScriptStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py): Adds a step to run a Python script in a Pipeline.\n", + "- [**AdlaStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep?view=azure-ml-py): Adds a step to run U-SQL script using Azure Data Lake Analytics.\n", + "- [**DataTransferStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.data_transfer_step.datatransferstep?view=azure-ml-py): Transfers data between Azure Blob and Data Lake accounts.\n", + "- [**DatabricksStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py): Adds a DataBricks notebook as a step in a Pipeline.\n", + "- [**HyperDriveStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.hyper_drive_step.hyperdrivestep?view=azure-ml-py): Creates a Hyper Drive step for Hyper Parameter Tuning in a Pipeline.\n", + "- [**AzureBatchStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.azurebatch_step.azurebatchstep?view=azure-ml-py): Creates a step for submitting jobs to Azure Batch\n", + "- [**EstimatorStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep?view=azure-ml-py): Adds a step to run Estimator in a Pipeline.\n", + "- [**MpiStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.mpi_step.mpistep?view=azure-ml-py): Adds a step to run a MPI job in a Pipeline.\n", + "- [**AutoMLStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.automlstep?view=azure-ml-py): Creates a AutoML step in a Pipeline.\n", + "\n", + "The following code will create a PythonScriptStep to be executed in the Azure Machine Learning Compute we created above using train.py, one of the files already made available in the `source_directory`.\n", + "\n", + "A **PythonScriptStep** is a basic, built-in step to run a Python Script on a compute target. It takes a script name and optionally other parameters like arguments for the script, compute target, inputs and outputs. If no compute target is specified, default compute target for the workspace is used. You can also use a [**RunConfiguration**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) to specify requirements for the PythonScriptStep, such as conda dependencies and docker image.\n", + "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uses default values for PythonScriptStep construct.\n", + "\n", + "source_directory = './train'\n", + "print('Source directory for the step is {}.'.format(os.path.realpath(source_directory)))\n", + "\n", + "# Syntax\n", + "# PythonScriptStep(\n", + "# script_name, \n", + "# name=None, \n", + "# arguments=None, \n", + "# compute_target=None, \n", + "# runconfig=None, \n", + "# inputs=None, \n", + "# outputs=None, \n", + "# params=None, \n", + "# source_directory=None, \n", + "# allow_reuse=True, \n", + "# version=None, \n", + "# hash_paths=None)\n", + "# This returns a Step\n", + "step1 = PythonScriptStep(name=\"train_step\",\n", + " script_name=\"train.py\", \n", + " compute_target=aml_compute, \n", + " source_directory=source_directory,\n", + " allow_reuse=True)\n", + "print(\"Step1 created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** In the above call to PythonScriptStep(), the flag *allow_reuse* determines whether the step should reuse previous results when run with the same settings/inputs. This flag's default value is *True*; the default is set to *True* because, when inputs and parameters have not changed, we typically do not want to re-run a given pipeline step. \n", + "\n", + "If *allow_reuse* is set to *False*, a new run will always be generated for this step during pipeline execution. The *allow_reuse* flag can come in handy in situations where you do *not* want to re-run a pipeline step." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running a few steps in parallel\n", + "Here we are looking at a simple scenario where we are running a few steps (all involving PythonScriptStep) in parallel. Running nodes in **parallel** is the default behavior for steps in a pipeline.\n", + "\n", + "We already have one step defined earlier. Let's define few more steps. For step3, we are using customized conda-dependency, and job might fail when \"azureml-defaults\" (or other meta package) is not in pip-package list. We need to be aware if we are not using any of the meta packages (azureml-sdk, azureml-defaults, azureml-core), and we recommend installing \"azureml-defaults\"." + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For this step, we use a different source_directory\n", + "source_directory = './compare'\n", + "print('Source directory for the step is {}.'.format(os.path.realpath(source_directory)))\n", + "\n", + "# All steps use the same Azure Machine Learning compute target as well\n", + "step2 = PythonScriptStep(name=\"compare_step\",\n", + " script_name=\"compare.py\", \n", + " compute_target=aml_compute, \n", + " source_directory=source_directory)\n", + "\n", + "# Use a RunConfiguration to specify some additional requirements for this step.\n", + "from azureml.core.runconfig import RunConfiguration\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n", + "\n", + "# create a new runconfig object\n", + "run_config = RunConfiguration()\n", + "\n", + "# enable Docker \n", + "run_config.environment.docker.enabled = True\n", + "\n", + "# set Docker base image to the default CPU-based image\n", + "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n", + "\n", + "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", + "run_config.environment.python.user_managed_dependencies = False\n", + "\n", + "# specify CondaDependencies obj\n", + "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n", + "\n", + "# For this step, we use yet another source_directory\n", + "source_directory = './extract'\n", + "print('Source directory for the step is {}.'.format(os.path.realpath(source_directory)))\n", + "\n", + "step3 = PythonScriptStep(name=\"extract_step\",\n", + " script_name=\"extract.py\", \n", + " compute_target=aml_compute, \n", + " source_directory=source_directory,\n", + " runconfig=run_config)\n", + "\n", + "# list of steps to run\n", + "steps = [step1, step2, step3]\n", + "print(\"Step lists created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build the pipeline\n", + "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py). By deafult, all these steps will run in **parallel** once we submit the pipeline for run.\n", + "\n", + "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment(class)?view=azure-ml-py#submit-config--tags-none----kwargs-). When submit is called, a [PipelineRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax\n", + "# Pipeline(workspace, \n", + "# steps, \n", + "# description=None, \n", + "# default_datastore_name=None, \n", + "# default_source_directory=None, \n", + "# resolve_closure=True, \n", + "# _workflow_provider=None, \n", + "# _service_endpoint=None)\n", + "\n", + "pipeline1 = Pipeline(workspace=ws, steps=steps)\n", + "print (\"Pipeline is built\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validate the pipeline\n", + "You have the option to [validate](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#validate--) the pipeline prior to submitting for run. The platform runs validation steps such as checking for circular dependencies and parameter checks etc. even if you do not explicitly call validate method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline1.validate()\n", + "print(\"Pipeline validation complete\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Submit the pipeline\n", + "[Submitting](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#submit) the pipeline involves creating an [Experiment](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment?view=azure-ml-py) object and providing the built pipeline for submission. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Submit syntax\n", + "# submit(experiment_name, \n", + "# pipeline_parameters=None, \n", + "# continue_on_step_failure=False, \n", + "# regenerate_outputs=False)\n", + "\n", + "pipeline_run1 = Experiment(ws, 'Hello_World1').submit(pipeline1, regenerate_outputs=False)\n", + "print(\"Pipeline is submitted for execution\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** If regenerate_outputs is set to True, a new submit will always force generation of all step outputs, and disallow data reuse for any step of this run. Once this run is complete, however, subsequent runs may reuse the results of this run.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examine the pipeline run\n", + "\n", + "#### Use RunDetails Widget\n", + "We are going to use the RunDetails widget to examine the run of the pipeline. You can click each row below to get more details on the step runs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RunDetails(pipeline_run1).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use Pipeline SDK objects\n", + "You can cycle through the node_run objects and examine job logs, stdout, and stderr of each of the steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "step_runs = pipeline_run1.get_children()\n", + "for step_run in step_runs:\n", + " status = step_run.get_status()\n", + " print('Script:', step_run.name, 'status:', status)\n", + " \n", + " # Change this if you want to see details even if the Step has succeeded.\n", + " if status == \"Failed\":\n", + " joblog = step_run.get_job_log()\n", + " print('job log:', joblog)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get additonal run details\n", + "If you wait until the pipeline_run is finished, you may be able to get additional details on the run. **Since this is a blocking call, the following code is commented out.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#pipeline_run1.wait_for_completion()\n", + "#for step_run in pipeline_run1.get_children():\n", + "# print(\"{}: {}\".format(step_run.name, step_run.get_metrics()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running a few steps in sequence\n", + "Now let's see how we run a few steps in sequence. We already have three steps defined earlier. Let's *reuse* those steps for this part.\n", + "\n", + "We will reuse step1, step2, step3, but build the pipeline in such a way that we chain step3 after step2 and step2 after step1. Note that there is no explicit data dependency between these steps, but still steps can be made dependent by using the [run_after](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep?view=azure-ml-py#run-after-step-) construct." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "step2.run_after(step1)\n", + "step3.run_after(step2)\n", + "\n", + "# Try a loop\n", + "#step2.run_after(step3)\n", + "\n", + "# Now, construct the pipeline using the steps.\n", + "\n", + "# We can specify the \"final step\" in the chain, \n", + "# Pipeline will take care of \"transitive closure\" and \n", + "# figure out the implicit or explicit dependencies\n", + "# https://www.geeksforgeeks.org/transitive-closure-of-a-graph/\n", + "pipeline2 = Pipeline(workspace=ws, steps=[step3])\n", + "print (\"Pipeline is built\")\n", + "\n", + "pipeline2.validate()\n", + "print(\"Simple validation complete\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run2 = Experiment(ws, 'Hello_World2').submit(pipeline2)\n", + "print(\"Pipeline is submitted for execution\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RunDetails(pipeline_run2).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next: Pipelines with data dependency\n", + "The next [notebook](https://aka.ms/pl-data-dep) demostrates how to construct a pipeline with data dependency." + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "Getting Started with Azure Machine Learning Pipelines", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + }, + "order_index": 1, + "tags": [ + "None" + ], + "task": "Getting Started notebook for ANML Pipelines" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb index f9f2d4c92..39338c8d7 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb @@ -1,414 +1,414 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Azure Machine Learning Pipeline with AzureBatchStep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is used to demonstrate the use of AzureBatchStep in Azure Machine Learning Pipeline.\n", - "An AzureBatchStep will submit a job to an AzureBatch Compute to run a simple windows executable." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Azure Machine Learning and Pipeline SDK-specific Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Experiment\n", - "from azureml.core.compute import ComputeTarget, BatchCompute\n", - "from azureml.core.datastore import Datastore\n", - "from azureml.data.data_reference import DataReference\n", - "from azureml.exceptions import ComputeTargetException\n", - "from azureml.pipeline.core import Pipeline, PipelineData\n", - "from azureml.pipeline.steps import AzureBatchStep\n", - "\n", - "import os\n", - "from os import path\n", - "from tempfile import mkdtemp\n", - "\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json\n", - "\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, If you don't have a config.json file, please go through the [configuration Notebook](https://aka.ms/pl-config) located [here](https://github.com/Azure/MachineLearningNotebooks). \n", - "\n", - "This sets you up with a working config file that has information on your workspace, subscription id, etc. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "print('Workspace Name: ' + ws.name, \n", - " 'Azure Region: ' + ws.location, \n", - " 'Subscription Id: ' + ws.subscription_id, \n", - " 'Resource Group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Attach Batch Compute to Workspace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To submit jobs to Azure Batch service, you must attach your Azure Batch account to the workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-batchcompute-attach" - ] - }, - "outputs": [], - "source": [ - "batch_compute_name = 'mybatchcompute' # Name to associate with new compute in workspace\n", - "\n", - "# Batch account details needed to attach as compute to workspace\n", - "batch_account_name = \"\" # Name of the Batch account\n", - "batch_resource_group = \"\" # Name of the resource group which contains this account\n", - "\n", - "try:\n", - " # check if already attached\n", - " batch_compute = BatchCompute(ws, batch_compute_name)\n", - "except ComputeTargetException:\n", - " print('Attaching Batch compute...')\n", - " provisioning_config = BatchCompute.attach_configuration(resource_group=batch_resource_group, \n", - " account_name=batch_account_name)\n", - " batch_compute = ComputeTarget.attach(ws, batch_compute_name, provisioning_config)\n", - " batch_compute.wait_for_completion()\n", - " print(\"Provisioning state:{}\".format(batch_compute.provisioning_state))\n", - " print(\"Provisioning errors:{}\".format(batch_compute.provisioning_errors))\n", - "\n", - "print(\"Using Batch compute:{}\".format(batch_compute.cluster_resource_id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup Datastore" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Setting up the Blob storage associated with the workspace. \n", - "The following call retrieves the Azure Blob Store associated with your workspace. \n", - "Note that workspaceblobstore is **the name of this store and CANNOT BE CHANGED and must be used as is**. \n", - " \n", - "If you want to register another Datastore, please follow the instructions from here:\n", - "https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data#register-a-datastore" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore = Datastore(ws, \"workspaceblobstore\")\n", - "\n", - "print('Datastore details:')\n", - "print('Datastore Account Name: ' + datastore.account_name)\n", - "print('Datastore Workspace Name: ' + datastore.workspace.name)\n", - "print('Datastore Container Name: ' + datastore.container_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup Input and Output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this example we will upload a file in the provided Datastore. These are some helper methods to achieve that." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def create_local_file(content, file_name):\n", - " # create a file in a local temporary directory\n", - " temp_dir = mkdtemp()\n", - " with open(path.join(temp_dir, file_name), 'w') as f:\n", - " f.write(content)\n", - " return temp_dir\n", - "\n", - "\n", - "def upload_file_to_datastore(datastore, file_name, content):\n", - " src_dir = create_local_file(content=content, file_name=file_name)\n", - " datastore.upload(src_dir=src_dir, overwrite=True, show_progress=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we associate the input DataReference with an existing file in the provided Datastore. Feel free to upload the file of your choice manually or use the *upload_file_to_datastore* method. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "file_name=\"input.txt\"\n", - "\n", - "upload_file_to_datastore(datastore=datastore, \n", - " file_name=file_name, \n", - " content=\"this is the content of the file\")\n", - "\n", - "testdata = DataReference(datastore=datastore, \n", - " path_on_datastore=file_name, \n", - " data_reference_name=\"input\")\n", - "\n", - "outputdata = PipelineData(name=\"output\", datastore=datastore)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup AzureBatch Job Binaries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "AzureBatch can run a task within the job and here we put a simple .cmd file to be executed. Feel free to put any binaries in the folder, or modify the .cmd file as needed, they will be uploaded once we create the AzureBatch Step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "binaries_folder = \"azurebatch/job_binaries\"\n", - "if not os.path.isdir(binaries_folder):\n", - " os.makedirs(binaries_folder)\n", - "\n", - "file_name=\"azurebatch.cmd\"\n", - "with open(path.join(binaries_folder, file_name), 'w') as f:\n", - " f.write(\"copy \\\"%1\\\" \\\"%2\\\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an AzureBatchStep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "AzureBatchStep is used to submit a job to the attached Azure Batch compute.\n", - "- **name:** Name of the step\n", - "- **pool_id:** Name of the pool, it can be an existing pool, or one that will be created when the job is submitted\n", - "- **inputs:** List of inputs that will be processed by the job\n", - "- **outputs:** List of outputs the job will create\n", - "- **executable:** The executable that will run as part of the job\n", - "- **arguments:** Arguments for the executable. They can be plain string format, inputs, outputs or parameters\n", - "- **compute_target:** The compute target where the job will run.\n", - "- **source_directory:** The local directory with binaries to be executed by the job\n", - "\n", - "Optional parameters:\n", - "\n", - "- **create_pool:** Boolean flag to indicate whether create the pool before running the jobs\n", - "- **delete_batch_job_after_finish:** Boolean flag to indicate whether to delete the job from Batch account after it's finished\n", - "- **delete_batch_pool_after_finish:** Boolean flag to indicate whether to delete the pool after the job finishes\n", - "- **is_positive_exit_code_failure:** Boolean flag to indicate if the job fails if the task exists with a positive code\n", - "- **vm_image_urn:** If create_pool is true and VM uses VirtualMachineConfiguration. \n", - " Value format: 'urn:publisher:offer:sku'. \n", - " Example: urn:MicrosoftWindowsServer:WindowsServer:2012-R2-Datacenter \n", - " For more details: \n", - " https://docs.microsoft.com/en-us/azure/virtual-machines/windows/cli-ps-findimage#table-of-commonly-used-windows-images and \n", - " https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage#find-specific-images\n", - "- **run_task_as_admin:** Boolean flag to indicate if the task should run with Admin privileges\n", - "- **target_compute_nodes:** Assumes create_pool is true, indicates how many compute nodes will be added to the pool\n", - "- **source_directory:** Local folder that contains the module binaries, executable, assemblies etc.\n", - "- **executable:** Name of the command/executable that will be executed as part of the job\n", - "- **arguments:** Arguments for the command/executable\n", - "- **inputs:** List of input port bindings\n", - "- **outputs:** List of output port bindings\n", - "- **vm_size:** If create_pool is true, indicating Virtual machine size of the compute nodes\n", - "- **compute_target:** BatchCompute compute\n", - "- **allow_reuse:** Whether the module should reuse previous results when run with the same settings/inputs\n", - "- **version:** A version tag to denote a change in functionality for the module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "azurebatchstep-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "step = AzureBatchStep(\n", - " name=\"Azure Batch Job\",\n", - " pool_id=\"MyPoolName\", # Replace this with the pool name of your choice\n", - " inputs=[testdata],\n", - " outputs=[outputdata],\n", - " executable=\"azurebatch.cmd\",\n", - " arguments=[testdata, outputdata],\n", - " compute_target=batch_compute,\n", - " source_directory=binaries_folder,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and Submit the Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline = Pipeline(workspace=ws, steps=[step])\n", - "pipeline_run = Experiment(ws, 'azurebatch_sample').submit(pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize the Running Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "tutorial", - "compute": [ - "Azure Batch" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Azure Machine Learning Pipeline with AzureBatchStep", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "order_index": 9, - "star_tag": [ - "None" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Azure Machine Learning Pipeline with AzureBatchStep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook is used to demonstrate the use of AzureBatchStep in Azure Machine Learning Pipeline.\n", + "An AzureBatchStep will submit a job to an AzureBatch Compute to run a simple windows executable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Azure Machine Learning and Pipeline SDK-specific Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "from azureml.core import Workspace, Experiment\n", + "from azureml.core.compute import ComputeTarget, BatchCompute\n", + "from azureml.core.datastore import Datastore\n", + "from azureml.data.data_reference import DataReference\n", + "from azureml.exceptions import ComputeTargetException\n", + "from azureml.pipeline.core import Pipeline, PipelineData\n", + "from azureml.pipeline.steps import AzureBatchStep\n", + "\n", + "import os\n", + "from os import path\n", + "from tempfile import mkdtemp\n", + "\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json\n", + "\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, If you don't have a config.json file, please go through the [configuration Notebook](https://aka.ms/pl-config) located [here](https://github.com/Azure/MachineLearningNotebooks). \n", + "\n", + "This sets you up with a working config file that has information on your workspace, subscription id, etc. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "\n", + "print('Workspace Name: ' + ws.name, \n", + " 'Azure Region: ' + ws.location, \n", + " 'Subscription Id: ' + ws.subscription_id, \n", + " 'Resource Group: ' + ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Attach Batch Compute to Workspace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To submit jobs to Azure Batch service, you must attach your Azure Batch account to the workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of AzureBatchStep" + "sample-batchcompute-attach" + ] + }, + "outputs": [], + "source": [ + "batch_compute_name = 'mybatchcompute' # Name to associate with new compute in workspace\n", + "\n", + "# Batch account details needed to attach as compute to workspace\n", + "batch_account_name = \"\" # Name of the Batch account\n", + "batch_resource_group = \"\" # Name of the resource group which contains this account\n", + "\n", + "try:\n", + " # check if already attached\n", + " batch_compute = BatchCompute(ws, batch_compute_name)\n", + "except ComputeTargetException:\n", + " print('Attaching Batch compute...')\n", + " provisioning_config = BatchCompute.attach_configuration(resource_group=batch_resource_group, \n", + " account_name=batch_account_name)\n", + " batch_compute = ComputeTarget.attach(ws, batch_compute_name, provisioning_config)\n", + " batch_compute.wait_for_completion()\n", + " print(\"Provisioning state:{}\".format(batch_compute.provisioning_state))\n", + " print(\"Provisioning errors:{}\".format(batch_compute.provisioning_errors))\n", + "\n", + "print(\"Using Batch compute:{}\".format(batch_compute.cluster_resource_id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup Datastore" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Setting up the Blob storage associated with the workspace. \n", + "The following call retrieves the Azure Blob Store associated with your workspace. \n", + "Note that workspaceblobstore is **the name of this store and CANNOT BE CHANGED and must be used as is**. \n", + " \n", + "If you want to register another Datastore, please follow the instructions from here:\n", + "https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data#register-a-datastore" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "datastore = Datastore(ws, \"workspaceblobstore\")\n", + "\n", + "print('Datastore details:')\n", + "print('Datastore Account Name: ' + datastore.account_name)\n", + "print('Datastore Workspace Name: ' + datastore.workspace.name)\n", + "print('Datastore Container Name: ' + datastore.container_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup Input and Output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this example we will upload a file in the provided Datastore. These are some helper methods to achieve that." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_local_file(content, file_name):\n", + " # create a file in a local temporary directory\n", + " temp_dir = mkdtemp()\n", + " with open(path.join(temp_dir, file_name), 'w') as f:\n", + " f.write(content)\n", + " return temp_dir\n", + "\n", + "\n", + "def upload_file_to_datastore(datastore, file_name, content):\n", + " src_dir = create_local_file(content=content, file_name=file_name)\n", + " datastore.upload(src_dir=src_dir, overwrite=True, show_progress=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we associate the input DataReference with an existing file in the provided Datastore. Feel free to upload the file of your choice manually or use the *upload_file_to_datastore* method. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file_name=\"input.txt\"\n", + "\n", + "upload_file_to_datastore(datastore=datastore, \n", + " file_name=file_name, \n", + " content=\"this is the content of the file\")\n", + "\n", + "testdata = DataReference(datastore=datastore, \n", + " path_on_datastore=file_name, \n", + " data_reference_name=\"input\")\n", + "\n", + "outputdata = PipelineData(name=\"output\", datastore=datastore)" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup AzureBatch Job Binaries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "AzureBatch can run a task within the job and here we put a simple .cmd file to be executed. Feel free to put any binaries in the folder, or modify the .cmd file as needed, they will be uploaded once we create the AzureBatch Step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "binaries_folder = \"azurebatch/job_binaries\"\n", + "if not os.path.isdir(binaries_folder):\n", + " os.makedirs(binaries_folder)\n", + "\n", + "file_name=\"azurebatch.cmd\"\n", + "with open(path.join(binaries_folder, file_name), 'w') as f:\n", + " f.write(\"copy \\\"%1\\\" \\\"%2\\\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an AzureBatchStep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "AzureBatchStep is used to submit a job to the attached Azure Batch compute.\n", + "- **name:** Name of the step\n", + "- **pool_id:** Name of the pool, it can be an existing pool, or one that will be created when the job is submitted\n", + "- **inputs:** List of inputs that will be processed by the job\n", + "- **outputs:** List of outputs the job will create\n", + "- **executable:** The executable that will run as part of the job\n", + "- **arguments:** Arguments for the executable. They can be plain string format, inputs, outputs or parameters\n", + "- **compute_target:** The compute target where the job will run.\n", + "- **source_directory:** The local directory with binaries to be executed by the job\n", + "\n", + "Optional parameters:\n", + "\n", + "- **create_pool:** Boolean flag to indicate whether create the pool before running the jobs\n", + "- **delete_batch_job_after_finish:** Boolean flag to indicate whether to delete the job from Batch account after it's finished\n", + "- **delete_batch_pool_after_finish:** Boolean flag to indicate whether to delete the pool after the job finishes\n", + "- **is_positive_exit_code_failure:** Boolean flag to indicate if the job fails if the task exists with a positive code\n", + "- **vm_image_urn:** If create_pool is true and VM uses VirtualMachineConfiguration. \n", + " Value format: 'urn:publisher:offer:sku'. \n", + " Example: urn:MicrosoftWindowsServer:WindowsServer:2012-R2-Datacenter \n", + " For more details: \n", + " https://docs.microsoft.com/en-us/azure/virtual-machines/windows/cli-ps-findimage#table-of-commonly-used-windows-images and \n", + " https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage#find-specific-images\n", + "- **run_task_as_admin:** Boolean flag to indicate if the task should run with Admin privileges\n", + "- **target_compute_nodes:** Assumes create_pool is true, indicates how many compute nodes will be added to the pool\n", + "- **source_directory:** Local folder that contains the module binaries, executable, assemblies etc.\n", + "- **executable:** Name of the command/executable that will be executed as part of the job\n", + "- **arguments:** Arguments for the command/executable\n", + "- **inputs:** List of input port bindings\n", + "- **outputs:** List of output port bindings\n", + "- **vm_size:** If create_pool is true, indicating Virtual machine size of the compute nodes\n", + "- **compute_target:** BatchCompute compute\n", + "- **allow_reuse:** Whether the module should reuse previous results when run with the same settings/inputs\n", + "- **version:** A version tag to denote a change in functionality for the module" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "azurebatchstep-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "step = AzureBatchStep(\n", + " name=\"Azure Batch Job\",\n", + " pool_id=\"MyPoolName\", # Replace this with the pool name of your choice\n", + " inputs=[testdata],\n", + " outputs=[outputdata],\n", + " executable=\"azurebatch.cmd\",\n", + " arguments=[testdata, outputdata],\n", + " compute_target=batch_compute,\n", + " source_directory=binaries_folder,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and Submit the Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline(workspace=ws, steps=[step])\n", + "pipeline_run = Experiment(ws, 'azurebatch_sample').submit(pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the Running Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(pipeline_run).show()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "tutorial", + "compute": [ + "Azure Batch" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "Azure Machine Learning Pipeline with AzureBatchStep", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "order_index": 9, + "star_tag": [ + "None" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of AzureBatchStep" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb index d349b5b1d..c20d03f71 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb @@ -1,500 +1,500 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# How to create Module, ModuleVersion, and use them in a pipeline with ModuleStep.\n", - "In this notebook, we introduce the concept of versioned modules and how to use them in an Azure Machine Learning Pipeline.\n", - "\n", - "The core idea behind introducing Module, ModuleVersion and ModuleStep is to allow the separation between reusable executable components and their actual usage. These reusable software components (such as scripts or executables) can be used in different scenarios and by different users. This follows the same idea of separating software frameworks/libraries and their actual usage in applications. Module and ModuleVersion take the role of the reusable executable components where ModuleStep is there to link them to an actual usage.\n", - "\n", - "A module is an elaborated container of its versions, where each version is the actual computational unit. It is up to users to define the semantics of this hierarchical structure of container and versions. For example, there could be different versions for different use cases, development progress, etc.\n", - "\n", - "Each ModuleVersion may have inputs, outputs and rely on parameters and its environment configuration to operate.\n", - "\n", - "Because Modules can now be separated from execution in a pipeline, there's a need for a mechanism to reconnect these again, and allow using Modules and their versions in a Pipeline. This is done using a new kind of Step called ModuleStep, which allows embedding a Module (and more precisely, a version of it) in a Pipeline.\n", - " \n", - "This notebook shows the usage of a module that computes the sum and product of two numbers. As a module can only be used as a step in a pipeline, we define two different versions for it, to be used in two different use cases:\n", - "\n", - "1) As the module powering the initial step of a pipeline, where the step does not receive any input from preceding steps.\n", - "\n", - "2) As a module powering a step in the pipeline that receives inputs from preceding steps.\n", - "\n", - "Once these two versions are defined, we show how to embed them as steps in the pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites and AML Basics\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n", - "\n", - "### Initialization Steps" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace, Experiment, Datastore, RunConfiguration\n", - "from azureml.core.compute import AmlCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "from azureml.pipeline.core import Pipeline, PipelineData, PipelineParameter\n", - "from azureml.pipeline.core.graph import InputPortDef, OutputPortDef\n", - "from azureml.pipeline.core.module import Module\n", - "from azureml.pipeline.steps import ModuleStep\n", - "\n", - "workspace = Workspace.from_config()\n", - "print(workspace.name, workspace.resource_group, workspace.location, workspace.subscription_id, sep = '\\n')\n", - "\n", - "aml_compute_target = \"cpu-cluster\"\n", - "try:\n", - " aml_compute = AmlCompute(workspace, aml_compute_target)\n", - " print(\"Found existing compute target: {}\".format(aml_compute_target))\n", - "except:\n", - " print(\"Creating new compute target: {}\".format(aml_compute_target))\n", - " \n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", - " min_nodes = 1, \n", - " max_nodes = 4) \n", - " aml_compute = ComputeTarget.create(workspace, aml_compute_target, provisioning_config)\n", - " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "datastore = Datastore(workspace=workspace, name=\"workspaceblobstore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a Module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Module is a container that manages computational units. Each such computational unit is a version of the module, and is called a ModuleVersion. We start by either creating a module or fetching an existing one by its ID or by its name." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "module = Module.create(workspace, name=\"AddAndMultiply\", description=\"A module that adds and multiplies\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculation entry ModuleVersion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A ModuleVersion is an actual computational unit. Defining it involves defining its inputs, outputs, the computation and other configuration items. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we define that this version is to be used at the beginning of the pipeline, hence does not have incoming ports, only outgoing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "module-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "out_sum = OutputPortDef(name=\"out_sum\", default_datastore_name=datastore.name, default_datastore_mode=\"mount\", \n", - " label=\"Sum of two numbers\")\n", - "out_prod = OutputPortDef(name=\"out_prod\", default_datastore_name=datastore.name, default_datastore_mode=\"mount\", \n", - " label=\"Product of two numbers\")\n", - "entry_version = module.publish_python_script(\"calculate.py\", \"initial\", \n", - " inputs=[], outputs=[out_sum, out_prod], params = {\"initialNum\":12},\n", - " version=\"1\", source_directory=\"./calc\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculation middle/end ModuleVersion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another version of the module performs a computation in the middle or at the end of the pipeline. This version has both outputs and inputs, as it is to be either followed by another computation, or emits its outputs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "moduleversion-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "in1_mid = InputPortDef(name=\"in1\", default_datastore_mode=\"mount\", \n", - " default_data_reference_name=datastore.name, label=\"First input number\")\n", - "in2_mid = InputPortDef(name=\"in2\", default_datastore_mode=\"mount\", \n", - " default_data_reference_name=datastore.name, label=\"Second input number\")\n", - "out_sum_mid = OutputPortDef(name=\"out_sum\", default_datastore_name=datastore.name, default_datastore_mode=\"mount\",\n", - " label=\"Sum of two numbers\")\n", - "out_prod_mid = OutputPortDef(name=\"out_prod\", default_datastore_name=datastore.name, default_datastore_mode=\"mount\",\n", - " label=\"Product of two numbers\")\n", - "module.publish_python_script(\n", - " \"calculate.py\", \"middle\", inputs=[in1_mid, in2_mid], outputs=[out_sum_mid, out_prod_mid], version=\"2\", is_default=True, \n", - " source_directory=\"./calc\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using a Module in a Pipeline with ModuleStep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Introduction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using a Module, and more precisely, a specific version, in a pipeline is done via a specialized kind of step. This step is called ModuleStep. It is used as a step in a pipeline, one that holds enough information that allows pinpointing to a specific ModuleVersion. \n", - "\n", - "Another responsibility of a ModuleStep is to wire the actual data that is used in the pipeline to the inputs/outputs definitions of the ModuleVersion. This wiring is done by mapping each of the inputs and the outputs definitions to a data element in the pipeline. Defining the wiring is done using a dictionary whose keys are the name of the inputs/outputs, and the mapped value is the data element (e.g., a PipelineData object)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Deciding which ModuleVersion to use - resolving" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is up to the ModuleStep to decide which ModuleVersion to use. That decision is based on the parameters given to the ModuleStep, and it follows this process:\n", - "1. If a ModuleVersion object was provided, use it.\n", - "2. For the given Module object, if a version was provided, use it.\n", - "3. The given Module object resolves which is the right version:\n", - " 1. If a default ModuleVersion was defined for the Module, use it.\n", - " 2. If all the versions of the ModuleVersions in the Module follow semantic versioning, take the one with the highest version.\n", - " 3. Take the ModuleVersion with the most recent update." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Step and its wires" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

      The first step in a pipeline does not have incoming inputs, but it does have outputs. For that we'd use the ModuleVersion that was designed for this use case.

      \n", - "We start off by preparing the outgoing edges as two PipelineData objects (to be later linked to another step), as well as wiring these to the moduleVersion's outputs by creating a dictionary mapping." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "first_sum = PipelineData(\"sum_out\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", - "first_prod = PipelineData(\"prod_out\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", - "step_output_wiring = {\"out_sum\":first_sum, \"out_prod\":first_prod}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initial ModuleStep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

      In order for the step to know which ModuleVersion to use, we provided the initial ModuleVersion object. We wire the ModuleVersion's outputs with the step_output_wiring map we just created.

      \n", - "The initial ModuleStep uses the ModuleVersion that does not have inputs from the pipeline, however, it still needs to receive two numbers to operate upon. We'll provide these numbers as arguments to the step. The first is provided as a parameter, the other one is hard coded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "first_num_param = PipelineParameter(name=\"initialNum\", default_value=17)\n", - "first_step = ModuleStep(module_version=entry_version,\n", - " inputs_map={}, outputs_map=step_output_wiring, \n", - " runconfig=RunConfiguration(), \n", - " compute_target=aml_compute, \n", - " arguments = [\"--output_sum\", first_sum, \n", - " \"--output_product\", first_prod,\n", - " \"--arg_num1\", first_num_param, \n", - " \"--arg_num2\", \"2\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Second step and its wires" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second step in the pipeline receives its inputs from the previous step, and emits its outputs to the next step. Thus the ModuleStep here needs a different kind of ModuleVersion, one that has both inputs and outputs defined for. We have defined such ModuleVersion, and moreover, defined it to be the default version of our Module. This allows us to provide to the ModuleStep the Module object, which would resolve to that default ModuleVersion when needed." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wires" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The wiring to the previous step relies on the PipelineData objects we defined before, and for them we create a new dictionary mapping to the ModuleVersion. The wiring to the next step requires us to define another pair of PipelineData objects, for which also a dictionary mapping is needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "modulestep-remarks-sample2" - ] - }, - "outputs": [], - "source": [ - "middle_step_input_wiring = {\"in1\":first_sum, \"in2\":first_prod}\n", - "middle_sum = PipelineData(\"middle_sum\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", - "middle_prod = PipelineData(\"middle_prod\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", - "middle_step_output_wiring = {\"out_sum\":middle_sum, \"out_prod\":middle_prod}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Middle ModuleStep - resolving to the default ModuleVersion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "modulestep-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "middle_step = ModuleStep(module=module,\n", - " inputs_map= middle_step_input_wiring, \n", - " outputs_map= middle_step_output_wiring,\n", - " runconfig=RunConfiguration(), compute_target=aml_compute,\n", - " arguments = [\"--file_num1\", first_sum, \"--file_num2\", first_prod,\n", - " \"--output_sum\", middle_sum, \"--output_product\", middle_prod])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## End step and its wires" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The last step in the pipeline also has input and outputs, thus its configuration would be similar to the previous step. In this case we would still use Pipeline data as the step's outputs, even though they are not read by any following step, but rather act as the end result of the pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Wires" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "last_step_input_wiring = {\"in1\":middle_sum, \"in2\":middle_prod}\n", - "end_sum = PipelineData(\"end_sum\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", - "end_prod = PipelineData(\"end_prod\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", - "last_step_output_wiring = {\"out_sum\":end_sum, \"out_prod\":end_prod}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Last ModuleStep - specifing the exact version" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "end_step = ModuleStep(module=module, version=\"2\",\n", - " inputs_map= last_step_input_wiring,\n", - " outputs_map= last_step_output_wiring,\n", - " runconfig=RunConfiguration(), compute_target=aml_compute,\n", - " arguments=[\"--file_num1\", middle_sum, \"--file_num2\", middle_prod,\n", - " \"--output_sum\", end_sum, \"--output_product\", end_prod])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pipeline, experiment, submission" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The last thing to be done is to create a pipeline out of the previously defined steps, then create an experiment and submit the pipeline to the experiment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline = Pipeline(workspace=workspace, steps=[first_step, middle_step, end_step])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment = Experiment(workspace, 'testmodulestesp')\n", - "experiment.submit(pipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "How to use ModuleStep with AML Pipelines", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "order_index": 14, - "star_tag": [ - "featured" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# How to create Module, ModuleVersion, and use them in a pipeline with ModuleStep.\n", + "In this notebook, we introduce the concept of versioned modules and how to use them in an Azure Machine Learning Pipeline.\n", + "\n", + "The core idea behind introducing Module, ModuleVersion and ModuleStep is to allow the separation between reusable executable components and their actual usage. These reusable software components (such as scripts or executables) can be used in different scenarios and by different users. This follows the same idea of separating software frameworks/libraries and their actual usage in applications. Module and ModuleVersion take the role of the reusable executable components where ModuleStep is there to link them to an actual usage.\n", + "\n", + "A module is an elaborated container of its versions, where each version is the actual computational unit. It is up to users to define the semantics of this hierarchical structure of container and versions. For example, there could be different versions for different use cases, development progress, etc.\n", + "\n", + "Each ModuleVersion may have inputs, outputs and rely on parameters and its environment configuration to operate.\n", + "\n", + "Because Modules can now be separated from execution in a pipeline, there's a need for a mechanism to reconnect these again, and allow using Modules and their versions in a Pipeline. This is done using a new kind of Step called ModuleStep, which allows embedding a Module (and more precisely, a version of it) in a Pipeline.\n", + " \n", + "This notebook shows the usage of a module that computes the sum and product of two numbers. As a module can only be used as a step in a pipeline, we define two different versions for it, to be used in two different use cases:\n", + "\n", + "1) As the module powering the initial step of a pipeline, where the step does not receive any input from preceding steps.\n", + "\n", + "2) As a module powering a step in the pipeline that receives inputs from preceding steps.\n", + "\n", + "Once these two versions are defined, we show how to embed them as steps in the pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites and AML Basics\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n", + "\n", + "### Initialization Steps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace, Experiment, Datastore, RunConfiguration\n", + "from azureml.core.compute import AmlCompute\n", + "from azureml.core.compute import ComputeTarget\n", + "from azureml.pipeline.core import Pipeline, PipelineData, PipelineParameter\n", + "from azureml.pipeline.core.graph import InputPortDef, OutputPortDef\n", + "from azureml.pipeline.core.module import Module\n", + "from azureml.pipeline.steps import ModuleStep\n", + "\n", + "workspace = Workspace.from_config()\n", + "print(workspace.name, workspace.resource_group, workspace.location, workspace.subscription_id, sep = '\\n')\n", + "\n", + "aml_compute_target = \"cpu-cluster\"\n", + "try:\n", + " aml_compute = AmlCompute(workspace, aml_compute_target)\n", + " print(\"Found existing compute target: {}\".format(aml_compute_target))\n", + "except:\n", + " print(\"Creating new compute target: {}\".format(aml_compute_target))\n", + " \n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", + " min_nodes = 1, \n", + " max_nodes = 4) \n", + " aml_compute = ComputeTarget.create(workspace, aml_compute_target, provisioning_config)\n", + " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", + "\n", + "datastore = Datastore(workspace=workspace, name=\"workspaceblobstore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Module is a container that manages computational units. Each such computational unit is a version of the module, and is called a ModuleVersion. We start by either creating a module or fetching an existing one by its ID or by its name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "module = Module.create(workspace, name=\"AddAndMultiply\", description=\"A module that adds and multiplies\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculation entry ModuleVersion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A ModuleVersion is an actual computational unit. Defining it involves defining its inputs, outputs, the computation and other configuration items. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we define that this version is to be used at the beginning of the pipeline, hence does not have incoming ports, only outgoing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "module-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "out_sum = OutputPortDef(name=\"out_sum\", default_datastore_name=datastore.name, default_datastore_mode=\"mount\", \n", + " label=\"Sum of two numbers\")\n", + "out_prod = OutputPortDef(name=\"out_prod\", default_datastore_name=datastore.name, default_datastore_mode=\"mount\", \n", + " label=\"Product of two numbers\")\n", + "entry_version = module.publish_python_script(\"calculate.py\", \"initial\", \n", + " inputs=[], outputs=[out_sum, out_prod], params = {\"initialNum\":12},\n", + " version=\"1\", source_directory=\"./calc\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculation middle/end ModuleVersion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another version of the module performs a computation in the middle or at the end of the pipeline. This version has both outputs and inputs, as it is to be either followed by another computation, or emits its outputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of ModuleStep" + "moduleversion-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "in1_mid = InputPortDef(name=\"in1\", default_datastore_mode=\"mount\", \n", + " default_data_reference_name=datastore.name, label=\"First input number\")\n", + "in2_mid = InputPortDef(name=\"in2\", default_datastore_mode=\"mount\", \n", + " default_data_reference_name=datastore.name, label=\"Second input number\")\n", + "out_sum_mid = OutputPortDef(name=\"out_sum\", default_datastore_name=datastore.name, default_datastore_mode=\"mount\",\n", + " label=\"Sum of two numbers\")\n", + "out_prod_mid = OutputPortDef(name=\"out_prod\", default_datastore_name=datastore.name, default_datastore_mode=\"mount\",\n", + " label=\"Product of two numbers\")\n", + "module.publish_python_script(\n", + " \"calculate.py\", \"middle\", inputs=[in1_mid, in2_mid], outputs=[out_sum_mid, out_prod_mid], version=\"2\", is_default=True, \n", + " source_directory=\"./calc\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using a Module in a Pipeline with ModuleStep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using a Module, and more precisely, a specific version, in a pipeline is done via a specialized kind of step. This step is called ModuleStep. It is used as a step in a pipeline, one that holds enough information that allows pinpointing to a specific ModuleVersion. \n", + "\n", + "Another responsibility of a ModuleStep is to wire the actual data that is used in the pipeline to the inputs/outputs definitions of the ModuleVersion. This wiring is done by mapping each of the inputs and the outputs definitions to a data element in the pipeline. Defining the wiring is done using a dictionary whose keys are the name of the inputs/outputs, and the mapped value is the data element (e.g., a PipelineData object)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Deciding which ModuleVersion to use - resolving" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is up to the ModuleStep to decide which ModuleVersion to use. That decision is based on the parameters given to the ModuleStep, and it follows this process:\n", + "1. If a ModuleVersion object was provided, use it.\n", + "2. For the given Module object, if a version was provided, use it.\n", + "3. The given Module object resolves which is the right version:\n", + " 1. If a default ModuleVersion was defined for the Module, use it.\n", + " 2. If all the versions of the ModuleVersions in the Module follow semantic versioning, take the one with the highest version.\n", + " 3. Take the ModuleVersion with the most recent update." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### First Step and its wires" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

      The first step in a pipeline does not have incoming inputs, but it does have outputs. For that we'd use the ModuleVersion that was designed for this use case.

      \n", + "We start off by preparing the outgoing edges as two PipelineData objects (to be later linked to another step), as well as wiring these to the moduleVersion's outputs by creating a dictionary mapping." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "first_sum = PipelineData(\"sum_out\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", + "first_prod = PipelineData(\"prod_out\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", + "step_output_wiring = {\"out_sum\":first_sum, \"out_prod\":first_prod}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initial ModuleStep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

      In order for the step to know which ModuleVersion to use, we provided the initial ModuleVersion object. We wire the ModuleVersion's outputs with the step_output_wiring map we just created.

      \n", + "The initial ModuleStep uses the ModuleVersion that does not have inputs from the pipeline, however, it still needs to receive two numbers to operate upon. We'll provide these numbers as arguments to the step. The first is provided as a parameter, the other one is hard coded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "first_num_param = PipelineParameter(name=\"initialNum\", default_value=17)\n", + "first_step = ModuleStep(module_version=entry_version,\n", + " inputs_map={}, outputs_map=step_output_wiring, \n", + " runconfig=RunConfiguration(), \n", + " compute_target=aml_compute, \n", + " arguments = [\"--output_sum\", first_sum, \n", + " \"--output_product\", first_prod,\n", + " \"--arg_num1\", first_num_param, \n", + " \"--arg_num2\", \"2\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Second step and its wires" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second step in the pipeline receives its inputs from the previous step, and emits its outputs to the next step. Thus the ModuleStep here needs a different kind of ModuleVersion, one that has both inputs and outputs defined for. We have defined such ModuleVersion, and moreover, defined it to be the default version of our Module. This allows us to provide to the ModuleStep the Module object, which would resolve to that default ModuleVersion when needed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wires" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The wiring to the previous step relies on the PipelineData objects we defined before, and for them we create a new dictionary mapping to the ModuleVersion. The wiring to the next step requires us to define another pair of PipelineData objects, for which also a dictionary mapping is needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "modulestep-remarks-sample2" + ] + }, + "outputs": [], + "source": [ + "middle_step_input_wiring = {\"in1\":first_sum, \"in2\":first_prod}\n", + "middle_sum = PipelineData(\"middle_sum\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", + "middle_prod = PipelineData(\"middle_prod\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", + "middle_step_output_wiring = {\"out_sum\":middle_sum, \"out_prod\":middle_prod}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Middle ModuleStep - resolving to the default ModuleVersion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "modulestep-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "middle_step = ModuleStep(module=module,\n", + " inputs_map= middle_step_input_wiring, \n", + " outputs_map= middle_step_output_wiring,\n", + " runconfig=RunConfiguration(), compute_target=aml_compute,\n", + " arguments = [\"--file_num1\", first_sum, \"--file_num2\", first_prod,\n", + " \"--output_sum\", middle_sum, \"--output_product\", middle_prod])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## End step and its wires" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last step in the pipeline also has input and outputs, thus its configuration would be similar to the previous step. In this case we would still use Pipeline data as the step's outputs, even though they are not read by any following step, but rather act as the end result of the pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Wires" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "last_step_input_wiring = {\"in1\":middle_sum, \"in2\":middle_prod}\n", + "end_sum = PipelineData(\"end_sum\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", + "end_prod = PipelineData(\"end_prod\", datastore=datastore, output_mode=\"mount\",is_directory=False)\n", + "last_step_output_wiring = {\"out_sum\":end_sum, \"out_prod\":end_prod}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Last ModuleStep - specifing the exact version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "end_step = ModuleStep(module=module, version=\"2\",\n", + " inputs_map= last_step_input_wiring,\n", + " outputs_map= last_step_output_wiring,\n", + " runconfig=RunConfiguration(), compute_target=aml_compute,\n", + " arguments=[\"--file_num1\", middle_sum, \"--file_num2\", middle_prod,\n", + " \"--output_sum\", end_sum, \"--output_product\", end_prod])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pipeline, experiment, submission" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last thing to be done is to create a pipeline out of the previously defined steps, then create an experiment and submit the pipeline to the experiment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline(workspace=workspace, steps=[first_step, middle_step, end_step])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "experiment = Experiment(workspace, 'testmodulestesp')\n", + "experiment.submit(pipeline)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "How to use ModuleStep with AML Pipelines", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "order_index": 14, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of ModuleStep" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb index 3b07b1d3d..2d83b6bf2 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb @@ -1,291 +1,291 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to Use Pipeline Drafts\n", - "In this notebook, we will show you how you can use Pipeline Drafts. Pipeline Drafts are mutable pipelines which can be used to submit runs and create Published Pipelines." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites and AML Basics\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n", - "\n", - "### Initialization Steps" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace\n", - "from azureml.widgets import RunDetails\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Target\n", - "Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "aml_compute_target = \"cpu-cluster\"\n", - "try:\n", - " aml_compute = AmlCompute(ws, aml_compute_target)\n", - " print(\"Found existing compute target: {}\".format(aml_compute_target))\n", - "except ComputeTargetException:\n", - " print(\"Creating new compute target: {}\".format(aml_compute_target))\n", - " \n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", - " min_nodes = 1, \n", - " max_nodes = 4) \n", - " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", - " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build a Pipeline\n", - "Build a simple pipeline to use to create a PipelineDraft." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "\n", - "source_directory = \"publish_run_train\"\n", - "\n", - "train_step = PythonScriptStep(\n", - " name=\"Training_Step\",\n", - " script_name=\"train.py\", \n", - " compute_target=aml_compute_target, \n", - " source_directory=source_directory)\n", - "print(\"train step created\")\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[train_step])\n", - "print (\"Pipeline is built\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a Pipeline Draft\n", - "Create a PipelineDraft by specifying a name, description, experiment_name and Pipeline. You can also specify tags, properties and pipeline_parameter values.\n", - "\n", - "In this example we use the previously created Pipeline object to create the Pipeline Draft. You can also create a Pipeline Draft from an existing Pipeline Run, Published Pipeline, or other Pipeline Draft." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineDraft\n", - "\n", - "pipeline_draft = PipelineDraft.create(ws, name=\"TestPipelineDraft\",\n", - " description=\"draft description\",\n", - " experiment_name=\"pipeline_draft_sample\",\n", - " pipeline=pipeline,\n", - " continue_on_step_failure=True,\n", - " tags={'dev': 'true'},\n", - " properties={'train': 'value'})\n", - "\n", - "created_pipeline_draft_id = pipeline_draft.id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### List Pipeline Drafts in a Workspace\n", - "Use the PipelineDraft.list() function to list all PipelineDrafts in a Workspace. You can use the optional tags parameter to filter on specified tag values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_drafts = PipelineDraft.list(ws, tags={'dev': 'true'})\n", - "\n", - "for pipeline_draft in pipeline_drafts:\n", - " print(pipeline_draft)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get a Pipeline Draft by Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_draft = PipelineDraft.get(ws, id=created_pipeline_draft_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Update a Pipeline Draft\n", - "The update() function of a pipeline draft can be used to update the name, description, experiment name, pipeline parameter assignments, continue on step failure setting and Pipeline associated with the PipelineDraft. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "new_train_step = PythonScriptStep(\n", - " name=\"New_Training_Step\",\n", - " script_name=\"train.py\", \n", - " compute_target=aml_compute_target, \n", - " source_directory=source_directory)\n", - "\n", - "new_pipeline = Pipeline(workspace=ws, steps=[new_train_step])\n", - "\n", - "pipeline_draft.update(name=\"UpdatedPipelineDraft\", description=\"has updated train step\", pipeline=new_pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit a Pipeline Run from a Pipeline Draft\n", - "Use the pipeline_draft.submit() function to submit a PipelineRun. After the run is submitted, the PipelineDraft can still be edited and used to submit new runs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run = pipeline_draft.submit_run()\n", - "pipeline_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a Published Pipeline from a Pipeline Draft\n", - "Use the pipeline_draft.publish() function to create a Published Pipeline from the Pipeline Draft. After creating a Published Pipeline, the Pipeline Draft can still be edited and used to create other Published Pipelines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline = pipeline_draft.publish()\n", - "published_pipeline" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Use Pipeline Drafts\n", + "In this notebook, we will show you how you can use Pipeline Drafts. Pipeline Drafts are mutable pipelines which can be used to submit runs and create Published Pipelines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites and AML Basics\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n", + "\n", + "### Initialization Steps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "from azureml.core import Workspace\n", + "from azureml.widgets import RunDetails\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Target\n", + "Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import AmlCompute, ComputeTarget\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "aml_compute_target = \"cpu-cluster\"\n", + "try:\n", + " aml_compute = AmlCompute(ws, aml_compute_target)\n", + " print(\"Found existing compute target: {}\".format(aml_compute_target))\n", + "except ComputeTargetException:\n", + " print(\"Creating new compute target: {}\".format(aml_compute_target))\n", + " \n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", + " min_nodes = 1, \n", + " max_nodes = 4) \n", + " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", + " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build a Pipeline\n", + "Build a simple pipeline to use to create a PipelineDraft." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Pipeline\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "\n", + "source_directory = \"publish_run_train\"\n", + "\n", + "train_step = PythonScriptStep(\n", + " name=\"Training_Step\",\n", + " script_name=\"train.py\", \n", + " compute_target=aml_compute_target, \n", + " source_directory=source_directory)\n", + "print(\"train step created\")\n", + "\n", + "pipeline = Pipeline(workspace=ws, steps=[train_step])\n", + "print (\"Pipeline is built\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a Pipeline Draft\n", + "Create a PipelineDraft by specifying a name, description, experiment_name and Pipeline. You can also specify tags, properties and pipeline_parameter values.\n", + "\n", + "In this example we use the previously created Pipeline object to create the Pipeline Draft. You can also create a Pipeline Draft from an existing Pipeline Run, Published Pipeline, or other Pipeline Draft." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineDraft\n", + "\n", + "pipeline_draft = PipelineDraft.create(ws, name=\"TestPipelineDraft\",\n", + " description=\"draft description\",\n", + " experiment_name=\"pipeline_draft_sample\",\n", + " pipeline=pipeline,\n", + " continue_on_step_failure=True,\n", + " tags={'dev': 'true'},\n", + " properties={'train': 'value'})\n", + "\n", + "created_pipeline_draft_id = pipeline_draft.id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List Pipeline Drafts in a Workspace\n", + "Use the PipelineDraft.list() function to list all PipelineDrafts in a Workspace. You can use the optional tags parameter to filter on specified tag values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_drafts = PipelineDraft.list(ws, tags={'dev': 'true'})\n", + "\n", + "for pipeline_draft in pipeline_drafts:\n", + " print(pipeline_draft)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get a Pipeline Draft by Id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_draft = PipelineDraft.get(ws, id=created_pipeline_draft_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Update a Pipeline Draft\n", + "The update() function of a pipeline draft can be used to update the name, description, experiment name, pipeline parameter assignments, continue on step failure setting and Pipeline associated with the PipelineDraft. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_train_step = PythonScriptStep(\n", + " name=\"New_Training_Step\",\n", + " script_name=\"train.py\", \n", + " compute_target=aml_compute_target, \n", + " source_directory=source_directory)\n", + "\n", + "new_pipeline = Pipeline(workspace=ws, steps=[new_train_step])\n", + "\n", + "pipeline_draft.update(name=\"UpdatedPipelineDraft\", description=\"has updated train step\", pipeline=new_pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Submit a Pipeline Run from a Pipeline Draft\n", + "Use the pipeline_draft.submit() function to submit a PipelineRun. After the run is submitted, the PipelineDraft can still be edited and used to submit new runs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run = pipeline_draft.submit_run()\n", + "pipeline_run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a Published Pipeline from a Pipeline Draft\n", + "Use the pipeline_draft.publish() function to create a Published Pipeline from the Pipeline Draft. After creating a Published Pipeline, the Pipeline Draft can still be edited and used to create other Published Pipelines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "published_pipeline = pipeline_draft.publish()\n", + "published_pipeline" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "How to use Pipeline Drafts to create a Published Pipeline", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - }, - "order_index": 14, - "star_tag": [ - "featured" - ], - "tags": [ - "None" - ], - "task": "Demonstrates the use of Pipeline Drafts" + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "How to use Pipeline Drafts to create a Published Pipeline", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + }, + "order_index": 14, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of Pipeline Drafts" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb index 7a617663c..ed658d14b 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb @@ -1,625 +1,625 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Azure Machine Learning Pipeline with HyperDriveStep\n", - "\n", - "\n", - "This notebook is used to demonstrate the use of HyperDriveStep in AML Pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites and Azure Machine Learning Basics\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n", - "\n", - "## Azure Machine Learning and Pipeline SDK-specific imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Environment, Experiment, Datastore, Dataset, ScriptRunConfig\n", - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.exceptions import ComputeTargetException\n", - "from azureml.pipeline.steps import HyperDriveStep, HyperDriveStepRun, PythonScriptStep\n", - "from azureml.pipeline.core import Pipeline, PipelineData, TrainingOutput\n", - "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n", - "from azureml.train.hyperdrive import choice, loguniform\n", - "\n", - "import os\n", - "import shutil\n", - "import urllib\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "\n", - "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an Azure ML experiment\n", - "Let's create an experiment named \"tf-mnist\" and a folder to hold the training scripts. \n", - "\n", - "> The best practice is to use separate folders for scripts and its dependent files for each step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step. \n", - "\n", - "> The script runs will be recorded under the experiment in Azure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "script_folder = './tf-mnist'\n", - "os.makedirs(script_folder, exist_ok=True)\n", - "\n", - "exp = Experiment(workspace=ws, name='Hyperdrive_sample')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download MNIST dataset\n", - "In order to train on the MNIST dataset we will first need to download it from Yan LeCun's web site directly and save them in a `data` folder locally." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_folder = os.path.join(os.getcwd(), 'data/mnist')\n", - "os.makedirs(data_folder, exist_ok=True)\n", - "\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-images-idx3-ubyte.gz',\n", - " filename=os.path.join(data_folder, 'train-images.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-labels-idx1-ubyte.gz',\n", - " filename=os.path.join(data_folder, 'train-labels.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n", - " filename=os.path.join(data_folder, 'test-images.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz',\n", - " filename=os.path.join(data_folder, 'test-labels.gz'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Show some sample images\n", - "Let's load the downloaded compressed file into numpy arrays using some utility functions included in the `utils.py` library file from the current folder. Then we use `matplotlib` to plot 30 random images from the dataset along with their labels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from utils import load_data\n", - "\n", - "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster.\n", - "X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / np.float32(255.0)\n", - "X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / np.float32(255.0)\n", - "y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)\n", - "y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)\n", - "\n", - "\n", - "count = 0\n", - "sample_size = 30\n", - "plt.figure(figsize = (16, 6))\n", - "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n", - " count = count + 1\n", - " plt.subplot(1, sample_size, count)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.text(x = 10, y = -10, s = y_train[i], fontsize = 18)\n", - " plt.imshow(X_train[i].reshape(28, 28), cmap = plt.cm.Greys)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Upload MNIST dataset to blob datastore \n", - "A [datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data) is a place where data can be stored that is then made accessible to a Run either by means of mounting or copying the data to the compute target. In the next step, we will use Azure Blob Storage and upload the training and test set into the Azure Blob datastore, which we will then later be mount on a Batch AI cluster for training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore = ws.get_default_datastore()\n", - "datastore.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Azure Machine Learning datasets\n", - "By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = Dataset.File.from_files(datastore.path('mnist'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrieve or create a Azure Machine Learning compute\n", - "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. This process is broken down into the following steps:\n", - "\n", - "1. Create the configuration\n", - "2. Create the Azure Machine Learning compute\n", - "\n", - "**This process will take a few minutes and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell.**\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cluster_name = \"amlcomp\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target {}.'.format(cluster_name))\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n", - " max_nodes=4)\n", - "\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "compute_target.wait_for_completion(show_output=True, timeout_in_minutes=20)\n", - "\n", - "print(\"Azure Machine Learning Compute attached\")\n", - "\n", - "cpu_cluster_name = \"cpu-cluster\"\n", - "\n", - "try:\n", - " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print(\"Found existing cpu-cluster\")\n", - "except ComputeTargetException:\n", - " print(\"Creating new cpu-cluster\")\n", - " \n", - " compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n", - " min_nodes=0,\n", - " max_nodes=4)\n", - " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - " \n", - "cpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Copy the training files into the script folder\n", - "The TensorFlow training script is already created for you. You can simply copy it into the script folder, together with the utility library used to load compressed data file into numpy array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# the training logic is in the tf_mnist.py file.\n", - "shutil.copy('./tf_mnist.py', script_folder)\n", - "\n", - "# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.\n", - "shutil.copy('./utils.py', script_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrieve an Environment\n", - "In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. Curated environments are available in your workspace by default. Specifically, we will use the TensorFlow 2.0 GPU curated environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup an input for the ScriptRunConfig step\n", - "You can mount dataset to remote compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_folder = dataset.as_mount()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure the training job\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='tf_mnist.py',\n", - " arguments=['--data-folder', data_folder],\n", - " compute_target=compute_target,\n", - " environment=tf_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Intelligent hyperparameter tuning\n", - "Now let's try hyperparameter tuning by launching multiple runs on the cluster. First let's define the parameter space using random sampling.\n", - "\n", - "In this example we will use random sampling to try different configuration sets of hyperparameters to maximize our primary metric, the best validation accuracy (`validation_acc`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ps = RandomParameterSampling(\n", - " {\n", - " '--batch-size': choice(25, 50, 100),\n", - " '--first-layer-neurons': choice(10, 50, 200, 300, 500),\n", - " '--second-layer-neurons': choice(10, 50, 200, 500),\n", - " '--learning-rate': loguniform(-6, -1)\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will define an early termnination policy. The `BanditPolicy` basically states to check the job every 2 iterations. If the primary metric (defined later) falls outside of the top 10% range, Azure ML terminate the job. This saves us from continuing to explore hyperparameters that don't show promise of helping reach our target metric.\n", - "\n", - "Refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-tune-hyperparameters#specify-an-early-termination-policy) for more information on the BanditPolicy and other policies available." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "early_termination_policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are ready to configure a run configuration object, and specify the primary metric `validation_acc` that's recorded in your training runs. If you go back to visit the training script, you will notice that this value is being logged after every epoch (a full batch set). We also want to tell the service that we are looking to maximizing this value. We also set the number of samples to 20, and maximal concurrent job to 4, which is the same as the number of nodes in our computer cluster." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hyperdriveconfig-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "hd_config = HyperDriveConfig(run_config=src, \n", - " hyperparameter_sampling=ps,\n", - " policy=early_termination_policy,\n", - " primary_metric_name='validation_acc', \n", - " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n", - " max_total_runs=4,\n", - " max_concurrent_runs=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### HyperDriveStep\n", - "HyperDriveStep can be used to run HyperDrive job as a step in pipeline.\n", - "- **name:** Name of the step\n", - "- **hyperdrive_config:** A HyperDriveConfig that defines the configuration for this HyperDrive run\n", - "- **inputs:** List of input port bindings\n", - "- **outputs:** List of output port bindings\n", - "- **metrics_output:** Optional value specifying the location to store HyperDrive run metrics as a JSON file\n", - "- **allow_reuse:** whether to allow reuse\n", - "- **version:** version\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hyperdrivestep-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "metrics_output_name = 'metrics_output'\n", - "metrics_data = PipelineData(name='metrics_data',\n", - " datastore=datastore,\n", - " pipeline_output_name=metrics_output_name,\n", - " training_output=TrainingOutput(\"Metrics\"))\n", - "\n", - "model_output_name = 'model_output'\n", - "saved_model = PipelineData(name='saved_model',\n", - " datastore=datastore,\n", - " pipeline_output_name=model_output_name,\n", - " training_output=TrainingOutput(\"Model\",\n", - " model_file=\"outputs/model/saved_model.pb\"))\n", - "\n", - "hd_step_name='hd_step01'\n", - "hd_step = HyperDriveStep(\n", - " name=hd_step_name,\n", - " hyperdrive_config=hd_config,\n", - " inputs=[data_folder],\n", - " outputs=[metrics_data, saved_model])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Find and register best model\n", - "When all the jobs finish, we can choose to register the model that has the highest accuracy through an additional PythonScriptStep.\n", - "\n", - "Through this additional register_model_step, we register the chosen files as a model named `tf-dnn-mnist` under the workspace for deployment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "conda_dep = CondaDependencies()\n", - "conda_dep.add_pip_package(\"azureml-sdk\")\n", - "\n", - "rcfg = RunConfiguration(conda_dependencies=conda_dep)\n", - "\n", - "register_model_step = PythonScriptStep(script_name='register_model.py',\n", - " name=\"register_model_step01\",\n", - " inputs=[saved_model],\n", - " compute_target=cpu_cluster,\n", - " arguments=[\"--saved-model\", saved_model],\n", - " allow_reuse=True,\n", - " runconfig=rcfg)\n", - "\n", - "register_model_step.run_after(hd_step)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline = Pipeline(workspace=ws, steps=[hd_step, register_model_step])\n", - "pipeline_run = exp.submit(pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor using widget" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wait for the completion of this Pipeline run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve the metrics\n", - "Outputs of above run can be used as inputs of other steps in pipeline. In this tutorial, we will show the result metrics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n", - "num_file_downloaded = metrics_output.download('.', show_progress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import json\n", - "with open(metrics_output._path_on_datastore) as f: \n", - " metrics_output_result = f.read()\n", - " \n", - "deserialized_metrics_output = json.loads(metrics_output_result)\n", - "df = pd.DataFrame(deserialized_metrics_output)\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For model deployment, please refer to [Training, hyperparameter tune, and deploy with TensorFlow](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb)." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "nagaur" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Azure Machine Learning Pipeline with HyperDriveStep", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "order_index": 8, - "star_tag": [ - "featured" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Azure Machine Learning Pipeline with HyperDriveStep\n", + "\n", + "\n", + "This notebook is used to demonstrate the use of HyperDriveStep in AML Pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites and Azure Machine Learning Basics\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n", + "\n", + "## Azure Machine Learning and Pipeline SDK-specific imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "from azureml.core import Workspace, Environment, Experiment, Datastore, Dataset, ScriptRunConfig\n", + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "from azureml.core.runconfig import RunConfiguration\n", + "from azureml.exceptions import ComputeTargetException\n", + "from azureml.pipeline.steps import HyperDriveStep, HyperDriveStepRun, PythonScriptStep\n", + "from azureml.pipeline.core import Pipeline, PipelineData, TrainingOutput\n", + "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n", + "from azureml.train.hyperdrive import choice, loguniform\n", + "\n", + "import os\n", + "import shutil\n", + "import urllib\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize workspace\n", + "\n", + "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an Azure ML experiment\n", + "Let's create an experiment named \"tf-mnist\" and a folder to hold the training scripts. \n", + "\n", + "> The best practice is to use separate folders for scripts and its dependent files for each step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step. \n", + "\n", + "> The script runs will be recorded under the experiment in Azure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "script_folder = './tf-mnist'\n", + "os.makedirs(script_folder, exist_ok=True)\n", + "\n", + "exp = Experiment(workspace=ws, name='Hyperdrive_sample')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download MNIST dataset\n", + "In order to train on the MNIST dataset we will first need to download it from Yan LeCun's web site directly and save them in a `data` folder locally." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_folder = os.path.join(os.getcwd(), 'data/mnist')\n", + "os.makedirs(data_folder, exist_ok=True)\n", + "\n", + "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-images-idx3-ubyte.gz',\n", + " filename=os.path.join(data_folder, 'train-images.gz'))\n", + "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-labels-idx1-ubyte.gz',\n", + " filename=os.path.join(data_folder, 'train-labels.gz'))\n", + "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n", + " filename=os.path.join(data_folder, 'test-images.gz'))\n", + "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz',\n", + " filename=os.path.join(data_folder, 'test-labels.gz'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show some sample images\n", + "Let's load the downloaded compressed file into numpy arrays using some utility functions included in the `utils.py` library file from the current folder. Then we use `matplotlib` to plot 30 random images from the dataset along with their labels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from utils import load_data\n", + "\n", + "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster.\n", + "X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / np.float32(255.0)\n", + "X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / np.float32(255.0)\n", + "y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)\n", + "y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)\n", + "\n", + "\n", + "count = 0\n", + "sample_size = 30\n", + "plt.figure(figsize = (16, 6))\n", + "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n", + " count = count + 1\n", + " plt.subplot(1, sample_size, count)\n", + " plt.axhline('')\n", + " plt.axvline('')\n", + " plt.text(x = 10, y = -10, s = y_train[i], fontsize = 18)\n", + " plt.imshow(X_train[i].reshape(28, 28), cmap = plt.cm.Greys)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Upload MNIST dataset to blob datastore \n", + "A [datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data) is a place where data can be stored that is then made accessible to a Run either by means of mounting or copying the data to the compute target. In the next step, we will use Azure Blob Storage and upload the training and test set into the Azure Blob datastore, which we will then later be mount on a Batch AI cluster for training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "datastore = ws.get_default_datastore()\n", + "datastore.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Azure Machine Learning datasets\n", + "By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = Dataset.File.from_files(datastore.path('mnist'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve or create a Azure Machine Learning compute\n", + "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\n", + "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. This process is broken down into the following steps:\n", + "\n", + "1. Create the configuration\n", + "2. Create the Azure Machine Learning compute\n", + "\n", + "**This process will take a few minutes and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell.**\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cluster_name = \"amlcomp\"\n", + "\n", + "try:\n", + " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", + " print('Found existing compute target {}.'.format(cluster_name))\n", + "except ComputeTargetException:\n", + " print('Creating a new compute target...')\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n", + " max_nodes=4)\n", + "\n", + " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", + "compute_target.wait_for_completion(show_output=True, timeout_in_minutes=20)\n", + "\n", + "print(\"Azure Machine Learning Compute attached\")\n", + "\n", + "cpu_cluster_name = \"cpu-cluster\"\n", + "\n", + "try:\n", + " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", + " print(\"Found existing cpu-cluster\")\n", + "except ComputeTargetException:\n", + " print(\"Creating new cpu-cluster\")\n", + " \n", + " compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n", + " min_nodes=0,\n", + " max_nodes=4)\n", + " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", + " \n", + "cpu_cluster.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Copy the training files into the script folder\n", + "The TensorFlow training script is already created for you. You can simply copy it into the script folder, together with the utility library used to load compressed data file into numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# the training logic is in the tf_mnist.py file.\n", + "shutil.copy('./tf_mnist.py', script_folder)\n", + "\n", + "# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.\n", + "shutil.copy('./utils.py', script_folder)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve an Environment\n", + "In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. Curated environments are available in your workspace by default. Specifically, we will use the TensorFlow 2.0 GPU curated environment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup an input for the ScriptRunConfig step\n", + "You can mount dataset to remote compute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_folder = dataset.as_mount()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configure the training job\n", + "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "src = ScriptRunConfig(source_directory=script_folder,\n", + " script='tf_mnist.py',\n", + " arguments=['--data-folder', data_folder],\n", + " compute_target=compute_target,\n", + " environment=tf_env)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intelligent hyperparameter tuning\n", + "Now let's try hyperparameter tuning by launching multiple runs on the cluster. First let's define the parameter space using random sampling.\n", + "\n", + "In this example we will use random sampling to try different configuration sets of hyperparameters to maximize our primary metric, the best validation accuracy (`validation_acc`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ps = RandomParameterSampling(\n", + " {\n", + " '--batch-size': choice(25, 50, 100),\n", + " '--first-layer-neurons': choice(10, 50, 200, 300, 500),\n", + " '--second-layer-neurons': choice(10, 50, 200, 500),\n", + " '--learning-rate': loguniform(-6, -1)\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will define an early termnination policy. The `BanditPolicy` basically states to check the job every 2 iterations. If the primary metric (defined later) falls outside of the top 10% range, Azure ML terminate the job. This saves us from continuing to explore hyperparameters that don't show promise of helping reach our target metric.\n", + "\n", + "Refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-tune-hyperparameters#specify-an-early-termination-policy) for more information on the BanditPolicy and other policies available." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "early_termination_policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to configure a run configuration object, and specify the primary metric `validation_acc` that's recorded in your training runs. If you go back to visit the training script, you will notice that this value is being logged after every epoch (a full batch set). We also want to tell the service that we are looking to maximizing this value. We also set the number of samples to 20, and maximal concurrent job to 4, which is the same as the number of nodes in our computer cluster." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hyperdriveconfig-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "hd_config = HyperDriveConfig(run_config=src, \n", + " hyperparameter_sampling=ps,\n", + " policy=early_termination_policy,\n", + " primary_metric_name='validation_acc', \n", + " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n", + " max_total_runs=4,\n", + " max_concurrent_runs=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### HyperDriveStep\n", + "HyperDriveStep can be used to run HyperDrive job as a step in pipeline.\n", + "- **name:** Name of the step\n", + "- **hyperdrive_config:** A HyperDriveConfig that defines the configuration for this HyperDrive run\n", + "- **inputs:** List of input port bindings\n", + "- **outputs:** List of output port bindings\n", + "- **metrics_output:** Optional value specifying the location to store HyperDrive run metrics as a JSON file\n", + "- **allow_reuse:** whether to allow reuse\n", + "- **version:** version\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of HyperDriveStep" + "hyperdrivestep-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "metrics_output_name = 'metrics_output'\n", + "metrics_data = PipelineData(name='metrics_data',\n", + " datastore=datastore,\n", + " pipeline_output_name=metrics_output_name,\n", + " training_output=TrainingOutput(\"Metrics\"))\n", + "\n", + "model_output_name = 'model_output'\n", + "saved_model = PipelineData(name='saved_model',\n", + " datastore=datastore,\n", + " pipeline_output_name=model_output_name,\n", + " training_output=TrainingOutput(\"Model\",\n", + " model_file=\"outputs/model/saved_model.pb\"))\n", + "\n", + "hd_step_name='hd_step01'\n", + "hd_step = HyperDriveStep(\n", + " name=hd_step_name,\n", + " hyperdrive_config=hd_config,\n", + " inputs=[data_folder],\n", + " outputs=[metrics_data, saved_model])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find and register best model\n", + "When all the jobs finish, we can choose to register the model that has the highest accuracy through an additional PythonScriptStep.\n", + "\n", + "Through this additional register_model_step, we register the chosen files as a model named `tf-dnn-mnist` under the workspace for deployment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conda_dep = CondaDependencies()\n", + "conda_dep.add_pip_package(\"azureml-sdk\")\n", + "\n", + "rcfg = RunConfiguration(conda_dependencies=conda_dep)\n", + "\n", + "register_model_step = PythonScriptStep(script_name='register_model.py',\n", + " name=\"register_model_step01\",\n", + " inputs=[saved_model],\n", + " compute_target=cpu_cluster,\n", + " arguments=[\"--saved-model\", saved_model],\n", + " allow_reuse=True,\n", + " runconfig=rcfg)\n", + "\n", + "register_model_step.run_after(hd_step)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline(workspace=ws, steps=[hd_step, register_model_step])\n", + "pipeline_run = exp.submit(pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Monitor using widget" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(pipeline_run).show()" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wait for the completion of this Pipeline run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Retrieve the metrics\n", + "Outputs of above run can be used as inputs of other steps in pipeline. In this tutorial, we will show the result metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n", + "num_file_downloaded = metrics_output.download('.', show_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import json\n", + "with open(metrics_output._path_on_datastore) as f: \n", + " metrics_output_result = f.read()\n", + " \n", + "deserialized_metrics_output = json.loads(metrics_output_result)\n", + "df = pd.DataFrame(deserialized_metrics_output)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For model deployment, please refer to [Training, hyperparameter tune, and deploy with TensorFlow](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb)." + ] + } + ], + "metadata": { + "authors": [ + { + "name": "nagaur" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "Azure Machine Learning Pipeline with HyperDriveStep", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "order_index": 8, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of HyperDriveStep" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb index 816e7f47c..1822e4c33 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb @@ -1,473 +1,473 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to Publish a Pipeline and Invoke the REST endpoint\n", - "In this notebook, we will see how we can publish a pipeline and then invoke the REST endpoint." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites and Azure Machine Learning Basics\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n", - "\n", - "### Initialization Steps" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Datastore, Experiment, Dataset\n", - "from azureml.data import OutputFileDatasetConfig\n", - "from azureml.core.compute import AmlCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)\n", - "\n", - "from azureml.pipeline.core import Pipeline\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "from azureml.pipeline.core.graph import PipelineParameter\n", - "\n", - "print(\"Pipeline SDK-specific imports completed\")\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n", - "\n", - "# Default datastore (Azure blob storage)\n", - "# def_blob_store = ws.get_default_datastore()\n", - "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n", - "print(\"Blobstore's name: {}\".format(def_blob_store.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Targets\n", - "#### Retrieve an already attached Azure Machine Learning Compute\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "aml_compute_target = \"cpu-cluster\"\n", - "try:\n", - " aml_compute = AmlCompute(ws, aml_compute_target)\n", - " print(\"found existing compute target.\")\n", - "except ComputeTargetException:\n", - " print(\"creating new compute target\")\n", - " \n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", - " min_nodes = 1, \n", - " max_nodes = 4) \n", - " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", - " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n", - "# example: un-comment the following line.\n", - "# print(aml_compute.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building Pipeline Steps with Inputs and Outputs\n", - "A step in the pipeline can take [dataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) as input. This dataset can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify a public dataset path\n", - "data_path = \"https://dprepdata.blob.core.windows.net/demo/Titanic.csv\"\n", - "# Or uploading data to the datastore\n", - "# data_path = def_blob_store.upload_files([\"./your_data.pkl\"], target_path=\"your_path\", overwrite=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Reference the data uploaded to blob storage using file dataset\n", - "# Assign the datasource to blob_input_data variable\n", - "blob_input_data = Dataset.File.from_files(data_path).as_named_input(\"test_data\")\n", - "print(\"Dataset created\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define intermediate data using OutputFileDatasetConfig\n", - "processed_data1 = OutputFileDatasetConfig(name=\"processed_data1\")\n", - "print(\"Output dataset object created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define a Step that consumes a dataset and produces intermediate data.\n", - "In this step, we define a step that consumes a dataset and produces intermediate data.\n", - "\n", - "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** \n", - "\n", - "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# trainStep consumes the datasource (Datareference) in the previous step\n", - "# and produces processed_data1\n", - "\n", - "source_directory = \"publish_run_train\"\n", - "\n", - "trainStep = PythonScriptStep(\n", - " script_name=\"train.py\", \n", - " arguments=[\"--input_data\", blob_input_data.as_mount(), \"--output_train\", processed_data1],\n", - " compute_target=aml_compute, \n", - " source_directory=source_directory\n", - ")\n", - "print(\"trainStep created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define a Step that consumes intermediate data and produces intermediate data\n", - "In this step, we define a step that consumes an intermediate data and produces intermediate data.\n", - "\n", - "**Open `extract.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# extractStep to use the intermediate data produced by trainStep\n", - "# This step also produces an output processed_data2\n", - "processed_data2 = OutputFileDatasetConfig(name=\"processed_data2\")\n", - "source_directory = \"publish_run_extract\"\n", - "\n", - "extractStep = PythonScriptStep(\n", - " script_name=\"extract.py\",\n", - " arguments=[\"--input_extract\", processed_data1.as_input(), \"--output_extract\", processed_data2],\n", - " compute_target=aml_compute, \n", - " source_directory=source_directory)\n", - "print(\"extractStep created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define a Step that consumes multiple intermediate data and produces intermediate data\n", - "In this step, we define a step that consumes multiple intermediate data and produces intermediate data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PipelineParameter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This step also has a [PipelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.pipelineparameter?view=azure-ml-py) argument that help with calling the REST endpoint of the published pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We will use this later in publishing pipeline\n", - "pipeline_param = PipelineParameter(name=\"pipeline_arg\", default_value=10)\n", - "print(\"pipeline parameter created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Open `compare.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Now define compareStep that takes two inputs (both intermediate data), and produce an output\n", - "processed_data3 = OutputFileDatasetConfig(name=\"processed_data3\")\n", - "\n", - "# You can register the output as dataset after job completion\n", - "processed_data3 = processed_data3.register_on_complete(\"compare_result\")\n", - "\n", - "source_directory = \"publish_run_compare\"\n", - "\n", - "compareStep = PythonScriptStep(\n", - " script_name=\"compare.py\",\n", - " arguments=[\"--compare_data1\", processed_data1.as_input(), \"--compare_data2\", processed_data2.as_input(), \"--output_compare\", processed_data3, \"--pipeline_param\", pipeline_param], \n", - " compute_target=aml_compute, \n", - " source_directory=source_directory)\n", - "print(\"compareStep created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Build the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\n", - "print (\"Pipeline is built\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run published pipeline\n", - "### Publish the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline1 = pipeline1.publish(name=\"My_New_Pipeline\", description=\"My Published Pipeline Description\", continue_on_step_failure=True)\n", - "published_pipeline1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: the continue_on_step_failure parameter specifies whether the execution of steps in the Pipeline will continue if one step fails. The default value is False, meaning when one step fails, the Pipeline execution will stop, canceling any running steps." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Publish the pipeline from a submitted PipelineRun\n", - "It is also possible to publish a pipeline from a submitted PipelineRun" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# submit a pipeline run\n", - "pipeline_run1 = Experiment(ws, 'Pipeline_experiment_sample').submit(pipeline1)\n", - "# publish a pipeline from the submitted pipeline run\n", - "published_pipeline2 = pipeline_run1.publish_pipeline(name=\"My_New_Pipeline2\", description=\"My Published Pipeline Description\", version=\"0.1\", continue_on_step_failure=True)\n", - "published_pipeline2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get published pipeline\n", - "\n", - "You can get the published pipeline using **pipeline id**.\n", - "\n", - "To get all the published pipelines for a given workspace(ws): \n", - "```css\n", - "all_pub_pipelines = PublishedPipeline.get_all(ws)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PublishedPipeline\n", - "\n", - "pipeline_id = published_pipeline1.id # use your published pipeline id\n", - "published_pipeline = PublishedPipeline.get(ws, pipeline_id)\n", - "published_pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run published pipeline using its REST endpoint\n", - "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.authentication import InteractiveLoginAuthentication\n", - "import requests\n", - "\n", - "auth = InteractiveLoginAuthentication()\n", - "aad_token = auth.get_authentication_header()\n", - "\n", - "rest_endpoint1 = published_pipeline.endpoint\n", - "\n", - "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint1))\n", - "\n", - "# specify the param when running the pipeline\n", - "response = requests.post(rest_endpoint1, \n", - " headers=aad_token, \n", - " json={\"ExperimentName\": \"My_Pipeline1\",\n", - " \"RunSource\": \"SDK\",\n", - " \"ParameterAssignments\": {\"pipeline_arg\": 45}})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " response.raise_for_status()\n", - "except Exception as ex: \n", - " raise Exception('Received bad response from the endpoint: {}\\n'\n", - " 'Response Code: {}\\n'\n", - " 'Headers: {}\\n'\n", - " 'Content: {}'.format(rest_endpoint1, response.status_code, response.headers, response.content)) from ex\n", - "\n", - "run_id = response.json().get('Id')\n", - "print('Submitted pipeline run: ', run_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next: Data Transfer\n", - "The next [notebook](https://aka.ms/pl-data-trans) will showcase data transfer steps between different types of data stores." - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Publish a Pipeline and Invoke the REST endpoint\n", + "In this notebook, we will see how we can publish a pipeline and then invoke the REST endpoint." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites and Azure Machine Learning Basics\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n", + "\n", + "### Initialization Steps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "from azureml.core import Workspace, Datastore, Experiment, Dataset\n", + "from azureml.data import OutputFileDatasetConfig\n", + "from azureml.core.compute import AmlCompute\n", + "from azureml.core.compute import ComputeTarget\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)\n", + "\n", + "from azureml.pipeline.core import Pipeline\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "from azureml.pipeline.core.graph import PipelineParameter\n", + "\n", + "print(\"Pipeline SDK-specific imports completed\")\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n", + "\n", + "# Default datastore (Azure blob storage)\n", + "# def_blob_store = ws.get_default_datastore()\n", + "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n", + "print(\"Blobstore's name: {}\".format(def_blob_store.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Targets\n", + "#### Retrieve an already attached Azure Machine Learning Compute\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "aml_compute_target = \"cpu-cluster\"\n", + "try:\n", + " aml_compute = AmlCompute(ws, aml_compute_target)\n", + " print(\"found existing compute target.\")\n", + "except ComputeTargetException:\n", + " print(\"creating new compute target\")\n", + " \n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", + " min_nodes = 1, \n", + " max_nodes = 4) \n", + " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", + " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n", + "# example: un-comment the following line.\n", + "# print(aml_compute.get_status().serialize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building Pipeline Steps with Inputs and Outputs\n", + "A step in the pipeline can take [dataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) as input. This dataset can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Specify a public dataset path\n", + "data_path = \"https://dprepdata.blob.core.windows.net/demo/Titanic.csv\"\n", + "# Or uploading data to the datastore\n", + "# data_path = def_blob_store.upload_files([\"./your_data.pkl\"], target_path=\"your_path\", overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reference the data uploaded to blob storage using file dataset\n", + "# Assign the datasource to blob_input_data variable\n", + "blob_input_data = Dataset.File.from_files(data_path).as_named_input(\"test_data\")\n", + "print(\"Dataset created\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define intermediate data using OutputFileDatasetConfig\n", + "processed_data1 = OutputFileDatasetConfig(name=\"processed_data1\")\n", + "print(\"Output dataset object created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define a Step that consumes a dataset and produces intermediate data.\n", + "In this step, we define a step that consumes a dataset and produces intermediate data.\n", + "\n", + "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** \n", + "\n", + "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# trainStep consumes the datasource (Datareference) in the previous step\n", + "# and produces processed_data1\n", + "\n", + "source_directory = \"publish_run_train\"\n", + "\n", + "trainStep = PythonScriptStep(\n", + " script_name=\"train.py\", \n", + " arguments=[\"--input_data\", blob_input_data.as_mount(), \"--output_train\", processed_data1],\n", + " compute_target=aml_compute, \n", + " source_directory=source_directory\n", + ")\n", + "print(\"trainStep created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define a Step that consumes intermediate data and produces intermediate data\n", + "In this step, we define a step that consumes an intermediate data and produces intermediate data.\n", + "\n", + "**Open `extract.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# extractStep to use the intermediate data produced by trainStep\n", + "# This step also produces an output processed_data2\n", + "processed_data2 = OutputFileDatasetConfig(name=\"processed_data2\")\n", + "source_directory = \"publish_run_extract\"\n", + "\n", + "extractStep = PythonScriptStep(\n", + " script_name=\"extract.py\",\n", + " arguments=[\"--input_extract\", processed_data1.as_input(), \"--output_extract\", processed_data2],\n", + " compute_target=aml_compute, \n", + " source_directory=source_directory)\n", + "print(\"extractStep created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define a Step that consumes multiple intermediate data and produces intermediate data\n", + "In this step, we define a step that consumes multiple intermediate data and produces intermediate data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PipelineParameter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This step also has a [PipelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.pipelineparameter?view=azure-ml-py) argument that help with calling the REST endpoint of the published pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We will use this later in publishing pipeline\n", + "pipeline_param = PipelineParameter(name=\"pipeline_arg\", default_value=10)\n", + "print(\"pipeline parameter created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Open `compare.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now define compareStep that takes two inputs (both intermediate data), and produce an output\n", + "processed_data3 = OutputFileDatasetConfig(name=\"processed_data3\")\n", + "\n", + "# You can register the output as dataset after job completion\n", + "processed_data3 = processed_data3.register_on_complete(\"compare_result\")\n", + "\n", + "source_directory = \"publish_run_compare\"\n", + "\n", + "compareStep = PythonScriptStep(\n", + " script_name=\"compare.py\",\n", + " arguments=[\"--compare_data1\", processed_data1.as_input(), \"--compare_data2\", processed_data2.as_input(), \"--output_compare\", processed_data3, \"--pipeline_param\", pipeline_param], \n", + " compute_target=aml_compute, \n", + " source_directory=source_directory)\n", + "print(\"compareStep created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\n", + "print (\"Pipeline is built\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run published pipeline\n", + "### Publish the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "published_pipeline1 = pipeline1.publish(name=\"My_New_Pipeline\", description=\"My Published Pipeline Description\", continue_on_step_failure=True)\n", + "published_pipeline1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: the continue_on_step_failure parameter specifies whether the execution of steps in the Pipeline will continue if one step fails. The default value is False, meaning when one step fails, the Pipeline execution will stop, canceling any running steps." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Publish the pipeline from a submitted PipelineRun\n", + "It is also possible to publish a pipeline from a submitted PipelineRun" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# submit a pipeline run\n", + "pipeline_run1 = Experiment(ws, 'Pipeline_experiment_sample').submit(pipeline1)\n", + "# publish a pipeline from the submitted pipeline run\n", + "published_pipeline2 = pipeline_run1.publish_pipeline(name=\"My_New_Pipeline2\", description=\"My Published Pipeline Description\", version=\"0.1\", continue_on_step_failure=True)\n", + "published_pipeline2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get published pipeline\n", + "\n", + "You can get the published pipeline using **pipeline id**.\n", + "\n", + "To get all the published pipelines for a given workspace(ws): \n", + "```css\n", + "all_pub_pipelines = PublishedPipeline.get_all(ws)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PublishedPipeline\n", + "\n", + "pipeline_id = published_pipeline1.id # use your published pipeline id\n", + "published_pipeline = PublishedPipeline.get(ws, pipeline_id)\n", + "published_pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run published pipeline using its REST endpoint\n", + "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.authentication import InteractiveLoginAuthentication\n", + "import requests\n", + "\n", + "auth = InteractiveLoginAuthentication()\n", + "aad_token = auth.get_authentication_header()\n", + "\n", + "rest_endpoint1 = published_pipeline.endpoint\n", + "\n", + "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint1))\n", + "\n", + "# specify the param when running the pipeline\n", + "response = requests.post(rest_endpoint1, \n", + " headers=aad_token, \n", + " json={\"ExperimentName\": \"My_Pipeline1\",\n", + " \"RunSource\": \"SDK\",\n", + " \"ParameterAssignments\": {\"pipeline_arg\": 45}})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " response.raise_for_status()\n", + "except Exception as ex: \n", + " raise Exception('Received bad response from the endpoint: {}\\n'\n", + " 'Response Code: {}\\n'\n", + " 'Headers: {}\\n'\n", + " 'Content: {}'.format(rest_endpoint1, response.status_code, response.headers, response.content)) from ex\n", + "\n", + "run_id = response.json().get('Id')\n", + "print('Submitted pipeline run: ', run_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next: Data Transfer\n", + "The next [notebook](https://aka.ms/pl-data-trans) will showcase data transfer steps between different types of data stores." + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "How to Publish a Pipeline and Invoke the REST endpoint", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "order_index": 3, + "star_tag": [ + "featured" + ], + "tags": [ + "None" ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "How to Publish a Pipeline and Invoke the REST endpoint", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "order_index": 3, - "star_tag": [ - "featured" - ], - "tags": [ - "None" - ], - "task": "Demonstrates the use of Published Pipelines" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "task": "Demonstrates the use of Published Pipelines" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb index 2acb6e067..0da729ecc 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb @@ -1,670 +1,670 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint\n", - "In this notebook, we will show you how you can run an already published pipeline or a pipeline endpoint on a schedule." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites and AML Basics\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n", - "\n", - "### Initialization Steps" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Targets\n", - "#### Retrieve an already attached Azure Machine Learning Compute\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "aml_compute_target = \"cpu-cluster\"\n", - "try:\n", - " aml_compute = AmlCompute(ws, aml_compute_target)\n", - " print(\"Found existing compute target: {}\".format(aml_compute_target))\n", - "except ComputeTargetException:\n", - " print(\"Creating new compute target: {}\".format(aml_compute_target))\n", - " \n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", - " min_nodes = 1, \n", - " max_nodes = 4) \n", - " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", - " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and Publish Pipeline\n", - "Build a simple pipeline, publish it and add a schedule to run it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define a pipeline step\n", - "Define a single step pipeline for demonstration purpose. The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import PythonScriptStep\n", - "\n", - "source_directory = \"publish_run_train\"\n", - "\n", - "trainStep = PythonScriptStep(\n", - " name=\"Training_Step\",\n", - " script_name=\"train.py\", \n", - " compute_target=aml_compute_target, \n", - " source_directory=source_directory\n", - ")\n", - "print(\"TrainStep created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "\n", - "pipeline1 = Pipeline(workspace=ws, steps=[trainStep])\n", - "print (\"Pipeline is built\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Publish the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "timenow = datetime.now().strftime('%m-%d-%Y-%H-%M')\n", - "\n", - "pipeline_name = timenow + \"-Pipeline\"\n", - "print(pipeline_name)\n", - "\n", - "published_pipeline1 = pipeline1.publish(\n", - " name=pipeline_name, \n", - " description=pipeline_name)\n", - "print(\"Newly published pipeline id: {}\".format(published_pipeline1.id))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Create a Pipeline Endpoint\n", - "Alternatively, you can create a schedule to run a pipeline endpoint instead of a published pipeline. You will need this to create a schedule against a pipeline endpoint in the last section of this notebook. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineEndpoint\n", - "\n", - "pipeline_endpoint = PipelineEndpoint.publish(workspace=ws, name=\"ScheduledPipelineEndpoint\",\n", - " pipeline=pipeline1, description=\"Publish pipeline endpoint for schedule test\")\n", - "pipeline_endpoint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Schedule Operations\n", - "Schedule operations require id of a published pipeline. You can get all published pipelines and do Schedule operations on them, or if you already know the id of the published pipeline, you can use it directly as well.\n", - "### Get published pipeline ID" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PublishedPipeline\n", - "\n", - "# You could retrieve all pipelines that are published, or \n", - "# just get the published pipeline object that you have the ID for.\n", - "\n", - "# Get all published pipeline objects in the workspace\n", - "all_pub_pipelines = PublishedPipeline.list(ws)\n", - "\n", - "# We will iterate through the list of published pipelines and \n", - "# use the last ID in the list for Schelue operations: \n", - "print(\"Published pipelines found in the workspace:\")\n", - "for pub_pipeline in all_pub_pipelines:\n", - " print(pub_pipeline.id)\n", - " pub_pipeline_id = pub_pipeline.id\n", - "\n", - "print(\"Published pipeline id to be used for Schedule operations: {}\".format(pub_pipeline_id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a schedule for the published pipeline using a recurrence\n", - "This schedule will run on a specified recurrence interval." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.pipeline.core.schedule import ScheduleRecurrence, Schedule\n", - "\n", - "recurrence = ScheduleRecurrence(frequency=\"Day\", interval=2, hours=[22], minutes=[30]) # Runs every other day at 10:30pm\n", - "\n", - "schedule = Schedule.create(workspace=ws, name=\"My_Schedule\",\n", - " pipeline_id=pub_pipeline_id, \n", - " experiment_name='Schedule-run-sample',\n", - " recurrence=recurrence,\n", - " wait_for_provisioning=True,\n", - " description=\"Schedule Run\")\n", - "\n", - "# You may want to make sure that the schedule is provisioned properly\n", - "# before making any further changes to the schedule\n", - "\n", - "print(\"Created schedule with id: {}\".format(schedule.id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: Set the `wait_for_provisioning` flag to False if you do not want to wait for the call to provision the schedule in the backend." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get all schedules for a given pipeline\n", - "Once you have the published pipeline ID, then you can get all schedules for that pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "schedules = Schedule.list(ws, pipeline_id=pub_pipeline_id)\n", - "\n", - "# We will iterate through the list of schedules and \n", - "# use the last recurrence schedule in the list for further operations: \n", - "print(\"Found these schedules for the pipeline id {}:\".format(pub_pipeline_id))\n", - "for schedule in schedules: \n", - " print(schedule.id)\n", - " if schedule.recurrence is not None:\n", - " schedule_id = schedule.id\n", - "\n", - "print(\"Schedule id to be used for schedule operations: {}\".format(schedule_id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get all schedules in your workspace\n", - "You can also iterate through all schedules in your workspace if needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use active_only=False to get all schedules including disabled schedules\n", - "schedules = Schedule.list(ws, active_only=True) \n", - "print(\"Your workspace has the following schedules set up:\")\n", - "for schedule in schedules:\n", - " print(\"{} (Published pipeline: {}\".format(schedule.id, schedule.pipeline_id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get the schedule" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fetched_schedule = Schedule.get(ws, schedule_id)\n", - "print(\"Using schedule with id: {}\".format(fetched_schedule.id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Disable the schedule\n", - "It is important to note the best practice of disabling schedules when not in use.\n", - "The number of schedule triggers allowed per month per region per subscription is 100,000.\n", - "This is calculated using the project trigger counts for all active schedules." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1606157800044 - } - }, - "outputs": [], - "source": [ - "# Set the wait_for_provisioning flag to False if you do not want to wait \n", - "# for the call to provision the schedule in the backend.\n", - "fetched_schedule.disable(wait_for_provisioning=True)\n", - "fetched_schedule = Schedule.get(ws, schedule_id)\n", - "print(\"Disabled schedule {}. New status is: {}\".format(fetched_schedule.id, fetched_schedule.status))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reenable the schedule" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the wait_for_provisioning flag to False if you do not want to wait \n", - "# for the call to provision the schedule in the backend.\n", - "fetched_schedule.enable(wait_for_provisioning=True)\n", - "fetched_schedule = Schedule.get(ws, schedule_id)\n", - "print(\"Enabled schedule {}. New status is: {}\".format(fetched_schedule.id, fetched_schedule.status))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Change recurrence of the schedule" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the wait_for_provisioning flag to False if you do not want to wait \n", - "# for the call to provision the schedule in the backend.\n", - "recurrence = ScheduleRecurrence(frequency=\"Hour\", interval=2) # Runs every two hours\n", - "\n", - "fetched_schedule = Schedule.get(ws, schedule_id)\n", - "\n", - "fetched_schedule.update(name=\"My_Updated_Schedule\", \n", - " description=\"Updated_Schedule_Run\", \n", - " status='Active', \n", - " wait_for_provisioning=True,\n", - " recurrence=recurrence)\n", - "\n", - "fetched_schedule = Schedule.get(ws, fetched_schedule.id)\n", - "\n", - "print(\"Updated schedule:\", fetched_schedule.id, \n", - " \"\\nNew name:\", fetched_schedule.name,\n", - " \"\\nNew frequency:\", fetched_schedule.recurrence.frequency,\n", - " \"\\nNew status:\", fetched_schedule.status)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a schedule for the pipeline using a Datastore\n", - "This schedule will run when additions or modifications are made to Blobs in the Datastore.\n", - "By default, the Datastore container is monitored for changes. Use the path_on_datastore parameter to instead specify a path on the Datastore to monitor for changes. Note: the path_on_datastore will be under the container for the datastore, so the actual path monitored will be container/path_on_datastore. Changes made to subfolders in the container/path will not trigger the schedule.\n", - "Note: Only Blob Datastores are supported.\n", - "Note: Not supported for CMK workspaces. Please review these [instructions](https://docs.microsoft.com/azure/machine-learning/how-to-trigger-published-pipeline) in order to setup a blob trigger submission schedule with CMK enabled. Also see those instructions to bring your own LogicApp to avoid the schedule triggers per month limit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.datastore import Datastore\n", - "\n", - "datastore = Datastore(workspace=ws, name=\"workspaceblobstore\")\n", - "\n", - "schedule = Schedule.create(workspace=ws, name=\"My_Schedule\",\n", - " pipeline_id=pub_pipeline_id, \n", - " experiment_name='Schedule-run-sample',\n", - " datastore=datastore,\n", - " wait_for_provisioning=True,\n", - " description=\"Schedule Run\")\n", - " #polling_interval=5, use polling_interval to specify how often to poll for blob additions/modifications. Default value is 5 minutes.\n", - " #path_on_datastore=\"file/path\") use path_on_datastore to specify a specific folder to monitor for changes.\n", - "\n", - "# You may want to make sure that the schedule is provisioned properly\n", - "# before making any further changes to the schedule\n", - "\n", - "print(\"Created schedule with id: {}\".format(schedule.id))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1606157862620 - } - }, - "outputs": [], - "source": [ - "# Set the wait_for_provisioning flag to False if you do not want to wait \n", - "# for the call to provision the schedule in the backend.\n", - "schedule.disable(wait_for_provisioning=True)\n", - "schedule = Schedule.get(ws, schedule_id)\n", - "print(\"Disabled schedule {}. New status is: {}\".format(schedule.id, schedule.status))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Create a schedule for a pipeline endpoint\n", - "Alternative to creating schedules for a published pipeline, you can also create schedules to run pipeline endpoints.\n", - "Retrieve the pipeline endpoint id to create a schedule. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1606157888851 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"ScheduledPipelineEndpoint\")\n", - "published_pipeline_endpoint_id = pipeline_endpoint_by_name.id\n", - "\n", - "recurrence = ScheduleRecurrence(frequency=\"Day\", interval=2, hours=[22], minutes=[30]) # Runs every other day at 10:30pm\n", - "\n", - "schedule = Schedule.create_for_pipeline_endpoint(workspace=ws, name=\"My_Endpoint_Schedule\",\n", - " pipeline_endpoint_id=published_pipeline_endpoint_id,\n", - " experiment_name='Schedule-run-sample',\n", - " recurrence=recurrence, description=\"Schedule_Run\",\n", - " wait_for_provisioning=True)\n", - "\n", - "# You may want to make sure that the schedule is provisioned properly\n", - "# before making any further changes to the schedule\n", - "\n", - "print(\"Created schedule with id: {}\".format(schedule.id))" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint\n", + "In this notebook, we will show you how you can run an already published pipeline or a pipeline endpoint on a schedule." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites and AML Basics\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n", + "\n", + "### Initialization Steps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "from azureml.core import Workspace\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Targets\n", + "#### Retrieve an already attached Azure Machine Learning Compute\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import AmlCompute, ComputeTarget\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "aml_compute_target = \"cpu-cluster\"\n", + "try:\n", + " aml_compute = AmlCompute(ws, aml_compute_target)\n", + " print(\"Found existing compute target: {}\".format(aml_compute_target))\n", + "except ComputeTargetException:\n", + " print(\"Creating new compute target: {}\".format(aml_compute_target))\n", + " \n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", + " min_nodes = 1, \n", + " max_nodes = 4) \n", + " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", + " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and Publish Pipeline\n", + "Build a simple pipeline, publish it and add a schedule to run it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a pipeline step\n", + "Define a single step pipeline for demonstration purpose. The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.steps import PythonScriptStep\n", + "\n", + "source_directory = \"publish_run_train\"\n", + "\n", + "trainStep = PythonScriptStep(\n", + " name=\"Training_Step\",\n", + " script_name=\"train.py\", \n", + " compute_target=aml_compute_target, \n", + " source_directory=source_directory\n", + ")\n", + "print(\"TrainStep created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Pipeline\n", + "\n", + "pipeline1 = Pipeline(workspace=ws, steps=[trainStep])\n", + "print (\"Pipeline is built\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Publish the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "timenow = datetime.now().strftime('%m-%d-%Y-%H-%M')\n", + "\n", + "pipeline_name = timenow + \"-Pipeline\"\n", + "print(pipeline_name)\n", + "\n", + "published_pipeline1 = pipeline1.publish(\n", + " name=pipeline_name, \n", + " description=pipeline_name)\n", + "print(\"Newly published pipeline id: {}\".format(published_pipeline1.id))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Create a Pipeline Endpoint\n", + "Alternatively, you can create a schedule to run a pipeline endpoint instead of a published pipeline. You will need this to create a schedule against a pipeline endpoint in the last section of this notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Get all schedules for a given pipeline endpoint\n", - "Once you have the pipeline endpoint ID, then you can get all schedules for that pipeline endopint." - ] + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineEndpoint\n", + "\n", + "pipeline_endpoint = PipelineEndpoint.publish(workspace=ws, name=\"ScheduledPipelineEndpoint\",\n", + " pipeline=pipeline1, description=\"Publish pipeline endpoint for schedule test\")\n", + "pipeline_endpoint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Schedule Operations\n", + "Schedule operations require id of a published pipeline. You can get all published pipelines and do Schedule operations on them, or if you already know the id of the published pipeline, you can use it directly as well.\n", + "### Get published pipeline ID" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PublishedPipeline\n", + "\n", + "# You could retrieve all pipelines that are published, or \n", + "# just get the published pipeline object that you have the ID for.\n", + "\n", + "# Get all published pipeline objects in the workspace\n", + "all_pub_pipelines = PublishedPipeline.list(ws)\n", + "\n", + "# We will iterate through the list of published pipelines and \n", + "# use the last ID in the list for Schelue operations: \n", + "print(\"Published pipelines found in the workspace:\")\n", + "for pub_pipeline in all_pub_pipelines:\n", + " print(pub_pipeline.id)\n", + " pub_pipeline_id = pub_pipeline.id\n", + "\n", + "print(\"Published pipeline id to be used for Schedule operations: {}\".format(pub_pipeline_id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a schedule for the published pipeline using a recurrence\n", + "This schedule will run on a specified recurrence interval." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "schedules_for_pipeline_endpoints = Schedule.\\\n", - " get_schedules_for_pipeline_endpoint_id(ws,\n", - " pipeline_endpoint_id=published_pipeline_endpoint_id)\n", - "print('Got all schedules for pipeline endpoint:', published_pipeline_endpoint_id, 'Count:',\n", - " len(schedules_for_pipeline_endpoints))\n", - "\n", - "print('done')" - ] + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "from azureml.pipeline.core.schedule import ScheduleRecurrence, Schedule\n", + "\n", + "recurrence = ScheduleRecurrence(frequency=\"Day\", interval=2, hours=[22], minutes=[30]) # Runs every other day at 10:30pm\n", + "\n", + "schedule = Schedule.create(workspace=ws, name=\"My_Schedule\",\n", + " pipeline_id=pub_pipeline_id, \n", + " experiment_name='Schedule-run-sample',\n", + " recurrence=recurrence,\n", + " wait_for_provisioning=True,\n", + " description=\"Schedule Run\")\n", + "\n", + "# You may want to make sure that the schedule is provisioned properly\n", + "# before making any further changes to the schedule\n", + "\n", + "print(\"Created schedule with id: {}\".format(schedule.id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: Set the `wait_for_provisioning` flag to False if you do not want to wait for the call to provision the schedule in the backend." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get all schedules for a given pipeline\n", + "Once you have the published pipeline ID, then you can get all schedules for that pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schedules = Schedule.list(ws, pipeline_id=pub_pipeline_id)\n", + "\n", + "# We will iterate through the list of schedules and \n", + "# use the last recurrence schedule in the list for further operations: \n", + "print(\"Found these schedules for the pipeline id {}:\".format(pub_pipeline_id))\n", + "for schedule in schedules: \n", + " print(schedule.id)\n", + " if schedule.recurrence is not None:\n", + " schedule_id = schedule.id\n", + "\n", + "print(\"Schedule id to be used for schedule operations: {}\".format(schedule_id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get all schedules in your workspace\n", + "You can also iterate through all schedules in your workspace if needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use active_only=False to get all schedules including disabled schedules\n", + "schedules = Schedule.list(ws, active_only=True) \n", + "print(\"Your workspace has the following schedules set up:\")\n", + "for schedule in schedules:\n", + " print(\"{} (Published pipeline: {}\".format(schedule.id, schedule.pipeline_id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the schedule" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fetched_schedule = Schedule.get(ws, schedule_id)\n", + "print(\"Using schedule with id: {}\".format(fetched_schedule.id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Disable the schedule\n", + "It is important to note the best practice of disabling schedules when not in use.\n", + "The number of schedule triggers allowed per month per region per subscription is 100,000.\n", + "This is calculated using the project trigger counts for all active schedules." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1606157800044 + } + }, + "outputs": [], + "source": [ + "# Set the wait_for_provisioning flag to False if you do not want to wait \n", + "# for the call to provision the schedule in the backend.\n", + "fetched_schedule.disable(wait_for_provisioning=True)\n", + "fetched_schedule = Schedule.get(ws, schedule_id)\n", + "print(\"Disabled schedule {}. New status is: {}\".format(fetched_schedule.id, fetched_schedule.status))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reenable the schedule" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the wait_for_provisioning flag to False if you do not want to wait \n", + "# for the call to provision the schedule in the backend.\n", + "fetched_schedule.enable(wait_for_provisioning=True)\n", + "fetched_schedule = Schedule.get(ws, schedule_id)\n", + "print(\"Enabled schedule {}. New status is: {}\".format(fetched_schedule.id, fetched_schedule.status))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Change recurrence of the schedule" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the wait_for_provisioning flag to False if you do not want to wait \n", + "# for the call to provision the schedule in the backend.\n", + "recurrence = ScheduleRecurrence(frequency=\"Hour\", interval=2) # Runs every two hours\n", + "\n", + "fetched_schedule = Schedule.get(ws, schedule_id)\n", + "\n", + "fetched_schedule.update(name=\"My_Updated_Schedule\", \n", + " description=\"Updated_Schedule_Run\", \n", + " status='Active', \n", + " wait_for_provisioning=True,\n", + " recurrence=recurrence)\n", + "\n", + "fetched_schedule = Schedule.get(ws, fetched_schedule.id)\n", + "\n", + "print(\"Updated schedule:\", fetched_schedule.id, \n", + " \"\\nNew name:\", fetched_schedule.name,\n", + " \"\\nNew frequency:\", fetched_schedule.recurrence.frequency,\n", + " \"\\nNew status:\", fetched_schedule.status)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a schedule for the pipeline using a Datastore\n", + "This schedule will run when additions or modifications are made to Blobs in the Datastore.\n", + "By default, the Datastore container is monitored for changes. Use the path_on_datastore parameter to instead specify a path on the Datastore to monitor for changes. Note: the path_on_datastore will be under the container for the datastore, so the actual path monitored will be container/path_on_datastore. Changes made to subfolders in the container/path will not trigger the schedule.\n", + "Note: Only Blob Datastores are supported.\n", + "Note: Not supported for CMK workspaces. Please review these [instructions](https://docs.microsoft.com/azure/machine-learning/how-to-trigger-published-pipeline) in order to setup a blob trigger submission schedule with CMK enabled. Also see those instructions to bring your own LogicApp to avoid the schedule triggers per month limit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.datastore import Datastore\n", + "\n", + "datastore = Datastore(workspace=ws, name=\"workspaceblobstore\")\n", + "\n", + "schedule = Schedule.create(workspace=ws, name=\"My_Schedule\",\n", + " pipeline_id=pub_pipeline_id, \n", + " experiment_name='Schedule-run-sample',\n", + " datastore=datastore,\n", + " wait_for_provisioning=True,\n", + " description=\"Schedule Run\")\n", + " #polling_interval=5, use polling_interval to specify how often to poll for blob additions/modifications. Default value is 5 minutes.\n", + " #path_on_datastore=\"file/path\") use path_on_datastore to specify a specific folder to monitor for changes.\n", + "\n", + "# You may want to make sure that the schedule is provisioned properly\n", + "# before making any further changes to the schedule\n", + "\n", + "print(\"Created schedule with id: {}\".format(schedule.id))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1606157862620 + } + }, + "outputs": [], + "source": [ + "# Set the wait_for_provisioning flag to False if you do not want to wait \n", + "# for the call to provision the schedule in the backend.\n", + "schedule.disable(wait_for_provisioning=True)\n", + "schedule = Schedule.get(ws, schedule_id)\n", + "print(\"Disabled schedule {}. New status is: {}\".format(schedule.id, schedule.status))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Create a schedule for a pipeline endpoint\n", + "Alternative to creating schedules for a published pipeline, you can also create schedules to run pipeline endpoints.\n", + "Retrieve the pipeline endpoint id to create a schedule. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1606157888851 }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Disable the schedule created for running the pipeline endpont\n", - "Recall the best practice of disabling schedules when not in use.\n", - "The number of schedule triggers allowed per month per region per subscription is 100,000.\n", - "This is calculated using the project trigger counts for all active schedules." - ] + "jupyter": { + "outputs_hidden": false, + "source_hidden": false }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "fetched_schedule = Schedule.get(ws, schedule_id)\n", - "print(\"Using schedule with id: {}\".format(fetched_schedule.id))\n", - "\n", - "# Set the wait_for_provisioning flag to False if you do not want to wait \n", - "# for the call to provision the schedule in the backend.\n", - "fetched_schedule.disable(wait_for_provisioning=True)\n", - "fetched_schedule = Schedule.get(ws, schedule_id)\n", - "print(\"Disabled schedule {}. New status is: {}\".format(fetched_schedule.id, fetched_schedule.status))" - ] + "nteract": { + "transient": { + "deleting": false + } } - ], - "metadata": { - "authors": [ - { - "name": "shbijlan" - } - ], - "categories": [ - "how-to-use-azureml", - "machine-learning-pipelines", - "intro-to-pipelines" - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" + }, + "outputs": [], + "source": [ + "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"ScheduledPipelineEndpoint\")\n", + "published_pipeline_endpoint_id = pipeline_endpoint_by_name.id\n", + "\n", + "recurrence = ScheduleRecurrence(frequency=\"Day\", interval=2, hours=[22], minutes=[30]) # Runs every other day at 10:30pm\n", + "\n", + "schedule = Schedule.create_for_pipeline_endpoint(workspace=ws, name=\"My_Endpoint_Schedule\",\n", + " pipeline_endpoint_id=published_pipeline_endpoint_id,\n", + " experiment_name='Schedule-run-sample',\n", + " recurrence=recurrence, description=\"Schedule_Run\",\n", + " wait_for_provisioning=True)\n", + "\n", + "# You may want to make sure that the schedule is provisioned properly\n", + "# before making any further changes to the schedule\n", + "\n", + "print(\"Created schedule with id: {}\".format(schedule.id))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Get all schedules for a given pipeline endpoint\n", + "Once you have the pipeline endpoint ID, then you can get all schedules for that pipeline endopint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false }, "nteract": { - "version": "nteract-front-end@1.0.0" + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "schedules_for_pipeline_endpoints = Schedule.\\\n", + " get_schedules_for_pipeline_endpoint_id(ws,\n", + " pipeline_endpoint_id=published_pipeline_endpoint_id)\n", + "print('Got all schedules for pipeline endpoint:', published_pipeline_endpoint_id, 'Count:',\n", + " len(schedules_for_pipeline_endpoints))\n", + "\n", + "print('done')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Disable the schedule created for running the pipeline endpont\n", + "Recall the best practice of disabling schedules when not in use.\n", + "The number of schedule triggers allowed per month per region per subscription is 100,000.\n", + "This is calculated using the project trigger counts for all active schedules." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false }, - "order_index": 10, - "star_tag": [ - "featured" - ], - "tags": [ - "None" - ], - "task": "Demonstrates the use of Schedules for Published Pipelines and Pipeline endpoints" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "fetched_schedule = Schedule.get(ws, schedule_id)\n", + "print(\"Using schedule with id: {}\".format(fetched_schedule.id))\n", + "\n", + "# Set the wait_for_provisioning flag to False if you do not want to wait \n", + "# for the call to provision the schedule in the backend.\n", + "fetched_schedule.disable(wait_for_provisioning=True)\n", + "fetched_schedule = Schedule.get(ws, schedule_id)\n", + "print(\"Disabled schedule {}. New status is: {}\".format(fetched_schedule.id, fetched_schedule.status))" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "shbijlan" + } + ], + "categories": [ + "how-to-use-azureml", + "machine-learning-pipelines", + "intro-to-pipelines" + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + }, + "order_index": 10, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of Schedules for Published Pipelines and Pipeline endpoints" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb index aefaf8f75..a9470a4fa 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb @@ -1,608 +1,608 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# How to Setup a PipelineEndpoint and Submit a Pipeline Using the PipelineEndpoint.\n", - "In this notebook, we will see how to setup a PipelineEndpoint and run a specific pipeline version.\n", - "\n", - "PipelineEndpoint can be used to update a published pipeline while maintaining the same endpoint.\n", - "PipelineEndpoint provides a way to keep track of [PublishedPipelines](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.publishedpipeline) using versions. PipelineEndpoint uses endpoint with version information to trigger an underlying published pipeline. Pipeline endpoints are uniquely named within a workspace. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prerequisites and AML Basics\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Notebook Overview\n", - "In this notebook, we provide an introduction to Azure machine learning PipelineEndpoints. It covers:\n", - "* [Create PipelineEndpoint](#Create-PipelineEndpoint), How to create PipelineEndpoint.\n", - "* [Retrieving PipelineEndpoint](#Retrieving-PipelineEndpoint), How to get specific PipelineEndpoint from worskpace by name/Id and get all [PipelineEndpoints](#Get-all-PipelineEndpoints-in-workspace) within workspace.\n", - "* [PipelineEndpoint Properties](#PipelineEndpoint-properties). How to get and set PipelineEndpoint properties, such as default version of PipelineEndpoint.\n", - "* [PipelineEndpoint Submission](#PipelineEndpoint-Submission). How to run a Pipeline using PipelineEndpoint." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create PipelineEndpoint\n", - "Following are required input parameters to create PipelineEndpoint:\n", - "\n", - "* *workspace*: AML workspace.\n", - "* *name*: name of PipelineEndpoint, it is unique within workspace.\n", - "* *description*: description details for PipelineEndpoint.\n", - "* *pipeline*: A [Pipeline](#Steps-to-create-simple-Pipeline) or [PublishedPipeline](#Publish-Pipeline), to set default version of PipelineEndpoint. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialization, Steps to create a Pipeline\n", - "\n", - "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "from azureml.pipeline.core import Pipeline\n", - "\n", - "#Retrieve an already attached Azure Machine Learning Compute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "aml_compute_target = \"cpu-cluster\"\n", - "try:\n", - " aml_compute = AmlCompute(ws, aml_compute_target)\n", - " print(\"Found existing compute target: {}\".format(aml_compute_target))\n", - "except ComputeTargetException:\n", - " print(\"Creating new compute target: {}\".format(aml_compute_target))\n", - " \n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", - " min_nodes = 1, \n", - " max_nodes = 4) \n", - " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", - " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "# source_directory\n", - "source_directory = 'publish_run_train'\n", - "# define a single step pipeline for demonstration purpose.\n", - "trainStep = PythonScriptStep(\n", - " name=\"Training_Step\",\n", - " script_name=\"train.py\", \n", - " compute_target=aml_compute_target, \n", - " source_directory=source_directory\n", - ")\n", - "print(\"TrainStep created\")\n", - "# build and validate Pipeline\n", - "pipeline = Pipeline(workspace=ws, steps=[trainStep])\n", - "print(\"Pipeline is built\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Publish Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "timenow = datetime.now().strftime('%m-%d-%Y-%H-%M')\n", - "\n", - "pipeline_name = timenow + \"-Pipeline\"\n", - "print(pipeline_name)\n", - "\n", - "published_pipeline = pipeline.publish(\n", - " name=pipeline_name, \n", - " description=pipeline_name)\n", - "print(\"Newly published pipeline id: {}\".format(published_pipeline.id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Publishing PipelineEndpoint\n", - "Create PipelineEndpoint with required parameters: workspace, name, description and pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineEndpoint\n", - "\n", - "pipeline_endpoint = PipelineEndpoint.publish(workspace=ws, name=\"PipelineEndpointTest\",\n", - " pipeline=pipeline, description=\"Test description Notebook\")\n", - "pipeline_endpoint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieving PipelineEndpoint\n", - "\n", - "PipelineEndpoint is uniquely defined by name and id within workspace. PipelineEndpoint in workspace can be retrived by Id or by name." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get PipelineEndpoint by Name\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"PipelineEndpointTest\")\n", - "pipeline_endpoint_by_name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get PipelineEndpoint by Id\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#get the PipelineEndpoint Id\n", - "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"PipelineEndpointTest\")\n", - "endpoint_id = pipeline_endpoint_by_name.id\n", - "\n", - "pipeline_endpoint_by_id = PipelineEndpoint.get(workspace=ws, id=endpoint_id)\n", - "pipeline_endpoint_by_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get all PipelineEndpoints in workspace\n", - "Returns all PipelineEndpoints within workspace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint_list = PipelineEndpoint.list(workspace=ws, active_only=True)\n", - "endpoint_list" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PipelineEndpoint properties" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Default Version of PipelineEndpoint\n", - "Default version of PipelineEndpoint starts from \"0\" and increments on addition of pipelines.\n", - "\n", - "##### Get the Default Version" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "default_version = pipeline_endpoint_by_name.get_default_version()\n", - "default_version" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Set default version \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_endpoint_by_name.set_default_version(\"0\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get the Published Pipeline corresponds to specific version of PipelineEndpoint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline = pipeline_endpoint_by_name.get_pipeline(\"0\")\n", - "pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get default version Published Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline = pipeline_endpoint_by_name.get_pipeline()\n", - "pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Add Published Pipeline to PipelineEndpoint, \n", - "Adds a published pipeline (if its not present) using add() and if you want to add and set to default use add_default()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_endpoint_by_name.add(published_pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Add Published pipeline to PipelineEndpoint and set it to default version\n", - "Adding published pipeline to PipelineEndpoint if not present and set it to default" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set Published Pipeline to PipelineEndpoint, if exists\n", - "pipeline_endpoint_by_name.set_default(published_pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get all Versions in PipelineEndpoint\n", - "Returns list of published pipelines and its versions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "versions = pipeline_endpoint_by_name.list_versions()\n", - "\n", - "for ve in versions:\n", - " print(ve.version)\n", - " print(ve.pipeline.id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get all Published Pipelines in PipelineEndpoint\n", - "Returns all active pipelines in PipelineEnpoint, if active_only flag is set to True." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipelines = pipeline_endpoint_by_name.list_pipelines(active_only=True)\n", - "pipelines" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Name property of PipelineEndpoint\n", - "PipelineEndpoint is uniquely identified by name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Set Name PipelineEndpoint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_endpoint_by_name.set_name(name=\"NewName\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PipelineEndpoint Submission\n", - "PipelineEndpoint triggers specific versioned pipeline or default pipeline by:\n", - "* Rest Endpoint \n", - "* Submit call." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Run Pipeline by endpoint property of PipelineEndpoint\n", - "Run specific pipeline using endpoint property of PipelineEndpoint and executing http post." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"NewName\")\n", - "\n", - "# endpoint with id \n", - "rest_endpoint_id = pipeline_endpoint_by_name.endpoint\n", - "\n", - "# for default version pipeline\n", - "rest_endpoint_id_without_version_with_id = rest_endpoint_id\n", - "\n", - "# for specific version pipeline just append version info\n", - "version=\"0\"\n", - "rest_endpoint_id_with_version = rest_endpoint_id_without_version_with_id+\"/\"+ version\n", - "print(rest_endpoint_id_with_version)\n", - "pipeline_endpoint_by_name" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# endpoint with name\n", - "rest_endpoint_name = rest_endpoint_id.split(\"Id\", 1)[0] + \"Name?name=\" + pipeline_endpoint_by_name.name\n", - "\n", - "# for default version pipeline\n", - "rest_endpoint_name_without_version = rest_endpoint_name\n", - "\n", - "# for specific version pipeline just append version info\n", - "version=\"0\"\n", - "rest_endpoint_name_with_version = rest_endpoint_name_without_version+\"&pipelineVersion=\"+ version\n", - "print(rest_endpoint_name_with_version)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.authentication import InteractiveLoginAuthentication\n", - "import requests\n", - "\n", - "auth = InteractiveLoginAuthentication()\n", - "aad_token = auth.get_authentication_header()\n", - "\n", - "#endpoint = pipeline_endpoint_by_name.url\n", - "\n", - "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint_name_with_version))\n", - "\n", - "# specify the param when running the pipeline\n", - "response = requests.post(rest_endpoint_name_with_version, \n", - " headers=aad_token, \n", - " json={\"ExperimentName\": \"default_pipeline\",\n", - " \"RunSource\": \"SDK\",\n", - " \"ParameterAssignments\": {\"1\": \"united\", \"2\":\"city\"}})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " response.raise_for_status()\n", - "except Exception: \n", - " raise Exception('Received bad response from the endpoint: {}\\n'\n", - " 'Response Code: {}\\n'\n", - " 'Headers: {}\\n'\n", - " 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n", - "\n", - "run_id = response.json().get('Id')\n", - "print('Submitted pipeline run: ', run_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Run Pipeline by Submit call of PipelineEndpoint \n", - "Run specific pipeline using Submit api of PipelineEndpoint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# submit pipeline with specific version\n", - "run_id = pipeline_endpoint_by_name.submit(\"NewName\", pipeline_version=\"0\")\n", - "print(run_id)\n", - "\n", - "# submit pipeline with default version\n", - "run_id = pipeline_endpoint_by_name.submit(\"NewName\")\n", - "print(run_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Use Experiment.Submit() to Submit Pipeline\n", - "Run specific pipeline using Experiment submit api" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "pipeline_run = Experiment(ws, name=\"submit_endpoint_sample\").submit(pipeline_endpoint_by_name, tags={'endpoint_tag': \"1\"}, pipeline_version=\"0\")" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# How to Setup a PipelineEndpoint and Submit a Pipeline Using the PipelineEndpoint.\n", + "In this notebook, we will see how to setup a PipelineEndpoint and run a specific pipeline version.\n", + "\n", + "PipelineEndpoint can be used to update a published pipeline while maintaining the same endpoint.\n", + "PipelineEndpoint provides a way to keep track of [PublishedPipelines](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.publishedpipeline) using versions. PipelineEndpoint uses endpoint with version information to trigger an underlying published pipeline. Pipeline endpoints are uniquely named within a workspace. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prerequisites and AML Basics\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Notebook Overview\n", + "In this notebook, we provide an introduction to Azure machine learning PipelineEndpoints. It covers:\n", + "* [Create PipelineEndpoint](#Create-PipelineEndpoint), How to create PipelineEndpoint.\n", + "* [Retrieving PipelineEndpoint](#Retrieving-PipelineEndpoint), How to get specific PipelineEndpoint from worskpace by name/Id and get all [PipelineEndpoints](#Get-all-PipelineEndpoints-in-workspace) within workspace.\n", + "* [PipelineEndpoint Properties](#PipelineEndpoint-properties). How to get and set PipelineEndpoint properties, such as default version of PipelineEndpoint.\n", + "* [PipelineEndpoint Submission](#PipelineEndpoint-Submission). How to run a Pipeline using PipelineEndpoint." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create PipelineEndpoint\n", + "Following are required input parameters to create PipelineEndpoint:\n", + "\n", + "* *workspace*: AML workspace.\n", + "* *name*: name of PipelineEndpoint, it is unique within workspace.\n", + "* *description*: description details for PipelineEndpoint.\n", + "* *pipeline*: A [Pipeline](#Steps-to-create-simple-Pipeline) or [PublishedPipeline](#Publish-Pipeline), to set default version of PipelineEndpoint. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialization, Steps to create a Pipeline\n", + "\n", + "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import AmlCompute, ComputeTarget\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "from azureml.pipeline.core import Pipeline\n", + "\n", + "#Retrieve an already attached Azure Machine Learning Compute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "aml_compute_target = \"cpu-cluster\"\n", + "try:\n", + " aml_compute = AmlCompute(ws, aml_compute_target)\n", + " print(\"Found existing compute target: {}\".format(aml_compute_target))\n", + "except ComputeTargetException:\n", + " print(\"Creating new compute target: {}\".format(aml_compute_target))\n", + " \n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", + " min_nodes = 1, \n", + " max_nodes = 4) \n", + " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", + " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", + "\n", + "# source_directory\n", + "source_directory = 'publish_run_train'\n", + "# define a single step pipeline for demonstration purpose.\n", + "trainStep = PythonScriptStep(\n", + " name=\"Training_Step\",\n", + " script_name=\"train.py\", \n", + " compute_target=aml_compute_target, \n", + " source_directory=source_directory\n", + ")\n", + "print(\"TrainStep created\")\n", + "# build and validate Pipeline\n", + "pipeline = Pipeline(workspace=ws, steps=[trainStep])\n", + "print(\"Pipeline is built\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Publish Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "timenow = datetime.now().strftime('%m-%d-%Y-%H-%M')\n", + "\n", + "pipeline_name = timenow + \"-Pipeline\"\n", + "print(pipeline_name)\n", + "\n", + "published_pipeline = pipeline.publish(\n", + " name=pipeline_name, \n", + " description=pipeline_name)\n", + "print(\"Newly published pipeline id: {}\".format(published_pipeline.id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Publishing PipelineEndpoint\n", + "Create PipelineEndpoint with required parameters: workspace, name, description and pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineEndpoint\n", + "\n", + "pipeline_endpoint = PipelineEndpoint.publish(workspace=ws, name=\"PipelineEndpointTest\",\n", + " pipeline=pipeline, description=\"Test description Notebook\")\n", + "pipeline_endpoint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Retrieving PipelineEndpoint\n", + "\n", + "PipelineEndpoint is uniquely defined by name and id within workspace. PipelineEndpoint in workspace can be retrived by Id or by name." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get PipelineEndpoint by Name\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"PipelineEndpointTest\")\n", + "pipeline_endpoint_by_name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get PipelineEndpoint by Id\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#get the PipelineEndpoint Id\n", + "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"PipelineEndpointTest\")\n", + "endpoint_id = pipeline_endpoint_by_name.id\n", + "\n", + "pipeline_endpoint_by_id = PipelineEndpoint.get(workspace=ws, id=endpoint_id)\n", + "pipeline_endpoint_by_id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get all PipelineEndpoints in workspace\n", + "Returns all PipelineEndpoints within workspace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "endpoint_list = PipelineEndpoint.list(workspace=ws, active_only=True)\n", + "endpoint_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PipelineEndpoint properties" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Default Version of PipelineEndpoint\n", + "Default version of PipelineEndpoint starts from \"0\" and increments on addition of pipelines.\n", + "\n", + "##### Get the Default Version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "default_version = pipeline_endpoint_by_name.get_default_version()\n", + "default_version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Set default version \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_endpoint_by_name.set_default_version(\"0\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get the Published Pipeline corresponds to specific version of PipelineEndpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = pipeline_endpoint_by_name.get_pipeline(\"0\")\n", + "pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get default version Published Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = pipeline_endpoint_by_name.get_pipeline()\n", + "pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Add Published Pipeline to PipelineEndpoint, \n", + "Adds a published pipeline (if its not present) using add() and if you want to add and set to default use add_default()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_endpoint_by_name.add(published_pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Add Published pipeline to PipelineEndpoint and set it to default version\n", + "Adding published pipeline to PipelineEndpoint if not present and set it to default" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set Published Pipeline to PipelineEndpoint, if exists\n", + "pipeline_endpoint_by_name.set_default(published_pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get all Versions in PipelineEndpoint\n", + "Returns list of published pipelines and its versions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "versions = pipeline_endpoint_by_name.list_versions()\n", + "\n", + "for ve in versions:\n", + " print(ve.version)\n", + " print(ve.pipeline.id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get all Published Pipelines in PipelineEndpoint\n", + "Returns all active pipelines in PipelineEnpoint, if active_only flag is set to True." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipelines = pipeline_endpoint_by_name.list_pipelines(active_only=True)\n", + "pipelines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Name property of PipelineEndpoint\n", + "PipelineEndpoint is uniquely identified by name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Set Name PipelineEndpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_endpoint_by_name.set_name(name=\"NewName\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PipelineEndpoint Submission\n", + "PipelineEndpoint triggers specific versioned pipeline or default pipeline by:\n", + "* Rest Endpoint \n", + "* Submit call." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Run Pipeline by endpoint property of PipelineEndpoint\n", + "Run specific pipeline using endpoint property of PipelineEndpoint and executing http post." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"NewName\")\n", + "\n", + "# endpoint with id \n", + "rest_endpoint_id = pipeline_endpoint_by_name.endpoint\n", + "\n", + "# for default version pipeline\n", + "rest_endpoint_id_without_version_with_id = rest_endpoint_id\n", + "\n", + "# for specific version pipeline just append version info\n", + "version=\"0\"\n", + "rest_endpoint_id_with_version = rest_endpoint_id_without_version_with_id+\"/\"+ version\n", + "print(rest_endpoint_id_with_version)\n", + "pipeline_endpoint_by_name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# endpoint with name\n", + "rest_endpoint_name = rest_endpoint_id.split(\"Id\", 1)[0] + \"Name?name=\" + pipeline_endpoint_by_name.name\n", + "\n", + "# for default version pipeline\n", + "rest_endpoint_name_without_version = rest_endpoint_name\n", + "\n", + "# for specific version pipeline just append version info\n", + "version=\"0\"\n", + "rest_endpoint_name_with_version = rest_endpoint_name_without_version+\"&pipelineVersion=\"+ version\n", + "print(rest_endpoint_name_with_version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.authentication import InteractiveLoginAuthentication\n", + "import requests\n", + "\n", + "auth = InteractiveLoginAuthentication()\n", + "aad_token = auth.get_authentication_header()\n", + "\n", + "#endpoint = pipeline_endpoint_by_name.url\n", + "\n", + "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint_name_with_version))\n", + "\n", + "# specify the param when running the pipeline\n", + "response = requests.post(rest_endpoint_name_with_version, \n", + " headers=aad_token, \n", + " json={\"ExperimentName\": \"default_pipeline\",\n", + " \"RunSource\": \"SDK\",\n", + " \"ParameterAssignments\": {\"1\": \"united\", \"2\":\"city\"}})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " response.raise_for_status()\n", + "except Exception: \n", + " raise Exception('Received bad response from the endpoint: {}\\n'\n", + " 'Response Code: {}\\n'\n", + " 'Headers: {}\\n'\n", + " 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n", + "\n", + "run_id = response.json().get('Id')\n", + "print('Submitted pipeline run: ', run_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Run Pipeline by Submit call of PipelineEndpoint \n", + "Run specific pipeline using Submit api of PipelineEndpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# submit pipeline with specific version\n", + "run_id = pipeline_endpoint_by_name.submit(\"NewName\", pipeline_version=\"0\")\n", + "print(run_id)\n", + "\n", + "# submit pipeline with default version\n", + "run_id = pipeline_endpoint_by_name.submit(\"NewName\")\n", + "print(run_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use Experiment.Submit() to Submit Pipeline\n", + "Run specific pipeline using Experiment submit api" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "pipeline_run = Experiment(ws, name=\"submit_endpoint_sample\").submit(pipeline_endpoint_by_name, tags={'endpoint_tag': \"1\"}, pipeline_version=\"0\")" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "How to setup a versioned Pipeline Endpoint", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "order_index": 12, + "tags": [ + "None" ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "How to setup a versioned Pipeline Endpoint", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "order_index": 12, - "tags": [ - "None" - ], - "task": "Demonstrates the use of PipelineEndpoint to run a specific version of the Published Pipeline" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "task": "Demonstrates the use of PipelineEndpoint to run a specific version of the Published Pipeline" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb index ff01d3a07..6a3237c74 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb @@ -1,530 +1,530 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Showcasing DataPath and PipelineParameter\n", - "\n", - "This notebook demonstrateas the use of [**DataPath**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapath?view=azure-ml-py) and [**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) in AML Pipeline. You will learn how strings and [**DataPath**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapath?view=azure-ml-py) can be parameterized and submitted to AML Pipelines via [**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py).\n", - "To see more about how parameters work between steps, please refer [aml-pipelines-with-data-dependency-steps](https://aka.ms/pl-data-dep).\n", - "\n", - "* [How to create a Pipeline with a DataPath PipelineParameter](#index1)\n", - "* [How to submit a Pipeline with a DataPath PipelineParameter](#index2)\n", - "* [How to submit a Pipeline and change the DataPath PipelineParameter value from the sdk](#index3)\n", - "* [How to submit a Pipeline and change the DataPath PipelineParameter value using a REST call](#index4)\n", - "* [How to create a datastore trigger schedule and use the data_path_parameter_name to get the path of the changed blob in the Pipeline](#index5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Azure Machine Learning and Pipeline SDK-specific imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Experiment\n", - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.data.datapath import DataPath, DataPathComputeBinding\n", - "from azureml.widgets import RunDetails\n", - "\n", - "from azureml.pipeline.core import PipelineParameter\n", - "from azureml.pipeline.core import Pipeline, PipelineRun\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json\n", - "\n", - "If you don't have a config.json file, go through the [configuration Notebook](https://aka.ms/pl-config) first.\n", - "\n", - "This sets you up with a working config file that has information on your workspace, subscription id, etc." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an Azure ML experiment\n", - "\n", - "Let's create an experiment named \"showcasing-datapath\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a name for the run history container in the workspace.\n", - "experiment_name = 'showcasing-datapath'\n", - "source_directory = '.'\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "experiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach an AmlCompute cluster\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a name for your cluster.\n", - "amlcompute_cluster_name = \"cpu-cluster\"\n", - "\n", - "found = False\n", - "# Check if this compute target already exists in the workspace.\n", - "cts = ws.compute_targets\n", - "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n", - " found = True\n", - " print('Found existing compute target.')\n", - " compute_target = cts[amlcompute_cluster_name]\n", - " \n", - "if not found:\n", - " print('Creating a new compute target...')\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n", - " #vm_priority = 'lowpriority', # optional\n", - " max_nodes = 4)\n", - "\n", - " # Create the cluster.\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n", - " \n", - " # Can poll for a minimum number of nodes and for a specific timeout.\n", - " # If no min_node_count is provided, it will use the scale settings for the cluster.\n", - " compute_target.wait_for_completion(show_output = True, timeout_in_minutes = 10)\n", - " \n", - " # For a more detailed view of current AmlCompute status, use get_status()." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data and arguments setup \n", - "\n", - "We will setup a trining script to run and its arguments to be used. The sample training script below will print the two arguments to show what has been passed to pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile train_with_datapath.py\n", - "import argparse\n", - "import os\n", - "\n", - "parser = argparse.ArgumentParser(\"train\")\n", - "parser.add_argument(\"--arg1\", type=str, help=\"sample string argument\")\n", - "parser.add_argument(\"--arg2\", type=str, help=\"sample datapath argument\")\n", - "args = parser.parse_args()\n", - "\n", - "print(\"Sample string argument : %s\" % args.arg1)\n", - "print(\"Sample datapath argument: %s\" % args.arg2)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's setup string and DataPath arguments using PipelineParameter. \n", - "\n", - "Note that Pipeline accepts a tuple of the form ([**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) , [**DataPathComputeBinding**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapathcomputebinding?view=azure-ml-py)) as an input. DataPath defines the location of input data. DataPathComputeBinding defines how the data is consumed during step execution. The DataPath can be modified at pipeline submission time with a DataPath parameter, while the compute binding does not change. For static data inputs, we use [**DataReference**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.data_reference.datareference?view=azure-ml-py) which defines both the data location and compute binding." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "datapath-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "def_blob_store = ws.get_default_datastore()\n", - "print(\"Default datastore's name: {}\".format(def_blob_store.name))\n", - "\n", - "data_path = DataPath(datastore=def_blob_store, path_on_datastore='sample_datapath1')\n", - "datapath1_pipeline_param = PipelineParameter(name=\"input_datapath\", default_value=data_path)\n", - "datapath_input = (datapath1_pipeline_param, DataPathComputeBinding(mode='mount'))\n", - "\n", - "string_pipeline_param = PipelineParameter(name=\"input_string\", default_value='sample_string1')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a Pipeline with a DataPath PipelineParameter\n", - "\n", - "Note that the ```datapath_input``` is specified on both arguments and inputs to create a step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_step = PythonScriptStep(\n", - " name='train_step',\n", - " script_name=\"train_with_datapath.py\",\n", - " arguments=[\"--arg1\", string_pipeline_param, \"--arg2\", datapath_input],\n", - " inputs=[datapath_input],\n", - " compute_target=compute_target, \n", - " source_directory=source_directory)\n", - "print(\"train_step created\")\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[train_step])\n", - "print(\"pipeline with the train_step created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit a Pipeline with a DataPath PipelineParameter\n", - "\n", - "Pipelines can be submitted with default values of PipelineParameters by not specifying any parameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run = experiment.submit(pipeline)\n", - "print(\"Pipeline is submitted for execution\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit a Pipeline and change the DataPath PipelineParameter value from the sdk\n", - "\n", - "Or Pipelines can be submitted with values other than default ones by using pipeline_parameters. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run_with_params = experiment.submit(pipeline, \\\n", - " pipeline_parameters={'input_datapath': DataPath(datastore=def_blob_store, path_on_datastore='sample_datapath2'),\n", - " 'input_string': 'sample_string2'}) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(pipeline_run_with_params).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run_with_params.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit a Pipeline and change the DataPath PipelineParameter value using a REST call\n", - "\n", - "Let's published the pipeline to use the rest endpoint of the published pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline = pipeline.publish(name=\"DataPath_Pipeline\", description=\"Pipeline to test Datapath\", continue_on_step_failure=True)\n", - "published_pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.authentication import InteractiveLoginAuthentication\n", - "import requests\n", - "\n", - "auth = InteractiveLoginAuthentication()\n", - "aad_token = auth.get_authentication_header()\n", - "\n", - "rest_endpoint = published_pipeline.endpoint\n", - "\n", - "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# specify the param when running the pipeline\n", - "response = requests.post(rest_endpoint, \n", - " headers=aad_token, \n", - " json={\"ExperimentName\": \"MyRestPipeline\",\n", - " \"RunSource\": \"SDK\",\n", - " \"DataPathAssignments\": {\n", - " \"input_datapath\": { \n", - " \"DataStoreName\": def_blob_store.name,\n", - " \"RelativePath\": 'sample_datapath3'\n", - " }\n", - " },\n", - " \"ParameterAssignments\": {\"input_string\": \"sample_string3\"}\n", - " }\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " response.raise_for_status()\n", - "except Exception: \n", - " raise Exception('Received bad response from the endpoint: {}\\n'\n", - " 'Response Code: {}\\n'\n", - " 'Headers: {}\\n'\n", - " 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n", - "\n", - "run_id = response.json().get('Id')\n", - "print('Submitted pipeline run: ', run_id)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline_run_via_rest = PipelineRun(ws.experiments[\"MyRestPipeline\"], run_id)\n", - "RunDetails(published_pipeline_run_via_rest).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline_run_via_rest.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a Datastore trigger schedule and use data path parameter\n", - "\n", - "When the Pipeline is scheduled with DataPath parameter, it will be triggered by the modified or added data in the DataPath. ```path_on_datastore``` should be a folder and the value of the DataPath will be replaced by the path of the modified data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Schedule\n", - "\n", - "schedule = Schedule.create(workspace=ws, \n", - " name=\"Datastore_trigger_schedule\",\n", - " pipeline_id=published_pipeline.id, \n", - " experiment_name='Scheduled_Pipeline',\n", - " datastore=def_blob_store,\n", - " wait_for_provisioning=True,\n", - " description=\"Datastore trigger schedule demo\",\n", - " path_on_datastore=\"sample_datapath_for_folder\",\n", - " data_path_parameter_name=\"input_datapath\") #Same name as used above to create PipelineParameter\n", - "\n", - "print(\"Created schedule with id: {}\".format(schedule.id))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "schedule.disable()\n", - "schedule" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "shbijlan" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "How to use DataPath as a PipelineParameter", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "order_index": 13, - "star_tag": [ - "featured" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Showcasing DataPath and PipelineParameter\n", + "\n", + "This notebook demonstrateas the use of [**DataPath**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapath?view=azure-ml-py) and [**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) in AML Pipeline. You will learn how strings and [**DataPath**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapath?view=azure-ml-py) can be parameterized and submitted to AML Pipelines via [**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py).\n", + "To see more about how parameters work between steps, please refer [aml-pipelines-with-data-dependency-steps](https://aka.ms/pl-data-dep).\n", + "\n", + "* [How to create a Pipeline with a DataPath PipelineParameter](#index1)\n", + "* [How to submit a Pipeline with a DataPath PipelineParameter](#index2)\n", + "* [How to submit a Pipeline and change the DataPath PipelineParameter value from the sdk](#index3)\n", + "* [How to submit a Pipeline and change the DataPath PipelineParameter value using a REST call](#index4)\n", + "* [How to create a datastore trigger schedule and use the data_path_parameter_name to get the path of the changed blob in the Pipeline](#index5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Azure Machine Learning and Pipeline SDK-specific imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "from azureml.core import Workspace, Experiment\n", + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.data.datapath import DataPath, DataPathComputeBinding\n", + "from azureml.widgets import RunDetails\n", + "\n", + "from azureml.pipeline.core import PipelineParameter\n", + "from azureml.pipeline.core import Pipeline, PipelineRun\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json\n", + "\n", + "If you don't have a config.json file, go through the [configuration Notebook](https://aka.ms/pl-config) first.\n", + "\n", + "This sets you up with a working config file that has information on your workspace, subscription id, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an Azure ML experiment\n", + "\n", + "Let's create an experiment named \"showcasing-datapath\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose a name for the run history container in the workspace.\n", + "experiment_name = 'showcasing-datapath'\n", + "source_directory = '.'\n", + "\n", + "experiment = Experiment(ws, experiment_name)\n", + "experiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create or Attach an AmlCompute cluster\n", + "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose a name for your cluster.\n", + "amlcompute_cluster_name = \"cpu-cluster\"\n", + "\n", + "found = False\n", + "# Check if this compute target already exists in the workspace.\n", + "cts = ws.compute_targets\n", + "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n", + " found = True\n", + " print('Found existing compute target.')\n", + " compute_target = cts[amlcompute_cluster_name]\n", + " \n", + "if not found:\n", + " print('Creating a new compute target...')\n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n", + " #vm_priority = 'lowpriority', # optional\n", + " max_nodes = 4)\n", + "\n", + " # Create the cluster.\n", + " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n", + " \n", + " # Can poll for a minimum number of nodes and for a specific timeout.\n", + " # If no min_node_count is provided, it will use the scale settings for the cluster.\n", + " compute_target.wait_for_completion(show_output = True, timeout_in_minutes = 10)\n", + " \n", + " # For a more detailed view of current AmlCompute status, use get_status()." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data and arguments setup \n", + "\n", + "We will setup a trining script to run and its arguments to be used. The sample training script below will print the two arguments to show what has been passed to pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile train_with_datapath.py\n", + "import argparse\n", + "import os\n", + "\n", + "parser = argparse.ArgumentParser(\"train\")\n", + "parser.add_argument(\"--arg1\", type=str, help=\"sample string argument\")\n", + "parser.add_argument(\"--arg2\", type=str, help=\"sample datapath argument\")\n", + "args = parser.parse_args()\n", + "\n", + "print(\"Sample string argument : %s\" % args.arg1)\n", + "print(\"Sample datapath argument: %s\" % args.arg2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's setup string and DataPath arguments using PipelineParameter. \n", + "\n", + "Note that Pipeline accepts a tuple of the form ([**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) , [**DataPathComputeBinding**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapathcomputebinding?view=azure-ml-py)) as an input. DataPath defines the location of input data. DataPathComputeBinding defines how the data is consumed during step execution. The DataPath can be modified at pipeline submission time with a DataPath parameter, while the compute binding does not change. For static data inputs, we use [**DataReference**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.data_reference.datareference?view=azure-ml-py) which defines both the data location and compute binding." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of DataPath as a PipelineParameter" + "datapath-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "def_blob_store = ws.get_default_datastore()\n", + "print(\"Default datastore's name: {}\".format(def_blob_store.name))\n", + "\n", + "data_path = DataPath(datastore=def_blob_store, path_on_datastore='sample_datapath1')\n", + "datapath1_pipeline_param = PipelineParameter(name=\"input_datapath\", default_value=data_path)\n", + "datapath_input = (datapath1_pipeline_param, DataPathComputeBinding(mode='mount'))\n", + "\n", + "string_pipeline_param = PipelineParameter(name=\"input_string\", default_value='sample_string1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Pipeline with a DataPath PipelineParameter\n", + "\n", + "Note that the ```datapath_input``` is specified on both arguments and inputs to create a step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_step = PythonScriptStep(\n", + " name='train_step',\n", + " script_name=\"train_with_datapath.py\",\n", + " arguments=[\"--arg1\", string_pipeline_param, \"--arg2\", datapath_input],\n", + " inputs=[datapath_input],\n", + " compute_target=compute_target, \n", + " source_directory=source_directory)\n", + "print(\"train_step created\")\n", + "\n", + "pipeline = Pipeline(workspace=ws, steps=[train_step])\n", + "print(\"pipeline with the train_step created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submit a Pipeline with a DataPath PipelineParameter\n", + "\n", + "Pipelines can be submitted with default values of PipelineParameters by not specifying any parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run = experiment.submit(pipeline)\n", + "print(\"Pipeline is submitted for execution\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RunDetails(pipeline_run).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submit a Pipeline and change the DataPath PipelineParameter value from the sdk\n", + "\n", + "Or Pipelines can be submitted with values other than default ones by using pipeline_parameters. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run_with_params = experiment.submit(pipeline, \\\n", + " pipeline_parameters={'input_datapath': DataPath(datastore=def_blob_store, path_on_datastore='sample_datapath2'),\n", + " 'input_string': 'sample_string2'}) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RunDetails(pipeline_run_with_params).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run_with_params.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submit a Pipeline and change the DataPath PipelineParameter value using a REST call\n", + "\n", + "Let's published the pipeline to use the rest endpoint of the published pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "published_pipeline = pipeline.publish(name=\"DataPath_Pipeline\", description=\"Pipeline to test Datapath\", continue_on_step_failure=True)\n", + "published_pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.authentication import InteractiveLoginAuthentication\n", + "import requests\n", + "\n", + "auth = InteractiveLoginAuthentication()\n", + "aad_token = auth.get_authentication_header()\n", + "\n", + "rest_endpoint = published_pipeline.endpoint\n", + "\n", + "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# specify the param when running the pipeline\n", + "response = requests.post(rest_endpoint, \n", + " headers=aad_token, \n", + " json={\"ExperimentName\": \"MyRestPipeline\",\n", + " \"RunSource\": \"SDK\",\n", + " \"DataPathAssignments\": {\n", + " \"input_datapath\": { \n", + " \"DataStoreName\": def_blob_store.name,\n", + " \"RelativePath\": 'sample_datapath3'\n", + " }\n", + " },\n", + " \"ParameterAssignments\": {\"input_string\": \"sample_string3\"}\n", + " }\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " response.raise_for_status()\n", + "except Exception: \n", + " raise Exception('Received bad response from the endpoint: {}\\n'\n", + " 'Response Code: {}\\n'\n", + " 'Headers: {}\\n'\n", + " 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n", + "\n", + "run_id = response.json().get('Id')\n", + "print('Submitted pipeline run: ', run_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "published_pipeline_run_via_rest = PipelineRun(ws.experiments[\"MyRestPipeline\"], run_id)\n", + "RunDetails(published_pipeline_run_via_rest).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "published_pipeline_run_via_rest.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Datastore trigger schedule and use data path parameter\n", + "\n", + "When the Pipeline is scheduled with DataPath parameter, it will be triggered by the modified or added data in the DataPath. ```path_on_datastore``` should be a folder and the value of the DataPath will be replaced by the path of the modified data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Schedule\n", + "\n", + "schedule = Schedule.create(workspace=ws, \n", + " name=\"Datastore_trigger_schedule\",\n", + " pipeline_id=published_pipeline.id, \n", + " experiment_name='Scheduled_Pipeline',\n", + " datastore=def_blob_store,\n", + " wait_for_provisioning=True,\n", + " description=\"Datastore trigger schedule demo\",\n", + " path_on_datastore=\"sample_datapath_for_folder\",\n", + " data_path_parameter_name=\"input_datapath\") #Same name as used above to create PipelineParameter\n", + "\n", + "print(\"Created schedule with id: {}\".format(schedule.id))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schedule.disable()\n", + "schedule" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "shbijlan" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "How to use DataPath as a PipelineParameter", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "order_index": 13, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of DataPath as a PipelineParameter" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb index 08490e403..8aec4af78 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb @@ -1,526 +1,526 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Showcasing Dataset and PipelineParameter\n", - "\n", - "This notebook demonstrates how a [**FileDataset**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) or [**TabularDataset**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) can be parametrized with [**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) in an AML [Pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline(class)?view=azure-ml-py). By parametrizing datasets, you can dynamically run pipeline experiments with different datasets without any code change.\n", - "\n", - "A common use case is building a training pipeline with a sample of your training data for quick iterative development. When you're ready to test and deploy your pipeline at scale, you can pass in your full training dataset to the pipeline experiment without making any changes to your training script. \n", - " \n", - "To see more about how parameters work between steps, please refer [aml-pipelines-with-data-dependency-steps](https://aka.ms/pl-data-dep).\n", - "\n", - "* [How to create a Pipeline with a Dataset PipelineParameter](#index1)\n", - "* [How to submit a Pipeline with a Dataset PipelineParameter](#index2)\n", - "* [How to submit a Pipeline and change the Dataset PipelineParameter value from the sdk](#index3)\n", - "* [How to submit a Pipeline and change the Dataset PipelineParameter value using a REST call](#index4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Azure Machine Learning and Pipeline SDK-specific imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Experiment, Dataset, RunConfiguration\n", - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.environment import CondaDependencies\n", - "from azureml.data.dataset_consumption_config import DatasetConsumptionConfig\n", - "from azureml.widgets import RunDetails\n", - "\n", - "from azureml.pipeline.core import PipelineParameter\n", - "from azureml.pipeline.core import Pipeline, PipelineRun\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json\n", - "\n", - "If you don't have a config.json file, go through the [configuration Notebook](https://aka.ms/pl-config) first.\n", - "\n", - "This sets you up with a working config file that has information on your workspace, subscription id, etc." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an Azure ML experiment\n", - "\n", - "Let's create an experiment named \"showcasing-dataset\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a name for the run history container in the workspace.\n", - "experiment_name = 'showcasing-dataset'\n", - "source_directory = '.'\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "experiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach an AmlCompute cluster\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a name for your cluster.\n", - "amlcompute_cluster_name = \"cpu-cluster\"\n", - "\n", - "found = False\n", - "# Check if this compute target already exists in the workspace.\n", - "cts = ws.compute_targets\n", - "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n", - " found = True\n", - " print('Found existing compute target.')\n", - " compute_target = cts[amlcompute_cluster_name]\n", - " \n", - "if not found:\n", - " print('Creating a new compute target...')\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n", - " #vm_priority = 'lowpriority', # optional\n", - " max_nodes = 4)\n", - "\n", - " # Create the cluster.\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n", - " \n", - " # Can poll for a minimum number of nodes and for a specific timeout.\n", - " # If no min_node_count is provided, it will use the scale settings for the cluster.\n", - " compute_target.wait_for_completion(show_output = True, timeout_in_minutes = 10)\n", - " \n", - " # For a more detailed view of current AmlCompute status, use get_status()." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset Configuration\n", - "\n", - "The following steps detail how to create a [FileDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) and [TabularDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) from an external CSV file, and configure them to be used by a [Pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline(class)?view=azure-ml-py):\n", - "\n", - "1. Create a dataset from a csv file\n", - "2. Create a [PipelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) object and set the `default_value` to the dataset. [PipelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) objects enabled arguments to be passed into Pipelines when they are resubmitted after creation. The `name` is referenced later on when we submit additional pipeline runs with different input datasets. \n", - "3. Create a [DatasetConsumptionConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_consumption_config.datasetconsumptionconfig?view=azure-ml-py) object from the [PiepelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py). The [DatasetConsumptionConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_consumption_config.datasetconsumptionconfig?view=azure-ml-py) object specifies how the dataset should be used by the remote compute where the pipeline is run. **NOTE** only [DatasetConsumptionConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_consumption_config.datasetconsumptionconfig?view=azure-ml-py) objects built on [FileDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) can be set `as_mount()` or `as_download()` on the remote compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "datapath-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "file_dataset = Dataset.File.from_files('https://dprepdata.blob.core.windows.net/demo/Titanic.csv')\n", - "file_pipeline_param = PipelineParameter(name=\"file_ds_param\", default_value=file_dataset)\n", - "file_ds_consumption = DatasetConsumptionConfig(\"file_dataset\", file_pipeline_param).as_mount()\n", - "\n", - "tabular_dataset = Dataset.Tabular.from_delimited_files('https://dprepdata.blob.core.windows.net/demo/Titanic.csv')\n", - "tabular_pipeline_param = PipelineParameter(name=\"tabular_ds_param\", default_value=tabular_dataset)\n", - "tabular_ds_consumption = DatasetConsumptionConfig(\"tabular_dataset\", tabular_pipeline_param)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will setup a training script to ingest our passed-in datasets and print their contents. **NOTE** the names of the datasets referenced inside the training script correspond to the `name` of their respective [DatasetConsumptionConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_consumption_config.datasetconsumptionconfig?view=azure-ml-py) objects we defined above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile train_with_dataset.py\n", - "from azureml.core import Run\n", - "\n", - "input_file_ds_path = Run.get_context().input_datasets['file_dataset']\n", - "with open(input_file_ds_path, 'r') as f:\n", - " content = f.read()\n", - " print(content)\n", - "\n", - "input_tabular_ds = Run.get_context().input_datasets['tabular_dataset']\n", - "tabular_df = input_tabular_ds.to_pandas_dataframe()\n", - "print(tabular_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a Pipeline with a Dataset PipelineParameter\n", - "\n", - "Note that the ```file_ds_consumption``` and ```tabular_ds_consumption``` are specified as both arguments and inputs to create a step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "conda_dep = CondaDependencies()\n", - "conda_dep.add_pip_package(\"pandas\")\n", - "\n", - "run_config = RunConfiguration(conda_dependencies=conda_dep)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_step = PythonScriptStep(\n", - " name=\"train_step\",\n", - " script_name=\"train_with_dataset.py\",\n", - " arguments=[\"--param1\", file_ds_consumption, \"--param2\", tabular_ds_consumption],\n", - " inputs=[file_ds_consumption, tabular_ds_consumption],\n", - " compute_target=compute_target,\n", - " source_directory=source_directory,\n", - " runconfig=run_config)\n", - "\n", - "print(\"train_step created\")\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[train_step])\n", - "print(\"pipeline with the train_step created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit a Pipeline with a Dataset PipelineParameter\n", - "\n", - "Pipelines can be submitted with default values of PipelineParameters by not specifying any parameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Pipeline will run with default file_ds and tabular_ds\n", - "pipeline_run = experiment.submit(pipeline)\n", - "print(\"Pipeline is submitted for execution\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit a Pipeline with a different Dataset PipelineParameter value from the SDK\n", - "\n", - "The training pipeline can be reused with different input datasets by passing them in as PipelineParameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "iris_file_ds = Dataset.File.from_files('https://raw.githubusercontent.com/Azure/MachineLearningNotebooks/'\n", - " '4e7b3784d50e81c313c62bcdf9a330194153d9cd/how-to-use-azureml/work-with-data/'\n", - " 'datasets-tutorial/train-with-datasets/train-dataset/iris.csv')\n", - "\n", - "iris_tabular_ds = Dataset.Tabular.from_delimited_files('https://raw.githubusercontent.com/Azure/MachineLearningNotebooks/'\n", - " '4e7b3784d50e81c313c62bcdf9a330194153d9cd/how-to-use-azureml/work-with-data/'\n", - " 'datasets-tutorial/train-with-datasets/train-dataset/iris.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run_with_params = experiment.submit(pipeline, pipeline_parameters={'file_ds_param': iris_file_ds, 'tabular_ds_param': iris_tabular_ds}) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(pipeline_run_with_params).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run_with_params.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dynamically Set the Dataset PipelineParameter Values using a REST Call\n", - "\n", - "Let's publish the pipeline we created previously, so we can generate a pipeline endpoint. We can then submit the iris datasets to the pipeline REST endpoint by passing in their IDs. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline = pipeline.publish(name=\"Dataset_Pipeline\", description=\"Pipeline to test Dataset PipelineParameter\", continue_on_step_failure=True)\n", - "published_pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline.submit(ws, experiment_name=\"publishedexperiment\", pipeline_parameters={'file_ds_param': iris_file_ds, 'tabular_ds_param': iris_tabular_ds})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.authentication import InteractiveLoginAuthentication\n", - "import requests\n", - "\n", - "auth = InteractiveLoginAuthentication()\n", - "aad_token = auth.get_authentication_header()\n", - "\n", - "rest_endpoint = published_pipeline.endpoint\n", - "\n", - "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# specify the param when running the pipeline\n", - "response = requests.post(rest_endpoint, \n", - " headers=aad_token, \n", - " json={\"ExperimentName\": \"MyRestPipeline\",\n", - " \"RunSource\": \"SDK\",\n", - " \"DataSetDefinitionValueAssignments\": {\"file_ds_param\": {\"SavedDataSetReference\": {\"Id\": iris_file_ds.id}},\n", - " \"tabular_ds_param\": {\"SavedDataSetReference\": {\"Id\": iris_tabular_ds.id}}}\n", - " }\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " response.raise_for_status()\n", - "except Exception: \n", - " raise Exception('Received bad response from the endpoint: {}\\n'\n", - " 'Response Code: {}\\n'\n", - " 'Headers: {}\\n'\n", - " 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n", - "\n", - "run_id = response.json().get('Id')\n", - "print('Submitted pipeline run: ', run_id)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline_run_via_rest = PipelineRun(ws.experiments[\"MyRestPipeline\"], run_id)\n", - "RunDetails(published_pipeline_run_via_rest).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline_run_via_rest.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "rafarmah" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "How to use Dataset as a PipelineParameter", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "order_index": 13.0, - "star_tag": [ - "featured" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Showcasing Dataset and PipelineParameter\n", + "\n", + "This notebook demonstrates how a [**FileDataset**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) or [**TabularDataset**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) can be parametrized with [**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) in an AML [Pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline(class)?view=azure-ml-py). By parametrizing datasets, you can dynamically run pipeline experiments with different datasets without any code change.\n", + "\n", + "A common use case is building a training pipeline with a sample of your training data for quick iterative development. When you're ready to test and deploy your pipeline at scale, you can pass in your full training dataset to the pipeline experiment without making any changes to your training script. \n", + " \n", + "To see more about how parameters work between steps, please refer [aml-pipelines-with-data-dependency-steps](https://aka.ms/pl-data-dep).\n", + "\n", + "* [How to create a Pipeline with a Dataset PipelineParameter](#index1)\n", + "* [How to submit a Pipeline with a Dataset PipelineParameter](#index2)\n", + "* [How to submit a Pipeline and change the Dataset PipelineParameter value from the sdk](#index3)\n", + "* [How to submit a Pipeline and change the Dataset PipelineParameter value using a REST call](#index4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Azure Machine Learning and Pipeline SDK-specific imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "from azureml.core import Workspace, Experiment, Dataset, RunConfiguration\n", + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.environment import CondaDependencies\n", + "from azureml.data.dataset_consumption_config import DatasetConsumptionConfig\n", + "from azureml.widgets import RunDetails\n", + "\n", + "from azureml.pipeline.core import PipelineParameter\n", + "from azureml.pipeline.core import Pipeline, PipelineRun\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json\n", + "\n", + "If you don't have a config.json file, go through the [configuration Notebook](https://aka.ms/pl-config) first.\n", + "\n", + "This sets you up with a working config file that has information on your workspace, subscription id, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an Azure ML experiment\n", + "\n", + "Let's create an experiment named \"showcasing-dataset\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose a name for the run history container in the workspace.\n", + "experiment_name = 'showcasing-dataset'\n", + "source_directory = '.'\n", + "\n", + "experiment = Experiment(ws, experiment_name)\n", + "experiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create or Attach an AmlCompute cluster\n", + "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose a name for your cluster.\n", + "amlcompute_cluster_name = \"cpu-cluster\"\n", + "\n", + "found = False\n", + "# Check if this compute target already exists in the workspace.\n", + "cts = ws.compute_targets\n", + "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n", + " found = True\n", + " print('Found existing compute target.')\n", + " compute_target = cts[amlcompute_cluster_name]\n", + " \n", + "if not found:\n", + " print('Creating a new compute target...')\n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n", + " #vm_priority = 'lowpriority', # optional\n", + " max_nodes = 4)\n", + "\n", + " # Create the cluster.\n", + " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n", + " \n", + " # Can poll for a minimum number of nodes and for a specific timeout.\n", + " # If no min_node_count is provided, it will use the scale settings for the cluster.\n", + " compute_target.wait_for_completion(show_output = True, timeout_in_minutes = 10)\n", + " \n", + " # For a more detailed view of current AmlCompute status, use get_status()." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset Configuration\n", + "\n", + "The following steps detail how to create a [FileDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) and [TabularDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) from an external CSV file, and configure them to be used by a [Pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline(class)?view=azure-ml-py):\n", + "\n", + "1. Create a dataset from a csv file\n", + "2. Create a [PipelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) object and set the `default_value` to the dataset. [PipelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) objects enabled arguments to be passed into Pipelines when they are resubmitted after creation. The `name` is referenced later on when we submit additional pipeline runs with different input datasets. \n", + "3. Create a [DatasetConsumptionConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_consumption_config.datasetconsumptionconfig?view=azure-ml-py) object from the [PiepelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py). The [DatasetConsumptionConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_consumption_config.datasetconsumptionconfig?view=azure-ml-py) object specifies how the dataset should be used by the remote compute where the pipeline is run. **NOTE** only [DatasetConsumptionConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_consumption_config.datasetconsumptionconfig?view=azure-ml-py) objects built on [FileDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) can be set `as_mount()` or `as_download()` on the remote compute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of Dataset as a PipelineParameter" + "datapath-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "file_dataset = Dataset.File.from_files('https://dprepdata.blob.core.windows.net/demo/Titanic.csv')\n", + "file_pipeline_param = PipelineParameter(name=\"file_ds_param\", default_value=file_dataset)\n", + "file_ds_consumption = DatasetConsumptionConfig(\"file_dataset\", file_pipeline_param).as_mount()\n", + "\n", + "tabular_dataset = Dataset.Tabular.from_delimited_files('https://dprepdata.blob.core.windows.net/demo/Titanic.csv')\n", + "tabular_pipeline_param = PipelineParameter(name=\"tabular_ds_param\", default_value=tabular_dataset)\n", + "tabular_ds_consumption = DatasetConsumptionConfig(\"tabular_dataset\", tabular_pipeline_param)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will setup a training script to ingest our passed-in datasets and print their contents. **NOTE** the names of the datasets referenced inside the training script correspond to the `name` of their respective [DatasetConsumptionConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_consumption_config.datasetconsumptionconfig?view=azure-ml-py) objects we defined above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile train_with_dataset.py\n", + "from azureml.core import Run\n", + "\n", + "input_file_ds_path = Run.get_context().input_datasets['file_dataset']\n", + "with open(input_file_ds_path, 'r') as f:\n", + " content = f.read()\n", + " print(content)\n", + "\n", + "input_tabular_ds = Run.get_context().input_datasets['tabular_dataset']\n", + "tabular_df = input_tabular_ds.to_pandas_dataframe()\n", + "print(tabular_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Pipeline with a Dataset PipelineParameter\n", + "\n", + "Note that the ```file_ds_consumption``` and ```tabular_ds_consumption``` are specified as both arguments and inputs to create a step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conda_dep = CondaDependencies()\n", + "conda_dep.add_pip_package(\"pandas\")\n", + "\n", + "run_config = RunConfiguration(conda_dependencies=conda_dep)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_step = PythonScriptStep(\n", + " name=\"train_step\",\n", + " script_name=\"train_with_dataset.py\",\n", + " arguments=[\"--param1\", file_ds_consumption, \"--param2\", tabular_ds_consumption],\n", + " inputs=[file_ds_consumption, tabular_ds_consumption],\n", + " compute_target=compute_target,\n", + " source_directory=source_directory,\n", + " runconfig=run_config)\n", + "\n", + "print(\"train_step created\")\n", + "\n", + "pipeline = Pipeline(workspace=ws, steps=[train_step])\n", + "print(\"pipeline with the train_step created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submit a Pipeline with a Dataset PipelineParameter\n", + "\n", + "Pipelines can be submitted with default values of PipelineParameters by not specifying any parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pipeline will run with default file_ds and tabular_ds\n", + "pipeline_run = experiment.submit(pipeline)\n", + "print(\"Pipeline is submitted for execution\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RunDetails(pipeline_run).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run.wait_for_completion()" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submit a Pipeline with a different Dataset PipelineParameter value from the SDK\n", + "\n", + "The training pipeline can be reused with different input datasets by passing them in as PipelineParameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iris_file_ds = Dataset.File.from_files('https://raw.githubusercontent.com/Azure/MachineLearningNotebooks/'\n", + " '4e7b3784d50e81c313c62bcdf9a330194153d9cd/how-to-use-azureml/work-with-data/'\n", + " 'datasets-tutorial/train-with-datasets/train-dataset/iris.csv')\n", + "\n", + "iris_tabular_ds = Dataset.Tabular.from_delimited_files('https://raw.githubusercontent.com/Azure/MachineLearningNotebooks/'\n", + " '4e7b3784d50e81c313c62bcdf9a330194153d9cd/how-to-use-azureml/work-with-data/'\n", + " 'datasets-tutorial/train-with-datasets/train-dataset/iris.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run_with_params = experiment.submit(pipeline, pipeline_parameters={'file_ds_param': iris_file_ds, 'tabular_ds_param': iris_tabular_ds}) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RunDetails(pipeline_run_with_params).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run_with_params.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dynamically Set the Dataset PipelineParameter Values using a REST Call\n", + "\n", + "Let's publish the pipeline we created previously, so we can generate a pipeline endpoint. We can then submit the iris datasets to the pipeline REST endpoint by passing in their IDs. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "published_pipeline = pipeline.publish(name=\"Dataset_Pipeline\", description=\"Pipeline to test Dataset PipelineParameter\", continue_on_step_failure=True)\n", + "published_pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "published_pipeline.submit(ws, experiment_name=\"publishedexperiment\", pipeline_parameters={'file_ds_param': iris_file_ds, 'tabular_ds_param': iris_tabular_ds})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.authentication import InteractiveLoginAuthentication\n", + "import requests\n", + "\n", + "auth = InteractiveLoginAuthentication()\n", + "aad_token = auth.get_authentication_header()\n", + "\n", + "rest_endpoint = published_pipeline.endpoint\n", + "\n", + "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# specify the param when running the pipeline\n", + "response = requests.post(rest_endpoint, \n", + " headers=aad_token, \n", + " json={\"ExperimentName\": \"MyRestPipeline\",\n", + " \"RunSource\": \"SDK\",\n", + " \"DataSetDefinitionValueAssignments\": {\"file_ds_param\": {\"SavedDataSetReference\": {\"Id\": iris_file_ds.id}},\n", + " \"tabular_ds_param\": {\"SavedDataSetReference\": {\"Id\": iris_tabular_ds.id}}}\n", + " }\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " response.raise_for_status()\n", + "except Exception: \n", + " raise Exception('Received bad response from the endpoint: {}\\n'\n", + " 'Response Code: {}\\n'\n", + " 'Headers: {}\\n'\n", + " 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n", + "\n", + "run_id = response.json().get('Id')\n", + "print('Submitted pipeline run: ', run_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "published_pipeline_run_via_rest = PipelineRun(ws.experiments[\"MyRestPipeline\"], run_id)\n", + "RunDetails(published_pipeline_run_via_rest).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "published_pipeline_run_via_rest.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "rafarmah" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "How to use Dataset as a PipelineParameter", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "order_index": 13.0, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of Dataset as a PipelineParameter" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb index 8c51bc3d5..e6e8e468f 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb @@ -1,404 +1,404 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AML Pipeline with AdlaStep\n", - "\n", - "This notebook is used to demonstrate the use of AdlaStep in AML Pipelines. [AdlaStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep?view=azure-ml-py) is used to run U-SQL scripts using Azure Data Lake Analytics service. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## AML and Pipeline SDK-specific imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from msrest.exceptions import HttpOperationError\n", - "\n", - "import azureml.core\n", - "from azureml.exceptions import ComputeTargetException\n", - "from azureml.core import Workspace, Experiment\n", - "from azureml.core.compute import ComputeTarget, AdlaCompute\n", - "from azureml.core.datastore import Datastore\n", - "from azureml.data.data_reference import DataReference\n", - "from azureml.pipeline.core import Pipeline, PipelineData\n", - "from azureml.pipeline.steps import AdlaStep\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Attach ADLA account to workspace\n", - "\n", - "To submit jobs to Azure Data Lake Analytics service, you must first attach your ADLA account to the workspace. You'll need to provide the account name and resource group of ADLA account to complete this part." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-adlacompute-attach" - ] - }, - "outputs": [], - "source": [ - "adla_compute_name = 'testadl' # Name to associate with new compute in workspace\n", - "\n", - "# ADLA account details needed to attach as compute to workspace\n", - "adla_account_name = \"\" # Name of the Azure Data Lake Analytics account\n", - "adla_resource_group = \"\" # Name of the resource group which contains this account\n", - "\n", - "try:\n", - " # check if already attached\n", - " adla_compute = AdlaCompute(ws, adla_compute_name)\n", - "except ComputeTargetException:\n", - " print('attaching adla compute...')\n", - " attach_config = AdlaCompute.attach_configuration(resource_group=adla_resource_group, account_name=adla_account_name)\n", - " adla_compute = ComputeTarget.attach(ws, adla_compute_name, attach_config)\n", - " adla_compute.wait_for_completion()\n", - "\n", - "print(\"Using ADLA compute:{}\".format(adla_compute.cluster_resource_id))\n", - "print(\"Provisioning state:{}\".format(adla_compute.provisioning_state))\n", - "print(\"Provisioning errors:{}\".format(adla_compute.provisioning_errors))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register Data Lake Storage as Datastore\n", - "\n", - "To register Data Lake Storage as Datastore in workspace, you'll need account information like account name, resource group and subscription Id. \n", - "\n", - "> AdlaStep can only work with data stored in the **default** Data Lake Storage of the Data Lake Analytics account provided above. If the data you need to work with is in a non-default storage, you can use a DataTransferStep to copy the data before training. You can find the default storage by opening your Data Lake Analytics account in Azure portal and then navigating to 'Data sources' item under Settings in the left pane.\n", - "\n", - "### Grant Azure AD application access to Data Lake Storage\n", - "\n", - "You'll also need to provide an Active Directory application which can access Data Lake Storage. [This document](https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory) contains step-by-step instructions on how to create an AAD application and assign to Data Lake Storage. Couple of important notes when assigning permissions to AAD app:\n", - "\n", - "- Access should be provided at root folder level.\n", - "- In 'Assign permissions' pane, select Read, Write, and Execute permissions for 'This folder and all children'. Add as 'An access permission entry and a default permission entry' to make sure application can access any new files created in the future." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore_name = 'MyAdlsDatastore' # Name to associate with data store in workspace\n", - "\n", - "# ADLS storage account details needed to register as a Datastore\n", - "subscription_id = os.getenv(\"ADL_SUBSCRIPTION_62\", \"\") # subscription id of ADLS account\n", - "resource_group = os.getenv(\"ADL_RESOURCE_GROUP_62\", \"\") # resource group of ADLS account\n", - "store_name = os.getenv(\"ADL_STORENAME_62\", \"\") # ADLS account name\n", - "tenant_id = os.getenv(\"ADL_TENANT_62\", \"\") # tenant id of service principal\n", - "client_id = os.getenv(\"ADL_CLIENTID_62\", \"\") # client id of service principal\n", - "client_secret = os.getenv(\"ADL_CLIENT_62_SECRET\", \"\") # the secret of service principal\n", - "\n", - "try:\n", - " adls_datastore = Datastore.get(ws, datastore_name)\n", - " print(\"found datastore with name: %s\" % datastore_name)\n", - "except HttpOperationError:\n", - " adls_datastore = Datastore.register_azure_data_lake(\n", - " workspace=ws,\n", - " datastore_name=datastore_name,\n", - " subscription_id=subscription_id, # subscription id of ADLS account\n", - " resource_group=resource_group, # resource group of ADLS account\n", - " store_name=store_name, # ADLS account name\n", - " tenant_id=tenant_id, # tenant id of service principal\n", - " client_id=client_id, # client id of service principal\n", - " client_secret=client_secret) # the secret of service principal\n", - " print(\"registered datastore with name: %s\" % datastore_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup inputs and outputs\n", - "\n", - "For purpose of this demo, we're going to execute a simple U-SQL script that reads a CSV file and writes portion of content to a new text file. First, let's create our sample input which contains 3 columns: employee Id, name and department Id." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# create a folder to store files for our job\n", - "sample_folder = \"adla_sample\"\n", - "\n", - "if not os.path.isdir(sample_folder):\n", - " os.mkdir(sample_folder)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile $sample_folder/sample_input.csv\n", - "1, Noah, 100\n", - "3, Liam, 100\n", - "4, Emma, 100\n", - "5, Jacob, 100\n", - "7, Jennie, 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Upload this file to Data Lake Storage at location `adla_sample/sample_input.csv` and create a DataReference to refer to this file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample_input = DataReference(\n", - " datastore=adls_datastore,\n", - " data_reference_name=\"employee_data\",\n", - " path_on_datastore=\"adla_sample/sample_input.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create PipelineData object to store output produced by AdlaStep." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample_output = PipelineData(\"sample_output\", datastore=adls_datastore)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Write your U-SQL script\n", - "\n", - "Now let's write a U-Sql script that reads above CSV file and writes the name column to a new file.\n", - "\n", - "Instead of hard-coding paths in your script, you can use `@@name@@` syntax to refer to inputs, outputs, and parameters.\n", - "\n", - "- If `name` is the name of an input or output port binding, any occurrences of `@@name@@` in the script are replaced with actual data path of corresponding port binding.\n", - "- If `name` matches any key in the `params` dictionary, any occurrences of `@@name@@` will be replaced with corresponding value in the dictionary.\n", - "\n", - "Note the use of @@ syntax in the below script. Before submitting the job to Data Lake Analytics service, `@@emplyee_data@@` will be replaced with actual path of `sample_input.csv` in Data Lake Storage. Similarly, `@@sample_output@@` will be replaced with a path in Data Lake Storage which will be used to store intermediate output produced by the step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile $sample_folder/sample_script.usql\n", - "\n", - "// Read employee information from csv file\n", - "@employees = \n", - " EXTRACT EmpId int, EmpName string, DeptId int\n", - " FROM \"@@employee_data@@\"\n", - " USING Extractors.Csv();\n", - "\n", - "// Export employee names to text file\n", - "OUTPUT\n", - "(\n", - " SELECT EmpName\n", - " FROM @employees\n", - ")\n", - "TO \"@@sample_output@@\"\n", - "USING Outputters.Text();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an AdlaStep\n", - "\n", - "**[AdlaStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep?view=azure-ml-py)** is used to run U-SQL script using Azure Data Lake Analytics.\n", - "\n", - "- **name:** Name of module\n", - "- **script_name:** name of U-SQL script file\n", - "- **inputs:** List of input port bindings\n", - "- **outputs:** List of output port bindings\n", - "- **compute_target:** the ADLA compute to use for this job\n", - "- **params:** Dictionary of name-value pairs to pass to U-SQL job *(optional)*\n", - "- **degree_of_parallelism:** the degree of parallelism to use for this job *(optional)*\n", - "- **priority:** the priority value to use for the current job *(optional)*\n", - "- **runtime_version:** the runtime version of the Data Lake Analytics engine *(optional)*\n", - "- **source_directory:** folder that contains the script, assemblies etc. *(optional)*\n", - "- **hash_paths:** list of paths to hash to detect a change (script file is always hashed) *(optional)*\n", - "\n", - "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "adlastep-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "adla_step = AdlaStep(\n", - " name='extract_employee_names',\n", - " script_name='sample_script.usql',\n", - " source_directory=sample_folder,\n", - " inputs=[sample_input],\n", - " outputs=[sample_output],\n", - " compute_target=adla_compute)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and Submit the Experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline = Pipeline(workspace=ws, steps=[adla_step])\n", - "\n", - "pipeline_run = Experiment(ws, 'adla_sample').submit(pipeline)\n", - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "tutorial", - "compute": [ - "Azure Data Lake Analytics" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "How to use AdlaStep with AML Pipelines", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "order_index": 6, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AML Pipeline with AdlaStep\n", + "\n", + "This notebook is used to demonstrate the use of AdlaStep in AML Pipelines. [AdlaStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep?view=azure-ml-py) is used to run U-SQL scripts using Azure Data Lake Analytics service. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AML and Pipeline SDK-specific imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from msrest.exceptions import HttpOperationError\n", + "\n", + "import azureml.core\n", + "from azureml.exceptions import ComputeTargetException\n", + "from azureml.core import Workspace, Experiment\n", + "from azureml.core.compute import ComputeTarget, AdlaCompute\n", + "from azureml.core.datastore import Datastore\n", + "from azureml.data.data_reference import DataReference\n", + "from azureml.pipeline.core import Pipeline, PipelineData\n", + "from azureml.pipeline.steps import AdlaStep\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Attach ADLA account to workspace\n", + "\n", + "To submit jobs to Azure Data Lake Analytics service, you must first attach your ADLA account to the workspace. You'll need to provide the account name and resource group of ADLA account to complete this part." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of AdlaStep" + "sample-adlacompute-attach" + ] + }, + "outputs": [], + "source": [ + "adla_compute_name = 'testadl' # Name to associate with new compute in workspace\n", + "\n", + "# ADLA account details needed to attach as compute to workspace\n", + "adla_account_name = \"\" # Name of the Azure Data Lake Analytics account\n", + "adla_resource_group = \"\" # Name of the resource group which contains this account\n", + "\n", + "try:\n", + " # check if already attached\n", + " adla_compute = AdlaCompute(ws, adla_compute_name)\n", + "except ComputeTargetException:\n", + " print('attaching adla compute...')\n", + " attach_config = AdlaCompute.attach_configuration(resource_group=adla_resource_group, account_name=adla_account_name)\n", + " adla_compute = ComputeTarget.attach(ws, adla_compute_name, attach_config)\n", + " adla_compute.wait_for_completion()\n", + "\n", + "print(\"Using ADLA compute:{}\".format(adla_compute.cluster_resource_id))\n", + "print(\"Provisioning state:{}\".format(adla_compute.provisioning_state))\n", + "print(\"Provisioning errors:{}\".format(adla_compute.provisioning_errors))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register Data Lake Storage as Datastore\n", + "\n", + "To register Data Lake Storage as Datastore in workspace, you'll need account information like account name, resource group and subscription Id. \n", + "\n", + "> AdlaStep can only work with data stored in the **default** Data Lake Storage of the Data Lake Analytics account provided above. If the data you need to work with is in a non-default storage, you can use a DataTransferStep to copy the data before training. You can find the default storage by opening your Data Lake Analytics account in Azure portal and then navigating to 'Data sources' item under Settings in the left pane.\n", + "\n", + "### Grant Azure AD application access to Data Lake Storage\n", + "\n", + "You'll also need to provide an Active Directory application which can access Data Lake Storage. [This document](https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory) contains step-by-step instructions on how to create an AAD application and assign to Data Lake Storage. Couple of important notes when assigning permissions to AAD app:\n", + "\n", + "- Access should be provided at root folder level.\n", + "- In 'Assign permissions' pane, select Read, Write, and Execute permissions for 'This folder and all children'. Add as 'An access permission entry and a default permission entry' to make sure application can access any new files created in the future." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "datastore_name = 'MyAdlsDatastore' # Name to associate with data store in workspace\n", + "\n", + "# ADLS storage account details needed to register as a Datastore\n", + "subscription_id = os.getenv(\"ADL_SUBSCRIPTION_62\", \"\") # subscription id of ADLS account\n", + "resource_group = os.getenv(\"ADL_RESOURCE_GROUP_62\", \"\") # resource group of ADLS account\n", + "store_name = os.getenv(\"ADL_STORENAME_62\", \"\") # ADLS account name\n", + "tenant_id = os.getenv(\"ADL_TENANT_62\", \"\") # tenant id of service principal\n", + "client_id = os.getenv(\"ADL_CLIENTID_62\", \"\") # client id of service principal\n", + "client_secret = os.getenv(\"ADL_CLIENT_62_SECRET\", \"\") # the secret of service principal\n", + "\n", + "try:\n", + " adls_datastore = Datastore.get(ws, datastore_name)\n", + " print(\"found datastore with name: %s\" % datastore_name)\n", + "except HttpOperationError:\n", + " adls_datastore = Datastore.register_azure_data_lake(\n", + " workspace=ws,\n", + " datastore_name=datastore_name,\n", + " subscription_id=subscription_id, # subscription id of ADLS account\n", + " resource_group=resource_group, # resource group of ADLS account\n", + " store_name=store_name, # ADLS account name\n", + " tenant_id=tenant_id, # tenant id of service principal\n", + " client_id=client_id, # client id of service principal\n", + " client_secret=client_secret) # the secret of service principal\n", + " print(\"registered datastore with name: %s\" % datastore_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup inputs and outputs\n", + "\n", + "For purpose of this demo, we're going to execute a simple U-SQL script that reads a CSV file and writes portion of content to a new text file. First, let's create our sample input which contains 3 columns: employee Id, name and department Id." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create a folder to store files for our job\n", + "sample_folder = \"adla_sample\"\n", + "\n", + "if not os.path.isdir(sample_folder):\n", + " os.mkdir(sample_folder)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile $sample_folder/sample_input.csv\n", + "1, Noah, 100\n", + "3, Liam, 100\n", + "4, Emma, 100\n", + "5, Jacob, 100\n", + "7, Jennie, 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upload this file to Data Lake Storage at location `adla_sample/sample_input.csv` and create a DataReference to refer to this file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sample_input = DataReference(\n", + " datastore=adls_datastore,\n", + " data_reference_name=\"employee_data\",\n", + " path_on_datastore=\"adla_sample/sample_input.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create PipelineData object to store output produced by AdlaStep." + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sample_output = PipelineData(\"sample_output\", datastore=adls_datastore)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Write your U-SQL script\n", + "\n", + "Now let's write a U-Sql script that reads above CSV file and writes the name column to a new file.\n", + "\n", + "Instead of hard-coding paths in your script, you can use `@@name@@` syntax to refer to inputs, outputs, and parameters.\n", + "\n", + "- If `name` is the name of an input or output port binding, any occurrences of `@@name@@` in the script are replaced with actual data path of corresponding port binding.\n", + "- If `name` matches any key in the `params` dictionary, any occurrences of `@@name@@` will be replaced with corresponding value in the dictionary.\n", + "\n", + "Note the use of @@ syntax in the below script. Before submitting the job to Data Lake Analytics service, `@@emplyee_data@@` will be replaced with actual path of `sample_input.csv` in Data Lake Storage. Similarly, `@@sample_output@@` will be replaced with a path in Data Lake Storage which will be used to store intermediate output produced by the step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile $sample_folder/sample_script.usql\n", + "\n", + "// Read employee information from csv file\n", + "@employees = \n", + " EXTRACT EmpId int, EmpName string, DeptId int\n", + " FROM \"@@employee_data@@\"\n", + " USING Extractors.Csv();\n", + "\n", + "// Export employee names to text file\n", + "OUTPUT\n", + "(\n", + " SELECT EmpName\n", + " FROM @employees\n", + ")\n", + "TO \"@@sample_output@@\"\n", + "USING Outputters.Text();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an AdlaStep\n", + "\n", + "**[AdlaStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep?view=azure-ml-py)** is used to run U-SQL script using Azure Data Lake Analytics.\n", + "\n", + "- **name:** Name of module\n", + "- **script_name:** name of U-SQL script file\n", + "- **inputs:** List of input port bindings\n", + "- **outputs:** List of output port bindings\n", + "- **compute_target:** the ADLA compute to use for this job\n", + "- **params:** Dictionary of name-value pairs to pass to U-SQL job *(optional)*\n", + "- **degree_of_parallelism:** the degree of parallelism to use for this job *(optional)*\n", + "- **priority:** the priority value to use for the current job *(optional)*\n", + "- **runtime_version:** the runtime version of the Data Lake Analytics engine *(optional)*\n", + "- **source_directory:** folder that contains the script, assemblies etc. *(optional)*\n", + "- **hash_paths:** list of paths to hash to detect a change (script file is always hashed) *(optional)*\n", + "\n", + "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "adlastep-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "adla_step = AdlaStep(\n", + " name='extract_employee_names',\n", + " script_name='sample_script.usql',\n", + " source_directory=sample_folder,\n", + " inputs=[sample_input],\n", + " outputs=[sample_output],\n", + " compute_target=adla_compute)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and Submit the Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline(workspace=ws, steps=[adla_step])\n", + "\n", + "pipeline_run = Experiment(ws, 'adla_sample').submit(pipeline)\n", + "pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(pipeline_run).show()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "tutorial", + "compute": [ + "Azure Data Lake Analytics" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "How to use AdlaStep with AML Pipelines", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + }, + "order_index": 6, + "tags": [ + "None" + ], + "task": "Demonstrates the use of AdlaStep" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb index 60c72fe4f..f9df2c3d0 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb @@ -1,971 +1,977 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n", - "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n", - "\n", - "The notebook will show:\n", - "1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n", - "2. Running an arbitrary Python script that the customer has in DBFS\n", - "3. Running an arbitrary Python script that is available on local computer (will upload to DBFS, and then run in Databricks) \n", - "4. Running a JAR job that the customer has in DBFS.\n", - "5. How to get run context in a Databricks interactive cluster\n", - "\n", - "## Before you begin:\n", - "\n", - "1. **Create an Azure Databricks workspace** in the same subscription where you have your Azure Machine Learning workspace. You will need details of this workspace later on to define DatabricksStep. [Click here](https://ms.portal.azure.com/#blade/HubsExtension/Resources/resourceType/Microsoft.Databricks%2Fworkspaces) for more information.\n", - "2. **Create PAT (access token)**: Manually create a Databricks access token at the Azure Databricks portal. See [this](https://docs.databricks.com/api/latest/authentication.html#generate-a-token) for more information.\n", - "3. **Add demo notebook to ADB**: This notebook has a sample you can use as is. Launch Azure Databricks attached to your Azure Machine Learning workspace and add a new notebook. \n", - "4. **Create/attach a Blob storage** for use from ADB" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add demo notebook to ADB Workspace\n", - "Copy and paste the below code to create a new notebook in your ADB workspace." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "# direct access\n", - "dbutils.widgets.get(\"myparam\")\n", - "p = getArgument(\"myparam\")\n", - "print (\"Param -\\'myparam':\")\n", - "print (p)\n", - "\n", - "dbutils.widgets.get(\"input\")\n", - "i = getArgument(\"input\")\n", - "print (\"Param -\\'input':\")\n", - "print (i)\n", - "\n", - "dbutils.widgets.get(\"output\")\n", - "o = getArgument(\"output\")\n", - "print (\"Param -\\'output':\")\n", - "print (o)\n", - "\n", - "n = i + \"/testdata.txt\"\n", - "df = spark.read.csv(n)\n", - "\n", - "display (df)\n", - "\n", - "data = [('value1', 'value2')]\n", - "df2 = spark.createDataFrame(data)\n", - "\n", - "z = o + \"/output.txt\"\n", - "df2.write.csv(z)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Azure Machine Learning and Pipeline SDK-specific imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import azureml.core\n", - "from azureml.core.runconfig import JarLibrary\n", - "from azureml.core.compute import ComputeTarget, DatabricksCompute\n", - "from azureml.exceptions import ComputeTargetException\n", - "from azureml.core import Workspace, Experiment\n", - "from azureml.pipeline.core import Pipeline, PipelineData\n", - "from azureml.pipeline.steps import DatabricksStep\n", - "from azureml.core.datastore import Datastore\n", - "from azureml.data.data_reference import DataReference\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Attach Databricks compute target\n", - "Next, you need to add your Databricks workspace to Azure Machine Learning as a compute target and give it a name. You will use this name to refer to your Databricks workspace compute target inside Azure Machine Learning.\n", - "\n", - "- **Resource Group** - The resource group name of your Azure Machine Learning workspace\n", - "- **Databricks Workspace Name** - The workspace name of your Azure Databricks workspace\n", - "- **Databricks Access Token** - The access token you created in ADB\n", - "\n", - "**The Databricks workspace need to be present in the same subscription as your AML workspace**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-databrickscompute-attach" - ] - }, - "outputs": [], - "source": [ - "# Replace with your account info before running.\n", - " \n", - "db_compute_name=os.getenv(\"DATABRICKS_COMPUTE_NAME\", \"\") # Databricks compute name\n", - "db_resource_group=os.getenv(\"DATABRICKS_RESOURCE_GROUP\", \"\") # Databricks resource group\n", - "db_workspace_name=os.getenv(\"DATABRICKS_WORKSPACE_NAME\", \"\") # Databricks workspace name\n", - "db_access_token=os.getenv(\"DATABRICKS_ACCESS_TOKEN\", \"\") # Databricks access token\n", - " \n", - "try:\n", - " databricks_compute = DatabricksCompute(workspace=ws, name=db_compute_name)\n", - " print('Compute target {} already exists'.format(db_compute_name))\n", - "except ComputeTargetException:\n", - " print('Compute not found, will use below parameters to attach new one')\n", - " print('db_compute_name {}'.format(db_compute_name))\n", - " print('db_resource_group {}'.format(db_resource_group))\n", - " print('db_workspace_name {}'.format(db_workspace_name))\n", - " print('db_access_token {}'.format(db_access_token))\n", - " \n", - " config = DatabricksCompute.attach_configuration(\n", - " resource_group = db_resource_group,\n", - " workspace_name = db_workspace_name,\n", - " access_token= db_access_token)\n", - " databricks_compute=ComputeTarget.attach(ws, db_compute_name, config)\n", - " databricks_compute.wait_for_completion(True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Connections with Inputs and Outputs\n", - "The DatabricksStep supports DBFS, Azure Blob and ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n", - "\n", - "- Databricks documentation on [Azure Blob](https://docs.azuredatabricks.net/spark/latest/data-sources/azure/azure-storage.html)\n", - "- Databricks documentation on [ADLS](https://docs.databricks.com/spark/latest/data-sources/azure/azure-datalake.html)\n", - "\n", - "### Type of Data Access\n", - "Databricks allows to interact with Azure Blob and ADLS in two ways.\n", - "- **Direct Access**: Databricks allows you to interact with Azure Blob or ADLS URIs directly. The input or output URIs will be mapped to a Databricks widget param in the Databricks notebook.\n", - "- **Mounting**: You will be supplied with additional parameters and secrets that will enable you to mount your ADLS or Azure Blob input or output location in your Databricks notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Direct Access: Python sample code\n", - "If you have a data reference named \"input\" it will represent the URI of the input and you can access it directly in the Databricks python notebook like so:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "dbutils.widgets.get(\"input\")\n", - "y = getArgument(\"input\")\n", - "df = spark.read.csv(y)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Mounting: Python sample code for Azure Blob\n", - "Given an Azure Blob data reference named \"input\" the following widget params will be made available in the Databricks notebook:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "# This contains the input URI\n", - "dbutils.widgets.get(\"input\")\n", - "myinput_uri = getArgument(\"input\")\n", - "\n", - "# How to get the input datastore name inside ADB notebook\n", - "# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n", - "# that contians an access key or sas for the Azure Blob input (this name is obtained by appending \n", - "# the name of the input with \"_blob_secretname\". \n", - "dbutils.widgets.get(\"input_blob_secretname\") \n", - "myinput_blob_secretname = getArgument(\"input_blob_secretname\")\n", - "\n", - "# This contains the required configuration for mounting\n", - "dbutils.widgets.get(\"input_blob_config\")\n", - "myinput_blob_config = getArgument(\"input_blob_config\")\n", - "\n", - "# Usage\n", - "dbutils.fs.mount(\n", - " source = myinput_uri,\n", - " mount_point = \"/mnt/input\",\n", - " extra_configs = {myinput_blob_config:dbutils.secrets.get(scope = \"amlscope\", key = myinput_blob_secretname)})\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Mounting: Python sample code for ADLS\n", - "Given an ADLS data reference named \"input\" the following widget params will be made available in the Databricks notebook:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "# This contains the input URI\n", - "dbutils.widgets.get(\"input\") \n", - "myinput_uri = getArgument(\"input\")\n", - "\n", - "# This contains the client id for the service principal \n", - "# that has access to the adls input\n", - "dbutils.widgets.get(\"input_adls_clientid\") \n", - "myinput_adls_clientid = getArgument(\"input_adls_clientid\")\n", - "\n", - "# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n", - "# that contains the secret for the above mentioned service principal\n", - "dbutils.widgets.get(\"input_adls_secretname\") \n", - "myinput_adls_secretname = getArgument(\"input_adls_secretname\")\n", - "\n", - "# This contains the refresh url for the mounting configs\n", - "dbutils.widgets.get(\"input_adls_refresh_url\") \n", - "myinput_adls_refresh_url = getArgument(\"input_adls_refresh_url\")\n", - "\n", - "# Usage \n", - "configs = {\"dfs.adls.oauth2.access.token.provider.type\": \"ClientCredential\",\n", - " \"dfs.adls.oauth2.client.id\": myinput_adls_clientid,\n", - " \"dfs.adls.oauth2.credential\": dbutils.secrets.get(scope = \"amlscope\", key =myinput_adls_secretname),\n", - " \"dfs.adls.oauth2.refresh.url\": myinput_adls_refresh_url}\n", - "\n", - "dbutils.fs.mount(\n", - " source = myinput_uri,\n", - " mount_point = \"/mnt/output\",\n", - " extra_configs = configs)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use Databricks from Azure Machine Learning Pipeline\n", - "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. Let's define a datasource (via DataReference), intermediate data (via PipelineData) and a pipeline parameter (via PipelineParameter) to be used in DatabricksStep." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineParameter\n", - "\n", - "# Use the default blob storage\n", - "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n", - "print('Datastore {} will be used'.format(def_blob_store.name))\n", - "\n", - "pipeline_param = PipelineParameter(name=\"my_pipeline_param\", default_value=\"pipeline_param1\")\n", - "\n", - "# We are uploading a sample file in the local directory to be used as a datasource\n", - "def_blob_store.upload_files(files=[\"./testdata.txt\"], target_path=\"dbtest\", overwrite=False)\n", - "\n", - "step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n", - " data_reference_name=\"input\")\n", - "\n", - "step_1_output = PipelineData(\"output\", datastore=def_blob_store)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add a DatabricksStep\n", - "Adds a Databricks notebook as a step in a Pipeline.\n", - "- ***name:** Name of the Module\n", - "- **inputs:** List of input connections for data consumed by this step. Fetch this inside the notebook using dbutils.widgets.get(\"input\")\n", - "- **outputs:** List of output port definitions for outputs produced by this step. Fetch this inside the notebook using dbutils.widgets.get(\"output\")\n", - "- **existing_cluster_id:** Cluster ID of an existing Interactive cluster on the Databricks workspace. If you are providing this, do not provide any of the parameters below that are used to create a new cluster such as spark_version, node_type, etc.\n", - "- **spark_version:** Version of spark for the databricks run cluster. default value: 4.0.x-scala2.11\n", - "- **node_type:** Azure vm node types for the databricks run cluster. default value: Standard_D3_v2\n", - "- **num_workers:** Specifies a static number of workers for the databricks run cluster\n", - "- **min_workers:** Specifies a min number of workers to use for auto-scaling the databricks run cluster\n", - "- **max_workers:** Specifies a max number of workers to use for auto-scaling the databricks run cluster\n", - "- **spark_env_variables:** Spark environment variables for the databricks run cluster (dictionary of {str:str}). default value: {'PYSPARK_PYTHON': '/databricks/python3/bin/python3'}\n", - "- **notebook_path:** Path to the notebook in the databricks instance. If you are providing this, do not provide python script related paramaters or JAR related parameters.\n", - "- **notebook_params:** Parameters for the databricks notebook (dictionary of {str:str}). Fetch this inside the notebook using dbutils.widgets.get(\"myparam\")\n", - "- **python_script_path:** The path to the python script in the DBFS or S3. If you are providing this, do not provide python_script_name which is used for uploading script from local machine.\n", - "- **python_script_params:** Parameters for the python script (list of str)\n", - "- **main_class_name:** The name of the entry point in a JAR module. If you are providing this, do not provide any python script or notebook related parameters.\n", - "- **jar_params:** Parameters for the JAR module (list of str)\n", - "- **python_script_name:** name of a python script on your local machine (relative to source_directory). If you are providing this do not provide python_script_path which is used to execute a remote python script; or any of the JAR or notebook related parameters.\n", - "- **source_directory:** folder that contains the script and other files\n", - "- **hash_paths:** list of paths to hash to detect a change in source_directory (script file is always hashed)\n", - "- **run_name:** Name in databricks for this run\n", - "- **timeout_seconds:** Timeout for the databricks run\n", - "- **runconfig:** Runconfig to use. Either pass runconfig or each library type as a separate parameter but do not mix the two\n", - "- **maven_libraries:** maven libraries for the databricks run\n", - "- **pypi_libraries:** pypi libraries for the databricks run\n", - "- **egg_libraries:** egg libraries for the databricks run\n", - "- **jar_libraries:** jar libraries for the databricks run\n", - "- **rcran_libraries:** rcran libraries for the databricks run\n", - "- **compute_target:** Azure Databricks compute\n", - "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs\n", - "- **version:** Optional version tag to denote a change in functionality for the step\n", - "\n", - "\\* *denotes required fields* \n", - "*You must provide exactly one of num_workers or min_workers and max_workers paramaters* \n", - "*You must provide exactly one of databricks_compute or databricks_compute_name parameters*\n", - "\n", - "## Use runconfig to specify library dependencies\n", - "You can use a runconfig to specify the library dependencies for your cluster in Databricks. The runconfig will contain a databricks section as follows:\n", - "\n", - "```yaml\n", - "environment:\n", - "# Databricks details\n", - " databricks:\n", - "# List of maven libraries.\n", - " mavenLibraries:\n", - " - coordinates: org.jsoup:jsoup:1.7.1\n", - " repo: ''\n", - " exclusions:\n", - " - slf4j:slf4j\n", - " - '*:hadoop-client'\n", - "# List of PyPi libraries\n", - " pypiLibraries:\n", - " - package: beautifulsoup4\n", - " repo: ''\n", - "# List of RCran libraries\n", - " rcranLibraries:\n", - " -\n", - "# Coordinates.\n", - " package: ada\n", - "# Repo\n", - " repo: http://cran.us.r-project.org\n", - "# List of JAR libraries\n", - " jarLibraries:\n", - " -\n", - "# Coordinates.\n", - " library: dbfs:/mnt/libraries/library.jar\n", - "# List of Egg libraries\n", - " eggLibraries:\n", - " -\n", - "# Coordinates.\n", - " library: dbfs:/mnt/libraries/library.egg\n", - "```\n", - "\n", - "You can then create a RunConfiguration object using this file and pass it as the runconfig parameter to DatabricksStep.\n", - "```python\n", - "from azureml.core.runconfig import RunConfiguration\n", - "\n", - "runconfig = RunConfiguration()\n", - "runconfig.load(path='', name='')\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Running the demo notebook already added to the Databricks workspace\n", - "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable\u00c2\u00a0notebook_path\u00c2\u00a0when you run the code cell below:\n", - "\n", - "your notebook's path in Azure Databricks UI by hovering over to notebook's title. A typical path of notebook looks like this `/Users/example@databricks.com/example`. See [Databricks Workspace](https://docs.azuredatabricks.net/user-guide/workspace.html) to learn about the folder structure.\n", - "\n", - "Note: DataPath `PipelineParameter` should be provided in list of inputs. Such parameters can be accessed by the datapath `name`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "notebook_path=os.getenv(\"DATABRICKS_NOTEBOOK_PATH\", \"\") # Databricks notebook path\n", - "\n", - "dbNbStep = DatabricksStep(\n", - " name=\"DBNotebookInWS\",\n", - " inputs=[step_1_input],\n", - " outputs=[step_1_output],\n", - " num_workers=1,\n", - " notebook_path=notebook_path,\n", - " notebook_params={'myparam': 'testparam', \n", - " 'myparam2': pipeline_param},\n", - " run_name='DB_Notebook_demo',\n", - " compute_target=databricks_compute,\n", - " allow_reuse=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Build and submit the Experiment\n", - "\n", - "Note: Default value of `pipeline_param` will be used if different value is not specified in pipeline parameters during submission" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "steps = [dbNbStep]\n", - "pipeline = Pipeline(workspace=ws, steps=steps)\n", - "pipeline_run = Experiment(ws, 'DB_Notebook_demo').submit(pipeline)\n", - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Running a Python script from DBFS\n", - "This shows how to run a Python script in DBFS. \n", - "\n", - "To complete this, you will need to first upload the Python script in your local machine to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html). The CLI command is given below:\n", - "\n", - "```\n", - "dbfs cp ./train-db-dbfs.py dbfs:/train-db-dbfs.py\n", - "```\n", - "\n", - "The code in the below cell assumes that you have completed the previous step of uploading the script `train-db-dbfs.py` to the root folder in DBFS.\n", - "\n", - "Note: `pipeline_param` will add two values in the python_script_params, a name followed by value. the name will be in this format `--MY_PIPELINE_PARAM`. For example, in the given case, python_script_params will be `[\"arg1\", \"--MY_PIPELINE_PARAM\", \"pipeline_param1\", \"arg2\"]`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "python_script_path = os.getenv(\"DATABRICKS_PYTHON_SCRIPT_PATH\", \"\") # Databricks python script path\n", - "\n", - "dbPythonInDbfsStep = DatabricksStep(\n", - " name=\"DBPythonInDBFS\",\n", - " inputs=[step_1_input],\n", - " num_workers=1,\n", - " python_script_path=python_script_path,\n", - " python_script_params={'arg1', pipeline_param, 'arg2'},\n", - " run_name='DB_Python_demo',\n", - " compute_target=databricks_compute,\n", - " allow_reuse=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Build and submit the Experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "steps = [dbPythonInDbfsStep]\n", - "pipeline = Pipeline(workspace=ws, steps=steps)\n", - "pipeline_run = Experiment(ws, 'DB_Python_demo').submit(pipeline)\n", - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Running a Python script in Databricks that currenlty is in local computer\n", - "To run a Python script that is currently in your local computer, follow the instructions below. \n", - "\n", - "The commented out code below code assumes that you have `train-db-local.py` in the `source_directory` subdirectory under the current working directory. \n", - "\n", - "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.\n", - "\n", - "In this case, the Python script will be uploaded first to DBFS, and then the script will be run in Databricks." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "python_script_name = \"train-db-local.py\"\n", - "source_directory = \"./databricks_train\"\n", - "\n", - "dbPythonInLocalMachineStep = DatabricksStep(\n", - " name=\"DBPythonInLocalMachine\",\n", - " inputs=[step_1_input],\n", - " num_workers=1,\n", - " python_script_name=python_script_name,\n", - " source_directory=source_directory,\n", - " run_name='DB_Python_Local_demo',\n", - " compute_target=databricks_compute,\n", - " allow_reuse=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Build and submit the Experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "steps = [dbPythonInLocalMachineStep]\n", - "pipeline = Pipeline(workspace=ws, steps=steps)\n", - "pipeline_run = Experiment(ws, 'DB_Python_Local_demo').submit(pipeline)\n", - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. Running a JAR job that is alreay added in DBFS\n", - "To run a JAR job that is already uploaded to DBFS, follow the instructions below. You will first upload the JAR file to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n", - "\n", - "The commented out code in the below cell assumes that you have uploaded `train-db-dbfs.jar` to the root folder in DBFS. You can upload `train-db-dbfs.jar` to the root folder in DBFS using this commandline so you can use `jar_library_dbfs_path = \"dbfs:/train-db-dbfs.jar\"`:\n", - "\n", - "```\n", - "dbfs cp ./train-db-dbfs.jar dbfs:/train-db-dbfs.jar\n", - "```\n", - "\n", - "Note: `pipeline_param` will add two values in the python_script_params, a name followed by value. the name will be in this format `--MY_PIPELINE_PARAM`. For example, in the given case, python_script_params will be `[\"arg1\", \"--MY_PIPELINE_PARAM\", \"pipeline_param1\", \"arg2\"]`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "main_jar_class_name = \"com.microsoft.aeva.Main\"\n", - "jar_library_dbfs_path = os.getenv(\"DATABRICKS_JAR_LIB_PATH\", \"\") # Databricks jar library path\n", - "\n", - "dbJarInDbfsStep = DatabricksStep(\n", - " name=\"DBJarInDBFS\",\n", - " inputs=[step_1_input],\n", - " num_workers=1,\n", - " main_class_name=main_jar_class_name,\n", - " jar_params={'arg1', pipeline_param, 'arg2'},\n", - " run_name='DB_JAR_demo',\n", - " jar_libraries=[JarLibrary(jar_library_dbfs_path)],\n", - " compute_target=databricks_compute,\n", - " allow_reuse=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Build and submit the Experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "steps = [dbJarInDbfsStep]\n", - "pipeline = Pipeline(workspace=ws, steps=steps)\n", - "pipeline_run = Experiment(ws, 'DB_JAR_demo').submit(pipeline)\n", - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5. Running demo notebook already added to the Databricks workspace using existing cluster\n", - "First you need register DBFS datastore and make sure path_on_datastore does exist in databricks file system, you can browser the files by refering [this](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n", - "\n", - "Find existing_cluster_id by opeing Azure Databricks UI with Clusters page and in url you will find a string connected with '-' right after \"clusters/\"." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " dbfs_ds = Datastore.get(workspace=ws, datastore_name='dbfs_datastore')\n", - " print('DBFS Datastore already exists')\n", - "except Exception as ex:\n", - " dbfs_ds = Datastore.register_dbfs(ws, datastore_name='dbfs_datastore')\n", - "\n", - "step_1_input = DataReference(datastore=dbfs_ds, path_on_datastore=\"FileStore\", data_reference_name=\"input\")\n", - "step_1_output = PipelineData(\"output\", datastore=dbfs_ds)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dbNbWithExistingClusterStep = DatabricksStep(\n", - " name=\"DBFSReferenceWithExisting\",\n", - " inputs=[step_1_input],\n", - " outputs=[step_1_output],\n", - " notebook_path=notebook_path,\n", - " notebook_params={'myparam': 'testparam', \n", - " 'myparam2': pipeline_param},\n", - " run_name='DBFS_Reference_With_Existing',\n", - " compute_target=databricks_compute,\n", - " existing_cluster_id=\"your existing cluster id\",\n", - " allow_reuse=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Build and submit the Experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "steps = [dbNbWithExistingClusterStep]\n", - "pipeline = Pipeline(workspace=ws, steps=steps)\n", - "pipeline_run = Experiment(ws, 'DBFS_Reference_With_Existing').submit(pipeline)\n", - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6. Running a Python script in Databricks that is currently in local computer with existing cluster\n", - "When you access azure blob or data lake storage from an existing (interactive) cluster, you need to ensure the Spark configuration is set up correctly to access this storage and this set up may require the cluster to be restarted.\n", - "\n", - "If you set permit_cluster_restart to True, AML will check if the spark configuration needs to be updated and restart the cluster for you if required. This will ensure that the storage can be correctly accessed from the Databricks cluster." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n", - " data_reference_name=\"input\")\n", - "\n", - "dbPythonInLocalWithExistingStep = DatabricksStep(\n", - " name=\"DBPythonInLocalMachineWithExisting\",\n", - " inputs=[step_1_input],\n", - " python_script_name=python_script_name,\n", - " source_directory=source_directory,\n", - " run_name='DB_Python_Local_existing_demo',\n", - " compute_target=databricks_compute,\n", - " existing_cluster_id=\"your existing cluster id\",\n", - " allow_reuse=False,\n", - " permit_cluster_restart=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Build and submit the Experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "steps = [dbPythonInLocalWithExistingStep]\n", - "pipeline = Pipeline(workspace=ws, steps=steps)\n", - "pipeline_run = Experiment(ws, 'DB_Python_Local_existing_demo').submit(pipeline)\n", - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### How to get run context in a Databricks interactive cluster\n", - "\n", - "Users are used to being able to use Run.get_context() to retrieve the parent_run_id for a given run_id. In DatabricksStep, however, a little more work is required to achieve this.\n", - "\n", - "The solution is to parse the script arguments and set corresponding environment variables to access the run context from within Databricks.\n", - "Note that this workaround is not required for job clusters. \n", - "\n", - "Here is a code sample:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "from azureml.core import Run\n", - "import argparse\n", - "import os\n", - "\n", - "\n", - "def populate_environ():\n", - " parser = argparse.ArgumentParser(description='Process arguments passed to script')\n", - "\n", - " # The AZUREML_SCRIPT_DIRECTORY_NAME argument will be filled in if the DatabricksStep\n", - " # was run using a local source_directory and python_script_name\n", - " parser.add_argument('--AZUREML_SCRIPT_DIRECTORY_NAME')\n", - "\n", - " # Remaining arguments are filled in for all databricks jobs and can be used to build the run context\n", - " parser.add_argument('--AZUREML_RUN_TOKEN')\n", - " parser.add_argument('--AZUREML_RUN_TOKEN_EXPIRY')\n", - " parser.add_argument('--AZUREML_RUN_ID')\n", - " parser.add_argument('--AZUREML_ARM_SUBSCRIPTION')\n", - " parser.add_argument('--AZUREML_ARM_RESOURCEGROUP')\n", - " parser.add_argument('--AZUREML_ARM_WORKSPACE_NAME')\n", - " parser.add_argument('--AZUREML_ARM_PROJECT_NAME')\n", - " parser.add_argument('--AZUREML_SERVICE_ENDPOINT')\n", - " parser.add_argument('--AZUREML_WORKSPACE_ID')\n", - " parser.add_argument('--AZUREML_EXPERIMENT_ID')\n", - "\n", - " (args, extra_args) = parser.parse_known_args()\n", - " os.environ['AZUREML_RUN_TOKEN'] = args.AZUREML_RUN_TOKEN\n", - " os.environ['AZUREML_RUN_TOKEN_EXPIRY'] = args.AZUREML_RUN_TOKEN_EXPIRY\n", - " os.environ['AZUREML_RUN_ID'] = args.AZUREML_RUN_ID\n", - " os.environ['AZUREML_ARM_SUBSCRIPTION'] = args.AZUREML_ARM_SUBSCRIPTION\n", - " os.environ['AZUREML_ARM_RESOURCEGROUP'] = args.AZUREML_ARM_RESOURCEGROUP\n", - " os.environ['AZUREML_ARM_WORKSPACE_NAME'] = args.AZUREML_ARM_WORKSPACE_NAME\n", - " os.environ['AZUREML_ARM_PROJECT_NAME'] = args.AZUREML_ARM_PROJECT_NAME\n", - " os.environ['AZUREML_SERVICE_ENDPOINT'] = args.AZUREML_SERVICE_ENDPOINT\n", - " os.environ['AZUREML_WORKSPACE_ID'] = args.AZUREML_WORKSPACE_ID\n", - " os.environ['AZUREML_EXPERIMENT_ID'] = args.AZUREML_EXPERIMENT_ID\n", - "\n", - "populate_environ()\n", - "run = Run.get_context(allow_offline=False)\n", - "print(run.parent.id)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next: ADLA as a Compute Target\n", - "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](https://aka.ms/pl-adla) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "shbijlan" - } - ], - "category": "tutorial", - "compute": [ - "Azure Databricks" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML, Azure Databricks" - ], - "friendly_name": "How to use DatabricksStep with AML Pipelines", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - }, - "order_index": 5, - "star_tag": [ - "featured" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n", + "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n", + "\n", + "The notebook will show:\n", + "1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n", + "2. Running an arbitrary Python script that the customer has in DBFS\n", + "3. Running an arbitrary Python script that is available on local computer (will upload to DBFS, and then run in Databricks) \n", + "4. Running a JAR job that the customer has in DBFS.\n", + "5. How to get run context in a Databricks interactive cluster\n", + "\n", + "## Before you begin:\n", + "\n", + "1. **Create an Azure Databricks workspace** in the same subscription where you have your Azure Machine Learning workspace. You will need details of this workspace later on to define DatabricksStep. [Click here](https://ms.portal.azure.com/#blade/HubsExtension/Resources/resourceType/Microsoft.Databricks%2Fworkspaces) for more information.\n", + "2. **Create PAT (access token)**: Manually create a Databricks access token at the Azure Databricks portal. See [this](https://docs.databricks.com/api/latest/authentication.html#generate-a-token) for more information.\n", + "3. **Add demo notebook to ADB**: This notebook has a sample you can use as is. Launch Azure Databricks attached to your Azure Machine Learning workspace and add a new notebook. \n", + "4. **Create/attach a Blob storage** for use from ADB" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add demo notebook to ADB Workspace\n", + "Copy and paste the below code to create a new notebook in your ADB workspace." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "# direct access\n", + "dbutils.widgets.get(\"myparam\")\n", + "p = getArgument(\"myparam\")\n", + "print (\"Param -\\'myparam':\")\n", + "print (p)\n", + "\n", + "dbutils.widgets.get(\"input\")\n", + "i = getArgument(\"input\")\n", + "print (\"Param -\\'input':\")\n", + "print (i)\n", + "\n", + "dbutils.widgets.get(\"output\")\n", + "o = getArgument(\"output\")\n", + "print (\"Param -\\'output':\")\n", + "print (o)\n", + "\n", + "n = i + \"/testdata.txt\"\n", + "df = spark.read.csv(n)\n", + "\n", + "display (df)\n", + "\n", + "data = [('value1', 'value2')]\n", + "df2 = spark.createDataFrame(data)\n", + "\n", + "z = o + \"/output.txt\"\n", + "df2.write.csv(z)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Azure Machine Learning and Pipeline SDK-specific imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import azureml.core\n", + "from azureml.core.runconfig import JarLibrary\n", + "from azureml.core.compute import ComputeTarget, DatabricksCompute\n", + "from azureml.exceptions import ComputeTargetException\n", + "from azureml.core import Workspace, Experiment\n", + "from azureml.pipeline.core import Pipeline, PipelineData\n", + "from azureml.pipeline.steps import DatabricksStep\n", + "from azureml.core.datastore import Datastore\n", + "from azureml.data.data_reference import DataReference\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Attach Databricks compute target\n", + "Next, you need to add your Databricks workspace to Azure Machine Learning as a compute target and give it a name. You will use this name to refer to your Databricks workspace compute target inside Azure Machine Learning.\n", + "\n", + "- **Resource Group** - The resource group name of your Azure Machine Learning workspace\n", + "- **Databricks Workspace Name** - The workspace name of your Azure Databricks workspace\n", + "- **Databricks Access Token** - The access token you created in ADB\n", + "\n", + "**The Databricks workspace need to be present in the same subscription as your AML workspace**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of DatabricksStep" + "sample-databrickscompute-attach" + ] + }, + "outputs": [], + "source": [ + "# Replace with your account info before running.\n", + " \n", + "db_compute_name=os.getenv(\"DATABRICKS_COMPUTE_NAME\", \"\") # Databricks compute name\n", + "db_resource_group=os.getenv(\"DATABRICKS_RESOURCE_GROUP\", \"\") # Databricks resource group\n", + "db_workspace_name=os.getenv(\"DATABRICKS_WORKSPACE_NAME\", \"\") # Databricks workspace name\n", + "db_access_token=os.getenv(\"DATABRICKS_ACCESS_TOKEN\", \"\") # Databricks access token\n", + " \n", + "try:\n", + " databricks_compute = DatabricksCompute(workspace=ws, name=db_compute_name)\n", + " print('Compute target {} already exists'.format(db_compute_name))\n", + "except ComputeTargetException:\n", + " print('Compute not found, will use below parameters to attach new one')\n", + " print('db_compute_name {}'.format(db_compute_name))\n", + " print('db_resource_group {}'.format(db_resource_group))\n", + " print('db_workspace_name {}'.format(db_workspace_name))\n", + " print('db_access_token {}'.format(db_access_token))\n", + " \n", + " config = DatabricksCompute.attach_configuration(\n", + " resource_group = db_resource_group,\n", + " workspace_name = db_workspace_name,\n", + " access_token= db_access_token)\n", + " databricks_compute=ComputeTarget.attach(ws, db_compute_name, config)\n", + " databricks_compute.wait_for_completion(True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Connections with Inputs and Outputs\n", + "The DatabricksStep supports DBFS, Azure Blob and ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n", + "\n", + "- Databricks documentation on [Azure Blob](https://docs.azuredatabricks.net/spark/latest/data-sources/azure/azure-storage.html)\n", + "- Databricks documentation on [ADLS](https://docs.databricks.com/spark/latest/data-sources/azure/azure-datalake.html)\n", + "\n", + "### Type of Data Access\n", + "Databricks allows to interact with Azure Blob and ADLS in two ways.\n", + "- **Direct Access**: Databricks allows you to interact with Azure Blob or ADLS URIs directly. The input or output URIs will be mapped to a Databricks widget param in the Databricks notebook.\n", + "- **Mounting**: You will be supplied with additional parameters and secrets that will enable you to mount your ADLS or Azure Blob input or output location in your Databricks notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Direct Access: Python sample code\n", + "If you have a data reference named \"input\" it will represent the URI of the input and you can access it directly in the Databricks python notebook like so:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "dbutils.widgets.get(\"input\")\n", + "y = getArgument(\"input\")\n", + "df = spark.read.csv(y)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Mounting: Python sample code for Azure Blob\n", + "Given an Azure Blob data reference named \"input\" the following widget params will be made available in the Databricks notebook:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "# This contains the input URI\n", + "dbutils.widgets.get(\"input\")\n", + "myinput_uri = getArgument(\"input\")\n", + "\n", + "# How to get the input datastore name inside ADB notebook\n", + "# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n", + "# that contians an access key or sas for the Azure Blob input (this name is obtained by appending \n", + "# the name of the input with \"_blob_secretname\". \n", + "dbutils.widgets.get(\"input_blob_secretname\") \n", + "myinput_blob_secretname = getArgument(\"input_blob_secretname\")\n", + "\n", + "# This contains the required configuration for mounting\n", + "dbutils.widgets.get(\"input_blob_config\")\n", + "myinput_blob_config = getArgument(\"input_blob_config\")\n", + "\n", + "# Usage\n", + "dbutils.fs.mount(\n", + " source = myinput_uri,\n", + " mount_point = \"/mnt/input\",\n", + " extra_configs = {myinput_blob_config:dbutils.secrets.get(scope = \"amlscope\", key = myinput_blob_secretname)})\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Mounting: Python sample code for ADLS\n", + "Given an ADLS data reference named \"input\" the following widget params will be made available in the Databricks notebook:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "# This contains the input URI\n", + "dbutils.widgets.get(\"input\") \n", + "myinput_uri = getArgument(\"input\")\n", + "\n", + "# This contains the client id for the service principal \n", + "# that has access to the adls input\n", + "dbutils.widgets.get(\"input_adls_clientid\") \n", + "myinput_adls_clientid = getArgument(\"input_adls_clientid\")\n", + "\n", + "# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n", + "# that contains the secret for the above mentioned service principal\n", + "dbutils.widgets.get(\"input_adls_secretname\") \n", + "myinput_adls_secretname = getArgument(\"input_adls_secretname\")\n", + "\n", + "# This contains the refresh url for the mounting configs\n", + "dbutils.widgets.get(\"input_adls_refresh_url\") \n", + "myinput_adls_refresh_url = getArgument(\"input_adls_refresh_url\")\n", + "\n", + "# Usage \n", + "configs = {\"dfs.adls.oauth2.access.token.provider.type\": \"ClientCredential\",\n", + " \"dfs.adls.oauth2.client.id\": myinput_adls_clientid,\n", + " \"dfs.adls.oauth2.credential\": dbutils.secrets.get(scope = \"amlscope\", key =myinput_adls_secretname),\n", + " \"dfs.adls.oauth2.refresh.url\": myinput_adls_refresh_url}\n", + "\n", + "dbutils.fs.mount(\n", + " source = myinput_uri,\n", + " mount_point = \"/mnt/output\",\n", + " extra_configs = configs)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Databricks from Azure Machine Learning Pipeline\n", + "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. Let's define a datasource (via DataReference), intermediate data (via PipelineData) and a pipeline parameter (via PipelineParameter) to be used in DatabricksStep." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineParameter\n", + "\n", + "# Use the default blob storage\n", + "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n", + "print('Datastore {} will be used'.format(def_blob_store.name))\n", + "\n", + "pipeline_param = PipelineParameter(name=\"my_pipeline_param\", default_value=\"pipeline_param1\")\n", + "\n", + "# We are uploading a sample file in the local directory to be used as a datasource\n", + "def_blob_store.upload_files(files=[\"./testdata.txt\"], target_path=\"dbtest\", overwrite=False)\n", + "\n", + "step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n", + " data_reference_name=\"input\")\n", + "\n", + "step_1_output = PipelineData(\"output\", datastore=def_blob_store)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add a DatabricksStep\n", + "Adds a Databricks notebook as a step in a Pipeline.\n", + "- ***name:** Name of the Module\n", + "- **inputs:** List of input connections for data consumed by this step. Fetch this inside the notebook using dbutils.widgets.get(\"input\")\n", + "- **outputs:** List of output port definitions for outputs produced by this step. Fetch this inside the notebook using dbutils.widgets.get(\"output\")\n", + "- **existing_cluster_id:** Cluster ID of an existing Interactive cluster on the Databricks workspace. If you are providing this, do not provide any of the parameters below that are used to create a new cluster such as spark_version, node_type, etc.\n", + "- **spark_version:** Version of spark for the databricks run cluster. default value: 4.0.x-scala2.11\n", + "- **node_type:** Azure vm node types for the databricks run cluster. default value: Standard_D3_v2\n", + "- **num_workers:** Specifies a static number of workers for the databricks run cluster\n", + "- **min_workers:** Specifies a min number of workers to use for auto-scaling the databricks run cluster\n", + "- **max_workers:** Specifies a max number of workers to use for auto-scaling the databricks run cluster\n", + "- **spark_env_variables:** Spark environment variables for the databricks run cluster (dictionary of {str:str}). default value: {'PYSPARK_PYTHON': '/databricks/python3/bin/python3'}\n", + "- **notebook_path:** Path to the notebook in the databricks instance. If you are providing this, do not provide python script related paramaters or JAR related parameters.\n", + "- **notebook_params:** Parameters for the databricks notebook (dictionary of {str:str}). Fetch this inside the notebook using dbutils.widgets.get(\"myparam\")\n", + "- **python_script_path:** The path to the python script in the DBFS or S3. If you are providing this, do not provide python_script_name which is used for uploading script from local machine.\n", + "- **python_script_params:** Parameters for the python script (list of str)\n", + "- **main_class_name:** The name of the entry point in a JAR module. If you are providing this, do not provide any python script or notebook related parameters.\n", + "- **jar_params:** Parameters for the JAR module (list of str)\n", + "- **python_script_name:** name of a python script on your local machine (relative to source_directory). If you are providing this do not provide python_script_path which is used to execute a remote python script; or any of the JAR or notebook related parameters.\n", + "- **source_directory:** folder that contains the script and other files\n", + "- **hash_paths:** list of paths to hash to detect a change in source_directory (script file is always hashed)\n", + "- **run_name:** Name in databricks for this run\n", + "- **timeout_seconds:** Timeout for the databricks run\n", + "- **runconfig:** Runconfig to use. Either pass runconfig or each library type as a separate parameter but do not mix the two\n", + "- **maven_libraries:** maven libraries for the databricks run\n", + "- **pypi_libraries:** pypi libraries for the databricks run\n", + "- **egg_libraries:** egg libraries for the databricks run\n", + "- **jar_libraries:** jar libraries for the databricks run\n", + "- **rcran_libraries:** rcran libraries for the databricks run\n", + "- **compute_target:** Azure Databricks compute\n", + "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs\n", + "- **version:** Optional version tag to denote a change in functionality for the step\n", + "\n", + "\\* *denotes required fields* \n", + "*You must provide exactly one of num_workers or min_workers and max_workers paramaters* \n", + "*You must provide exactly one of databricks_compute or databricks_compute_name parameters*\n", + "\n", + "## Use runconfig to specify library dependencies\n", + "You can use a runconfig to specify the library dependencies for your cluster in Databricks. The runconfig will contain a databricks section as follows:\n", + "\n", + "```yaml\n", + "environment:\n", + "# Databricks details\n", + " databricks:\n", + "# List of maven libraries.\n", + " mavenLibraries:\n", + " - coordinates: org.jsoup:jsoup:1.7.1\n", + " repo: ''\n", + " exclusions:\n", + " - slf4j:slf4j\n", + " - '*:hadoop-client'\n", + "# List of PyPi libraries\n", + " pypiLibraries:\n", + " - package: beautifulsoup4\n", + " repo: ''\n", + "# List of RCran libraries\n", + " rcranLibraries:\n", + " -\n", + "# Coordinates.\n", + " package: ada\n", + "# Repo\n", + " repo: http://cran.us.r-project.org\n", + "# List of JAR libraries\n", + " jarLibraries:\n", + " -\n", + "# Coordinates.\n", + " library: dbfs:/mnt/libraries/library.jar\n", + "# List of Egg libraries\n", + " eggLibraries:\n", + " -\n", + "# Coordinates.\n", + " library: dbfs:/mnt/libraries/library.egg\n", + "```\n", + "\n", + "You can then create a RunConfiguration object using this file and pass it as the runconfig parameter to DatabricksStep.\n", + "```python\n", + "from azureml.core.runconfig import RunConfiguration\n", + "\n", + "runconfig = RunConfiguration()\n", + "runconfig.load(path='', name='')\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Running the demo notebook already added to the Databricks workspace\n", + "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable notebook_path when you run the code cell below:\n", + "\n", + "your notebook's path in Azure Databricks UI by hovering over to notebook's title. A typical path of notebook looks like this `/Users/example@databricks.com/example`. See [Databricks Workspace](https://docs.azuredatabricks.net/user-guide/workspace.html) to learn about the folder structure.\n", + "\n", + "Note: DataPath `PipelineParameter` should be provided in list of inputs. Such parameters can be accessed by the datapath `name`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "notebook_path=os.getenv(\"DATABRICKS_NOTEBOOK_PATH\", \"\") # Databricks notebook path\n", + "\n", + "dbNbStep = DatabricksStep(\n", + " name=\"DBNotebookInWS\",\n", + " inputs=[step_1_input],\n", + " outputs=[step_1_output],\n", + " num_workers=1,\n", + " notebook_path=notebook_path,\n", + " notebook_params={'myparam': 'testparam', \n", + " 'myparam2': pipeline_param},\n", + " run_name='DB_Notebook_demo',\n", + " compute_target=databricks_compute,\n", + " allow_reuse=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build and submit the Experiment\n", + "\n", + "Note: Default value of `pipeline_param` will be used if different value is not specified in pipeline parameters during submission" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#steps = [dbNbStep]\n", + "#pipeline = Pipeline(workspace=ws, steps=steps)\n", + "#pipeline_run = Experiment(ws, 'DB_Notebook_demo').submit(pipeline)\n", + "#pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#from azureml.widgets import RunDetails\n", + "#RunDetails(pipeline_run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Running a Python script from DBFS\n", + "This shows how to run a Python script in DBFS. \n", + "\n", + "To complete this, you will need to first upload the Python script in your local machine to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html). The CLI command is given below:\n", + "\n", + "```\n", + "dbfs cp ./train-db-dbfs.py dbfs:/train-db-dbfs.py\n", + "```\n", + "\n", + "The code in the below cell assumes that you have completed the previous step of uploading the script `train-db-dbfs.py` to the root folder in DBFS.\n", + "\n", + "Note: `pipeline_param` will add two values in the python_script_params, a name followed by value. the name will be in this format `--MY_PIPELINE_PARAM`. For example, in the given case, python_script_params will be `[\"arg1\", \"--MY_PIPELINE_PARAM\", \"pipeline_param1\", \"arg2\"]`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "python_script_path = os.getenv(\"DATABRICKS_PYTHON_SCRIPT_PATH\", \"\") # Databricks python script path\n", + "\n", + "dbPythonInDbfsStep = DatabricksStep(\n", + " name=\"DBPythonInDBFS\",\n", + " inputs=[step_1_input],\n", + " num_workers=1,\n", + " python_script_path=python_script_path,\n", + " python_script_params={'arg1', pipeline_param, 'arg2'},\n", + " run_name='DB_Python_demo',\n", + " compute_target=databricks_compute,\n", + " allow_reuse=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build and submit the Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#steps = [dbPythonInDbfsStep]\n", + "#pipeline = Pipeline(workspace=ws, steps=steps)\n", + "#pipeline_run = Experiment(ws, 'DB_Python_demo').submit(pipeline)\n", + "#pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#from azureml.widgets import RunDetails\n", + "#RunDetails(pipeline_run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Running a Python script in Databricks that currenlty is in local computer\n", + "To run a Python script that is currently in your local computer, follow the instructions below. \n", + "\n", + "The commented out code below code assumes that you have `train-db-local.py` in the `source_directory` subdirectory under the current working directory. \n", + "\n", + "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.\n", + "\n", + "In this case, the Python script will be uploaded first to DBFS, and then the script will be run in Databricks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "python_script_name = \"train-db-local.py\"\n", + "source_directory = \"./databricks_train\"\n", + "\n", + "dbPythonInLocalMachineStep = DatabricksStep(\n", + " name=\"DBPythonInLocalMachine\",\n", + " inputs=[step_1_input],\n", + " num_workers=1,\n", + " python_script_name=python_script_name,\n", + " source_directory=source_directory,\n", + " run_name='DB_Python_Local_demo',\n", + " compute_target=databricks_compute,\n", + " allow_reuse=True\n", + ")" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build and submit the Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "steps = [dbPythonInLocalMachineStep]\n", + "pipeline = Pipeline(workspace=ws, steps=steps)\n", + "pipeline_run = Experiment(ws, 'DB_Python_Local_demo').submit(pipeline)\n", + "pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(pipeline_run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Running a JAR job that is alreay added in DBFS\n", + "To run a JAR job that is already uploaded to DBFS, follow the instructions below. You will first upload the JAR file to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n", + "\n", + "The commented out code in the below cell assumes that you have uploaded `train-db-dbfs.jar` to the root folder in DBFS. You can upload `train-db-dbfs.jar` to the root folder in DBFS using this commandline so you can use `jar_library_dbfs_path = \"dbfs:/train-db-dbfs.jar\"`:\n", + "\n", + "```\n", + "dbfs cp ./train-db-dbfs.jar dbfs:/train-db-dbfs.jar\n", + "```\n", + "\n", + "Note: `pipeline_param` will add two values in the python_script_params, a name followed by value. the name will be in this format `--MY_PIPELINE_PARAM`. For example, in the given case, python_script_params will be `[\"arg1\", \"--MY_PIPELINE_PARAM\", \"pipeline_param1\", \"arg2\"]`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main_jar_class_name = \"com.microsoft.aeva.Main\"\n", + "jar_library_dbfs_path = os.getenv(\"DATABRICKS_JAR_LIB_PATH\", \"\") # Databricks jar library path\n", + "\n", + "dbJarInDbfsStep = DatabricksStep(\n", + " name=\"DBJarInDBFS\",\n", + " inputs=[step_1_input],\n", + " num_workers=1,\n", + " main_class_name=main_jar_class_name,\n", + " jar_params={'arg1', pipeline_param, 'arg2'},\n", + " run_name='DB_JAR_demo',\n", + " jar_libraries=[JarLibrary(jar_library_dbfs_path)],\n", + " compute_target=databricks_compute,\n", + " allow_reuse=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build and submit the Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#steps = [dbJarInDbfsStep]\n", + "#pipeline = Pipeline(workspace=ws, steps=steps)\n", + "#pipeline_run = Experiment(ws, 'DB_JAR_demo').submit(pipeline)\n", + "#pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PUBLISHONLY\n", + "#from azureml.widgets import RunDetails\n", + "#RunDetails(pipeline_run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Running demo notebook already added to the Databricks workspace using existing cluster\n", + "First you need register DBFS datastore and make sure path_on_datastore does exist in databricks file system, you can browser the files by refering [this](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n", + "\n", + "Find existing_cluster_id by opeing Azure Databricks UI with Clusters page and in url you will find a string connected with '-' right after \"clusters/\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " dbfs_ds = Datastore.get(workspace=ws, datastore_name='dbfs_datastore')\n", + " print('DBFS Datastore already exists')\n", + "except Exception as ex:\n", + " dbfs_ds = Datastore.register_dbfs(ws, datastore_name='dbfs_datastore')\n", + "\n", + "step_1_input = DataReference(datastore=dbfs_ds, path_on_datastore=\"FileStore\", data_reference_name=\"input\")\n", + "step_1_output = PipelineData(\"output\", datastore=dbfs_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbNbWithExistingClusterStep = DatabricksStep(\n", + " name=\"DBFSReferenceWithExisting\",\n", + " inputs=[step_1_input],\n", + " outputs=[step_1_output],\n", + " notebook_path=notebook_path,\n", + " notebook_params={'myparam': 'testparam', \n", + " 'myparam2': pipeline_param},\n", + " run_name='DBFS_Reference_With_Existing',\n", + " compute_target=databricks_compute,\n", + " existing_cluster_id=\"your existing cluster id\",\n", + " allow_reuse=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build and submit the Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "steps = [dbNbWithExistingClusterStep]\n", + "pipeline = Pipeline(workspace=ws, steps=steps)\n", + "pipeline_run = Experiment(ws, 'DBFS_Reference_With_Existing').submit(pipeline)\n", + "pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(pipeline_run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Running a Python script in Databricks that is currently in local computer with existing cluster\n", + "When you access azure blob or data lake storage from an existing (interactive) cluster, you need to ensure the Spark configuration is set up correctly to access this storage and this set up may require the cluster to be restarted.\n", + "\n", + "If you set permit_cluster_restart to True, AML will check if the spark configuration needs to be updated and restart the cluster for you if required. This will ensure that the storage can be correctly accessed from the Databricks cluster." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n", + " data_reference_name=\"input\")\n", + "\n", + "dbPythonInLocalWithExistingStep = DatabricksStep(\n", + " name=\"DBPythonInLocalMachineWithExisting\",\n", + " inputs=[step_1_input],\n", + " python_script_name=python_script_name,\n", + " source_directory=source_directory,\n", + " run_name='DB_Python_Local_existing_demo',\n", + " compute_target=databricks_compute,\n", + " existing_cluster_id=\"your existing cluster id\",\n", + " allow_reuse=False,\n", + " permit_cluster_restart=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build and submit the Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "steps = [dbPythonInLocalWithExistingStep]\n", + "pipeline = Pipeline(workspace=ws, steps=steps)\n", + "pipeline_run = Experiment(ws, 'DB_Python_Local_existing_demo').submit(pipeline)\n", + "pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(pipeline_run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to get run context in a Databricks interactive cluster\n", + "\n", + "Users are used to being able to use Run.get_context() to retrieve the parent_run_id for a given run_id. In DatabricksStep, however, a little more work is required to achieve this.\n", + "\n", + "The solution is to parse the script arguments and set corresponding environment variables to access the run context from within Databricks.\n", + "Note that this workaround is not required for job clusters. \n", + "\n", + "Here is a code sample:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "from azureml.core import Run\n", + "import argparse\n", + "import os\n", + "\n", + "\n", + "def populate_environ():\n", + " parser = argparse.ArgumentParser(description='Process arguments passed to script')\n", + "\n", + " # The AZUREML_SCRIPT_DIRECTORY_NAME argument will be filled in if the DatabricksStep\n", + " # was run using a local source_directory and python_script_name\n", + " parser.add_argument('--AZUREML_SCRIPT_DIRECTORY_NAME')\n", + "\n", + " # Remaining arguments are filled in for all databricks jobs and can be used to build the run context\n", + " parser.add_argument('--AZUREML_RUN_TOKEN')\n", + " parser.add_argument('--AZUREML_RUN_TOKEN_EXPIRY')\n", + " parser.add_argument('--AZUREML_RUN_ID')\n", + " parser.add_argument('--AZUREML_ARM_SUBSCRIPTION')\n", + " parser.add_argument('--AZUREML_ARM_RESOURCEGROUP')\n", + " parser.add_argument('--AZUREML_ARM_WORKSPACE_NAME')\n", + " parser.add_argument('--AZUREML_ARM_PROJECT_NAME')\n", + " parser.add_argument('--AZUREML_SERVICE_ENDPOINT')\n", + " parser.add_argument('--AZUREML_WORKSPACE_ID')\n", + " parser.add_argument('--AZUREML_EXPERIMENT_ID')\n", + "\n", + " (args, extra_args) = parser.parse_known_args()\n", + " os.environ['AZUREML_RUN_TOKEN'] = args.AZUREML_RUN_TOKEN\n", + " os.environ['AZUREML_RUN_TOKEN_EXPIRY'] = args.AZUREML_RUN_TOKEN_EXPIRY\n", + " os.environ['AZUREML_RUN_ID'] = args.AZUREML_RUN_ID\n", + " os.environ['AZUREML_ARM_SUBSCRIPTION'] = args.AZUREML_ARM_SUBSCRIPTION\n", + " os.environ['AZUREML_ARM_RESOURCEGROUP'] = args.AZUREML_ARM_RESOURCEGROUP\n", + " os.environ['AZUREML_ARM_WORKSPACE_NAME'] = args.AZUREML_ARM_WORKSPACE_NAME\n", + " os.environ['AZUREML_ARM_PROJECT_NAME'] = args.AZUREML_ARM_PROJECT_NAME\n", + " os.environ['AZUREML_SERVICE_ENDPOINT'] = args.AZUREML_SERVICE_ENDPOINT\n", + " os.environ['AZUREML_WORKSPACE_ID'] = args.AZUREML_WORKSPACE_ID\n", + " os.environ['AZUREML_EXPERIMENT_ID'] = args.AZUREML_EXPERIMENT_ID\n", + "\n", + "populate_environ()\n", + "run = Run.get_context(allow_offline=False)\n", + "print(run.parent.id)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next: ADLA as a Compute Target\n", + "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](https://aka.ms/pl-adla) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline." + ] + } + ], + "metadata": { + "authors": [ + { + "name": "shbijlan" + } + ], + "category": "tutorial", + "compute": [ + "Azure Databricks" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML, Azure Databricks" + ], + "friendly_name": "How to use DatabricksStep with AML Pipelines", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + }, + "order_index": 5, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of DatabricksStep" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-kusto-as-compute-target.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-kusto-as-compute-target.ipynb index 3959bd04a..f6fa77088 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-kusto-as-compute-target.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-kusto-as-compute-target.ipynb @@ -1,274 +1,274 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Azure Machine Learning Pipeline with KustoStep\n", - "To use Kusto as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a KustoStep is used. A KustoStep enables the functionality of running Kusto queries on a target Kusto cluster in Azure ML Pipelines. Each KustoStep can target one Kusto cluster and perform multiple queries on them. This notebook demonstrates the use of KustoStep in Azure Machine Learning (AML) Pipeline.\n", - "\n", - "## Before you begin:\n", - "\n", - "1. **Have an Azure Machine Learning workspace**: You will need details of this workspace later on to define KustoStep.\n", - "2. **Have a Service Principal**: You will need a service principal and use its credentials to access your cluster. See [this](https://docs.microsoft.com/en-us/azure/active-directory/develop/howto-create-service-principal-portal) for more information.\n", - "3. **Have a Blob storage**: You will need a Azure Blob storage for uploading the output of your Kusto query." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Azure Machine Learning and Pipeline SDK-specific imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import azureml.core\n", - "from azureml.core.runconfig import JarLibrary\n", - "from azureml.core.compute import ComputeTarget, KustoCompute\n", - "from azureml.exceptions import ComputeTargetException\n", - "from azureml.core import Workspace, Experiment\n", - "from azureml.pipeline.core import Pipeline, PipelineData\n", - "from azureml.pipeline.steps import KustoStep\n", - "from azureml.core.datastore import Datastore\n", - "from azureml.data.data_reference import DataReference\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Attach Kusto compute target\n", - "Next, you need to create a Kusto compute target and give it a name. You will use this name to refer to your Kusto compute target inside Azure Machine Learning. Your workspace will be associated to this Kusto compute target. You will also need to provide some credentials that will be used to enable access to your target Kusto cluster and database.\n", - "\n", - "- **Resource Group** - The resource group name of your Azure Machine Learning workspace\n", - "- **Workspace Name** - The workspace name of your Azure Machine Learning workspace\n", - "- **Resource ID** - The resource ID of your Kusto cluster\n", - "- **Tenant ID** - The tenant ID associated to your Kusto cluster\n", - "- **Application ID** - The Application ID associated to your Kusto cluster\n", - "- **Application Key** - The Application key associated to your Kusto cluster\n", - "- **Kusto Connection String** - The connection string of your Kusto cluster\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-databrickscompute-attach" - ] - }, - "outputs": [], - "source": [ - "compute_name = \"\" # Name to associate with new compute in workspace\n", - "\n", - "# Account details associated to the target Kusto cluster\n", - "resource_id = \"\" # Resource ID of the Kusto cluster\n", - "kusto_connection_string = \"\" # Connection string of the Kusto cluster\n", - "application_id = \"\" # Application ID associated to the Kusto cluster\n", - "application_key = \"\" # Application Key associated to the Kusto cluster\n", - "tenant_id = \"\" # Tenant ID associated to the Kusto cluster\n", - "\n", - "try:\n", - " kusto_compute = KustoCompute(workspace=ws, name=compute_name)\n", - " print('Compute target {} already exists'.format(compute_name))\n", - "except ComputeTargetException:\n", - " print('Compute not found, will use provided parameters to attach new one')\n", - " config = KustoCompute.attach_configuration(resource_group=ws.resource_group, workspace_name=ws.name, \n", - " resource_id=resource_id, tenant_id=tenant_id, \n", - " kusto_connection_string=kusto_connection_string, \n", - " application_id=application_id, application_key=application_key)\n", - " kusto_compute=ComputeTarget.attach(ws, compute_name, config)\n", - " kusto_compute.wait_for_completion(True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup output\n", - "To use Kusto as a compute target for Azure Machine Learning Pipeline, a KustoStep is used. Currently KustoStep only supports uploading results to Azure Blob store. Let's define an output datastore via PipelineData to be used in KustoStep." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineParameter\n", - "\n", - "# Use the default blob storage\n", - "def_blob_store = Datastore.get(ws, \"workspaceblobstore\")\n", - "print('Datastore {} will be used'.format(def_blob_store.name))\n", - "\n", - "step_1_output = PipelineData(\"output\", datastore=def_blob_store)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Add a KustoStep to Pipeline\n", - "Adds a Kusto query as a step in a Pipeline.\n", - "- **name:** Name of the Module\n", - "- **compute_target:** Name of Kusto compute target\n", - "- **database_name:** Name of the database to perform Kusto query on\n", - "- **query_directory:** Path to folder that contains only a text file with Kusto queries (see [here](https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/) for more details on Kusto queries). \n", - " - If the query is parameterized, then the text file must also include any declaration of query parameters (see [here](https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/queryparametersstatement?pivots=azuredataexplorer) for more details on query parameters declaration statements). \n", - " - An example of the query text file could just contain the query \"StormEvents | count | as HowManyRecords;\", where StormEvents is the table name. \n", - " - Note. the text file should just contain the declarations and queries without quotation marks around them.\n", - "- **outputs:** Output binding to an Azure Blob Store.\n", - "- **parameter_dict (optional):** Dictionary that contains the values of parameters declared in the query text file in the **query_directory** mentioned above.\n", - " - Dictionary key is the parameter name, and dictionary value is the parameter value.\n", - " - For example, parameter_dict = {\"paramName1\": \"paramValue1\", \"paramName2\": \"paramValue2\"}\n", - "- **allow_reuse (optional):** Whether the step should reuse previous results when run with the same settings/inputs (default to False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "database_name = \"\" # Name of the database to perform Kusto queries on\n", - "query_directory = \"\" # Path to folder that contains a text file with Kusto queries\n", - "\n", - "kustoStep = KustoStep(\n", - " name='KustoNotebook',\n", - " compute_target=compute_name,\n", - " database_name=database_name,\n", - " query_directory=query_directory,\n", - " output=step_1_output,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Build and submit the Experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "steps = [kustoStep]\n", - "pipeline = Pipeline(workspace=ws, steps=steps)\n", - "pipeline_run = Experiment(ws, 'Notebook_demo').submit(pipeline)\n", - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "t-kachia" - } - ], - "category": "tutorial", - "compute": [ - "Kusto" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML, Kusto" - ], - "friendly_name": "How to use KustoStep with AML Pipelines", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - }, - "order_index": 5, - "star_tag": [ - "featured" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Azure Machine Learning Pipeline with KustoStep\n", + "To use Kusto as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a KustoStep is used. A KustoStep enables the functionality of running Kusto queries on a target Kusto cluster in Azure ML Pipelines. Each KustoStep can target one Kusto cluster and perform multiple queries on them. This notebook demonstrates the use of KustoStep in Azure Machine Learning (AML) Pipeline.\n", + "\n", + "## Before you begin:\n", + "\n", + "1. **Have an Azure Machine Learning workspace**: You will need details of this workspace later on to define KustoStep.\n", + "2. **Have a Service Principal**: You will need a service principal and use its credentials to access your cluster. See [this](https://docs.microsoft.com/en-us/azure/active-directory/develop/howto-create-service-principal-portal) for more information.\n", + "3. **Have a Blob storage**: You will need a Azure Blob storage for uploading the output of your Kusto query." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Azure Machine Learning and Pipeline SDK-specific imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import azureml.core\n", + "from azureml.core.runconfig import JarLibrary\n", + "from azureml.core.compute import ComputeTarget, KustoCompute\n", + "from azureml.exceptions import ComputeTargetException\n", + "from azureml.core import Workspace, Experiment\n", + "from azureml.pipeline.core import Pipeline, PipelineData\n", + "from azureml.pipeline.steps import KustoStep\n", + "from azureml.core.datastore import Datastore\n", + "from azureml.data.data_reference import DataReference\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Attach Kusto compute target\n", + "Next, you need to create a Kusto compute target and give it a name. You will use this name to refer to your Kusto compute target inside Azure Machine Learning. Your workspace will be associated to this Kusto compute target. You will also need to provide some credentials that will be used to enable access to your target Kusto cluster and database.\n", + "\n", + "- **Resource Group** - The resource group name of your Azure Machine Learning workspace\n", + "- **Workspace Name** - The workspace name of your Azure Machine Learning workspace\n", + "- **Resource ID** - The resource ID of your Kusto cluster\n", + "- **Tenant ID** - The tenant ID associated to your Kusto cluster\n", + "- **Application ID** - The Application ID associated to your Kusto cluster\n", + "- **Application Key** - The Application key associated to your Kusto cluster\n", + "- **Kusto Connection String** - The connection string of your Kusto cluster\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of KustoStep" + "sample-databrickscompute-attach" + ] + }, + "outputs": [], + "source": [ + "compute_name = \"\" # Name to associate with new compute in workspace\n", + "\n", + "# Account details associated to the target Kusto cluster\n", + "resource_id = \"\" # Resource ID of the Kusto cluster\n", + "kusto_connection_string = \"\" # Connection string of the Kusto cluster\n", + "application_id = \"\" # Application ID associated to the Kusto cluster\n", + "application_key = \"\" # Application Key associated to the Kusto cluster\n", + "tenant_id = \"\" # Tenant ID associated to the Kusto cluster\n", + "\n", + "try:\n", + " kusto_compute = KustoCompute(workspace=ws, name=compute_name)\n", + " print('Compute target {} already exists'.format(compute_name))\n", + "except ComputeTargetException:\n", + " print('Compute not found, will use provided parameters to attach new one')\n", + " config = KustoCompute.attach_configuration(resource_group=ws.resource_group, workspace_name=ws.name, \n", + " resource_id=resource_id, tenant_id=tenant_id, \n", + " kusto_connection_string=kusto_connection_string, \n", + " application_id=application_id, application_key=application_key)\n", + " kusto_compute=ComputeTarget.attach(ws, compute_name, config)\n", + " kusto_compute.wait_for_completion(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup output\n", + "To use Kusto as a compute target for Azure Machine Learning Pipeline, a KustoStep is used. Currently KustoStep only supports uploading results to Azure Blob store. Let's define an output datastore via PipelineData to be used in KustoStep." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineParameter\n", + "\n", + "# Use the default blob storage\n", + "def_blob_store = Datastore.get(ws, \"workspaceblobstore\")\n", + "print('Datastore {} will be used'.format(def_blob_store.name))\n", + "\n", + "step_1_output = PipelineData(\"output\", datastore=def_blob_store)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Add a KustoStep to Pipeline\n", + "Adds a Kusto query as a step in a Pipeline.\n", + "- **name:** Name of the Module\n", + "- **compute_target:** Name of Kusto compute target\n", + "- **database_name:** Name of the database to perform Kusto query on\n", + "- **query_directory:** Path to folder that contains only a text file with Kusto queries (see [here](https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/) for more details on Kusto queries). \n", + " - If the query is parameterized, then the text file must also include any declaration of query parameters (see [here](https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/queryparametersstatement?pivots=azuredataexplorer) for more details on query parameters declaration statements). \n", + " - An example of the query text file could just contain the query \"StormEvents | count | as HowManyRecords;\", where StormEvents is the table name. \n", + " - Note. the text file should just contain the declarations and queries without quotation marks around them.\n", + "- **outputs:** Output binding to an Azure Blob Store.\n", + "- **parameter_dict (optional):** Dictionary that contains the values of parameters declared in the query text file in the **query_directory** mentioned above.\n", + " - Dictionary key is the parameter name, and dictionary value is the parameter value.\n", + " - For example, parameter_dict = {\"paramName1\": \"paramValue1\", \"paramName2\": \"paramValue2\"}\n", + "- **allow_reuse (optional):** Whether the step should reuse previous results when run with the same settings/inputs (default to False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "database_name = \"\" # Name of the database to perform Kusto queries on\n", + "query_directory = \"\" # Path to folder that contains a text file with Kusto queries\n", + "\n", + "kustoStep = KustoStep(\n", + " name='KustoNotebook',\n", + " compute_target=compute_name,\n", + " database_name=database_name,\n", + " query_directory=query_directory,\n", + " output=step_1_output,\n", + ")" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build and submit the Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "steps = [kustoStep]\n", + "pipeline = Pipeline(workspace=ws, steps=steps)\n", + "pipeline_run = Experiment(ws, 'Notebook_demo').submit(pipeline)\n", + "pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(pipeline_run).show()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "t-kachia" + } + ], + "category": "tutorial", + "compute": [ + "Kusto" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML, Kusto" + ], + "friendly_name": "How to use KustoStep with AML Pipelines", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "order_index": 5, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of KustoStep" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb index 6683fab95..6783d5ad0 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb @@ -1,528 +1,538 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Azure Machine Learning Pipeline with AutoMLStep\n", - "This notebook demonstrates the use of AutoMLStep in Azure Machine Learning Pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "In this example we showcase how you can use AzureML Dataset to load data for AutoML via AML Pipeline. \n", - "\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook, please also take a look at the [Automated ML setup-using-a-local-conda-environment](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning#setup-using-a-local-conda-environment) section to setup the environment.\n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create an `Experiment` in an existing `Workspace`.\n", - "2. Create or Attach existing AmlCompute to a workspace.\n", - "3. Define data loading in a `TabularDataset`.\n", - "4. Configure AutoML using `AutoMLConfig`.\n", - "5. Use AutoMLStep\n", - "6. Train the model using AmlCompute\n", - "7. Explore the results.\n", - "8. Test the best fitted model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Azure Machine Learning and Pipeline SDK-specific imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import os\n", - "import csv\n", - "\n", - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn import datasets\n", - "import pkg_resources\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.train.automl import AutoMLConfig\n", - "from azureml.core.dataset import Dataset\n", - "\n", - "from azureml.pipeline.steps import AutoMLStep\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an Azure ML experiment\n", - "Let's create an experiment named \"automlstep-sample\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n", - "\n", - "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a name for the run history container in the workspace.\n", - "experiment_name = 'automlstep-sample'\n", - "project_folder = './project'\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "experiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach an AmlCompute cluster\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "amlcompute_cluster_name = \"cpu-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',# for GPU, use \"STANDARD_NC6\"\n", - " #vm_priority = 'lowpriority', # optional\n", - " max_nodes=4)\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True, min_node_count = 1, timeout_in_minutes = 10)\n", - "# For a more detailed view of current AmlCompute status, use get_status()." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Try to load the dataset from the Workspace. Otherwise, create it from the file\n", - "found = False\n", - "key = \"Crime-Dataset\"\n", - "description_text = \"Crime Dataset (used in the the aml-pipelines-with-automated-machine-learning-step.ipynb notebook)\"\n", - "\n", - "if key in ws.datasets.keys(): \n", - " found = True\n", - " dataset = ws.datasets[key] \n", - "\n", - "if not found:\n", - " # Create AML Dataset and register it into Workspace\n", - " # The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n", - " example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n", - " dataset = Dataset.Tabular.from_delimited_files(example_data)\n", - " dataset = dataset.drop_columns(['FBI Code'])\n", - " \n", - " #Register Dataset in Workspace\n", - " dataset = dataset.register(workspace=ws,\n", - " name=key,\n", - " description=description_text)\n", - "\n", - "\n", - "df = dataset.to_pandas_dataframe()\n", - "df.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Review the Dataset Result\n", - "\n", - "You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records for all the steps in the TabularDataset, which makes it fast even against large datasets.\n", - "\n", - "`TabularDataset` objects are composed of a list of transformation steps (optional)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset.take(5).to_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "This creates a general AutoML settings object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"experiment_timeout_minutes\": 20,\n", - " \"max_concurrent_iterations\": 4,\n", - " \"primary_metric\" : 'AUC_weighted'\n", - "}\n", - "automl_config = AutoMLConfig(compute_target=compute_target,\n", - " task = \"classification\",\n", - " training_data=dataset,\n", - " label_column_name=\"Primary Type\", \n", - " path = project_folder,\n", - " enable_early_stopping= True,\n", - " featurization= 'auto',\n", - " debug_log = \"automl_errors.log\",\n", - " **automl_settings\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create Pipeline and AutoMLStep\n", - "\n", - "You can define outputs for the AutoMLStep using TrainingOutput." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineData, TrainingOutput\n", - "\n", - "ds = ws.get_default_datastore()\n", - "metrics_output_name = 'metrics_output'\n", - "best_model_output_name = 'best_model_output'\n", - "\n", - "metrics_data = PipelineData(name='metrics_data',\n", - " datastore=ds,\n", - " pipeline_output_name=metrics_output_name,\n", - " training_output=TrainingOutput(type='Metrics'))\n", - "model_data = PipelineData(name='model_data',\n", - " datastore=ds,\n", - " pipeline_output_name=best_model_output_name,\n", - " training_output=TrainingOutput(type='Model'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create an AutoMLStep." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "automlstep-remarks-sample1" - ] - }, - "outputs": [], - "source": [ - "automl_step = AutoMLStep(\n", - " name='automl_module',\n", - " automl_config=automl_config,\n", - " outputs=[metrics_data, model_data],\n", - " allow_reuse=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "automlstep-remarks-sample2" - ] - }, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "pipeline = Pipeline(\n", - " description=\"pipeline_with_automlstep\",\n", - " workspace=ws, \n", - " steps=[automl_step])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run = experiment.submit(pipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examine Results\n", - "\n", - "### Retrieve the metrics of all child runs\n", - "Outputs of above run can be used as inputs of other steps in pipeline. In this tutorial, we will examine the outputs by retrieve output data and running some tests." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n", - "num_file_downloaded = metrics_output.download('.', show_progress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "with open(metrics_output._path_on_datastore) as f:\n", - " metrics_output_result = f.read()\n", - " \n", - "deserialized_metrics_output = json.loads(metrics_output_result)\n", - "df = pd.DataFrame(deserialized_metrics_output)\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve the Best Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve best model from Pipeline Run\n", - "best_model_output = pipeline_run.get_pipeline_output(best_model_output_name)\n", - "num_file_downloaded = best_model_output.download('.', show_progress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "\n", - "with open(best_model_output._path_on_datastore, \"rb\" ) as f:\n", - " best_model = pickle.load(f)\n", - "best_model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_model.steps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the Model\n", - "#### Load Test Data\n", - "For the test data, it should have the same preparation step as the train data. Otherwise it might get failed at the preprocessing step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n", - "df_test = dataset_test.to_pandas_dataframe()\n", - "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n", - "\n", - "y_test = df_test['Primary Type']\n", - "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Testing Our Best Fitted Model\n", - "\n", - "We will use confusion matrix to see how our model works." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "ypred = best_model.predict(X_test)\n", - "cm = confusion_matrix(y_test, ypred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Visualize the confusion matrix\n", - "pd.DataFrame(cm).style.background_gradient(cmap='Blues', low=0, high=0.9)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "anshirga" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Automated Machine Learning" - ], - "friendly_name": "How to use AutoMLStep with AML Pipelines", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "order_index": 11, - "star_tag": [ - "featured" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Azure Machine Learning Pipeline with AutoMLStep\n", + "This notebook demonstrates the use of AutoMLStep in Azure Machine Learning Pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "In this example we showcase how you can use AzureML Dataset to load data for AutoML via AML Pipeline. \n", + "\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook, please also take a look at the [Automated ML setup-using-a-local-conda-environment](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning#setup-using-a-local-conda-environment) section to setup the environment.\n", + "\n", + "In this notebook you will learn how to:\n", + "1. Create an `Experiment` in an existing `Workspace`.\n", + "2. Create or Attach existing AmlCompute to a workspace.\n", + "3. Define data loading in a `TabularDataset`.\n", + "4. Configure AutoML using `AutoMLConfig`.\n", + "5. Use AutoMLStep\n", + "6. Train the model using AmlCompute\n", + "7. Explore the results.\n", + "8. Test the best fitted model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Azure Machine Learning and Pipeline SDK-specific imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import os\n", + "import csv\n", + "\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets\n", + "import pkg_resources\n", + "\n", + "import azureml.core\n", + "from azureml.core.experiment import Experiment\n", + "from azureml.core.workspace import Workspace\n", + "from azureml.train.automl import AutoMLConfig\n", + "from azureml.core.dataset import Dataset\n", + "\n", + "from azureml.pipeline.steps import AutoMLStep\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an Azure ML experiment\n", + "Let's create an experiment named \"automlstep-sample\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n", + "\n", + "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose a name for the run history container in the workspace.\n", + "experiment_name = 'automlstep-sample'\n", + "project_folder = './project'\n", + "\n", + "experiment = Experiment(ws, experiment_name)\n", + "experiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create or Attach an AmlCompute cluster\n", + "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import AmlCompute\n", + "from azureml.core.compute import ComputeTarget\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your CPU cluster\n", + "amlcompute_cluster_name = \"cpu-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", + " print('Found existing cluster, use it.')\n", + "except ComputeTargetException:\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',# for GPU, use \"STANDARD_NC6\"\n", + " #vm_priority = 'lowpriority', # optional\n", + " max_nodes=4)\n", + " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", + "\n", + "compute_target.wait_for_completion(show_output=True, min_node_count = 1, timeout_in_minutes = 10)\n", + "# For a more detailed view of current AmlCompute status, use get_status()." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Try to load the dataset from the Workspace. Otherwise, create it from the file\n", + "found = False\n", + "key = \"Crime-Dataset\"\n", + "description_text = \"Crime Dataset (used in the the aml-pipelines-with-automated-machine-learning-step.ipynb notebook)\"\n", + "\n", + "if key in ws.datasets.keys(): \n", + " found = True\n", + " dataset = ws.datasets[key] \n", + "\n", + "if not found:\n", + " # Create AML Dataset and register it into Workspace\n", + " # The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n", + " example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n", + " dataset = Dataset.Tabular.from_delimited_files(example_data)\n", + " dataset = dataset.drop_columns(['FBI Code'])\n", + " \n", + " #Register Dataset in Workspace\n", + " dataset = dataset.register(workspace=ws,\n", + " name=key,\n", + " description=description_text)\n", + "\n", + "\n", + "df = dataset.to_pandas_dataframe()\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Review the Dataset Result\n", + "\n", + "You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records for all the steps in the TabularDataset, which makes it fast even against large datasets.\n", + "\n", + "`TabularDataset` objects are composed of a list of transformation steps (optional)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.take(5).to_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train\n", + "This creates a general AutoML settings object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "automl_settings = {\n", + " \"experiment_timeout_minutes\": 20,\n", + " \"max_concurrent_iterations\": 4,\n", + " \"primary_metric\" : 'AUC_weighted'\n", + "}\n", + "automl_config = AutoMLConfig(compute_target=compute_target,\n", + " task = \"classification\",\n", + " training_data=dataset,\n", + " label_column_name=\"Primary Type\", \n", + " path = project_folder,\n", + " enable_early_stopping= True,\n", + " featurization= 'auto',\n", + " debug_log = \"automl_errors.log\",\n", + " **automl_settings\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create Pipeline and AutoMLStep\n", + "\n", + "You can define outputs for the AutoMLStep using TrainingOutput." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineData, TrainingOutput\n", + "\n", + "ds = ws.get_default_datastore()\n", + "metrics_output_name = 'metrics_output'\n", + "best_model_output_name = 'best_model_output'\n", + "\n", + "metrics_data = PipelineData(name='metrics_data',\n", + " datastore=ds,\n", + " pipeline_output_name=metrics_output_name,\n", + " training_output=TrainingOutput(type='Metrics'))\n", + "model_data = PipelineData(name='model_data',\n", + " datastore=ds,\n", + " pipeline_output_name=best_model_output_name,\n", + " training_output=TrainingOutput(type='Model'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create an AutoMLStep." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "automlstep-remarks-sample1" + ] + }, + "outputs": [], + "source": [ + "automl_step = AutoMLStep(\n", + " name='automl_module',\n", + " automl_config=automl_config,\n", + " outputs=[metrics_data, model_data],\n", + " allow_reuse=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of AutoMLStep" + "automlstep-remarks-sample2" + ] + }, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Pipeline\n", + "pipeline = Pipeline(\n", + " description=\"pipeline_with_automlstep\",\n", + " workspace=ws, \n", + " steps=[automl_step])" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run = experiment.submit(pipeline)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "#from azureml.widgets import RunDetails\n", + "#RunDetails(pipeline_run).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "#pipeline_run.wait_for_completion()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examine Results\n", + "\n", + "### Retrieve the metrics of all child runs\n", + "Outputs of above run can be used as inputs of other steps in pipeline. In this tutorial, we will examine the outputs by retrieve output data and running some tests." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "#metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n", + "#num_file_downloaded = metrics_output.download('.', show_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "#import json\n", + "#with open(metrics_output._path_on_datastore) as f:\n", + "# metrics_output_result = f.read()\n", + "# \n", + "#deserialized_metrics_output = json.loads(metrics_output_result)\n", + "#df = pd.DataFrame(deserialized_metrics_output)\n", + "#df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Retrieve the Best Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "## Retrieve best model from Pipeline Run\n", + "#best_model_output = pipeline_run.get_pipeline_output(best_model_output_name)\n", + "#num_file_downloaded = best_model_output.download('.', show_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "#import pickle\n", + "\n", + "#with open(best_model_output._path_on_datastore, \"rb\" ) as f:\n", + "# best_model = pickle.load(f)\n", + "#best_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "#best_model.steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test the Model\n", + "#### Load Test Data\n", + "For the test data, it should have the same preparation step as the train data. Otherwise it might get failed at the preprocessing step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "#dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n", + "#df_test = dataset_test.to_pandas_dataframe()\n", + "#df_test = df_test[pd.notnull(df_test['Primary Type'])]\n", + "#\n", + "#y_test = df_test['Primary Type']\n", + "#X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Testing Our Best Fitted Model\n", + "\n", + "We will use confusion matrix to see how our model works." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "#from sklearn.metrics import confusion_matrix\n", + "#ypred = best_model.predict(X_test)\n", + "#cm = confusion_matrix(y_test, ypred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "## Visualize the confusion matrix\n", + "#pd.DataFrame(cm).style.background_gradient(cmap='Blues', low=0, high=0.9)" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "anshirga" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Automated Machine Learning" + ], + "friendly_name": "How to use AutoMLStep with AML Pipelines", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "order_index": 11, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of AutoMLStep" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb index 4d7a3af51..eecde362b 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb @@ -1,345 +1,345 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to use CommandStep in Azure ML Pipelines\n", - "\n", - "This notebook shows how to use the CommandStep with Azure Machine Learning Pipelines for running R scripts in a pipeline.\n", - "\n", - "The example shows training a model in R to predict probability of fatality for vehicle crashes.\n", - "\n", - "\n", - "## Prerequisite:\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://aka.ms/pl-config) to:\n", - " * install the Azure ML SDK\n", - " * create a workspace and its configuration file (`config.json`)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's get started. First let's import some Python libraries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "# check core SDK version number\n", - "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_D2_V2` CPU VMs. This process is broken down into 3 steps:\n", - "1. create the configuration (this step is local and only takes a second)\n", - "2. create the cluster (this step will take about **20 seconds**)\n", - "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"cpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - " # can poll for a minimum number of nodes and for a specific timeout. \n", - " # if no min node count is provided it uses the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'cpu-cluster' of type `AmlCompute`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a CommandStep\n", - "CommandStep adds a step to run a command in a Pipeline. For the full set of configurable options see the CommandStep [reference docs](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.commandstep?view=azure-ml-py).\n", - "\n", - "- **name:** Name of the step\n", - "- **runconfig:** ScriptRunConfig object. You can configure a ScriptRunConfig object as you would for a standalone non-pipeline run and pass it in to this parameter. If using this option, you do not have to specify the `command`, `source_directory`, `compute_target` parameters of the CommandStep constructor as they are already defined in your ScriptRunConfig.\n", - "- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n", - "- **command:** The command to run or path of the executable/script relative to `source_directory`. It is required unless the `runconfig` parameter is specified. It can be specified with string arguments in a single string or with input/output/PipelineParameter in a list.\n", - "- **source_directory:** A folder containing the script and other resources used in the step.\n", - "- **compute_target:** Compute target to use \n", - "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n", - "- **version:** Optional version tag to denote a change in functionality for the step\n", - "\n", - "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure environment\n", - "\n", - "Configure the environment for the train step. In this example we will create an environment from the Dockerfile we have included.\n", - "\n", - "> Azure ML currently requires Python as an implicit dependency, so Python must installed in your image even if your training script does not have this dependency." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "import os\n", - "\n", - "src_dir = 'commandstep_r'\n", - "\n", - "env = Environment.from_dockerfile(name='r_env', dockerfile=os.path.join(src_dir, 'Dockerfile'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure input training dataset\n", - "\n", - "This tutorial uses data from the US National Highway Traffic Safety Administration. This dataset includes data from over 25,000 car crashes in the US, with variables you can use to predict the likelihood of a fatality. We have included an Rdata file that includes the accidents data for analysis.\n", - "\n", - "Here we use the workspace's default datastore to upload the training data file (**accidents.Rd**); in practice you can use any datastore you want." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore = ws.get_default_datastore()\n", - "data_ref = datastore.upload_files(files=[os.path.join(src_dir, 'accidents.Rd')], target_path='accidentdata')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now create a FileDataset from the data, which will be used as an input to the train step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "dataset = Dataset.File.from_files(datastore.path('accidentdata'))\n", - "dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now create a ScriptRunConfig that configures the training run. Note that in the `command` we include the input dataset for the training data.\n", - "\n", - "> For detailed guidance on how to move data in pipelines for input and output data, see the documentation [Moving data into and between ML pipelines](https://docs.microsoft.com/azure/machine-learning/how-to-move-data-in-out-of-pipelines)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "train_config = ScriptRunConfig(source_directory=src_dir,\n", - " command=['Rscript accidents.R --data_folder', dataset.as_mount(), '--output_folder outputs'],\n", - " compute_target=compute_target,\n", - " environment=env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now create a CommandStep and pass in the ScriptRunConfig object to the `runconfig` parameter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import CommandStep\n", - "\n", - "train = CommandStep(name='train', runconfig=train_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and Submit the Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "from azureml.core import Experiment\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[train])\n", - "pipeline_run = Experiment(ws, 'r-commandstep-pipeline').submit(pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to use CommandStep in Azure ML Pipelines\n", + "\n", + "This notebook shows how to use the CommandStep with Azure Machine Learning Pipelines for running R scripts in a pipeline.\n", + "\n", + "The example shows training a model in R to predict probability of fatality for vehicle crashes.\n", + "\n", + "\n", + "## Prerequisite:\n", + "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", + "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://aka.ms/pl-config) to:\n", + " * install the Azure ML SDK\n", + " * create a workspace and its configuration file (`config.json`)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get started. First let's import some Python libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "# check core SDK version number\n", + "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize workspace\n", + "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "ws = Workspace.from_config()\n", + "print('Workspace name: ' + ws.name, \n", + " 'Azure region: ' + ws.location, \n", + " 'Subscription id: ' + ws.subscription_id, \n", + " 'Resource group: ' + ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create or Attach existing AmlCompute\n", + "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_D2_V2` CPU VMs. This process is broken down into 3 steps:\n", + "1. create the configuration (this step is local and only takes a second)\n", + "2. create the cluster (this step will take about **20 seconds**)\n", + "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# choose a name for your cluster\n", + "cluster_name = \"cpu-cluster\"\n", + "\n", + "try:\n", + " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", + " print('Found existing compute target')\n", + "except ComputeTargetException:\n", + " print('Creating a new compute target...')\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', max_nodes=4)\n", + "\n", + " # create the cluster\n", + " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", + "\n", + " # can poll for a minimum number of nodes and for a specific timeout. \n", + " # if no min node count is provided it uses the scale settings for the cluster\n", + " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", + "\n", + "# use get_status() to get a detailed status for the current cluster. \n", + "print(compute_target.get_status().serialize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'cpu-cluster' of type `AmlCompute`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a CommandStep\n", + "CommandStep adds a step to run a command in a Pipeline. For the full set of configurable options see the CommandStep [reference docs](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.commandstep?view=azure-ml-py).\n", + "\n", + "- **name:** Name of the step\n", + "- **runconfig:** ScriptRunConfig object. You can configure a ScriptRunConfig object as you would for a standalone non-pipeline run and pass it in to this parameter. If using this option, you do not have to specify the `command`, `source_directory`, `compute_target` parameters of the CommandStep constructor as they are already defined in your ScriptRunConfig.\n", + "- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n", + "- **command:** The command to run or path of the executable/script relative to `source_directory`. It is required unless the `runconfig` parameter is specified. It can be specified with string arguments in a single string or with input/output/PipelineParameter in a list.\n", + "- **source_directory:** A folder containing the script and other resources used in the step.\n", + "- **compute_target:** Compute target to use \n", + "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n", + "- **version:** Optional version tag to denote a change in functionality for the step\n", + "\n", + "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure environment\n", + "\n", + "Configure the environment for the train step. In this example we will create an environment from the Dockerfile we have included.\n", + "\n", + "> Azure ML currently requires Python as an implicit dependency, so Python must installed in your image even if your training script does not have this dependency." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "import os\n", + "\n", + "src_dir = 'commandstep_r'\n", + "\n", + "env = Environment.from_dockerfile(name='r_env', dockerfile=os.path.join(src_dir, 'Dockerfile'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure input training dataset\n", + "\n", + "This tutorial uses data from the US National Highway Traffic Safety Administration. This dataset includes data from over 25,000 car crashes in the US, with variables you can use to predict the likelihood of a fatality. We have included an Rdata file that includes the accidents data for analysis.\n", + "\n", + "Here we use the workspace's default datastore to upload the training data file (**accidents.Rd**); in practice you can use any datastore you want." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "datastore = ws.get_default_datastore()\n", + "data_ref = datastore.upload_files(files=[os.path.join(src_dir, 'accidents.Rd')], target_path='accidentdata')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a FileDataset from the data, which will be used as an input to the train step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Dataset\n", + "dataset = Dataset.File.from_files(datastore.path('accidentdata'))\n", + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a ScriptRunConfig that configures the training run. Note that in the `command` we include the input dataset for the training data.\n", + "\n", + "> For detailed guidance on how to move data in pipelines for input and output data, see the documentation [Moving data into and between ML pipelines](https://docs.microsoft.com/azure/machine-learning/how-to-move-data-in-out-of-pipelines)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import ScriptRunConfig\n", + "\n", + "train_config = ScriptRunConfig(source_directory=src_dir,\n", + " command=['Rscript accidents.R --data_folder', dataset.as_mount(), '--output_folder outputs'],\n", + " compute_target=compute_target,\n", + " environment=env)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a CommandStep and pass in the ScriptRunConfig object to the `runconfig` parameter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.steps import CommandStep\n", + "\n", + "train = CommandStep(name='train', runconfig=train_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and Submit the Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Pipeline\n", + "from azureml.core import Experiment\n", + "\n", + "pipeline = Pipeline(workspace=ws, steps=[train])\n", + "pipeline_run = Experiment(ws, 'r-commandstep-pipeline').submit(pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(pipeline_run).show()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "minxia" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" ], - "metadata": { - "authors": [ - { - "name": "minxia" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Azure Machine Learning Pipeline with CommandStep for R", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "order_index": 7, - "star_tag": [ - "None" - ], - "tags": [ - "None" - ], - "task": "Demonstrates the use of CommandStep for running R scripts" + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "Azure Machine Learning Pipeline with CommandStep for R", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "order_index": 7, + "star_tag": [ + "None" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of CommandStep for running R scripts" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep.ipynb index fd719bb62..618329d7f 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep.ipynb @@ -1,308 +1,308 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to use CommandStep in Azure ML Pipelines\n", - "\n", - "This notebook shows how to use the CommandStep with Azure Machine Learning Pipelines for running commands in steps. The example shows running distributed TensorFlow training from within a pipeline.\n", - "\n", - "\n", - "## Prerequisite:\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://aka.ms/pl-config) to:\n", - " * install the Azure ML SDK\n", - " * create a workspace and its configuration file (`config.json`)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's get started. First let's import some Python libraries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "# check core SDK version number\n", - "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n", - "1. create the configuration (this step is local and only takes a second)\n", - "2. create the cluster (this step will take about **20 seconds**)\n", - "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"gpu-cluster\"\n", - "\n", - "try:\n", - " gpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', max_nodes=4)\n", - "\n", - " # create the cluster\n", - " gpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - " # can poll for a minimum number of nodes and for a specific timeout. \n", - " # if no min node count is provided it uses the scale settings for the cluster\n", - " gpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(gpu_cluster.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'gpu-cluster' of type `AmlCompute`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a CommandStep\n", - "CommandStep adds a step to run a command in a Pipeline. For the full set of configurable options see the CommandStep [reference docs](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.commandstep?view=azure-ml-py).\n", - "\n", - "- **name:** Name of the step\n", - "- **runconfig:** ScriptRunConfig object. You can configure a ScriptRunConfig object as you would for a standalone non-pipeline run and pass it in to this parameter. If using this option, you do not have to specify the `command`, `source_directory`, `compute_target` parameters of the CommandStep constructor as they are already defined in your ScriptRunConfig.\n", - "- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n", - "- **command:** The command to run or path of the executable/script relative to `source_directory`. It is required unless the `runconfig` parameter is specified. It can be specified with string arguments in a single string or with input/output/PipelineParameter in a list.\n", - "- **source_directory:** A folder containing the script and other resources used in the step.\n", - "- **compute_target:** Compute target to use \n", - "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n", - "- **version:** Optional version tag to denote a change in functionality for the step\n", - "\n", - "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First define the environment that you want to step to run in. This example users a curated TensorFlow environment, but in practice you can configure any environment you want." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.3-GPU')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example will first create a ScriptRunConfig object that configures the training job. Since we are running a distributed job, specify the `distributed_job_config` parameter. If you are just running a single-node job, omit that parameter.\n", - "\n", - "> If you have an input dataset you want to use in this step, you can specify that as part of the command. For example, if you have a FileDataset object called `dataset` and a `--data-dir` script argument, you can do the following: `command=['python train.py --epochs 30 --data-dir', dataset.as_mount()]`.\n", - "\n", - "> For detailed guidance on how to move data in pipelines for input and output data, see the documentation [Moving data into and between ML pipelines](https://docs.microsoft.com/azure/machine-learning/how-to-move-data-in-out-of-pipelines)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "from azureml.core.runconfig import MpiConfiguration\n", - "\n", - "src_dir = 'commandstep_train'\n", - "distr_config = MpiConfiguration(node_count=2) # you can also specify the process_count_per_node parameter for multi-process-per-node training\n", - "\n", - "src = ScriptRunConfig(source_directory=src_dir,\n", - " command=['python train.py --epochs 30'],\n", - " compute_target=gpu_cluster,\n", - " environment=tf_env,\n", - " distributed_job_config=distr_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now create a CommandStep and pass in the ScriptRunConfig object to the `runconfig` parameter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "estimatorstep-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import CommandStep\n", - "\n", - "train = CommandStep(name='train-mnist', runconfig=src)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and Submit the Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "from azureml.core import Experiment\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[train])\n", - "pipeline_run = Experiment(ws, 'train-commandstep-pipeline').submit(pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run).show()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Azure Machine Learning Pipeline with CommandStep", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "order_index": 7, - "star_tag": [ - "None" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to use CommandStep in Azure ML Pipelines\n", + "\n", + "This notebook shows how to use the CommandStep with Azure Machine Learning Pipelines for running commands in steps. The example shows running distributed TensorFlow training from within a pipeline.\n", + "\n", + "\n", + "## Prerequisite:\n", + "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", + "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://aka.ms/pl-config) to:\n", + " * install the Azure ML SDK\n", + " * create a workspace and its configuration file (`config.json`)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get started. First let's import some Python libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "# check core SDK version number\n", + "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize workspace\n", + "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "ws = Workspace.from_config()\n", + "print('Workspace name: ' + ws.name, \n", + " 'Azure region: ' + ws.location, \n", + " 'Subscription id: ' + ws.subscription_id, \n", + " 'Resource group: ' + ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create or Attach existing AmlCompute\n", + "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n", + "1. create the configuration (this step is local and only takes a second)\n", + "2. create the cluster (this step will take about **20 seconds**)\n", + "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# choose a name for your cluster\n", + "cluster_name = \"gpu-cluster\"\n", + "\n", + "try:\n", + " gpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n", + " print('Found existing compute target')\n", + "except ComputeTargetException:\n", + " print('Creating a new compute target...')\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', max_nodes=4)\n", + "\n", + " # create the cluster\n", + " gpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n", + "\n", + " # can poll for a minimum number of nodes and for a specific timeout. \n", + " # if no min node count is provided it uses the scale settings for the cluster\n", + " gpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", + "\n", + "# use get_status() to get a detailed status for the current cluster. \n", + "print(gpu_cluster.get_status().serialize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'gpu-cluster' of type `AmlCompute`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a CommandStep\n", + "CommandStep adds a step to run a command in a Pipeline. For the full set of configurable options see the CommandStep [reference docs](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.commandstep?view=azure-ml-py).\n", + "\n", + "- **name:** Name of the step\n", + "- **runconfig:** ScriptRunConfig object. You can configure a ScriptRunConfig object as you would for a standalone non-pipeline run and pass it in to this parameter. If using this option, you do not have to specify the `command`, `source_directory`, `compute_target` parameters of the CommandStep constructor as they are already defined in your ScriptRunConfig.\n", + "- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n", + "- **command:** The command to run or path of the executable/script relative to `source_directory`. It is required unless the `runconfig` parameter is specified. It can be specified with string arguments in a single string or with input/output/PipelineParameter in a list.\n", + "- **source_directory:** A folder containing the script and other resources used in the step.\n", + "- **compute_target:** Compute target to use \n", + "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n", + "- **version:** Optional version tag to denote a change in functionality for the step\n", + "\n", + "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First define the environment that you want to step to run in. This example users a curated TensorFlow environment, but in practice you can configure any environment you want." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "\n", + "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.3-GPU')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example will first create a ScriptRunConfig object that configures the training job. Since we are running a distributed job, specify the `distributed_job_config` parameter. If you are just running a single-node job, omit that parameter.\n", + "\n", + "> If you have an input dataset you want to use in this step, you can specify that as part of the command. For example, if you have a FileDataset object called `dataset` and a `--data-dir` script argument, you can do the following: `command=['python train.py --epochs 30 --data-dir', dataset.as_mount()]`.\n", + "\n", + "> For detailed guidance on how to move data in pipelines for input and output data, see the documentation [Moving data into and between ML pipelines](https://docs.microsoft.com/azure/machine-learning/how-to-move-data-in-out-of-pipelines)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import ScriptRunConfig\n", + "from azureml.core.runconfig import MpiConfiguration\n", + "\n", + "src_dir = 'commandstep_train'\n", + "distr_config = MpiConfiguration(node_count=2) # you can also specify the process_count_per_node parameter for multi-process-per-node training\n", + "\n", + "src = ScriptRunConfig(source_directory=src_dir,\n", + " command=['python train.py --epochs 30'],\n", + " compute_target=gpu_cluster,\n", + " environment=tf_env,\n", + " distributed_job_config=distr_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a CommandStep and pass in the ScriptRunConfig object to the `runconfig` parameter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates the use of CommandStep" + "estimatorstep-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "from azureml.pipeline.steps import CommandStep\n", + "\n", + "train = CommandStep(name='train-mnist', runconfig=src)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and Submit the Pipeline" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Pipeline\n", + "from azureml.core import Experiment\n", + "\n", + "pipeline = Pipeline(workspace=ws, steps=[train])\n", + "pipeline_run = Experiment(ws, 'train-commandstep-pipeline').submit(pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View Run Details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(pipeline_run).show()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "Azure Machine Learning Pipeline with CommandStep", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "order_index": 7, + "star_tag": [ + "None" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of CommandStep" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb index fa89e5c3b..0ef3bfcc8 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb @@ -1,575 +1,575 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Azure Machine Learning Pipelines with Data Dependency\n", - "In this notebook, we will see how we can build a pipeline with implicit data dependency." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites and Azure Machine Learning Basics\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n", - "\n", - "### Azure Machine Learning and Pipeline SDK-specific Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Experiment, Datastore\n", - "from azureml.core.compute import AmlCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "from azureml.widgets import RunDetails\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)\n", - "\n", - "from azureml.data.data_reference import DataReference\n", - "from azureml.pipeline.core import Pipeline, PipelineData\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "print(\"Pipeline SDK-specific imports completed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize Workspace\n", - "\n", - "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n", - "\n", - "# Default datastore (Azure blob storage)\n", - "# def_blob_store = ws.get_default_datastore()\n", - "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n", - "print(\"Blobstore's name: {}\".format(def_blob_store.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Source Directory\n", - "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# source directory\n", - "source_directory = 'data_dependency_run_train'\n", - " \n", - "print('Sample scripts will be created in {} directory.'.format(source_directory))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Required data and script files for the the tutorial\n", - "Sample files required to finish this tutorial are already copied to the project folder specified above. Even though the .py provided in the samples don't have much \"ML work,\" as a data scientist, you will work on this extensively as part of your work. To complete this tutorial, the contents of these files are not very important. The one-line files are for demostration purpose only." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Targets\n", - "See the list of Compute Targets on the workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cts = ws.compute_targets\n", - "for ct in cts:\n", - " print(ct)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Retrieve or create an Aml compute\n", - "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's get the default Aml Compute in the current workspace. We will then run the training script on this compute target.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "aml_compute_target = \"cpu-cluster\"\n", - "try:\n", - " aml_compute = AmlCompute(ws, aml_compute_target)\n", - " print(\"found existing compute target.\")\n", - "except ComputeTargetException:\n", - " print(\"creating new compute target\")\n", - " \n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", - " min_nodes = 1, \n", - " max_nodes = 4) \n", - " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", - " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - "print(\"Aml Compute attached\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n", - "# example: un-comment the following line.\n", - "# print(aml_compute.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Wait for this call to finish before proceeding (you will see the asterisk turning to a number).**\n", - "\n", - "Now that you have created the compute target, let's see what the workspace's compute_targets() function returns. You should now see one entry named 'amlcompute' of type AmlCompute." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building Pipeline Steps with Inputs and Outputs\n", - "As mentioned earlier, a step in the pipeline can take data as input. This data can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline.\n", - "\n", - "### Datasources\n", - "Datasource is represented by **[DataReference](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.data_reference.datareference?view=azure-ml-py)** object and points to data that lives in or is accessible from Datastore. DataReference could be a pointer to a file or a directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Reference the data uploaded to blob storage using DataReference\n", - "# Assign the datasource to blob_input_data variable\n", - "\n", - "# DataReference(datastore, \n", - "# data_reference_name=None, \n", - "# path_on_datastore=None, \n", - "# mode='mount', \n", - "# path_on_compute=None, \n", - "# overwrite=False)\n", - "\n", - "blob_input_data = DataReference(\n", - " datastore=def_blob_store,\n", - " data_reference_name=\"test_data\",\n", - " path_on_datastore=\"titanic/Titanic.csv\")\n", - "print(\"DataReference object created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Intermediate/Output Data\n", - "Intermediate data (or output of a Step) is represented by **[PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py)** object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.\n", - "\n", - "#### Constructing PipelineData\n", - "- **name:** [*Required*] Name of the data item within the pipeline graph\n", - "- **datastore_name:** Name of the Datastore to write this output to\n", - "- **output_name:** Name of the output\n", - "- **output_mode:** Specifies \"upload\" or \"mount\" modes for producing output (default: mount)\n", - "- **output_path_on_compute:** For \"upload\" mode, the path to which the module writes this output during execution\n", - "- **output_overwrite:** Flag to overwrite pre-existing data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define intermediate data using PipelineData\n", - "# Syntax\n", - "\n", - "# PipelineData(name, \n", - "# datastore=None, \n", - "# output_name=None, \n", - "# output_mode='mount', \n", - "# output_path_on_compute=None, \n", - "# output_overwrite=None, \n", - "# data_type=None, \n", - "# is_directory=None)\n", - "\n", - "# Naming the intermediate data as processed_data1 and assigning it to the variable processed_data1.\n", - "processed_data1 = PipelineData(\"processed_data1\",datastore=def_blob_store)\n", - "print(\"PipelineData object created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pipelines steps using datasources and intermediate data\n", - "Machine learning pipelines can have many steps and these steps could use or reuse datasources and intermediate data. Here's how we construct such a pipeline:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define a Step that consumes a datasource and produces intermediate data.\n", - "In this step, we define a step that consumes a datasource and produces intermediate data.\n", - "\n", - "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Specify conda dependencies and a base docker image through a RunConfiguration\n", - "\n", - "This step uses a docker image and scikit-learn, use a [**RunConfiguration**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) to specify these requirements and use when creating the PythonScriptStep. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n", - "\n", - "# create a new runconfig object\n", - "run_config = RunConfiguration()\n", - "\n", - "# enable Docker \n", - "run_config.environment.docker.enabled = True\n", - "\n", - "# set Docker base image to the default CPU-based image\n", - "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n", - "\n", - "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", - "run_config.environment.python.user_managed_dependencies = False\n", - "\n", - "# specify CondaDependencies obj\n", - "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# step4 consumes the datasource (Datareference) in the previous step\n", - "# and produces processed_data1\n", - "trainStep = PythonScriptStep(\n", - " script_name=\"train.py\", \n", - " arguments=[\"--input_data\", blob_input_data, \"--output_train\", processed_data1],\n", - " inputs=[blob_input_data],\n", - " outputs=[processed_data1],\n", - " compute_target=aml_compute, \n", - " source_directory=source_directory,\n", - " runconfig=run_config\n", - ")\n", - "print(\"trainStep created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define a Step that consumes intermediate data and produces intermediate data\n", - "In this step, we define a step that consumes an intermediate data and produces intermediate data.\n", - "\n", - "**Open `extract.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# step5 to use the intermediate data produced by step4\n", - "# This step also produces an output processed_data2\n", - "processed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\n", - "source_directory = \"data_dependency_run_extract\"\n", - "\n", - "extractStep = PythonScriptStep(\n", - " script_name=\"extract.py\",\n", - " arguments=[\"--input_extract\", processed_data1, \"--output_extract\", processed_data2],\n", - " inputs=[processed_data1],\n", - " outputs=[processed_data2],\n", - " compute_target=aml_compute, \n", - " source_directory=source_directory)\n", - "print(\"extractStep created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define a Step that consumes intermediate data and existing data and produces intermediate data\n", - "In this step, we define a step that consumes multiple data types and produces intermediate data.\n", - "\n", - "This step uses the output generated from the previous step as well as existing data on a DataStore. The location of the existing data is specified using a [**PipelineParameter**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) and a [**DataPath**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapath?view=azure-ml-py). Using a PipelineParameter enables easy modification of the data location when the Pipeline is published and resubmitted.\n", - "\n", - "**Open `compare.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Reference the data uploaded to blob storage using a PipelineParameter and a DataPath\n", - "from azureml.pipeline.core import PipelineParameter\n", - "from azureml.data.datapath import DataPath, DataPathComputeBinding\n", - "\n", - "datapath = DataPath(datastore=def_blob_store, path_on_datastore='titanic/Titanic.csv')\n", - "datapath_param = PipelineParameter(name=\"compare_data\", default_value=datapath)\n", - "data_parameter1 = (datapath_param, DataPathComputeBinding(mode='mount'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Now define the compare step which takes two inputs and produces an output\n", - "processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n", - "source_directory = \"data_dependency_run_compare\"\n", - "\n", - "compareStep = PythonScriptStep(\n", - " script_name=\"compare.py\",\n", - " arguments=[\"--compare_data1\", data_parameter1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3],\n", - " inputs=[data_parameter1, processed_data2],\n", - " outputs=[processed_data3], \n", - " compute_target=aml_compute, \n", - " source_directory=source_directory)\n", - "print(\"compareStep created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Build the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\n", - "print (\"Pipeline is built\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run1 = Experiment(ws, 'Data_dependency_sample').submit(pipeline1)\n", - "print(\"Pipeline is submitted for execution\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(pipeline_run1).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Wait for pipeline run to complete" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run1.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### See Outputs\n", - "\n", - "See where outputs of each pipeline step are located on your datastore.\n", - "\n", - "***Wait for pipeline run to complete, to make sure all the outputs are ready***" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get Steps\n", - "for step in pipeline_run1.get_steps():\n", - " print(\"Outputs of step \" + step.name)\n", - " \n", - " # Get a dictionary of StepRunOutputs with the output name as the key \n", - " output_dict = step.get_outputs()\n", - " \n", - " for name, output in output_dict.items():\n", - " \n", - " output_reference = output.get_port_data_reference() # Get output port data reference\n", - " print(\"\\tname: \" + name)\n", - " print(\"\\tdatastore: \" + output_reference.datastore_name)\n", - " print(\"\\tpath on datastore: \" + output_reference.path_on_datastore)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download Outputs\n", - "\n", - "We can download the output of any step to our local machine using the SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve the step runs by name 'train.py'\n", - "train_step = pipeline_run1.find_step_run('train.py')\n", - "\n", - "if train_step:\n", - " train_step_obj = train_step[0] # since we have only one step by name 'train.py'\n", - " train_step_obj.get_output_data('processed_data1').download(\"./outputs\") # download the output to current directory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next: Publishing the Pipeline and calling it from the REST endpoint\n", - "See this [notebook](https://aka.ms/pl-pub-rep) to understand how the pipeline is published and you can call the REST endpoint to run the pipeline." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Azure Machine Learning Pipelines with Data Dependency", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "order_index": 2, - "star_tag": [ - "featured" - ], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Azure Machine Learning Pipelines with Data Dependency\n", + "In this notebook, we will see how we can build a pipeline with implicit data dependency." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites and Azure Machine Learning Basics\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n", + "\n", + "### Azure Machine Learning and Pipeline SDK-specific Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "from azureml.core import Workspace, Experiment, Datastore\n", + "from azureml.core.compute import AmlCompute\n", + "from azureml.core.compute import ComputeTarget\n", + "from azureml.widgets import RunDetails\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)\n", + "\n", + "from azureml.data.data_reference import DataReference\n", + "from azureml.pipeline.core import Pipeline, PipelineData\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "print(\"Pipeline SDK-specific imports completed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize Workspace\n", + "\n", + "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Demonstrates how to construct a Pipeline with data dependency between steps" + "create workspace" + ] + }, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n", + "\n", + "# Default datastore (Azure blob storage)\n", + "# def_blob_store = ws.get_default_datastore()\n", + "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n", + "print(\"Blobstore's name: {}\".format(def_blob_store.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Source Directory\n", + "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# source directory\n", + "source_directory = 'data_dependency_run_train'\n", + " \n", + "print('Sample scripts will be created in {} directory.'.format(source_directory))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Required data and script files for the the tutorial\n", + "Sample files required to finish this tutorial are already copied to the project folder specified above. Even though the .py provided in the samples don't have much \"ML work,\" as a data scientist, you will work on this extensively as part of your work. To complete this tutorial, the contents of these files are not very important. The one-line files are for demostration purpose only." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Targets\n", + "See the list of Compute Targets on the workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cts = ws.compute_targets\n", + "for ct in cts:\n", + " print(ct)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Retrieve or create an Aml compute\n", + "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's get the default Aml Compute in the current workspace. We will then run the training script on this compute target.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "aml_compute_target = \"cpu-cluster\"\n", + "try:\n", + " aml_compute = AmlCompute(ws, aml_compute_target)\n", + " print(\"found existing compute target.\")\n", + "except ComputeTargetException:\n", + " print(\"creating new compute target\")\n", + " \n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n", + " min_nodes = 1, \n", + " max_nodes = 4) \n", + " aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n", + " aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", + " \n", + "print(\"Aml Compute attached\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n", + "# example: un-comment the following line.\n", + "# print(aml_compute.get_status().serialize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Wait for this call to finish before proceeding (you will see the asterisk turning to a number).**\n", + "\n", + "Now that you have created the compute target, let's see what the workspace's compute_targets() function returns. You should now see one entry named 'amlcompute' of type AmlCompute." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building Pipeline Steps with Inputs and Outputs\n", + "As mentioned earlier, a step in the pipeline can take data as input. This data can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline.\n", + "\n", + "### Datasources\n", + "Datasource is represented by **[DataReference](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.data_reference.datareference?view=azure-ml-py)** object and points to data that lives in or is accessible from Datastore. DataReference could be a pointer to a file or a directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reference the data uploaded to blob storage using DataReference\n", + "# Assign the datasource to blob_input_data variable\n", + "\n", + "# DataReference(datastore, \n", + "# data_reference_name=None, \n", + "# path_on_datastore=None, \n", + "# mode='mount', \n", + "# path_on_compute=None, \n", + "# overwrite=False)\n", + "\n", + "blob_input_data = DataReference(\n", + " datastore=def_blob_store,\n", + " data_reference_name=\"test_data\",\n", + " path_on_datastore=\"titanic/Titanic.csv\")\n", + "print(\"DataReference object created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intermediate/Output Data\n", + "Intermediate data (or output of a Step) is represented by **[PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py)** object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.\n", + "\n", + "#### Constructing PipelineData\n", + "- **name:** [*Required*] Name of the data item within the pipeline graph\n", + "- **datastore_name:** Name of the Datastore to write this output to\n", + "- **output_name:** Name of the output\n", + "- **output_mode:** Specifies \"upload\" or \"mount\" modes for producing output (default: mount)\n", + "- **output_path_on_compute:** For \"upload\" mode, the path to which the module writes this output during execution\n", + "- **output_overwrite:** Flag to overwrite pre-existing data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define intermediate data using PipelineData\n", + "# Syntax\n", + "\n", + "# PipelineData(name, \n", + "# datastore=None, \n", + "# output_name=None, \n", + "# output_mode='mount', \n", + "# output_path_on_compute=None, \n", + "# output_overwrite=None, \n", + "# data_type=None, \n", + "# is_directory=None)\n", + "\n", + "# Naming the intermediate data as processed_data1 and assigning it to the variable processed_data1.\n", + "processed_data1 = PipelineData(\"processed_data1\",datastore=def_blob_store)\n", + "print(\"PipelineData object created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pipelines steps using datasources and intermediate data\n", + "Machine learning pipelines can have many steps and these steps could use or reuse datasources and intermediate data. Here's how we construct such a pipeline:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define a Step that consumes a datasource and produces intermediate data.\n", + "In this step, we define a step that consumes a datasource and produces intermediate data.\n", + "\n", + "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Specify conda dependencies and a base docker image through a RunConfiguration\n", + "\n", + "This step uses a docker image and scikit-learn, use a [**RunConfiguration**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) to specify these requirements and use when creating the PythonScriptStep. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.runconfig import RunConfiguration\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n", + "\n", + "# create a new runconfig object\n", + "run_config = RunConfiguration()\n", + "\n", + "# enable Docker \n", + "run_config.environment.docker.enabled = True\n", + "\n", + "# set Docker base image to the default CPU-based image\n", + "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n", + "\n", + "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", + "run_config.environment.python.user_managed_dependencies = False\n", + "\n", + "# specify CondaDependencies obj\n", + "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# step4 consumes the datasource (Datareference) in the previous step\n", + "# and produces processed_data1\n", + "trainStep = PythonScriptStep(\n", + " script_name=\"train.py\", \n", + " arguments=[\"--input_data\", blob_input_data, \"--output_train\", processed_data1],\n", + " inputs=[blob_input_data],\n", + " outputs=[processed_data1],\n", + " compute_target=aml_compute, \n", + " source_directory=source_directory,\n", + " runconfig=run_config\n", + ")\n", + "print(\"trainStep created\")" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define a Step that consumes intermediate data and produces intermediate data\n", + "In this step, we define a step that consumes an intermediate data and produces intermediate data.\n", + "\n", + "**Open `extract.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# step5 to use the intermediate data produced by step4\n", + "# This step also produces an output processed_data2\n", + "processed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\n", + "source_directory = \"data_dependency_run_extract\"\n", + "\n", + "extractStep = PythonScriptStep(\n", + " script_name=\"extract.py\",\n", + " arguments=[\"--input_extract\", processed_data1, \"--output_extract\", processed_data2],\n", + " inputs=[processed_data1],\n", + " outputs=[processed_data2],\n", + " compute_target=aml_compute, \n", + " source_directory=source_directory)\n", + "print(\"extractStep created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define a Step that consumes intermediate data and existing data and produces intermediate data\n", + "In this step, we define a step that consumes multiple data types and produces intermediate data.\n", + "\n", + "This step uses the output generated from the previous step as well as existing data on a DataStore. The location of the existing data is specified using a [**PipelineParameter**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) and a [**DataPath**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapath?view=azure-ml-py). Using a PipelineParameter enables easy modification of the data location when the Pipeline is published and resubmitted.\n", + "\n", + "**Open `compare.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reference the data uploaded to blob storage using a PipelineParameter and a DataPath\n", + "from azureml.pipeline.core import PipelineParameter\n", + "from azureml.data.datapath import DataPath, DataPathComputeBinding\n", + "\n", + "datapath = DataPath(datastore=def_blob_store, path_on_datastore='titanic/Titanic.csv')\n", + "datapath_param = PipelineParameter(name=\"compare_data\", default_value=datapath)\n", + "data_parameter1 = (datapath_param, DataPathComputeBinding(mode='mount'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now define the compare step which takes two inputs and produces an output\n", + "processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n", + "source_directory = \"data_dependency_run_compare\"\n", + "\n", + "compareStep = PythonScriptStep(\n", + " script_name=\"compare.py\",\n", + " arguments=[\"--compare_data1\", data_parameter1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3],\n", + " inputs=[data_parameter1, processed_data2],\n", + " outputs=[processed_data3], \n", + " compute_target=aml_compute, \n", + " source_directory=source_directory)\n", + "print(\"compareStep created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\n", + "print (\"Pipeline is built\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run1 = Experiment(ws, 'Data_dependency_sample').submit(pipeline1)\n", + "print(\"Pipeline is submitted for execution\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RunDetails(pipeline_run1).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Wait for pipeline run to complete" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run1.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### See Outputs\n", + "\n", + "See where outputs of each pipeline step are located on your datastore.\n", + "\n", + "***Wait for pipeline run to complete, to make sure all the outputs are ready***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get Steps\n", + "for step in pipeline_run1.get_steps():\n", + " print(\"Outputs of step \" + step.name)\n", + " \n", + " # Get a dictionary of StepRunOutputs with the output name as the key \n", + " output_dict = step.get_outputs()\n", + " \n", + " for name, output in output_dict.items():\n", + " \n", + " output_reference = output.get_port_data_reference() # Get output port data reference\n", + " print(\"\\tname: \" + name)\n", + " print(\"\\tdatastore: \" + output_reference.datastore_name)\n", + " print(\"\\tpath on datastore: \" + output_reference.path_on_datastore)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download Outputs\n", + "\n", + "We can download the output of any step to our local machine using the SDK." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve the step runs by name 'train.py'\n", + "train_step = pipeline_run1.find_step_run('train.py')\n", + "\n", + "if train_step:\n", + " train_step_obj = train_step[0] # since we have only one step by name 'train.py'\n", + " train_step_obj.get_output_data('processed_data1').download(\"./outputs\") # download the output to current directory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next: Publishing the Pipeline and calling it from the REST endpoint\n", + "See this [notebook](https://aka.ms/pl-pub-rep) to understand how the pipeline is published and you can call the REST endpoint to run the pipeline." + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "Azure Machine Learning Pipelines with Data Dependency", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "order_index": 2, + "star_tag": [ + "featured" + ], + "tags": [ + "None" + ], + "task": "Demonstrates how to construct a Pipeline with data dependency between steps" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb index 2d17c662f..0a20fb71e 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb @@ -1,439 +1,441 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Azure Machine Learning Pipeline with NotebookRunnerStep\n", - "This notebook demonstrates the use of `NotebookRunnerStep`. It allows you to run a local notebook as a step in Azure Machine Learning Pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "In this example we showcase how you can run another notebook `notebook_runner/training_notebook.ipynb` as a step in Azure Machine Learning Pipeline.\n", - "\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook.\n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create an `Experiment` in an existing `Workspace`.\n", - "2. Create or Attach existing AmlCompute to a workspace.\n", - "3. Configure NotebookRun using `NotebokRunConfig`.\n", - "5. Use NotebookRunnerStep.\n", - "6. Run the notebook on `AmlCompute` as a pipeline step consuming the output of a python script step.\n", - "\n", - "Advantages of running your notebook as a step in pipeline:\n", - "1. Run your notebook like a python script without converting into .py files, leveraging complete end to end experience of Azure Machine Learning Pipelines.\n", - "2. Use pipeline intermediate data to and from the notebook along with other steps in pipeline.\n", - "3. Parameterize your notebook with [Pipeline Parameters](./aml-pipelines-publish-and-run-using-rest-endpoint.ipynb).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Azure Machine Learning and Pipeline SDK-specific imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import requests\n", - "import tempfile\n", - "\n", - "import azureml.core\n", - "\n", - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.data.data_reference import DataReference\n", - "from azureml.pipeline.core import PipelineData\n", - "from azureml.core.datastore import Datastore\n", - "\n", - "from azureml.core import Workspace, Experiment\n", - "from azureml.contrib.notebook import NotebookRunConfig, AzureMLNotebookHandler\n", - "\n", - "from azureml.pipeline.core import Pipeline\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "from azureml.contrib.notebook import NotebookRunnerStep\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize Workspace\n", - "\n", - "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n", - "ws.set_default_datastore(\"workspaceblobstore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Upload data to datastore" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# download data file from remote\n", - "response = requests.get(\"https://dprepdata.blob.core.windows.net/demo/Titanic.csv\")\n", - "titanic_file = os.path.join(tempfile.mkdtemp(), \"Titanic.csv\")\n", - "with open(titanic_file, \"w\") as f:\n", - " f.write(response.content.decode(\"utf-8\"))\n", - "Datastore.get(ws, \"workspaceblobstore\").upload_files([titanic_file], target_path=\"titanic\", overwrite=True)\n", - "print(\"Upload call completed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an Azure ML experiment\n", - "Let's create an experiment named \"notebook-step-run-example\" and a folder to holding the notebook and other scripts. The script runs will be recorded under the experiment in Azure.\n", - "\n", - "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a name for the run history container in the workspace.\n", - "experiment_name = 'notebook-step-run-example'\n", - "source_directory = 'notebook_runner'\n", - "\n", - "experiment = Experiment(ws, experiment_name)\n", - "experiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach an AmlCompute cluster\n", - "You will need to create a [compute target](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.computetarget?view=azure-ml-py) for your remote run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a name for your cluster.\n", - "amlcompute_cluster_name = \"cpu-cluster\"\n", - "\n", - "found = False\n", - "# Check if this compute target already exists in the workspace.\n", - "cts = ws.compute_targets\n", - "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n", - " found = True\n", - " print('Found existing compute target.')\n", - " compute_target = cts[amlcompute_cluster_name]\n", - " \n", - "if not found:\n", - " print('Creating a new compute target...')\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n", - " #vm_priority = 'lowpriority', # optional\n", - " max_nodes = 4)\n", - "\n", - " # Create the cluster.\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n", - " \n", - " # Can poll for a minimum number of nodes and for a specific timeout.\n", - " # If no min_node_count is provided, it will use the scale settings for the cluster.\n", - " compute_target.wait_for_completion(show_output = True, min_node_count = 1, timeout_in_minutes = 10)\n", - " \n", - " # For a more detailed view of current AmlCompute status, use get_status()." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a new RunConfig object" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "conda_run_config = RunConfiguration(framework=\"python\")\n", - "\n", - "conda_run_config.environment.docker.enabled = True\n", - "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n", - "\n", - "cd = CondaDependencies.create(pip_packages=['azureml-sdk'])\n", - "conda_run_config.environment.python.conda_dependencies = cd\n", - "\n", - "print('run config is ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define input and outputs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "input_data = DataReference(\n", - " datastore=Datastore.get(ws, \"workspaceblobstore\"),\n", - " data_reference_name=\"blob_test_data\",\n", - " path_on_datastore=\"titanic/Titanic.csv\")\n", - "\n", - "output_data = PipelineData(name=\"processed_data\",\n", - " datastore=Datastore.get(ws, \"workspaceblobstore\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create notebook run configuration and set parameters values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "handler = AzureMLNotebookHandler(timeout=600, progress_bar=False, log_output=True)\n", - "\n", - "cfg = NotebookRunConfig(source_directory=source_directory, notebook=\"training_notebook.ipynb\",\n", - " handler = handler,\n", - " parameters={\"arg1\": \"Machine Learning\"},\n", - " run_config=conda_run_config)\n", - "\n", - "print(\"Notebook Run Config is created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define PythonScriptStep" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Source directory for the step is {}.'.format(os.path.realpath('./train')))\n", - "python_script_step = PythonScriptStep(\n", - " script_name=\"train.py\",\n", - " arguments=[\"--input_data\", input_data],\n", - " inputs=[input_data],\n", - " outputs=[output_data],\n", - " compute_target=compute_target, \n", - " source_directory=\"./train\",\n", - " allow_reuse=True)\n", - "print(\"python_script_step created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define NotebookRunnerStep\n", - "\n", - "This step will consume intermediate output produced by `python_script_step` as an input.\n", - "\n", - "Optionally, a output of type `output_notebook_pipeline_data_name` can be added to the `NotebookRunnerStep` to redirect the `output_notebook` of notebook run to `NotebookRunnerStep`'s step output produced as `PipelineData` and can be further passed along the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineParameter\n", - "\n", - "output_from_notebook = PipelineData(name=\"notebook_processed_data\",\n", - " datastore=Datastore.get(ws, \"workspaceblobstore\"))\n", - "\n", - "my_pipeline_param = PipelineParameter(name=\"pipeline_param\", default_value=\"my_param\")\n", - "\n", - "print('Source directory for the step is {}.'.format(os.path.realpath(source_directory)))\n", - "notebook_runner_step = NotebookRunnerStep(name=\"training_notebook_step\",\n", - " notebook_run_config=cfg,\n", - " params={\"my_pipeline_param\": my_pipeline_param},\n", - " inputs=[output_data],\n", - " outputs=[output_from_notebook],\n", - " allow_reuse=True,\n", - " compute_target=compute_target,\n", - " output_notebook_pipeline_data_name=\"notebook_result\")\n", - "\n", - "print(\"Notebook Runner Step is Created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build Pipeline\n", - "\n", - "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py). By deafult, all these steps will run in **parallel** once we submit the pipeline for run.\n", - "\n", - "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#submit-experiment-name--pipeline-parameters-none--continue-on-step-failure-false--regenerate-outputs-false--parent-run-id-none----kwargs-). When submit is called, a [PipelineRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline1 = Pipeline(workspace=ws, steps=[notebook_runner_step])\n", - "print(\"Pipeline creation complete\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run1 = experiment.submit(pipeline1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(pipeline_run1).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download output notebook\n", - "\n", - "`output_notebook` can be retrieved via pipeline step output if `output_notebook_pipeline_data_name` is provided to the `NotebookRunnerStep`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run1.wait_for_completion()\n", - "train_step = pipeline_run1.find_step_run('training_notebook_step') # Retrieve the step runs by name `train.py`\n", - "\n", - "if train_step:\n", - " train_step_obj = train_step[0] # since we have only one step by name `training_notebook_step`\n", - " train_step_obj.get_output_data('notebook_result').download(source_directory) # download the output to source_directory" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Azure Machine Learning Pipeline with NotebookRunnerStep\n", + "This notebook demonstrates the use of `NotebookRunnerStep`. It allows you to run a local notebook as a step in Azure Machine Learning Pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "In this example we showcase how you can run another notebook `notebook_runner/training_notebook.ipynb` as a step in Azure Machine Learning Pipeline.\n", + "\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook.\n", + "\n", + "In this notebook you will learn how to:\n", + "1. Create an `Experiment` in an existing `Workspace`.\n", + "2. Create or Attach existing AmlCompute to a workspace.\n", + "3. Configure NotebookRun using `NotebokRunConfig`.\n", + "5. Use NotebookRunnerStep.\n", + "6. Run the notebook on `AmlCompute` as a pipeline step consuming the output of a python script step.\n", + "\n", + "Advantages of running your notebook as a step in pipeline:\n", + "1. Run your notebook like a python script without converting into .py files, leveraging complete end to end experience of Azure Machine Learning Pipelines.\n", + "2. Use pipeline intermediate data to and from the notebook along with other steps in pipeline.\n", + "3. Parameterize your notebook with [Pipeline Parameters](./aml-pipelines-publish-and-run-using-rest-endpoint.ipynb).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Azure Machine Learning and Pipeline SDK-specific imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "import tempfile\n", + "\n", + "import azureml.core\n", + "\n", + "from azureml.core.compute import AmlCompute, ComputeTarget\n", + "from azureml.core.runconfig import RunConfiguration\n", + "from azureml.data.data_reference import DataReference\n", + "from azureml.pipeline.core import PipelineData\n", + "from azureml.core.datastore import Datastore\n", + "\n", + "from azureml.core import Workspace, Experiment\n", + "from azureml.contrib.notebook import NotebookRunConfig, AzureMLNotebookHandler\n", + "\n", + "from azureml.pipeline.core import Pipeline\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "from azureml.contrib.notebook import NotebookRunnerStep\n", + "\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize Workspace\n", + "\n", + "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n", + "ws.set_default_datastore(\"workspaceblobstore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Upload data to datastore" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# download data file from remote\n", + "response = requests.get(\"https://dprepdata.blob.core.windows.net/demo/Titanic.csv\")\n", + "titanic_file = os.path.join(tempfile.mkdtemp(), \"Titanic.csv\")\n", + "with open(titanic_file, \"w\") as f:\n", + " f.write(response.content.decode(\"utf-8\"))\n", + "Datastore.get(ws, \"workspaceblobstore\").upload_files([titanic_file], target_path=\"titanic\", overwrite=True)\n", + "print(\"Upload call completed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an Azure ML experiment\n", + "Let's create an experiment named \"notebook-step-run-example\" and a folder to holding the notebook and other scripts. The script runs will be recorded under the experiment in Azure.\n", + "\n", + "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose a name for the run history container in the workspace.\n", + "experiment_name = 'notebook-step-run-example'\n", + "source_directory = 'notebook_runner'\n", + "\n", + "experiment = Experiment(ws, experiment_name)\n", + "experiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create or Attach an AmlCompute cluster\n", + "You will need to create a [compute target](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.computetarget?view=azure-ml-py) for your remote run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose a name for your cluster.\n", + "amlcompute_cluster_name = \"cpu-cluster\"\n", + "\n", + "found = False\n", + "# Check if this compute target already exists in the workspace.\n", + "cts = ws.compute_targets\n", + "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n", + " found = True\n", + " print('Found existing compute target.')\n", + " compute_target = cts[amlcompute_cluster_name]\n", + " \n", + "if not found:\n", + " print('Creating a new compute target...')\n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n", + " #vm_priority = 'lowpriority', # optional\n", + " max_nodes = 4)\n", + "\n", + " # Create the cluster.\n", + " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n", + " \n", + " # Can poll for a minimum number of nodes and for a specific timeout.\n", + " # If no min_node_count is provided, it will use the scale settings for the cluster.\n", + " compute_target.wait_for_completion(show_output = True, min_node_count = 1, timeout_in_minutes = 10)\n", + " \n", + " # For a more detailed view of current AmlCompute status, use get_status()." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a new RunConfig object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "conda_run_config = RunConfiguration(framework=\"python\")\n", + "\n", + "conda_run_config.environment.docker.enabled = True\n", + "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n", + "\n", + "cd = CondaDependencies.create(pip_packages=['azureml-sdk'])\n", + "conda_run_config.environment.python.conda_dependencies = cd\n", + "\n", + "print('run config is ready')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define input and outputs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_data = DataReference(\n", + " datastore=Datastore.get(ws, \"workspaceblobstore\"),\n", + " data_reference_name=\"blob_test_data\",\n", + " path_on_datastore=\"titanic/Titanic.csv\")\n", + "\n", + "output_data = PipelineData(name=\"processed_data\",\n", + " datastore=Datastore.get(ws, \"workspaceblobstore\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create notebook run configuration and set parameters values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "handler = AzureMLNotebookHandler(timeout=600, progress_bar=False, log_output=True)\n", + "\n", + "cfg = NotebookRunConfig(source_directory=source_directory, notebook=\"training_notebook.ipynb\",\n", + " handler = handler,\n", + " parameters={\"arg1\": \"Machine Learning\"},\n", + " run_config=conda_run_config)\n", + "\n", + "print(\"Notebook Run Config is created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define PythonScriptStep" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Source directory for the step is {}.'.format(os.path.realpath('./train')))\n", + "python_script_step = PythonScriptStep(\n", + " script_name=\"train.py\",\n", + " arguments=[\"--input_data\", input_data],\n", + " inputs=[input_data],\n", + " outputs=[output_data],\n", + " compute_target=compute_target, \n", + " source_directory=\"./train\",\n", + " allow_reuse=True)\n", + "print(\"python_script_step created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define NotebookRunnerStep\n", + "\n", + "This step will consume intermediate output produced by `python_script_step` as an input.\n", + "\n", + "Optionally, a output of type `output_notebook_pipeline_data_name` can be added to the `NotebookRunnerStep` to redirect the `output_notebook` of notebook run to `NotebookRunnerStep`'s step output produced as `PipelineData` and can be further passed along the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineParameter\n", + "\n", + "output_from_notebook = PipelineData(name=\"notebook_processed_data\",\n", + " datastore=Datastore.get(ws, \"workspaceblobstore\"))\n", + "\n", + "my_pipeline_param = PipelineParameter(name=\"pipeline_param\", default_value=\"my_param\")\n", + "\n", + "print('Source directory for the step is {}.'.format(os.path.realpath(source_directory)))\n", + "notebook_runner_step = NotebookRunnerStep(name=\"training_notebook_step\",\n", + " notebook_run_config=cfg,\n", + " params={\"my_pipeline_param\": my_pipeline_param},\n", + " inputs=[output_data],\n", + " outputs=[output_from_notebook],\n", + " allow_reuse=True,\n", + " compute_target=compute_target,\n", + " output_notebook_pipeline_data_name=\"notebook_result\")\n", + "\n", + "print(\"Notebook Runner Step is Created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build Pipeline\n", + "\n", + "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py). By deafult, all these steps will run in **parallel** once we submit the pipeline for run.\n", + "\n", + "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#submit-experiment-name--pipeline-parameters-none--continue-on-step-failure-false--regenerate-outputs-false--parent-run-id-none----kwargs-). When submit is called, a [PipelineRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline1 = Pipeline(workspace=ws, steps=[notebook_runner_step])\n", + "print(\"Pipeline creation complete\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run1 = experiment.submit(pipeline1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "#from azureml.widgets import RunDetails\n", + "#RunDetails(pipeline_run1).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download output notebook\n", + "\n", + "`output_notebook` can be retrieved via pipeline step output if `output_notebook_pipeline_data_name` is provided to the `NotebookRunnerStep`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "#pipeline_run1.wait_for_completion()\n", + "#train_step = pipeline_run1.find_step_run('training_notebook_step') # Retrieve the step runs by name `train.py`\n", + "#\n", + "#if train_step:\n", + "# train_step_obj = train_step[0] # since we have only one step by name `training_notebook_step`\n", + "# train_step_obj.get_output_data('notebook_result').download(source_directory) # download the output to source_directory" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "tutorial", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Custom" + ], + "deployment": [ + "None" ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "How to use run a notebook as a step in AML Pipelines", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "order_index": 12, - "star_tag": [ - "None" - ], - "tags": [ - "None" - ], - "task": "Demonstrates the use of NotebookRunnerStep" + "exclude_from_index": false, + "framework": [ + "Azure ML" + ], + "friendly_name": "How to use run a notebook as a step in AML Pipelines", + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "order_index": 12, + "star_tag": [ + "None" + ], + "tags": [ + "None" + ], + "task": "Demonstrates the use of NotebookRunnerStep" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb index db234669f..004ecca9d 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb @@ -1,106 +1,106 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"In training_notebook.ipynb\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "# declaring parameters to override\n", - "\n", - "arg1 = \"Azure\"\n", - "processed_data = None\n", - "notebook_processed_data = None\n", - "my_pipeline_param = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Final parameter values\n", - "\n", - "print(\"arg1: %s\" % arg1)\n", - "print(\"input from previous step: %s\" % processed_data)\n", - "print(\"output from notebook: %s\" % notebook_processed_data)\n", - "print(\"pipeline_parameter: %s\" % my_pipeline_param)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if not (notebook_processed_data is None):\n", - " os.makedirs(notebook_processed_data, exist_ok=True)\n", - " print(\"%s created\" % notebook_processed_data)" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"In training_notebook.ipynb\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# declaring parameters to override\n", + "\n", + "arg1 = \"Azure\"\n", + "processed_data = None\n", + "notebook_processed_data = None\n", + "my_pipeline_param = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Final parameter values\n", + "\n", + "print(\"arg1: %s\" % arg1)\n", + "print(\"input from previous step: %s\" % processed_data)\n", + "print(\"output from notebook: %s\" % notebook_processed_data)\n", + "print(\"pipeline_parameter: %s\" % my_pipeline_param)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if not (notebook_processed_data is None):\n", + " os.makedirs(notebook_processed_data, exist_ok=True)\n", + " print(\"%s created\" % notebook_processed_data)" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb b/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb index 8325b4bd1..b7a79db6c 100644 --- a/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb @@ -1,1068 +1,1068 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NYC Taxi Data Regression Model\n", - "This is an [Azure Machine Learning Pipelines](https://aka.ms/aml-pipelines) version of two-part tutorial ([Part 1](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-data-prep), [Part 2](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-auto-train-models)) available for Azure Machine Learning.\n", - "\n", - "You can combine the two part tutorial into one using AzureML Pipelines as Pipelines provide a way to stitch together various steps involved (like data preparation and training in this case) in a machine learning workflow.\n", - "\n", - "In this notebook, you learn how to prepare data for regression modeling by using open source library [pandas](https://pandas.pydata.org/). You run various transformations to filter and combine two different NYC taxi datasets. Once you prepare the NYC taxi data for regression modeling, then you will use [AutoMLStep](https://docs.microsoft.com/python/api/azureml-train-automl-runtime/azureml.train.automl.runtime.automl_step.automlstep?view=azure-ml-py) available with [Azure Machine Learning Pipelines](https://aka.ms/aml-pipelines) to define your machine learning goals and constraints as well as to launch the automated machine learning process. The automated machine learning technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.\n", - "\n", - "After you complete building the model, you can predict the cost of a taxi trip by training a model on data features. These features include the pickup day and time, the number of passengers, and the pickup location.\n", - "\n", - "## Prerequisite\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare data for regression modeling\n", - "First, we will prepare data for regression modeling. We will leverage the convenience of Azure Open Datasets along with the power of Azure Machine Learning service to create a regression model to predict NYC taxi fare prices. Perform `pip install azureml-opendatasets` to get the open dataset package. The Open Datasets package contains a class representing each data source (NycTlcGreen and NycTlcYellow) to easily filter date parameters before downloading.\n", - "\n", - "\n", - "### Load data\n", - "Begin by creating a dataframe to hold the taxi data. When working in a non-Spark environment, Open Datasets only allows downloading one month of data at a time with certain classes to avoid MemoryError with large datasets. To download a year of taxi data, iteratively fetch one month at a time, and before appending it to green_df_raw, randomly sample 500 records from each month to avoid bloating the dataframe. Then preview the data. To keep this process short, we are sampling data of only 1 month.\n", - "\n", - "Note: Open Datasets has mirroring classes for working in Spark environments where data size and memory aren't a concern." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.opendatasets import NycTlcGreen, NycTlcYellow\n", - "import pandas as pd\n", - "from datetime import datetime\n", - "from dateutil.relativedelta import relativedelta\n", - "\n", - "green_df_raw = pd.DataFrame([])\n", - "start = datetime.strptime(\"1/1/2016\",\"%m/%d/%Y\")\n", - "end = datetime.strptime(\"1/31/2016\",\"%m/%d/%Y\")\n", - "\n", - "number_of_months = 1\n", - "sample_size = 5000\n", - "\n", - "for sample_month in range(number_of_months):\n", - " temp_df_green = NycTlcGreen(start + relativedelta(months=sample_month), end + relativedelta(months=sample_month)) \\\n", - " .to_pandas_dataframe()\n", - " green_df_raw = green_df_raw.append(temp_df_green.sample(sample_size))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "yellow_df_raw = pd.DataFrame([])\n", - "start = datetime.strptime(\"1/1/2016\",\"%m/%d/%Y\")\n", - "end = datetime.strptime(\"1/31/2016\",\"%m/%d/%Y\")\n", - "\n", - "sample_size = 500\n", - "\n", - "for sample_month in range(number_of_months):\n", - " temp_df_yellow = NycTlcYellow(start + relativedelta(months=sample_month), end + relativedelta(months=sample_month)) \\\n", - " .to_pandas_dataframe()\n", - " yellow_df_raw = yellow_df_raw.append(temp_df_yellow.sample(sample_size))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### See the data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import display\n", - "\n", - "display(green_df_raw.head(5))\n", - "display(yellow_df_raw.head(5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download data locally and then upload to Azure Blob\n", - "This is a one-time process to save the dave in the default datastore. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "dataDir = \"data\"\n", - "\n", - "if not os.path.exists(dataDir):\n", - " os.mkdir(dataDir)\n", - "\n", - "greenDir = dataDir + \"/green\"\n", - "yelloDir = dataDir + \"/yellow\"\n", - "\n", - "if not os.path.exists(greenDir):\n", - " os.mkdir(greenDir)\n", - " \n", - "if not os.path.exists(yelloDir):\n", - " os.mkdir(yelloDir)\n", - " \n", - "greenTaxiData = greenDir + \"/unprepared.parquet\"\n", - "yellowTaxiData = yelloDir + \"/unprepared.parquet\"\n", - "\n", - "green_df_raw.to_csv(greenTaxiData, index=False)\n", - "yellow_df_raw.to_csv(yellowTaxiData, index=False)\n", - "\n", - "print(\"Data written to local folder.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(\"Workspace: \" + ws.name, \"Region: \" + ws.location, sep = '\\n')\n", - "\n", - "# Default datastore\n", - "default_store = ws.get_default_datastore() \n", - "\n", - "default_store.upload_files([greenTaxiData], \n", - " target_path = 'green', \n", - " overwrite = True, \n", - " show_progress = True)\n", - "\n", - "default_store.upload_files([yellowTaxiData], \n", - " target_path = 'yellow', \n", - " overwrite = True, \n", - " show_progress = True)\n", - "\n", - "print(\"Upload calls completed.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create and register datasets\n", - "\n", - "By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. You can learn more about the what subsetting capabilities are supported by referring to [our documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset?view=azure-ml-py#remarks). The data remains in its existing location, so no extra storage cost is incurred." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "green_taxi_data = Dataset.Tabular.from_delimited_files(default_store.path('green/unprepared.parquet'))\n", - "yellow_taxi_data = Dataset.Tabular.from_delimited_files(default_store.path('yellow/unprepared.parquet'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Register the taxi datasets with the workspace so that you can reuse them in other experiments or share with your colleagues who have access to your workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "green_taxi_data = green_taxi_data.register(ws, 'green_taxi_data')\n", - "yellow_taxi_data = yellow_taxi_data.register(ws, 'yellow_taxi_data')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup Compute\n", - "#### Create new or use an existing compute\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "amlcompute_cluster_name = \"cpu-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " aml_compute = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n", - " max_nodes=4)\n", - " aml_compute = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "aml_compute.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define RunConfig for the compute\n", - "We will also use `pandas`, `scikit-learn` and `automl`, `pyarrow` for the pipeline steps. Defining the `runconfig` for that." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "# Create a new runconfig object\n", - "aml_run_config = RunConfiguration()\n", - "\n", - "# Use the aml_compute you created above. \n", - "aml_run_config.target = aml_compute\n", - "\n", - "# Enable Docker\n", - "aml_run_config.environment.docker.enabled = True\n", - "\n", - "# Use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", - "aml_run_config.environment.python.user_managed_dependencies = False\n", - "\n", - "# Specify CondaDependencies obj, add necessary packages\n", - "aml_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n", - " conda_packages=['pandas','scikit-learn'], \n", - " pip_packages=['azureml-sdk[automl]', 'pyarrow'])\n", - "\n", - "print (\"Run configuration created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare data\n", - "Now we will prepare for regression modeling by using `pandas`. We run various transformations to filter and combine two different NYC taxi datasets.\n", - "\n", - "We achieve this by creating a separate step for each transformation as this allows us to reuse the steps and saves us from running all over again in case of any change. We will keep data preparation scripts in one subfolder and training scripts in another.\n", - "\n", - "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define Useful Columns\n", - "Here we are defining a set of \"useful\" columns for both Green and Yellow taxi data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "display(green_df_raw.columns)\n", - "display(yellow_df_raw.columns)\n", - "\n", - "# useful columns needed for the Azure Machine Learning NYC Taxi tutorial\n", - "useful_columns = str([\"cost\", \"distance\", \"dropoff_datetime\", \"dropoff_latitude\", \n", - " \"dropoff_longitude\", \"passengers\", \"pickup_datetime\", \n", - " \"pickup_latitude\", \"pickup_longitude\", \"store_forward\", \"vendor\"]).replace(\",\", \";\")\n", - "\n", - "print(\"Useful columns defined.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Cleanse Green taxi data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineData\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "\n", - "# python scripts folder\n", - "prepare_data_folder = './scripts/prepdata'\n", - "\n", - "# rename columns as per Azure Machine Learning NYC Taxi tutorial\n", - "green_columns = str({ \n", - " \"vendorID\": \"vendor\",\n", - " \"lpepPickupDatetime\": \"pickup_datetime\",\n", - " \"lpepDropoffDatetime\": \"dropoff_datetime\",\n", - " \"storeAndFwdFlag\": \"store_forward\",\n", - " \"pickupLongitude\": \"pickup_longitude\",\n", - " \"pickupLatitude\": \"pickup_latitude\",\n", - " \"dropoffLongitude\": \"dropoff_longitude\",\n", - " \"dropoffLatitude\": \"dropoff_latitude\",\n", - " \"passengerCount\": \"passengers\",\n", - " \"fareAmount\": \"cost\",\n", - " \"tripDistance\": \"distance\"\n", - "}).replace(\",\", \";\")\n", - "\n", - "# Define output after cleansing step\n", - "cleansed_green_data = PipelineData(\"cleansed_green_data\", datastore=default_store).as_dataset()\n", - "\n", - "print('Cleanse script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", - "\n", - "# cleansing step creation\n", - "# See the cleanse.py for details about input and output\n", - "cleansingStepGreen = PythonScriptStep(\n", - " name=\"Cleanse Green Taxi Data\",\n", - " script_name=\"cleanse.py\", \n", - " arguments=[\"--useful_columns\", useful_columns,\n", - " \"--columns\", green_columns,\n", - " \"--output_cleanse\", cleansed_green_data],\n", - " inputs=[green_taxi_data.as_named_input('raw_data')],\n", - " outputs=[cleansed_green_data],\n", - " compute_target=aml_compute,\n", - " runconfig=aml_run_config,\n", - " source_directory=prepare_data_folder,\n", - " allow_reuse=True\n", - ")\n", - "\n", - "print(\"cleansingStepGreen created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Cleanse Yellow taxi data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "yellow_columns = str({\n", - " \"vendorID\": \"vendor\",\n", - " \"tpepPickupDateTime\": \"pickup_datetime\",\n", - " \"tpepDropoffDateTime\": \"dropoff_datetime\",\n", - " \"storeAndFwdFlag\": \"store_forward\",\n", - " \"startLon\": \"pickup_longitude\",\n", - " \"startLat\": \"pickup_latitude\",\n", - " \"endLon\": \"dropoff_longitude\",\n", - " \"endLat\": \"dropoff_latitude\",\n", - " \"passengerCount\": \"passengers\",\n", - " \"fareAmount\": \"cost\",\n", - " \"tripDistance\": \"distance\"\n", - "}).replace(\",\", \";\")\n", - "\n", - "# Define output after cleansing step\n", - "cleansed_yellow_data = PipelineData(\"cleansed_yellow_data\", datastore=default_store).as_dataset()\n", - "\n", - "print('Cleanse script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", - "\n", - "# cleansing step creation\n", - "# See the cleanse.py for details about input and output\n", - "cleansingStepYellow = PythonScriptStep(\n", - " name=\"Cleanse Yellow Taxi Data\",\n", - " script_name=\"cleanse.py\", \n", - " arguments=[\"--useful_columns\", useful_columns,\n", - " \"--columns\", yellow_columns,\n", - " \"--output_cleanse\", cleansed_yellow_data],\n", - " inputs=[yellow_taxi_data.as_named_input('raw_data')],\n", - " outputs=[cleansed_yellow_data],\n", - " compute_target=aml_compute,\n", - " runconfig=aml_run_config,\n", - " source_directory=prepare_data_folder,\n", - " allow_reuse=True\n", - ")\n", - "\n", - "print(\"cleansingStepYellow created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Merge cleansed Green and Yellow datasets\n", - "We are creating a single data source by merging the cleansed versions of Green and Yellow taxi data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define output after merging step\n", - "merged_data = PipelineData(\"merged_data\", datastore=default_store).as_dataset()\n", - "\n", - "print('Merge script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", - "\n", - "# merging step creation\n", - "# See the merge.py for details about input and output\n", - "mergingStep = PythonScriptStep(\n", - " name=\"Merge Taxi Data\",\n", - " script_name=\"merge.py\", \n", - " arguments=[\"--output_merge\", merged_data],\n", - " inputs=[cleansed_green_data.parse_parquet_files(),\n", - " cleansed_yellow_data.parse_parquet_files()],\n", - " outputs=[merged_data],\n", - " compute_target=aml_compute,\n", - " runconfig=aml_run_config,\n", - " source_directory=prepare_data_folder,\n", - " allow_reuse=True\n", - ")\n", - "\n", - "print(\"mergingStep created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Filter data\n", - "This step filters out coordinates for locations that are outside the city border. We use a TypeConverter object to change the latitude and longitude fields to decimal type. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define output after merging step\n", - "filtered_data = PipelineData(\"filtered_data\", datastore=default_store).as_dataset()\n", - "\n", - "print('Filter script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", - "\n", - "# filter step creation\n", - "# See the filter.py for details about input and output\n", - "filterStep = PythonScriptStep(\n", - " name=\"Filter Taxi Data\",\n", - " script_name=\"filter.py\", \n", - " arguments=[\"--output_filter\", filtered_data],\n", - " inputs=[merged_data.parse_parquet_files()],\n", - " outputs=[filtered_data],\n", - " compute_target=aml_compute,\n", - " runconfig = aml_run_config,\n", - " source_directory=prepare_data_folder,\n", - " allow_reuse=True\n", - ")\n", - "\n", - "print(\"FilterStep created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Normalize data\n", - "In this step, we split the pickup and dropoff datetime values into the respective date and time columns and then we rename the columns to use meaningful names." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define output after normalize step\n", - "normalized_data = PipelineData(\"normalized_data\", datastore=default_store).as_dataset()\n", - "\n", - "print('Normalize script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", - "\n", - "# normalize step creation\n", - "# See the normalize.py for details about input and output\n", - "normalizeStep = PythonScriptStep(\n", - " name=\"Normalize Taxi Data\",\n", - " script_name=\"normalize.py\", \n", - " arguments=[\"--output_normalize\", normalized_data],\n", - " inputs=[filtered_data.parse_parquet_files()],\n", - " outputs=[normalized_data],\n", - " compute_target=aml_compute,\n", - " runconfig = aml_run_config,\n", - " source_directory=prepare_data_folder,\n", - " allow_reuse=True\n", - ")\n", - "\n", - "print(\"normalizeStep created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Transform data\n", - "Transform the normalized taxi data to final required format. This steps does the following:\n", - "\n", - "- Split the pickup and dropoff date further into the day of the week, day of the month, and month values. \n", - "- To get the day of the week value, uses the derive_column_by_example() function. The function takes an array parameter of example objects that define the input data, and the preferred output. The function automatically determines the preferred transformation. For the pickup and dropoff time columns, split the time into the hour, minute, and second by using the split_column_by_example() function with no example parameter.\n", - "- After new features are generated, use the drop_columns() function to delete the original fields as the newly generated features are preferred. \n", - "- Rename the rest of the fields to use meaningful descriptions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define output after transform step\n", - "transformed_data = PipelineData(\"transformed_data\", datastore=default_store).as_dataset()\n", - "\n", - "print('Transform script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", - "\n", - "# transform step creation\n", - "# See the transform.py for details about input and output\n", - "transformStep = PythonScriptStep(\n", - " name=\"Transform Taxi Data\",\n", - " script_name=\"transform.py\", \n", - " arguments=[\"--output_transform\", transformed_data],\n", - " inputs=[normalized_data.parse_parquet_files()],\n", - " outputs=[transformed_data],\n", - " compute_target=aml_compute,\n", - " runconfig = aml_run_config,\n", - " source_directory=prepare_data_folder,\n", - " allow_reuse=True\n", - ")\n", - "\n", - "print(\"transformStep created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Split the data into train and test sets\n", - "This function segregates the data into dataset for model training and dataset for testing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_model_folder = './scripts/trainmodel'\n", - "\n", - "# train and test splits output\n", - "output_split_train = PipelineData(\"output_split_train\", datastore=default_store).as_dataset()\n", - "output_split_test = PipelineData(\"output_split_test\", datastore=default_store).as_dataset()\n", - "\n", - "print('Data spilt script is in {}.'.format(os.path.realpath(train_model_folder)))\n", - "\n", - "# test train split step creation\n", - "# See the train_test_split.py for details about input and output\n", - "testTrainSplitStep = PythonScriptStep(\n", - " name=\"Train Test Data Split\",\n", - " script_name=\"train_test_split.py\", \n", - " arguments=[\"--output_split_train\", output_split_train,\n", - " \"--output_split_test\", output_split_test],\n", - " inputs=[transformed_data.parse_parquet_files()],\n", - " outputs=[output_split_train, output_split_test],\n", - " compute_target=aml_compute,\n", - " runconfig = aml_run_config,\n", - " source_directory=train_model_folder,\n", - " allow_reuse=True\n", - ")\n", - "\n", - "print(\"testTrainSplitStep created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use automated machine learning to build regression model\n", - "Now we will use **automated machine learning** to build the regression model. We will use [AutoMLStep](https://docs.microsoft.com/python/api/azureml-train-automl-runtime/azureml.train.automl.runtime.automl_step.automlstep?view=azure-ml-py) in AML Pipelines for this part. Perform `pip install azureml-sdk[automl]`to get the automated machine learning package. These functions use various features from the data set and allow an automated model to build relationships between the features and the price of a taxi trip." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Automatically train a model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment = Experiment(ws, 'NYCTaxi_Tutorial_Pipelines')\n", - "\n", - "print(\"Experiment created\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define settings for autogeneration and tuning\n", - "\n", - "Here we define the experiment parameter and model settings for autogeneration and tuning. We can specify automl_settings as **kwargs as well.\n", - "\n", - "Use your defined training settings as a parameter to an `AutoMLConfig` object. Additionally, specify your training data and the type of model, which is `regression` in this case.\n", - "\n", - "Note: When using AmlCompute, we can't pass Numpy arrays directly to the fit method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.automl import AutoMLConfig\n", - "\n", - "# Change iterations to a reasonable number (50) to get better accuracy\n", - "automl_settings = {\n", - " \"iteration_timeout_minutes\" : 10,\n", - " \"iterations\" : 2,\n", - " \"primary_metric\" : 'spearman_correlation',\n", - " \"n_cross_validations\": 5\n", - "}\n", - "\n", - "training_dataset = output_split_train.parse_parquet_files().keep_columns(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor', 'cost'])\n", - "\n", - "automl_config = AutoMLConfig(task = 'regression',\n", - " debug_log = 'automated_ml_errors.log',\n", - " path = train_model_folder,\n", - " compute_target = aml_compute,\n", - " featurization = 'auto',\n", - " training_data = training_dataset,\n", - " label_column_name = 'cost',\n", - " **automl_settings)\n", - " \n", - "print(\"AutoML config created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define AutoMLStep" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import AutoMLStep\n", - "\n", - "trainWithAutomlStep = AutoMLStep(name='AutoML_Regression',\n", - " automl_config=automl_config,\n", - " allow_reuse=True)\n", - "print(\"trainWithAutomlStep created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Build and run the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline\n", - "from azureml.widgets import RunDetails\n", - "\n", - "pipeline_steps = [trainWithAutomlStep]\n", - "\n", - "pipeline = Pipeline(workspace = ws, steps=pipeline_steps)\n", - "print(\"Pipeline is built.\")\n", - "\n", - "pipeline_run = experiment.submit(pipeline, regenerate_outputs=False)\n", - "\n", - "print(\"Pipeline submitted for execution.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(pipeline_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Explore the results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Before we proceed we need to wait for the run to complete.\n", - "pipeline_run.wait_for_completion(show_output=False)\n", - "\n", - "# functions to download output to local and fetch as dataframe\n", - "def get_download_path(download_path, output_name):\n", - " output_folder = os.listdir(download_path + '/azureml')[0]\n", - " path = download_path + '/azureml/' + output_folder + '/' + output_name\n", - " return path\n", - "\n", - "def fetch_df(current_step, output_name):\n", - " output_data = current_step.get_output_data(output_name) \n", - " download_path = './outputs/' + output_name\n", - " output_data.download(download_path, overwrite=True)\n", - " df_path = get_download_path(download_path, output_name) + '/processed.parquet'\n", - " return pd.read_parquet(df_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View cleansed taxi data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "green_cleanse_step = pipeline_run.find_step_run(cleansingStepGreen.name)[0]\n", - "yellow_cleanse_step = pipeline_run.find_step_run(cleansingStepYellow.name)[0]\n", - "\n", - "cleansed_green_df = fetch_df(green_cleanse_step, cleansed_green_data.name)\n", - "cleansed_yellow_df = fetch_df(yellow_cleanse_step, cleansed_yellow_data.name)\n", - "\n", - "display(cleansed_green_df.head(5))\n", - "display(cleansed_yellow_df.head(5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View the combined taxi data profile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "merge_step = pipeline_run.find_step_run(mergingStep.name)[0]\n", - "combined_df = fetch_df(merge_step, merged_data.name)\n", - "\n", - "display(combined_df.describe())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View the filtered taxi data profile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "filter_step = pipeline_run.find_step_run(filterStep.name)[0]\n", - "filtered_df = fetch_df(filter_step, filtered_data.name)\n", - "\n", - "display(filtered_df.describe())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View normalized taxi data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normalize_step = pipeline_run.find_step_run(normalizeStep.name)[0]\n", - "normalized_df = fetch_df(normalize_step, normalized_data.name)\n", - "\n", - "display(normalized_df.head(5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View transformed taxi data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "transform_step = pipeline_run.find_step_run(transformStep.name)[0]\n", - "transformed_df = fetch_df(transform_step, transformed_data.name)\n", - "\n", - "display(transformed_df.describe())\n", - "display(transformed_df.head(5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View training data used by AutoML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "split_step = pipeline_run.find_step_run(testTrainSplitStep.name)[0]\n", - "train_split = fetch_df(split_step, output_split_train.name)\n", - "\n", - "display(train_split.describe())\n", - "display(train_split.head(5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View the details of the AutoML run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.automl.run import AutoMLRun\n", - "#from azureml.widgets import RunDetails\n", - "\n", - "# workaround to get the automl run as its the last step in the pipeline \n", - "# and get_steps() returns the steps from latest to first\n", - "\n", - "for step in pipeline_run.get_steps():\n", - " automl_step_run_id = step.id\n", - " print(step.name)\n", - " print(automl_step_run_id)\n", - " break\n", - "\n", - "automl_run = AutoMLRun(experiment = experiment, run_id=automl_step_run_id)\n", - "#RunDetails(automl_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retreive the best model\n", - "\n", - "Uncomment the below cell to retrieve the best model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# best_run, fitted_model = automl_run.get_output()\n", - "# print(best_run)\n", - "# print(fitted_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get test data\n", - "\n", - "Uncomment the below cell to get test data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# split_step = pipeline_run.find_step_run(testTrainSplitStep.name)[0]\n", - "\n", - "# x_test = fetch_df(split_step, output_split_test.name)[['distance','passengers', 'vendor','pickup_weekday','pickup_hour']]\n", - "# y_test = fetch_df(split_step, output_split_test.name)[['cost']]\n", - "\n", - "# display(x_test.head(5))\n", - "# display(y_test.head(5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Test the best fitted model\n", - "\n", - "Uncomment the below cell to test the best fitted model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# y_predict = fitted_model.predict(x_test)\n", - "\n", - "# y_actual = y_test.values.tolist()\n", - "\n", - "# display(pd.DataFrame({'Actual':y_actual, 'Predicted':y_predict}).head(5))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import matplotlib.pyplot as plt\n", - "\n", - "# fig = plt.figure(figsize=(14, 10))\n", - "# ax1 = fig.add_subplot(111)\n", - "\n", - "# distance_vals = [x[0] for x in x_test.values]\n", - "\n", - "# ax1.scatter(distance_vals[:100], y_predict[:100], s=18, c='b', marker=\"s\", label='Predicted')\n", - "# ax1.scatter(distance_vals[:100], y_actual[:100], s=18, c='r', marker=\"o\", label='Actual')\n", - "\n", - "# ax1.set_xlabel('distance (mi)')\n", - "# ax1.set_title('Predicted and Actual Cost/Distance')\n", - "# ax1.set_ylabel('Cost ($)')\n", - "\n", - "# plt.legend(loc='upper left', prop={'size': 12})\n", - "# plt.rcParams.update({'font.size': 14})\n", - "# plt.show()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NYC Taxi Data Regression Model\n", + "This is an [Azure Machine Learning Pipelines](https://aka.ms/aml-pipelines) version of two-part tutorial ([Part 1](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-data-prep), [Part 2](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-auto-train-models)) available for Azure Machine Learning.\n", + "\n", + "You can combine the two part tutorial into one using AzureML Pipelines as Pipelines provide a way to stitch together various steps involved (like data preparation and training in this case) in a machine learning workflow.\n", + "\n", + "In this notebook, you learn how to prepare data for regression modeling by using open source library [pandas](https://pandas.pydata.org/). You run various transformations to filter and combine two different NYC taxi datasets. Once you prepare the NYC taxi data for regression modeling, then you will use [AutoMLStep](https://docs.microsoft.com/python/api/azureml-train-automl-runtime/azureml.train.automl.runtime.automl_step.automlstep?view=azure-ml-py) available with [Azure Machine Learning Pipelines](https://aka.ms/aml-pipelines) to define your machine learning goals and constraints as well as to launch the automated machine learning process. The automated machine learning technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.\n", + "\n", + "After you complete building the model, you can predict the cost of a taxi trip by training a model on data features. These features include the pickup day and time, the number of passengers, and the pickup location.\n", + "\n", + "## Prerequisite\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare data for regression modeling\n", + "First, we will prepare data for regression modeling. We will leverage the convenience of Azure Open Datasets along with the power of Azure Machine Learning service to create a regression model to predict NYC taxi fare prices. Perform `pip install azureml-opendatasets` to get the open dataset package. The Open Datasets package contains a class representing each data source (NycTlcGreen and NycTlcYellow) to easily filter date parameters before downloading.\n", + "\n", + "\n", + "### Load data\n", + "Begin by creating a dataframe to hold the taxi data. When working in a non-Spark environment, Open Datasets only allows downloading one month of data at a time with certain classes to avoid MemoryError with large datasets. To download a year of taxi data, iteratively fetch one month at a time, and before appending it to green_df_raw, randomly sample 500 records from each month to avoid bloating the dataframe. Then preview the data. To keep this process short, we are sampling data of only 1 month.\n", + "\n", + "Note: Open Datasets has mirroring classes for working in Spark environments where data size and memory aren't a concern." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "# Check core SDK version number\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.opendatasets import NycTlcGreen, NycTlcYellow\n", + "import pandas as pd\n", + "from datetime import datetime\n", + "from dateutil.relativedelta import relativedelta\n", + "\n", + "green_df_raw = pd.DataFrame([])\n", + "start = datetime.strptime(\"1/1/2016\",\"%m/%d/%Y\")\n", + "end = datetime.strptime(\"1/31/2016\",\"%m/%d/%Y\")\n", + "\n", + "number_of_months = 1\n", + "sample_size = 5000\n", + "\n", + "for sample_month in range(number_of_months):\n", + " temp_df_green = NycTlcGreen(start + relativedelta(months=sample_month), end + relativedelta(months=sample_month)) \\\n", + " .to_pandas_dataframe()\n", + " green_df_raw = green_df_raw.append(temp_df_green.sample(sample_size))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yellow_df_raw = pd.DataFrame([])\n", + "start = datetime.strptime(\"1/1/2016\",\"%m/%d/%Y\")\n", + "end = datetime.strptime(\"1/31/2016\",\"%m/%d/%Y\")\n", + "\n", + "sample_size = 500\n", + "\n", + "for sample_month in range(number_of_months):\n", + " temp_df_yellow = NycTlcYellow(start + relativedelta(months=sample_month), end + relativedelta(months=sample_month)) \\\n", + " .to_pandas_dataframe()\n", + " yellow_df_raw = yellow_df_raw.append(temp_df_yellow.sample(sample_size))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### See the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display\n", + "\n", + "display(green_df_raw.head(5))\n", + "display(yellow_df_raw.head(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download data locally and then upload to Azure Blob\n", + "This is a one-time process to save the dave in the default datastore. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "dataDir = \"data\"\n", + "\n", + "if not os.path.exists(dataDir):\n", + " os.mkdir(dataDir)\n", + "\n", + "greenDir = dataDir + \"/green\"\n", + "yelloDir = dataDir + \"/yellow\"\n", + "\n", + "if not os.path.exists(greenDir):\n", + " os.mkdir(greenDir)\n", + " \n", + "if not os.path.exists(yelloDir):\n", + " os.mkdir(yelloDir)\n", + " \n", + "greenTaxiData = greenDir + \"/unprepared.parquet\"\n", + "yellowTaxiData = yelloDir + \"/unprepared.parquet\"\n", + "\n", + "green_df_raw.to_csv(greenTaxiData, index=False)\n", + "yellow_df_raw.to_csv(yellowTaxiData, index=False)\n", + "\n", + "print(\"Data written to local folder.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(\"Workspace: \" + ws.name, \"Region: \" + ws.location, sep = '\\n')\n", + "\n", + "# Default datastore\n", + "default_store = ws.get_default_datastore() \n", + "\n", + "default_store.upload_files([greenTaxiData], \n", + " target_path = 'green', \n", + " overwrite = True, \n", + " show_progress = True)\n", + "\n", + "default_store.upload_files([yellowTaxiData], \n", + " target_path = 'yellow', \n", + " overwrite = True, \n", + " show_progress = True)\n", + "\n", + "print(\"Upload calls completed.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create and register datasets\n", + "\n", + "By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. You can learn more about the what subsetting capabilities are supported by referring to [our documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset?view=azure-ml-py#remarks). The data remains in its existing location, so no extra storage cost is incurred." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Dataset\n", + "green_taxi_data = Dataset.Tabular.from_delimited_files(default_store.path('green/unprepared.parquet'))\n", + "yellow_taxi_data = Dataset.Tabular.from_delimited_files(default_store.path('yellow/unprepared.parquet'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Register the taxi datasets with the workspace so that you can reuse them in other experiments or share with your colleagues who have access to your workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "green_taxi_data = green_taxi_data.register(ws, 'green_taxi_data')\n", + "yellow_taxi_data = yellow_taxi_data.register(ws, 'yellow_taxi_data')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup Compute\n", + "#### Create new or use an existing compute\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your CPU cluster\n", + "amlcompute_cluster_name = \"cpu-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " aml_compute = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", + " print('Found existing cluster, use it.')\n", + "except ComputeTargetException:\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n", + " max_nodes=4)\n", + " aml_compute = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", + "\n", + "aml_compute.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define RunConfig for the compute\n", + "We will also use `pandas`, `scikit-learn` and `automl`, `pyarrow` for the pipeline steps. Defining the `runconfig` for that." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.runconfig import RunConfiguration\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "# Create a new runconfig object\n", + "aml_run_config = RunConfiguration()\n", + "\n", + "# Use the aml_compute you created above. \n", + "aml_run_config.target = aml_compute\n", + "\n", + "# Enable Docker\n", + "aml_run_config.environment.docker.enabled = True\n", + "\n", + "# Use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", + "aml_run_config.environment.python.user_managed_dependencies = False\n", + "\n", + "# Specify CondaDependencies obj, add necessary packages\n", + "aml_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n", + " conda_packages=['pandas','scikit-learn'], \n", + " pip_packages=['azureml-sdk[automl]', 'pyarrow'])\n", + "\n", + "print (\"Run configuration created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare data\n", + "Now we will prepare for regression modeling by using `pandas`. We run various transformations to filter and combine two different NYC taxi datasets.\n", + "\n", + "We achieve this by creating a separate step for each transformation as this allows us to reuse the steps and saves us from running all over again in case of any change. We will keep data preparation scripts in one subfolder and training scripts in another.\n", + "\n", + "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define Useful Columns\n", + "Here we are defining a set of \"useful\" columns for both Green and Yellow taxi data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(green_df_raw.columns)\n", + "display(yellow_df_raw.columns)\n", + "\n", + "# useful columns needed for the Azure Machine Learning NYC Taxi tutorial\n", + "useful_columns = str([\"cost\", \"distance\", \"dropoff_datetime\", \"dropoff_latitude\", \n", + " \"dropoff_longitude\", \"passengers\", \"pickup_datetime\", \n", + " \"pickup_latitude\", \"pickup_longitude\", \"store_forward\", \"vendor\"]).replace(\",\", \";\")\n", + "\n", + "print(\"Useful columns defined.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Cleanse Green taxi data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineData\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "\n", + "# python scripts folder\n", + "prepare_data_folder = './scripts/prepdata'\n", + "\n", + "# rename columns as per Azure Machine Learning NYC Taxi tutorial\n", + "green_columns = str({ \n", + " \"vendorID\": \"vendor\",\n", + " \"lpepPickupDatetime\": \"pickup_datetime\",\n", + " \"lpepDropoffDatetime\": \"dropoff_datetime\",\n", + " \"storeAndFwdFlag\": \"store_forward\",\n", + " \"pickupLongitude\": \"pickup_longitude\",\n", + " \"pickupLatitude\": \"pickup_latitude\",\n", + " \"dropoffLongitude\": \"dropoff_longitude\",\n", + " \"dropoffLatitude\": \"dropoff_latitude\",\n", + " \"passengerCount\": \"passengers\",\n", + " \"fareAmount\": \"cost\",\n", + " \"tripDistance\": \"distance\"\n", + "}).replace(\",\", \";\")\n", + "\n", + "# Define output after cleansing step\n", + "cleansed_green_data = PipelineData(\"cleansed_green_data\", datastore=default_store).as_dataset()\n", + "\n", + "print('Cleanse script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", + "\n", + "# cleansing step creation\n", + "# See the cleanse.py for details about input and output\n", + "cleansingStepGreen = PythonScriptStep(\n", + " name=\"Cleanse Green Taxi Data\",\n", + " script_name=\"cleanse.py\", \n", + " arguments=[\"--useful_columns\", useful_columns,\n", + " \"--columns\", green_columns,\n", + " \"--output_cleanse\", cleansed_green_data],\n", + " inputs=[green_taxi_data.as_named_input('raw_data')],\n", + " outputs=[cleansed_green_data],\n", + " compute_target=aml_compute,\n", + " runconfig=aml_run_config,\n", + " source_directory=prepare_data_folder,\n", + " allow_reuse=True\n", + ")\n", + "\n", + "print(\"cleansingStepGreen created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Cleanse Yellow taxi data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yellow_columns = str({\n", + " \"vendorID\": \"vendor\",\n", + " \"tpepPickupDateTime\": \"pickup_datetime\",\n", + " \"tpepDropoffDateTime\": \"dropoff_datetime\",\n", + " \"storeAndFwdFlag\": \"store_forward\",\n", + " \"startLon\": \"pickup_longitude\",\n", + " \"startLat\": \"pickup_latitude\",\n", + " \"endLon\": \"dropoff_longitude\",\n", + " \"endLat\": \"dropoff_latitude\",\n", + " \"passengerCount\": \"passengers\",\n", + " \"fareAmount\": \"cost\",\n", + " \"tripDistance\": \"distance\"\n", + "}).replace(\",\", \";\")\n", + "\n", + "# Define output after cleansing step\n", + "cleansed_yellow_data = PipelineData(\"cleansed_yellow_data\", datastore=default_store).as_dataset()\n", + "\n", + "print('Cleanse script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", + "\n", + "# cleansing step creation\n", + "# See the cleanse.py for details about input and output\n", + "cleansingStepYellow = PythonScriptStep(\n", + " name=\"Cleanse Yellow Taxi Data\",\n", + " script_name=\"cleanse.py\", \n", + " arguments=[\"--useful_columns\", useful_columns,\n", + " \"--columns\", yellow_columns,\n", + " \"--output_cleanse\", cleansed_yellow_data],\n", + " inputs=[yellow_taxi_data.as_named_input('raw_data')],\n", + " outputs=[cleansed_yellow_data],\n", + " compute_target=aml_compute,\n", + " runconfig=aml_run_config,\n", + " source_directory=prepare_data_folder,\n", + " allow_reuse=True\n", + ")\n", + "\n", + "print(\"cleansingStepYellow created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Merge cleansed Green and Yellow datasets\n", + "We are creating a single data source by merging the cleansed versions of Green and Yellow taxi data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define output after merging step\n", + "merged_data = PipelineData(\"merged_data\", datastore=default_store).as_dataset()\n", + "\n", + "print('Merge script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", + "\n", + "# merging step creation\n", + "# See the merge.py for details about input and output\n", + "mergingStep = PythonScriptStep(\n", + " name=\"Merge Taxi Data\",\n", + " script_name=\"merge.py\", \n", + " arguments=[\"--output_merge\", merged_data],\n", + " inputs=[cleansed_green_data.parse_parquet_files(),\n", + " cleansed_yellow_data.parse_parquet_files()],\n", + " outputs=[merged_data],\n", + " compute_target=aml_compute,\n", + " runconfig=aml_run_config,\n", + " source_directory=prepare_data_folder,\n", + " allow_reuse=True\n", + ")\n", + "\n", + "print(\"mergingStep created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Filter data\n", + "This step filters out coordinates for locations that are outside the city border. We use a TypeConverter object to change the latitude and longitude fields to decimal type. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define output after merging step\n", + "filtered_data = PipelineData(\"filtered_data\", datastore=default_store).as_dataset()\n", + "\n", + "print('Filter script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", + "\n", + "# filter step creation\n", + "# See the filter.py for details about input and output\n", + "filterStep = PythonScriptStep(\n", + " name=\"Filter Taxi Data\",\n", + " script_name=\"filter.py\", \n", + " arguments=[\"--output_filter\", filtered_data],\n", + " inputs=[merged_data.parse_parquet_files()],\n", + " outputs=[filtered_data],\n", + " compute_target=aml_compute,\n", + " runconfig = aml_run_config,\n", + " source_directory=prepare_data_folder,\n", + " allow_reuse=True\n", + ")\n", + "\n", + "print(\"FilterStep created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Normalize data\n", + "In this step, we split the pickup and dropoff datetime values into the respective date and time columns and then we rename the columns to use meaningful names." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define output after normalize step\n", + "normalized_data = PipelineData(\"normalized_data\", datastore=default_store).as_dataset()\n", + "\n", + "print('Normalize script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", + "\n", + "# normalize step creation\n", + "# See the normalize.py for details about input and output\n", + "normalizeStep = PythonScriptStep(\n", + " name=\"Normalize Taxi Data\",\n", + " script_name=\"normalize.py\", \n", + " arguments=[\"--output_normalize\", normalized_data],\n", + " inputs=[filtered_data.parse_parquet_files()],\n", + " outputs=[normalized_data],\n", + " compute_target=aml_compute,\n", + " runconfig = aml_run_config,\n", + " source_directory=prepare_data_folder,\n", + " allow_reuse=True\n", + ")\n", + "\n", + "print(\"normalizeStep created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Transform data\n", + "Transform the normalized taxi data to final required format. This steps does the following:\n", + "\n", + "- Split the pickup and dropoff date further into the day of the week, day of the month, and month values. \n", + "- To get the day of the week value, uses the derive_column_by_example() function. The function takes an array parameter of example objects that define the input data, and the preferred output. The function automatically determines the preferred transformation. For the pickup and dropoff time columns, split the time into the hour, minute, and second by using the split_column_by_example() function with no example parameter.\n", + "- After new features are generated, use the drop_columns() function to delete the original fields as the newly generated features are preferred. \n", + "- Rename the rest of the fields to use meaningful descriptions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define output after transform step\n", + "transformed_data = PipelineData(\"transformed_data\", datastore=default_store).as_dataset()\n", + "\n", + "print('Transform script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n", + "\n", + "# transform step creation\n", + "# See the transform.py for details about input and output\n", + "transformStep = PythonScriptStep(\n", + " name=\"Transform Taxi Data\",\n", + " script_name=\"transform.py\", \n", + " arguments=[\"--output_transform\", transformed_data],\n", + " inputs=[normalized_data.parse_parquet_files()],\n", + " outputs=[transformed_data],\n", + " compute_target=aml_compute,\n", + " runconfig = aml_run_config,\n", + " source_directory=prepare_data_folder,\n", + " allow_reuse=True\n", + ")\n", + "\n", + "print(\"transformStep created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Split the data into train and test sets\n", + "This function segregates the data into dataset for model training and dataset for testing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_model_folder = './scripts/trainmodel'\n", + "\n", + "# train and test splits output\n", + "output_split_train = PipelineData(\"output_split_train\", datastore=default_store).as_dataset()\n", + "output_split_test = PipelineData(\"output_split_test\", datastore=default_store).as_dataset()\n", + "\n", + "print('Data spilt script is in {}.'.format(os.path.realpath(train_model_folder)))\n", + "\n", + "# test train split step creation\n", + "# See the train_test_split.py for details about input and output\n", + "testTrainSplitStep = PythonScriptStep(\n", + " name=\"Train Test Data Split\",\n", + " script_name=\"train_test_split.py\", \n", + " arguments=[\"--output_split_train\", output_split_train,\n", + " \"--output_split_test\", output_split_test],\n", + " inputs=[transformed_data.parse_parquet_files()],\n", + " outputs=[output_split_train, output_split_test],\n", + " compute_target=aml_compute,\n", + " runconfig = aml_run_config,\n", + " source_directory=train_model_folder,\n", + " allow_reuse=True\n", + ")\n", + "\n", + "print(\"testTrainSplitStep created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use automated machine learning to build regression model\n", + "Now we will use **automated machine learning** to build the regression model. We will use [AutoMLStep](https://docs.microsoft.com/python/api/azureml-train-automl-runtime/azureml.train.automl.runtime.automl_step.automlstep?view=azure-ml-py) in AML Pipelines for this part. Perform `pip install azureml-sdk[automl]`to get the automated machine learning package. These functions use various features from the data set and allow an automated model to build relationships between the features and the price of a taxi trip." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Automatically train a model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "\n", + "experiment = Experiment(ws, 'NYCTaxi_Tutorial_Pipelines')\n", + "\n", + "print(\"Experiment created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define settings for autogeneration and tuning\n", + "\n", + "Here we define the experiment parameter and model settings for autogeneration and tuning. We can specify automl_settings as **kwargs as well.\n", + "\n", + "Use your defined training settings as a parameter to an `AutoMLConfig` object. Additionally, specify your training data and the type of model, which is `regression` in this case.\n", + "\n", + "Note: When using AmlCompute, we can't pass Numpy arrays directly to the fit method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.train.automl import AutoMLConfig\n", + "\n", + "# Change iterations to a reasonable number (50) to get better accuracy\n", + "automl_settings = {\n", + " \"iteration_timeout_minutes\" : 10,\n", + " \"iterations\" : 2,\n", + " \"primary_metric\" : 'spearman_correlation',\n", + " \"n_cross_validations\": 5\n", + "}\n", + "\n", + "training_dataset = output_split_train.parse_parquet_files().keep_columns(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor', 'cost'])\n", + "\n", + "automl_config = AutoMLConfig(task = 'regression',\n", + " debug_log = 'automated_ml_errors.log',\n", + " path = train_model_folder,\n", + " compute_target = aml_compute,\n", + " featurization = 'auto',\n", + " training_data = training_dataset,\n", + " label_column_name = 'cost',\n", + " **automl_settings)\n", + " \n", + "print(\"AutoML config created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define AutoMLStep" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.steps import AutoMLStep\n", + "\n", + "trainWithAutomlStep = AutoMLStep(name='AutoML_Regression',\n", + " automl_config=automl_config,\n", + " allow_reuse=True)\n", + "print(\"trainWithAutomlStep created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build and run the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Pipeline\n", + "from azureml.widgets import RunDetails\n", + "\n", + "pipeline_steps = [trainWithAutomlStep]\n", + "\n", + "pipeline = Pipeline(workspace = ws, steps=pipeline_steps)\n", + "print(\"Pipeline is built.\")\n", + "\n", + "pipeline_run = experiment.submit(pipeline, regenerate_outputs=False)\n", + "\n", + "print(\"Pipeline submitted for execution.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RunDetails(pipeline_run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explore the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Before we proceed we need to wait for the run to complete.\n", + "pipeline_run.wait_for_completion(show_output=False)\n", + "\n", + "# functions to download output to local and fetch as dataframe\n", + "def get_download_path(download_path, output_name):\n", + " output_folder = os.listdir(download_path + '/azureml')[0]\n", + " path = download_path + '/azureml/' + output_folder + '/' + output_name\n", + " return path\n", + "\n", + "def fetch_df(current_step, output_name):\n", + " output_data = current_step.get_output_data(output_name) \n", + " download_path = './outputs/' + output_name\n", + " output_data.download(download_path, overwrite=True)\n", + " df_path = get_download_path(download_path, output_name) + '/processed.parquet'\n", + " return pd.read_parquet(df_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View cleansed taxi data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "green_cleanse_step = pipeline_run.find_step_run(cleansingStepGreen.name)[0]\n", + "yellow_cleanse_step = pipeline_run.find_step_run(cleansingStepYellow.name)[0]\n", + "\n", + "cleansed_green_df = fetch_df(green_cleanse_step, cleansed_green_data.name)\n", + "cleansed_yellow_df = fetch_df(yellow_cleanse_step, cleansed_yellow_data.name)\n", + "\n", + "display(cleansed_green_df.head(5))\n", + "display(cleansed_yellow_df.head(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View the combined taxi data profile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "merge_step = pipeline_run.find_step_run(mergingStep.name)[0]\n", + "combined_df = fetch_df(merge_step, merged_data.name)\n", + "\n", + "display(combined_df.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View the filtered taxi data profile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filter_step = pipeline_run.find_step_run(filterStep.name)[0]\n", + "filtered_df = fetch_df(filter_step, filtered_data.name)\n", + "\n", + "display(filtered_df.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View normalized taxi data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "normalize_step = pipeline_run.find_step_run(normalizeStep.name)[0]\n", + "normalized_df = fetch_df(normalize_step, normalized_data.name)\n", + "\n", + "display(normalized_df.head(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View transformed taxi data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "transform_step = pipeline_run.find_step_run(transformStep.name)[0]\n", + "transformed_df = fetch_df(transform_step, transformed_data.name)\n", + "\n", + "display(transformed_df.describe())\n", + "display(transformed_df.head(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View training data used by AutoML" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "split_step = pipeline_run.find_step_run(testTrainSplitStep.name)[0]\n", + "train_split = fetch_df(split_step, output_split_train.name)\n", + "\n", + "display(train_split.describe())\n", + "display(train_split.head(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View the details of the AutoML run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.train.automl.run import AutoMLRun\n", + "#from azureml.widgets import RunDetails\n", + "\n", + "# workaround to get the automl run as its the last step in the pipeline \n", + "# and get_steps() returns the steps from latest to first\n", + "\n", + "for step in pipeline_run.get_steps():\n", + " automl_step_run_id = step.id\n", + " print(step.name)\n", + " print(automl_step_run_id)\n", + " break\n", + "\n", + "automl_run = AutoMLRun(experiment = experiment, run_id=automl_step_run_id)\n", + "#RunDetails(automl_run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Retreive the best model\n", + "\n", + "Uncomment the below cell to retrieve the best model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# best_run, fitted_model = automl_run.get_output()\n", + "# print(best_run)\n", + "# print(fitted_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get test data\n", + "\n", + "Uncomment the below cell to get test data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# split_step = pipeline_run.find_step_run(testTrainSplitStep.name)[0]\n", + "\n", + "# x_test = fetch_df(split_step, output_split_test.name)[['distance','passengers', 'vendor','pickup_weekday','pickup_hour']]\n", + "# y_test = fetch_df(split_step, output_split_test.name)[['cost']]\n", + "\n", + "# display(x_test.head(5))\n", + "# display(y_test.head(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Test the best fitted model\n", + "\n", + "Uncomment the below cell to test the best fitted model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# y_predict = fitted_model.predict(x_test)\n", + "\n", + "# y_actual = y_test.values.tolist()\n", + "\n", + "# display(pd.DataFrame({'Actual':y_actual, 'Predicted':y_predict}).head(5))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import matplotlib.pyplot as plt\n", + "\n", + "# fig = plt.figure(figsize=(14, 10))\n", + "# ax1 = fig.add_subplot(111)\n", + "\n", + "# distance_vals = [x[0] for x in x_test.values]\n", + "\n", + "# ax1.scatter(distance_vals[:100], y_predict[:100], s=18, c='b', marker=\"s\", label='Predicted')\n", + "# ax1.scatter(distance_vals[:100], y_actual[:100], s=18, c='r', marker=\"o\", label='Actual')\n", + "\n", + "# ax1.set_xlabel('distance (mi)')\n", + "# ax1.set_title('Predicted and Actual Cost/Distance')\n", + "# ax1.set_ylabel('Cost ($)')\n", + "\n", + "# plt.legend(loc='upper left', prop={'size': 12})\n", + "# plt.rcParams.update({'font.size': 14})\n", + "# plt.show()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "anshirga" + } ], - "metadata": { - "authors": [ - { - "name": "anshirga" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb index 50736a545..a82149bcf 100644 --- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb @@ -1,635 +1,635 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Azure Machine Learning Pipelines for Batch Inference\n", - "\n", - "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", - "\n", - "> **Tip**\n", - "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", - "\n", - "In this example will be take a digit identification model already-trained on MNIST dataset using the [AzureML training with deep learning example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb), and run that trained model on some of the MNIST test images in batch. \n", - "\n", - "The input dataset used for this notebook differs from a standard MNIST dataset in that it has been converted to PNG images to demonstrate use of files as inputs to Batch Inference. A sample of PNG-converted images of the MNIST dataset were take from [this repository](https://github.com/myleott/mnist_png). \n", - "\n", - "The outline of this notebook is as follows:\n", - "\n", - "- Create a DataStore referencing MNIST images stored in a blob container.\n", - "- Register the pretrained MNIST model into the model registry. \n", - "- Use the registered model to do batch inference on the images in the data blob container.\n", - "\n", - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Connect to workspace\n", - "Create a workspace object from the existing workspace. Workspace.from_config() reads the file config.json and loads the details into an object named ws." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach existing compute resource\n", - "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of compute takes approximately 5 minutes. If the AmlCompute with that name is already in your workspace the code will skip the creation process.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "\n", - "# choose a name for your cluster\n", - "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n", - "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n", - "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 4)\n", - "\n", - "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n", - "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n", - "\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " print('found compute target. just use it. ' + compute_name)\n", - "else:\n", - " print('creating a new compute target...')\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n", - " min_nodes = compute_min_nodes, \n", - " max_nodes = compute_max_nodes)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - " \n", - " # can poll for a minimum number of nodes and for a specific timeout. \n", - " # if no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - " # For a more detailed view of current AmlCompute status, use get_status()\n", - " print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a datastore containing sample images\n", - "The input dataset used for this notebook differs from a standard MNIST dataset in that it has been converted to PNG images to demonstrate use of files as inputs to Batch Inference. A sample of PNG-converted images of the MNIST dataset were take from [this repository](https://github.com/myleott/mnist_png).\n", - "\n", - "We have created a public blob container `sampledata` on an account named `pipelinedata`, containing these images from the MNIST dataset. In the next step, we create a datastore with the name `images_datastore`, which points to this blob container. In the call to `register_azure_blob_container` below, setting the `overwrite` flag to `True` overwrites any datastore that was created previously with that name. \n", - "\n", - "This step can be changed to point to your blob container by providing your own `datastore_name`, `container_name`, and `account_name`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.datastore import Datastore\n", - "\n", - "account_name = \"pipelinedata\"\n", - "datastore_name = \"mnist_datastore\"\n", - "container_name = \"sampledata\"\n", - "\n", - "mnist_data = Datastore.register_azure_blob_container(ws, \n", - " datastore_name=datastore_name, \n", - " container_name=container_name, \n", - " account_name=account_name,\n", - " overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's specify the default datastore for the outputs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def_data_store = ws.get_default_datastore()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a FileDataset\n", - "A [FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.\n", - "You can use dataset objects as inputs. Register the datasets to the workspace if you want to reuse them later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.dataset import Dataset\n", - "\n", - "mnist_ds_name = 'mnist_sample_data'\n", - "\n", - "path_on_datastore = mnist_data.path('mnist')\n", - "input_mnist_ds = Dataset.File.from_files(path=path_on_datastore, validate=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The input dataset can be specified as a pipeline parameter, so that you can pass in new data when rerun the PRS pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.data.dataset_consumption_config import DatasetConsumptionConfig\n", - "from azureml.pipeline.core import PipelineParameter\n", - "\n", - "pipeline_param = PipelineParameter(name=\"mnist_param\", default_value=input_mnist_ds)\n", - "input_mnist_ds_consumption = DatasetConsumptionConfig(\"minist_param_config\", pipeline_param).as_mount()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Intermediate/Output Data\n", - "Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline, PipelineData\n", - "\n", - "output_dir = PipelineData(name=\"inferences\", datastore=def_data_store)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download the Model\n", - "\n", - "Download and extract the model from https://pipelinedata.blob.core.windows.net/mnist-model/mnist-tf.tar.gz to \"models\" directory" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tarfile\n", - "import urllib.request\n", - "\n", - "# create directory for model\n", - "model_dir = 'models'\n", - "if not os.path.isdir(model_dir):\n", - " os.mkdir(model_dir)\n", - "\n", - "url=\"https://pipelinedata.blob.core.windows.net/mnist-model/mnist-tf.tar.gz\"\n", - "response = urllib.request.urlretrieve(url, \"model.tar.gz\")\n", - "tar = tarfile.open(\"model.tar.gz\", \"r:gz\")\n", - "tar.extractall(model_dir)\n", - "\n", - "os.listdir(model_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Register the model with Workspace\n", - "A registered model is a logical container for one or more files that make up your model. For example, if you have a model that's stored in multiple files, you can register them as a single model in the workspace. After you register the files, you can then download or deploy the registered model and receive all the files that you registered.\n", - "\n", - "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where#registermodel) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "# register downloaded model \n", - "model = Model.register(model_path=\"models/\",\n", - " model_name=\"mnist-prs\", # this is the name the model is registered as\n", - " tags={'pretrained': \"mnist\"},\n", - " description=\"Mnist trained tensorflow model\",\n", - " workspace=ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using your model to make batch predictions\n", - "To use the model to make batch predictions, you need an **entry script** and a list of **dependencies**:\n", - "\n", - "#### An entry script\n", - "This script accepts requests, scores the requests by using the model, and returns the results.\n", - "- __init()__ - Typically this function loads the model into a global object. This function is run only once at the start of batch processing per worker node/process. Init method can make use of following environment variables (ParallelRunStep input):\n", - " 1.\tAZUREML_BI_OUTPUT_PATH \u00e2\u20ac\u201c output folder path\n", - "- __run(mini_batch)__ - The method to be parallelized. Each invocation will have one minibatch.
      \n", - "__mini_batch__: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.
      \n", - "__run__ method response: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.\n", - " User should make sure that enough data is included in inference result to map input to inference. Inference output will be written in output file and not guaranteed to be in order, user should use some key in the output to map it to input.\n", - " \n", - "\n", - "#### Dependencies\n", - "Helper scripts or Python/Conda packages required to run the entry script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scripts_folder = \"Code\"\n", - "script_file = \"digit_identification.py\"\n", - "\n", - "# peek at contents\n", - "with open(os.path.join(scripts_folder, script_file)) as inference_file:\n", - " print(inference_file.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and run the batch inference pipeline\n", - "The data, models, and compute resource are now available. Let's put all these together in a pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Specify the environment to run the script\n", - "Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment.\n", - "* Always include **azureml-core** and **azureml-dataset-runtime\\[fuse\\]** in the pip package list to make ParallelRunStep run properly.\n", - "\n", - "If you're using custom image (`batch_env.python.user_managed_dependencies = True`), you need to install the package to your image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n", - "\n", - "batch_conda_deps = CondaDependencies.create(pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \n", - " \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n", - "batch_env = Environment(name=\"batch_environment\")\n", - "batch_env.python.conda_dependencies = batch_conda_deps\n", - "batch_env.docker.base_image = DEFAULT_CPU_IMAGE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the configuration to wrap the inference script" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig\n", - "\n", - "parallel_run_config = ParallelRunConfig(\n", - " source_directory=scripts_folder,\n", - " entry_script=script_file,\n", - " mini_batch_size=PipelineParameter(name=\"batch_size_param\", default_value=\"5\"),\n", - " error_threshold=10,\n", - " output_action=\"append_row\",\n", - " append_row_file_name=\"mnist_outputs.txt\",\n", - " environment=batch_env,\n", - " compute_target=compute_target,\n", - " process_count_per_node=PipelineParameter(name=\"process_count_param\", default_value=2),\n", - " node_count=2\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the pipeline step\n", - "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use ParallelRunStep to create the pipeline step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "parallelrun_step = ParallelRunStep(\n", - " name=\"predict-digits-mnist\",\n", - " parallel_run_config=parallel_run_config,\n", - " inputs=[ input_mnist_ds_consumption ],\n", - " output=output_dir,\n", - " allow_reuse=False\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the pipeline\n", - "At this point you can run the pipeline and examine the output it produced. The Experiment object is used to track the run of the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])\n", - "experiment = Experiment(ws, 'digit_identification')\n", - "pipeline_run = experiment.submit(pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor the run\n", - "\n", - "The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will output information of the pipeline run, including the link to the details page of portal.\n", - "pipeline_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optional: View detailed logs (streaming) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Wait the run for completion and show output log to console\n", - "pipeline_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View the prediction results per input image\n", - "In the digit_identification.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import tempfile\n", - "\n", - "batch_run = pipeline_run.find_step_run(parallelrun_step.name)[0]\n", - "batch_output = batch_run.get_output_data(output_dir.name)\n", - "\n", - "target_dir = tempfile.mkdtemp()\n", - "batch_output.download(local_path=target_dir)\n", - "result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n", - "\n", - "df = pd.read_csv(result_file, delimiter=\":\", header=None)\n", - "df.columns = [\"Filename\", \"Prediction\"]\n", - "print(\"Prediction has \", df.shape[0], \" rows\")\n", - "df.head(10) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Resubmit a with different dataset\n", - "Since we made the input a `PipelineParameter`, we can resubmit with a different dataset without having to create an entirely new experiment. We'll use the same datastore but use only a single image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "path_on_datastore = mnist_data.path('mnist/0.png')\n", - "single_image_ds = Dataset.File.from_files(path=path_on_datastore, validate=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run_2 = experiment.submit(pipeline, \n", - " pipeline_parameters={\"mnist_param\": single_image_ds, \n", - " \"batch_size_param\": \"1\",\n", - " \"process_count_param\": 1}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will output information of the pipeline run, including the link to the details page of portal.\n", - "pipeline_run_2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Wait the run for completion and show output log to console\n", - "pipeline_run_2.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cleanup Compute resources\n", - "\n", - "For re-occurring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single-run job, we are free to release the allocated compute resources." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment below and run if compute resources are no longer needed \n", - "# compute_target.delete() " - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Azure Machine Learning Pipelines for Batch Inference\n", + "\n", + "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", + "\n", + "> **Tip**\n", + "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", + "\n", + "In this example will be take a digit identification model already-trained on MNIST dataset using the [AzureML training with deep learning example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb), and run that trained model on some of the MNIST test images in batch. \n", + "\n", + "The input dataset used for this notebook differs from a standard MNIST dataset in that it has been converted to PNG images to demonstrate use of files as inputs to Batch Inference. A sample of PNG-converted images of the MNIST dataset were take from [this repository](https://github.com/myleott/mnist_png). \n", + "\n", + "The outline of this notebook is as follows:\n", + "\n", + "- Create a DataStore referencing MNIST images stored in a blob container.\n", + "- Register the pretrained MNIST model into the model registry. \n", + "- Use the registered model to do batch inference on the images in the data blob container.\n", + "\n", + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to workspace\n", + "Create a workspace object from the existing workspace. Workspace.from_config() reads the file config.json and loads the details into an object named ws." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print('Workspace name: ' + ws.name, \n", + " 'Azure region: ' + ws.location, \n", + " 'Subscription id: ' + ws.subscription_id, \n", + " 'Resource group: ' + ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create or Attach existing compute resource\n", + "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\n", + "**Creation of compute takes approximately 5 minutes. If the AmlCompute with that name is already in your workspace the code will skip the creation process.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from azureml.core.compute import AmlCompute, ComputeTarget\n", + "\n", + "# choose a name for your cluster\n", + "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n", + "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n", + "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 4)\n", + "\n", + "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n", + "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n", + "\n", + "\n", + "if compute_name in ws.compute_targets:\n", + " compute_target = ws.compute_targets[compute_name]\n", + " if compute_target and type(compute_target) is AmlCompute:\n", + " print('found compute target. just use it. ' + compute_name)\n", + "else:\n", + " print('creating a new compute target...')\n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n", + " min_nodes = compute_min_nodes, \n", + " max_nodes = compute_max_nodes)\n", + "\n", + " # create the cluster\n", + " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", + " \n", + " # can poll for a minimum number of nodes and for a specific timeout. \n", + " # if no min node count is provided it will use the scale settings for the cluster\n", + " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", + " \n", + " # For a more detailed view of current AmlCompute status, use get_status()\n", + " print(compute_target.get_status().serialize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a datastore containing sample images\n", + "The input dataset used for this notebook differs from a standard MNIST dataset in that it has been converted to PNG images to demonstrate use of files as inputs to Batch Inference. A sample of PNG-converted images of the MNIST dataset were take from [this repository](https://github.com/myleott/mnist_png).\n", + "\n", + "We have created a public blob container `sampledata` on an account named `pipelinedata`, containing these images from the MNIST dataset. In the next step, we create a datastore with the name `images_datastore`, which points to this blob container. In the call to `register_azure_blob_container` below, setting the `overwrite` flag to `True` overwrites any datastore that was created previously with that name. \n", + "\n", + "This step can be changed to point to your blob container by providing your own `datastore_name`, `container_name`, and `account_name`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.datastore import Datastore\n", + "\n", + "account_name = \"pipelinedata\"\n", + "datastore_name = \"mnist_datastore\"\n", + "container_name = \"sampledata\"\n", + "\n", + "mnist_data = Datastore.register_azure_blob_container(ws, \n", + " datastore_name=datastore_name, \n", + " container_name=container_name, \n", + " account_name=account_name,\n", + " overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's specify the default datastore for the outputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def_data_store = ws.get_default_datastore()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a FileDataset\n", + "A [FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.\n", + "You can use dataset objects as inputs. Register the datasets to the workspace if you want to reuse them later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.dataset import Dataset\n", + "\n", + "mnist_ds_name = 'mnist_sample_data'\n", + "\n", + "path_on_datastore = mnist_data.path('mnist')\n", + "input_mnist_ds = Dataset.File.from_files(path=path_on_datastore, validate=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input dataset can be specified as a pipeline parameter, so that you can pass in new data when rerun the PRS pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.data.dataset_consumption_config import DatasetConsumptionConfig\n", + "from azureml.pipeline.core import PipelineParameter\n", + "\n", + "pipeline_param = PipelineParameter(name=\"mnist_param\", default_value=input_mnist_ds)\n", + "input_mnist_ds_consumption = DatasetConsumptionConfig(\"minist_param_config\", pipeline_param).as_mount()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intermediate/Output Data\n", + "Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Pipeline, PipelineData\n", + "\n", + "output_dir = PipelineData(name=\"inferences\", datastore=def_data_store)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download the Model\n", + "\n", + "Download and extract the model from https://pipelinedata.blob.core.windows.net/mnist-model/mnist-tf.tar.gz to \"models\" directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tarfile\n", + "import urllib.request\n", + "\n", + "# create directory for model\n", + "model_dir = 'models'\n", + "if not os.path.isdir(model_dir):\n", + " os.mkdir(model_dir)\n", + "\n", + "url=\"https://pipelinedata.blob.core.windows.net/mnist-model/mnist-tf.tar.gz\"\n", + "response = urllib.request.urlretrieve(url, \"model.tar.gz\")\n", + "tar = tarfile.open(\"model.tar.gz\", \"r:gz\")\n", + "tar.extractall(model_dir)\n", + "\n", + "os.listdir(model_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register the model with Workspace\n", + "A registered model is a logical container for one or more files that make up your model. For example, if you have a model that's stored in multiple files, you can register them as a single model in the workspace. After you register the files, you can then download or deploy the registered model and receive all the files that you registered.\n", + "\n", + "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where#registermodel) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "# register downloaded model \n", + "model = Model.register(model_path=\"models/\",\n", + " model_name=\"mnist-prs\", # this is the name the model is registered as\n", + " tags={'pretrained': \"mnist\"},\n", + " description=\"Mnist trained tensorflow model\",\n", + " workspace=ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using your model to make batch predictions\n", + "To use the model to make batch predictions, you need an **entry script** and a list of **dependencies**:\n", + "\n", + "#### An entry script\n", + "This script accepts requests, scores the requests by using the model, and returns the results.\n", + "- __init()__ - Typically this function loads the model into a global object. This function is run only once at the start of batch processing per worker node/process. Init method can make use of following environment variables (ParallelRunStep input):\n", + " 1.\tAZUREML_BI_OUTPUT_PATH – output folder path\n", + "- __run(mini_batch)__ - The method to be parallelized. Each invocation will have one minibatch.
      \n", + "__mini_batch__: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.
      \n", + "__run__ method response: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.\n", + " User should make sure that enough data is included in inference result to map input to inference. Inference output will be written in output file and not guaranteed to be in order, user should use some key in the output to map it to input.\n", + " \n", + "\n", + "#### Dependencies\n", + "Helper scripts or Python/Conda packages required to run the entry script." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scripts_folder = \"Code\"\n", + "script_file = \"digit_identification.py\"\n", + "\n", + "# peek at contents\n", + "with open(os.path.join(scripts_folder, script_file)) as inference_file:\n", + " print(inference_file.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and run the batch inference pipeline\n", + "The data, models, and compute resource are now available. Let's put all these together in a pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specify the environment to run the script\n", + "Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment.\n", + "* Always include **azureml-core** and **azureml-dataset-runtime\\[fuse\\]** in the pip package list to make ParallelRunStep run properly.\n", + "\n", + "If you're using custom image (`batch_env.python.user_managed_dependencies = True`), you need to install the package to your image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n", + "\n", + "batch_conda_deps = CondaDependencies.create(pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \n", + " \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n", + "batch_env = Environment(name=\"batch_environment\")\n", + "batch_env.python.conda_dependencies = batch_conda_deps\n", + "batch_env.docker.base_image = DEFAULT_CPU_IMAGE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the configuration to wrap the inference script" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig\n", + "\n", + "parallel_run_config = ParallelRunConfig(\n", + " source_directory=scripts_folder,\n", + " entry_script=script_file,\n", + " mini_batch_size=PipelineParameter(name=\"batch_size_param\", default_value=\"5\"),\n", + " error_threshold=10,\n", + " output_action=\"append_row\",\n", + " append_row_file_name=\"mnist_outputs.txt\",\n", + " environment=batch_env,\n", + " compute_target=compute_target,\n", + " process_count_per_node=PipelineParameter(name=\"process_count_param\", default_value=2),\n", + " node_count=2\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the pipeline step\n", + "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use ParallelRunStep to create the pipeline step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parallelrun_step = ParallelRunStep(\n", + " name=\"predict-digits-mnist\",\n", + " parallel_run_config=parallel_run_config,\n", + " inputs=[ input_mnist_ds_consumption ],\n", + " output=output_dir,\n", + " allow_reuse=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the pipeline\n", + "At this point you can run the pipeline and examine the output it produced. The Experiment object is used to track the run of the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "\n", + "pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])\n", + "experiment = Experiment(ws, 'digit_identification')\n", + "pipeline_run = experiment.submit(pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Monitor the run\n", + "\n", + "The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This will output information of the pipeline run, including the link to the details page of portal.\n", + "pipeline_run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional: View detailed logs (streaming) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Wait the run for completion and show output log to console\n", + "pipeline_run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View the prediction results per input image\n", + "In the digit_identification.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import tempfile\n", + "\n", + "batch_run = pipeline_run.find_step_run(parallelrun_step.name)[0]\n", + "batch_output = batch_run.get_output_data(output_dir.name)\n", + "\n", + "target_dir = tempfile.mkdtemp()\n", + "batch_output.download(local_path=target_dir)\n", + "result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n", + "\n", + "df = pd.read_csv(result_file, delimiter=\":\", header=None)\n", + "df.columns = [\"Filename\", \"Prediction\"]\n", + "print(\"Prediction has \", df.shape[0], \" rows\")\n", + "df.head(10) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resubmit a with different dataset\n", + "Since we made the input a `PipelineParameter`, we can resubmit with a different dataset without having to create an entirely new experiment. We'll use the same datastore but use only a single image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "path_on_datastore = mnist_data.path('mnist/0.png')\n", + "single_image_ds = Dataset.File.from_files(path=path_on_datastore, validate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run_2 = experiment.submit(pipeline, \n", + " pipeline_parameters={\"mnist_param\": single_image_ds, \n", + " \"batch_size_param\": \"1\",\n", + " \"process_count_param\": 1}\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This will output information of the pipeline run, including the link to the details page of portal.\n", + "pipeline_run_2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Wait the run for completion and show output log to console\n", + "pipeline_run_2.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup Compute resources\n", + "\n", + "For re-occurring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single-run job, we are free to release the allocated compute resources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment below and run if compute resources are no longer needed \n", + "# compute_target.delete() " + ] + } + ], + "metadata": { + "authors": [ + { + "name": "joringer" + }, + { + "name": "asraniwa" + }, + { + "name": "pansav" + }, + { + "name": "tracych" + } + ], + "category": "Other notebooks", + "compute": [ + "AML Compute" + ], + "datasets": [ + "MNIST" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "None" + ], + "friendly_name": "MNIST data inferencing using ParallelRunStep", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "tags": [ + "Batch Inferencing", + "Pipeline" ], - "metadata": { - "authors": [ - { - "name": "joringer" - }, - { - "name": "asraniwa" - }, - { - "name": "pansav" - }, - { - "name": "tracych" - } - ], - "category": "Other notebooks", - "compute": [ - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "MNIST data inferencing using ParallelRunStep", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "tags": [ - "Batch Inferencing", - "Pipeline" - ], - "task": "Digit identification" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "task": "Digit identification" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb index c18ca0156..96587b116 100644 --- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb @@ -1,404 +1,404 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Azure Machine Learning Pipelines for Batch Inference for files input partitioned by folder structure\n", - "\n", - "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", - "\n", - "> **Tip**\n", - "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", - "\n", - "This example will create a sample dataset with nested folder structure, where the folder name corresponds to the attribute of the files inside it. The Batch Inference job would split the files inside the dataset according to their attributes, so that all files with identical value on the specified attribute will form up a single mini-batch to be processed.\n", - "\n", - "The outline of this notebook is as follows:\n", - "\n", - "- Create a dataset with nested folder structure and `partition_format` to interpret the folder structure into the attributes of files inside.\n", - "- Do batch inference on each mini-batch defined by the folder structure.\n", - "\n", - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Connect to workspace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')\n", - "\n", - "datastore = ws.get_default_datastore()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "print(azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Upload local test data to datastore\n", - "The destination folder in the datastore is structured so that the name of each folder layer corresponds to a property of all the files inside the foler." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "\n", - "datastore.upload('test_files/disco', 'dataset_partition_test/user1/winter', overwrite=True, show_progress=False)\n", - "datastore.upload('test_files/orchestra', 'dataset_partition_test/user1/fall', overwrite=True, show_progress=False)\n", - "datastore.upload('test_files/piano', 'dataset_partition_test/user2/summer', overwrite=True, show_progress=False)\n", - "datastore.upload('test_files/spirituality', 'dataset_partition_test/user3/fall', overwrite=True, show_progress=False)\n", - "datastore.upload('test_files/piano', 'dataset_partition_test/user4/spring', overwrite=True, show_progress=False)\n", - "datastore.upload('test_files/piano', 'dataset_partition_test/user4/fall', overwrite=True, show_progress=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create partitioned file dataset\n", - "Create a file dataset partitioned by 'user', 'season', and 'genres', each corresponds to a folder layer specified in `partition_format`. You can get a partition of data by specifying the value of one or more partition keys. E.g., by specifying `user=user1 and genres=piano`, you can get all the file that matches `dataset_partition_test/user1/*/piano.wav`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "partitioned_file_dataset = Dataset.File.from_files(path=(datastore, 'dataset_partition_test/*/*/*.wav'),\n", - " partition_format=\"dataset_partition_test/{user}/{season}/{genres}.wav\",\n", - " validate=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "partitioned_file_dataset.partition_keys" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach existing compute resource" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "\n", - "# choose a name for your cluster\n", - "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n", - "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n", - "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 2)\n", - "\n", - "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n", - "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n", - "\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " print('found compute target. just use it. ' + compute_name)\n", - "else:\n", - " print('creating a new compute target...')\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n", - " min_nodes = compute_min_nodes, \n", - " max_nodes = compute_max_nodes)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - " \n", - " # can poll for a minimum number of nodes and for a specific timeout. \n", - " # if no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - " # For a more detailed view of current AmlCompute status, use get_status()\n", - " print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Intermediate/Output Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline, PipelineData\n", - "\n", - "output_dir = PipelineData(name=\"file_dataset_inferences\", datastore=datastore)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate total file size of each mini-batch partitioned by dataset partition key(s)\n", - "The script is to sum up the total size of files in each mini-batch." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scripts_folder = \"Code\"\n", - "script_file = \"total_file_size.py\"\n", - "\n", - "# peek at contents\n", - "with open(os.path.join(scripts_folder, script_file)) as inference_file:\n", - " print(inference_file.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and run the batch inference pipeline\n", - "### Specify the environment to run the script\n", - "You would need to specify the required private azureml packages in dependencies. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n", - "\n", - "batch_conda_deps = CondaDependencies.create(pip_packages=[\"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n", - "batch_env = Environment(name=\"batch_environment\")\n", - "batch_env.python.conda_dependencies = batch_conda_deps\n", - "batch_env.docker.base_image = DEFAULT_CPU_IMAGE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the configuration to wrap the inference script\n", - "The parameter `partition_keys` is a list containing a subset of the dataset partition keys, specifying how is the input dataset partitioned. Each and every possible combination of values of partition_keys will form up a mini-batch. E.g., by specifying `partition_keys=['user', 'genres']` will result in 5 mini-batches, i.e. `user=halit && genres=disco`, `user=halit && genres=orchestra`, `user=chunyu && genres=piano`, `user=kin && genres=spirituality` and `user=ramandeep && genres=piano`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig\n", - "\n", - "# In a real-world scenario, you'll want to shape your process per node and nodes to fit your problem domain.\n", - "parallel_run_config = ParallelRunConfig(\n", - " source_directory=scripts_folder,\n", - " entry_script=script_file, # the user script to run against each input\n", - " partition_keys=['user', 'genres'],\n", - " error_threshold=5,\n", - " output_action='append_row',\n", - " append_row_file_name=\"file_size_outputs.txt\",\n", - " environment=batch_env,\n", - " compute_target=compute_target, \n", - " node_count=2,\n", - " run_invocation_timeout=600\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the pipeline step" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "parallel_run_step = ParallelRunStep(\n", - " name='summarize-file-size',\n", - " inputs=[partitioned_file_dataset.as_named_input(\"partitioned_file_input\")],\n", - " output=output_dir,\n", - " parallel_run_config=parallel_run_config,\n", - " allow_reuse=False\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "from azureml.pipeline.core import Pipeline\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[parallel_run_step])\n", - "\n", - "pipeline_run = Experiment(ws, 'file-dataset-partition').submit(pipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View the prediction results\n", - "In the total_file_size.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called inferences. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import tempfile\n", - "\n", - "batch_run = pipeline_run.find_step_run(parallel_run_step.name)[0]\n", - "batch_output = batch_run.get_output_data(output_dir.name)\n", - "\n", - "target_dir = tempfile.mkdtemp()\n", - "batch_output.download(local_path=target_dir)\n", - "result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n", - "\n", - "df = pd.read_csv(result_file, delimiter=\",\", header=None)\n", - "df.columns = [\"File Name\", \"File Size\", \"Ratio of Size in Partition\", \"user\", \"genres\", \"Total File Size of Partition\"]\n", - "print(\"Prediction has\", df.shape[0], \"rows\")\n", - "df.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Azure Machine Learning Pipelines for Batch Inference for files input partitioned by folder structure\n", + "\n", + "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", + "\n", + "> **Tip**\n", + "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", + "\n", + "This example will create a sample dataset with nested folder structure, where the folder name corresponds to the attribute of the files inside it. The Batch Inference job would split the files inside the dataset according to their attributes, so that all files with identical value on the specified attribute will form up a single mini-batch to be processed.\n", + "\n", + "The outline of this notebook is as follows:\n", + "\n", + "- Create a dataset with nested folder structure and `partition_format` to interpret the folder structure into the attributes of files inside.\n", + "- Do batch inference on each mini-batch defined by the folder structure.\n", + "\n", + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to workspace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.workspace import Workspace\n", + "ws = Workspace.from_config()\n", + "print('Workspace name: ' + ws.name, \n", + " 'Azure region: ' + ws.location, \n", + " 'Subscription id: ' + ws.subscription_id, \n", + " 'Resource group: ' + ws.resource_group, sep = '\\n')\n", + "\n", + "datastore = ws.get_default_datastore()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "print(azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Upload local test data to datastore\n", + "The destination folder in the datastore is structured so that the name of each folder layer corresponds to a property of all the files inside the foler." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Dataset\n", + "\n", + "datastore.upload('test_files/disco', 'dataset_partition_test/user1/winter', overwrite=True, show_progress=False)\n", + "datastore.upload('test_files/orchestra', 'dataset_partition_test/user1/fall', overwrite=True, show_progress=False)\n", + "datastore.upload('test_files/piano', 'dataset_partition_test/user2/summer', overwrite=True, show_progress=False)\n", + "datastore.upload('test_files/spirituality', 'dataset_partition_test/user3/fall', overwrite=True, show_progress=False)\n", + "datastore.upload('test_files/piano', 'dataset_partition_test/user4/spring', overwrite=True, show_progress=False)\n", + "datastore.upload('test_files/piano', 'dataset_partition_test/user4/fall', overwrite=True, show_progress=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create partitioned file dataset\n", + "Create a file dataset partitioned by 'user', 'season', and 'genres', each corresponds to a folder layer specified in `partition_format`. You can get a partition of data by specifying the value of one or more partition keys. E.g., by specifying `user=user1 and genres=piano`, you can get all the file that matches `dataset_partition_test/user1/*/piano.wav`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "partitioned_file_dataset = Dataset.File.from_files(path=(datastore, 'dataset_partition_test/*/*/*.wav'),\n", + " partition_format=\"dataset_partition_test/{user}/{season}/{genres}.wav\",\n", + " validate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "partitioned_file_dataset.partition_keys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create or Attach existing compute resource" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from azureml.core.compute import AmlCompute, ComputeTarget\n", + "\n", + "# choose a name for your cluster\n", + "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n", + "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n", + "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 2)\n", + "\n", + "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n", + "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n", + "\n", + "\n", + "if compute_name in ws.compute_targets:\n", + " compute_target = ws.compute_targets[compute_name]\n", + " if compute_target and type(compute_target) is AmlCompute:\n", + " print('found compute target. just use it. ' + compute_name)\n", + "else:\n", + " print('creating a new compute target...')\n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n", + " min_nodes = compute_min_nodes, \n", + " max_nodes = compute_max_nodes)\n", + "\n", + " # create the cluster\n", + " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", + " \n", + " # can poll for a minimum number of nodes and for a specific timeout. \n", + " # if no min node count is provided it will use the scale settings for the cluster\n", + " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", + " \n", + " # For a more detailed view of current AmlCompute status, use get_status()\n", + " print(compute_target.get_status().serialize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intermediate/Output Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Pipeline, PipelineData\n", + "\n", + "output_dir = PipelineData(name=\"file_dataset_inferences\", datastore=datastore)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate total file size of each mini-batch partitioned by dataset partition key(s)\n", + "The script is to sum up the total size of files in each mini-batch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scripts_folder = \"Code\"\n", + "script_file = \"total_file_size.py\"\n", + "\n", + "# peek at contents\n", + "with open(os.path.join(scripts_folder, script_file)) as inference_file:\n", + " print(inference_file.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and run the batch inference pipeline\n", + "### Specify the environment to run the script\n", + "You would need to specify the required private azureml packages in dependencies. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n", + "\n", + "batch_conda_deps = CondaDependencies.create(pip_packages=[\"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n", + "batch_env = Environment(name=\"batch_environment\")\n", + "batch_env.python.conda_dependencies = batch_conda_deps\n", + "batch_env.docker.base_image = DEFAULT_CPU_IMAGE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the configuration to wrap the inference script\n", + "The parameter `partition_keys` is a list containing a subset of the dataset partition keys, specifying how is the input dataset partitioned. Each and every possible combination of values of partition_keys will form up a mini-batch. E.g., by specifying `partition_keys=['user', 'genres']` will result in 5 mini-batches, i.e. `user=halit && genres=disco`, `user=halit && genres=orchestra`, `user=chunyu && genres=piano`, `user=kin && genres=spirituality` and `user=ramandeep && genres=piano`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig\n", + "\n", + "# In a real-world scenario, you'll want to shape your process per node and nodes to fit your problem domain.\n", + "parallel_run_config = ParallelRunConfig(\n", + " source_directory=scripts_folder,\n", + " entry_script=script_file, # the user script to run against each input\n", + " partition_keys=['user', 'genres'],\n", + " error_threshold=5,\n", + " output_action='append_row',\n", + " append_row_file_name=\"file_size_outputs.txt\",\n", + " environment=batch_env,\n", + " compute_target=compute_target, \n", + " node_count=2,\n", + " run_invocation_timeout=600\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the pipeline step" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parallel_run_step = ParallelRunStep(\n", + " name='summarize-file-size',\n", + " inputs=[partitioned_file_dataset.as_named_input(\"partitioned_file_input\")],\n", + " output=output_dir,\n", + " parallel_run_config=parallel_run_config,\n", + " allow_reuse=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "from azureml.pipeline.core import Pipeline\n", + "\n", + "pipeline = Pipeline(workspace=ws, steps=[parallel_run_step])\n", + "\n", + "pipeline_run = Experiment(ws, 'file-dataset-partition').submit(pipeline)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View the prediction results\n", + "In the total_file_size.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called inferences. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import tempfile\n", + "\n", + "batch_run = pipeline_run.find_step_run(parallel_run_step.name)[0]\n", + "batch_output = batch_run.get_output_data(output_dir.name)\n", + "\n", + "target_dir = tempfile.mkdtemp()\n", + "batch_output.download(local_path=target_dir)\n", + "result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n", + "\n", + "df = pd.read_csv(result_file, delimiter=\",\", header=None)\n", + "df.columns = [\"File Name\", \"File Size\", \"Ratio of Size in Partition\", \"user\", \"genres\", \"Total File Size of Partition\"]\n", + "print(\"Prediction has\", df.shape[0], \"rows\")\n", + "df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "authors": [ + { + "name": "pansav" + }, + { + "name": "tracych" + }, + { + "name": "migu" + } + ], + "category": "Other notebooks", + "compute": [ + "AML Compute" + ], + "datasets": [ + "None" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "None" ], - "metadata": { - "authors": [ - { - "name": "pansav" - }, - { - "name": "tracych" - }, - { - "name": "migu" - } - ], - "category": "Other notebooks", - "compute": [ - "AML Compute" - ], - "datasets": [ - "None" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Batch inferencing file data partitioned by folder using ParallelRunStep", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } + "friendly_name": "Batch inferencing file data partitioned by folder using ParallelRunStep", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb index 205d2fbdf..40a15fe70 100644 --- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb @@ -1,541 +1,541 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Azure Machine Learning Pipelines for Batch Inference for CSV Files\n", - "\n", - "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", - "\n", - "> **Tip**\n", - "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", - "\n", - "In this example we will take use a machine learning model already trained to predict different types of iris flowers and run that trained model on some of the data in a CSV file which has characteristics of different iris flowers. However, the same example can be extended to manipulating data to any embarrassingly-parallel processing through a python script.\n", - "\n", - "The outline of this notebook is as follows:\n", - "\n", - "- Create a DataStore referencing the CSV files stored in a blob container.\n", - "- Register the pretrained model into the model registry. \n", - "- Use the registered model to do batch inference on the CSV files in the data blob container.\n", - "\n", - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Connect to workspace\n", - "Create a workspace object from the existing workspace. Workspace.from_config() reads the file config.json and loads the details into an object named ws." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach existing compute resource\n", - "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of compute takes approximately 5 minutes. If the AmlCompute with that name is already in your workspace the code will skip the creation process.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "\n", - "# choose a name for your cluster\n", - "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n", - "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n", - "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 4)\n", - "\n", - "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n", - "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n", - "\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " print('found compute target. just use it. ' + compute_name)\n", - "else:\n", - " print('creating a new compute target...')\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n", - " min_nodes = compute_min_nodes, \n", - " max_nodes = compute_max_nodes)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - " \n", - " # can poll for a minimum number of nodes and for a specific timeout.\n", - " # if no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - " # For a more detailed view of current AmlCompute status, use get_status()\n", - " print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a datastore containing sample images\n", - "The input dataset used for this notebook is CSV data which has attributes of different iris flowers. We have created a public blob container `sampledata` on an account named `pipelinedata`, containing iris data set. In the next step, we create a datastore with the name `iris_datastore`, which points to this container. In the call to `register_azure_blob_container` below, setting the `overwrite` flag to `True` overwrites any datastore that was created previously with that name. \n", - "\n", - "This step can be changed to point to your blob container by providing your own `datastore_name`, `container_name`, and `account_name`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.datastore import Datastore\n", - "\n", - "account_name = \"pipelinedata\"\n", - "datastore_name=\"iris_datastore_data\"\n", - "container_name=\"sampledata\"\n", - "\n", - "iris_data = Datastore.register_azure_blob_container(ws, \n", - " datastore_name=datastore_name, \n", - " container_name= container_name, \n", - " account_name=account_name, \n", - " overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a TabularDataset\n", - "A [TabularDataSet](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) references single or multiple files which contain data in a tabular structure (ie like CSV files) in your datastores or public urls. TabularDatasets provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.\n", - "You can use dataset objects as inputs. Register the datasets to the workspace if you want to reuse them later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.dataset import Dataset\n", - "\n", - "iris_ds_name = 'iris_data'\n", - "\n", - "path_on_datastore = iris_data.path('iris/')\n", - "input_iris_ds = Dataset.Tabular.from_delimited_files(path=path_on_datastore, validate=False)\n", - "named_iris_ds = input_iris_ds.as_named_input(iris_ds_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Intermediate/Output Data\n", - "Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineData\n", - "\n", - "datastore = ws.get_default_datastore()\n", - "output_folder = PipelineData(name='inferences', datastore=datastore)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Registering the Model with the Workspace\n", - "Get the pretrained model from a publicly available Azure Blob container, then register it to use in your workspace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_container_name=\"iris-model\"\n", - "model_datastore_name=\"iris_model_datastore\"\n", - "\n", - "model_datastore = Datastore.register_azure_blob_container(ws, \n", - " datastore_name=model_datastore_name, \n", - " container_name= model_container_name, \n", - " account_name=account_name, \n", - " overwrite=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "\n", - "model_datastore.download('iris_model.pkl')\n", - "\n", - "# register downloaded model\n", - "model = Model.register(model_path = \"iris_model.pkl/iris_model.pkl\",\n", - " model_name = \"iris-prs\", # this is the name the model is registered as\n", - " tags = {'pretrained': \"iris\"},\n", - " workspace = ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using your model to make batch predictions\n", - "To use the model to make batch predictions, you need an **entry script** and a list of **dependencies**:\n", - "\n", - "#### An entry script\n", - "This script accepts requests, scores the requests by using the model, and returns the results.\n", - "- __init()__ - Typically this function loads the model into a global object. This function is run only once at the start of batch processing per worker node/process. init method can make use of following environment variables (ParallelRunStep input):\n", - " 1.\tAZUREML_BI_OUTPUT_PATH \u00e2\u20ac\u201c output folder path\n", - "- __run(mini_batch)__ - The method to be parallelized. Each invocation will have one minibatch.
      \n", - "__mini_batch__: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.
      \n", - "__run__ method response: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.\n", - " User should make sure that enough data is included in inference result to map input to inference. Inference output will be written in output file and not guaranteed to be in order, user should use some key in the output to map it to input.\n", - " \n", - "\n", - "#### Dependencies\n", - "Helper scripts or Python/Conda packages required to run the entry script.\n", - "\n", - "## Print inferencing script" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scripts_folder = \"Code\"\n", - "script_file = \"iris_score.py\"\n", - "\n", - "# peek at contents\n", - "with open(os.path.join(scripts_folder, script_file)) as inference_file:\n", - " print(inference_file.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and run the batch inference pipeline\n", - "The data, models, and compute resource are now available. Let's put all these together in a pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Specify the environment to run the script\n", - "Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment.\n", - "* Always include **azureml-core** and **azureml-dataset-runtime\\[fuse\\]** in the pip package list to make ParallelRunStep run properly.\n", - "* For TabularDataset, add **pandas** as `run(mini_batch)` uses `pandas.DataFrame` as mini_batch type.\n", - "\n", - "If you're using custom image (`batch_env.python.user_managed_dependencies = True`), you need to install the package to your image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.runconfig import CondaDependencies\n", - "\n", - "predict_conda_deps = CondaDependencies.create(pip_packages=[\"scikit-learn==0.20.3\",\n", - " \"azureml-core\", \"azureml-dataset-runtime[pandas,fuse]\"])\n", - "\n", - "predict_env = Environment(name=\"predict_environment\")\n", - "predict_env.python.conda_dependencies = predict_conda_deps\n", - "predict_env.spark.precache_packages = False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the configuration to wrap the inference script" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig\n", - "\n", - "# In a real-world scenario, you'll want to shape your process per node and nodes to fit your problem domain.\n", - "parallel_run_config = ParallelRunConfig(\n", - " source_directory=scripts_folder,\n", - " entry_script=script_file, # the user script to run against each input\n", - " mini_batch_size='1KB',\n", - " error_threshold=5,\n", - " output_action='append_row',\n", - " append_row_file_name=\"iris_outputs.txt\",\n", - " environment=predict_env,\n", - " compute_target=compute_target, \n", - " node_count=2,\n", - " run_invocation_timeout=600\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the pipeline step\n", - "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use ParallelRunStep to create the pipeline step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "distributed_csv_iris_step = ParallelRunStep(\n", - " name='example-iris',\n", - " inputs=[named_iris_ds],\n", - " output=output_folder,\n", - " parallel_run_config=parallel_run_config,\n", - " arguments=['--model_name', 'iris-prs'],\n", - " allow_reuse=False\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the pipeline\n", - "At this point you can run the pipeline and examine the output it produced. The Experiment object is used to track the run of the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "from azureml.pipeline.core import Pipeline\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[distributed_csv_iris_step])\n", - "\n", - "pipeline_run = Experiment(ws, 'iris-prs').submit(pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View progress of Pipeline run\n", - "\n", - "The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will output information of the pipeline run, including the link to the details page of portal.\n", - "pipeline_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optional: View detailed logs (streaming) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "## Wait the run for completion and show output log to console\n", - "pipeline_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View Results\n", - "In the iris_score.py file above you can see that the Result with the prediction of the iris variety gets returned and then appended to the original input of the row from the csv file. These results are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This contains the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to a random 20 rows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import tempfile\n", - "\n", - "prediction_run = pipeline_run.find_step_run(distributed_csv_iris_step.name)[0]\n", - "prediction_output = prediction_run.get_output_data(output_folder.name)\n", - "\n", - "target_dir = tempfile.mkdtemp()\n", - "prediction_output.download(local_path=target_dir)\n", - "result_file = os.path.join(target_dir, prediction_output.path_on_datastore, parallel_run_config.append_row_file_name)\n", - "\n", - "# cleanup output format\n", - "df = pd.read_csv(result_file, delimiter=\" \", header=None)\n", - "df.columns = [\"sepal.length\", \"sepal.width\", \"petal.length\", \"petal.width\", \"variety\"]\n", - "print(\"Prediction has \", df.shape[0], \" rows\")\n", - "\n", - "random_subset = df.sample(n=20)\n", - "random_subset.head(20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cleanup compute resources\n", - "For re-occurring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single run job, we are free to release the allocated compute resources." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment below and run if compute resources are no longer needed \n", - "# compute_target.delete()" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Azure Machine Learning Pipelines for Batch Inference for CSV Files\n", + "\n", + "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", + "\n", + "> **Tip**\n", + "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", + "\n", + "In this example we will take use a machine learning model already trained to predict different types of iris flowers and run that trained model on some of the data in a CSV file which has characteristics of different iris flowers. However, the same example can be extended to manipulating data to any embarrassingly-parallel processing through a python script.\n", + "\n", + "The outline of this notebook is as follows:\n", + "\n", + "- Create a DataStore referencing the CSV files stored in a blob container.\n", + "- Register the pretrained model into the model registry. \n", + "- Use the registered model to do batch inference on the CSV files in the data blob container.\n", + "\n", + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to workspace\n", + "Create a workspace object from the existing workspace. Workspace.from_config() reads the file config.json and loads the details into an object named ws." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print('Workspace name: ' + ws.name, \n", + " 'Azure region: ' + ws.location, \n", + " 'Subscription id: ' + ws.subscription_id, \n", + " 'Resource group: ' + ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create or Attach existing compute resource\n", + "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\n", + "**Creation of compute takes approximately 5 minutes. If the AmlCompute with that name is already in your workspace the code will skip the creation process.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from azureml.core.compute import AmlCompute, ComputeTarget\n", + "\n", + "# choose a name for your cluster\n", + "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n", + "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n", + "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 4)\n", + "\n", + "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n", + "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n", + "\n", + "\n", + "if compute_name in ws.compute_targets:\n", + " compute_target = ws.compute_targets[compute_name]\n", + " if compute_target and type(compute_target) is AmlCompute:\n", + " print('found compute target. just use it. ' + compute_name)\n", + "else:\n", + " print('creating a new compute target...')\n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n", + " min_nodes = compute_min_nodes, \n", + " max_nodes = compute_max_nodes)\n", + "\n", + " # create the cluster\n", + " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", + " \n", + " # can poll for a minimum number of nodes and for a specific timeout.\n", + " # if no min node count is provided it will use the scale settings for the cluster\n", + " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", + " \n", + " # For a more detailed view of current AmlCompute status, use get_status()\n", + " print(compute_target.get_status().serialize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a datastore containing sample images\n", + "The input dataset used for this notebook is CSV data which has attributes of different iris flowers. We have created a public blob container `sampledata` on an account named `pipelinedata`, containing iris data set. In the next step, we create a datastore with the name `iris_datastore`, which points to this container. In the call to `register_azure_blob_container` below, setting the `overwrite` flag to `True` overwrites any datastore that was created previously with that name. \n", + "\n", + "This step can be changed to point to your blob container by providing your own `datastore_name`, `container_name`, and `account_name`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.datastore import Datastore\n", + "\n", + "account_name = \"pipelinedata\"\n", + "datastore_name=\"iris_datastore_data\"\n", + "container_name=\"sampledata\"\n", + "\n", + "iris_data = Datastore.register_azure_blob_container(ws, \n", + " datastore_name=datastore_name, \n", + " container_name= container_name, \n", + " account_name=account_name, \n", + " overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a TabularDataset\n", + "A [TabularDataSet](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) references single or multiple files which contain data in a tabular structure (ie like CSV files) in your datastores or public urls. TabularDatasets provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.\n", + "You can use dataset objects as inputs. Register the datasets to the workspace if you want to reuse them later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.dataset import Dataset\n", + "\n", + "iris_ds_name = 'iris_data'\n", + "\n", + "path_on_datastore = iris_data.path('iris/')\n", + "input_iris_ds = Dataset.Tabular.from_delimited_files(path=path_on_datastore, validate=False)\n", + "named_iris_ds = input_iris_ds.as_named_input(iris_ds_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intermediate/Output Data\n", + "Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineData\n", + "\n", + "datastore = ws.get_default_datastore()\n", + "output_folder = PipelineData(name='inferences', datastore=datastore)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Registering the Model with the Workspace\n", + "Get the pretrained model from a publicly available Azure Blob container, then register it to use in your workspace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_container_name=\"iris-model\"\n", + "model_datastore_name=\"iris_model_datastore\"\n", + "\n", + "model_datastore = Datastore.register_azure_blob_container(ws, \n", + " datastore_name=model_datastore_name, \n", + " container_name= model_container_name, \n", + " account_name=account_name, \n", + " overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "\n", + "model_datastore.download('iris_model.pkl')\n", + "\n", + "# register downloaded model\n", + "model = Model.register(model_path = \"iris_model.pkl/iris_model.pkl\",\n", + " model_name = \"iris-prs\", # this is the name the model is registered as\n", + " tags = {'pretrained': \"iris\"},\n", + " workspace = ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using your model to make batch predictions\n", + "To use the model to make batch predictions, you need an **entry script** and a list of **dependencies**:\n", + "\n", + "#### An entry script\n", + "This script accepts requests, scores the requests by using the model, and returns the results.\n", + "- __init()__ - Typically this function loads the model into a global object. This function is run only once at the start of batch processing per worker node/process. init method can make use of following environment variables (ParallelRunStep input):\n", + " 1.\tAZUREML_BI_OUTPUT_PATH – output folder path\n", + "- __run(mini_batch)__ - The method to be parallelized. Each invocation will have one minibatch.
      \n", + "__mini_batch__: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.
      \n", + "__run__ method response: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.\n", + " User should make sure that enough data is included in inference result to map input to inference. Inference output will be written in output file and not guaranteed to be in order, user should use some key in the output to map it to input.\n", + " \n", + "\n", + "#### Dependencies\n", + "Helper scripts or Python/Conda packages required to run the entry script.\n", + "\n", + "## Print inferencing script" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scripts_folder = \"Code\"\n", + "script_file = \"iris_score.py\"\n", + "\n", + "# peek at contents\n", + "with open(os.path.join(scripts_folder, script_file)) as inference_file:\n", + " print(inference_file.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and run the batch inference pipeline\n", + "The data, models, and compute resource are now available. Let's put all these together in a pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specify the environment to run the script\n", + "Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment.\n", + "* Always include **azureml-core** and **azureml-dataset-runtime\\[fuse\\]** in the pip package list to make ParallelRunStep run properly.\n", + "* For TabularDataset, add **pandas** as `run(mini_batch)` uses `pandas.DataFrame` as mini_batch type.\n", + "\n", + "If you're using custom image (`batch_env.python.user_managed_dependencies = True`), you need to install the package to your image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.runconfig import CondaDependencies\n", + "\n", + "predict_conda_deps = CondaDependencies.create(pip_packages=[\"scikit-learn==0.20.3\",\n", + " \"azureml-core\", \"azureml-dataset-runtime[pandas,fuse]\"])\n", + "\n", + "predict_env = Environment(name=\"predict_environment\")\n", + "predict_env.python.conda_dependencies = predict_conda_deps\n", + "predict_env.spark.precache_packages = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the configuration to wrap the inference script" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig\n", + "\n", + "# In a real-world scenario, you'll want to shape your process per node and nodes to fit your problem domain.\n", + "parallel_run_config = ParallelRunConfig(\n", + " source_directory=scripts_folder,\n", + " entry_script=script_file, # the user script to run against each input\n", + " mini_batch_size='1KB',\n", + " error_threshold=5,\n", + " output_action='append_row',\n", + " append_row_file_name=\"iris_outputs.txt\",\n", + " environment=predict_env,\n", + " compute_target=compute_target, \n", + " node_count=2,\n", + " run_invocation_timeout=600\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the pipeline step\n", + "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use ParallelRunStep to create the pipeline step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "distributed_csv_iris_step = ParallelRunStep(\n", + " name='example-iris',\n", + " inputs=[named_iris_ds],\n", + " output=output_folder,\n", + " parallel_run_config=parallel_run_config,\n", + " arguments=['--model_name', 'iris-prs'],\n", + " allow_reuse=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the pipeline\n", + "At this point you can run the pipeline and examine the output it produced. The Experiment object is used to track the run of the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "from azureml.pipeline.core import Pipeline\n", + "\n", + "pipeline = Pipeline(workspace=ws, steps=[distributed_csv_iris_step])\n", + "\n", + "pipeline_run = Experiment(ws, 'iris-prs').submit(pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View progress of Pipeline run\n", + "\n", + "The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This will output information of the pipeline run, including the link to the details page of portal.\n", + "pipeline_run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional: View detailed logs (streaming) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "## Wait the run for completion and show output log to console\n", + "pipeline_run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View Results\n", + "In the iris_score.py file above you can see that the Result with the prediction of the iris variety gets returned and then appended to the original input of the row from the csv file. These results are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This contains the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to a random 20 rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import tempfile\n", + "\n", + "prediction_run = pipeline_run.find_step_run(distributed_csv_iris_step.name)[0]\n", + "prediction_output = prediction_run.get_output_data(output_folder.name)\n", + "\n", + "target_dir = tempfile.mkdtemp()\n", + "prediction_output.download(local_path=target_dir)\n", + "result_file = os.path.join(target_dir, prediction_output.path_on_datastore, parallel_run_config.append_row_file_name)\n", + "\n", + "# cleanup output format\n", + "df = pd.read_csv(result_file, delimiter=\" \", header=None)\n", + "df.columns = [\"sepal.length\", \"sepal.width\", \"petal.length\", \"petal.width\", \"variety\"]\n", + "print(\"Prediction has \", df.shape[0], \" rows\")\n", + "\n", + "random_subset = df.sample(n=20)\n", + "random_subset.head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup compute resources\n", + "For re-occurring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single run job, we are free to release the allocated compute resources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment below and run if compute resources are no longer needed \n", + "# compute_target.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "joringer" + }, + { + "name": "asraniwa" + }, + { + "name": "pansav" + }, + { + "name": "tracych" + } + ], + "category": "Other notebooks", + "compute": [ + "AML Compute" + ], + "datasets": [ + "IRIS" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "None" + ], + "friendly_name": "IRIS data inferencing using ParallelRunStep", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + }, + "tags": [ + "Batch Inferencing", + "Pipeline" ], - "metadata": { - "authors": [ - { - "name": "joringer" - }, - { - "name": "asraniwa" - }, - { - "name": "pansav" - }, - { - "name": "tracych" - } - ], - "category": "Other notebooks", - "compute": [ - "AML Compute" - ], - "datasets": [ - "IRIS" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "IRIS data inferencing using ParallelRunStep", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - }, - "tags": [ - "Batch Inferencing", - "Pipeline" - ], - "task": "Recognize flower type" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "task": "Recognize flower type" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb index 647fcb490..9ee3df17a 100644 --- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb @@ -1,427 +1,427 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Azure Machine Learning Pipelines for Batch Inference for tabular input partitioned by column value\n", - "\n", - "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", - "\n", - "> **Tip**\n", - "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", - "\n", - "This example will create a partitioned tabular dataset by splitting the rows in a large csv file by its value on specified column. Each partition will form up a mini-batch in the parallel processing procedure.\n", - "\n", - "The outline of this notebook is as follows:\n", - "\n", - "- Create a tabular dataset partitioned by value on specified column.\n", - "- Do batch inference on the dataset with each mini-batch corresponds to one partition.\n", - "\n", - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Connect to workspace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')\n", - "\n", - "datastore = ws.get_default_datastore()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "print(azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download OJ sales data from opendataset url" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "\n", - "oj_sales_path = \"./oj.csv\"\n", - "r = requests.get(\"http://www.cs.unitn.it/~taufer/Data/oj.csv\")\n", - "open(oj_sales_path, \"wb\").write(r.content)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Upload OJ sales data to datastore" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore.upload_files([oj_sales_path], \".\", \"oj_sales_data\", overwrite=True, show_progress=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create tabular dataset\n", - "Create normal tabular dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "\n", - "dataset = Dataset.Tabular.from_delimited_files(path=(datastore, 'oj_sales_data/*.csv'))\n", - "print(dataset.to_pandas_dataframe())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Partition the tabular dataset\n", - "Partition the dataset by column 'store' and 'brand'. You can get a partition of data by specifying the value of one or more partition keys. E.g., by specifying `store=1000 and brand='tropicana'`, you can get all the rows that matches this condition in the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "partitioned_dataset = dataset.partition_by(partition_keys=['store', 'brand'], target=(datastore, \"partition_by_key_res\"), name=\"partitioned_oj_data\")\n", - "partitioned_dataset.partition_keys" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach existing compute resource" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "\n", - "# choose a name for your cluster\n", - "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n", - "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n", - "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 2)\n", - "\n", - "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n", - "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n", - "\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " print('found compute target. just use it. ' + compute_name)\n", - "else:\n", - " print('creating a new compute target...')\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n", - " min_nodes = compute_min_nodes, \n", - " max_nodes = compute_max_nodes)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - " \n", - " # can poll for a minimum number of nodes and for a specific timeout. \n", - " # if no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - " # For a more detailed view of current AmlCompute status, use get_status()\n", - " print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Intermediate/Output Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import Pipeline, PipelineData\n", - "\n", - "output_dir = PipelineData(name=\"inferences\", datastore=datastore)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate total revenue of each mini-batch partitioned by dataset partition key(s)\n", - "The script sum up the total revenue of a mini-batch." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scripts_folder = \"Code\"\n", - "script_file = \"total_income.py\"\n", - "\n", - "# peek at contents\n", - "with open(os.path.join(scripts_folder, script_file)) as inference_file:\n", - " print(inference_file.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and run the batch inference pipeline\n", - "### Specify the environment to run the script\n", - "You would need to specify the required private azureml packages in dependencies. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n", - "\n", - "batch_conda_deps = CondaDependencies.create(pip_packages=[\"azureml-core\", \"azureml-dataset-runtime[fuse,pandas]\"])\n", - "batch_env = Environment(name=\"batch_environment\")\n", - "batch_env.python.conda_dependencies = batch_conda_deps\n", - "batch_env.docker.base_image = DEFAULT_CPU_IMAGE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the configuration to wrap the inference script\n", - "The parameter `partition_keys` is a list containing a subset of the dataset partition keys, specifying how is the input dataset partitioned. Each and every possible combination of values of partition_keys will form up a mini-batch. E.g., by specifying `partition_keys=['store', 'brand']` will result in mini-batches like `store=1000 && brand=tropicana`, `store=1000 && brand=dominicks`, `store=1001 && brand=dominicks`, ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig\n", - "\n", - "# In a real-world scenario, you'll want to shape your process per node and nodes to fit your problem domain.\n", - "parallel_run_config = ParallelRunConfig(\n", - " source_directory=scripts_folder,\n", - " entry_script=script_file, # the user script to run against each input\n", - " partition_keys=['store', 'brand'],\n", - " error_threshold=5,\n", - " output_action='append_row',\n", - " append_row_file_name=\"revenue_outputs.txt\",\n", - " environment=batch_env,\n", - " compute_target=compute_target, \n", - " node_count=2,\n", - " run_invocation_timeout=600\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the pipeline step" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "parallel_run_step = ParallelRunStep(\n", - " name='summarize-revenue',\n", - " inputs=[partitioned_dataset.as_named_input(\"partitioned_tabular_input\")],\n", - " output=output_dir,\n", - " parallel_run_config=parallel_run_config,\n", - " allow_reuse=False\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "from azureml.pipeline.core import Pipeline\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[parallel_run_step])\n", - "\n", - "pipeline_run = Experiment(ws, 'tabular-dataset-partition').submit(pipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View the prediction results\n", - "In the total_income.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called inferences. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import tempfile\n", - "\n", - "batch_run = pipeline_run.find_step_run(parallel_run_step.name)[0]\n", - "batch_output = batch_run.get_output_data(output_dir.name)\n", - "\n", - "target_dir = tempfile.mkdtemp()\n", - "batch_output.download(local_path=target_dir)\n", - "result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n", - "\n", - "df = pd.read_csv(result_file, delimiter=\" \", header=None)\n", - "\n", - "df.columns = [\"week\", \"logmove\", \"feat\", \"price\", \"AGE60\", \"EDUC\", \"ETHNIC\", \"INCOME\", \"HHLARGE\", \"WORKWOM\", \"HVAL150\", \"SSTRDIST\", \"SSTRVOL\", \"CPDIST5\", \"CPWVOL5\", \"store\", \"brand\", \"total_income\"]\n", - "print(\"Prediction has \", df.shape[0], \" rows\")\n", - "df.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved. \n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Azure Machine Learning Pipelines for Batch Inference for tabular input partitioned by column value\n", + "\n", + "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", + "\n", + "> **Tip**\n", + "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", + "\n", + "This example will create a partitioned tabular dataset by splitting the rows in a large csv file by its value on specified column. Each partition will form up a mini-batch in the parallel processing procedure.\n", + "\n", + "The outline of this notebook is as follows:\n", + "\n", + "- Create a tabular dataset partitioned by value on specified column.\n", + "- Do batch inference on the dataset with each mini-batch corresponds to one partition.\n", + "\n", + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to workspace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.workspace import Workspace\n", + "ws = Workspace.from_config()\n", + "print('Workspace name: ' + ws.name, \n", + " 'Azure region: ' + ws.location, \n", + " 'Subscription id: ' + ws.subscription_id, \n", + " 'Resource group: ' + ws.resource_group, sep = '\\n')\n", + "\n", + "datastore = ws.get_default_datastore()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "print(azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download OJ sales data from opendataset url" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "oj_sales_path = \"./oj.csv\"\n", + "r = requests.get(\"http://www.cs.unitn.it/~taufer/Data/oj.csv\")\n", + "open(oj_sales_path, \"wb\").write(r.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Upload OJ sales data to datastore" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "datastore.upload_files([oj_sales_path], \".\", \"oj_sales_data\", overwrite=True, show_progress=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create tabular dataset\n", + "Create normal tabular dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Dataset\n", + "\n", + "dataset = Dataset.Tabular.from_delimited_files(path=(datastore, 'oj_sales_data/*.csv'))\n", + "print(dataset.to_pandas_dataframe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Partition the tabular dataset\n", + "Partition the dataset by column 'store' and 'brand'. You can get a partition of data by specifying the value of one or more partition keys. E.g., by specifying `store=1000 and brand='tropicana'`, you can get all the rows that matches this condition in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "partitioned_dataset = dataset.partition_by(partition_keys=['store', 'brand'], target=(datastore, \"partition_by_key_res\"), name=\"partitioned_oj_data\")\n", + "partitioned_dataset.partition_keys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create or Attach existing compute resource" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from azureml.core.compute import AmlCompute, ComputeTarget\n", + "\n", + "# choose a name for your cluster\n", + "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n", + "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n", + "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 2)\n", + "\n", + "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n", + "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n", + "\n", + "\n", + "if compute_name in ws.compute_targets:\n", + " compute_target = ws.compute_targets[compute_name]\n", + " if compute_target and type(compute_target) is AmlCompute:\n", + " print('found compute target. just use it. ' + compute_name)\n", + "else:\n", + " print('creating a new compute target...')\n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n", + " min_nodes = compute_min_nodes, \n", + " max_nodes = compute_max_nodes)\n", + "\n", + " # create the cluster\n", + " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", + " \n", + " # can poll for a minimum number of nodes and for a specific timeout. \n", + " # if no min node count is provided it will use the scale settings for the cluster\n", + " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", + " \n", + " # For a more detailed view of current AmlCompute status, use get_status()\n", + " print(compute_target.get_status().serialize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intermediate/Output Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import Pipeline, PipelineData\n", + "\n", + "output_dir = PipelineData(name=\"inferences\", datastore=datastore)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate total revenue of each mini-batch partitioned by dataset partition key(s)\n", + "The script sum up the total revenue of a mini-batch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scripts_folder = \"Code\"\n", + "script_file = \"total_income.py\"\n", + "\n", + "# peek at contents\n", + "with open(os.path.join(scripts_folder, script_file)) as inference_file:\n", + " print(inference_file.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and run the batch inference pipeline\n", + "### Specify the environment to run the script\n", + "You would need to specify the required private azureml packages in dependencies. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n", + "\n", + "batch_conda_deps = CondaDependencies.create(pip_packages=[\"azureml-core\", \"azureml-dataset-runtime[fuse,pandas]\"])\n", + "batch_env = Environment(name=\"batch_environment\")\n", + "batch_env.python.conda_dependencies = batch_conda_deps\n", + "batch_env.docker.base_image = DEFAULT_CPU_IMAGE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the configuration to wrap the inference script\n", + "The parameter `partition_keys` is a list containing a subset of the dataset partition keys, specifying how is the input dataset partitioned. Each and every possible combination of values of partition_keys will form up a mini-batch. E.g., by specifying `partition_keys=['store', 'brand']` will result in mini-batches like `store=1000 && brand=tropicana`, `store=1000 && brand=dominicks`, `store=1001 && brand=dominicks`, ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig\n", + "\n", + "# In a real-world scenario, you'll want to shape your process per node and nodes to fit your problem domain.\n", + "parallel_run_config = ParallelRunConfig(\n", + " source_directory=scripts_folder,\n", + " entry_script=script_file, # the user script to run against each input\n", + " partition_keys=['store', 'brand'],\n", + " error_threshold=5,\n", + " output_action='append_row',\n", + " append_row_file_name=\"revenue_outputs.txt\",\n", + " environment=batch_env,\n", + " compute_target=compute_target, \n", + " node_count=2,\n", + " run_invocation_timeout=600\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the pipeline step" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parallel_run_step = ParallelRunStep(\n", + " name='summarize-revenue',\n", + " inputs=[partitioned_dataset.as_named_input(\"partitioned_tabular_input\")],\n", + " output=output_dir,\n", + " parallel_run_config=parallel_run_config,\n", + " allow_reuse=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "from azureml.pipeline.core import Pipeline\n", + "\n", + "pipeline = Pipeline(workspace=ws, steps=[parallel_run_step])\n", + "\n", + "pipeline_run = Experiment(ws, 'tabular-dataset-partition').submit(pipeline)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View the prediction results\n", + "In the total_income.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called inferences. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import tempfile\n", + "\n", + "batch_run = pipeline_run.find_step_run(parallel_run_step.name)[0]\n", + "batch_output = batch_run.get_output_data(output_dir.name)\n", + "\n", + "target_dir = tempfile.mkdtemp()\n", + "batch_output.download(local_path=target_dir)\n", + "result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n", + "\n", + "df = pd.read_csv(result_file, delimiter=\" \", header=None)\n", + "\n", + "df.columns = [\"week\", \"logmove\", \"feat\", \"price\", \"AGE60\", \"EDUC\", \"ETHNIC\", \"INCOME\", \"HHLARGE\", \"WORKWOM\", \"HVAL150\", \"SSTRDIST\", \"SSTRVOL\", \"CPDIST5\", \"CPWVOL5\", \"store\", \"brand\", \"total_income\"]\n", + "print(\"Prediction has \", df.shape[0], \" rows\")\n", + "df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "authors": [ + { + "name": "pansav" + }, + { + "name": "tracych" + }, + { + "name": "migu" + } + ], + "category": "Other notebooks", + "compute": [ + "AML Compute" ], - "metadata": { - "authors": [ - { - "name": "pansav" - }, - { - "name": "tracych" - }, - { - "name": "migu" - } - ], - "category": "Other notebooks", - "compute": [ - "AML Compute" - ], - "datasets": [ - "OJ Sales Data" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Batch inferencing OJ Sales Data partitioned by column using ParallelRunStep", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file + "datasets": [ + "OJ Sales Data" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "None" + ], + "friendly_name": "Batch inferencing OJ Sales Data partitioned by column using ParallelRunStep", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb index 32000b075..309ab60f1 100644 --- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb @@ -1,754 +1,754 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Neural style transfer on video\n", - "Using modified code from `pytorch`'s neural style [example](https://pytorch.org/tutorials/advanced/neural_style_tutorial.html), we show how to setup a pipeline for doing style transfer on video. The pipeline has following steps:\n", - "1. Split a video into images\n", - "2. Run neural style on each image using one of the provided models (from `pytorch` pretrained models for this example).\n", - "3. Stitch the image back into a video.\n", - "\n", - "> **Tip**\n", - "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace, Experiment\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "from azureml.core import Datastore, Dataset\n", - "from azureml.pipeline.core import Pipeline\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "from azureml.data import OutputFileDatasetConfig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "# create directory for model\n", - "model_dir = 'models'\n", - "if not os.path.isdir(model_dir):\n", - " os.mkdir(model_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib.request\n", - "\n", - "def download_model(model_name):\n", - " # downloaded models from https://pytorch.org/tutorials/advanced/neural_style_tutorial.html are kept here\n", - " url = \"https://pipelinedata.blob.core.windows.net/styletransfer/saved_models/\" + model_name\n", - " local_path = os.path.join(model_dir, model_name)\n", - " urllib.request.urlretrieve(url, local_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Register all Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.model import Model\n", - "mosaic_model = None\n", - "candy_model = None\n", - "\n", - "models = Model.list(workspace=ws, tags=['scenario'])\n", - "for m in models:\n", - " print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)\n", - " if m.name == 'mosaic' and mosaic_model is None:\n", - " mosaic_model = m\n", - " elif m.name == 'candy' and candy_model is None:\n", - " candy_model = m\n", - "\n", - "if mosaic_model is None:\n", - " print('Mosaic model does not exist, registering it')\n", - " download_model('mosaic.pth')\n", - " mosaic_model = Model.register(model_path = os.path.join(model_dir, \"mosaic.pth\"),\n", - " model_name = \"mosaic\",\n", - " tags = {'type': \"mosaic\", 'scenario': \"Style transfer using batch inference\"},\n", - " description = \"Style transfer - Mosaic\",\n", - " workspace = ws)\n", - "else:\n", - " print('Reusing existing mosaic model')\n", - " \n", - "\n", - "if candy_model is None:\n", - " print('Candy model does not exist, registering it')\n", - " download_model('candy.pth')\n", - " candy_model = Model.register(model_path = os.path.join(model_dir, \"candy.pth\"),\n", - " model_name = \"candy\",\n", - " tags = {'type': \"candy\", 'scenario': \"Style transfer using batch inference\"},\n", - " description = \"Style transfer - Candy\",\n", - " workspace = ws)\n", - "else:\n", - " print('Reusing existing candy model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create or use existing compute\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# AmlCompute\n", - "cpu_cluster_name = \"cpu-cluster\"\n", - "try:\n", - " cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n", - " print(\"found existing cluster.\")\n", - "except ComputeTargetException:\n", - " print(\"creating new cluster\")\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_v2\",\n", - " max_nodes = 1)\n", - "\n", - " # create the cluster\n", - " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n", - " cpu_cluster.wait_for_completion(show_output=True)\n", - " \n", - "# AmlCompute\n", - "gpu_cluster_name = \"gpu-cluster\"\n", - "try:\n", - " gpu_cluster = AmlCompute(ws, gpu_cluster_name)\n", - " print(\"found existing cluster.\")\n", - "except ComputeTargetException:\n", - " print(\"creating new cluster\")\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n", - " max_nodes = 3)\n", - "\n", - " # create the cluster\n", - " gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n", - " gpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Python Scripts\n", - "We use an edited version of `neural_style_mpi.py` (original is [here](https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py)). Scripts to split and stitch the video are thin wrappers to calls to `ffmpeg`. \n", - "\n", - "We install `ffmpeg` through conda dependencies." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scripts_folder = \"scripts\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "process_video_script_file = \"process_video.py\"\n", - "\n", - "# peek at contents\n", - "with open(os.path.join(scripts_folder, process_video_script_file)) as process_video_file:\n", - " print(process_video_file.read())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stitch_video_script_file = \"stitch_video.py\"\n", - "\n", - "# peek at contents\n", - "with open(os.path.join(scripts_folder, stitch_video_script_file)) as stitch_video_file:\n", - " print(stitch_video_file.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The sample video **organutan.mp4** is stored at a publicly shared datastore. We are registering the datastore below. If you want to take a look at the original video, click here. (https://pipelinedata.blob.core.windows.net/sample-videos/orangutan.mp4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# datastore for input video\n", - "account_name = \"pipelinedata\"\n", - "video_ds = Datastore.register_azure_blob_container(ws, \"videos\", \"sample-videos\",\n", - " account_name=account_name, overwrite=True)\n", - "\n", - "# the default blob store attached to a workspace\n", - "default_datastore = ws.get_default_datastore()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sample video" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "video_name=os.getenv(\"STYLE_TRANSFER_VIDEO_NAME\", \"orangutan.mp4\") \n", - "orangutan_video = Dataset.File.from_files((video_ds,video_name))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cd = CondaDependencies()\n", - "\n", - "cd.add_channel(\"conda-forge\")\n", - "cd.add_conda_package(\"ffmpeg==4.0.2\")\n", - "\n", - "# Runconfig\n", - "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n", - "amlcompute_run_config.environment.docker.base_image = \"pytorch/pytorch\"\n", - "amlcompute_run_config.environment.spark.precache_packages = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ffmpeg_audio = OutputFileDatasetConfig(name=\"ffmpeg_audio\")\n", - "processed_images = OutputFileDatasetConfig(name=\"processed_images\")\n", - "output_video = OutputFileDatasetConfig(name=\"output_video\")\n", - "\n", - "ffmpeg_images = OutputFileDatasetConfig(name=\"ffmpeg_images\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define tweakable parameters to pipeline\n", - "These parameters can be changed when the pipeline is published and rerun from a REST call.\n", - "As part of ParallelRunStep following 2 pipeline parameters will be created which can be used to override values.\n", - " node_count\n", - " process_count_per_node" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core.graph import PipelineParameter\n", - "# create a parameter for style (one of \"candy\", \"mosaic\") to transfer the images to\n", - "style_param = PipelineParameter(name=\"style\", default_value=\"mosaic\")\n", - "# create a parameter for the number of nodes to use in step no. 2 (style transfer)\n", - "nodecount_param = PipelineParameter(name=\"nodecount\", default_value=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "split_video_step = PythonScriptStep(\n", - " name=\"split video\",\n", - " script_name=\"process_video.py\",\n", - " arguments=[\"--input_video\", orangutan_video.as_mount(),\n", - " \"--output_audio\", ffmpeg_audio,\n", - " \"--output_images\", ffmpeg_images],\n", - " compute_target=cpu_cluster,\n", - " runconfig=amlcompute_run_config,\n", - " source_directory=scripts_folder\n", - ")\n", - "\n", - "stitch_video_step = PythonScriptStep(\n", - " name=\"stitch\",\n", - " script_name=\"stitch_video.py\",\n", - " arguments=[\"--images_dir\", processed_images.as_input(), \n", - " \"--input_audio\", ffmpeg_audio.as_input(), \n", - " \"--output_dir\", output_video],\n", - " compute_target=cpu_cluster,\n", - " runconfig=amlcompute_run_config,\n", - " source_directory=scripts_folder\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create environment, parallel step run config and parallel run step" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n", - "\n", - "parallel_cd = CondaDependencies()\n", - "\n", - "parallel_cd.add_channel(\"pytorch\")\n", - "parallel_cd.add_conda_package(\"pytorch\")\n", - "parallel_cd.add_conda_package(\"torchvision\")\n", - "parallel_cd.add_conda_package(\"pillow<7\") # needed for torchvision==0.4.0\n", - "parallel_cd.add_pip_package(\"azureml-core\")\n", - "\n", - "styleenvironment = Environment(name=\"styleenvironment\")\n", - "styleenvironment.python.conda_dependencies=parallel_cd\n", - "styleenvironment.docker.base_image = DEFAULT_GPU_IMAGE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PipelineParameter\n", - "from azureml.pipeline.steps import ParallelRunConfig\n", - "\n", - "parallel_run_config = ParallelRunConfig(\n", - " environment=styleenvironment,\n", - " entry_script='transform.py',\n", - " output_action='summary_only',\n", - " mini_batch_size=\"1\",\n", - " error_threshold=1,\n", - " source_directory=scripts_folder,\n", - " compute_target=gpu_cluster, \n", - " node_count=nodecount_param,\n", - " process_count_per_node=2\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import ParallelRunStep\n", - "from datetime import datetime\n", - "\n", - "parallel_step_name = 'styletransfer-' + datetime.now().strftime('%Y%m%d%H%M')\n", - "\n", - "distributed_style_transfer_step = ParallelRunStep(\n", - " name=parallel_step_name,\n", - " inputs=[ffmpeg_images], # Input file share/blob container/file dataset\n", - " output=processed_images, # Output file share/blob container\n", - " arguments=[\"--style\", style_param],\n", - " parallel_run_config=parallel_run_config,\n", - " allow_reuse=False #[optional - default value True]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline = Pipeline(workspace=ws, steps=[stitch_video_step])\n", - "\n", - "pipeline.validate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# submit the pipeline and provide values for the PipelineParameters used in the pipeline\n", - "pipeline_run = Experiment(ws, 'styletransfer_parallel_mosaic').submit(pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Monitor pipeline run\n", - "\n", - "The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will output information of the pipeline run, including the link to the details page of portal.\n", - "pipeline_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optional: View detailed logs (streaming) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Wait the run for completion and show output log to console\n", - "pipeline_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download output video" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Downloads the video in `output_video` folder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def download_video(run, target_dir=None):\n", - " stitch_run = run.find_step_run(stitch_video_step.name)[0]\n", - " port_data = stitch_run.get_details()['outputDatasets'][0]['dataset']\n", - " port_data.download(target_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_run.wait_for_completion()\n", - "download_video(pipeline_run, \"output_video_mosaic\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Publish pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_name = \"style-transfer-batch-inference\"\n", - "print(pipeline_name)\n", - "\n", - "published_pipeline = pipeline.publish(\n", - " name=pipeline_name, \n", - " description=pipeline_name)\n", - "print(\"Newly published pipeline id: {}\".format(published_pipeline.id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Get published pipeline\n", - "This is another way to get the published pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core import PublishedPipeline\n", - "\n", - "# You could retrieve all pipelines that are published, or \n", - "# just get the published pipeline object that you have the ID for.\n", - "\n", - "# Get all published pipeline objects in the workspace\n", - "all_pub_pipelines = PublishedPipeline.list(ws)\n", - "\n", - "# We will iterate through the list of published pipelines and \n", - "# use the last ID in the list for Schelue operations: \n", - "print(\"Published pipelines found in the workspace:\")\n", - "for pub_pipeline in all_pub_pipelines:\n", - " print(\"Name:\", pub_pipeline.name,\"\\tDescription:\", pub_pipeline.description, \"\\tId:\", pub_pipeline.id, \"\\tStatus:\", pub_pipeline.status)\n", - " if(pub_pipeline.name == pipeline_name):\n", - " published_pipeline = pub_pipeline\n", - "\n", - "print(\"Published pipeline id: {}\".format(published_pipeline.id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run pipeline through REST calls for other styles\n", - "\n", - "# Get AAD token" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.authentication import InteractiveLoginAuthentication\n", - "import requests\n", - "\n", - "auth = InteractiveLoginAuthentication()\n", - "aad_token = auth.get_authentication_header()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Get endpoint URL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rest_endpoint = published_pipeline.endpoint\n", - "print(\"Pipeline REST endpoing: {}\".format(rest_endpoint))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Send request and monitor" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_name = 'styletransfer_parallel_candy'\n", - "response = requests.post(rest_endpoint, \n", - " headers=aad_token,\n", - " json={\"ExperimentName\": experiment_name,\n", - " \"ParameterAssignments\": {\"style\": \"candy\", \"NodeCount\": 3}})\n", - "\n", - "run_id = response.json()[\"Id\"]\n", - "\n", - "from azureml.pipeline.core.run import PipelineRun\n", - "published_pipeline_run_candy = PipelineRun(ws.experiments[experiment_name], run_id)\n", - "\n", - "# Show detail information of run\n", - "published_pipeline_run_candy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download output from re-run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline_run_candy.wait_for_completion()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "download_video(published_pipeline_run_candy, target_dir=\"output_video_candy\")" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural style transfer on video\n", + "Using modified code from `pytorch`'s neural style [example](https://pytorch.org/tutorials/advanced/neural_style_tutorial.html), we show how to setup a pipeline for doing style transfer on video. The pipeline has following steps:\n", + "1. Split a video into images\n", + "2. Run neural style on each image using one of the provided models (from `pytorch` pretrained models for this example).\n", + "3. Stitch the image back into a video.\n", + "\n", + "> **Tip**\n", + "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace, Experiment\n", + "\n", + "ws = Workspace.from_config()\n", + "print('Workspace name: ' + ws.name, \n", + " 'Azure region: ' + ws.location, \n", + " 'Subscription id: ' + ws.subscription_id, \n", + " 'Resource group: ' + ws.resource_group, sep = '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import AmlCompute, ComputeTarget\n", + "from azureml.core import Datastore, Dataset\n", + "from azureml.pipeline.core import Pipeline\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "from azureml.data import OutputFileDatasetConfig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# create directory for model\n", + "model_dir = 'models'\n", + "if not os.path.isdir(model_dir):\n", + " os.mkdir(model_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import urllib.request\n", + "\n", + "def download_model(model_name):\n", + " # downloaded models from https://pytorch.org/tutorials/advanced/neural_style_tutorial.html are kept here\n", + " url = \"https://pipelinedata.blob.core.windows.net/styletransfer/saved_models/\" + model_name\n", + " local_path = os.path.join(model_dir, model_name)\n", + " urllib.request.urlretrieve(url, local_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Register all Models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.model import Model\n", + "mosaic_model = None\n", + "candy_model = None\n", + "\n", + "models = Model.list(workspace=ws, tags=['scenario'])\n", + "for m in models:\n", + " print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)\n", + " if m.name == 'mosaic' and mosaic_model is None:\n", + " mosaic_model = m\n", + " elif m.name == 'candy' and candy_model is None:\n", + " candy_model = m\n", + "\n", + "if mosaic_model is None:\n", + " print('Mosaic model does not exist, registering it')\n", + " download_model('mosaic.pth')\n", + " mosaic_model = Model.register(model_path = os.path.join(model_dir, \"mosaic.pth\"),\n", + " model_name = \"mosaic\",\n", + " tags = {'type': \"mosaic\", 'scenario': \"Style transfer using batch inference\"},\n", + " description = \"Style transfer - Mosaic\",\n", + " workspace = ws)\n", + "else:\n", + " print('Reusing existing mosaic model')\n", + " \n", + "\n", + "if candy_model is None:\n", + " print('Candy model does not exist, registering it')\n", + " download_model('candy.pth')\n", + " candy_model = Model.register(model_path = os.path.join(model_dir, \"candy.pth\"),\n", + " model_name = \"candy\",\n", + " tags = {'type': \"candy\", 'scenario': \"Style transfer using batch inference\"},\n", + " description = \"Style transfer - Candy\",\n", + " workspace = ws)\n", + "else:\n", + " print('Reusing existing candy model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create or use existing compute\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# AmlCompute\n", + "cpu_cluster_name = \"cpu-cluster\"\n", + "try:\n", + " cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n", + " print(\"found existing cluster.\")\n", + "except ComputeTargetException:\n", + " print(\"creating new cluster\")\n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_v2\",\n", + " max_nodes = 1)\n", + "\n", + " # create the cluster\n", + " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n", + " cpu_cluster.wait_for_completion(show_output=True)\n", + " \n", + "# AmlCompute\n", + "gpu_cluster_name = \"gpu-cluster\"\n", + "try:\n", + " gpu_cluster = AmlCompute(ws, gpu_cluster_name)\n", + " print(\"found existing cluster.\")\n", + "except ComputeTargetException:\n", + " print(\"creating new cluster\")\n", + " provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n", + " max_nodes = 3)\n", + "\n", + " # create the cluster\n", + " gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n", + " gpu_cluster.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python Scripts\n", + "We use an edited version of `neural_style_mpi.py` (original is [here](https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py)). Scripts to split and stitch the video are thin wrappers to calls to `ffmpeg`. \n", + "\n", + "We install `ffmpeg` through conda dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scripts_folder = \"scripts\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "process_video_script_file = \"process_video.py\"\n", + "\n", + "# peek at contents\n", + "with open(os.path.join(scripts_folder, process_video_script_file)) as process_video_file:\n", + " print(process_video_file.read())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stitch_video_script_file = \"stitch_video.py\"\n", + "\n", + "# peek at contents\n", + "with open(os.path.join(scripts_folder, stitch_video_script_file)) as stitch_video_file:\n", + " print(stitch_video_file.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The sample video **organutan.mp4** is stored at a publicly shared datastore. We are registering the datastore below. If you want to take a look at the original video, click here. (https://pipelinedata.blob.core.windows.net/sample-videos/orangutan.mp4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# datastore for input video\n", + "account_name = \"pipelinedata\"\n", + "video_ds = Datastore.register_azure_blob_container(ws, \"videos\", \"sample-videos\",\n", + " account_name=account_name, overwrite=True)\n", + "\n", + "# the default blob store attached to a workspace\n", + "default_datastore = ws.get_default_datastore()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sample video" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "video_name=os.getenv(\"STYLE_TRANSFER_VIDEO_NAME\", \"orangutan.mp4\") \n", + "orangutan_video = Dataset.File.from_files((video_ds,video_name))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cd = CondaDependencies()\n", + "\n", + "cd.add_channel(\"conda-forge\")\n", + "cd.add_conda_package(\"ffmpeg==4.0.2\")\n", + "\n", + "# Runconfig\n", + "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n", + "amlcompute_run_config.environment.docker.base_image = \"pytorch/pytorch\"\n", + "amlcompute_run_config.environment.spark.precache_packages = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ffmpeg_audio = OutputFileDatasetConfig(name=\"ffmpeg_audio\")\n", + "processed_images = OutputFileDatasetConfig(name=\"processed_images\")\n", + "output_video = OutputFileDatasetConfig(name=\"output_video\")\n", + "\n", + "ffmpeg_images = OutputFileDatasetConfig(name=\"ffmpeg_images\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define tweakable parameters to pipeline\n", + "These parameters can be changed when the pipeline is published and rerun from a REST call.\n", + "As part of ParallelRunStep following 2 pipeline parameters will be created which can be used to override values.\n", + " node_count\n", + " process_count_per_node" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core.graph import PipelineParameter\n", + "# create a parameter for style (one of \"candy\", \"mosaic\") to transfer the images to\n", + "style_param = PipelineParameter(name=\"style\", default_value=\"mosaic\")\n", + "# create a parameter for the number of nodes to use in step no. 2 (style transfer)\n", + "nodecount_param = PipelineParameter(name=\"nodecount\", default_value=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "split_video_step = PythonScriptStep(\n", + " name=\"split video\",\n", + " script_name=\"process_video.py\",\n", + " arguments=[\"--input_video\", orangutan_video.as_mount(),\n", + " \"--output_audio\", ffmpeg_audio,\n", + " \"--output_images\", ffmpeg_images],\n", + " compute_target=cpu_cluster,\n", + " runconfig=amlcompute_run_config,\n", + " source_directory=scripts_folder\n", + ")\n", + "\n", + "stitch_video_step = PythonScriptStep(\n", + " name=\"stitch\",\n", + " script_name=\"stitch_video.py\",\n", + " arguments=[\"--images_dir\", processed_images.as_input(), \n", + " \"--input_audio\", ffmpeg_audio.as_input(), \n", + " \"--output_dir\", output_video],\n", + " compute_target=cpu_cluster,\n", + " runconfig=amlcompute_run_config,\n", + " source_directory=scripts_folder\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create environment, parallel step run config and parallel run step" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n", + "\n", + "parallel_cd = CondaDependencies()\n", + "\n", + "parallel_cd.add_channel(\"pytorch\")\n", + "parallel_cd.add_conda_package(\"pytorch\")\n", + "parallel_cd.add_conda_package(\"torchvision\")\n", + "parallel_cd.add_conda_package(\"pillow<7\") # needed for torchvision==0.4.0\n", + "parallel_cd.add_pip_package(\"azureml-core\")\n", + "\n", + "styleenvironment = Environment(name=\"styleenvironment\")\n", + "styleenvironment.python.conda_dependencies=parallel_cd\n", + "styleenvironment.docker.base_image = DEFAULT_GPU_IMAGE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineParameter\n", + "from azureml.pipeline.steps import ParallelRunConfig\n", + "\n", + "parallel_run_config = ParallelRunConfig(\n", + " environment=styleenvironment,\n", + " entry_script='transform.py',\n", + " output_action='summary_only',\n", + " mini_batch_size=\"1\",\n", + " error_threshold=1,\n", + " source_directory=scripts_folder,\n", + " compute_target=gpu_cluster, \n", + " node_count=nodecount_param,\n", + " process_count_per_node=2\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.steps import ParallelRunStep\n", + "from datetime import datetime\n", + "\n", + "parallel_step_name = 'styletransfer-' + datetime.now().strftime('%Y%m%d%H%M')\n", + "\n", + "distributed_style_transfer_step = ParallelRunStep(\n", + " name=parallel_step_name,\n", + " inputs=[ffmpeg_images], # Input file share/blob container/file dataset\n", + " output=processed_images, # Output file share/blob container\n", + " arguments=[\"--style\", style_param],\n", + " parallel_run_config=parallel_run_config,\n", + " allow_reuse=False #[optional - default value True]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline(workspace=ws, steps=[stitch_video_step])\n", + "\n", + "pipeline.validate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# submit the pipeline and provide values for the PipelineParameters used in the pipeline\n", + "pipeline_run = Experiment(ws, 'styletransfer_parallel_mosaic').submit(pipeline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Monitor pipeline run\n", + "\n", + "The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This will output information of the pipeline run, including the link to the details page of portal.\n", + "pipeline_run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional: View detailed logs (streaming) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Wait the run for completion and show output log to console\n", + "pipeline_run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download output video" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Downloads the video in `output_video` folder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def download_video(run, target_dir=None):\n", + " stitch_run = run.find_step_run(stitch_video_step.name)[0]\n", + " port_data = stitch_run.get_details()['outputDatasets'][0]['dataset']\n", + " port_data.download(target_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_run.wait_for_completion()\n", + "download_video(pipeline_run, \"output_video_mosaic\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Publish pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_name = \"style-transfer-batch-inference\"\n", + "print(pipeline_name)\n", + "\n", + "published_pipeline = pipeline.publish(\n", + " name=pipeline_name, \n", + " description=pipeline_name)\n", + "print(\"Newly published pipeline id: {}\".format(published_pipeline.id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get published pipeline\n", + "This is another way to get the published pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PublishedPipeline\n", + "\n", + "# You could retrieve all pipelines that are published, or \n", + "# just get the published pipeline object that you have the ID for.\n", + "\n", + "# Get all published pipeline objects in the workspace\n", + "all_pub_pipelines = PublishedPipeline.list(ws)\n", + "\n", + "# We will iterate through the list of published pipelines and \n", + "# use the last ID in the list for Schelue operations: \n", + "print(\"Published pipelines found in the workspace:\")\n", + "for pub_pipeline in all_pub_pipelines:\n", + " print(\"Name:\", pub_pipeline.name,\"\\tDescription:\", pub_pipeline.description, \"\\tId:\", pub_pipeline.id, \"\\tStatus:\", pub_pipeline.status)\n", + " if(pub_pipeline.name == pipeline_name):\n", + " published_pipeline = pub_pipeline\n", + "\n", + "print(\"Published pipeline id: {}\".format(published_pipeline.id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run pipeline through REST calls for other styles\n", + "\n", + "# Get AAD token" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.authentication import InteractiveLoginAuthentication\n", + "import requests\n", + "\n", + "auth = InteractiveLoginAuthentication()\n", + "aad_token = auth.get_authentication_header()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get endpoint URL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rest_endpoint = published_pipeline.endpoint\n", + "print(\"Pipeline REST endpoing: {}\".format(rest_endpoint))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Send request and monitor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "experiment_name = 'styletransfer_parallel_candy'\n", + "response = requests.post(rest_endpoint, \n", + " headers=aad_token,\n", + " json={\"ExperimentName\": experiment_name,\n", + " \"ParameterAssignments\": {\"style\": \"candy\", \"NodeCount\": 3}})\n", + "\n", + "run_id = response.json()[\"Id\"]\n", + "\n", + "from azureml.pipeline.core.run import PipelineRun\n", + "published_pipeline_run_candy = PipelineRun(ws.experiments[experiment_name], run_id)\n", + "\n", + "# Show detail information of run\n", + "published_pipeline_run_candy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download output from re-run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "published_pipeline_run_candy.wait_for_completion()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "download_video(published_pipeline_run_candy, target_dir=\"output_video_candy\")" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil joringer asraniwa pansav tracych" + } + ], + "category": "Other notebooks", + "compute": [ + "AML Compute" + ], + "datasets": [], + "deployment": [ + "None" + ], + "exclude_from_index": true, + "framework": [ + "None" + ], + "friendly_name": "Style transfer using ParallelRunStep", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "tags": [ + "Batch Inferencing", + "Pipeline" ], - "metadata": { - "authors": [ - { - "name": "sanpil joringer asraniwa pansav tracych" - } - ], - "category": "Other notebooks", - "compute": [ - "AML Compute" - ], - "datasets": [], - "deployment": [ - "None" - ], - "exclude_from_index": true, - "framework": [ - "None" - ], - "friendly_name": "Style transfer using ParallelRunStep", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "tags": [ - "Batch Inferencing", - "Pipeline" - ], - "task": "Style transfer" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "task": "Style transfer" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb b/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb deleted file mode 100644 index 9354a50be..000000000 --- a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb +++ /dev/null @@ -1,543 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Authentication in Azure Machine Learning\n", - "\n", - "This notebook shows you how to authenticate to your Azure ML Workspace using\n", - "\n", - " 1. Interactive Login Authentication\n", - " 2. Azure CLI Authentication\n", - " 3. Managed Service Identity (MSI) Authentication\n", - " 4. Service Principal Authentication\n", - " 5. Token Authentication\n", - " \n", - "The interactive authentication is suitable for local experimentation on your own computer. Azure CLI authentication is suitable if you are already using Azure CLI for managing Azure resources, and want to sign in only once. The MSI and Service Principal authentication are suitable for automated workflows, for example as part of Azure Devops build." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interactive Authentication\n", - "\n", - "Interactive authentication is the default mode when using Azure ML SDK.\n", - "\n", - "When you connect to your workspace using workspace.from_config, you will get an interactive login dialog." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also, if you explicitly specify the subscription ID, resource group and workspace name, you will get the dialog." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace(subscription_id=\"my-subscription-id\",\n", - " resource_group=\"my-ml-rg\",\n", - " workspace_name=\"my-ml-workspace\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the user you're authenticated as must have access to the subscription and resource group. If you receive an error\n", - "\n", - "```\n", - "AuthenticationException: You don't have access to xxxxxx-xxxx-xxx-xxx-xxxxxxxxxx subscription. All the subscriptions that you have access to = ...\n", - "```\n", - "\n", - "check that the you used correct login and entered the correct subscription ID." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In some cases, you may see a version of the error message containing text: ```All the subscriptions that you have access to = []```\n", - "\n", - "In such a case, you may have to specify the tenant ID of the Azure Active Directory you're using. An example would be accessing a subscription as a guest to a tenant that is not your default. You specify the tenant by explicitly instantiating _InteractiveLoginAuthentication_ with Tenant ID as argument. The Tenant ID can be found, for example, from https://portal.azure.com under **Azure Active Directory**, **Properties** as Directory ID." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-interactiveloginauth-tenantid" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.authentication import InteractiveLoginAuthentication\n", - "\n", - "interactive_auth = InteractiveLoginAuthentication(tenant_id=\"my-tenant-id\")\n", - "\n", - "ws = Workspace(subscription_id=\"my-subscription-id\",\n", - " resource_group=\"my-ml-rg\",\n", - " workspace_name=\"my-ml-workspace\",\n", - " auth=interactive_auth)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Despite having access to the workspace, you may sometimes see the following error when retrieving it:\n", - "\n", - "```\n", - "You are currently logged-in to xxxxxxxx-xxx-xxxx-xxxx-xxxxxxxxxxxx tenant. You don't have access to xxxxxx-xxxx-xxx-xxx-xxxxxxxxxx subscription, please check if it is in this tenant.\n", - "```\n", - "\n", - "This error sometimes occurs when you are trying to access a subscription to which you were recently added. In this case, you need to force authentication again to avoid using a cached authentication token that has not picked up the new permissions. You can do so by setting `force=true` on the `InteractiveLoginAuthentication()` object's constructor as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "forced_interactive_auth = InteractiveLoginAuthentication(tenant_id=\"my-tenant-id\", force=True)\n", - "\n", - "ws = Workspace(subscription_id=\"my-subscription-id\",\n", - " resource_group=\"my-ml-rg\",\n", - " workspace_name=\"my-ml-workspace\",\n", - " auth=forced_interactive_auth)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure CLI Authentication\n", - "\n", - "If you have installed azure-cli package, and used ```az login``` command to log in to your Azure Subscription, you can use _AzureCliAuthentication_ class.\n", - "\n", - "Note that interactive authentication described above won't use existing Azure CLI auth tokens. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-azurecliauth" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.authentication import AzureCliAuthentication\n", - "\n", - "cli_auth = AzureCliAuthentication()\n", - "\n", - "ws = Workspace(subscription_id=\"my-subscription-id\",\n", - " resource_group=\"my-ml-rg\",\n", - " workspace_name=\"my-ml-workspace\",\n", - " auth=cli_auth)\n", - "\n", - "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### MSI Authentication\n", - "\n", - "__Note__: _MSI authentication is supported only when using SDK from Azure Virtual Machine. The code below will fail on local computer._\n", - "\n", - "When using Azure ML SDK on Azure Virtual Machine (VM), you can use Managed Service Identity (MSI) based authentication. This mode allows the VM connect to the Workspace without storing credentials in the Python code.\n", - "\n", - "As a prerequisite, enable System-assigned Managed Identity for your VM as described in [Configure managed identities for Azure resources on a VM using the Azure portal](https://docs.microsoft.com/azure/active-directory/managed-identities-azure-resources/qs-configure-portal-windows-vm).\n", - "\n", - "Then, assign the VM access to your Workspace. For example from Azure Portal, navigate to your workspace, select __Access Control (IAM)__, __Add Role Assignment__, specify __Virtual Machine__ for __Assign Access To__ dropdown, and select your VM's identity.\n", - "\n", - "![msi assignment](images/msiaccess.PNG)\n", - "\n", - "After completing these steps, you can use authenticate using MsiAuthentication instance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-msiauth" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.authentication import MsiAuthentication\n", - "\n", - "msi_auth = MsiAuthentication()\n", - "\n", - "ws = Workspace(subscription_id=\"my-subscription-id\",\n", - " resource_group=\"my-ml-rg\",\n", - " workspace_name=\"my-ml-workspace\",\n", - " auth=msi_auth)\n", - "\n", - "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Service Principal Authentication\n", - "\n", - "When setting up a machine learning workflow as an automated process, we recommend using Service Principal Authentication. This approach decouples the authentication from any specific user login, and allows managed access control.\n", - "\n", - "Note that you must have administrator privileges over the Azure subscription to complete these steps.\n", - "\n", - "The first step is to create a service principal. First, go to [Azure Portal](https://portal.azure.com), select **Azure Active Directory** and **App Registrations**. Then select **+New application**, give your service principal a name, for example _my-svc-principal_. You can leave other parameters as is.\n", - "\n", - "Then click **Register**.\n", - "\n", - "![service principal creation](images/svc-pr-1.PNG)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the page for your newly created service principal, copy the _Application ID_ and _Tenant ID_ as they are needed later.\n", - "![application and tenant id](images/svc-pr-2.PNG)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then select **Certificates & secrets**, and **+New client secret** write a description for your key, and select duration. Then click **Add**, and copy the value of client secret to a secure location.\n", - "\n", - "\n", - "![tenant id](images/svc-pr-3.PNG)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, you need to give the service principal permissions to access your workspace. Navigate to **Resource Groups**, to the resource group for your Machine Learning Workspace. \n", - "\n", - "Then select **Access Control (IAM)** and **Add a role assignment**. For _Role_, specify which level of access you need to grant, for example _Contributor_. Start entering your service principal name and once it is found, select it, and click **Save**.\n", - "\n", - "![add role](images/svc-pr-4.PNG)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now you are ready to use the service principal authentication. For example, to connect to your Workspace, see code below and enter your own values for tenant ID, application ID, subscription ID, resource group and workspace.\n", - "\n", - "**We strongly recommended that you do not insert the secret password to code**. Instead, you can use environment variables to pass it to your code, for example through Azure Key Vault, or through secret build variables in Azure DevOps. For local testing, you can for example use following PowerShell command to set the environment variable.\n", - "\n", - "```\n", - "$env:AZUREML_PASSWORD = \"my-password\"\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-serviceprincipalauth-tenantid" - ] - }, - "outputs": [], - "source": [ - "import os\n", - "from azureml.core.authentication import ServicePrincipalAuthentication\n", - "\n", - "svc_pr_password = os.environ.get(\"AZUREML_PASSWORD\")\n", - "\n", - "svc_pr = ServicePrincipalAuthentication(\n", - " tenant_id=\"my-tenant-id\",\n", - " service_principal_id=\"my-application-id\",\n", - " service_principal_password=svc_pr_password)\n", - "\n", - "\n", - "ws = Workspace(\n", - " subscription_id=\"my-subscription-id\",\n", - " resource_group=\"my-ml-rg\",\n", - " workspace_name=\"my-ml-workspace\",\n", - " auth=svc_pr\n", - " )\n", - "\n", - "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See [Register an application with the Microsoft identity platform](https://docs.microsoft.com/en-us/azure/active-directory/develop/quickstart-register-app) quickstart for more details about application registrations. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Token Authentication\n", - "\n", - "When token generation and its refresh needs to be outside on AML SDK, we recommend using Token Authentication. It can be used for getting token for AML or ARM audience. Thus giving more granular control over token generated.\n", - "\n", - "This authentication class requires users to provide method `get_token_for_audience` which will be called to retrieve the token based on the audience passed.\n", - "\n", - "Audience that is passed to `get_token_for_audience` can be ARM or AML. Exact value that will be passed as audience will depend on cloud and type for audience." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.authentication import TokenAuthentication, Audience\n", - "\n", - "# This is a sample method to retrieve token and will be passed to TokenAuthentication\n", - "def get_token_for_audience(audience):\n", - " from adal import AuthenticationContext\n", - " client_id = \"my-client-id\"\n", - " client_secret = \"my-client-secret\"\n", - " tenant_id = \"my-tenant-id\"\n", - " auth_context = AuthenticationContext(\"https://login.microsoftonline.com/{}\".format(tenant_id))\n", - " resp = auth_context.acquire_token_with_client_credentials(audience,client_id,client_secret)\n", - " token = resp[\"accessToken\"]\n", - " return token\n", - "\n", - "\n", - "token_auth = TokenAuthentication(get_token_for_audience=get_token_for_audience)\n", - "\n", - "ws = Workspace(\n", - " subscription_id=\"my-subscription-id\",\n", - " resource_group=\"my-ml-rg\",\n", - " workspace_name=\"my-ml-workspace\",\n", - " auth=token_auth\n", - " )\n", - "\n", - "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))\n", - "\n", - "token_aml_audience = token_auth.get_token(Audience.aml)\n", - "token_arm_audience = token_auth.get_token(Audience.arm)\n", - "\n", - "# Value of audience pass to `get_token_for_audience` can be retrieved as follows:\n", - "# aud_aml_val = token_auth.get_aml_resource_id() # For AML\n", - "# aud_arm_val = token_auth._cloud_type.endpoints.active_directory_resource_id # For ARM\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Token authentication object can be used to retrieve token for either AML or ARM audience,\n", - "which can be used by other clients to authenticate to AML or ARM" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using Secrets in Remote Runs\n", - "\n", - "Sometimes, you may have to pass a secret to a remote run, for example username and password to authenticate against external data source.\n", - "\n", - "Azure ML SDK enables this use case through Key Vault associated with your workspace. The workflow for adding a secret is following.\n", - "\n", - "On local computer:\n", - "\n", - " 1. Read in a local secret, for example from environment variable or user input. To keep them secret, do not insert secret values into code as hard-coded strings.\n", - " 2. Obtain a reference to the keyvault\n", - " 3. Add the secret name-value pair in the key vault.\n", - " \n", - "The secret is then available for remote runs as shown further below.\n", - "\n", - "__Note__: The _azureml.core.keyvault.Keyvault_ is different from _azure.keyvault_ library. It is intended as simplified wrapper for setting, getting and listing user secrets in Workspace Key Vault." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-keyvault" - ] - }, - "outputs": [], - "source": [ - "import uuid\n", - "\n", - "local_secret = os.environ.get(\"LOCAL_SECRET\", default = str(uuid.uuid4())) # Use random UUID as a substitute for real secret.\n", - "keyvault = ws.get_default_keyvault()\n", - "keyvault.set_secret(name=\"secret-name\", value = local_secret)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The _set_secret_ method adds a new secret if one doesn't exist, or updates an existing one with new value.\n", - "\n", - "You can list secret names you've added. This method doesn't return the values of the secrets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "keyvault.list_secrets()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can retrieve the value of the secret, and validate that it matches the original value. \n", - "\n", - "__Note__: This method returns the secret value. Take care not to write the the secret value to output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "retrieved_secret = keyvault.get_secret(name=\"secret-name\")\n", - "local_secret==retrieved_secret" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In submitted runs on local and remote compute, you can use the get_secret method of Run instance to get the secret value from Key Vault. \n", - "\n", - "The method gives you a simple shortcut: the Run instance is aware of its Workspace and Keyvault, so it can directly obtain the secret without you having to instantiate the Workspace and Keyvault within remote run.\n", - "\n", - "__Note__: This method returns the secret value. Take care not to write the secret to output.\n", - "\n", - "For example, let's create a simple script _get_secret.py_ that gets the secret we set earlier. In an actual appication, you would use the secret, for example to access a database or other password-protected resource." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile get_secret.py\n", - "\n", - "from azureml.core import Run\n", - "\n", - "run = Run.get_context()\n", - "secret_value = run.get_secret(name=\"secret-name\")\n", - "print(\"Got secret value {} , but don't write it out!\".format(len(secret_value) * \"*\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, submit the script as a regular script run, and find the obfuscated secret value in run output. You can use the same approach to other kinds of runs, such as Estimator ones." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "from azureml.core.script_run_config import ScriptRunConfig\n", - "\n", - "exp = Experiment(workspace = ws, name=\"try-secret\")\n", - "src = ScriptRunConfig(source_directory=\".\", script=\"get_secret.py\")\n", - "\n", - "run = exp.submit(src)\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Furthermore, you can set and get multiple secrets using set_secrets and get_secrets methods." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "roastala" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-1.PNG b/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-1.PNG deleted file mode 100644 index 26d6b3351..000000000 Binary files a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-1.PNG and /dev/null differ diff --git a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-2.PNG b/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-2.PNG deleted file mode 100644 index 086a6a049..000000000 Binary files a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-2.PNG and /dev/null differ diff --git a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-3.PNG b/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-3.PNG deleted file mode 100644 index 3c4b8bd8e..000000000 Binary files a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-3.PNG and /dev/null differ diff --git a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-4.PNG b/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-4.PNG deleted file mode 100644 index 0756a22bc..000000000 Binary files a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/images/svc-pr-4.PNG and /dev/null differ diff --git a/how-to-use-azureml/ml-frameworks/README.md b/how-to-use-azureml/ml-frameworks/README.md deleted file mode 100644 index 863f61abb..000000000 --- a/how-to-use-azureml/ml-frameworks/README.md +++ /dev/null @@ -1,11 +0,0 @@ -## Training and deployment examples with ML frameworks -These sample notebooks show you how to train and deploy models with popular machine learning frameworks using Azure Machine Learning. - -1. [Scikit-learn](scikit-learn): Train, hyperparameter tune and deploy scikit-learn models. -2. [PyTorch](pytorch): Train, hyperparameter tune and deploy PyTorch models. Distributed training with PyTorch. -3. [TensorFlow](tensorflow): Train, hyperparameter tune and deploy TensorFlow models. Distributed training with TensorFlow. -4. [Keras](keras): Train, hyperparameter tune and deploy Keras models. -5. [Chainer](chainer): Train, hyperparameter tune and deploy Chainer models. Distributed training with Chainer. -6. [Fastai](fastai): Train, hyperparameter tune and deploy Fastai models. - - ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/README.png) diff --git a/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.ipynb b/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.ipynb deleted file mode 100644 index eb21490d8..000000000 --- a/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.ipynb +++ /dev/null @@ -1,358 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Distributed Chainer\n", - "In this tutorial, you will run a Chainer training example on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using ChainerMN distributed training across a GPU cluster." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* If you are using an Azure Machine Learning compute instance, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"gpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target.')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - " compute_target.wait_for_completion(show_output=True)\n", - "\n", - "# use get_status() to get a detailed status for the current AmlCompute. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model on the remote compute\n", - "Now that we have the AmlCompute ready to go, let's run our distributed training job." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project directory\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "project_folder = './chainer-distr'\n", - "os.makedirs(project_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare training script\n", - "Now you will need to create your training script. In this tutorial, the script for distributed training of MNIST is already provided for you at `train_mnist.py`. In practice, you should be able to take any custom Chainer training script as is and run it with Azure ML without having to modify your code." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once your script is ready, copy the training script `train_mnist.py` into the project directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "shutil.copy('train_mnist.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment\n", - "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed Chainer tutorial. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = 'chainer-distr'\n", - "experiment = Experiment(ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "In this tutorial, we will use one of the Azure ML Chainer curated environments for training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "chainer_env = Environment.get(ws, name='AzureML-Chainer-5.1.0-GPU')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure your training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on.\n", - "\n", - "In order to execute a distributed run using MPI, you must create an `MpiConfiguration` object and specify it to the `distributed_job_config` parameter. The below code will configure a 2-node distributed job. If you would also like to run multiple processes per node (i.e. if your cluster SKU has multiple GPUs), additionally specify the `process_count_per_node` parameter in MpiConfiguration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "from azureml.core.runconfig import MpiConfiguration\n", - "\n", - "src = ScriptRunConfig(source_directory=project_folder,\n", - " script='train_mnist.py',\n", - " compute_target=compute_target,\n", - " environment=chainer_env,\n", - " distributed_job_config=MpiConfiguration(node_count=2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job\n", - "Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = experiment.submit(src)\n", - "print(run)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor your run\n", - "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes. You can see that the widget automatically plots and visualizes the loss metric that we logged to the Azure ML run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "ninhu" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Chainer" - ], - "friendly_name": "Distributed Training with Chainer", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "tags": [ - "None" - ], - "task": "Use the Chainer estimator to perform distributed training" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.yml b/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.yml deleted file mode 100644 index 0c2ef761c..000000000 --- a/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.yml +++ /dev/null @@ -1,5 +0,0 @@ -name: distributed-chainer -dependencies: -- pip: - - azureml-sdk - - azureml-widgets diff --git a/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/train_mnist.py b/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/train_mnist.py deleted file mode 100644 index 29c77f2da..000000000 --- a/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/train_mnist.py +++ /dev/null @@ -1,125 +0,0 @@ -# Official ChainerMN example taken from -# https://github.com/chainer/chainer/blob/master/examples/chainermn/mnist/train_mnist.py - -from __future__ import print_function - -import argparse - -import chainer -import chainer.functions as F -import chainer.links as L -from chainer import training -from chainer.training import extensions - -import chainermn - - -class MLP(chainer.Chain): - - def __init__(self, n_units, n_out): - super(MLP, self).__init__( - # the size of the inputs to each layer will be inferred - l1=L.Linear(784, n_units), # n_in -> n_units - l2=L.Linear(n_units, n_units), # n_units -> n_units - l3=L.Linear(n_units, n_out), # n_units -> n_out - ) - - def __call__(self, x): - h1 = F.relu(self.l1(x)) - h2 = F.relu(self.l2(h1)) - return self.l3(h2) - - -def main(): - parser = argparse.ArgumentParser(description='ChainerMN example: MNIST') - parser.add_argument('--batchsize', '-b', type=int, default=100, - help='Number of images in each mini-batch') - parser.add_argument('--communicator', type=str, - default='non_cuda_aware', help='Type of communicator') - parser.add_argument('--epoch', '-e', type=int, default=20, - help='Number of sweeps over the dataset to train') - parser.add_argument('--gpu', '-g', default=True, - help='Use GPU') - parser.add_argument('--out', '-o', default='result', - help='Directory to output the result') - parser.add_argument('--resume', '-r', default='', - help='Resume the training from snapshot') - parser.add_argument('--unit', '-u', type=int, default=1000, - help='Number of units') - args = parser.parse_args() - - # Prepare ChainerMN communicator. - - if args.gpu: - if args.communicator == 'naive': - print("Error: 'naive' communicator does not support GPU.\n") - exit(-1) - comm = chainermn.create_communicator(args.communicator) - device = comm.intra_rank - else: - if args.communicator != 'naive': - print('Warning: using naive communicator ' - 'because only naive supports CPU-only execution') - comm = chainermn.create_communicator('naive') - device = -1 - - if comm.rank == 0: - print('==========================================') - print('Num process (COMM_WORLD): {}'.format(comm.size)) - if args.gpu: - print('Using GPUs') - print('Using {} communicator'.format(args.communicator)) - print('Num unit: {}'.format(args.unit)) - print('Num Minibatch-size: {}'.format(args.batchsize)) - print('Num epoch: {}'.format(args.epoch)) - print('==========================================') - - model = L.Classifier(MLP(args.unit, 10)) - if device >= 0: - chainer.cuda.get_device_from_id(device).use() - model.to_gpu() - - # Create a multi node optimizer from a standard Chainer optimizer. - optimizer = chainermn.create_multi_node_optimizer( - chainer.optimizers.Adam(), comm) - optimizer.setup(model) - - # Split and distribute the dataset. Only worker 0 loads the whole dataset. - # Datasets of worker 0 are evenly split and distributed to all workers. - if comm.rank == 0: - train, test = chainer.datasets.get_mnist() - else: - train, test = None, None - train = chainermn.scatter_dataset(train, comm, shuffle=True) - test = chainermn.scatter_dataset(test, comm, shuffle=True) - - train_iter = chainer.iterators.SerialIterator(train, args.batchsize) - test_iter = chainer.iterators.SerialIterator(test, args.batchsize, - repeat=False, shuffle=False) - - updater = training.StandardUpdater(train_iter, optimizer, device=device) - trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out) - - # Create a multi node evaluator from a standard Chainer evaluator. - evaluator = extensions.Evaluator(test_iter, model, device=device) - evaluator = chainermn.create_multi_node_evaluator(evaluator, comm) - trainer.extend(evaluator) - - # Some display and output extensions are necessary only for one worker. - # (Otherwise, there would just be repeated outputs.) - if comm.rank == 0: - trainer.extend(extensions.dump_graph('main/loss')) - trainer.extend(extensions.LogReport()) - trainer.extend(extensions.PrintReport( - ['epoch', 'main/loss', 'validation/main/loss', - 'main/accuracy', 'validation/main/accuracy', 'elapsed_time'])) - trainer.extend(extensions.ProgressBar()) - - if args.resume: - chainer.serializers.load_npz(args.resume, trainer) - - trainer.run() - - -if __name__ == '__main__': - main() diff --git a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/chainer_mnist.py b/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/chainer_mnist.py deleted file mode 100644 index 59a2f5571..000000000 --- a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/chainer_mnist.py +++ /dev/null @@ -1,142 +0,0 @@ - -import argparse -import os - -import numpy as np - -from utils import download_mnist - -import chainer -from chainer import backend -from chainer import backends -from chainer.backends import cuda -from chainer import Function, gradient_check, report, training, utils, Variable -from chainer import datasets, iterators, optimizers, serializers -from chainer import Link, Chain, ChainList -import chainer.functions as F -import chainer.links as L -from chainer.training import extensions -from chainer.dataset import concat_examples -from chainer.backends.cuda import to_cpu - - -from azureml.core.run import Run -run = Run.get_context() - - -class MyNetwork(Chain): - - def __init__(self, n_mid_units=100, n_out=10): - super(MyNetwork, self).__init__() - with self.init_scope(): - self.l1 = L.Linear(None, n_mid_units) - self.l2 = L.Linear(n_mid_units, n_mid_units) - self.l3 = L.Linear(n_mid_units, n_out) - - def forward(self, x): - h = F.relu(self.l1(x)) - h = F.relu(self.l2(h)) - return self.l3(h) - - -def main(): - parser = argparse.ArgumentParser(description='Chainer example: MNIST') - parser.add_argument('--batchsize', '-b', type=int, default=100, - help='Number of images in each mini-batch') - parser.add_argument('--epochs', '-e', type=int, default=20, - help='Number of sweeps over the dataset to train') - parser.add_argument('--output_dir', '-o', default='./outputs', - help='Directory to output the result') - parser.add_argument('--gpu_id', '-g', default=0, - help='ID of the GPU to be used. Set to -1 if you use CPU') - args = parser.parse_args() - - # Download the MNIST data if you haven't downloaded it yet - train, test = download_mnist() - - gpu_id = args.gpu_id - batchsize = args.batchsize - epochs = args.epochs - run.log('Batch size', np.int(batchsize)) - run.log('Epochs', np.int(epochs)) - - train_iter = iterators.SerialIterator(train, batchsize) - test_iter = iterators.SerialIterator(test, batchsize, - repeat=False, shuffle=False) - - model = MyNetwork() - - if gpu_id >= 0: - # Make a specified GPU current - chainer.backends.cuda.get_device_from_id(0).use() - model.to_gpu() # Copy the model to the GPU - - # Choose an optimizer algorithm - optimizer = optimizers.MomentumSGD(lr=0.01, momentum=0.9) - - # Give the optimizer a reference to the model so that it - # can locate the model's parameters. - optimizer.setup(model) - - while train_iter.epoch < epochs: - # ---------- One iteration of the training loop ---------- - train_batch = train_iter.next() - image_train, target_train = concat_examples(train_batch, gpu_id) - - # Calculate the prediction of the network - prediction_train = model(image_train) - - # Calculate the loss with softmax_cross_entropy - loss = F.softmax_cross_entropy(prediction_train, target_train) - - # Calculate the gradients in the network - model.cleargrads() - loss.backward() - - # Update all the trainable parameters - optimizer.update() - # --------------------- until here --------------------- - - # Check the validation accuracy of prediction after every epoch - if train_iter.is_new_epoch: # If this iteration is the final iteration of the current epoch - - # Display the training loss - print('epoch:{:02d} train_loss:{:.04f} '.format( - train_iter.epoch, float(to_cpu(loss.array))), end='') - - test_losses = [] - test_accuracies = [] - while True: - test_batch = test_iter.next() - image_test, target_test = concat_examples(test_batch, gpu_id) - - # Forward the test data - prediction_test = model(image_test) - - # Calculate the loss - loss_test = F.softmax_cross_entropy(prediction_test, target_test) - test_losses.append(to_cpu(loss_test.array)) - - # Calculate the accuracy - accuracy = F.accuracy(prediction_test, target_test) - accuracy.to_cpu() - test_accuracies.append(accuracy.array) - - if test_iter.is_new_epoch: - test_iter.epoch = 0 - test_iter.current_position = 0 - test_iter.is_new_epoch = False - test_iter._pushed_position = None - break - - val_accuracy = np.mean(test_accuracies) - print('val_loss:{:.04f} val_accuracy:{:.04f}'.format( - np.mean(test_losses), val_accuracy)) - - run.log("Accuracy", np.float(val_accuracy)) - - serializers.save_npz(os.path.join(args.output_dir, 'model.npz'), model) - - -if __name__ == '__main__': - main() diff --git a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/chainer_score.py b/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/chainer_score.py deleted file mode 100644 index e7bd88549..000000000 --- a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/chainer_score.py +++ /dev/null @@ -1,50 +0,0 @@ -import numpy as np -import os -import json - -from utils import download_mnist - -from chainer import serializers, using_config, Variable, datasets -import chainer.functions as F -import chainer.links as L -from chainer import Chain - -from azureml.core.model import Model - - -class MyNetwork(Chain): - - def __init__(self, n_mid_units=100, n_out=10): - super(MyNetwork, self).__init__() - with self.init_scope(): - self.l1 = L.Linear(None, n_mid_units) - self.l2 = L.Linear(n_mid_units, n_mid_units) - self.l3 = L.Linear(n_mid_units, n_out) - - def forward(self, x): - h = F.relu(self.l1(x)) - h = F.relu(self.l2(h)) - return self.l3(h) - - -def init(): - global model - - # AZUREML_MODEL_DIR is an environment variable created during deployment. - # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION) - # For multiple models, it points to the folder containing all deployed models (./azureml-models) - model_root = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.npz') - - # Load our saved artifacts - model = MyNetwork() - serializers.load_npz(model_root, model) - - -def run(input_data): - i = np.array(json.loads(input_data)['data']) - - _, test = download_mnist() - x = Variable(np.asarray([test[i][0]])) - y = model(x) - - return np.ndarray.tolist(y.data.argmax(axis=1)) diff --git a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb b/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb deleted file mode 100644 index 835d37e41..000000000 --- a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb +++ /dev/null @@ -1,809 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train and hyperparameter tune with Chainer\n", - "\n", - "In this tutorial, we demonstrate how to use the Azure ML Python SDK to train a Convolutional Neural Network (CNN) on a single-node GPU with Chainer to perform handwritten digit recognition on the popular MNIST dataset. We will also demonstrate how to perform hyperparameter tuning of the model using Azure ML's HyperDrive service." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"gpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target.')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model on the remote compute\n", - "Now that you have your data and training script prepared, you are ready to train on your remote compute cluster. You can take advantage of Azure compute to leverage GPUs to cut down your training time. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project directory\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "project_folder = './chainer-mnist'\n", - "os.makedirs(project_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare training script\n", - "Now you will need to create your training script. In this tutorial, the training script is already provided for you at `chainer_mnist.py`. In practice, you should be able to take any custom training script as is and run it with Azure ML without having to modify your code.\n", - "\n", - "However, if you would like to use Azure ML's [tracking and metrics](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#metrics) capabilities, you will have to add a small amount of Azure ML code inside your training script. \n", - "\n", - "In `chainer_mnist.py`, we will log some metrics to our Azure ML run. To do so, we will access the Azure ML `Run` object within the script:\n", - "```Python\n", - "from azureml.core.run import Run\n", - "run = Run.get_context()\n", - "```\n", - "Further within `chainer_mnist.py`, we log the batchsize and epochs parameters, and the highest accuracy the model achieves:\n", - "```Python\n", - "run.log('Batch size', np.int(args.batchsize))\n", - "run.log('Epochs', np.int(args.epochs))\n", - "\n", - "run.log('Accuracy', np.float(val_accuracy))\n", - "```\n", - "These run metrics will become particularly important when we begin hyperparameter tuning our model in the \"Tune model hyperparameters\" section." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once your script is ready, copy the training script `chainer_mnist.py` into your project directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "shutil.copy('chainer_mnist.py', project_folder)\n", - "shutil.copy('chainer_score.py', project_folder)\n", - "shutil.copy('utils.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment\n", - "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this Chainer tutorial. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = 'chainer-mnist'\n", - "experiment = Experiment(ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "Define a conda environment YAML file with your training script dependencies and create an Azure ML environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile conda_dependencies.yml\n", - "\n", - "channels:\n", - "- conda-forge\n", - "dependencies:\n", - "- python=3.6.2\n", - "- pip=21.3.1\n", - "- pip:\n", - " - azureml-defaults\n", - " - azureml-opendatasets\n", - " - chainer==5.1.0\n", - " - cupy-cuda100==5.1.0\n", - " - mpi4py==3.0.0\n", - " - pytest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.runconfig import DockerConfiguration\n", - "\n", - "chainer_env = Environment.from_conda_specification(name = 'chainer-5.1.0-gpu', file_path = './conda_dependencies.yml')\n", - "\n", - "# Specify a GPU base image\n", - "chainer_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.0-cudnn7-ubuntu18.04'\n", - "\n", - "docker_config = DockerConfiguration(use_docker=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure your training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=project_folder,\n", - " script='chainer_mnist.py',\n", - " arguments=['--epochs', 10, '--batchsize', 128, '--output_dir', './outputs'],\n", - " compute_target=compute_target,\n", - " environment=chainer_env,\n", - " docker_runtime_config=docker_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job\n", - "Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = experiment.submit(src)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor your run\n", - "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# to get more details of your run\n", - "print(run.get_details())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tune model hyperparameters\n", - "Now that we've seen how to do a simple Chainer training run using the SDK, let's see if we can further improve the accuracy of our model. We can optimize our model's hyperparameters using Azure Machine Learning's hyperparameter tuning capabilities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start a hyperparameter sweep\n", - "First, we will define the hyperparameter space to sweep over. Let's tune the batch size and epochs parameters. In this example we will use random sampling to try different configuration sets of hyperparameters to maximize our primary metric, accuracy.\n", - "\n", - "Then, we specify the early termination policy to use to early terminate poorly performing runs. Here we use the `BanditPolicy`, which will terminate any run that doesn't fall within the slack factor of our primary evaluation metric. In this tutorial, we will apply this policy every epoch (since we report our `Accuracy` metric every epoch and `evaluation_interval=1`). Notice we will delay the first policy evaluation until after the first `3` epochs (`delay_evaluation=3`).\n", - "Refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-tune-hyperparameters#specify-an-early-termination-policy) for more information on the BanditPolicy and other policies available." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n", - "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n", - "from azureml.train.hyperdrive.policy import BanditPolicy\n", - "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n", - "from azureml.train.hyperdrive.parameter_expressions import choice\n", - " \n", - "\n", - "param_sampling = RandomParameterSampling( {\n", - " \"--batchsize\": choice(128, 256),\n", - " \"--epochs\": choice(5, 10, 20, 40)\n", - " }\n", - ")\n", - "\n", - "hyperdrive_config = HyperDriveConfig(run_config=src,\n", - " hyperparameter_sampling=param_sampling, \n", - " primary_metric_name='Accuracy',\n", - " policy=BanditPolicy(evaluation_interval=1, slack_factor=0.1, delay_evaluation=3),\n", - " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n", - " max_total_runs=8,\n", - " max_concurrent_runs=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, lauch the hyperparameter tuning job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# start the HyperDrive run\n", - "hyperdrive_run = experiment.submit(hyperdrive_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor HyperDrive runs\n", - "You can monitor the progress of the runs with the following Jupyter widget. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(hyperdrive_run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hyperdrive_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert(hyperdrive_run.get_status() == \"Completed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Warm start a Hyperparameter Tuning experiment and resuming child runs\n", - "Often times, finding the best hyperparameter values for your model can be an iterative process, needing multiple tuning runs that learn from previous hyperparameter tuning runs. Reusing knowledge from these previous runs will accelerate the hyperparameter tuning process, thereby reducing the cost of tuning the model and will potentially improve the primary metric of the resulting model. When warm starting a hyperparameter tuning experiment with Bayesian sampling, trials from the previous run will be used as prior knowledge to intelligently pick new samples, so as to improve the primary metric. Additionally, when using Random or Grid sampling, any early termination decisions will leverage metrics from the previous runs to determine poorly performing training runs. \n", - "\n", - "Azure Machine Learning allows you to warm start your hyperparameter tuning run by leveraging knowledge from up to 5 previously completed hyperparameter tuning parent runs. \n", - "\n", - "Additionally, there might be occasions when individual training runs of a hyperparameter tuning experiment are cancelled due to budget constraints or fail due to other reasons. It is now possible to resume such individual training runs from the last checkpoint (assuming your training script handles checkpoints). Resuming an individual training run will use the same hyperparameter configuration and mount the storage used for that run. The training script should accept the \"--resume-from\" argument, which contains the checkpoint or model files from which to resume the training run. You can also resume individual runs as part of an experiment that spends additional budget on hyperparameter tuning. Any additional budget, after resuming the specified training runs is used for exploring additional configurations.\n", - "\n", - "For more information on warm starting and resuming hyperparameter tuning runs, please refer to the [Hyperparameter Tuning for Azure Machine Learning documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters) \n", - "\n", - "### Find and register best model\n", - "When all jobs finish, we can find out the one that has the highest accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n", - "print(best_run.get_details()['runDefinition']['arguments'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's list the model files uploaded during the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(best_run.get_file_names())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then register the folder (and all files in it) as a model named `chainer-dnn-mnist` under the workspace for deployment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = best_run.register_model(model_name='chainer-dnn-mnist', model_path='outputs/model.npz')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy the model in ACI\n", - "Now, we are ready to deploy the model as a web service running in Azure Container Instance, [ACI](https://azure.microsoft.com/en-us/services/container-instances/). Azure Machine Learning accomplishes this by constructing a Docker image with the scoring logic and model baked in.\n", - "\n", - "### Create scoring script\n", - "First, we will create a scoring script that will be invoked by the web service call.\n", - "+ Now that the scoring script must have two required functions, `init()` and `run(input_data)`.\n", - " + In `init()`, you typically load the model into a global object. This function is executed only once when the Docker contianer is started.\n", - " + In `run(input_data)`, the model is used to predict a value based on the input data. The input and output to `run` uses NPZ as the serialization and de-serialization format because it is the preferred format for Chainer, but you are not limited to it.\n", - " \n", - "Refer to the scoring script `chainer_score.py` for this tutorial. Our web service will use this file to predict. When writing your own scoring script, don't forget to test it locally first before you go and deploy the web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "shutil.copy('chainer_score.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create myenv.yml\n", - "We also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify conda package `numpy` and pip install `chainer`. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import CondaDependencies\n", - "\n", - "cd = CondaDependencies.create()\n", - "cd.add_conda_package('numpy')\n", - "cd.add_pip_package('chainer==5.1.0')\n", - "cd.add_pip_package(\"azureml-defaults\")\n", - "cd.add_pip_package(\"azureml-opendatasets\")\n", - "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n", - "\n", - "print(cd.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy to ACI\n", - "We are almost ready to deploy. Create the inference configuration and deployment configuration and deploy to ACI. This cell will run for about 7-8 minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.webservice import Webservice\n", - "from azureml.core.model import Model\n", - "from azureml.core.environment import Environment\n", - "\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"chainer_score.py\", environment=myenv,\n", - " source_directory=project_folder)\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1,\n", - " auth_enabled=True, # this flag generates API keys to secure access\n", - " memory_gb=2,\n", - " tags={'name': 'mnist', 'framework': 'Chainer'},\n", - " description='Chainer DNN with MNIST')\n", - "\n", - "service = Model.deploy(workspace=ws,\n", - " name='chainer-mnist-1',\n", - " models=[model],\n", - " inference_config=inference_config,\n", - " deployment_config=aciconfig)\n", - "service.wait_for_deployment(True)\n", - "print(service.state)\n", - "print(service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:** " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(service.get_logs())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is the scoring web service endpoint:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the deployed model\n", - "Let's test the deployed model. Pick a random sample from the test set, and send it to the web service hosted in ACI for a prediction. Note, here we are using the an HTTP request to invoke the service.\n", - "\n", - "We can retrieve the API keys used for accessing the HTTP endpoint and construct a raw HTTP request to send to the service. Don't forget to add key to the HTTP header." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# retreive the API keys. two keys were generated.\n", - "key1, Key2 = service.get_keys()\n", - "print(key1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import urllib\n", - "import gzip\n", - "import numpy as np\n", - "import struct\n", - "import requests\n", - "\n", - "\n", - "# load compressed MNIST gz files and return numpy arrays\n", - "def load_data(filename, label=False):\n", - " with gzip.open(filename) as gz:\n", - " struct.unpack('I', gz.read(4))\n", - " n_items = struct.unpack('>I', gz.read(4))\n", - " if not label:\n", - " n_rows = struct.unpack('>I', gz.read(4))[0]\n", - " n_cols = struct.unpack('>I', gz.read(4))[0]\n", - " res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)\n", - " res = res.reshape(n_items[0], n_rows * n_cols)\n", - " else:\n", - " res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)\n", - " res = res.reshape(n_items[0], 1)\n", - " return res\n", - "\n", - "data_folder = os.path.join(os.getcwd(), 'data/mnist')\n", - "os.makedirs(data_folder, exist_ok=True)\n", - "\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n", - " filename=os.path.join(data_folder, 't10k-images-idx3-ubyte.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz',\n", - " filename=os.path.join(data_folder, 't10k-labels-idx1-ubyte.gz'))\n", - "\n", - "X_test = load_data(os.path.join(data_folder, 't10k-images-idx3-ubyte.gz'), False) / np.float32(255.0)\n", - "y_test = load_data(os.path.join(data_folder, 't10k-labels-idx1-ubyte.gz'), True).reshape(-1)\n", - "\n", - "# send a random row from the test set to score\n", - "random_index = np.random.randint(0, len(X_test)-1)\n", - "input_data = \"{\\\"data\\\": [\" + str(random_index) + \"]}\"\n", - "\n", - "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n", - "\n", - "# send sample to service for scoring\n", - "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n", - "\n", - "print(\"label:\", y_test[random_index])\n", - "print(\"prediction:\", resp.text[1])\n", - "\n", - "plt.imshow(X_test[random_index].reshape((28,28)), cmap='gray')\n", - "plt.axis('off')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the workspace after the web service was deployed. You should see\n", - "\n", - " + a registered model named 'chainer-dnn-mnist' and with the id 'chainer-dnn-mnist:1'\n", - " + a webservice called 'chainer-mnist-svc' with some scoring URL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = ws.models['chainer-dnn-mnist']\n", - "print(\"Model: {}, ID: {}\".format('chainer-dnn-mnist', model.id))\n", - " \n", - "webservice = ws.webservices['chainer-mnist-1']\n", - "print(\"Webservice: {}, scoring URI: {}\".format('chainer-mnist-1', webservice.scoring_uri))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can delete the ACI deployment with a simple delete API call." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "nagaur" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "Chainer" - ], - "friendly_name": "Train a model with hyperparameter tuning", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "tags": [ - "None" - ], - "task": "Train a Convolutional Neural Network (CNN)" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.yml b/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.yml deleted file mode 100644 index 7f35763c5..000000000 --- a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.yml +++ /dev/null @@ -1,13 +0,0 @@ -name: train-hyperparameter-tune-deploy-with-chainer -dependencies: -- pip: - - azureml-sdk - - azureml-widgets - - numpy - - matplotlib - - json - - urllib - - gzip - - struct - - requests - - azureml-opendatasets diff --git a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/utils.py b/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/utils.py deleted file mode 100644 index 68f98c44c..000000000 --- a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/utils.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import glob -import gzip -import numpy as np -import os -import struct - -from azureml.core import Dataset -from azureml.opendatasets import MNIST -from chainer.datasets import tuple_dataset - - -# load compressed MNIST gz files and return numpy arrays -def load_data(filename, label=False): - with gzip.open(filename) as gz: - struct.unpack('I', gz.read(4)) - n_items = struct.unpack('>I', gz.read(4)) - if not label: - n_rows = struct.unpack('>I', gz.read(4))[0] - n_cols = struct.unpack('>I', gz.read(4))[0] - res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8) - res = res.reshape(n_items[0], n_rows * n_cols) - else: - res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8) - res = res.reshape(n_items[0], 1) - return res - - -def download_mnist(): - data_folder = os.path.join(os.getcwd(), 'data/mnist') - os.makedirs(data_folder, exist_ok=True) - - mnist_file_dataset = MNIST.get_file_dataset() - mnist_file_dataset.download(data_folder, overwrite=True) - - X_train = load_data(glob.glob(os.path.join(data_folder, "**/train-images-idx3-ubyte.gz"), - recursive=True)[0], False) / 255.0 - X_test = load_data(glob.glob(os.path.join(data_folder, "**/t10k-images-idx3-ubyte.gz"), - recursive=True)[0], False) / 255.0 - y_train = load_data(glob.glob(os.path.join(data_folder, "**/train-labels-idx1-ubyte.gz"), - recursive=True)[0], True).reshape(-1) - y_test = load_data(glob.glob(os.path.join(data_folder, "**/t10k-labels-idx1-ubyte.gz"), - recursive=True)[0], True).reshape(-1) - - train = tuple_dataset.TupleDataset(X_train.astype(np.float32), y_train.astype(np.int32)) - test = tuple_dataset.TupleDataset(X_test.astype(np.float32), y_test.astype(np.int32)) - - return train, test diff --git a/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb b/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb deleted file mode 100644 index d70ba8674..000000000 --- a/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb +++ /dev/null @@ -1,373 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/fastai/train-with-custom-docker/fastai-with-custom-docker.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train a model using a custom Docker image" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial, learn how to use a custom Docker image when training models with Azure Machine Learning.\n", - "\n", - "The example scripts in this article are used to classify pet images by creating a convolutional neural network. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up the experiment\n", - "This section sets up the training experiment by initializing a workspace, creating an experiment, and uploading the training data and training scripts." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize a workspace\n", - "The Azure Machine Learning workspace is the top-level resource for the service. It provides you with a centralized place to work with all the artifacts you create. In the Python SDK, you can access the workspace artifacts by creating a `workspace` object.\n", - "\n", - "Create a workspace object from the config.json file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare scripts\n", - "Create a directory titled `fastai-example`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.makedirs('fastai-example', exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then run the cell below to create the training script train.py in the directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "%%writefile fastai-example/train.py\n", - "\n", - "from fastai.vision.all import *\n", - "\n", - "path = untar_data(URLs.PETS)\n", - "path.ls()\n", - "\n", - "files = get_image_files(path/\"images\")\n", - "len(files)\n", - "\n", - "#(Path('/home/ashwin/.fastai/data/oxford-iiit-pet/images/yorkshire_terrier_102.jpg'),Path('/home/ashwin/.fastai/data/oxford-iiit-pet/images/great_pyrenees_102.jpg'))\n", - "\n", - "def label_func(f): return f[0].isupper()\n", - "\n", - "#To get our data ready for a model, we need to put it in a DataLoaders object. Here we have a function that labels using the file names, so we will use ImageDataLoaders.from_name_func. There are other factory methods of ImageDataLoaders that could be more suitable for your problem, so make sure to check them all in vision.data.\n", - "\n", - "dls = ImageDataLoaders.from_name_func(path, files, label_func, item_tfms=Resize(224))\n", - "\n", - "#We have passed to this function the directory we're working in, the files we grabbed, our label_func and one last piece as item_tfms: this is a Transform applied on all items of our dataset that will resize each imge to 224 by 224, by using a random crop on the largest dimension to make it a square, then resizing to 224 by 224. If we didn't pass this, we would get an error later as it would be impossible to batch the items together.\n", - "\n", - "dls.show_batch()\n", - "\n", - "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n", - "learn.fine_tune(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define your environment\n", - "Create an environment object and enable Docker." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "fastai_env = Environment(\"fastai\")\n", - "fastai_env.docker.enabled = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This specified base image supports the fast.ai library which allows for distributed deep learning capabilities. For more information, see the [fast.ai DockerHub](https://hub.docker.com/u/fastdotai). \n", - "\n", - "When you are using your custom Docker image, you might already have your Python environment properly set up. In that case, set the `user_managed_dependencies` flag to True in order to leverage your custom image's built-in python environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fastai_env.docker.base_image = \"fastdotai/fastai:2021-02-11\"\n", - "fastai_env.python.user_managed_dependencies = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To use an image from a private container registry that is not in your workspace, you must use `docker.base_image_registry` to specify the address of the repository as well as a username and password." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "fastai_env.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n", - "fastai_env.docker.base_image_registry.username = \"username\"\n", - "fastai_env.docker.base_image_registry.password = \"password\"\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is also possible to use a custom Dockerfile. Use this approach if you need to install non-Python packages as dependencies and remember to set the base image to None. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specify docker steps as a string:\n", - "```python \n", - "dockerfile = r\"\"\" \\\n", - "FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\n", - "RUN echo \"Hello from custom container!\" \\\n", - "\"\"\"\n", - "```\n", - "Set base image to None, because the image is defined by dockerfile:\n", - "```python\n", - "fastai_env.docker.base_image = None \\\n", - "fastai_env.docker.base_dockerfile = dockerfile\n", - "```\n", - "Alternatively, load the string from a file:\n", - "```python\n", - "fastai_env.docker.base_image = None \\\n", - "fastai_env.docker.base_dockerfile = \"./Dockerfile\"\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"gpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target.')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - " compute_target.wait_for_completion(show_output=True)\n", - "\n", - "# use get_status() to get a detailed status for the current AmlCompute\n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a ScriptRunConfig\n", - "This ScriptRunConfig will configure your job for execution on the desired compute target." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "fastai_config = ScriptRunConfig(source_directory='fastai-example',\n", - " script='train.py',\n", - " compute_target=compute_target,\n", - " environment=fastai_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit your run\n", - "When a training run is submitted using a ScriptRunConfig object, the submit method returns an object of type ScriptRun. The returned ScriptRun object gives you programmatic access to information about the training run. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "run = Experiment(ws,'fastai-custom-image').submit(fastai_config)\n", - "run.wait_for_completion(show_output=True)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sagopal" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Oxford IIIT Pet" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Pytorch" - ], - "friendly_name": "Train a model with a custom Docker image", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "nteract": { - "version": "nteract-front-end@1.0.0" - }, - "tags": [ - "None" - ], - "task": "Train with custom Docker image" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.yml b/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.yml deleted file mode 100644 index 3e5f80ae5..000000000 --- a/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.yml +++ /dev/null @@ -1,5 +0,0 @@ -name: fastai-with-custom-docker -dependencies: -- pip: - - azureml-sdk - - fastai==1.0.61 diff --git a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/keras_mnist.py b/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/keras_mnist.py deleted file mode 100644 index 6b67b9bfd..000000000 --- a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/keras_mnist.py +++ /dev/null @@ -1,128 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import numpy as np -import argparse -import os -import glob - -import matplotlib.pyplot as plt - -import keras -from keras.models import Sequential, model_from_json -from keras.layers import Dense -from keras.optimizers import RMSprop -from keras.callbacks import Callback - -import tensorflow as tf - -from azureml.core import Run -from utils import load_data, one_hot_encode - -print("Keras version:", keras.__version__) -print("Tensorflow version:", tf.__version__) - -parser = argparse.ArgumentParser() -parser.add_argument('--data-folder', type=str, dest='data_folder', default='data', help='data folder mounting point') -parser.add_argument('--batch-size', type=int, dest='batch_size', default=50, help='mini batch size for training') -parser.add_argument('--first-layer-neurons', type=int, dest='n_hidden_1', default=100, - help='# of neurons in the first layer') -parser.add_argument('--second-layer-neurons', type=int, dest='n_hidden_2', default=100, - help='# of neurons in the second layer') -parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.001, help='learning rate') - -args = parser.parse_args() - -data_folder = args.data_folder - -print('training dataset is stored here:', data_folder) - -X_train_path = glob.glob(os.path.join(data_folder, '**/train-images-idx3-ubyte.gz'), recursive=True)[0] -X_test_path = glob.glob(os.path.join(data_folder, '**/t10k-images-idx3-ubyte.gz'), recursive=True)[0] -y_train_path = glob.glob(os.path.join(data_folder, '**/train-labels-idx1-ubyte.gz'), recursive=True)[0] -y_test_path = glob.glob(os.path.join(data_folder, '**/t10k-labels-idx1-ubyte.gz'), recursive=True)[0] - -X_train = load_data(X_train_path, False) / 255.0 -X_test = load_data(X_test_path, False) / 255.0 -y_train = load_data(y_train_path, True).reshape(-1) -y_test = load_data(y_test_path, True).reshape(-1) - -training_set_size = X_train.shape[0] - -n_inputs = 28 * 28 -n_h1 = args.n_hidden_1 -n_h2 = args.n_hidden_2 -n_outputs = 10 -n_epochs = 20 -batch_size = args.batch_size -learning_rate = args.learning_rate - -y_train = one_hot_encode(y_train, n_outputs) -y_test = one_hot_encode(y_test, n_outputs) -print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n') - -# Build a simple MLP model -model = Sequential() -# first hidden layer -model.add(Dense(n_h1, activation='relu', input_shape=(n_inputs,))) -# second hidden layer -model.add(Dense(n_h2, activation='relu')) -# output layer -model.add(Dense(n_outputs, activation='softmax')) - -model.summary() - -model.compile(loss='categorical_crossentropy', - optimizer=RMSprop(lr=learning_rate), - metrics=['accuracy']) - -# start an Azure ML run -run = Run.get_context() - - -class LogRunMetrics(Callback): - # callback at the end of every epoch - def on_epoch_end(self, epoch, log): - # log a value repeated which creates a list - run.log('Loss', log['val_loss']) - run.log('Accuracy', log['val_accuracy']) - - -history = model.fit(X_train, y_train, - batch_size=batch_size, - epochs=n_epochs, - verbose=2, - validation_data=(X_test, y_test), - callbacks=[LogRunMetrics()]) - -score = model.evaluate(X_test, y_test, verbose=0) - -# log a single value -run.log("Final test loss", score[0]) -print('Test loss:', score[0]) - -run.log('Final test accuracy', score[1]) -print('Test accuracy:', score[1]) - -plt.figure(figsize=(6, 3)) -plt.title('MNIST with Keras MLP ({} epochs)'.format(n_epochs), fontsize=14) -plt.plot(history.history['val_accuracy'], 'b-', label='Accuracy', lw=4, alpha=0.5) -plt.plot(history.history['val_loss'], 'r--', label='Loss', lw=4, alpha=0.5) -plt.legend(fontsize=12) -plt.grid(True) - -# log an image -run.log_image('Accuracy vs Loss', plot=plt) - -# create a ./outputs/model folder in the compute target -# files saved in the "./outputs" folder are automatically uploaded into run history -os.makedirs('./outputs/model', exist_ok=True) - -# serialize NN architecture to JSON -model_json = model.to_json() -# save model JSON -with open('./outputs/model/model.json', 'w') as f: - f.write(model_json) -# save model weights -model.save_weights('./outputs/model/model.h5') -print("model saved in ./outputs/model folder") diff --git a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/nn.png b/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/nn.png deleted file mode 100644 index 8910281ea..000000000 Binary files a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/nn.png and /dev/null differ diff --git a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb b/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb deleted file mode 100644 index 3da5e83a1..000000000 --- a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb +++ /dev/null @@ -1,1248 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "bf74d2e9-2708-49b1-934b-e0ede342f475" - } - }, - "source": [ - "# Training, hyperparameter tune, and deploy with Keras\n", - "\n", - "## Introduction\n", - "This tutorial shows how to train a simple deep neural network using the MNIST dataset and Keras on Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of `28x28` pixels, representing number from 0 to 9. The goal is to create a multi-class classifier to identify the digit each image represents, and deploy it as a web service in Azure.\n", - "\n", - "For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/).\n", - "\n", - "## Prerequisite:\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../../configuration.ipynb) to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (`config.json`)\n", - "* For local scoring test, you will also need to have `tensorflow` and `keras` installed in the current Jupyter kernel." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's get started. First let's import some Python libraries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "c377ea0c-0cd9-4345-9be2-e20fb29c94c3" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import os\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "edaa7f2f-2439-4148-b57a-8c794c0945ec" - } - }, - "outputs": [], - "source": [ - "import azureml\n", - "from azureml.core import Workspace\n", - "\n", - "# check core SDK version number\n", - "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "59f52294-4a25-4c92-bab8-3b07f0f44d15" - } - }, - "source": [ - "## Create an Azure ML experiment\n", - "Let's create an experiment named \"keras-mnist\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "bc70f780-c240-4779-96f3-bc5ef9a37d59" - } - }, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "script_folder = './keras-mnist'\n", - "os.makedirs(script_folder, exist_ok=True)\n", - "\n", - "exp = Experiment(workspace=ws, name='keras-mnist')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explore data\n", - "\n", - "Before you train a model, you need to understand the data that you are using to train it. In this section you learn how to:\n", - "\n", - "* Download the MNIST dataset\n", - "* Display some sample images\n", - "\n", - "### Download the MNIST dataset\n", - "\n", - "Download the MNIST dataset and save the files into a `data` directory locally. Images and labels for both training and testing are downloaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib.request\n", - "\n", - "data_folder = os.path.join(os.getcwd(), 'data')\n", - "os.makedirs(data_folder, exist_ok=True)\n", - "\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-images-idx3-ubyte.gz',\n", - " filename=os.path.join(data_folder, 'train-images-idx3-ubyte.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-labels-idx1-ubyte.gz',\n", - " filename=os.path.join(data_folder, 'train-labels-idx1-ubyte.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n", - " filename=os.path.join(data_folder, 't10k-images-idx3-ubyte.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz',\n", - " filename=os.path.join(data_folder, 't10k-labels-idx1-ubyte.gz'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display some sample images\n", - "\n", - "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `utils.py` file. This file is included in the sample folder. Please make sure it is placed in the same folder as this notebook. The `load_data` function simply parses the compressed files into numpy arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# make sure utils.py is in the same directory as this code\n", - "from utils import load_data\n", - "\n", - "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.\n", - "X_train = load_data(os.path.join(data_folder, 'train-images-idx3-ubyte.gz'), False) / 255.0\n", - "X_test = load_data(os.path.join(data_folder, 't10k-images-idx3-ubyte.gz'), False) / 255.0\n", - "y_train = load_data(os.path.join(data_folder, 'train-labels-idx1-ubyte.gz'), True).reshape(-1)\n", - "y_test = load_data(os.path.join(data_folder, 't10k-labels-idx1-ubyte.gz'), True).reshape(-1)\n", - "\n", - "# now let's show some randomly chosen images from the training set.\n", - "count = 0\n", - "sample_size = 30\n", - "plt.figure(figsize = (16, 6))\n", - "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n", - " count = count + 1\n", - " plt.subplot(1, sample_size, count)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.text(x=10, y=-10, s=y_train[i], fontsize=18)\n", - " plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now you have an idea of what these images look like and the expected prediction outcome." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "defe921f-8097-44c3-8336-8af6700804a7" - } - }, - "source": [ - "## Create a FileDataset\n", - "A FileDataset references one or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.dataset import Dataset\n", - "web_paths = ['https://azureopendatastorage.blob.core.windows.net/mnist/train-images-idx3-ubyte.gz',\n", - " 'https://azureopendatastorage.blob.core.windows.net/mnist/train-labels-idx1-ubyte.gz',\n", - " 'https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n", - " 'https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz'\n", - " ]\n", - "dataset = Dataset.File.from_files(path = web_paths)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the `register()` method to register datasets to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script.\n", - "You can try get the dataset first to see if it's already registered." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.exceptions import UserErrorException\n", - "dataset_registered = False\n", - "try:\n", - " temp = Dataset.get_by_name(workspace = ws, name = 'mnist-dataset')\n", - " dataset_registered = True\n", - "except UserErrorException:\n", - " print(\"The dataset mnist-dataset is not registered in workspace yet.\")\n", - "\n", - "if not dataset_registered:\n", - " dataset = dataset.register(workspace = ws,\n", - " name = 'mnist-dataset',\n", - " description='training and test dataset',\n", - " create_new_version=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n", - "1. create the configuration (this step is local and only takes a second)\n", - "2. create the cluster (this step will take about **20 seconds**)\n", - "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"hd-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - "# can poll for a minimum number of nodes and for a specific timeout. \n", - "# if no min node count is provided it uses the scale settings for the cluster\n", - "compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named \"gpu-cluster\" of type `AmlCompute`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "compute_targets = ws.compute_targets\n", - "for name, ct in compute_targets.items():\n", - " print(name, ct.type, ct.provisioning_state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Copy the training files into the script folder\n", - "The Keras training script is already created for you. You can simply copy it into the script folder, together with the utility library used to load compressed data file into numpy array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "# the training logic is in the keras_mnist.py file.\n", - "shutil.copy('./keras_mnist.py', script_folder)\n", - "\n", - "# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.\n", - "shutil.copy('./utils.py', script_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "2039d2d5-aca6-4f25-a12f-df9ae6529cae" - } - }, - "source": [ - "## Construct neural network in Keras\n", - "In the training script `keras_mnist.py`, it creates a very simple DNN (deep neural network), with just 2 hidden layers. The input layer has 28 * 28 = 784 neurons, each representing a pixel in an image. The first hidden layer has 300 neurons, and the second hidden layer has 100 neurons. The output layer has 10 neurons, each representing a targeted label from 0 to 9.\n", - "\n", - "![DNN](nn.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure ML concepts \n", - "Please note the following three things in the code below:\n", - "1. The script accepts arguments using the argparse package. In this case there is one argument `--data_folder` which specifies the FileDataset in which the script can find the MNIST data\n", - "```\n", - " parser = argparse.ArgumentParser()\n", - " parser.add_argument('--data_folder')\n", - "```\n", - "2. The script is accessing the Azure ML `Run` object by executing `run = Run.get_context()`. Further down the script is using the `run` to report the loss and accuracy at the end of each epoch via callback.\n", - "```\n", - " run.log('Loss', log['val_loss'])\n", - " run.log('Accuracy', log['val_accuracy'])\n", - "```\n", - "3. When running the script on Azure ML, you can write files out to a folder `./outputs` that is relative to the root directory. This folder is specially tracked by Azure ML in the sense that any files written to that folder during script execution on the remote target will be picked up by Run History; these files (known as artifacts) will be available as part of the run history record." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next cell will print out the training code for you to inspect." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(os.path.join(script_folder, './keras_mnist.py'), 'r') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an environment\n", - "\n", - "Define a conda environment YAML file with your training script dependencies, which include TensorFlow, Keras and matplotlib, and create an Azure ML environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile conda_dependencies.yml\n", - "\n", - "channels:\n", - "- conda-forge\n", - "dependencies:\n", - "- python=3.6.2\n", - "- pip=21.3.1\n", - "- pip:\n", - " - h5py<=2.10.0\n", - " - azureml-defaults\n", - " - tensorflow-gpu==2.0.0\n", - " - keras<=2.3.1\n", - " - matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "keras_env = Environment.from_conda_specification(name = 'keras-2.3.1', file_path = './conda_dependencies.yml')\n", - "\n", - "# Specify a GPU base image\n", - "keras_env.docker.enabled = True\n", - "keras_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.0-cudnn7-ubuntu18.04'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure the training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on.\n", - "\n", - "Note that we are specifying a DatasetConsumptionConfig for our FileDataset as an argument to the training script. Azure ML will resolve this DatasetConsumptionConfig to the mount-point of the backing datastore, which we access from the training script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = Dataset.get_by_name(ws, 'mnist-dataset')\n", - "\n", - "# list the files referenced by mnist dataset\n", - "dataset.to_path()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "args = ['--data-folder', dataset.as_named_input('mnist').as_mount(),\n", - " '--batch-size', 50,\n", - " '--first-layer-neurons', 300,\n", - " '--second-layer-neurons', 100,\n", - " '--learning-rate', 0.001]\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='keras_mnist.py',\n", - " arguments=args,\n", - " compute_target=compute_target,\n", - " environment=keras_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit job to run\n", - "Submit the ScriptRunConfig to the Azure ML experiment to kick off the execution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = exp.submit(src)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor the Run\n", - "As the Run is executed, it will go through the following stages:\n", - "1. Preparing: A docker image is created matching the Python environment specified by the Azure ML environment, and it will be uploaded to the workspace's Azure Container Registry. This step will only happen once for each Python environment -- the container will then be cached for subsequent runs. Creating and uploading the image takes about **5 minutes**. While the job is preparing, logs are streamed to the run history and can be viewed to monitor the progress of the image creation.\n", - "\n", - "2. Scaling: If the compute needs to be scaled up (i.e. the AmlCompute cluster requires more nodes to execute the run than currently available), the cluster will attempt to scale up in order to make the required amount of nodes available. Scaling typically takes about **5 minutes**.\n", - "\n", - "3. Running: All scripts in the script folder are uploaded to the compute target, data stores are mounted/copied and the `entry_script` is executed. While the job is running, stdout and the `./logs` folder are streamed to the run history and can be viewed to monitor the progress of the run.\n", - "\n", - "4. Post-Processing: The `./outputs` folder of the run is copied over to the run history\n", - "\n", - "There are multiple ways to check the progress of a running job. We can use a Jupyter notebook widget. \n", - "\n", - "**Note: The widget will automatically update ever 10-15 seconds, always showing you the most up-to-date information about the run**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also periodically check the status of the run object, and navigate to Azure portal to monitor the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the outputs of the training script, it prints out the Keras version number. Please make a note of it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The Run object\n", - "The Run object provides the interface to the run history -- both to the job and to the control plane (this notebook), and both while the job is running and after it has completed. It provides a number of interesting features for instance:\n", - "* `run.get_details()`: Provides a rich set of properties of the run\n", - "* `run.get_metrics()`: Provides a dictionary with all the metrics that were reported for the Run\n", - "* `run.get_file_names()`: List all the files that were uploaded to the run history for this Run. This will include the `outputs` and `logs` folder, azureml-logs and other logs, as well as files that were explicitly uploaded to the run using `run.upload_file()`\n", - "\n", - "Below are some examples -- please run through them and inspect their output. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_details()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_metrics()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_file_names()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download the saved model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the training script, the Keras model is saved into two files, `model.json` and `model.h5`, in the `outputs/models` folder on the gpu-cluster AmlCompute node. Azure ML automatically uploaded anything written in the `./outputs` folder into run history file store. Subsequently, we can use the `run` object to download the model files. They are under the the `outputs/model` folder in the run history file store, and are downloaded into a local folder named `model`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# create a model folder in the current directory\n", - "os.makedirs('./model', exist_ok=True)\n", - "\n", - "for f in run.get_file_names():\n", - " if f.startswith('outputs/model'):\n", - " output_file_path = os.path.join('./model', f.split('/')[-1])\n", - " print('Downloading from {} to {} ...'.format(f, output_file_path))\n", - " run.download_file(name=f, output_file_path=output_file_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predict on the test set (Optional)\n", - "Let's check the version of the local Keras. Make sure it matches with the version number printed out in the training script. Otherwise you might not be able to load the model properly." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " import keras\n", - " import tensorflow as tf\n", - "\n", - " print(\"Keras version:\", keras.__version__)\n", - " print(\"Tensorflow version:\", tf.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's load the downloaded model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " from keras.models import model_from_json\n", - "\n", - " # load json and create model\n", - " json_file = open('model/model.json', 'r')\n", - " loaded_model_json = json_file.read()\n", - " json_file.close()\n", - " loaded_model = model_from_json(loaded_model_json)\n", - " # load weights into new model\n", - " loaded_model.load_weights(\"model/model.h5\")\n", - " print(\"Model loaded from disk.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Feed test dataset to the persisted model to get predictions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " # evaluate loaded model on test data\n", - " loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", - " y_test_ohe = one_hot_encode(y_test, 10)\n", - " y_hat = np.argmax(loaded_model.predict(X_test), axis=1)\n", - "\n", - " # print the first 30 labels and predictions\n", - " print('labels: \\t', y_test[:30])\n", - " print('predictions:\\t', y_hat[:30])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculate the overall accuracy by comparing the predicted value against the test set." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " print(\"Accuracy on the test set:\", np.average(y_hat == y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Intelligent hyperparameter tuning\n", - "We have trained the model with one set of hyperparameters, now let's how we can do hyperparameter tuning by launching multiple runs on the cluster. First let's define the parameter space using random sampling." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n", - "from azureml.train.hyperdrive import choice, loguniform\n", - "\n", - "ps = RandomParameterSampling(\n", - " {\n", - " '--batch-size': choice(25, 50, 100),\n", - " '--first-layer-neurons': choice(10, 50, 200, 300, 500),\n", - " '--second-layer-neurons': choice(10, 50, 200, 500),\n", - " '--learning-rate': loguniform(-6, -1)\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will create a new ScriptRunConfig without the above arguments since they will be passed in later by our Hyperdrive configuration. Note we still need to keep the `data-folder` parameter since that's not a hyperparameter we will sweep." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "args = ['--data-folder', dataset.as_named_input('mnist').as_mount()]\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='keras_mnist.py',\n", - " arguments=args,\n", - " compute_target=compute_target,\n", - " environment=keras_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will define an early termnination policy. The `BanditPolicy` basically states to check the job every 2 iterations. If the primary metric (defined later) falls outside of the top 10% range, Azure ML terminate the job. This saves us from continuing to explore hyperparameters that don't show promise of helping reach our target metric." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are ready to configure a run configuration object, and specify the primary metric `Accuracy` that's recorded in your training runs. If you go back to visit the training script, you will notice that this value is being logged after every epoch (a full batch set). We also want to tell the service that we are looking to maximizing this value. We also set the number of samples to 20, and maximal concurrent job to 4, which is the same as the number of nodes in our computer cluster." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hyperdrive_config = HyperDriveConfig(run_config=src,\n", - " hyperparameter_sampling=ps,\n", - " policy=policy,\n", - " primary_metric_name='Accuracy',\n", - " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n", - " max_total_runs=20,\n", - " max_concurrent_runs=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's launch the hyperparameter tuning job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hyperdrive_run = exp.submit(config=hyperdrive_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use a run history widget to show the progress. Be patient as this might take a while to complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(hyperdrive_run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hyperdrive_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert(hyperdrive_run.get_status() == \"Completed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Warm start a Hyperparameter Tuning experiment and resuming child runs\n", - "Often times, finding the best hyperparameter values for your model can be an iterative process, needing multiple tuning runs that learn from previous hyperparameter tuning runs. Reusing knowledge from these previous runs will accelerate the hyperparameter tuning process, thereby reducing the cost of tuning the model and will potentially improve the primary metric of the resulting model. When warm starting a hyperparameter tuning experiment with Bayesian sampling, trials from the previous run will be used as prior knowledge to intelligently pick new samples, so as to improve the primary metric. Additionally, when using Random or Grid sampling, any early termination decisions will leverage metrics from the previous runs to determine poorly performing training runs. \n", - "\n", - "Azure Machine Learning allows you to warm start your hyperparameter tuning run by leveraging knowledge from up to 5 previously completed hyperparameter tuning parent runs. \n", - "\n", - "Additionally, there might be occasions when individual training runs of a hyperparameter tuning experiment are cancelled due to budget constraints or fail due to other reasons. It is now possible to resume such individual training runs from the last checkpoint (assuming your training script handles checkpoints). Resuming an individual training run will use the same hyperparameter configuration and mount the storage used for that run. The training script should accept the \"--resume-from\" argument, which contains the checkpoint or model files from which to resume the training run. You can also resume individual runs as part of an experiment that spends additional budget on hyperparameter tuning. Any additional budget, after resuming the specified training runs is used for exploring additional configurations.\n", - "\n", - "For more information on warm starting and resuming hyperparameter tuning runs, please refer to the [Hyperparameter Tuning for Azure Machine Learning documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters) \n", - "\n", - "## Find and register best model\n", - "When all the jobs finish, we can find out the one that has the highest accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n", - "print(best_run.get_details()['runDefinition']['arguments'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's list the model files uploaded during the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(best_run.get_file_names())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then register the folder (and all files in it) as a model named `keras-dnn-mnist` under the workspace for deployment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = best_run.register_model(model_name='keras-mlp-mnist', model_path='outputs/model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy the model in ACI\n", - "Now we are ready to deploy the model as a web service running in Azure Container Instance [ACI](https://azure.microsoft.com/en-us/services/container-instances/). Azure Machine Learning accomplishes this by constructing a Docker image with the scoring logic and model baked in.\n", - "### Create score.py\n", - "First, we will create a scoring script that will be invoked by the web service call. \n", - "\n", - "* Note that the scoring script must have two required functions, `init()` and `run(input_data)`. \n", - " * In `init()` function, you typically load the model into a global object. This function is executed only once when the Docker container is started. \n", - " * In `run(input_data)` function, the model is used to predict a value based on the input data. The input and output to `run` typically use JSON as serialization and de-serialization format but you are not limited to that." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import json\n", - "import numpy as np\n", - "import os\n", - "from keras.models import model_from_json\n", - "\n", - "from azureml.core.model import Model\n", - "\n", - "def init():\n", - " global model\n", - " \n", - " model_root = Model.get_model_path('keras-mlp-mnist')\n", - " # load json and create model\n", - " json_file = open(os.path.join(model_root, 'model.json'), 'r')\n", - " model_json = json_file.read()\n", - " json_file.close()\n", - " model = model_from_json(model_json)\n", - " # load weights into new model\n", - " model.load_weights(os.path.join(model_root, \"model.h5\")) \n", - " model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", - " \n", - "def run(raw_data):\n", - " data = np.array(json.loads(raw_data)['data'])\n", - " # make prediction\n", - " y_hat = np.argmax(model.predict(data), axis=1)\n", - " return y_hat.tolist()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create myenv.yml\n", - "We also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify conda packages `tensorflow` and `keras`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "cd = CondaDependencies.create()\n", - "cd.add_tensorflow_conda_package()\n", - "cd.add_conda_package('h5py<=2.10.0')\n", - "cd.add_conda_package('keras<=2.3.1')\n", - "cd.add_pip_package(\"azureml-defaults\")\n", - "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n", - "\n", - "print(cd.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy to ACI\n", - "We are almost ready to deploy. Create the inference configuration and deployment configuration and deploy to ACI. This cell will run for about 7-8 minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.model import Model\n", - "\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores=2,\n", - " auth_enabled=True, # this flag generates API keys to secure access\n", - " memory_gb=2,\n", - " tags={'name': 'mnist', 'framework': 'Keras'},\n", - " description='Keras MLP on MNIST')\n", - "\n", - "service = Model.deploy(workspace=ws, \n", - " name='keras-mnist-svc', \n", - " models=[model], \n", - " inference_config=inference_config, \n", - " deployment_config=aciconfig)\n", - "\n", - "service.wait_for_deployment(True)\n", - "print(service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(service.get_logs())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is the scoring web service endpoint:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the deployed model\n", - "Let's test the deployed model. Pick 30 random samples from the test set, and send it to the web service hosted in ACI. Note here we are using the `run` API in the SDK to invoke the service. You can also make raw HTTP calls using any HTTP tool such as curl.\n", - "\n", - "After the invocation, we print the returned predictions and plot them along with the input images. Use red font color and inversed image (white on black) to highlight the misclassified samples. Note since the model accuracy is pretty high, you might have to run the below cell a few times before you can see a misclassified sample." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "# find 30 random samples from test set\n", - "n = 30\n", - "sample_indices = np.random.permutation(X_test.shape[0])[0:n]\n", - "\n", - "test_samples = json.dumps({\"data\": X_test[sample_indices].tolist()})\n", - "test_samples = bytes(test_samples, encoding='utf8')\n", - "\n", - "# predict using the deployed model\n", - "result = service.run(input_data=test_samples)\n", - "\n", - "# compare actual value vs. the predicted values:\n", - "i = 0\n", - "plt.figure(figsize = (20, 1))\n", - "\n", - "for s in sample_indices:\n", - " plt.subplot(1, n, i + 1)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " \n", - " # use different color for misclassified sample\n", - " font_color = 'red' if y_test[s] != result[i] else 'black'\n", - " clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n", - " \n", - " plt.text(x=10, y=-10, s=result[i], fontsize=18, color=font_color)\n", - " plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)\n", - " \n", - " i = i + 1\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can retrieve the API keys used for accessing the HTTP endpoint." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve the API keys. Two keys were generated.\n", - "key1, Key2 = service.get_keys()\n", - "print(key1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now send construct raw HTTP request and send to the service. Don't forget to add key to the HTTP header." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "\n", - "# send a random row from the test set to score\n", - "random_index = np.random.randint(0, len(X_test)-1)\n", - "input_data = \"{\\\"data\\\": [\" + str(list(X_test[random_index])) + \"]}\"\n", - "\n", - "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n", - "\n", - "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n", - "\n", - "print(\"POST to url\", service.scoring_uri)\n", - "#print(\"input data:\", input_data)\n", - "print(\"label:\", y_test[random_index])\n", - "print(\"prediction:\", resp.text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the workspace after the web service was deployed. You should see \n", - "* a registered model named 'keras-mlp-mnist' and with the id 'model:1' \n", - "* a webservice called 'keras-mnist-svc' with some scoring URL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = ws.models['keras-mlp-mnist']\n", - "print(\"Model: {}, ID: {}\".format('keras-mlp-mnist', model.id))\n", - " \n", - "webservice = ws.webservices['keras-mnist-svc']\n", - "print(\"Webservice: {}, scoring URI: {}\".format('keras-mnist-svc', webservice.scoring_uri))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up\n", - "You can delete the ACI deployment with a simple delete API call." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "swatig" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "TensorFlow" - ], - "friendly_name": "Train a DNN using hyperparameter tuning and deploying with Keras", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "tags": [ - "None" - ], - "task": "Create a multi-class classifier" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.yml b/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.yml deleted file mode 100644 index 8fa4d3521..000000000 --- a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.yml +++ /dev/null @@ -1,8 +0,0 @@ -name: train-hyperparameter-tune-deploy-with-keras -dependencies: -- pip: - - azureml-sdk - - azureml-widgets - - tensorflow - - keras<=2.3.1 - - matplotlib diff --git a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/utils.py b/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/utils.py deleted file mode 100644 index 98170adae..000000000 --- a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/utils.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import gzip -import numpy as np -import struct - - -# load compressed MNIST gz files and return numpy arrays -def load_data(filename, label=False): - with gzip.open(filename) as gz: - struct.unpack('I', gz.read(4)) - n_items = struct.unpack('>I', gz.read(4)) - if not label: - n_rows = struct.unpack('>I', gz.read(4))[0] - n_cols = struct.unpack('>I', gz.read(4))[0] - res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8) - res = res.reshape(n_items[0], n_rows * n_cols) - else: - res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8) - res = res.reshape(n_items[0], 1) - return res - - -# one-hot encode a 1-D array -def one_hot_encode(array, num_of_classes): - return np.eye(num_of_classes)[array.reshape(-1)] diff --git a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.ipynb b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.ipynb deleted file mode 100644 index 088233e38..000000000 --- a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.ipynb +++ /dev/null @@ -1,497 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Distributed PyTorch with DistributedDataParallel\n", - "\n", - "In this tutorial, you will train a PyTorch model on the [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset using distributed training with PyTorch's `DistributedDataParallel` module across a GPU cluster." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = 'gpu-cluster'\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target.')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - " compute_target.wait_for_completion(show_output=True)\n", - "\n", - "# use get_status() to get a detailed status for the current AmlCompute. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare dataset\n", - "\n", - "Prepare the dataset used for training. We will first download and extract the publicly available CIFAR-10 dataset from the cs.toronto.edu website and then create an Azure ML FileDataset to use the data for training." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download and extract CIFAR-10 data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib\n", - "import tarfile\n", - "import os\n", - "\n", - "url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n", - "filename = 'cifar-10-python.tar.gz'\n", - "data_root = 'cifar-10'\n", - "filepath = os.path.join(data_root, filename)\n", - "\n", - "if not os.path.isdir(data_root):\n", - " os.makedirs(data_root, exist_ok=True)\n", - " urllib.request.urlretrieve(url, filepath)\n", - " with tarfile.open(filepath, \"r:gz\") as tar:\n", - " tar.extractall(path=data_root)\n", - " os.remove(filepath) # delete tar.gz file after extraction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Azure ML dataset\n", - "\n", - "The `upload_directory` method will upload the data to a datastore and create a FileDataset from it. In this tutorial we will use the workspace's default datastore." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "\n", - "datastore = ws.get_default_datastore()\n", - "dataset = Dataset.File.upload_directory(\n", - " src_dir=data_root, target=(datastore, data_root)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model on the remote compute\n", - "Now that we have the AmlCompute ready to go, let's run our distributed training job." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project directory\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "project_folder = './pytorch-distr'\n", - "os.makedirs(project_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare training script\n", - "Now you will need to create your training script. In this tutorial, the script for distributed training on CIFAR-10 is already provided for you at `train.py`. In practice, you should be able to take any custom PyTorch training script as is and run it with Azure ML without having to modify your code." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once your script is ready, copy the training script `train.py` into the project directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "shutil.copy('train.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment\n", - "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = 'pytorch-distr'\n", - "experiment = Experiment(ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "In this tutorial, we will use one of Azure ML's curated PyTorch environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the PyTorch 1.6 GPU curated environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "pytorch_env = Environment.get(ws, name='AzureML-PyTorch-1.6-GPU')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the training job\n", - "\n", - "To launch a distributed PyTorch job on Azure ML, you have two options:\n", - "\n", - "1. Per-process launch - specify the total # of worker processes (typically one per GPU) you want to run, and\n", - "Azure ML will handle launching each process.\n", - "2. Per-node launch with [torch.distributed.launch](https://pytorch.org/docs/stable/distributed.html#launch-utility) - provide the `torch.distributed.launch` command you want to\n", - "run on each node.\n", - "\n", - "For more information, see the [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch#distributeddataparallel).\n", - "\n", - "Both options are shown below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Per-process launch\n", - "\n", - "To use the per-process launch option in which Azure ML will handle launching each of the processes to run your training script,\n", - "\n", - "1. Specify the training script and arguments\n", - "2. Create a `PyTorchConfiguration` and specify `node_count` and `process_count`. The `process_count` is the total number of processes you want to run for the job; this should typically equal the # of GPUs available on each node multiplied by the # of nodes. Since this tutorial uses the `STANDARD_NC6` SKU, which has one GPU, the total process count for a 2-node job is `2`. If you are using a SKU with >1 GPUs, adjust the `process_count` accordingly.\n", - "\n", - "Azure ML will set the `MASTER_ADDR`, `MASTER_PORT`, `NODE_RANK`, `WORLD_SIZE` environment variables on each node, in addition to the process-level `RANK` and `LOCAL_RANK` environment variables, that are needed for distributed PyTorch training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "from azureml.core.runconfig import PyTorchConfiguration\n", - "\n", - "# create distributed config\n", - "distr_config = PyTorchConfiguration(process_count=2, node_count=2)\n", - "\n", - "# create args\n", - "args = [\"--data-dir\", dataset.as_download(), \"--epochs\", 25]\n", - "\n", - "# create job config\n", - "src = ScriptRunConfig(source_directory=project_folder,\n", - " script='train.py',\n", - " arguments=args,\n", - " compute_target=compute_target,\n", - " environment=pytorch_env,\n", - " distributed_job_config=distr_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Per-node launch with `torch.distributed.launch`\n", - "\n", - "If you would instead like to use the PyTorch-provided launch utility `torch.distributed.launch` to handle launching the worker processes on each node, you can do so as well. \n", - "\n", - "1. Provide the launch command to the `command` parameter of ScriptRunConfig. For PyTorch jobs Azure ML will set the `MASTER_ADDR`, `MASTER_PORT`, and `NODE_RANK` environment variables on each node, so you can simply just reference those environment variables in your command. If you are using a SKU with >1 GPUs, adjust the `--nproc_per_node` argument accordingly.\n", - "\n", - "2. Create a `PyTorchConfiguration` and specify the `node_count`. You do not need to specify the `process_count`; by default Azure ML will launch one process per node to run the `command` you provided.\n", - "\n", - "Uncomment the code below to configure a job with this method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "from azureml.core import ScriptRunConfig\n", - "from azureml.core.runconfig import PyTorchConfiguration\n", - "\n", - "# create distributed config\n", - "distr_config = PyTorchConfiguration(node_count=2)\n", - "\n", - "# define command\n", - "launch_cmd = [\"python -m torch.distributed.launch --nproc_per_node 1 --nnodes 2 \" \\\n", - " \"--node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT --use_env \" \\\n", - " \"train.py --data-dir\", dataset.as_download(), \"--epochs 25\"]\n", - "\n", - "# create job config\n", - "src = ScriptRunConfig(source_directory=project_folder,\n", - " command=launch_cmd,\n", - " compute_target=compute_target,\n", - " environment=pytorch_env,\n", - " distributed_job_config=distr_config)\n", - "'''" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job\n", - "Run your experiment by submitting your `ScriptRunConfig` object. Note that this call is asynchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = experiment.submit(src)\n", - "print(run)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor your run\n", - "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes. You can see that the widget automatically plots and visualizes the loss metric that we logged to the Azure ML run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can block until the script has completed training before running more code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True) # this provides a verbose log" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "minxia" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "CIFAR-10" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "PyTorch" - ], - "friendly_name": "Distributed training with PyTorch", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "tags": [ - "None" - ], - "task": "Train a model using distributed training via PyTorch DistributedDataParallel" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.yml b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.yml deleted file mode 100644 index 8fa7e81d4..000000000 --- a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.yml +++ /dev/null @@ -1,5 +0,0 @@ -name: distributed-pytorch-with-distributeddataparallel -dependencies: -- pip: - - azureml-sdk - - azureml-widgets diff --git a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/train.py b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/train.py deleted file mode 100644 index c6c302dd5..000000000 --- a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/train.py +++ /dev/null @@ -1,238 +0,0 @@ -# Copyright (c) 2017 Facebook, Inc. All rights reserved. -# BSD 3-Clause License -# -# Script adapted from: -# https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html -# ============================================================================== - -# imports -import torch -import torchvision -import torchvision.transforms as transforms -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -import os -import argparse - - -# define network architecture -class Net(nn.Module): - def __init__(self): - super(Net, self).__init__() - self.conv1 = nn.Conv2d(3, 32, 3) - self.pool = nn.MaxPool2d(2, 2) - self.conv2 = nn.Conv2d(32, 64, 3) - self.conv3 = nn.Conv2d(64, 128, 3) - self.fc1 = nn.Linear(128 * 6 * 6, 120) - self.dropout = nn.Dropout(p=0.2) - self.fc2 = nn.Linear(120, 84) - self.fc3 = nn.Linear(84, 10) - - def forward(self, x): - x = F.relu(self.conv1(x)) - x = self.pool(F.relu(self.conv2(x))) - x = self.pool(F.relu(self.conv3(x))) - x = x.view(-1, 128 * 6 * 6) - x = self.dropout(F.relu(self.fc1(x))) - x = F.relu(self.fc2(x)) - x = self.fc3(x) - return x - - -def train(train_loader, model, criterion, optimizer, epoch, device, print_freq, rank): - running_loss = 0.0 - for i, data in enumerate(train_loader, 0): - # get the inputs; data is a list of [inputs, labels] - inputs, labels = data[0].to(device), data[1].to(device) - - # zero the parameter gradients - optimizer.zero_grad() - - # forward + backward + optimize - outputs = model(inputs) - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - - # print statistics - running_loss += loss.item() - if i % print_freq == 0: # print every print_freq mini-batches - print( - "Rank %d: [%d, %5d] loss: %.3f" - % (rank, epoch + 1, i + 1, running_loss / print_freq) - ) - running_loss = 0.0 - - -def evaluate(test_loader, model, device): - classes = ( - "plane", - "car", - "bird", - "cat", - "deer", - "dog", - "frog", - "horse", - "ship", - "truck", - ) - - model.eval() - - correct = 0 - total = 0 - class_correct = list(0.0 for i in range(10)) - class_total = list(0.0 for i in range(10)) - with torch.no_grad(): - for data in test_loader: - images, labels = data[0].to(device), data[1].to(device) - outputs = model(images) - _, predicted = torch.max(outputs.data, 1) - total += labels.size(0) - correct += (predicted == labels).sum().item() - c = (predicted == labels).squeeze() - for i in range(10): - label = labels[i] - class_correct[label] += c[i].item() - class_total[label] += 1 - - # print total test set accuracy - print( - "Accuracy of the network on the 10000 test images: %d %%" - % (100 * correct / total) - ) - - # print test accuracy for each of the classes - for i in range(10): - print( - "Accuracy of %5s : %2d %%" - % (classes[i], 100 * class_correct[i] / class_total[i]) - ) - - -def main(args): - # get PyTorch environment variables - world_size = int(os.environ["WORLD_SIZE"]) - rank = int(os.environ["RANK"]) - local_rank = int(os.environ["LOCAL_RANK"]) - - distributed = world_size > 1 - - # set device - if distributed: - device = torch.device("cuda", local_rank) - else: - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - # initialize distributed process group using default env:// method - if distributed: - torch.distributed.init_process_group(backend="nccl") - - # define train and test dataset DataLoaders - transform = transforms.Compose( - [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] - ) - - train_set = torchvision.datasets.CIFAR10( - root=args.data_dir, train=True, download=False, transform=transform - ) - - if distributed: - train_sampler = torch.utils.data.distributed.DistributedSampler(train_set) - else: - train_sampler = None - - train_loader = torch.utils.data.DataLoader( - train_set, - batch_size=args.batch_size, - shuffle=(train_sampler is None), - num_workers=args.workers, - sampler=train_sampler, - ) - - test_set = torchvision.datasets.CIFAR10( - root=args.data_dir, train=False, download=False, transform=transform - ) - test_loader = torch.utils.data.DataLoader( - test_set, batch_size=args.batch_size, shuffle=False, num_workers=args.workers - ) - - model = Net().to(device) - - # wrap model with DDP - if distributed: - model = nn.parallel.DistributedDataParallel( - model, device_ids=[local_rank], output_device=local_rank - ) - - # define loss function and optimizer - criterion = nn.CrossEntropyLoss() - optimizer = optim.SGD( - model.parameters(), lr=args.learning_rate, momentum=args.momentum - ) - - # train the model - for epoch in range(args.epochs): - print("Rank %d: Starting epoch %d" % (rank, epoch)) - if distributed: - train_sampler.set_epoch(epoch) - model.train() - train( - train_loader, - model, - criterion, - optimizer, - epoch, - device, - args.print_freq, - rank, - ) - - print("Rank %d: Finished Training" % (rank)) - - if not distributed or rank == 0: - os.makedirs(args.output_dir, exist_ok=True) - model_path = os.path.join(args.output_dir, "cifar_net.pt") - torch.save(model.state_dict(), model_path) - - # evaluate on full test dataset - evaluate(test_loader, model, device) - - -if __name__ == "__main__": - # setup argparse - parser = argparse.ArgumentParser() - parser.add_argument( - "--data-dir", type=str, help="directory containing CIFAR-10 dataset" - ) - parser.add_argument("--epochs", default=10, type=int, help="number of epochs") - parser.add_argument( - "--batch-size", - default=16, - type=int, - help="mini batch size for each gpu/process", - ) - parser.add_argument( - "--workers", - default=2, - type=int, - help="number of data loading workers for each gpu/process", - ) - parser.add_argument( - "--learning-rate", default=0.001, type=float, help="learning rate" - ) - parser.add_argument("--momentum", default=0.9, type=float, help="momentum") - parser.add_argument( - "--output-dir", default="outputs", type=str, help="directory to save model to" - ) - parser.add_argument( - "--print-freq", - default=200, - type=int, - help="frequency of printing training statistics", - ) - args = parser.parse_args() - - main(args) diff --git a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb deleted file mode 100644 index 00fc8ad2e..000000000 --- a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb +++ /dev/null @@ -1,378 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Distributed PyTorch with Horovod\n", - "In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod) across a GPU cluster." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`\n", - "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) on single-node PyTorch training using Azure Machine Learning" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"gpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target.')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - " compute_target.wait_for_completion(show_output=True)\n", - "\n", - "# use get_status() to get a detailed status for the current AmlCompute. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model on the remote compute\n", - "Now that we have the AmlCompute ready to go, let's run our distributed training job." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project directory\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "project_folder = './pytorch-distr-hvd'\n", - "os.makedirs(project_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare training script\n", - "Now you will need to create your training script. In this tutorial, the script for distributed training of MNIST is already provided for you at `pytorch_horovod_mnist.py`. In practice, you should be able to take any custom PyTorch training script as is and run it with Azure ML without having to modify your code.\n", - "\n", - "However, if you would like to use Azure ML's [metric logging](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#logging) capabilities, you will have to add a small amount of Azure ML logic inside your training script. In this example, at each logging interval, we will log the loss for that minibatch to our Azure ML run.\n", - "\n", - "To do so, in `pytorch_horovod_mnist.py`, we will first access the Azure ML `Run` object within the script:\n", - "```Python\n", - "from azureml.core.run import Run\n", - "run = Run.get_context()\n", - "```\n", - "Later within the script, we log the loss metric to our run:\n", - "```Python\n", - "run.log('loss', loss.item())\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once your script is ready, copy the training script `pytorch_horovod_mnist.py` into the project directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "shutil.copy('pytorch_horovod_mnist.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment\n", - "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = 'pytorch-distr-hvd'\n", - "experiment = Experiment(ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "In this tutorial, we will use one of Azure ML's curated PyTorch environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the PyTorch 1.6 GPU curated environment. The curated environment includes the `torch`, `torchvision` and `horovod` packages required by the training script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "pytorch_env = Environment.get(ws, name='AzureML-PyTorch-1.6-GPU')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on.\n", - "\n", - "In order to execute a distributed run using MPI/Horovod, you must create an `MpiConfiguration` object and pass it to the `distributed_job_config` parameter of the ScriptRunConfig constructor. The below code will configure a 2-node distributed job running one process per node. If you would also like to run multiple processes per node (i.e. if your cluster SKU has multiple GPUs), additionally specify the `process_count_per_node` parameter in `MpiConfiguration` (the default is `1`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "from azureml.core.runconfig import MpiConfiguration\n", - "\n", - "src = ScriptRunConfig(source_directory=project_folder,\n", - " script='pytorch_horovod_mnist.py',\n", - " compute_target=compute_target,\n", - " environment=pytorch_env,\n", - " distributed_job_config=MpiConfiguration(node_count=2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job\n", - "Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = experiment.submit(src)\n", - "print(run)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor your run\n", - "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes. You can see that the widget automatically plots and visualizes the loss metric that we logged to the Azure ML run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can block until the script has completed training before running more code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True) # this provides a verbose log" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "ninhu" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "PyTorch" - ], - "friendly_name": "Distributed PyTorch", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "tags": [ - "None" - ], - "task": "Train a model using the distributed training via Horovod" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.yml b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.yml deleted file mode 100644 index 58bb77d8d..000000000 --- a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.yml +++ /dev/null @@ -1,5 +0,0 @@ -name: distributed-pytorch-with-horovod -dependencies: -- pip: - - azureml-sdk - - azureml-widgets diff --git a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py deleted file mode 100644 index 617ebef48..000000000 --- a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright (c) 2017, PyTorch contributors -# Modifications copyright (C) Microsoft Corporation -# Licensed under the BSD license -# Adapted from https://github.com/uber/horovod/blob/master/examples/pytorch_mnist.py - -from __future__ import print_function -import argparse -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -from torchvision import datasets, transforms -import torch.utils.data.distributed -import horovod.torch as hvd - -from azureml.core.run import Run -# get the Azure ML run object -run = Run.get_context() - -print("Torch version:", torch.__version__) - -# Training settings -parser = argparse.ArgumentParser(description='PyTorch MNIST Example') -parser.add_argument('--batch-size', type=int, default=64, metavar='N', - help='input batch size for training (default: 64)') -parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', - help='input batch size for testing (default: 1000)') -parser.add_argument('--epochs', type=int, default=10, metavar='N', - help='number of epochs to train (default: 10)') -parser.add_argument('--lr', type=float, default=0.01, metavar='LR', - help='learning rate (default: 0.01)') -parser.add_argument('--momentum', type=float, default=0.5, metavar='M', - help='SGD momentum (default: 0.5)') -parser.add_argument('--no-cuda', action='store_true', default=False, - help='disables CUDA training') -parser.add_argument('--seed', type=int, default=42, metavar='S', - help='random seed (default: 42)') -parser.add_argument('--log-interval', type=int, default=10, metavar='N', - help='how many batches to wait before logging training status') -parser.add_argument('--fp16-allreduce', action='store_true', default=False, - help='use fp16 compression during allreduce') -args = parser.parse_args() -args.cuda = not args.no_cuda and torch.cuda.is_available() - -hvd.init() -torch.manual_seed(args.seed) - -if args.cuda: - # Horovod: pin GPU to local rank. - torch.cuda.set_device(hvd.local_rank()) - torch.cuda.manual_seed(args.seed) - - -kwargs = {} -# Use Azure Open Datasets for MNIST dataset -datasets.MNIST.resources = [ - ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz", - "f68b3c2dcbeaaa9fbdd348bbdeb94873"), - ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz", - "d53e105ee54ea40749a09fcbcd1e9432"), - ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz", - "9fb629c4189551a2d022fa330f9573f3"), - ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz", - "ec29112dd5afa0611ce80d1b7f02629c") -] -train_dataset = \ - datasets.MNIST('data-%d' % hvd.rank(), train=True, download=True, - transform=transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.1307,), (0.3081,)) - ])) -train_sampler = torch.utils.data.distributed.DistributedSampler( - train_dataset, num_replicas=hvd.size(), rank=hvd.rank()) -train_loader = torch.utils.data.DataLoader( - train_dataset, batch_size=args.batch_size, sampler=train_sampler, **kwargs) - -test_dataset = \ - datasets.MNIST('data-%d' % hvd.rank(), train=False, transform=transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.1307,), (0.3081,)) - ])) -test_sampler = torch.utils.data.distributed.DistributedSampler( - test_dataset, num_replicas=hvd.size(), rank=hvd.rank()) -test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size, - sampler=test_sampler, **kwargs) - - -class Net(nn.Module): - def __init__(self): - super(Net, self).__init__() - self.conv1 = nn.Conv2d(1, 10, kernel_size=5) - self.conv2 = nn.Conv2d(10, 20, kernel_size=5) - self.conv2_drop = nn.Dropout2d() - self.fc1 = nn.Linear(320, 50) - self.fc2 = nn.Linear(50, 10) - - def forward(self, x): - x = F.relu(F.max_pool2d(self.conv1(x), 2)) - x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) - x = x.view(-1, 320) - x = F.relu(self.fc1(x)) - x = F.dropout(x, training=self.training) - x = self.fc2(x) - return F.log_softmax(x) - - -model = Net() - -if args.cuda: - # Move model to GPU. - model.cuda() - -# Horovod: broadcast parameters. -hvd.broadcast_parameters(model.state_dict(), root_rank=0) - -# Horovod: scale learning rate by the number of GPUs. -optimizer = optim.SGD(model.parameters(), lr=args.lr * hvd.size(), - momentum=args.momentum) - -# Horovod: (optional) compression algorithm. -compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none - -# Horovod: wrap optimizer with DistributedOptimizer. -optimizer = hvd.DistributedOptimizer(optimizer, - named_parameters=model.named_parameters(), - compression=compression) - - -def train(epoch): - model.train() - train_sampler.set_epoch(epoch) - for batch_idx, (data, target) in enumerate(train_loader): - if args.cuda: - data, target = data.cuda(), target.cuda() - optimizer.zero_grad() - output = model(data) - loss = F.nll_loss(output, target) - loss.backward() - optimizer.step() - if batch_idx % args.log_interval == 0: - print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( - epoch, batch_idx * len(data), len(train_sampler), - 100. * batch_idx / len(train_loader), loss.item())) - - # log the loss to the Azure ML run - run.log('loss', loss.item()) - - -def metric_average(val, name): - tensor = torch.tensor(val) - avg_tensor = hvd.allreduce(tensor, name=name) - return avg_tensor.item() - - -def test(): - model.eval() - test_loss = 0. - test_accuracy = 0. - for data, target in test_loader: - if args.cuda: - data, target = data.cuda(), target.cuda() - output = model(data) - # sum up batch loss - test_loss += F.nll_loss(output, target, size_average=False).item() - # get the index of the max log-probability - pred = output.data.max(1, keepdim=True)[1] - test_accuracy += pred.eq(target.data.view_as(pred)).cpu().float().sum() - - test_loss /= len(test_sampler) - test_accuracy /= len(test_sampler) - - test_loss = metric_average(test_loss, 'avg_loss') - test_accuracy = metric_average(test_accuracy, 'avg_accuracy') - - if hvd.rank() == 0: - print('\nTest set: Average loss: {:.4f}, Accuracy: {:.2f}%\n'.format( - test_loss, 100. * test_accuracy)) - - -for epoch in range(1, args.epochs + 1): - train(epoch) - test() diff --git a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py deleted file mode 100644 index bc6e50451..000000000 --- a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. -# Licensed under the MIT license. - -import os -import torch -import torch.nn as nn -from torchvision import transforms -import json - -from azureml.core.model import Model - - -def init(): - global model - # AZUREML_MODEL_DIR is an environment variable created during deployment. - # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION) - # For multiple models, it points to the folder containing all deployed models (./azureml-models) - model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.pt') - model = torch.load(model_path, map_location=lambda storage, loc: storage) - model.eval() - - -def run(input_data): - input_data = torch.tensor(json.loads(input_data)['data']) - - # get prediction - with torch.no_grad(): - output = model(input_data) - classes = ['chicken', 'turkey'] - softmax = nn.Softmax(dim=1) - pred_probs = softmax(output).numpy()[0] - index = torch.argmax(output, 1) - - result = {"label": classes[index], "probability": str(pred_probs[index])} - return result diff --git a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py deleted file mode 100644 index 85efb6ac9..000000000 --- a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py +++ /dev/null @@ -1,206 +0,0 @@ -# Copyright (c) 2017, PyTorch contributors -# Modifications copyright (C) Microsoft Corporation -# Licensed under the BSD license -# Adapted from https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html - -from __future__ import print_function, division -import torch -import torch.nn as nn -import torch.optim as optim -from torch.optim import lr_scheduler -from torchvision import datasets, models, transforms -import numpy as np -import time -import os -import copy -import argparse - -from azureml.core.run import Run -# get the Azure ML run object -run = Run.get_context() - - -def load_data(data_dir): - """Load the train/val data.""" - - # Data augmentation and normalization for training - # Just normalization for validation - data_transforms = { - 'train': transforms.Compose([ - transforms.RandomResizedCrop(224), - transforms.RandomHorizontalFlip(), - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]), - 'val': transforms.Compose([ - transforms.Resize(256), - transforms.CenterCrop(224), - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]), - } - - image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), - data_transforms[x]) - for x in ['train', 'val']} - dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, - shuffle=True, num_workers=4) - for x in ['train', 'val']} - dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} - class_names = image_datasets['train'].classes - - return dataloaders, dataset_sizes, class_names - - -def train_model(model, criterion, optimizer, scheduler, num_epochs, data_dir): - """Train the model.""" - - # load training/validation data - dataloaders, dataset_sizes, class_names = load_data(data_dir) - - device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') - - since = time.time() - - best_model_wts = copy.deepcopy(model.state_dict()) - best_acc = 0.0 - - for epoch in range(num_epochs): - print('Epoch {}/{}'.format(epoch, num_epochs - 1)) - print('-' * 10) - - # Each epoch has a training and validation phase - for phase in ['train', 'val']: - if phase == 'train': - scheduler.step() - model.train() # Set model to training mode - else: - model.eval() # Set model to evaluate mode - - running_loss = 0.0 - running_corrects = 0 - - # Iterate over data. - for inputs, labels in dataloaders[phase]: - inputs = inputs.to(device) - labels = labels.to(device) - - # zero the parameter gradients - optimizer.zero_grad() - - # forward - # track history if only in train - with torch.set_grad_enabled(phase == 'train'): - outputs = model(inputs) - _, preds = torch.max(outputs, 1) - loss = criterion(outputs, labels) - - # backward + optimize only if in training phase - if phase == 'train': - loss.backward() - optimizer.step() - - # statistics - running_loss += loss.item() * inputs.size(0) - running_corrects += torch.sum(preds == labels.data) - - epoch_loss = running_loss / dataset_sizes[phase] - epoch_acc = running_corrects.double() / dataset_sizes[phase] - - print('{} Loss: {:.4f} Acc: {:.4f}'.format( - phase, epoch_loss, epoch_acc)) - - # deep copy the model - if phase == 'val' and epoch_acc > best_acc: - best_acc = epoch_acc - best_model_wts = copy.deepcopy(model.state_dict()) - - # log the best val accuracy to AML run - run.log('best_val_acc', np.float(best_acc)) - - print() - - time_elapsed = time.time() - since - print('Training complete in {:.0f}m {:.0f}s'.format( - time_elapsed // 60, time_elapsed % 60)) - print('Best val Acc: {:4f}'.format(best_acc)) - - # load best model weights - model.load_state_dict(best_model_wts) - return model - - -def fine_tune_model(num_epochs, data_dir, learning_rate, momentum): - """Load a pretrained model and reset the final fully connected layer.""" - - # log the hyperparameter metrics to the AML run - run.log('lr', np.float(learning_rate)) - run.log('momentum', np.float(momentum)) - - model_ft = models.resnet18(pretrained=True) - num_ftrs = model_ft.fc.in_features - model_ft.fc = nn.Linear(num_ftrs, 2) # only 2 classes to predict - - device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') - model_ft = model_ft.to(device) - - criterion = nn.CrossEntropyLoss() - - # Observe that all parameters are being optimized - optimizer_ft = optim.SGD(model_ft.parameters(), - lr=learning_rate, momentum=momentum) - - # Decay LR by a factor of 0.1 every 7 epochs - exp_lr_scheduler = lr_scheduler.StepLR( - optimizer_ft, step_size=7, gamma=0.1) - - model = train_model(model_ft, criterion, optimizer_ft, - exp_lr_scheduler, num_epochs, data_dir) - - return model - - -def download_data(): - """Download and extract the training data.""" - import urllib - from zipfile import ZipFile - # download data - data_file = './fowl_data.zip' - download_url = 'https://azureopendatastorage.blob.core.windows.net/testpublic/temp/fowl_data.zip' - urllib.request.urlretrieve(download_url, filename=data_file) - - # extract files - with ZipFile(data_file, 'r') as zip: - print('extracting files...') - zip.extractall() - print('finished extracting') - data_dir = zip.namelist()[0] - - # delete zip file - os.remove(data_file) - return data_dir - - -def main(): - print("Torch version:", torch.__version__) - - # get command-line arguments - parser = argparse.ArgumentParser() - parser.add_argument('--num_epochs', type=int, default=25, - help='number of epochs to train') - parser.add_argument('--output_dir', type=str, help='output directory') - parser.add_argument('--learning_rate', type=float, - default=0.001, help='learning rate') - parser.add_argument('--momentum', type=float, default=0.9, help='momentum') - args = parser.parse_args() - - data_dir = download_data() - print("data directory is: " + data_dir) - model = fine_tune_model(args.num_epochs, data_dir, - args.learning_rate, args.momentum) - os.makedirs(args.output_dir, exist_ok=True) - torch.save(model, os.path.join(args.output_dir, 'model.pt')) - - -if __name__ == "__main__": - main() diff --git a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/test_img.jpg b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/test_img.jpg deleted file mode 100644 index f2878b48b..000000000 Binary files a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/test_img.jpg and /dev/null differ diff --git a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb deleted file mode 100644 index 99e21ff64..000000000 --- a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb +++ /dev/null @@ -1,762 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train, hyperparameter tune, and deploy with PyTorch\n", - "\n", - "In this tutorial, you will train, hyperparameter tune, and deploy a PyTorch model using the Azure Machine Learning (Azure ML) Python SDK.\n", - "\n", - "This tutorial will train an image classification model using transfer learning, based on PyTorch's [Transfer Learning tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html). The model is trained to classify chickens and turkeys by first using a pretrained ResNet18 model that has been trained on the [ImageNet](http://image-net.org/index) dataset." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"hd-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target.')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model on the remote compute\n", - "Now that you have your data and training script prepared, you are ready to train on your remote compute cluster. You can take advantage of Azure compute to leverage GPUs to cut down your training time. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project directory\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "project_folder = './pytorch-birds'\n", - "os.makedirs(project_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download training data\n", - "The dataset we will use (located on a public blob [here](https://azureopendatastorage.blob.core.windows.net/testpublic/temp/fowl_data.zip) as a zip file) consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html). We will download and extract the dataset as part of our training script `pytorch_train.py`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare training script\n", - "Now you will need to create your training script. In this tutorial, the training script is already provided for you at `pytorch_train.py`. In practice, you should be able to take any custom training script as is and run it with Azure ML without having to modify your code.\n", - "\n", - "However, if you would like to use Azure ML's [tracking and metrics](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#metrics) capabilities, you will have to add a small amount of Azure ML code inside your training script. \n", - "\n", - "In `pytorch_train.py`, we will log some metrics to our Azure ML run. To do so, we will access the Azure ML `Run` object within the script:\n", - "```Python\n", - "from azureml.core.run import Run\n", - "run = Run.get_context()\n", - "```\n", - "Further within `pytorch_train.py`, we log the learning rate and momentum parameters, and the best validation accuracy the model achieves:\n", - "```Python\n", - "run.log('lr', np.float(learning_rate))\n", - "run.log('momentum', np.float(momentum))\n", - "\n", - "run.log('best_val_acc', np.float(best_acc))\n", - "```\n", - "These run metrics will become particularly important when we begin hyperparameter tuning our model in the \"Tune model hyperparameters\" section." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once your script is ready, copy the training script `pytorch_train.py` into your project directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "shutil.copy('pytorch_train.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment\n", - "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this transfer learning PyTorch tutorial. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = 'pytorch-birds'\n", - "experiment = Experiment(ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "Define a conda environment YAML file with your training script dependencies and create an Azure ML environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile conda_dependencies.yml\n", - "\n", - "channels:\n", - "- conda-forge\n", - "dependencies:\n", - "- python=3.6.2\n", - "- pip=21.3.1\n", - "- pip:\n", - " - azureml-defaults\n", - " - torch==1.6.0\n", - " - torchvision==0.7.0\n", - " - future==0.17.1\n", - " - pillow" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "pytorch_env = Environment.from_conda_specification(name = 'pytorch-1.6-gpu', file_path = './conda_dependencies.yml')\n", - "\n", - "# Specify a GPU base image\n", - "pytorch_env.docker.enabled = True\n", - "pytorch_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.1-cudnn7-ubuntu18.04'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on. The following code will configure a single-node PyTorch job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=project_folder,\n", - " script='pytorch_train.py',\n", - " arguments=['--num_epochs', 30, '--output_dir', './outputs'],\n", - " compute_target=compute_target,\n", - " environment=pytorch_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job\n", - "Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = experiment.submit(src)\n", - "print(run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# to get more details of your run\n", - "print(run.get_details())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor your run\n", - "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can block until the script has completed training before running more code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tune model hyperparameters\n", - "Now that we've seen how to do a simple PyTorch training run using the SDK, let's see if we can further improve the accuracy of our model. We can optimize our model's hyperparameters using Azure Machine Learning's hyperparameter tuning capabilities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start a hyperparameter sweep\n", - "First, we will define the hyperparameter space to sweep over. Since our training script uses a learning rate schedule to decay the learning rate every several epochs, let's tune the initial learning rate and the momentum parameters. In this example we will use random sampling to try different configuration sets of hyperparameters to maximize our primary metric, the best validation accuracy (`best_val_acc`).\n", - "\n", - "Then, we specify the early termination policy to use to early terminate poorly performing runs. Here we use the `BanditPolicy`, which will terminate any run that doesn't fall within the slack factor of our primary evaluation metric. In this tutorial, we will apply this policy every epoch (since we report our `best_val_acc` metric every epoch and `evaluation_interval=1`). Notice we will delay the first policy evaluation until after the first `10` epochs (`delay_evaluation=10`).\n", - "Refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-tune-hyperparameters#specify-an-early-termination-policy) for more information on the BanditPolicy and other policies available." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, uniform, PrimaryMetricGoal\n", - "\n", - "param_sampling = RandomParameterSampling( {\n", - " 'learning_rate': uniform(0.0005, 0.005),\n", - " 'momentum': uniform(0.9, 0.99)\n", - " }\n", - ")\n", - "\n", - "early_termination_policy = BanditPolicy(slack_factor=0.15, evaluation_interval=1, delay_evaluation=10)\n", - "\n", - "hyperdrive_config = HyperDriveConfig(run_config=src,\n", - " hyperparameter_sampling=param_sampling, \n", - " policy=early_termination_policy,\n", - " primary_metric_name='best_val_acc',\n", - " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n", - " max_total_runs=8,\n", - " max_concurrent_runs=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, lauch the hyperparameter tuning job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# start the HyperDrive run\n", - "hyperdrive_run = experiment.submit(hyperdrive_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor HyperDrive runs\n", - "You can monitor the progress of the runs with the following Jupyter widget. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(hyperdrive_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or block until the HyperDrive sweep has completed:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hyperdrive_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert(hyperdrive_run.get_status() == \"Completed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Warm start a Hyperparameter Tuning experiment and resuming child runs\n", - "Often times, finding the best hyperparameter values for your model can be an iterative process, needing multiple tuning runs that learn from previous hyperparameter tuning runs. Reusing knowledge from these previous runs will accelerate the hyperparameter tuning process, thereby reducing the cost of tuning the model and will potentially improve the primary metric of the resulting model. When warm starting a hyperparameter tuning experiment with Bayesian sampling, trials from the previous run will be used as prior knowledge to intelligently pick new samples, so as to improve the primary metric. Additionally, when using Random or Grid sampling, any early termination decisions will leverage metrics from the previous runs to determine poorly performing training runs. \n", - "\n", - "Azure Machine Learning allows you to warm start your hyperparameter tuning run by leveraging knowledge from up to 5 previously completed hyperparameter tuning parent runs. \n", - "\n", - "Additionally, there might be occasions when individual training runs of a hyperparameter tuning experiment are cancelled due to budget constraints or fail due to other reasons. It is now possible to resume such individual training runs from the last checkpoint (assuming your training script handles checkpoints). Resuming an individual training run will use the same hyperparameter configuration and mount the storage used for that run. The training script should accept the \"--resume-from\" argument, which contains the checkpoint or model files from which to resume the training run. You can also resume individual runs as part of an experiment that spends additional budget on hyperparameter tuning. Any additional budget, after resuming the specified training runs is used for exploring additional configurations.\n", - "\n", - "For more information on warm starting and resuming hyperparameter tuning runs, please refer to the [Hyperparameter Tuning for Azure Machine Learning documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters) \n", - "\n", - "### Find and register the best model\n", - "Once all the runs complete, we can find the run that produced the model with the highest accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n", - "best_run_metrics = best_run.get_metrics()\n", - "print(best_run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Best Run is:\\n Validation accuracy: {0:.5f} \\n Learning rate: {1:.5f} \\n Momentum: {2:.5f}'.format(\n", - " best_run_metrics['best_val_acc'][-1],\n", - " best_run_metrics['lr'],\n", - " best_run_metrics['momentum'])\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, register the model from your best-performing run to your workspace. The `model_path` parameter takes in the relative path on the remote VM to the model file in your `outputs` directory. In the next section, we will deploy this registered model as a web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = best_run.register_model(model_name = 'pytorch-birds', model_path = 'outputs/model.pt')\n", - "print(model.name, model.id, model.version, sep = '\\t')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy model as web service\n", - "Once you have your trained model, you can deploy the model on Azure. In this tutorial, we will deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) (ACI). For more information on deploying models using Azure ML, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create scoring script\n", - "\n", - "First, we will create a scoring script that will be invoked by the web service call. Note that the scoring script must have two required functions:\n", - "* `init()`: In this function, you typically load the model into a `global` object. This function is executed only once when the Docker container is started. \n", - "* `run(input_data)`: In this function, the model is used to predict a value based on the input data. The input and output typically use JSON as serialization and deserialization format, but you are not limited to that.\n", - "\n", - "Refer to the scoring script `pytorch_score.py` for this tutorial. Our web service will use this file to predict whether an image is a chicken or a turkey. When writing your own scoring script, don't forget to test it locally first before you go and deploy the web service." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define the environment\n", - "\n", - "Then, we will need to create an Azure ML environment that specifies all of the scoring script's package dependencies. In this tutorial, we will reuse the same environment, `pytorch_env`, that we created for training." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy to ACI container\n", - "We are ready to deploy. Create an inference configuration which gives specifies the inferencing environment and scripts. Create a deployment configuration file to specify the number of CPUs and gigabytes of RAM needed for your ACI container. While it depends on your model, the default of `1` core and `1` gigabyte of RAM is usually sufficient for many models. This cell will run for about 7-8 minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.model import Model\n", - "\n", - "inference_config = InferenceConfig(entry_script=\"pytorch_score.py\", environment=pytorch_env)\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores=2, \n", - " memory_gb=2, \n", - " tags={'data': 'birds', 'method':'transfer learning', 'framework':'pytorch'},\n", - " description='Classify turkey/chickens using transfer learning with PyTorch')\n", - "\n", - "service = Model.deploy(workspace=ws, \n", - " name='aci-birds', \n", - " models=[model], \n", - " inference_config=inference_config, \n", - " deployment_config=aciconfig)\n", - "service.wait_for_deployment(True)\n", - "print(service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If your deployment fails for any reason and you need to redeploy, make sure to delete the service before you do so: `service.delete()`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.get_logs()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the web service's HTTP endpoint, which accepts REST client calls. This endpoint can be shared with anyone who wants to test the web service or integrate it into an application." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the web service\n", - "Finally, let's test our deployed web service. We will send the data as a JSON string to the web service hosted in ACI and use the SDK's `run` API to invoke the service. Here we will take an image from our validation data to predict on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from PIL import Image\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "plt.imshow(Image.open('test_img.jpg'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "from torchvision import transforms\n", - " \n", - "def preprocess(image_file):\n", - " \"\"\"Preprocess the input image.\"\"\"\n", - " data_transforms = transforms.Compose([\n", - " transforms.Resize(256),\n", - " transforms.CenterCrop(224),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", - " ])\n", - "\n", - " image = Image.open(image_file)\n", - " image = data_transforms(image).float()\n", - " image = torch.tensor(image)\n", - " image = image.unsqueeze(0)\n", - " return image.numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "input_data = preprocess('test_img.jpg')\n", - "result = service.run(input_data=json.dumps({'data': input_data.tolist()}))\n", - "print(result)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up\n", - "Once you no longer need the web service, you can delete it with a simple API call." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "nagaur" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "ImageNet" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "PyTorch" - ], - "friendly_name": "Training with hyperparameter tuning using PyTorch", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "tags": [ - "None" - ], - "task": "Train an image classification model using transfer learning with the PyTorch estimator" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml deleted file mode 100644 index a1368b4dd..000000000 --- a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml +++ /dev/null @@ -1,10 +0,0 @@ -name: train-hyperparameter-tune-deploy-with-pytorch -dependencies: -- pip: - - azureml-sdk - - azureml-widgets - - pillow==5.4.1 - - matplotlib - - numpy==1.19.3 - - https://download.pytorch.org/whl/cpu/torch-1.6.0%2Bcpu-cp38-cp38-win_amd64.whl - - https://download.pytorch.org/whl/cpu/torchvision-0.7.0%2Bcpu-cp38-cp38-win_amd64.whl diff --git a/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb b/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb deleted file mode 100644 index 7936fb154..000000000 --- a/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb +++ /dev/null @@ -1,621 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train and hyperparameter tune on Iris Dataset with Scikit-learn\n", - "In this tutorial, we demonstrate how to use the Azure ML Python SDK to train a support vector machine (SVM) on a single-node CPU with Scikit-learn to perform classification on the popular [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris). We will also demonstrate how to perform hyperparameter tuning of the model using Azure ML's HyperDrive service." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Go through the [Configuration](../../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML Workspace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create AmlCompute" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_D2_V2` CPU VMs. This process is broken down into 3 steps:\n", - "1. create the configuration (this step is local and only takes a second)\n", - "2. create the cluster (this step will take about **20 seconds**)\n", - "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"hd-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', \n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - "# can poll for a minimum number of nodes and for a specific timeout. \n", - "# if no min node count is provided it uses the scale settings for the cluster\n", - "compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code retrieves a CPU compute target. Scikit-learn does not support GPU computing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model on the remote compute" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that you have your data and training script prepared, you are ready to train on your remote compute. You can take advantage of Azure compute to leverage a CPU cluster." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project directory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "project_folder = './sklearn-iris'\n", - "os.makedirs(project_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare training script" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now you will need to create your training script. In this tutorial, the training script is already provided for you at `train_iris`.py. In practice, you should be able to take any custom training script as is and run it with Azure ML without having to modify your code.\n", - "\n", - "However, if you would like to use Azure ML's [tracking and metrics](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#metrics) capabilities, you will have to add a small amount of Azure ML code inside your training script.\n", - "\n", - "In `train_iris.py`, we will log some metrics to our Azure ML run. To do so, we will access the Azure ML Run object within the script:\n", - "\n", - "```python\n", - "from azureml.core.run import Run\n", - "run = Run.get_context()\n", - "```\n", - "\n", - "Further within `train_iris.py`, we log the kernel and penalty parameters, and the highest accuracy the model achieves:\n", - "\n", - "```python\n", - "run.log('Kernel type', np.string(args.kernel))\n", - "run.log('Penalty', np.float(args.penalty))\n", - "\n", - "run.log('Accuracy', np.float(accuracy))\n", - "```\n", - "\n", - "These run metrics will become particularly important when we begin hyperparameter tuning our model in the \"Tune model hyperparameters\" section.\n", - "\n", - "Once your script is ready, copy the training script `train_iris.py` into your project directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "shutil.copy('train_iris.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this Scikit-learn tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = 'train_iris'\n", - "experiment = Experiment(ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "Define a conda environment YAML file with your training script dependencies and create an Azure ML environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile conda_dependencies.yml\n", - "\n", - "dependencies:\n", - "- python=3.6.2\n", - "- scikit-learn\n", - "- pip:\n", - " - azureml-defaults" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "sklearn_env = Environment.from_conda_specification(name = 'sklearn-env', file_path = './conda_dependencies.yml')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=project_folder,\n", - " script='train_iris.py',\n", - " arguments=['--kernel', 'linear', '--penalty', 1.0],\n", - " compute_target=compute_target,\n", - " environment=sklearn_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = experiment.submit(src)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Monitor your run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tune model hyperparameters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we've seen how to do a simple Scikit-learn training run using the SDK, let's see if we can further improve the accuracy of our model. We can optimize our model's hyperparameters using Azure Machine Learning's hyperparameter tuning capabilities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start a hyperparameter sweep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we will define the hyperparameter space to sweep over. Let's tune the `kernel` and `penalty` parameters. In this example we will use random sampling to try different configuration sets of hyperparameters to maximize our primary metric, `Accuracy`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n", - "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n", - "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n", - "from azureml.train.hyperdrive.parameter_expressions import choice\n", - " \n", - "\n", - "param_sampling = RandomParameterSampling( {\n", - " \"--kernel\": choice('linear', 'rbf', 'poly', 'sigmoid'),\n", - " \"--penalty\": choice(0.5, 1, 1.5)\n", - " }\n", - ")\n", - "\n", - "hyperdrive_config = HyperDriveConfig(run_config=src,\n", - " hyperparameter_sampling=param_sampling, \n", - " primary_metric_name='Accuracy',\n", - " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n", - " max_total_runs=12,\n", - " max_concurrent_runs=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, lauch the hyperparameter tuning job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# start the HyperDrive run\n", - "hyperdrive_run = experiment.submit(hyperdrive_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Monitor HyperDrive runs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can monitor the progress of the runs with the following Jupyter widget." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(hyperdrive_run).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hyperdrive_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert(hyperdrive_run.get_status() == \"Completed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Warm start a Hyperparameter Tuning experiment and resuming child runs\n", - "Often times, finding the best hyperparameter values for your model can be an iterative process, needing multiple tuning runs that learn from previous hyperparameter tuning runs. Reusing knowledge from these previous runs will accelerate the hyperparameter tuning process, thereby reducing the cost of tuning the model and will potentially improve the primary metric of the resulting model. When warm starting a hyperparameter tuning experiment with Bayesian sampling, trials from the previous run will be used as prior knowledge to intelligently pick new samples, so as to improve the primary metric. Additionally, when using Random or Grid sampling, any early termination decisions will leverage metrics from the previous runs to determine poorly performing training runs. \n", - "\n", - "Azure Machine Learning allows you to warm start your hyperparameter tuning run by leveraging knowledge from up to 5 previously completed hyperparameter tuning parent runs. \n", - "\n", - "Additionally, there might be occasions when individual training runs of a hyperparameter tuning experiment are cancelled due to budget constraints or fail due to other reasons. It is now possible to resume such individual training runs from the last checkpoint (assuming your training script handles checkpoints). Resuming an individual training run will use the same hyperparameter configuration and mount the storage used for that run. The training script should accept the \"--resume-from\" argument, which contains the checkpoint or model files from which to resume the training run. You can also resume individual runs as part of an experiment that spends additional budget on hyperparameter tuning. Any additional budget, after resuming the specified training runs is used for exploring additional configurations.\n", - "\n", - "For more information on warm starting and resuming hyperparameter tuning runs, please refer to the [Hyperparameter Tuning for Azure Machine Learning documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters) \n", - "\n", - "### Find and register best model\n", - "When all jobs finish, we can find out the one that has the highest accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n", - "print(best_run.get_details()['runDefinition']['arguments'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's list the model files uploaded during the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(best_run.get_file_names())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then register the folder (and all files in it) as a model named `sklearn-iris` under the workspace for deployment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = best_run.register_model(model_name='sklearn-iris', model_path='outputs/model.joblib')" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "nagaur" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Iris" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Scikit-learn" - ], - "friendly_name": "Training and hyperparameter tuning with Scikit-learn", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "msauthor": "dipeck", - "tags": [ - "None" - ], - "task": "Train a support vector machine (SVM) to perform classification" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.yml b/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.yml deleted file mode 100644 index 2691a8497..000000000 --- a/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.yml +++ /dev/null @@ -1,6 +0,0 @@ -name: train-hyperparameter-tune-deploy-with-sklearn -dependencies: -- pip: - - azureml-sdk - - azureml-widgets - - numpy diff --git a/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py b/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py deleted file mode 100644 index bc9099d8b..000000000 --- a/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py +++ /dev/null @@ -1,60 +0,0 @@ -# Modified from https://www.geeksforgeeks.org/multiclass-classification-using-scikit-learn/ - -import argparse -import os - -# importing necessary libraries -import numpy as np - -from sklearn import datasets -from sklearn.metrics import confusion_matrix -from sklearn.model_selection import train_test_split - -import joblib - -from azureml.core.run import Run -run = Run.get_context() - - -def main(): - parser = argparse.ArgumentParser() - - parser.add_argument('--kernel', type=str, default='linear', - help='Kernel type to be used in the algorithm') - parser.add_argument('--penalty', type=float, default=1.0, - help='Penalty parameter of the error term') - - args = parser.parse_args() - run.log('Kernel type', np.str(args.kernel)) - run.log('Penalty', np.float(args.penalty)) - - # loading the iris dataset - iris = datasets.load_iris() - - # X -> features, y -> label - X = iris.data - y = iris.target - - # dividing X, y into train and test data - X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) - - # training a linear SVM classifier - from sklearn.svm import SVC - svm_model_linear = SVC(kernel=args.kernel, C=args.penalty).fit(X_train, y_train) - svm_predictions = svm_model_linear.predict(X_test) - - # model accuracy for X_test - accuracy = svm_model_linear.score(X_test, y_test) - print('Accuracy of SVM classifier on test set: {:.2f}'.format(accuracy)) - run.log('Accuracy', np.float(accuracy)) - # creating a confusion matrix - cm = confusion_matrix(y_test, svm_predictions) - print(cm) - - os.makedirs('outputs', exist_ok=True) - # files saved in the "outputs" folder are automatically uploaded into run history - joblib.dump(svm_model_linear, 'outputs/model.joblib') - - -if __name__ == '__main__': - main() diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb deleted file mode 100644 index 10139f41b..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb +++ /dev/null @@ -1,408 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Distributed TensorFlow with Horovod\n", - "In this tutorial, you will train a word2vec model in TensorFlow using distributed training via [Horovod](https://github.com/uber/horovod)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../../configuration.ipynb) to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (`config.json`)\n", - "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"gpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - " compute_target.wait_for_completion(show_output=True)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a Dataset for Files\n", - "A Dataset can reference single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "\n", - "web_paths = ['https://azureopendatastorage.blob.core.windows.net/testpublic/text8.zip']\n", - "dataset = Dataset.File.from_files(path=web_paths)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may want to register datasets using the register() method to your workspace so that the dataset can be shared with others, reused across various experiments, and referred to by name in your training script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = dataset.register(workspace=ws,\n", - " name='wikipedia-text',\n", - " description='Wikipedia text training and test dataset',\n", - " create_new_version=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# list the files referenced by the dataset\n", - "dataset.to_path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model on the remote compute" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project directory\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "project_folder = './tf-distr-hvd'\n", - "os.makedirs(project_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copy the training script `tf_horovod_word2vec.py` into this project directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "shutil.copy('tf_horovod_word2vec.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment\n", - "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = 'tf-distr-hvd'\n", - "experiment = Experiment(ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the TensorFlow 1.13 GPU curated environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "tf_env = Environment.get(ws, name='AzureML-TensorFlow-1.13-GPU')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on.\n", - "\n", - "In order to execute a distributed run using MPI/Horovod, you must create an `MpiConfiguration` object and pass it to the `distributed_job_config` parameter of the ScriptRunConfig constructor. The below code will configure a 2-node distributed job running one process per node. If you would also like to run multiple processes per node (i.e. if your cluster SKU has multiple GPUs), additionally specify the `process_count_per_node` parameter in `MpiConfiguration` (the default is `1`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "from azureml.core.runconfig import MpiConfiguration\n", - "\n", - "src = ScriptRunConfig(source_directory=project_folder,\n", - " script='tf_horovod_word2vec.py',\n", - " arguments=['--input_data', dataset.as_mount()],\n", - " compute_target=compute_target,\n", - " environment=tf_env,\n", - " distributed_job_config=MpiConfiguration(node_count=2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job\n", - "Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = experiment.submit(src)\n", - "print(run)\n", - "run.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor your run\n", - "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can block until the script has completed training before running more code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "minxia" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "None" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "TensorFlow" - ], - "friendly_name": "Distributed training using TensorFlow with Horovod", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "tags": [ - "None" - ], - "task": "Use the TensorFlow estimator to train a word2vec model" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.yml b/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.yml deleted file mode 100644 index 3fbd7704f..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.yml +++ /dev/null @@ -1,11 +0,0 @@ -name: distributed-tensorflow-with-horovod -dependencies: -- pip: - - azureml-sdk - - azureml-widgets - - keras - - tensorflow-gpu==1.13.2 - - horovod==0.19.1 - - matplotlib - - pandas - - fuse diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/tf_horovod_word2vec.py b/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/tf_horovod_word2vec.py deleted file mode 100644 index 483053302..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/tf_horovod_word2vec.py +++ /dev/null @@ -1,238 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# Modifications copyright (C) 2017 Uber Technologies, Inc. -# Additional modifications copyright (C) Microsoft Corporation -# Licensed under the Apache License, Version 2.0 -# Script adapted from: https://github.com/uber/horovod/blob/master/examples/tensorflow_word2vec.py -# ====================================== -"""Basic word2vec example.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import math -import os -import random -import zipfile -import argparse -import glob - -import numpy as np -from six.moves import urllib -from six.moves import xrange # pylint: disable=redefined-builtin -import tensorflow as tf -import horovod.tensorflow as hvd -from azureml.core.run import Run - -# Horovod: initialize Horovod. -hvd.init() - -parser = argparse.ArgumentParser() -parser.add_argument('--input_data', type=str, help='training data') - -args = parser.parse_args() - -input_data = args.input_data -print("the input data is at %s" % input_data) - -# Step 1: Read data. -filename = input_data - - -# Read the data into a list of strings. -def read_data(filename): - """Extract the first file enclosed in a zip file as a list of words.""" - with zipfile.ZipFile(filename) as f: - data = tf.compat.as_str(f.read(f.namelist()[0])).split() - return data - - -vocabulary = read_data(filename) -print('Data size', len(vocabulary)) - -# Step 2: Build the dictionary and replace rare words with UNK token. -vocabulary_size = 50000 - - -def build_dataset(words, n_words): - """Process raw inputs into a dataset.""" - count = [['UNK', -1]] - count.extend(collections.Counter(words).most_common(n_words - 1)) - dictionary = dict() - for word, _ in count: - dictionary[word] = len(dictionary) - data = list() - unk_count = 0 - for word in words: - if word in dictionary: - index = dictionary[word] - else: - index = 0 # dictionary['UNK'] - unk_count += 1 - data.append(index) - count[0][1] = unk_count - reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys())) - return data, count, dictionary, reversed_dictionary - - -data, count, dictionary, reverse_dictionary = build_dataset(vocabulary, - vocabulary_size) -del vocabulary # Hint to reduce memory. -print('Most common words (+UNK)', count[:5]) -print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]]) - - -# Step 3: Function to generate a training batch for the skip-gram model. -def generate_batch(batch_size, num_skips, skip_window): - assert num_skips <= 2 * skip_window - # Adjust batch_size to match num_skips - batch_size = batch_size // num_skips * num_skips - span = 2 * skip_window + 1 # [ skip_window target skip_window ] - # Backtrack a little bit to avoid skipping words in the end of a batch - data_index = random.randint(0, len(data) - span - 1) - batch = np.ndarray(shape=(batch_size), dtype=np.int32) - labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) - buffer = collections.deque(maxlen=span) - for _ in range(span): - buffer.append(data[data_index]) - data_index = (data_index + 1) % len(data) - for i in range(batch_size // num_skips): - target = skip_window # target label at the center of the buffer - targets_to_avoid = [skip_window] - for j in range(num_skips): - while target in targets_to_avoid: - target = random.randint(0, span - 1) - targets_to_avoid.append(target) - batch[i * num_skips + j] = buffer[skip_window] - labels[i * num_skips + j, 0] = buffer[target] - buffer.append(data[data_index]) - data_index = (data_index + 1) % len(data) - return batch, labels - - -batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1) -for i in range(8): - print(batch[i], reverse_dictionary[batch[i]], - '->', labels[i, 0], reverse_dictionary[labels[i, 0]]) - -# Step 4: Build and train a skip-gram model. - -max_batch_size = 128 -embedding_size = 128 # Dimension of the embedding vector. -skip_window = 1 # How many words to consider left and right. -num_skips = 2 # How many times to reuse an input to generate a label. - -# We pick a random validation set to sample nearest neighbors. Here we limit the -# validation samples to the words that have a low numeric ID, which by -# construction are also the most frequent. -valid_size = 16 # Random set of words to evaluate similarity on. -valid_window = 100 # Only pick dev samples in the head of the distribution. -valid_examples = np.random.choice(valid_window, valid_size, replace=False) -num_sampled = 64 # Number of negative examples to sample. - -graph = tf.Graph() - -with graph.as_default(): - - # Input data. - train_inputs = tf.placeholder(tf.int32, shape=[None]) - train_labels = tf.placeholder(tf.int32, shape=[None, 1]) - valid_dataset = tf.constant(valid_examples, dtype=tf.int32) - - # Look up embeddings for inputs. - embeddings = tf.Variable( - tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) - embed = tf.nn.embedding_lookup(embeddings, train_inputs) - - # Construct the variables for the NCE loss - nce_weights = tf.Variable( - tf.truncated_normal([vocabulary_size, embedding_size], - stddev=1.0 / math.sqrt(embedding_size))) - nce_biases = tf.Variable(tf.zeros([vocabulary_size])) - - # Compute the average NCE loss for the batch. - # tf.nce_loss automatically draws a new sample of the negative labels each - # time we evaluate the loss. - loss = tf.reduce_mean( - tf.nn.nce_loss(weights=nce_weights, - biases=nce_biases, - labels=train_labels, - inputs=embed, - num_sampled=num_sampled, - num_classes=vocabulary_size)) - - # Horovod: adjust learning rate based on number of GPUs. - optimizer = tf.train.GradientDescentOptimizer(1.0 * hvd.size()) - - # Horovod: add Horovod Distributed Optimizer. - optimizer = hvd.DistributedOptimizer(optimizer) - - train_op = optimizer.minimize(loss) - - # Compute the cosine similarity between minibatch examples and all embeddings. - norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True)) - normalized_embeddings = embeddings / norm - valid_embeddings = tf.nn.embedding_lookup( - normalized_embeddings, valid_dataset) - similarity = tf.matmul( - valid_embeddings, normalized_embeddings, transpose_b=True) - - # Add variable initializer. - init = tf.global_variables_initializer() - - # Horovod: broadcast initial variable states from rank 0 to all other processes. - # This is necessary to ensure consistent initialization of all workers when - # training is started with random weights or restored from a checkpoint. - bcast = hvd.broadcast_global_variables(0) - -# Step 5: Begin training. - -# Horovod: adjust number of steps based on number of GPUs. -num_steps = 4000 // hvd.size() + 1 - -# Horovod: pin GPU to be used to process local rank (one GPU per process) -config = tf.ConfigProto() -config.gpu_options.allow_growth = True -config.gpu_options.visible_device_list = str(hvd.local_rank()) - -with tf.Session(graph=graph, config=config) as session: - # We must initialize all variables before we use them. - init.run() - bcast.run() - print('Initialized') - run = Run.get_context() - average_loss = 0 - for step in xrange(num_steps): - # simulate various sentence length by randomization - batch_size = random.randint(max_batch_size // 2, max_batch_size) - batch_inputs, batch_labels = generate_batch( - batch_size, num_skips, skip_window) - feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels} - - # We perform one update step by evaluating the optimizer op (including it - # in the list of returned values for session.run() - _, loss_val = session.run([train_op, loss], feed_dict=feed_dict) - average_loss += loss_val - - if step % 2000 == 0: - if step > 0: - average_loss /= 2000 - # The average loss is an estimate of the loss over the last 2000 batches. - print('Average loss at step ', step, ': ', average_loss) - run.log("Loss", average_loss) - average_loss = 0 - final_embeddings = normalized_embeddings.eval() - - # Evaluate similarity in the end on worker 0. - if hvd.rank() == 0: - sim = similarity.eval() - for i in xrange(valid_size): - valid_word = reverse_dictionary[valid_examples[i]] - top_k = 8 # number of nearest neighbors - nearest = (-sim[i, :]).argsort()[1:top_k + 1] - log_str = 'Nearest to %s:' % valid_word - for k in xrange(top_k): - close_word = reverse_dictionary[nearest[k]] - log_str = '%s %s,' % (log_str, close_word) - print(log_str) diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb deleted file mode 100644 index db1df133b..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb +++ /dev/null @@ -1,354 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Distributed TensorFlow with parameter server\n", - "In this tutorial, you will train a TensorFlow model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using native [distributed TensorFlow](https://www.tensorflow.org/deploy/distributed)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../../configuration.ipynb) to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (`config.json`)\n", - "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"gpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target.')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - " compute_target.wait_for_completion(show_output=True)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model on the remote compute\n", - "Now that we have the cluster ready to go, let's run our distributed training job." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project directory\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "project_folder = './tf-distr-ps'\n", - "os.makedirs(project_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copy the training script `tf_mnist_replica.py` into this project directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "shutil.copy('tf_mnist_replica.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment\n", - "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = 'tf-distr-ps'\n", - "experiment = Experiment(ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the TensorFlow 1.13 GPU curated environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "tf_env = Environment.get(ws, name='AzureML-TensorFlow-1.13-GPU')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on.\n", - "\n", - "In order to execute a distributed TensorFlow run with the parameter server strategy, you must create a `TensorflowConfiguration` object and pass it to the `distributed_job_config` parameter of the ScriptRunConfig constructor. The below code configures a distributed TensorFlow run with `2` workers and `1` parameter server." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "from azureml.core.runconfig import TensorflowConfiguration\n", - "\n", - "src = ScriptRunConfig(source_directory=project_folder,\n", - " script='tf_mnist_replica.py',\n", - " arguments=['--num_gpus', 1, '--train_steps', 500],\n", - " compute_target=compute_target,\n", - " environment=tf_env,\n", - " distributed_job_config=TensorflowConfiguration(worker_count=2, parameter_server_count=1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job\n", - "Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = experiment.submit(src)\n", - "print(run)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor your run\n", - "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can block until the script has completed training before running more code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True) # this provides a verbose log" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "minxia" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "TensorFlow" - ], - "friendly_name": "Distributed TensorFlow with parameter server", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "tags": [ - "None" - ], - "task": "Use the TensorFlow estimator to train a model using distributed training" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.yml b/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.yml deleted file mode 100644 index bc5a30ebb..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.yml +++ /dev/null @@ -1,5 +0,0 @@ -name: distributed-tensorflow-with-parameter-server -dependencies: -- pip: - - azureml-sdk - - azureml-widgets diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/tf_mnist_replica.py b/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/tf_mnist_replica.py deleted file mode 100644 index 96d40fed1..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/tf_mnist_replica.py +++ /dev/null @@ -1,271 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 -# Script adapted from: -# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dist_test/python/mnist_replica.py -# ============================================================================== -"""Distributed MNIST training and validation, with model replicas. -A simple softmax model with one hidden layer is defined. The parameters -(weights and biases) are located on one parameter server (ps), while the ops -are executed on two worker nodes by default. The TF sessions also run on the -worker node. -Multiple invocations of this script can be done in parallel, with different -values for --task_index. There should be exactly one invocation with ---task_index, which will create a master session that carries out variable -initialization. The other, non-master, sessions will wait for the master -session to finish the initialization before proceeding to the training stage. -The coordination between the multiple worker invocations occurs due to -the definition of the parameters on the same ps devices. The parameter updates -from one worker is visible to all other workers. As such, the workers can -perform forward computation and gradient calculation in parallel, which -should lead to increased training speed for the simple model. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import math -import sys -import tempfile -import time -import json - -import tensorflow as tf -from tensorflow.examples.tutorials.mnist import input_data -from azureml.core.run import Run - -flags = tf.app.flags -flags.DEFINE_string("data_dir", "/tmp/mnist-data", - "Directory for storing mnist data") -flags.DEFINE_boolean("download_only", False, - "Only perform downloading of data; Do not proceed to " - "session preparation, model definition or training") -flags.DEFINE_integer("num_gpus", 0, "Total number of gpus for each machine." - "If you don't use GPU, please set it to '0'") -flags.DEFINE_integer("replicas_to_aggregate", None, - "Number of replicas to aggregate before parameter update " - "is applied (For sync_replicas mode only; default: " - "num_workers)") -flags.DEFINE_integer("hidden_units", 100, - "Number of units in the hidden layer of the NN") -flags.DEFINE_integer("train_steps", 200, - "Number of (global) training steps to perform") -flags.DEFINE_integer("batch_size", 100, "Training batch size") -flags.DEFINE_float("learning_rate", 0.01, "Learning rate") -flags.DEFINE_boolean( - "sync_replicas", False, - "Use the sync_replicas (synchronized replicas) mode, " - "wherein the parameter updates from workers are aggregated " - "before applied to avoid stale gradients") -flags.DEFINE_boolean( - "existing_servers", False, "Whether servers already exists. If True, " - "will use the worker hosts via their GRPC URLs (one client process " - "per worker host). Otherwise, will create an in-process TensorFlow " - "server.") - -FLAGS = flags.FLAGS - -IMAGE_PIXELS = 28 - - -def main(unused_argv): - data_root = os.path.join("outputs", "MNIST") - mnist = None - tf_config = os.environ.get("TF_CONFIG") - if not tf_config or tf_config == "": - raise ValueError("TF_CONFIG not found.") - tf_config_json = json.loads(tf_config) - cluster = tf_config_json.get('cluster') - job_name = tf_config_json.get('task', {}).get('type') - task_index = tf_config_json.get('task', {}).get('index') - job_name = "worker" if job_name == "master" else job_name - sentinel_path = os.path.join(data_root, "complete.txt") - if job_name == "worker" and task_index == 0: - mnist = input_data.read_data_sets(data_root, one_hot=True) - with open(sentinel_path, 'w+') as f: - f.write("download complete") - else: - while not os.path.exists(sentinel_path): - time.sleep(0.01) - mnist = input_data.read_data_sets(data_root, one_hot=True) - - if FLAGS.download_only: - sys.exit(0) - - print("job name = %s" % job_name) - print("task index = %d" % task_index) - print("number of GPUs = %d" % FLAGS.num_gpus) - - # Construct the cluster and start the server - cluster_spec = tf.train.ClusterSpec(cluster) - - # Get the number of workers. - num_workers = len(cluster_spec.task_indices("worker")) - - if not FLAGS.existing_servers: - # Not using existing servers. Create an in-process server. - server = tf.train.Server( - cluster_spec, job_name=job_name, task_index=task_index) - if job_name == "ps": - server.join() - - is_chief = (task_index == 0) - if FLAGS.num_gpus > 0: - # Avoid gpu allocation conflict: now allocate task_num -> #gpu - # for each worker in the corresponding machine - gpu = (task_index % FLAGS.num_gpus) - worker_device = "/job:worker/task:%d/gpu:%d" % (task_index, gpu) - elif FLAGS.num_gpus == 0: - # Just allocate the CPU to worker server - cpu = 0 - worker_device = "/job:worker/task:%d/cpu:%d" % (task_index, cpu) - # The device setter will automatically place Variables ops on separate - # parameter servers (ps). The non-Variable ops will be placed on the workers. - # The ps use CPU and workers use corresponding GPU - with tf.device( - tf.train.replica_device_setter( - worker_device=worker_device, - ps_device="/job:ps/cpu:0", - cluster=cluster)): - global_step = tf.Variable(0, name="global_step", trainable=False) - - # Variables of the hidden layer - hid_w = tf.Variable( - tf.truncated_normal( - [IMAGE_PIXELS * IMAGE_PIXELS, FLAGS.hidden_units], - stddev=1.0 / IMAGE_PIXELS), - name="hid_w") - hid_b = tf.Variable(tf.zeros([FLAGS.hidden_units]), name="hid_b") - - # Variables of the softmax layer - sm_w = tf.Variable( - tf.truncated_normal( - [FLAGS.hidden_units, 10], - stddev=1.0 / math.sqrt(FLAGS.hidden_units)), - name="sm_w") - sm_b = tf.Variable(tf.zeros([10]), name="sm_b") - - # Ops: located on the worker specified with task_index - x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS * IMAGE_PIXELS]) - y_ = tf.placeholder(tf.float32, [None, 10]) - - hid_lin = tf.nn.xw_plus_b(x, hid_w, hid_b) - hid = tf.nn.relu(hid_lin) - - y = tf.nn.softmax(tf.nn.xw_plus_b(hid, sm_w, sm_b)) - cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0))) - - opt = tf.train.AdamOptimizer(FLAGS.learning_rate) - - if FLAGS.sync_replicas: - if FLAGS.replicas_to_aggregate is None: - replicas_to_aggregate = num_workers - else: - replicas_to_aggregate = FLAGS.replicas_to_aggregate - - opt = tf.train.SyncReplicasOptimizer( - opt, - replicas_to_aggregate=replicas_to_aggregate, - total_num_replicas=num_workers, - name="mnist_sync_replicas") - - train_step = opt.minimize(cross_entropy, global_step=global_step) - - if FLAGS.sync_replicas: - local_init_op = opt.local_step_init_op - if is_chief: - local_init_op = opt.chief_init_op - - ready_for_local_init_op = opt.ready_for_local_init_op - - # Initial token and chief queue runners required by the sync_replicas mode - chief_queue_runner = opt.get_chief_queue_runner() - sync_init_op = opt.get_init_tokens_op() - - init_op = tf.global_variables_initializer() - train_dir = tempfile.mkdtemp() - - if FLAGS.sync_replicas: - sv = tf.train.Supervisor( - is_chief=is_chief, - logdir=train_dir, - init_op=init_op, - local_init_op=local_init_op, - ready_for_local_init_op=ready_for_local_init_op, - recovery_wait_secs=1, - global_step=global_step) - else: - sv = tf.train.Supervisor( - is_chief=is_chief, - logdir=train_dir, - init_op=init_op, - recovery_wait_secs=1, - global_step=global_step) - - sess_config = tf.ConfigProto( - allow_soft_placement=True, - log_device_placement=False, - device_filters=["/job:ps", - "/job:worker/task:%d" % task_index]) - - # The chief worker (task_index==0) session will prepare the session, - # while the remaining workers will wait for the preparation to complete. - if is_chief: - print("Worker %d: Initializing session..." % task_index) - else: - print("Worker %d: Waiting for session to be initialized..." % - task_index) - - if FLAGS.existing_servers: - server_grpc_url = "grpc://" + task_index - print("Using existing server at: %s" % server_grpc_url) - - sess = sv.prepare_or_wait_for_session(server_grpc_url, config=sess_config) - else: - sess = sv.prepare_or_wait_for_session(server.target, config=sess_config) - - print("Worker %d: Session initialization complete." % task_index) - - if FLAGS.sync_replicas and is_chief: - # Chief worker will start the chief queue runner and call the init op. - sess.run(sync_init_op) - sv.start_queue_runners(sess, [chief_queue_runner]) - - # Perform training - time_begin = time.time() - print("Training begins @ %f" % time_begin) - - local_step = 0 - while True: - # Training feed - batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size) - train_feed = {x: batch_xs, y_: batch_ys} - - _, step = sess.run([train_step, global_step], feed_dict=train_feed) - local_step += 1 - - now = time.time() - print("%f: Worker %d: training step %d done (global step: %d)" % - (now, task_index, local_step, step)) - - if step >= FLAGS.train_steps: - break - - time_end = time.time() - print("Training ends @ %f" % time_end) - training_time = time_end - time_begin - print("Training elapsed time: %f s" % training_time) - - # Validation feed - val_feed = {x: mnist.validation.images, y_: mnist.validation.labels} - val_xent = sess.run(cross_entropy, feed_dict=val_feed) - print("After %d training step(s), validation cross entropy = %g" % - (FLAGS.train_steps, val_xent)) - if job_name == "worker" and task_index == 0: - run = Run.get_context() - run.log("CrossEntropy", val_xent) - - -if __name__ == "__main__": - tf.app.run() diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb deleted file mode 100644 index f7aa0b33e..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb +++ /dev/null @@ -1,900 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Warm start hyperparameter tuning\n", - "In this tutorial, you will learn how to warm start a hyperparameter tuning run from a previous tuning run." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's get started. First let's import some Python libraries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "c377ea0c-0cd9-4345-9be2-e20fb29c94c3" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import os\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "edaa7f2f-2439-4148-b57a-8c794c0945ec" - } - }, - "outputs": [], - "source": [ - "import azureml\n", - "from azureml.core import Workspace\n", - "\n", - "# check core SDK version number\n", - "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "59f52294-4a25-4c92-bab8-3b07f0f44d15" - } - }, - "source": [ - "## Create an Azure ML experiment\n", - "Let's create an experiment named \"tf-mnist\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "bc70f780-c240-4779-96f3-bc5ef9a37d59" - } - }, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "script_folder = './tf-mnist'\n", - "os.makedirs(script_folder, exist_ok=True)\n", - "\n", - "exp = Experiment(workspace=ws, name='tf-mnist-2')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "defe921f-8097-44c3-8336-8af6700804a7" - } - }, - "source": [ - "## Download MNIST dataset\n", - "In order to train on the MNIST dataset we will first need to download it from Yan LeCun's web site directly and save them in a `data` folder locally." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib\n", - "data_folder = 'data'\n", - "os.makedirs(data_folder, exist_ok=True)\n", - "\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-images-idx3-ubyte.gz',\n", - " filename=os.path.join(data_folder, 'train-images-idx3-ubyte.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-labels-idx1-ubyte.gz',\n", - " filename=os.path.join(data_folder, 'train-labels-idx1-ubyte.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n", - " filename=os.path.join(data_folder, 't10k-images-idx3-ubyte.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz',\n", - " filename=os.path.join(data_folder, 't10k-labels-idx1-ubyte.gz'))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea" - } - }, - "source": [ - "## Show some sample images\n", - "Let's load the downloaded compressed file into numpy arrays using some utility functions included in the `utils.py` library file from the current folder. Then we use `matplotlib` to plot 30 random images from the dataset along with their labels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "396d478b-34aa-4afa-9898-cdce8222a516" - } - }, - "outputs": [], - "source": [ - "from utils import load_data\n", - "\n", - "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.\n", - "X_train = load_data(os.path.join(data_folder, 'train-images-idx3-ubyte.gz'), False) / 255.0\n", - "X_test = load_data(os.path.join(data_folder, 't10k-images-idx3-ubyte.gz'), False) / 255.0\n", - "y_train = load_data(os.path.join(data_folder, 'train-labels-idx1-ubyte.gz'), True).reshape(-1)\n", - "y_test = load_data(os.path.join(data_folder, 't10k-labels-idx1-ubyte.gz'), True).reshape(-1)\n", - "\n", - "# now let's show some randomly chosen images from the training set.\n", - "count = 0\n", - "sample_size = 30\n", - "plt.figure(figsize = (16, 6))\n", - "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n", - " count = count + 1\n", - " plt.subplot(1, sample_size, count)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.text(x=10, y=-10, s=y_train[i], fontsize=18)\n", - " plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a FileDataset\n", - "A FileDataset references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.dataset import Dataset\n", - "web_paths = ['https://azureopendatastorage.blob.core.windows.net/mnist/train-images-idx3-ubyte.gz',\n", - " 'https://azureopendatastorage.blob.core.windows.net/mnist/train-labels-idx1-ubyte.gz',\n", - " 'https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n", - " 'https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz'\n", - " ]\n", - "dataset = Dataset.File.from_files(path = web_paths)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the register() method to register datasets to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script.\n", - "You can try get the dataset first to see if it's already registered." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_registered = False\n", - "try:\n", - " temp = Dataset.get_by_name(workspace = ws, name = 'mnist-dataset')\n", - " dataset_registered = True\n", - "except:\n", - " print(\"The dataset mnist-dataset is not registered in workspace yet.\")\n", - "\n", - "if not dataset_registered:\n", - " dataset = dataset.register(workspace = ws,\n", - " name = 'mnist-dataset',\n", - " description='training and test dataset',\n", - " create_new_version=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n", - "1. create the configuration (this step is local and only takes a second)\n", - "2. create the cluster (this step will take about **20 seconds**)\n", - "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"hd-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - "# can poll for a minimum number of nodes and for a specific timeout. \n", - "# if no min node count is provided it uses the scale settings for the cluster\n", - "compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'gpu-cluster' of type `AmlCompute`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "compute_targets = ws.compute_targets\n", - "for name, ct in compute_targets.items():\n", - " print(name, ct.type, ct.provisioning_state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Copy the training files into the script folder\n", - "The TensorFlow training script is already created for you. You can simply copy it into the script folder, together with the utility library used to load compressed data file into numpy array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "# the training logic is in the tf_mnist.py file.\n", - "shutil.copy('./tf_mnist.py', script_folder)\n", - "\n", - "# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.\n", - "shutil.copy('./utils.py', script_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "2039d2d5-aca6-4f25-a12f-df9ae6529cae" - } - }, - "source": [ - "## Construct neural network in TensorFlow\n", - "In the training script `tf_mnist.py`, it creates a very simple DNN (deep neural network), with just 2 hidden layers. The input layer has 28 * 28 = 784 neurons, each representing a pixel in an image. The first hidden layer has 300 neurons, and the second hidden layer has 100 neurons. The output layer has 10 neurons, each representing a targeted label from 0 to 9.\n", - "\n", - "![DNN](nn.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure ML concepts \n", - "Please note the following three things in the code below:\n", - "1. The script accepts arguments using the argparse package. In this case there is one argument `--data_folder` which specifies the file system folder in which the script can find the MNIST data\n", - "```\n", - " parser = argparse.ArgumentParser()\n", - " parser.add_argument('--data_folder')\n", - "```\n", - "2. The script is accessing the Azure ML `Run` object by executing `run = Run.get_context()`. Further down the script is using the `run` to report the training accuracy and the validation accuracy as training progresses.\n", - "```\n", - " run.log('training_acc', np.float(acc_train))\n", - " run.log('validation_acc', np.float(acc_val))\n", - "```\n", - "3. When running the script on Azure ML, you can write files out to a folder `./outputs` that is relative to the root directory. This folder is specially tracked by Azure ML in the sense that any files written to that folder during script execution on the remote target will be picked up by Run History; these files (known as artifacts) will be available as part of the run history record." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next cell will print out the training code for you to inspect it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(os.path.join(script_folder, './tf_mnist.py'), 'r') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the TensorFlow 2.0 GPU curated environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the training job\u00c2\u00b6\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "args = ['--data-folder', dataset.as_mount(),\n", - " '--batch-size', 64,\n", - " '--first-layer-neurons', 256,\n", - " '--second-layer-neurons', 128,\n", - " '--learning-rate', 0.01]\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='tf_mnist.py',\n", - " arguments=args,\n", - " compute_target=compute_target,\n", - " environment=tf_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit job to run\n", - "Submit the ScriptRunConfig to an Azure ML experiment to kick off the execution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = exp.submit(src)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Intelligent hyperparameter tuning\n", - "Now that we have trained the model with one set of hyperparameters, we can tune the model hyperparameters to optimize model performance. First let's define the parameter space using random sampling. Typically, the hyperparameter exploration process is painstakingly manual, given that the search space is vast and evaluation of each configuration can be expensive.\n", - "\n", - "Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We start by defining the hyperparameter space. In this case, we will tune 4 hyperparameters - '--batch-size', '--first-layer-neurons', '--second-layer-neurons' and '--learning-rate'. For each of these hyperparameters, we specify the range of values they can take. In this example, we will use Random Sampling to randomly select hyperparameter values from the defined search space." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.hyperdrive import RandomParameterSampling, choice, loguniform\n", - "\n", - "ps = RandomParameterSampling(\n", - " {\n", - " '--batch-size': choice(32, 64, 128),\n", - " '--first-layer-neurons': choice(16, 64, 128, 256, 512),\n", - " '--second-layer-neurons': choice(16, 64, 256, 512),\n", - " '--learning-rate': loguniform(-6, -1)\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will create a new ScriptRunConfig without the above parameters since they will be passed in later. Note we still need to keep the `data-folder` parameter since that's not a hyperparamter we will sweep." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "args = ['--data-folder', dataset.as_mount()]\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='tf_mnist.py',\n", - " arguments=args,\n", - " compute_target=compute_target,\n", - " environment=tf_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we will define an early termnination policy. This will terminate poorly performing runs automatically, reducing wastage of resources and instead efficiently using these resources for exploring other parameter configurations. In this example, we will use the `TruncationSelectionPolicy`, truncating the bottom performing 25% runs. It states to check the job every 2 iterations. If the primary metric (defined later) falls in the bottom 25% range, Azure ML terminate the job. This saves us from continuing to explore hyperparameters that don't show promise of helping reach our target metric." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.hyperdrive import TruncationSelectionPolicy\n", - "policy = TruncationSelectionPolicy(evaluation_interval=2, truncation_percentage=25)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are ready to configure a run configuration object, and specify the primary metric `validation_acc` that's recorded in your training runs. If you go back to visit the training script, you will notice that this value is being logged after every epoch (a full batch set). We also want to tell the service that we are looking to maximizing this value. We also set the number of samples to 15, and maximal concurrent job to 4, which is the same as the number of nodes in our computer cluster." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.hyperdrive import HyperDriveConfig, PrimaryMetricGoal\n", - "htc = HyperDriveConfig(run_config=src, \n", - " hyperparameter_sampling=ps, \n", - " policy=policy, \n", - " primary_metric_name='validation_acc', \n", - " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n", - " max_total_runs=15,\n", - " max_concurrent_runs=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's launch the hyperparameter tuning job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "htr = exp.submit(config=htc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use a run history widget to show the progress. Be patient as this might take a while to complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(htr).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "htr.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert(htr.get_status() == \"Completed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Find and register best model \n", - "When all the jobs finish, we can find out the one that has the highest accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = htr.get_best_run_by_primary_metric()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's list the model files uploaded during the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(best_run.get_file_names())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then register the folder (and all files in it) as a model named `tf-dnn-mnist` under the workspace for deployment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = best_run.register_model(model_name='tf-dnn-mnist', model_path='outputs/model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Warm start a Hyperparameter Tuning experiment\n", - "Often times, finding the best hyperparameter values for your model can be an iterative process, needing multiple tuning runs that learn from previous hyperparameter tuning runs. Reusing knowledge from these previous runs will accelerate the hyperparameter tuning process, thereby reducing the cost of tuning the model and will potentially improve the primary metric of the resulting model. When warm starting a hyperparameter tuning experiment with Bayesian sampling, trials from the previous run will be used as prior knowledge to intelligently pick new samples, so as to improve the primary metric. Additionally, when using Random or Grid sampling, any early termination decisions will leverage metrics from the previous runs to determine poorly performing training runs. \n", - "\n", - "Azure Machine Learning allows you to warm start your hyperparameter tuning run by leveraging knowledge from up to 5 previously completed hyperparameter tuning parent runs. In this example, we shall warm start from the initial hyperparameter tuning run in this notebook" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "warm_start_parents_to_resume_from=[htr]\n", - "\n", - "warm_start_htc = HyperDriveConfig(run_config=src, \n", - " hyperparameter_sampling=ps, \n", - " policy=policy, \n", - " resume_from=warm_start_parents_to_resume_from, \n", - " primary_metric_name='validation_acc', \n", - " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n", - " max_total_runs=20,\n", - " max_concurrent_runs=4)\n", - "\n", - "warm_start_htr = exp.submit(config=warm_start_htc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the run history widget to show the progress of this warm start run. Be patient as this might take a while to complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(warm_start_htr).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "warm_start_htr.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find and register best model from the warm start run\n", - "When all the jobs finish, we can find out the one that has the highest accuracy and register the folder (and all files in it) as a model named tf-dnn-mnist-warm-start under the workspace for deployment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_warm_start_run = warm_start_htr.get_best_run_by_primary_metric()\n", - "warm_start_model = best_warm_start_run.register_model(model_name='tf-dnn-mnist-warm-start', model_path='outputs/model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resuming individual training runs in a hyperparameter tuning experiment\n", - "\n", - "In the previous section, we saw how you can warm start a hyperparameter tuning run, to learn from a previously completed run. Additionally, there might be occasions when individual training runs of a hyperparameter tuning experiment are cancelled due to budget constraints or fail due to other reasons. It is now possible to resume such individual training runs from the last checkpoint (assuming your training script handles checkpoints). Resuming an individual training run will use the same hyperparameter configuration and mount the storage used for that run. The training script should accept the \"--resume-from\" argument, which contains the checkpoint or model files from which to resume the training run. \n", - "\n", - "You can also resume individual runs as part of an experiment that spends additional budget on hyperparameter tuning. Any additional budget, after resuming the specified training runs is used for exploring additional configurations.\n", - "\n", - "In this example, we will resume one of the child runs cancelled in the previous hyperparameter tuning run in this notebook" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cancelled_child_runs = []\n", - "for child_run in htr.get_children():\n", - " if child_run.status == \"Canceled\":\n", - " cancelled_child_runs.append(child_run)\n", - " \n", - "if len(cancelled_child_runs) != 0:\n", - " child_runs_to_resume=[cancelled_child_runs[0]]\n", - "else:\n", - " child_runs_to_resume=[]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will configure the hyperparameter tuning experiment to warm start from the previous experiment and resume individual training runs and submit this warm start hyperparameter tuning run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "resume_child_runs_htc = HyperDriveConfig(run_config=src, \n", - " hyperparameter_sampling=ps, \n", - " policy=policy, \n", - " resume_child_runs=child_runs_to_resume, \n", - " primary_metric_name='validation_acc', \n", - " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n", - " max_total_runs=10,\n", - " max_concurrent_runs=4)\n", - "\n", - "resume_child_runs_htr = exp.submit(config=resume_child_runs_htc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can use the run history widget to show the progress of this resumed run. Be patient as this might take a while to complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(resume_child_runs_htr).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "resume_child_runs_htr.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find and register best model from the resumed run\n", - "When all the jobs finish, we can find out the one that has the highest accuracy and register the folder (and all files in it) as a model named tf-dnn-mnist-resumed under the workspace for deployment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_resume_child_run = resume_child_runs_htr.get_best_run_by_primary_metric()\n", - "resume_child_run_model = best_resume_child_run.register_model(model_name='tf-dnn-mnist-resumed', model_path='outputs/model')" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "nagaur" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "TensorFlow" - ], - "friendly_name": "Hyperparameter tuning and warm start using the TensorFlow estimator", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "tags": [ - "None" - ], - "task": "Train a deep neural network" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/nn.png b/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/nn.png deleted file mode 100644 index 8910281ea..000000000 Binary files a/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/nn.png and /dev/null differ diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/tf_mnist.py b/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/tf_mnist.py deleted file mode 100644 index d4ae34258..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/tf_mnist.py +++ /dev/null @@ -1,191 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import numpy as np -import argparse -import os -import re -import tensorflow as tf -import time -import glob - -from azureml.core import Run -from utils import load_data -from tensorflow.keras import Model, layers - - -# Create TF Model. -class NeuralNet(Model): - # Set layers. - def __init__(self): - super(NeuralNet, self).__init__() - # First hidden layer. - self.h1 = layers.Dense(n_h1, activation=tf.nn.relu) - # Second hidden layer. - self.h2 = layers.Dense(n_h2, activation=tf.nn.relu) - self.out = layers.Dense(n_outputs) - - # Set forward pass. - def call(self, x, is_training=False): - x = self.h1(x) - x = self.h2(x) - x = self.out(x) - if not is_training: - # Apply softmax when not training. - x = tf.nn.softmax(x) - return x - - -def cross_entropy_loss(y, logits): - # Convert labels to int 64 for tf cross-entropy function. - y = tf.cast(y, tf.int64) - # Apply softmax to logits and compute cross-entropy. - loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) - # Average loss across the batch. - return tf.reduce_mean(loss) - - -# Accuracy metric. -def accuracy(y_pred, y_true): - # Predicted class is the index of highest score in prediction vector (i.e. argmax). - correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64)) - return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1) - - -# Optimization process. -def run_optimization(x, y): - # Wrap computation inside a GradientTape for automatic differentiation. - with tf.GradientTape() as g: - # Forward pass. - logits = neural_net(x, is_training=True) - # Compute loss. - loss = cross_entropy_loss(y, logits) - - # Variables to update, i.e. trainable variables. - trainable_variables = neural_net.trainable_variables - - # Compute gradients. - gradients = g.gradient(loss, trainable_variables) - - # Update W and b following gradients. - optimizer.apply_gradients(zip(gradients, trainable_variables)) - - -print("TensorFlow version:", tf.__version__) - -parser = argparse.ArgumentParser() -parser.add_argument('--data-folder', type=str, dest='data_folder', default='data', help='data folder mounting point') -parser.add_argument('--batch-size', type=int, dest='batch_size', default=128, help='mini batch size for training') -parser.add_argument('--first-layer-neurons', type=int, dest='n_hidden_1', default=128, - help='# of neurons in the first layer') -parser.add_argument('--second-layer-neurons', type=int, dest='n_hidden_2', default=128, - help='# of neurons in the second layer') -parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.01, help='learning rate') -parser.add_argument('--resume-from', type=str, default=None, - help='location of the model or checkpoint files from where to resume the training') -args = parser.parse_args() - -previous_model_location = args.resume_from -# You can also use environment variable to get the model/checkpoint files location -# previous_model_location = os.path.expandvars(os.getenv("AZUREML_DATAREFERENCE_MODEL_LOCATION", None)) - -data_folder = args.data_folder -print('Data folder:', data_folder) - -# load train and test set into numpy arrays -# note we scale the pixel intensity values to 0-1 (by dividing it with 255.0) so the model can converge faster. -X_train = load_data(glob.glob(os.path.join(data_folder, '**/train-images-idx3-ubyte.gz'), - recursive=True)[0], False) / np.float32(255.0) -X_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-images-idx3-ubyte.gz'), - recursive=True)[0], False) / np.float32(255.0) -y_train = load_data(glob.glob(os.path.join(data_folder, '**/train-labels-idx1-ubyte.gz'), - recursive=True)[0], True).reshape(-1) -y_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-labels-idx1-ubyte.gz'), - recursive=True)[0], True).reshape(-1) - -print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n') - -training_set_size = X_train.shape[0] - -n_inputs = 28 * 28 -n_h1 = args.n_hidden_1 -n_h2 = args.n_hidden_2 -n_outputs = 10 -learning_rate = args.learning_rate -n_epochs = 20 -batch_size = args.batch_size - -# Build neural network model. -neural_net = NeuralNet() - -# Stochastic gradient descent optimizer. -optimizer = tf.optimizers.SGD(learning_rate) - -# start an Azure ML run -run = Run.get_context() - -if previous_model_location: - # Restore variables from latest checkpoint. - checkpoint = tf.train.Checkpoint(model=neural_net, optimizer=optimizer) - checkpoint_file_path = tf.train.latest_checkpoint(previous_model_location) - checkpoint.restore(checkpoint_file_path) - checkpoint_filename = os.path.basename(checkpoint_file_path) - num_found = re.search(r'\d+', checkpoint_filename) - if num_found: - start_epoch = int(num_found.group(0)) - print("Resuming from epoch {}".format(str(start_epoch))) - -start_time = time.perf_counter() -for epoch in range(0, n_epochs): - - # randomly shuffle training set - indices = np.random.permutation(training_set_size) - X_train = X_train[indices] - y_train = y_train[indices] - - # batch index - b_start = 0 - b_end = b_start + batch_size - for _ in range(training_set_size // batch_size): - # get a batch - X_batch, y_batch = X_train[b_start: b_end], y_train[b_start: b_end] - - # update batch index for the next batch - b_start = b_start + batch_size - b_end = min(b_start + batch_size, training_set_size) - - # train - run_optimization(X_batch, y_batch) - - # evaluate training set - pred = neural_net(X_batch, is_training=False) - acc_train = accuracy(pred, y_batch) - - # evaluate validation set - pred = neural_net(X_test, is_training=False) - acc_val = accuracy(pred, y_test) - - # log accuracies - run.log('training_acc', np.float(acc_train)) - run.log('validation_acc', np.float(acc_val)) - print(epoch, '-- Training accuracy:', acc_train, '\b Validation accuracy:', acc_val) - - # Save checkpoints in the "./outputs" folder so that they are automatically uploaded into run history. - checkpoint_dir = './outputs/' - checkpoint = tf.train.Checkpoint(model=neural_net, optimizer=optimizer) - - if epoch % 2 == 0: - checkpoint.save(checkpoint_dir) - time.sleep(3) - -run.log('final_acc', np.float(acc_val)) -os.makedirs('./outputs/model', exist_ok=True) - -# files saved in the "./outputs" folder are automatically uploaded into run history -# this is workaround for https://github.com/tensorflow/tensorflow/issues/33913 and will be fixed once we move to >tf2.1 -neural_net._set_inputs(X_train) -tf.saved_model.save(neural_net, './outputs/model/') - -stop_time = time.perf_counter() -training_time = (stop_time - start_time) * 1000 -print("Total time in milliseconds for training: {}".format(str(training_time))) diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/utils.py b/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/utils.py deleted file mode 100644 index 98170adae..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/utils.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import gzip -import numpy as np -import struct - - -# load compressed MNIST gz files and return numpy arrays -def load_data(filename, label=False): - with gzip.open(filename) as gz: - struct.unpack('I', gz.read(4)) - n_items = struct.unpack('>I', gz.read(4)) - if not label: - n_rows = struct.unpack('>I', gz.read(4))[0] - n_cols = struct.unpack('>I', gz.read(4))[0] - res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8) - res = res.reshape(n_items[0], n_rows * n_cols) - else: - res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8) - res = res.reshape(n_items[0], 1) - return res - - -# one-hot encode a 1-D array -def one_hot_encode(array, num_of_classes): - return np.eye(num_of_classes)[array.reshape(-1)] diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/nn.png b/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/nn.png deleted file mode 100644 index 8910281ea..000000000 Binary files a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/nn.png and /dev/null differ diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/tf_mnist.py b/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/tf_mnist.py deleted file mode 100644 index 87be1ab3f..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/tf_mnist.py +++ /dev/null @@ -1,190 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import numpy as np -import argparse -import os -import re -import tensorflow as tf -import time -import glob - -from azureml.core import Run -from utils import load_data -from tensorflow.keras import Model, layers - - -# Create TF Model. -class NeuralNet(Model): - # Set layers. - def __init__(self): - super(NeuralNet, self).__init__() - # First hidden layer. - self.h1 = layers.Dense(n_h1, activation=tf.nn.relu) - # Second hidden layer. - self.h2 = layers.Dense(n_h2, activation=tf.nn.relu) - self.out = layers.Dense(n_outputs) - - # Set forward pass. - def call(self, x, is_training=False): - x = self.h1(x) - x = self.h2(x) - x = self.out(x) - if not is_training: - # Apply softmax when not training. - x = tf.nn.softmax(x) - return x - - -def cross_entropy_loss(y, logits): - # Convert labels to int 64 for tf cross-entropy function. - y = tf.cast(y, tf.int64) - # Apply softmax to logits and compute cross-entropy. - loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) - # Average loss across the batch. - return tf.reduce_mean(loss) - - -# Accuracy metric. -def accuracy(y_pred, y_true): - # Predicted class is the index of highest score in prediction vector (i.e. argmax). - correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64)) - return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1) - - -# Optimization process. -def run_optimization(x, y): - # Wrap computation inside a GradientTape for automatic differentiation. - with tf.GradientTape() as g: - # Forward pass. - logits = neural_net(x, is_training=True) - # Compute loss. - loss = cross_entropy_loss(y, logits) - - # Variables to update, i.e. trainable variables. - trainable_variables = neural_net.trainable_variables - - # Compute gradients. - gradients = g.gradient(loss, trainable_variables) - - # Update W and b following gradients. - optimizer.apply_gradients(zip(gradients, trainable_variables)) - - -print("TensorFlow version:", tf.__version__) - -parser = argparse.ArgumentParser() -parser.add_argument('--data-folder', type=str, dest='data_folder', default='data', help='data folder mounting point') -parser.add_argument('--batch-size', type=int, dest='batch_size', default=128, help='mini batch size for training') -parser.add_argument('--first-layer-neurons', type=int, dest='n_hidden_1', default=128, - help='# of neurons in the first layer') -parser.add_argument('--second-layer-neurons', type=int, dest='n_hidden_2', default=128, - help='# of neurons in the second layer') -parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.01, help='learning rate') -parser.add_argument('--resume-from', type=str, default=None, - help='location of the model or checkpoint files from where to resume the training') -args = parser.parse_args() - -previous_model_location = args.resume_from -# You can also use environment variable to get the model/checkpoint files location -# previous_model_location = os.path.expandvars(os.getenv("AZUREML_DATAREFERENCE_MODEL_LOCATION", None)) - -data_folder = args.data_folder -print('Data folder:', data_folder) - -# load train and test set into numpy arrays -# note we scale the pixel intensity values to 0-1 (by dividing it with 255.0) so the model can converge faster. -X_train = load_data(glob.glob(os.path.join(data_folder, '**/train-images-idx3-ubyte.gz'), - recursive=True)[0], False) / np.float32(255.0) -X_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-images-idx3-ubyte.gz'), - recursive=True)[0], False) / np.float32(255.0) -y_train = load_data(glob.glob(os.path.join(data_folder, '**/train-labels-idx1-ubyte.gz'), - recursive=True)[0], True).reshape(-1) -y_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-labels-idx1-ubyte.gz'), - recursive=True)[0], True).reshape(-1) - -print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n') - -training_set_size = X_train.shape[0] - -n_inputs = 28 * 28 -n_h1 = args.n_hidden_1 -n_h2 = args.n_hidden_2 -n_outputs = 10 -learning_rate = args.learning_rate -n_epochs = 20 -batch_size = args.batch_size - -# Build neural network model. -neural_net = NeuralNet() - -# Stochastic gradient descent optimizer. -optimizer = tf.optimizers.SGD(learning_rate) - -# start an Azure ML run -run = Run.get_context() - -if previous_model_location: - # Restore variables from latest checkpoint. - checkpoint = tf.train.Checkpoint(model=neural_net, optimizer=optimizer) - checkpoint_file_path = tf.train.latest_checkpoint(previous_model_location) - checkpoint.restore(checkpoint_file_path) - checkpoint_filename = os.path.basename(checkpoint_file_path) - num_found = re.search(r'\d+', checkpoint_filename) - if num_found: - start_epoch = int(num_found.group(0)) - print("Resuming from epoch {}".format(str(start_epoch))) - -start_time = time.perf_counter() -for epoch in range(0, n_epochs): - - # randomly shuffle training set - indices = np.random.permutation(training_set_size) - X_train = X_train[indices] - y_train = y_train[indices] - - # batch index - b_start = 0 - b_end = b_start + batch_size - for _ in range(training_set_size // batch_size): - # get a batch - X_batch, y_batch = X_train[b_start: b_end], y_train[b_start: b_end] - - # update batch index for the next batch - b_start = b_start + batch_size - b_end = min(b_start + batch_size, training_set_size) - - # train - run_optimization(X_batch, y_batch) - - # evaluate training set - pred = neural_net(X_batch, is_training=False) - acc_train = accuracy(pred, y_batch) - - # evaluate validation set - pred = neural_net(X_test, is_training=False) - acc_val = accuracy(pred, y_test) - - # log accuracies - run.log('training_acc', np.float(acc_train)) - run.log('validation_acc', np.float(acc_val)) - print(epoch, '-- Training accuracy:', acc_train, '\b Validation accuracy:', acc_val) - - # Save checkpoints in the "./outputs" folder so that they are automatically uploaded into run history. - checkpoint_dir = './outputs/' - checkpoint = tf.train.Checkpoint(model=neural_net, optimizer=optimizer) - - if epoch % 2 == 0: - checkpoint.save(checkpoint_dir) - -run.log('final_acc', np.float(acc_val)) -os.makedirs('./outputs/model', exist_ok=True) - -# files saved in the "./outputs" folder are automatically uploaded into run history -# this is workaround for https://github.com/tensorflow/tensorflow/issues/33913 and will be fixed once we move to >tf2.1 -neural_net._set_inputs(X_train) -tf.saved_model.save(neural_net, './outputs/model/') - -stop_time = time.perf_counter() -training_time = (stop_time - start_time) * 1000 -print("Total time in milliseconds for training: {}".format(str(training_time))) diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb deleted file mode 100644 index aca27bb7a..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb +++ /dev/null @@ -1,1183 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "bf74d2e9-2708-49b1-934b-e0ede342f475" - } - }, - "source": [ - "# Training, hyperparameter tune, and deploy with TensorFlow\n", - "\n", - "## Introduction\n", - "This tutorial shows how to train a simple deep neural network using the MNIST dataset and TensorFlow on Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of `28x28` pixels, representing number from 0 to 9. The goal is to create a multi-class classifier to identify the digit each image represents, and deploy it as a web service in Azure.\n", - "\n", - "For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/).\n", - "\n", - "## Prerequisite:\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../../configuration.ipynb) to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (`config.json`)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's get started. First let's import some Python libraries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "c377ea0c-0cd9-4345-9be2-e20fb29c94c3" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import os\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "edaa7f2f-2439-4148-b57a-8c794c0945ec" - } - }, - "outputs": [], - "source": [ - "import azureml\n", - "from azureml.core import Workspace\n", - "\n", - "# check core SDK version number\n", - "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "59f52294-4a25-4c92-bab8-3b07f0f44d15" - } - }, - "source": [ - "## Create an Azure ML experiment\n", - "Let's create an experiment named \"tf-mnist\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "bc70f780-c240-4779-96f3-bc5ef9a37d59" - } - }, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "script_folder = './tf-mnist'\n", - "os.makedirs(script_folder, exist_ok=True)\n", - "\n", - "exp = Experiment(workspace=ws, name='tf-mnist')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "defe921f-8097-44c3-8336-8af6700804a7" - } - }, - "source": [ - "## Download MNIST dataset\n", - "In order to train on the MNIST dataset we will first need to download it from azuremlopendatasets blob directly and save them in a `data` folder locally. If you want you can directly download the same data from Yan LeCun's web site." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib.request\n", - "\n", - "data_folder = os.path.join(os.getcwd(), 'data')\n", - "os.makedirs(data_folder, exist_ok=True)\n", - "\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-images-idx3-ubyte.gz',\n", - " filename=os.path.join(data_folder, 'train-images-idx3-ubyte.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-labels-idx1-ubyte.gz',\n", - " filename=os.path.join(data_folder, 'train-labels-idx1-ubyte.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n", - " filename=os.path.join(data_folder, 't10k-images-idx3-ubyte.gz'))\n", - "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz',\n", - " filename=os.path.join(data_folder, 't10k-labels-idx1-ubyte.gz'))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea" - } - }, - "source": [ - "## Show some sample images\n", - "Let's load the downloaded compressed file into numpy arrays using some utility functions included in the `utils.py` library file from the current folder. Then we use `matplotlib` to plot 30 random images from the dataset along with their labels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "396d478b-34aa-4afa-9898-cdce8222a516" - } - }, - "outputs": [], - "source": [ - "from utils import load_data\n", - "\n", - "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster.\n", - "X_train = load_data(os.path.join(data_folder, 'train-images-idx3-ubyte.gz'), False) / np.float32(255.0)\n", - "X_test = load_data(os.path.join(data_folder, 't10k-images-idx3-ubyte.gz'), False) / np.float32(255.0)\n", - "y_train = load_data(os.path.join(data_folder, 'train-labels-idx1-ubyte.gz'), True).reshape(-1)\n", - "y_test = load_data(os.path.join(data_folder, 't10k-labels-idx1-ubyte.gz'), True).reshape(-1)\n", - "\n", - "count = 0\n", - "sample_size = 30\n", - "plt.figure(figsize = (16, 6))\n", - "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n", - " count = count + 1\n", - " plt.subplot(1, sample_size, count)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.text(x = 10, y = -10, s = y_train[i], fontsize = 18)\n", - " plt.imshow(X_train[i].reshape(28, 28), cmap = plt.cm.Greys)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a Dataset for Files\n", - "A Dataset can reference single or multiple files in your datastores or public urls. The files can be of any format. Dataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.dataset import Dataset\n", - "web_paths = ['https://azureopendatastorage.blob.core.windows.net/mnist/train-images-idx3-ubyte.gz',\n", - " 'https://azureopendatastorage.blob.core.windows.net/mnist/train-labels-idx1-ubyte.gz',\n", - " 'https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n", - " 'https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz'\n", - " ]\n", - "dataset = Dataset.File.from_files(path = web_paths)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may want to regiester datasets using the register() method to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script.\n", - "You can try get the dataset first to see if it's already registered." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.exceptions import UserErrorException\n", - "dataset_registered = False\n", - "try:\n", - " temp = Dataset.get_by_name(workspace = ws, name = 'mnist-dataset')\n", - " dataset_registered = True\n", - "except UserErrorException:\n", - " print(\"The dataset mnist-dataset is not registered in workspace yet.\")\n", - "\n", - "if not dataset_registered:\n", - " dataset = dataset.register(workspace = ws,\n", - " name = 'mnist-dataset',\n", - " description='training and test dataset',\n", - " create_new_version=True)\n", - "# list the files referenced by dataset\n", - "dataset.to_path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n", - "1. create the configuration (this step is local and only takes a second)\n", - "2. create the cluster (this step will take about **20 seconds**)\n", - "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"hd-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - "# can poll for a minimum number of nodes and for a specific timeout. \n", - "# if no min node count is provided it uses the scale settings for the cluster\n", - "compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'gpu-cluster' of type `AmlCompute`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "compute_targets = ws.compute_targets\n", - "for name, ct in compute_targets.items():\n", - " print(name, ct.type, ct.provisioning_state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Copy the training files into the script folder\n", - "The TensorFlow training script is already created for you. You can simply copy it into the script folder, together with the utility library used to load compressed data file into numpy array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "# the training logic is in the tf_mnist.py file.\n", - "shutil.copy('./tf_mnist.py', script_folder)\n", - "\n", - "# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.\n", - "shutil.copy('./utils.py', script_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "2039d2d5-aca6-4f25-a12f-df9ae6529cae" - } - }, - "source": [ - "## Construct neural network in TensorFlow\n", - "In the training script `tf_mnist.py`, it creates a very simple DNN (deep neural network), with just 2 hidden layers. The input layer has 28 * 28 = 784 neurons, each representing a pixel in an image. The first hidden layer has 300 neurons, and the second hidden layer has 100 neurons. The output layer has 10 neurons, each representing a targeted label from 0 to 9.\n", - "\n", - "![DNN](nn.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure ML concepts \n", - "Please note the following three things in the code below:\n", - "1. The script accepts arguments using the argparse package. In this case there is one argument `--data_folder` which specifies the file system folder in which the script can find the MNIST data\n", - "```\n", - " parser = argparse.ArgumentParser()\n", - " parser.add_argument('--data_folder')\n", - "```\n", - "2. The script is accessing the Azure ML `Run` object by executing `run = Run.get_context()`. Further down the script is using the `run` to report the training accuracy and the validation accuracy as training progresses.\n", - "```\n", - " run.log('training_acc', np.float(acc_train))\n", - " run.log('validation_acc', np.float(acc_val))\n", - "```\n", - "3. When running the script on Azure ML, you can write files out to a folder `./outputs` that is relative to the root directory. This folder is specially tracked by Azure ML in the sense that any files written to that folder during script execution on the remote target will be picked up by Run History; these files (known as artifacts) will be available as part of the run history record." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next cell will print out the training code for you to inspect it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(os.path.join(script_folder, './tf_mnist.py'), 'r') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an environment\n", - "\n", - "In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the TensorFlow 2.0 GPU curated environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "args = ['--data-folder', dataset.as_named_input('mnist').as_mount(),\n", - " '--batch-size', 64,\n", - " '--first-layer-neurons', 256,\n", - " '--second-layer-neurons', 128,\n", - " '--learning-rate', 0.01]\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='tf_mnist.py',\n", - " arguments=args,\n", - " compute_target=compute_target,\n", - " environment=tf_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit job to run\n", - "Submit the ScriptRunConfig to an Azure ML experiment to kick off the execution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = exp.submit(src)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor the Run \n", - "As the Run is executed, it will go through the following stages:\n", - "1. Preparing: A docker image is created based on the specifications of the Azure ML environment and it will be uploaded to the workspace's Azure Container Registry. This step will only happen once for each Python environment -- the container will then be cached for subsequent runs. Creating and uploading the image takes about **5 minutes**. While the job is preparing, logs are streamed to the run history and can be viewed to monitor the progress of the image creation.\n", - "\n", - "2. Scaling: If the compute needs to be scaled up (i.e. the Batch AI cluster requires more nodes to execute the run than currently available), the cluster will attempt to scale up in order to make the required amount of nodes available. Scaling typically takes about **5 minutes**.\n", - "\n", - "3. Running: All scripts in the script folder are uploaded to the compute target, data stores are mounted/copied and the `entry_script` is executed. While the job is running, stdout and the `./logs` folder are streamed to the run history and can be viewed to monitor the progress of the run.\n", - "\n", - "4. Post-Processing: The `./outputs` folder of the run is copied over to the run history\n", - "\n", - "There are multiple ways to check the progress of a running job. We can use a Jupyter notebook widget. \n", - "\n", - "**Note: The widget will automatically update ever 10-15 seconds, always showing you the most up-to-date information about the run**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also periodically check the status of the run object, and navigate to Azure portal to monitor the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The Run object \n", - "The Run object provides the interface to the run history -- both to the job and to the control plane (this notebook), and both while the job is running and after it has completed. It provides a number of interesting features for instance:\n", - "* `run.get_details()`: Provides a rich set of properties of the run\n", - "* `run.get_metrics()`: Provides a dictionary with all the metrics that were reported for the Run\n", - "* `run.get_file_names()`: List all the files that were uploaded to the run history for this Run. This will include the `outputs` and `logs` folder, azureml-logs and other logs, as well as files that were explicitly uploaded to the run using `run.upload_file()`\n", - "\n", - "Below are some examples -- please run through them and inspect their output. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_details()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_metrics()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_file_names()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot accuracy over epochs\n", - "Since we can retrieve the metrics from the run, we can easily make plots using `matplotlib` in the notebook. Then we can add the plotted image to the run using `run.log_image()`, so all information about the run is kept together." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "os.makedirs('./imgs', exist_ok=True)\n", - "metrics = run.get_metrics()\n", - "\n", - "plt.figure(figsize = (13,5))\n", - "plt.plot(metrics['validation_acc'], 'r-', lw=4, alpha=.6)\n", - "plt.plot(metrics['training_acc'], 'b--', alpha=0.5)\n", - "plt.legend(['Full evaluation set', 'Training set mini-batch'])\n", - "plt.xlabel('epochs', fontsize=14)\n", - "plt.ylabel('accuracy', fontsize=14)\n", - "plt.title('Accuracy over Epochs', fontsize=16)\n", - "run.log_image(name='acc_over_epochs.png', plot=plt)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download the saved model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the training script, a TensorFlow `saver` object is used to persist the model in a local folder (local to the compute target). The model was saved to the `./outputs` folder on the disk of the Batch AI cluster node where the job is run. Azure ML automatically uploaded anything written in the `./outputs` folder into run history file store. Subsequently, we can use the `Run` object to download the model files the `saver` object saved. They are under the the `outputs/model` folder in the run history file store, and are downloaded into a local folder named `model`. Note the TensorFlow model consists of four files in binary format and they are not human-readable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.download_files(prefix='outputs/model', output_directory='./model', append_prefix=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predict on the test set (Optional)\n", - "Now load the saved TensorFlow graph, and list all operations under the `network` scope. This way we can discover the input tensor `network/X:0` and the output tensor `network/output/MatMul:0`, and use them in the scoring script in the next step.\n", - "\n", - "Note: if your local TensorFlow version is different than the version running in the cluster where the model is trained, you might see a \"compiletime version mismatch\" warning. You can ignore it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " import tensorflow as tf\n", - " imported_model = tf.saved_model.load('./model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " pred = imported_model(X_test)\n", - " y_hat = np.argmax(pred, axis=1)\n", - "\n", - " # print the first 30 labels and predictions\n", - " print('labels: \\t', y_test[:30])\n", - " print('predictions:\\t', y_hat[:30])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " print(\"Accuracy on the test set:\", np.average(y_hat == y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " print(\"Accuracy on the test set:\", np.average(y_hat == y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Intelligent hyperparameter tuning\n", - "We have trained the model with one set of hyperparameters, now let's how we can do hyperparameter tuning by launching multiple runs on the cluster. First let's define the parameter space using random sampling." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n", - "from azureml.train.hyperdrive import choice, loguniform\n", - "\n", - "ps = RandomParameterSampling(\n", - " {\n", - " '--batch-size': choice(32, 64, 128),\n", - " '--first-layer-neurons': choice(16, 64, 128, 256, 512),\n", - " '--second-layer-neurons': choice(16, 64, 256, 512),\n", - " '--learning-rate': loguniform(-6, -1)\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will create a new ScriptRunConfig without the above parameters since they will be passed in later. Note we still need to keep the `data-folder` parameter since that's not a hyperparamter we will sweep." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "args = ['--data-folder', dataset.as_named_input('mnist').as_mount()]\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='tf_mnist.py',\n", - " arguments=args,\n", - " compute_target=compute_target,\n", - " environment=tf_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will define an early termnination policy. The `BanditPolicy` basically states to check the job every 2 iterations. If the primary metric (defined later) falls outside of the top 10% range, Azure ML terminate the job. This saves us from continuing to explore hyperparameters that don't show promise of helping reach our target metric." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are ready to configure a run configuration object, and specify the primary metric `validation_acc` that's recorded in your training runs. If you go back to visit the training script, you will notice that this value is being logged after every epoch (a full batch set). We also want to tell the service that we are looking to maximizing this value. We also set the number of samples to 20, and maximal concurrent job to 4, which is the same as the number of nodes in our computer cluster." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "htc = HyperDriveConfig(run_config=src, \n", - " hyperparameter_sampling=ps, \n", - " policy=policy, \n", - " primary_metric_name='validation_acc', \n", - " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n", - " max_total_runs=8,\n", - " max_concurrent_runs=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's launch the hyperparameter tuning job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "htr = exp.submit(config=htc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use a run history widget to show the progress. Be patient as this might take a while to complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(htr).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "htr.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert(htr.get_status() == \"Completed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Warm start a Hyperparameter Tuning experiment and resuming child runs\n", - "Often times, finding the best hyperparameter values for your model can be an iterative process, needing multiple tuning runs that learn from previous hyperparameter tuning runs. Reusing knowledge from these previous runs will accelerate the hyperparameter tuning process, thereby reducing the cost of tuning the model and will potentially improve the primary metric of the resulting model. When warm starting a hyperparameter tuning experiment with Bayesian sampling, trials from the previous run will be used as prior knowledge to intelligently pick new samples, so as to improve the primary metric. Additionally, when using Random or Grid sampling, any early termination decisions will leverage metrics from the previous runs to determine poorly performing training runs. \n", - "\n", - "Azure Machine Learning allows you to warm start your hyperparameter tuning run by leveraging knowledge from up to 5 previously completed hyperparameter tuning parent runs. \n", - "\n", - "Additionally, there might be occasions when individual training runs of a hyperparameter tuning experiment are cancelled due to budget constraints or fail due to other reasons. It is now possible to resume such individual training runs from the last checkpoint (assuming your training script handles checkpoints). Resuming an individual training run will use the same hyperparameter configuration and mount the storage used for that run. The training script should accept the \"--resume-from\" argument, which contains the checkpoint or model files from which to resume the training run. You can also resume individual runs as part of an experiment that spends additional budget on hyperparameter tuning. Any additional budget, after resuming the specified training runs is used for exploring additional configurations.\n", - "\n", - "For more information on warm starting and resuming hyperparameter tuning runs, please refer to the [Hyperparameter Tuning for Azure Machine Learning documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters) \n", - "\n", - "## Find and register best model \n", - "When all the jobs finish, we can find out the one that has the highest accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = htr.get_best_run_by_primary_metric()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's list the model files uploaded during the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(best_run.get_file_names())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then register the folder (and all files in it) as a model named `tf-dnn-mnist` under the workspace for deployment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = best_run.register_model(model_name='tf-dnn-mnist', model_path='outputs/model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy the model in ACI\n", - "Now we are ready to deploy the model as a web service running in Azure Container Instance [ACI](https://azure.microsoft.com/en-us/services/container-instances/). Azure Machine Learning accomplishes this by constructing a Docker image with the scoring logic and model baked in.\n", - "### Create score.py\n", - "First, we will create a scoring script that will be invoked by the web service call. \n", - "\n", - "* Note that the scoring script must have two required functions, `init()` and `run(input_data)`. \n", - " * In `init()` function, you typically load the model into a global object. This function is executed only once when the Docker container is started. \n", - " * In `run(input_data)` function, the model is used to predict a value based on the input data. The input and output to `run` typically use JSON as serialization and de-serialization format but you are not limited to that." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import json\n", - "import numpy as np\n", - "import os\n", - "import tensorflow as tf\n", - "\n", - "from azureml.core.model import Model\n", - "\n", - "def init():\n", - " global tf_model\n", - " model_root = os.getenv('AZUREML_MODEL_DIR')\n", - " # the name of the folder in which to look for tensorflow model files\n", - " tf_model_folder = 'model'\n", - " \n", - " tf_model = tf.saved_model.load(os.path.join(model_root, tf_model_folder))\n", - "\n", - "def run(raw_data):\n", - " data = np.array(json.loads(raw_data)['data'], dtype=np.float32)\n", - " \n", - " # make prediction\n", - " out = tf_model(data)\n", - " y_hat = np.argmax(out, axis=1)\n", - "\n", - " return y_hat.tolist()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create myenv.yml\n", - "We also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify packages `numpy`, `tensorflow`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import CondaDependencies\n", - "\n", - "cd = CondaDependencies.create()\n", - "cd.add_conda_package('numpy')\n", - "cd.add_pip_package('tensorflow==2.0.0')\n", - "cd.add_pip_package(\"azureml-defaults\")\n", - "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n", - "\n", - "print(cd.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy to ACI\n", - "We are almost ready to deploy. Create the inference configuration and deployment configuration and deploy to ACI. This cell will run for about 7-8 minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.model import Model\n", - "\n", - "\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores=2, \n", - " memory_gb=2, \n", - " tags={'name':'mnist', 'framework': 'TensorFlow DNN'},\n", - " description='Tensorflow DNN on MNIST')\n", - "\n", - "service = Model.deploy(workspace=ws, \n", - " name='tf-mnist-svc', \n", - " models=[model], \n", - " inference_config=inference_config, \n", - " deployment_config=aciconfig)\n", - "\n", - "service.wait_for_deployment(True)\n", - "print(service.state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(service.get_logs())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is the scoring web service endpoint:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the deployed model\n", - "Let's test the deployed model. Pick 30 random samples from the test set, and send it to the web service hosted in ACI. Note here we are using the `run` API in the SDK to invoke the service. You can also make raw HTTP calls using any HTTP tool such as curl.\n", - "\n", - "After the invocation, we print the returned predictions and plot them along with the input images. Use red font color and inversed image (white on black) to highlight the misclassified samples. Note since the model accuracy is pretty high, you might have to run the below cell a few times before you can see a misclassified sample." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "# find 30 random samples from test set\n", - "n = 30\n", - "sample_indices = np.random.permutation(X_test.shape[0])[0:n]\n", - "\n", - "test_samples = json.dumps({\"data\": X_test[sample_indices].tolist()})\n", - "test_samples = bytes(test_samples, encoding='utf8')\n", - "\n", - "# predict using the deployed model\n", - "result = service.run(input_data=test_samples)\n", - "\n", - "# compare actual value vs. the predicted values:\n", - "i = 0\n", - "plt.figure(figsize = (20, 1))\n", - "\n", - "for s in sample_indices:\n", - " plt.subplot(1, n, i + 1)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " \n", - " # use different color for misclassified sample\n", - " font_color = 'red' if y_test[s] != result[i] else 'black'\n", - " clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n", - " \n", - " plt.text(x=10, y=-10, s=result[i], fontsize=18, color=font_color)\n", - " plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)\n", - " \n", - " i = i + 1\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also send raw HTTP request to the service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "\n", - "# send a random row from the test set to score\n", - "random_index = np.random.randint(0, len(X_test)-1)\n", - "input_data = \"{\\\"data\\\": [\" + str(list(X_test[random_index])) + \"]}\"\n", - "\n", - "headers = {'Content-Type':'application/json'}\n", - "\n", - "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n", - "\n", - "print(\"POST to url\", service.scoring_uri)\n", - "#print(\"input data:\", input_data)\n", - "print(\"label:\", y_test[random_index])\n", - "print(\"prediction:\", resp.text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the workspace after the web service was deployed. You should see \n", - "* a registered model named 'model' and with the id 'model:1'\n", - "* a webservice called 'tf-mnist' with some scoring URL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = ws.models['tf-dnn-mnist']\n", - "print(\"Model: {}, ID: {}\".format('tf-dnn-mnist', model.id))\n", - " \n", - "webservice = ws.webservices['tf-mnist-svc']\n", - "print(\"Webservice: {}, scoring URI: {}\".format('tf-mnist-svc', webservice.scoring_uri))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up\n", - "You can delete the ACI deployment with a simple delete API call." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "nagaur" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "TensorFlow" - ], - "friendly_name": "Training and hyperparameter tuning using the TensorFlow estimator", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "tags": [ - "None" - ], - "task": "Train a deep neural network" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.yml b/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.yml deleted file mode 100644 index 76b7eabcf..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.yml +++ /dev/null @@ -1,12 +0,0 @@ -name: train-hyperparameter-tune-deploy-with-tensorflow -dependencies: -- numpy -- matplotlib -- pip: - - azureml-sdk - - azureml-widgets - - pandas - - keras - - tensorflow==2.0.0 - - matplotlib - - fuse diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/utils.py b/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/utils.py deleted file mode 100644 index 98170adae..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/utils.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import gzip -import numpy as np -import struct - - -# load compressed MNIST gz files and return numpy arrays -def load_data(filename, label=False): - with gzip.open(filename) as gz: - struct.unpack('I', gz.read(4)) - n_items = struct.unpack('>I', gz.read(4)) - if not label: - n_rows = struct.unpack('>I', gz.read(4))[0] - n_cols = struct.unpack('>I', gz.read(4))[0] - res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8) - res = res.reshape(n_items[0], n_rows * n_cols) - else: - res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8) - res = res.reshape(n_items[0], 1) - return res - - -# one-hot encode a 1-D array -def one_hot_encode(array, num_of_classes): - return np.eye(num_of_classes)[array.reshape(-1)] diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/tf_mnist_with_checkpoint.py b/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/tf_mnist_with_checkpoint.py deleted file mode 100644 index 598c4b08b..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/tf_mnist_with_checkpoint.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import numpy as np -import argparse -import os -import re -import tensorflow as tf -import glob - -from azureml.core import Run -from utils import load_data - -print("TensorFlow version:", tf.__version__) - -parser = argparse.ArgumentParser() -parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point') - -parser.add_argument('--resume-from', type=str, default=None, - help='location of the model or checkpoint files from where to resume the training') -args = parser.parse_args() - - -previous_model_location = args.resume_from -# You can also use environment variable to get the model/checkpoint files location -# previous_model_location = os.path.expandvars(os.getenv("AZUREML_DATAREFERENCE_MODEL_LOCATION", None)) - -data_folder = args.data_folder -print('Data folder:', data_folder) - -# load train and test set into numpy arrays -# note we scale the pixel intensity values to 0-1 (by dividing it with 255.0) so the model can converge faster. - -X_train = load_data(glob.glob(os.path.join(data_folder, '**/train-images-idx3-ubyte.gz'), - recursive=True)[0], False) / 255.0 -X_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-images-idx3-ubyte.gz'), - recursive=True)[0], False) / 255.0 -y_train = load_data(glob.glob(os.path.join(data_folder, '**/train-labels-idx1-ubyte.gz'), - recursive=True)[0], True).reshape(-1) -y_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-labels-idx1-ubyte.gz'), - recursive=True)[0], True).reshape(-1) - -print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n') - -training_set_size = X_train.shape[0] - -n_inputs = 28 * 28 -n_h1 = 100 -n_h2 = 100 -n_outputs = 10 -learning_rate = 0.01 -n_epochs = 20 -batch_size = 50 - -with tf.name_scope('network'): - # construct the DNN - X = tf.placeholder(tf.float32, shape=(None, n_inputs), name='X') - y = tf.placeholder(tf.int64, shape=(None), name='y') - h1 = tf.layers.dense(X, n_h1, activation=tf.nn.relu, name='h1') - h2 = tf.layers.dense(h1, n_h2, activation=tf.nn.relu, name='h2') - output = tf.layers.dense(h2, n_outputs, name='output') - -with tf.name_scope('train'): - cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=output) - loss = tf.reduce_mean(cross_entropy, name='loss') - optimizer = tf.train.GradientDescentOptimizer(learning_rate) - train_op = optimizer.minimize(loss) - -with tf.name_scope('eval'): - correct = tf.nn.in_top_k(output, y, 1) - acc_op = tf.reduce_mean(tf.cast(correct, tf.float32)) - -init = tf.global_variables_initializer() -saver = tf.train.Saver() - -# start an Azure ML run -run = Run.get_context() - -with tf.Session() as sess: - start_epoch = 0 - if previous_model_location: - checkpoint_file_path = tf.train.latest_checkpoint(previous_model_location) - saver.restore(sess, checkpoint_file_path) - checkpoint_filename = os.path.basename(checkpoint_file_path) - num_found = re.search(r'\d+', checkpoint_filename) - if num_found: - start_epoch = int(num_found.group(0)) - print("Resuming from epoch {}".format(str(start_epoch))) - else: - init.run() - - for epoch in range(start_epoch, n_epochs): - - # randomly shuffle training set - indices = np.random.permutation(training_set_size) - X_train = X_train[indices] - y_train = y_train[indices] - - # batch index - b_start = 0 - b_end = b_start + batch_size - for _ in range(training_set_size // batch_size): - # get a batch - X_batch, y_batch = X_train[b_start: b_end], y_train[b_start: b_end] - - # update batch index for the next batch - b_start = b_start + batch_size - b_end = min(b_start + batch_size, training_set_size) - - # train - sess.run(train_op, feed_dict={X: X_batch, y: y_batch}) - # evaluate training set - acc_train = acc_op.eval(feed_dict={X: X_batch, y: y_batch}) - # evaluate validation set - acc_val = acc_op.eval(feed_dict={X: X_test, y: y_test}) - - # log accuracies - run.log('training_acc', np.float(acc_train)) - run.log('validation_acc', np.float(acc_val)) - print(epoch, '-- Training accuracy:', acc_train, '\b Validation accuracy:', acc_val) - y_hat = np.argmax(output.eval(feed_dict={X: X_test}), axis=1) - - if epoch % 5 == 0: - saver.save(sess, './outputs/', global_step=epoch) - - # saving only half of the model and resuming again from same epoch - if not previous_model_location and epoch == 10: - break - - run.log('final_acc', np.float(acc_val)) diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb deleted file mode 100644 index c6d097bae..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb +++ /dev/null @@ -1,504 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Resuming Tensorflow training from previous run\n", - "In this tutorial, you will resume a mnist model in TensorFlow from a previously submitted run." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n", - "* Go through the [configuration notebook](../../../../configuration.ipynb) to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (`config.json`)\n", - "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"gpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target.')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - " compute_target.wait_for_completion(show_output=True)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a Dataset for Files\n", - "A Dataset can reference single or multiple files in your datastores or public urls. The files can be of any format. Dataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#initialize file dataset \n", - "from azureml.core.dataset import Dataset\n", - "web_paths = ['http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',\n", - " 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',\n", - " 'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',\n", - " 'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'\n", - " ]\n", - "dataset = Dataset.File.from_files(path = web_paths)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "you may want to register datasets using the register() method to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script.\n", - "You can try get the dataset first to see if it's already registered." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_registered = False\n", - "try:\n", - " temp = Dataset.get_by_name(workspace = ws, name = 'mnist-dataset')\n", - " dataset_registered = True\n", - "except:\n", - " print(\"The dataset mnist-dataset is not registered in workspace yet.\")\n", - "\n", - "if not dataset_registered:\n", - " #register dataset to workspace\n", - " dataset = dataset.register(workspace = ws,\n", - " name = 'mnist-dataset',\n", - " description='training and test dataset',\n", - " create_new_version=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# list the files referenced by dataset\n", - "dataset.to_path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model on the remote compute" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a project directory\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "script_folder = './tf-resume-training'\n", - "os.makedirs(script_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copy the training script `tf_mnist_with_checkpoint.py` into this project directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "# the training logic is in the tf_mnist_with_checkpoint.py file.\n", - "shutil.copy('./tf_mnist_with_checkpoint.py', script_folder)\n", - "\n", - "# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.\n", - "shutil.copy('./utils.py', script_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment\n", - "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = 'tf-resume-training'\n", - "experiment = Experiment(ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the TensorFlow 1.13 GPU curated environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "tf_env = Environment.get(ws, name='AzureML-TensorFlow-1.13-GPU')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "args = ['--data-folder', dataset.as_mount()]\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='tf_mnist_with_checkpoint.py',\n", - " arguments=args,\n", - " compute_target=compute_target,\n", - " environment=tf_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job\n", - "Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = experiment.submit(src)\n", - "print(run)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor your run\n", - "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can block until the script has completed training before running more code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Now let's resume training from the above run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we will get the DataPath to the outputs directory of the above run which\n", - "contains the checkpoint files. We will create a DataReference from this DataPath and specify the compute binding as mount mode; this will tell Azure ML to mount the checkpoint files on the compute target for the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.data.datapath import DataPathComputeBinding\n", - "\n", - "checkpoint_path = run._get_outputs_datapath()\n", - "checkpoint_data_ref = checkpoint_path.create_data_reference(datapath_compute_binding=DataPathComputeBinding(mode=\"mount\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we will create a new ScriptRunConfig and append the additional `'--resume-from'` argument with the corresponding checkpoint location to the `arguments` parameter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "args = ['--data-folder', dataset.as_mount(),\n", - " '--resume-from', str(checkpoint_data_ref)]\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='tf_mnist_with_checkpoint.py',\n", - " arguments=args,\n", - " compute_target=compute_target,\n", - " environment=tf_env)\n", - "\n", - "src.run_config.data_references = {checkpoint_data_ref.data_reference_name : checkpoint_data_ref.to_config()}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now you can submit the experiment and it should resume from previous run's checkpoint files." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "resumed_run = experiment.submit(src)\n", - "print(resumed_run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "resumed_run.wait_for_completion(show_output=True)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "hesuri" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "TensorFlow" - ], - "friendly_name": "Resuming a model", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "msauthor": "hesuri", - "tags": [ - "None" - ], - "task": "Resume a model in TensorFlow from a previously submitted run" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/utils.py b/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/utils.py deleted file mode 100644 index 98170adae..000000000 --- a/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/utils.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import gzip -import numpy as np -import struct - - -# load compressed MNIST gz files and return numpy arrays -def load_data(filename, label=False): - with gzip.open(filename) as gz: - struct.unpack('I', gz.read(4)) - n_items = struct.unpack('>I', gz.read(4)) - if not label: - n_rows = struct.unpack('>I', gz.read(4))[0] - n_cols = struct.unpack('>I', gz.read(4))[0] - res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8) - res = res.reshape(n_items[0], n_rows * n_cols) - else: - res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8) - res = res.reshape(n_items[0], 1) - return res - - -# one-hot encode a 1-D array -def one_hot_encode(array, num_of_classes): - return np.eye(num_of_classes)[array.reshape(-1)] diff --git a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/scripts/train.py b/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/scripts/train.py deleted file mode 100644 index 688fc4bfc..000000000 --- a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/scripts/train.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import mlflow -import mlflow.keras -import numpy as np -import warnings - -from tensorflow import keras -from tensorflow.keras.models import Sequential -from tensorflow.keras.layers import Dense -from tensorflow.keras.optimizers import RMSprop - -print("Keras version:", keras.__version__) - -# Enable auto-logging to MLflow to capture Keras metrics. -mlflow.autolog() - -# Model / data parameters -n_inputs = 28 * 28 -n_h1 = 300 -n_h2 = 100 -n_outputs = 10 -learning_rate = 0.001 - -# the data, split between train and test sets -(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() - -# Scale images to the [0, 1] range -x_train = x_train.astype("float32") / 255 -x_test = x_test.astype("float32") / 255 - -# Flatten image to be (n, 28 * 28) -x_train = x_train.reshape(len(x_train), -1) -x_test = x_test.reshape(len(x_test), -1) - -print("x_train shape:", x_train.shape) -print(x_train.shape[0], "train samples") -print(x_test.shape[0], "test samples") - -# convert class vectors to binary class matrices -y_train = keras.utils.to_categorical(y_train, n_outputs) -y_test = keras.utils.to_categorical(y_test, n_outputs) - - -def driver(): - warnings.filterwarnings("ignore") - - with mlflow.start_run() as run: - - # Build a simple MLP model - model = Sequential() - # first hidden layer - model.add(Dense(n_h1, activation='relu', input_shape=(n_inputs,))) - # second hidden layer - model.add(Dense(n_h2, activation='relu')) - # output layer - model.add(Dense(n_outputs, activation='softmax')) - model.summary() - - batch_size = 128 - epochs = 5 - - model.compile(loss='categorical_crossentropy', - optimizer=RMSprop(lr=learning_rate), - metrics=['accuracy']) - - model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1) - - score = model.evaluate(x_test, y_test, verbose=0) - print('Test loss:', score[0]) - print('Test accuracy:', score[1]) - - return run - - -if __name__ == "__main__": - driver() diff --git a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.ipynb b/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.ipynb deleted file mode 100644 index 7d75d8fd1..000000000 --- a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.ipynb +++ /dev/null @@ -1,387 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier\n", - "\n", - "This example shows you how to use MLflow together with Azure Machine Learning services for tracking the metrics and artifacts while training a Keras model to classify MNIST digit images and deploy the model as a web service. You'll learn how to:\n", - "\n", - " 1. Set up MLflow tracking URI so as to use Azure ML\n", - " 2. Create experiment\n", - " 3. Instrument your model with MLflow tracking\n", - " 4. Train a Keras model locally with MLflow auto logging\n", - " 5. Train a model on GPU compute on Azure with MLflow auto logging\n", - " 6. View your experiment within your Azure ML Workspace in Azure Portal\n", - " 7. Deploy the model as a web service on Azure Container Instance\n", - " 8. Call the model to make predictions\n", - " \n", - "### Pre-requisites\n", - " \n", - "If you are using a Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n", - "\n", - "Install TensorFlow and Keras, this notebook has been tested with TensorFlow version 2.1.0 and Keras version 2.3.1.\n", - "\n", - "Also, install azureml-mlflow package using ```pip install azureml-mlflow```. Note that azureml-mlflow installs mlflow package itself as a dependency if you haven't done so previously.\n", - "\n", - "### Set-up\n", - "\n", - "Import packages and check versions of Azure ML SDK and MLflow installed on your computer. Then connect to your Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys, os\n", - "import mlflow\n", - "import mlflow.azureml\n", - "\n", - "import azureml.core\n", - "from azureml.core import Workspace\n", - "\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)\n", - "print(\"MLflow version:\", mlflow.version.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "ws.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set tracking URI\n", - "\n", - "Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Experiment\n", - "\n", - "In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_name = \"keras-with-mlflow\"\n", - "mlflow.set_experiment(experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train model locally while logging metrics and artifacts\n", - "\n", - "The ```scripts/train.py``` program contains the code to load the image dataset, train and test the model. Within this program, the train.driver function wraps the end-to-end workflow.\n", - "\n", - "Within the driver, the ```mlflow.start_run``` starts MLflow tracking. Then, MLflow's automatic logging is used to log metrics, parameters and model for the Keras run.\n", - "\n", - "Let's add the program to search path, import it as a module and invoke the driver function. Note that the training can take few minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lib_path = os.path.abspath(\"scripts\")\n", - "sys.path.append(lib_path)\n", - "\n", - "import train" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = train.driver()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train model on GPU compute on Azure\n", - "\n", - "Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n", - "\n", - "Clone an environment object from the Tensorflow 2.1 Azure ML curated environment. Azure ML curated environments are pre-configured environments to simplify ML setup, reference [this doc](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments#use-a-curated-environment) for more information. To enable MLflow tracking, add ```azureml-mlflow``` as pip package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "env = Environment.get(workspace=ws, name=\"AzureML-TensorFlow-2.1-GPU\").clone(\"mlflow-env\")\n", - "\n", - "env.python.conda_dependencies.add_pip_package(\"azureml-mlflow\")\n", - "env.python.conda_dependencies.add_pip_package(\"keras==2.3.1\")\n", - "env.python.conda_dependencies.add_pip_package(\"numpy\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a ScriptRunConfig to specify the training configuration: script, compute as well as environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=\"./scripts\", script=\"train.py\")\n", - "src.run_config.environment = env\n", - "src.run_config.target = \"gpu-cluster\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get a reference to the experiment you created previously, but this time, as an Azure Machine Learning experiment object.\n", - "\n", - "Then, use the ```Experiment.submit``` method to start the remote training run. Note that the first training run often takes longer as Azure Machine Learning service builds the Docker image for executing the script. Subsequent runs will be faster as the cached image is used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "exp = Experiment(ws, experiment_name)\n", - "run = exp.submit(src)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can monitor the run and its metrics on Azure Portal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also, you can wait for run to complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy model as web service\n", - "\n", - "The ```client.create_deployment``` function registers the logged Keras+Tensorflow model and deploys the model in a framework-aware manner. It automatically creates the Tensorflow-specific inferencing wrapper code and specifies package dependencies for you. See [this doc](https://mlflow.org/docs/latest/models.html#id34) for more information on deploying models on Azure ML using MLflow.\n", - "\n", - "In this example, we deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n", - "\n", - "[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n", - "\n", - "Note that the service deployment can take several minutes." - ] - }, - { - "source": [ - "First define your deployment target and customize parameters in the deployment config. Refer to [this documentation](https://docs.microsoft.com/azure/machine-learning/reference-azure-machine-learning-cli#azure-container-instance-deployment-configuration-schema) for more information. " - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - " \n", - "# Data to be written\n", - "deploy_config ={\n", - " \"computeType\": \"aci\"\n", - "}\n", - "# Serializing json \n", - "json_object = json.dumps(deploy_config)\n", - " \n", - "# Writing to sample.json\n", - "with open(\"deployment_config.json\", \"w\") as outfile:\n", - " outfile.write(json_object)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mlflow.deployments import get_deploy_client\n", - "\n", - "# set the tracking uri as the deployment client\n", - "client = get_deploy_client(mlflow.get_tracking_uri())\n", - "\n", - "# set the model path \n", - "model_path = \"model\"\n", - "\n", - "# set the deployment config\n", - "deployment_config_path = \"deployment_config.json\"\n", - "test_config = {'deploy-config-file': deployment_config_path}\n", - "\n", - "# define the model path and the name is the service name\n", - "# the model gets registered automatically and a name is autogenerated using the \"name\" parameter below \n", - "client.create_deployment(model_uri='runs:/{}/{}'.format(run.id, model_path),\n", - " config=test_config,\n", - " name=\"keras-aci-deployment\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once the deployment has completed you can check the scoring URI of the web service in AzureML studio UI in the endpoints tab. Refer [mlflow predict](https://mlflow.org/docs/latest/python_api/mlflow.deployments.html#mlflow.deployments.BaseDeploymentClient.predict) on how to test your deployment. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up\n", - "You can delete the ACI deployment with a delete API call." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.delete(\"keras-aci-deployment\")" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "hancwang" - } - ], - "category": "tutorial", - "celltoolbar": "Edit Metadata", - "compute": [ - "Local", - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "Keras" - ], - "friendly_name": "Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "tags": [ - "mlflow", - "keras" - ], - "task": "Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier, leveraging MLflow auto logging" - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/scripts/train.py b/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/scripts/train.py deleted file mode 100644 index 956cd0335..000000000 --- a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/scripts/train.py +++ /dev/null @@ -1,160 +0,0 @@ -# Copyright (c) 2017, PyTorch Team -# All rights reserved -# Licensed under BSD 3-Clause License. - -# This example is based on PyTorch MNIST example: -# https://github.com/pytorch/examples/blob/master/mnist/main.py - -import mlflow -import mlflow.pytorch -from mlflow.utils.environment import _mlflow_conda_env -import warnings -import cloudpickle -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -import torchvision -from torchvision import datasets, transforms - - -class Net(nn.Module): - def __init__(self): - super(Net, self).__init__() - self.conv1 = nn.Conv2d(1, 20, 5, 1) - self.conv2 = nn.Conv2d(20, 50, 5, 1) - self.fc1 = nn.Linear(4 * 4 * 50, 500) - self.fc2 = nn.Linear(500, 10) - - def forward(self, x): - # Added the view for reshaping score requests - x = x.view(-1, 1, 28, 28) - x = F.relu(self.conv1(x)) - x = F.max_pool2d(x, 2, 2) - x = F.relu(self.conv2(x)) - x = F.max_pool2d(x, 2, 2) - x = x.view(-1, 4 * 4 * 50) - x = F.relu(self.fc1(x)) - x = self.fc2(x) - return F.log_softmax(x, dim=1) - - -def train(args, model, device, train_loader, optimizer, epoch): - model.train() - for batch_idx, (data, target) in enumerate(train_loader): - data, target = data.to(device), target.to(device) - optimizer.zero_grad() - output = model(data) - loss = F.nll_loss(output, target) - loss.backward() - optimizer.step() - if batch_idx % args.log_interval == 0: - print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( - epoch, batch_idx * len(data), len(train_loader.dataset), - 100. * batch_idx / len(train_loader), loss.item())) - # Use MLflow logging - mlflow.log_metric("epoch_loss", loss.item()) - - -def test(args, model, device, test_loader): - model.eval() - test_loss = 0 - correct = 0 - with torch.no_grad(): - for data, target in test_loader: - data, target = data.to(device), target.to(device) - output = model(data) - # sum up batch loss - test_loss += F.nll_loss(output, target, reduction="sum").item() - # get the index of the max log-probability - pred = output.argmax(dim=1, keepdim=True) - correct += pred.eq(target.view_as(pred)).sum().item() - - test_loss /= len(test_loader.dataset) - print("\n") - print("Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( - test_loss, correct, len(test_loader.dataset), - 100. * correct / len(test_loader.dataset))) - # Use MLflow logging - mlflow.log_metric("average_loss", test_loss) - - -class Args(object): - pass - - -# Training settings -args = Args() -setattr(args, 'batch_size', 64) -setattr(args, 'test_batch_size', 1000) -setattr(args, 'epochs', 3) # Higher number for better convergence -setattr(args, 'lr', 0.01) -setattr(args, 'momentum', 0.5) -setattr(args, 'no_cuda', True) -setattr(args, 'seed', 1) -setattr(args, 'log_interval', 10) -setattr(args, 'save_model', True) - -use_cuda = not args.no_cuda and torch.cuda.is_available() - -torch.manual_seed(args.seed) - -device = torch.device("cuda" if use_cuda else "cpu") - -kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {} -# Use Azure Open Datasets for MNIST dataset -datasets.MNIST.mirrors = [ - "https://azureopendatastorage.azurefd.net/mnist/" -] -datasets.MNIST.resources = [ - ("train-images-idx3-ubyte.gz", "f68b3c2dcbeaaa9fbdd348bbdeb94873"), - ("train-labels-idx1-ubyte.gz", "d53e105ee54ea40749a09fcbcd1e9432"), - ("t10k-images-idx3-ubyte.gz", "9fb629c4189551a2d022fa330f9573f3"), - ("t10k-labels-idx1-ubyte.gz", "ec29112dd5afa0611ce80d1b7f02629c") -] -train_loader = torch.utils.data.DataLoader( - datasets.MNIST('../data', train=True, download=True, - transform=transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.1307,), (0.3081,)) - ])), - batch_size=args.batch_size, shuffle=True, **kwargs) -test_loader = torch.utils.data.DataLoader( - datasets.MNIST( - '../data', - train=False, - transform=transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.1307,), (0.3081,))])), - batch_size=args.test_batch_size, shuffle=True, **kwargs) - - -def driver(): - warnings.filterwarnings("ignore") - # Dependencies for deploying the model - pytorch_index = "https://download.pytorch.org/whl/" - pytorch_version = "cpu/torch-1.1.0-cp36-cp36m-linux_x86_64.whl" - deps = [ - "cloudpickle=={}".format(cloudpickle.__version__), - pytorch_index + pytorch_version, - "torchvision=={}".format(torchvision.__version__), - "Pillow=={}".format("6.0.0") - ] - with mlflow.start_run() as run: - model = Net().to(device) - optimizer = optim.SGD( - model.parameters(), - lr=args.lr, - momentum=args.momentum) - for epoch in range(1, args.epochs + 1): - train(args, model, device, train_loader, optimizer, epoch) - test(args, model, device, test_loader) - # Log model to run history using MLflow - if args.save_model: - model_env = _mlflow_conda_env(additional_pip_deps=deps) - mlflow.pytorch.log_model(model, "model", conda_env=model_env) - return run - - -if __name__ == "__main__": - driver() diff --git a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb b/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb deleted file mode 100644 index 4d91702c2..000000000 --- a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb +++ /dev/null @@ -1,380 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier\n", - "\n", - "This example shows you how to use MLflow together with Azure Machine Learning services for tracking the metrics and artifacts while training a PyTorch model to classify MNIST digit images and deploy the model as a web service. You'll learn how to:\n", - "\n", - " 1. Set up MLflow tracking URI so as to use Azure ML\n", - " 2. Create experiment\n", - " 3. Instrument your model with MLflow tracking\n", - " 4. Train a PyTorch model locally\n", - " 5. Train a model on GPU compute on Azure\n", - " 6. View your experiment within your Azure ML Workspace in Azure Portal\n", - " 7. Deploy the model as a web service on Azure Container Instance\n", - " 8. Call the model to make predictions\n", - " \n", - "## Pre-requisites\n", - " \n", - "If you are using a Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n", - "\n", - "Install PyTorch, this notebook has been tested with torch==1.4\n", - "\n", - "Also, install azureml-mlflow package using ```pip install azureml-mlflow```. Note that azureml-mlflow installs mlflow package itself as a dependency if you haven't done so previously.\n", - "\n", - "## Set-up\n", - "\n", - "Import packages and check versions of Azure ML SDK and MLflow installed on your computer. Then connect to your Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys, os\n", - "import mlflow\n", - "import mlflow.azureml\n", - "\n", - "import azureml.core\n", - "from azureml.core import Workspace\n", - "\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)\n", - "print(\"MLflow version:\", mlflow.version.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "ws.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set tracking URI\n", - "\n", - "Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Experiment\n", - "\n", - "In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_name = \"pytorch-with-mlflow\"\n", - "mlflow.set_experiment(experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train model locally while logging metrics and artifacts\n", - "\n", - "The ```scripts/train.py``` program contains the code to load the image dataset, train and test the model. Within this program, the train.driver function wraps the end-to-end workflow.\n", - "\n", - "Within the driver, the ```mlflow.start_run``` starts MLflow tracking. Then, ```mlflow.log_metric``` functions are used to track the convergence of the neural network training iterations. Finally ```mlflow.pytorch.save_model``` is used to save the trained model in framework-aware manner.\n", - "\n", - "Let's add the program to search path, import it as a module and invoke the driver function. Note that the training can take few minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lib_path = os.path.abspath(\"scripts\")\n", - "sys.path.append(lib_path)\n", - "\n", - "import train" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = train.driver()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train model on GPU compute on Azure\n", - "\n", - "Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n", - "\n", - "Clone an environment object from the PyTorch 1.4 Azure ML curated environment. Azure ML curated environments are pre-configured environments to simplify ML setup, reference [this doc](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments#use-a-curated-environment) for more information. To enable MLflow tracking, add ```azureml-mlflow``` as pip package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "env = Environment.get(workspace=ws, name=\"AzureML-PyTorch-1.4-GPU\").clone(\"mlflow-env\")\n", - "\n", - "env.python.conda_dependencies.add_pip_package(\"azureml-mlflow\")\n", - "env.python.conda_dependencies.add_pip_package(\"Pillow==6.0.0\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a ScriptRunConfig to specify the training configuration: script, compute as well as environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=\"./scripts\", script=\"train.py\")\n", - "src.run_config.environment = env\n", - "src.run_config.target = \"gpu-cluster\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get a reference to the experiment you created previously, but this time, as an Azure Machine Learning experiment object.\n", - "\n", - "Then, use the ```Experiment.submit``` method to start the remote training run. Note that the first training run often takes longer as Azure Machine Learning service builds the Docker image for executing the script. Subsequent runs will be faster as the cached image is used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "exp = Experiment(ws, experiment_name)\n", - "run = exp.submit(src)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can monitor the run and its metrics on Azure Portal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also, you can wait for run to complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy model as web service\n", - "\n", - "The ```client.create_deployment``` function registers the logged PyTorch model and deploys the model in a framework-aware manner. It automatically creates the PyTorch-specific inferencing wrapper code and specifies package dependencies for you. See [this doc](https://mlflow.org/docs/latest/models.html#id34) for more information on deploying models on Azure ML using MLflow.\n", - "\n", - "In this example, we deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n", - "\n", - "[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n", - "\n", - "Note that the service deployment can take several minutes." - ] - }, - { - "source": [ - "First define your deployment target and customize parameters in the deployment config. Refer to [this documentation](https://docs.microsoft.com/azure/machine-learning/reference-azure-machine-learning-cli#azure-container-instance-deployment-configuration-schema) for more information. " - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - " \n", - "# Data to be written\n", - "deploy_config ={\n", - " \"computeType\": \"aci\"\n", - "}\n", - "# Serializing json \n", - "json_object = json.dumps(deploy_config)\n", - " \n", - "# Writing to sample.json\n", - "with open(\"deployment_config.json\", \"w\") as outfile:\n", - " outfile.write(json_object)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mlflow.deployments import get_deploy_client\n", - "\n", - "# set the tracking uri as the deployment client\n", - "client = get_deploy_client(mlflow.get_tracking_uri())\n", - "\n", - "# set the model path \n", - "model_path = \"model\"\n", - "\n", - "# set the deployment config\n", - "deployment_config_path = \"deployment_config.json\"\n", - "test_config = {'deploy-config-file': deployment_config_path}\n", - "\n", - "# define the model path and the name is the service name\n", - "# the model gets registered automatically and a name is autogenerated using the \"name\" parameter below \n", - "client.create_deployment(model_uri='runs:/{}/{}'.format(run.id, model_path),\n", - " config=test_config,\n", - " name=\"keras-aci-deployment\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once the deployment has completed you can check the scoring URI of the web service in AzureML studio UI in the endpoints tab. Refer [mlflow predict](https://mlflow.org/docs/latest/python_api/mlflow.deployments.html#mlflow.deployments.BaseDeploymentClient.predict) on how to test your deployment. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.delete(\"keras-aci-deployment\")" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "shipatel" - } - ], - "category": "tutorial", - "celltoolbar": "Edit Metadata", - "compute": [ - "Local", - "AML Compute" - ], - "datasets": [ - "MNIST" - ], - "deployment": [ - "Azure Container Instance" - ], - "exclude_from_index": false, - "framework": [ - "PyTorch" - ], - "friendly_name": "Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "name": "mlflow-sparksummit-pytorch", - "notebookId": 2495374963457641, - "tags": [ - "mlflow", - "pytorch" - ], - "task": "Use MLflow with Azure Machine Learning to train and deploy PyTorch image classifier model" - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/reinforcement-learning/README.md b/how-to-use-azureml/reinforcement-learning/README.md deleted file mode 100644 index d7965894b..000000000 --- a/how-to-use-azureml/reinforcement-learning/README.md +++ /dev/null @@ -1,130 +0,0 @@ - -# Important Note -Azure Machine Learning reinforcement learning via the `azureml.contrib.train.rl` package that is used on this page will no longer be supported after June 2022. We recommend customers use Ray-on-AML library to facilitate execution of reinforcement learning experiments on Azure Machine Learning. The sample notebooks referenced in [this section](#contents) are updated accordingly to use Ray on AML library. - -# Azure Machine Learning - Reinforcement Learning (Public Preview) - - - -This is an introduction to the [Azure Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/service/) Reinforcement Learning (Public Preview) using the [Ray](https://github.com/ray-project/ray/) framework. - -## What is reinforcement learning? - -Reinforcement learning is an approach to machine learning to train agents to make a sequence of decisions. This technique has gained popularity over the last few years as breakthroughs have been made to teach reinforcement learning agents to excel at complex tasks like playing video games. There are many practical real-world use cases as well, including robotics, chemistry, online recommendations, advertising and more. - -In reinforcement learning, the goal is to train an agent *policy* that outputs actions based on the agent’s observations of its environment. Actions result in further observations and *rewards* for taking the actions. In reinforcement learning, the full reward for policy actions may take many steps to obtain. Learning a policy involves many trial-and-error runs of the agent interacting with the environment and improving its policy. - -## Reinforcement learning on Azure Machine Learning - -Reinforcement learning support in Azure Machine Learning service enables data scientists to scale training to many powerful CPU or GPU enabled VMs using [Azure Machine Learning compute clusters](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets#amlcompute) which automatically provision, manage, and scale down these VMs to help manage your costs. - -Using these samples, you will learn how to do the following. - -1. Use an Azure Machine Learning workspace, set up virtual network and create compute clusters for distributed training. -2. Train reinforcement learning agents using Ray RLlib. - -## Contents - -| File/folder | Description | -|-------------------|--------------------------------------------| -| [cartpole_ci.ipynb](cartpole-on-compute-instance/cartpole_ci.ipynb) | Notebook to train a Cartpole playing agent on an Azure Machine Learning Compute Instance | -| [cartpole_sc.ipynb](cartpole-on-single-compute/cartpole_sc.ipynb) | Notebook to train a Cartpole playing agent on an Azure Machine Learning Compute Cluster (single node) | -| [pong_rllib.ipynb](atari-on-distributed-compute/pong_rllib.ipynb) | Notebook for distributed training of Pong agent using RLlib on multiple compute targets | -| [minecraft.ipynb](minecraft-on-distributed-compute/minecraft.ipynb) | Notebook to train an agent to navigate through a lava maze in the Minecraft game | -| [particle.ipynb](multiagent-particle-envs/particle.ipynb) | Notebook to train policies in a multiagent cooperative navigation scenario based on OpenAI's Particle environments | - -## Prerequisites - -To make use of these samples, you need the following. - -* A Microsoft Azure subscription. -* A Microsoft Azure resource group. -* An Azure Machine Learning Workspace in the resource group. -* Azure Machine Learning training compute. These samples use the VM sizes `STANDARD_NC6` and `STANDARD_D2_V2`. If these are not available in your region, -you can replace them with other sizes. -* A virtual network set up in the resource group for samples that use multiple compute targets. The Cartpole and Multi-agent Particle examples do not need a virtual network. Any network security group defined on the virtual network must allow network traffic on ports used by Azure infrastructure services. Sample instructions are provided in Atari Pong and Minecraft example notebooks. - - -## Setup - -You can run these samples in the following ways. - -* On an Azure Machine Learning Compute Instance or Azure Data Science Virtual Machine (DSVM). -* On a workstation with Python and the Azure ML Python SDK installed. - -### Compute Instance or DSVM -#### Update packages - - -We recommend that you update the required Python packages before you proceed. The following commands are for entering in a Python interpreter such as a notebook. - -```shell -# We recommend updating pip to the latest version. -!pip install --upgrade pip -# Update matplotlib for plotting charts -!pip install --upgrade matplotlib -# Update Azure Machine Learning SDK to the latest version -!pip install --upgrade azureml-sdk -# For Jupyter notebook widget used in samples -!pip install --upgrade azureml-widgets -# For Tensorboard used in samples -!pip install --upgrade azureml-tensorboard -# Install Azure Machine Learning Reinforcement Learning SDK -!pip install --upgrade azureml-contrib-reinforcementlearning -``` - -### Your own workstation -#### Install/update packages - -For a local workstation, create a Python environment and install [Azure Machine Learning SDK](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/install?view=azure-ml-py) and the RL SDK. We recommend Python 3.6 and higher. - -```shell -# Activate your environment first. -# e.g., -# conda activate amlrl -# We recommend updating pip to the latest version. -pip install --upgrade pip -# Install/upgrade matplotlib for plotting charts -pip install --upgrade matplotlib -# Install/upgrade tensorboard used in samples -pip install --upgrade tensorboard -# Install/upgrade Azure ML SDK to the latest version -pip install --upgrade azureml-sdk -# For Jupyter notebook widget used in samples -pip install --upgrade azureml-widgets -# For Tensorboard used in samples -pip install --upgrade azureml-tensorboard -# Install Azure Machine Learning Reinforcement Learning SDK -pip install --upgrade azureml-contrib-reinforcementlearning -# To use the notebook widget, you may need to register and enable the Azure ML extensions first. -jupyter nbextension install --py --user azureml.widgets -jupyter nbextension enable --py --user azureml.widgets -``` - -## Contributing - -This project welcomes contributions and suggestions. Most contributions require you to agree to a -Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us -the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. - -When you submit a pull request, a CLA bot will automatically determine whether you need to provide -a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions -provided by the bot. You will only need to do this once across all repos using our CLA. - -This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). -For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or -contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. - -For more on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks). - -**Please let us know your [feedback](https://github.com/Azure/MachineLearningNotebooks/labels/Reinforcement%20Learning).** - - - -![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/reinforcement-learning/README.png) \ No newline at end of file diff --git a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/networkutils.py b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/networkutils.py deleted file mode 100644 index 64af7d8ba..000000000 --- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/networkutils.py +++ /dev/null @@ -1,237 +0,0 @@ -import sys -import csv -from azure.mgmt.network import NetworkManagementClient - - -def check_port_in_port_range(expected_port: str, - dest_port_range: str): - """ - Check if a port is within a port range - Port range maybe like *, 8080 or 8888-8889 - """ - - if dest_port_range == '*': - return True - - dest_ports = dest_port_range.split('-') - - if len(dest_ports) == 1 and \ - int(dest_ports[0]) == int(expected_port): - return True - - if len(dest_ports) == 2 and \ - int(dest_ports[0]) <= int(expected_port) and \ - int(dest_ports[1]) >= int(expected_port): - return True - - return False - - -def check_port_in_destination_port_ranges(expected_port: str, - dest_port_ranges: list): - """ - Check if a port is within a given list of port ranges - i.e. check if port 8080 is in port ranges of 22,80,8080-8090,443 - """ - - for dest_port_range in dest_port_ranges: - if check_port_in_port_range(expected_port, dest_port_range) is True: - return True - - return False - - -def check_ports_in_destination_port_ranges(expected_ports: list, - dest_port_ranges: list): - """ - Check if all ports in a given port list are within a given list - of port ranges - i.e. check if port 8080,8081 are in port ranges of 22,80,8080-8090,443 - """ - - for expected_port in expected_ports: - if check_port_in_destination_port_ranges( - expected_port, dest_port_ranges) is False: - return False - - return True - - -def check_source_address_prefix(source_address_prefix: str): - """Check if source address prefix is BatchNodeManagement or default""" - - required_prefix = 'BatchNodeManagement' - default_prefix = 'default' - - if source_address_prefix.lower() == required_prefix.lower() or \ - source_address_prefix.lower() == default_prefix.lower(): - return True - - return False - - -def check_protocol(protocol: str): - """Check if protocol is supported - Tcp/Any""" - - required_protocol = 'Tcp' - any_protocol = 'Any' - - if required_protocol.lower() == protocol.lower() or \ - any_protocol.lower() == protocol.lower(): - return True - - return False - - -def check_direction(direction: str): - """Check if port direction is inbound""" - - required_direction = 'Inbound' - - if required_direction.lower() == direction.lower(): - return True - - return False - - -def check_provisioning_state(provisioning_state: str): - """Check if the provisioning state is succeeded""" - - required_provisioning_state = 'Succeeded' - - if required_provisioning_state.lower() == provisioning_state.lower(): - return True - - return False - - -def check_rule_for_Azure_ML(rule): - """Check if the ports required for Azure Machine Learning are open""" - - required_ports = ['29876', '29877'] - - if check_source_address_prefix(rule.source_address_prefix) is False: - return False - - if check_protocol(rule.protocol) is False: - return False - - if check_direction(rule.direction) is False: - return False - - if check_provisioning_state(rule.provisioning_state) is False: - return False - - if rule.destination_port_range is not None: - if check_ports_in_destination_port_ranges( - required_ports, - [rule.destination_port_range]) is False: - return False - else: - if check_ports_in_destination_port_ranges( - required_ports, - rule.destination_port_ranges) is False: - return False - - return True - - -def check_vnet_security_rules(auth_object, - vnet_subscription_id, - vnet_resource_group, - vnet_name, - save_to_file=False): - """ - Check all the rules of virtual network if required ports for Azure Machine - Learning are open - """ - - network_client = NetworkManagementClient( - auth_object, - vnet_subscription_id) - - # get the vnet - vnet = network_client.virtual_networks.get( - resource_group_name=vnet_resource_group, - virtual_network_name=vnet_name) - - vnet_location = vnet.location - vnet_info = [] - - if vnet.subnets is None or len(vnet.subnets) == 0: - print('WARNING: No subnet found for VNet:', vnet_name) - - # for each subnet of the vnet - for subnet in vnet.subnets: - if subnet.network_security_group is None: - print('WARNING: No network security group found for subnet.', - 'Subnet', - subnet.id.split("/")[-1]) - else: - # get all the rules - network_security_group_name = \ - subnet.network_security_group.id.split("/")[-1] - network_security_group_resource_group_name = \ - subnet.network_security_group.id.split("/")[4] - network_security_group_subscription_id = \ - subnet.network_security_group.id.split("/")[2] - - security_rules = list(network_client.security_rules.list( - network_security_group_resource_group_name, - network_security_group_name)) - - rule_matched = None - for rule in security_rules: - rule_info = [] - # add vnet details - rule_info.append(vnet_name) - rule_info.append(vnet_subscription_id) - rule_info.append(vnet_resource_group) - rule_info.append(vnet_location) - # add subnet details - rule_info.append(subnet.id.split("/")[-1]) - rule_info.append(network_security_group_name) - rule_info.append(network_security_group_subscription_id) - rule_info.append(network_security_group_resource_group_name) - # add rule details - rule_info.append(rule.priority) - rule_info.append(rule.name) - rule_info.append(rule.source_address_prefix) - if rule.destination_port_range is not None: - rule_info.append(rule.destination_port_range) - else: - rule_info.append(rule.destination_port_ranges) - rule_info.append(rule.direction) - rule_info.append(rule.provisioning_state) - vnet_info.append(rule_info) - - if check_rule_for_Azure_ML(rule) is True: - rule_matched = rule - - if rule_matched is not None: - print("INFORMATION: Rule matched with required ports. Subnet:", - subnet.id.split("/")[-1], "Rule:", rule.name) - else: - print("WARNING: No rule matched with required ports. Subnet:", - subnet.id.split("/")[-1]) - - if save_to_file is True: - file_name = vnet_name + ".csv" - with open(file_name, mode='w') as vnet_rule_file: - vnet_rule_file_writer = csv.writer( - vnet_rule_file, - delimiter=',', - quotechar='"', - quoting=csv.QUOTE_MINIMAL) - header = ['VNet_Name', 'VNet_Subscription_ID', - 'VNet_Resource_Group', 'VNet_Location', - 'Subnet_Name', 'NSG_Name', - 'NSG_Subscription_ID', 'NSG_Resource_Group', - 'Rule_Priority', 'Rule_Name', 'Rule_Source', - 'Rule_Destination_Ports', 'Rule_Direction', - 'Rule_Provisioning_State'] - vnet_rule_file_writer.writerow(header) - vnet_rule_file_writer.writerows(vnet_info) - - print("INFORMATION: Network security group rules for your virtual \ -network are saved in file", file_name) diff --git a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/pong_rllib.py b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/pong_rllib.py deleted file mode 100644 index e2953f004..000000000 --- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/pong_rllib.py +++ /dev/null @@ -1,35 +0,0 @@ -from ray_on_aml.core import Ray_On_AML - -import ray.tune as tune -from ray.rllib import train - -from utils import callbacks - -if __name__ == "__main__": - - ray_on_aml = Ray_On_AML() - ray = ray_on_aml.getRay() - if ray: # in the headnode - # Parse arguments - train_parser = train.create_parser() - - args = train_parser.parse_args() - print("Algorithm config:", args.config) - - tune.run( - run_or_experiment=args.run, - config={ - "env": args.env, - "num_gpus": args.config["num_gpus"], - "num_workers": args.config["num_workers"], - "callbacks": {"on_train_result": callbacks.on_train_result}, - "sample_batch_size": 50, - "train_batch_size": 1000, - "num_sgd_iter": 2, - "num_data_loader_buffers": 2, - "model": {"dim": 42}, - }, - stop=args.stop, - local_dir='./logs') - else: - print("in worker node") diff --git a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/utils/callbacks.py b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/utils/callbacks.py deleted file mode 100644 index f34a4e8c8..000000000 --- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/files/utils/callbacks.py +++ /dev/null @@ -1,17 +0,0 @@ -'''RLlib callbacks module: - Common callback methods to be passed to RLlib trainer. -''' - -from azureml.core import Run - - -def on_train_result(info): - '''Callback on train result to record metrics returned by trainer. - ''' - run = Run.get_context() - run.log( - name='episode_reward_mean', - value=info["result"]["episode_reward_mean"]) - run.log( - name='episodes_total', - value=info["result"]["episodes_total"]) diff --git a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/images/pong.gif b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/images/pong.gif deleted file mode 100644 index c29cc4a3b..000000000 Binary files a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/images/pong.gif and /dev/null differ diff --git a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb deleted file mode 100644 index a6ee8eebc..000000000 --- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb +++ /dev/null @@ -1,557 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reinforcement Learning in Azure Machine Learning - Pong problem\n", - "Reinforcement Learning in Azure Machine Learning is a managed service for running distributed reinforcement learning training and simulation using the open source Ray framework.\n", - "This example uses Ray RLlib to train a Pong playing agent on a multi-node cluster.\n", - "\n", - "## Pong problem\n", - "[Pong](https://en.wikipedia.org/wiki/Pong) is a two-dimensional sports game that simulates table tennis. The player controls an in-game paddle by moving it vertically across the left or right side of the screen. They can compete against another player controlling a second paddle on the opposing side. Players use the paddles to hit a ball back and forth." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      \"Pong
      Fig 1. Pong game animation (from towardsdatascience.com).
      " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The goal here is to train an agent to win an episode of Pong game against opponent with the score of at least 18 points. An episode in Pong runs until one of the players reaches a score of 21. Episodes are a terminology that is used across all the [OpenAI gym](https://gym.openai.com/envs/Pong-v0/) environments that contains a strictly defined task.\n", - "\n", - "Training a Pong agent is a compute-intensive task and this example demonstrates the use of Reinforcement Learning in Azure Machine Learning service to train an agent faster in a distributed, parallel environment. You'll learn more about using the head and the worker compute targets to train an agent in this notebook below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisite\n", - "\n", - "It is highly recommended that the user should go through the [Reinforcement Learning in Azure Machine Learning - Cartpole Problem on Single Compute](../cartpole-on-single-compute/cartpole_sc.ipynb) to understand the basics of Reinforcement Learning in Azure Machine Learning and Ray RLlib used in this notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up Development Environment\n", - "The following subsections show typical steps to setup your development environment. Setup includes:\n", - "\n", - "* Connecting to a workspace to enable communication between your local machine and remote resources\n", - "* Creating an experiment to track all your runs\n", - "* Setting up a virtual network\n", - "* Creating remote head and worker compute target on a virtual network to use for training" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure Machine Learning SDK\n", - "Display the Azure Machine Learning SDK version." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646081765827 - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "# Azure Machine Learning core imports\n", - "import azureml.core\n", - "\n", - "# Check core SDK version number\n", - "print(\"Azure Machine Learning SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get Azure Machine Learning workspace\n", - "Get a reference to an existing Azure Machine Learning workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646081772340 - } - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.location, ws.resource_group, sep = ' | ')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Azure Machine Learning experiment\n", - "Create an experiment to track the runs in your workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646081775643 - } - }, - "outputs": [], - "source": [ - "from azureml.core.experiment import Experiment\n", - "\n", - "# Experiment name\n", - "experiment_name = 'rllib-pong-multi-node'\n", - "exp = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create compute targets\n", - "\n", - "In this example, we show how to set up separate compute targets for the Ray nodes.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "#### Create head compute target\n", - "\n", - "First we define the head cluster with GPU for the Ray head node. One CPU of the head node will be used for the Ray head process and the rest of the CPUs will be used by the Ray worker processes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646086081229 - } - }, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "\n", - "# Choose a name for the Ray cluster\n", - "compute_name = 'compute-gpu'\n", - "compute_min_nodes = 0\n", - "compute_max_nodes = 2\n", - "\n", - "# This example uses GPU VM. For using CPU VM, set SKU to STANDARD_D2_V2\n", - "vm_size = 'STANDARD_NC6'\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " if compute_target.provisioning_state == 'Succeeded':\n", - " print('found compute target. just use it', compute_name)\n", - " else: \n", - " raise Exception(\n", - " 'found compute target but it is in state', compute_target.provisioning_state)\n", - "else:\n", - " print('creating a new compute target...')\n", - " provisioning_config = AmlCompute.provisioning_configuration(\n", - " vm_size=vm_size,\n", - " min_nodes=compute_min_nodes, \n", - " max_nodes=compute_max_nodes,\n", - " )\n", - "\n", - " # Create the cluster\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - " \n", - " # Can poll for a minimum number of nodes and for a specific timeout. \n", - " # If no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - " # For a more detailed view of current AmlCompute status, use get_status()\n", - " print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646093795069 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "import os\n", - "\n", - "ray_environment_name = 'pong-cpu'\n", - "ray_environment_dockerfile_path = os.path.join(os.getcwd(), 'docker', 'Dockerfile-cpu')\n", - "\n", - "# Build CPU image\n", - "ray_cpu_env = Environment. \\\n", - " from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n", - " register(workspace=ws)\n", - "ray_cpu_build_details = ray_cpu_env.build(workspace=ws)\n", - "\n", - "import time\n", - "while ray_cpu_build_details.status not in ['Succeeded', 'Failed']:\n", - " print(f'Awaiting completion of ray CPU environment build. Current status is: {ray_cpu_build_details.status}')\n", - " time.sleep(30)\n", - "print(f'status={ray_cpu_build_details.status}')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646160884910 - }, - "jupyter": { - "outputs_hidden": true, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "ray_environment_name = 'pong-gpu'\n", - "ray_environment_dockerfile_path = os.path.join(os.getcwd(), 'docker', 'Dockerfile-gpu')\n", - "\n", - "# Build GPU image\n", - "ray_gpu_env = Environment. \\\n", - " from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n", - " register(workspace=ws)\n", - "ray_gpu_build_details = ray_gpu_env.build(workspace=ws)\n", - "\n", - "import time\n", - "while ray_gpu_build_details.status not in ['Succeeded', 'Failed']:\n", - " print(f'Awaiting completion of ray GPU environment build. Current status is: {ray_gpu_build_details.status}')\n", - " time.sleep(30)\n", - "print(f'status={ray_gpu_build_details.status}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create reinforcement learning training run\n", - "\n", - "The code below submits the training run using a `ScriptRunConfig`. By providing the\n", - "command to run the training, and a `RunConfig` object configured with your\n", - "compute target, number of nodes, and environment image to use.\n", - "\n", - "We specify `episode_reward_mean` to 18 as we want to stop the training as soon as the trained agent reaches an average win margin of at least 18 point over opponent over all episodes in the training epoch.\n", - "Number of Ray worker processes are defined by parameter `num_workers`. We set it to 13 as we have 13 CPUs available in our compute targets. Multiple Ray worker processes parallelizes agent training and helps in achieving our goal faster. \n", - "\n", - "```\n", - "Number of CPUs in head_compute_target = 6 CPUs in 1 node = 6\n", - "Number of CPUs in worker_compute_target = 2 CPUs in each of 4 nodes = 8\n", - "Number of CPUs available = (Number of CPUs in head_compute_target) + (Number of CPUs in worker_compute_target) - (1 CPU for head node) = 6 + 8 - 1 = 13\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646162435310 - } - }, - "outputs": [], - "source": [ - "from azureml.core import RunConfiguration, ScriptRunConfig, Experiment\n", - "from azureml.core.runconfig import DockerConfiguration, RunConfiguration\n", - "\n", - "experiment_name = 'rllib-pong-multi-node'\n", - "\n", - "experiment = Experiment(workspace=ws, name=experiment_name)\n", - "ray_environment = Environment.get(workspace=ws, name=ray_environment_name)\n", - "\n", - "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n", - "aml_run_config_ml.target = compute_target\n", - "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n", - "aml_run_config_ml.node_count = 2\n", - "aml_run_config_ml.environment = ray_environment\n", - "\n", - "training_algorithm = \"IMPALA\"\n", - "rl_environment = \"PongNoFrameskip-v4\"\n", - "script_name='pong_rllib.py'\n", - "\n", - "command=[\n", - " 'python', script_name,\n", - " '--run', training_algorithm,\n", - " '--env', rl_environment,\n", - " '--config', '\\'{\"num_gpus\": 1, \"num_workers\": 11}\\'',\n", - " '--stop', '\\'{\"episode_reward_mean\": 18, \"time_total_s\": 3600}\\''\n", - "]\n", - "\n", - "config = ScriptRunConfig(source_directory='./files',\n", - " command=command,\n", - " run_config = aml_run_config_ml\n", - " )\n", - "training_run = experiment.submit(config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training script\n", - "As recommended in [RLlib](https://ray.readthedocs.io/en/latest/rllib.html) documentations, we use Ray [Tune](https://ray.readthedocs.io/en/latest/tune.html) API to run the training algorithm. All the RLlib built-in trainers are compatible with the Tune API. Here we use tune.run() to execute a built-in training algorithm. For convenience, down below you can see part of the entry script where we make this call.\n", - "\n", - "```python\n", - " tune.run(\n", - " run_or_experiment=args.run,\n", - " config={\n", - " \"env\": args.env,\n", - " \"num_gpus\": args.config[\"num_gpus\"],\n", - " \"num_workers\": args.config[\"num_workers\"],\n", - " \"callbacks\": {\"on_train_result\": callbacks.on_train_result},\n", - " \"sample_batch_size\": 50,\n", - " \"train_batch_size\": 1000,\n", - " \"num_sgd_iter\": 2,\n", - " \"num_data_loader_buffers\": 2,\n", - " \"model\": {\"dim\": 42},\n", - " },\n", - " stop=args.stop,\n", - " local_dir='./logs')\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor the run\n", - "\n", - "Azure Machine Learning provides a Jupyter widget to show the status of an experiment run. You could use this widget to monitor the status of the runs. The widget shows the list of two child runs, one for head compute target run and one for worker compute target run. You can click on the link under **Status** to see the details of the child run. It will also show the metrics being logged." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(training_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Stop the run\n", - "\n", - "To stop the run, call `training_run.cancel()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment line below to cancel the run\n", - "# training_run.cancel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wait for completion\n", - "Wait for the run to complete before proceeding. If you want to stop the run, you may skip this and move to next section below. \n", - "\n", - "**Note: The run may take anywhere from 30 minutes to 45 minutes to complete.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Performance of the agent during training\n", - "\n", - "Let's get the reward metrics for the training run agent and observe how the agent's rewards improved over the training iterations and how the agent learns to win the Pong game. \n", - "\n", - "Collect the episode reward metrics from the worker run's metrics. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the reward metrics from training_run\n", - "episode_reward_mean = training_run.get_metrics(name='episode_reward_mean')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot the reward metrics. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.plot(episode_reward_mean['episode_reward_mean'])\n", - "plt.xlabel('training_iteration')\n", - "plt.ylabel('episode_reward_mean')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We observe that during the training over multiple episodes, the agent learns to win the Pong game against opponent with our target of 18 points in each episode of 21 points.\n", - "**Congratulations!! You have trained your Pong agent to win a game.**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cleaning up\n", - "For your convenience, below you can find code snippets to clean up any resources created as part of this tutorial that you don't wish to retain." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# To archive the created experiment:\n", - "#experiment.archive()\n", - "\n", - "# To delete the compute targets:\n", - "#head_compute_target.delete()\n", - "#worker_compute_target.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next\n", - "In this example, you learned how to solve distributed reinforcement learning training problems using head and worker compute targets. This was an introductory tutorial on Reinforement Learning in Azure Machine Learning service offering. We would love to hear your feedback to build the features you need!" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "vineetg" - } - ], - "categories": [ - "how-to-use-azureml", - "reinforcement-learning" - ], - "interpreter": { - "hash": "13382f70c1d0595120591d2e358c8d446daf961bf951d1fba9a32631e205d5ab" - }, - "kernel_info": { - "name": "python3-azureml" - }, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.0" - }, - "notice": "Copyright (c) Microsoft Corporation. All rights reserved.\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00afLicensed under the MIT License.\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00af ", - "nteract": { - "version": "nteract-front-end@1.0.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.yml b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.yml deleted file mode 100644 index a79b3da06..000000000 --- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.yml +++ /dev/null @@ -1,9 +0,0 @@ -name: pong_rllib -dependencies: -- pip: - - azureml-sdk - - azureml-contrib-reinforcementlearning - - azureml-widgets - - matplotlib - - azure-mgmt-network - - azure-cli diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb deleted file mode 100644 index ab567f56f..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb +++ /dev/null @@ -1,778 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reinforcement Learning in Azure Machine Learning - Cartpole Problem on Compute Instance\n", - "\n", - "Reinforcement Learning in Azure Machine Learning is a managed service for running reinforcement learning training and simulation. With Reinforcement Learning in Azure Machine Learning, data scientists can start developing reinforcement learning systems on one machine, and scale to compute targets with 100s of nodes if needed.\n", - "\n", - "This example shows how to use Reinforcement Learning in Azure Machine Learning to train a Cartpole playing agent on a compute instance." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cartpole problem\n", - "\n", - "Cartpole, also known as [Inverted Pendulum](https://en.wikipedia.org/wiki/Inverted_pendulum), is a pendulum with a center of mass above its pivot point. This formation is essentially unstable and will easily fall over but can be kept balanced by applying appropriate horizontal forces to the pivot point.\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      \n", - " \"Cartpole \n", - "

      Fig 1. Cartpole problem schematic description (from towardsdatascience.com).

      \n", - "\n", - "The goal here is to train an agent to keep the cartpole balanced by applying appropriate forces to the pivot point.\n", - "\n", - "See [this video](https://www.youtube.com/watch?v=XiigTGKZfks) for a real-world demonstration of cartpole problem." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prerequisite\n", - "The user should have completed the Azure Machine Learning Tutorial: [Get started creating your first ML experiment with the Python SDK](https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-sdk-setup). You will need to make sure that you have a valid subscription ID, a resource group, and an Azure Machine Learning workspace. All datastores and datasets you use should be associated with your workspace." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up Development Environment\n", - "The following subsections show typical steps to setup your development environment. Setup includes:\n", - "\n", - "* Connecting to a workspace to enable communication between your local machine and remote resources\n", - "* Creating an experiment to track all your runs\n", - "* Using a Compute Instance as compute target" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure Machine Learning SDK \n", - "Display the Azure Machine Learning SDK version." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646344676671 - } - }, - "outputs": [], - "source": [ - "import azureml.core\n", - "print(\"Azure Machine Learning SDK Version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get Azure Machine Learning workspace\n", - "Get a reference to an existing Azure Machine Learning workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646344680982 - } - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.location, ws.resource_group, sep = ' | ')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use Compute Instance as compute target\n", - "\n", - "A compute target is a designated compute resource where you run your training and simulation scripts. This location may be your local machine or a cloud-based compute resource. For more information see [What are compute targets in Azure Machine Learning?](https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target)\n", - "\n", - "The code below shows how to use current compute instance as a compute target. First some helper functions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646344684217 - } - }, - "outputs": [], - "source": [ - "import os.path\n", - "\n", - "# Get information about the currently running compute instance (notebook VM), like its name and prefix.\n", - "def load_nbvm():\n", - " if not os.path.isfile(\"/mnt/azmnt/.nbvm\"):\n", - " return None\n", - " with open(\"/mnt/azmnt/.nbvm\", 'r') as nbvm_file:\n", - " return { key:value for (key, value) in [ line.strip().split('=') for line in nbvm_file if '=' in line ] }\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we use these helper functions to get a handle to current compute instance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646344690768 - } - }, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeInstance\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "import random\n", - "import string\n", - "\n", - "# Load current compute instance info\n", - "current_compute_instance = load_nbvm()\n", - "\n", - "# For this demo, let's use the current compute instance as the compute target, if available\n", - "if current_compute_instance:\n", - " print(\"Current compute instance:\", current_compute_instance)\n", - " instance_name = current_compute_instance['instance']\n", - "else:\n", - " # Compute instance name needs to be unique across all existing compute instances within an Azure region\n", - " instance_name = \"cartpole-ci-\" + \"\".join(random.choice(string.ascii_lowercase) for _ in range(5))\n", - " try:\n", - " instance = ComputeInstance(workspace=ws, name=instance_name)\n", - " print('Found existing instance, use it.')\n", - " except ComputeTargetException:\n", - " print(\"Creating new compute instance...\")\n", - " compute_config = ComputeInstance.provisioning_configuration(\n", - " vm_size='STANDARD_D2_V2'\n", - " )\n", - " instance = ComputeInstance.create(ws, instance_name, compute_config)\n", - " instance.wait_for_completion(show_output=True)\n", - " print(\"Instance name:\", instance_name)\n", - "\n", - "compute_target = ws.compute_targets[instance_name]\n", - "\n", - "print(\"Compute target status:\")\n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Azure Machine Learning experiment\n", - "Create an experiment to track the runs in your workspace. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646344835579 - } - }, - "outputs": [], - "source": [ - "from azureml.core.experiment import Experiment\n", - "\n", - "experiment_name = 'CartPole-v0-CI'\n", - "experiment = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646346293902 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "import os\n", - "import time\n", - "\n", - "ray_environment_name = 'cartpole-ray-ci'\n", - "ray_environment_dockerfile_path = os.path.join(os.getcwd(), 'files', 'docker', 'Dockerfile')\n", - "\n", - "# Build environment image\n", - "ray_environment = Environment. \\\n", - " from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n", - " register(workspace=ws)\n", - "ray_env_build_details = ray_environment.build(workspace=ws)\n", - "\n", - "# import time\n", - "while ray_env_build_details.status not in ['Succeeded', 'Failed']:\n", - " print(f'Awaiting completion of environment build. Current status is: {ray_env_build_details.status}')\n", - " time.sleep(30)\n", - "print(f'status={ray_env_build_details.status}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train Cartpole Agent\n", - "In this section, we show how to use Azure Machine Learning jobs and Ray/RLlib framework to train a cartpole playing agent. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create reinforcement learning training run\n", - "\n", - "The code below submits the training run using a `ScriptRunConfig`. By providing the\n", - "command to run the training, and a `RunConfig` object configured with your\n", - "compute target, number of nodes, and environment image to use." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347120585 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core import RunConfiguration, ScriptRunConfig, Experiment\n", - "from azureml.core.runconfig import DockerConfiguration, RunConfiguration\n", - "\n", - "training_algorithm = 'PPO'\n", - "rl_environment = 'CartPole-v0'\n", - "\n", - "script_name = 'cartpole_training.py'\n", - "script_arguments = [\n", - " '--run', training_algorithm,\n", - " '--env', rl_environment,\n", - " '--config', '{\"num_gpus\": 0, \"num_workers\": 1}',\n", - " '--stop', '{\"episode_reward_mean\": 200, \"time_total_s\": 300}',\n", - " '--checkpoint-freq', '2',\n", - " '--checkpoint-at-end',\n", - " '--local-dir', './logs'\n", - "]\n", - "\n", - "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n", - "aml_run_config_ml.target = compute_target\n", - "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n", - "aml_run_config_ml.node_count = 1\n", - "aml_run_config_ml.environment = ray_environment\n", - "\n", - "training_config = ScriptRunConfig(source_directory='./files',\n", - " script=script_name,\n", - " arguments=script_arguments,\n", - " run_config = aml_run_config_ml\n", - " )\n", - "training_run = experiment.submit(training_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training script\n", - "\n", - "As recommended in RLlib documentations, we use Ray Tune API to run the training algorithm. All the RLlib built-in trainers are compatible with the Tune API. Here we use `tune.run()` to execute a built-in training algorithm. For convenience, down below you can see part of the entry script where we make this call.\n", - "\n", - "This is the list of parameters we are passing into `tune.run()` via the `script_params` parameter:\n", - "\n", - "- `run_or_experiment`: name of the [built-in algorithm](https://ray.readthedocs.io/en/latest/rllib-algorithms.html#rllib-algorithms), 'PPO' in our example,\n", - "- `config`: Algorithm-specific configuration. This includes specifying the environment, `env`, which in our example is the gym **[CartPole-v0](https://gym.openai.com/envs/CartPole-v0/)** environment,\n", - "- `stop`: stopping conditions, which could be any of the metrics returned by the trainer. Here we use \"mean of episode reward\", and \"total training time in seconds\" as stop conditions, and\n", - "- `checkpoint_freq` and `checkpoint_at_end`: Frequency of taking checkpoints (number of training iterations between checkpoints), and if a checkpoint should be taken at the end.\n", - "\n", - "We also specify the `local_dir`, the directory in which the training logs, checkpoints and other training artificats will be recorded. \n", - "\n", - "See [RLlib Training APIs](https://ray.readthedocs.io/en/latest/rllib-training.html#rllib-training-apis) for more details, and also [Training (tune.run, tune.Experiment)](https://ray.readthedocs.io/en/latest/tune/api_docs/execution.html#training-tune-run-tune-experiment) for the complete list of parameters.\n", - "\n", - "```python\n", - "import os\n", - "import ray\n", - "import ray.tune as tune\n", - "\n", - "if __name__ == \"__main__\":\n", - "\n", - " # parse arguments ...\n", - " \n", - " # Start ray head (single node)\n", - " os.system('ray start --head')\n", - " ray.init(address='auto')\n", - "\n", - " # Run training task using tune.run\n", - " tune.run(\n", - " run_or_experiment=args.run,\n", - " config=dict(args.config, env=args.env),\n", - " stop=args.stop,\n", - " checkpoint_freq=args.checkpoint_freq,\n", - " checkpoint_at_end=args.checkpoint_at_end,\n", - " local_dir=args.local_dir\n", - " )\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor experiment\n", - "Azure Machine Learning provides a Jupyter widget to show the status of an experiment run. You could use this widget to monitor the status of the runs.\n", - "\n", - "You can click on the link under **Status** to see the details of a child run. It will also show the metrics being logged." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347127671 - } - }, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(training_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Stop the run\n", - "\n", - "To stop the run, call `training_run.cancel()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment line below to cancel the run\n", - "# training_run.cancel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wait for completion\n", - "Wait for the run to complete before proceeding.\n", - "\n", - "**Note: The run may take a few minutes to complete.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347318682 - } - }, - "outputs": [], - "source": [ - "training_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate Trained Agent and See Results\n", - "\n", - "We can evaluate a previously trained policy using the `cartpole_rollout.py` helper script provided by RLlib (see [Evaluating Trained Policies](https://ray.readthedocs.io/en/latest/rllib-training.html#evaluating-trained-policies) for more details). Here we use an adaptation of this script to reconstruct a policy from a checkpoint taken and saved during training. We took these checkpoints by setting `checkpoint-freq` and `checkpoint-at-end` parameters above.\n", - "\n", - "In this section we show how to get access to these checkpoints data, and then how to use them to evaluate the trained policy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a dataset of training artifacts\n", - "To evaluate a trained policy (a checkpoint) we need to make the checkpoint accessible to the rollout script.\n", - "We can use the Run API to download policy training artifacts (saved model and checkpoints) to local compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347328505 - } - }, - "outputs": [], - "source": [ - "from os import path\n", - "from distutils import dir_util\n", - "\n", - "training_artifacts_path = path.join(\"logs\", training_algorithm)\n", - "print(\"Training artifacts path:\", training_artifacts_path)\n", - "\n", - "if path.exists(training_artifacts_path):\n", - " dir_util.remove_tree(training_artifacts_path)\n", - "\n", - "# Download run artifacts to local compute\n", - "training_run.download_files(training_artifacts_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's find the checkpoints and the last checkpoint number." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347334571 - } - }, - "outputs": [], - "source": [ - "# A helper function to find checkpoint files in a directory\n", - "def find_checkpoints(file_path):\n", - " print(\"Looking in path:\", file_path)\n", - " checkpoints = []\n", - " for root, _, files in os.walk(file_path):\n", - " for name in files:\n", - " if os.path.basename(root).startswith('checkpoint_'):\n", - " checkpoints.append(path.join(root, name))\n", - " return checkpoints" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347337724 - } - }, - "outputs": [], - "source": [ - "# Find checkpoints and last checkpoint number\n", - "checkpoint_files = find_checkpoints(training_artifacts_path)\n", - "\n", - "checkpoint_numbers = []\n", - "for file in checkpoint_files:\n", - " file = os.path.basename(file)\n", - " if file.startswith('checkpoint-') and not file.endswith('.tune_metadata'):\n", - " checkpoint_numbers.append(int(file.split('-')[1]))\n", - "\n", - "print(\"Checkpoints:\", checkpoint_numbers)\n", - "\n", - "last_checkpoint_number = max(checkpoint_numbers)\n", - "print(\"Last checkpoint number:\", last_checkpoint_number)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we upload checkpoints to default datastore and create a file dataset. This dataset will be used to pass in the checkpoints to the rollout script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347346085 - } - }, - "outputs": [], - "source": [ - "# Upload the checkpoint files and create a DataSet\n", - "from azureml.core import Dataset\n", - "\n", - "datastore = ws.get_default_datastore()\n", - "checkpoint_dataref = datastore.upload_files(checkpoint_files, target_path='cartpole_checkpoints_' + training_run.id, overwrite=True)\n", - "checkpoint_ds = Dataset.File.from_files(checkpoint_dataref)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To verify, we can print out the number (and paths) of all the files in the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347354726 - } - }, - "outputs": [], - "source": [ - "artifacts_paths = checkpoint_ds.to_path()\n", - "print(\"Number of files in dataset:\", len(artifacts_paths))\n", - "\n", - "# Uncomment line below to print all file paths\n", - "#print(\"Artifacts dataset file paths: \", artifacts_paths)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate Trained Agent and See Results\n", - "\n", - "We can evaluate a previously trained policy using the `cartpole_rollout.py` helper script provided by RLlib (see [Evaluating Trained Policies](https://ray.readthedocs.io/en/latest/rllib-training.html#evaluating-trained-policies) for more details). Here we use an adaptation of this script to reconstruct a policy from a checkpoint taken and saved during training. We took these checkpoints by setting `checkpoint-freq` and `checkpoint-at-end` parameters above.\n", - "In this section we show how to use these checkpoints to evaluate the trained policy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347414835 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "ray_environment_name = 'cartpole-ray-ci'\n", - "\n", - "experiment_name = 'CartPole-v0-CI'\n", - "training_algorithm = 'PPO'\n", - "rl_environment = 'CartPole-v0'\n", - "\n", - "experiment = Experiment(workspace=ws, name=experiment_name)\n", - "ray_environment = Environment.get(workspace=ws, name=ray_environment_name)\n", - "\n", - "script_name = 'cartpole_rollout.py'\n", - "script_arguments = [\n", - " '--run', training_algorithm,\n", - " '--env', rl_environment,\n", - " '--config', '{}',\n", - " '--steps', '2000',\n", - " '--checkpoint-number', str(last_checkpoint_number),\n", - " '--no-render',\n", - " '--artifacts-dataset', checkpoint_ds.as_named_input('artifacts_dataset'),\n", - " '--artifacts-path', checkpoint_ds.as_named_input('artifacts_path').as_mount()\n", - "]\n", - "\n", - "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n", - "aml_run_config_ml.target = compute_target\n", - "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n", - "aml_run_config_ml.node_count = 1\n", - "aml_run_config_ml.environment = ray_environment\n", - "aml_run_config_ml.data\n", - "\n", - "rollout_config = ScriptRunConfig(\n", - " source_directory='./files',\n", - " script=script_name,\n", - " arguments=script_arguments,\n", - " run_config = aml_run_config_ml\n", - " )\n", - " \n", - "rollout_run = experiment.submit(rollout_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And then, similar to the training section, we can monitor the real-time progress of the rollout run and its chid as follows. If you browse logs of the child run you can see the evaluation results recorded in driver_log.txt file. Note that you may need to wait several minutes before these results become available." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347429626 - } - }, - "outputs": [], - "source": [ - "RunDetails(rollout_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Wait for completion of the rollout run, or you may cancel the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment line below to cancel the run\n", - "#rollout_run.cancel()\n", - "rollout_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cleaning up\n", - "For your convenience, below you can find code snippets to clean up any resources created as part of this tutorial that you don't wish to retain." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# To archive the created experiment:\n", - "#exp.archive()\n", - "\n", - "# To delete created compute instance\n", - "if not current_compute_instance:\n", - " compute_target.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next\n", - "This example was about running Reinforcement Learning in Azure Machine Learning (Ray/RLlib Framework) on a compute instance. Please see [Cartpole Problem on Single Compute](../cartpole-on-single-compute/cartpole_sc.ipynb)\n", - "example which uses Ray RLlib to train a Cartpole playing agent on a single node remote compute.\n" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "adrosa" - }, - { - "name": "hoazari" - } - ], - "categories": [ - "how-to-use-azureml", - "reinforcement-learning" - ], - "interpreter": { - "hash": "13382f70c1d0595120591d2e358c8d446daf961bf951d1fba9a32631e205d5ab" - }, - "kernel_info": { - "name": "python3-azureml" - }, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - }, - "microsoft": { - "host": { - "AzureML": { - "notebookHasBeenCompleted": true - } - } - }, - "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.", - "nteract": { - "version": "nteract-front-end@1.0.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.yml b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.yml deleted file mode 100644 index c5a2ed398..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.yml +++ /dev/null @@ -1,6 +0,0 @@ -name: cartpole_ci -dependencies: -- pip: - - azureml-sdk - - azureml-contrib-reinforcementlearning - - azureml-widgets diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/cartpole_rollout.py b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/cartpole_rollout.py deleted file mode 100644 index 0c7f09885..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/cartpole_rollout.py +++ /dev/null @@ -1,121 +0,0 @@ -import os -import sys - -import ray -from ray.rllib import rollout -from ray.tune.registry import get_trainable_cls - -from azureml.core import Run - -from utils import callbacks - - -def run_rollout(args, parser): - - config = args.config - if not args.env: - if not config.get("env"): - parser.error("the following arguments are required: --env") - args.env = config.get("env") - - # Create the Trainer from config. - cls = get_trainable_cls(args.run) - agent = cls(env=args.env, config=config) - - # Load state from checkpoint. - agent.restore(args.checkpoint) - num_steps = int(args.steps) - num_episodes = int(args.episodes) - - # Determine the video output directory. - use_arg_monitor = False - try: - args.video_dir - except AttributeError: - print("There is no such attribute: args.video_dir") - use_arg_monitor = True - - video_dir = None - if not use_arg_monitor: - if args.monitor: - video_dir = os.path.join("./logs", "video") - elif args.video_dir: - video_dir = os.path.expanduser(args.video_dir) - - # Do the actual rollout. - with rollout.RolloutSaver( - args.out, - args.use_shelve, - write_update_file=args.track_progress, - target_steps=num_steps, - target_episodes=num_episodes, - save_info=args.save_info) as saver: - if use_arg_monitor: - rollout.rollout( - agent, - args.env, - num_steps, - num_episodes, - saver, - args.no_render, - args.monitor) - else: - rollout.rollout( - agent, args.env, - num_steps, - num_episodes, - saver, - args.no_render, video_dir) - - -if __name__ == "__main__": - - # Start ray head (single node) - os.system('ray start --head') - ray.init(address='auto') - - # Add positional argument - serves as placeholder for checkpoint - argvc = sys.argv[1:] - argvc.insert(0, 'checkpoint-placeholder') - - # Parse arguments - rollout_parser = rollout.create_parser() - - rollout_parser.add_argument( - '--checkpoint-number', required=False, type=int, default=1, - help='Checkpoint number of the checkpoint from which to roll out') - - rollout_parser.add_argument( - '--artifacts-dataset', required=True, - help='The checkpoints artifacts dataset') - - rollout_parser.add_argument( - '--artifacts-path', required=True, - help='The checkpoints artifacts path') - - args = rollout_parser.parse_args(argvc) - - # Get a handle to run - run = Run.get_context() - - # Get handles to the tarining artifacts dataset and mount path - artifacts_dataset = run.input_datasets['artifacts_dataset'] - artifacts_path = run.input_datasets['artifacts_path'] - - # Find checkpoint file to be evaluated - checkpoint_id = '-' + str(args.checkpoint_number) - checkpoint_files = list(filter( - lambda filename: filename.endswith(checkpoint_id), - artifacts_dataset.to_path())) - - checkpoint_file = checkpoint_files[0] - if checkpoint_file[0] == '/': - checkpoint_file = checkpoint_file[1:] - checkpoint = os.path.join(artifacts_path, checkpoint_file) - print('Checkpoint:', checkpoint) - - # Set rollout checkpoint - args.checkpoint = checkpoint - - # Start rollout - run_rollout(args, rollout_parser) diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/cartpole_training.py b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/cartpole_training.py deleted file mode 100644 index 1a0e3c490..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/cartpole_training.py +++ /dev/null @@ -1,32 +0,0 @@ -import ray -from ray.rllib import train -from ray import tune -import os - -from utils import callbacks - -if __name__ == "__main__": - - # Parse arguments and add callbacks to config - train_parser = train.create_parser() - - args = train_parser.parse_args() - args.config["callbacks"] = {"on_train_result": callbacks.on_train_result} - - # Trace if video capturing is on - if 'monitor' in args.config and args.config['monitor']: - print("Video capturing is ON!") - - # Start ray head (single node) - os.system('ray start --head') - ray.init(address='auto') - - # Run training task using tune.run - tune.run( - run_or_experiment=args.run, - config=dict(args.config, env=args.env), - stop=args.stop, - checkpoint_freq=args.checkpoint_freq, - checkpoint_at_end=args.checkpoint_at_end, - local_dir=args.local_dir - ) diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile deleted file mode 100644 index a4bfb39e5..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile +++ /dev/null @@ -1,17 +0,0 @@ -FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04 - -RUN pip install ray-on-aml==0.1.6 -RUN pip install gym[atari]==0.19.0 -RUN pip install gym[accept-rom-license]==0.19.0 -RUN pip install ale-py==0.7.0 -RUN pip install azureml-core -RUN pip install azureml-dataset-runtime -RUN pip install ray==0.8.7 -RUN pip install ray[rllib,tune,serve]==0.8.7 -RUN pip install tensorflow==1.14.0 - -USER root - -RUN apt-get update -RUN apt-get install -y jq -RUN apt-get install -y rsync diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/utils/callbacks.py b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/utils/callbacks.py deleted file mode 100644 index f34a4e8c8..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/utils/callbacks.py +++ /dev/null @@ -1,17 +0,0 @@ -'''RLlib callbacks module: - Common callback methods to be passed to RLlib trainer. -''' - -from azureml.core import Run - - -def on_train_result(info): - '''Callback on train result to record metrics returned by trainer. - ''' - run = Run.get_context() - run.log( - name='episode_reward_mean', - value=info["result"]["episode_reward_mean"]) - run.log( - name='episodes_total', - value=info["result"]["episodes_total"]) diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/utils/misc.py b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/utils/misc.py deleted file mode 100644 index f123324e0..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/utils/misc.py +++ /dev/null @@ -1,13 +0,0 @@ -'''Misc module: - Miscellaneous helper functions and utilities. -''' - -import os -import glob - - -# Helper function to find a file or folder path -def find_path(name, path_prefix): - for root, _, _ in os.walk(path_prefix): - if glob.glob(os.path.join(root, name)): - return root diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/images/cartpole.png b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/images/cartpole.png deleted file mode 100644 index f37c084ed..000000000 Binary files a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/images/cartpole.png and /dev/null differ diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb deleted file mode 100644 index d274986cd..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb +++ /dev/null @@ -1,896 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/reinforcement-learning/cartpole_on_single_compute/cartpole_sc.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reinforcement Learning in Azure Machine Learning - Cartpole Problem on Single Compute\n", - "\n", - "Reinforcement Learning in Azure Machine Learning is a managed service for running reinforcement learning training and simulation. With Reinforcement Learning in Azure Machine Learning, data scientists can start developing reinforcement learning systems on one machine, and scale to compute targets with 100s of nodes if needed.\n", - "\n", - "This example shows how to use Reinforcement Learning in Azure Machine Learning to train a Cartpole playing agent on a single compute. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cartpole problem\n", - "\n", - "Cartpole, also known as [Inverted Pendulum](https://en.wikipedia.org/wiki/Inverted_pendulum), is a pendulum with a center of mass above its pivot point. This formation is essentially unstable and will easily fall over but can be kept balanced by applying appropriate horizontal forces to the pivot point.\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      \n", - " \"Cartpole \n", - "

      Fig 1. Cartpole problem schematic description (from towardsdatascience.com).

      \n", - "\n", - "The goal here is to train an agent to keep the cartpole balanced by applying appropriate forces to the pivot point.\n", - "\n", - "See [this video](https://www.youtube.com/watch?v=XiigTGKZfks) for a real-world demonstration of cartpole problem." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prerequisite\n", - "The user should have completed the Azure Machine Learning Tutorial: [Get started creating your first ML experiment with the Python SDK](https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-sdk-setup). You will need to make sure that you have a valid subscription ID, a resource group, and an Azure Machine Learning workspace. All datastores and datasets you use should be associated with your workspace." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up Development Environment\n", - "The following subsections show typical steps to setup your development environment. Setup includes:\n", - "\n", - "* Connecting to a workspace to enable communication between your local machine and remote resources\n", - "* Creating an experiment to track all your runs\n", - "* Creating a remote compute target to use for training" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure Machine Learning SDK \n", - "Display the Azure Machine Learning SDK version." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646347616697 - } - }, - "outputs": [], - "source": [ - "import azureml.core\n", - "\n", - "print(\"Azure Machine Learning SDK Version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get Azure Machine Learning workspace\n", - "Get a reference to an existing Azure Machine Learning workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646429058500 - } - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.location, ws.resource_group, sep = ' | ')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a new compute resource or attach an existing one\n", - "\n", - "A compute target is a designated compute resource where you run your training and simulation scripts. This location may be your local machine or a cloud-based compute resource. The code below shows how to create a cloud-based compute target. For more information see [What are compute targets in Azure Machine Learning?](https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target)\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Note: Creation of a compute resource can take several minutes**. Please make sure to change `STANDARD_D2_V2` to a [size available in your region](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=virtual-machines)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646359152101 - } - }, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "import os\n", - "\n", - "# Choose a name and maximum size for your cluster\n", - "compute_name = \"cpu-cluster-d2\"\n", - "compute_min_nodes = 0\n", - "compute_max_nodes = 4\n", - "vm_size = \"STANDARD_D2_V2\"\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " print(\"Found an existing compute target of name: \" + compute_name)\n", - " compute_target = ws.compute_targets[compute_name]\n", - " # Note: you may want to make sure compute_target is of type AmlCompute \n", - "else:\n", - " print(\"Creating new compute target...\")\n", - " provisioning_config = AmlCompute.provisioning_configuration(\n", - " vm_size=vm_size,\n", - " min_nodes=compute_min_nodes, \n", - " max_nodes=compute_max_nodes)\n", - " \n", - " # Create the cluster\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Azure Machine Learning experiment\n", - "Create an experiment to track the runs in your workspace. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646348040613 - } - }, - "outputs": [], - "source": [ - "from azureml.core.experiment import Experiment\n", - "\n", - "experiment_name = 'CartPole-v0-SC'\n", - "experiment = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646417962898 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "import os\n", - "\n", - "ray_environment_name = 'cartpole-ray-sc'\n", - "ray_environment_dockerfile_path = os.path.join(os.getcwd(), 'files', 'docker', 'Dockerfile')\n", - "\n", - "# Build environment image\n", - "ray_environment = Environment. \\\n", - " from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n", - " register(workspace=ws)\n", - "ray_env_build_details = ray_environment.build(workspace=ws)\n", - "\n", - "import time\n", - "while ray_env_build_details.status not in ['Succeeded', 'Failed']:\n", - " print(f'Awaiting completion of environment build. Current status is: {ray_env_build_details.status}')\n", - " time.sleep(30)\n", - "print(f'status={ray_env_build_details.status}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train Cartpole Agent\n", - "In this section, we show how to use Azure Machine Learning jobs and Ray/RLlib framework to train a cartpole playing agent. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create reinforcement learning training run\n", - "\n", - "The code below submits the training run using a `ScriptRunConfig`. By providing the\n", - "command to run the training, and a `RunConfig` object configured with your\n", - "compute target, number of nodes, and environment image to use." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646437786449 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import RunConfiguration, ScriptRunConfig, Experiment\n", - "from azureml.core.runconfig import DockerConfiguration, RunConfiguration\n", - "\n", - "training_algorithm = \"PPO\"\n", - "rl_environment = \"CartPole-v0\"\n", - "video_capture = True\n", - "if video_capture:\n", - " algorithm_config = '\\'{\"num_gpus\": 0, \"num_workers\": 1, \"monitor\": true}\\''\n", - "else:\n", - " algorithm_config = '\\'{\"num_gpus\": 0, \"num_workers\": 1, \"monitor\": false}\\''\n", - "\n", - "script_name = 'cartpole_training.py'\n", - "script_arguments = [\n", - " '--run', training_algorithm,\n", - " '--env', rl_environment,\n", - " '--stop', '\\'{\"episode_reward_mean\": 200, \"time_total_s\": 300}\\'',\n", - " '--config', algorithm_config,\n", - " '--checkpoint-freq', '2',\n", - " '--checkpoint-at-end',\n", - " '--local-dir', './logs'\n", - "]\n", - "\n", - "ray_environment = Environment.get(ws, name=ray_environment_name)\n", - "run_config = RunConfiguration(communicator='OpenMpi')\n", - "run_config.target = compute_target\n", - "run_config.docker = DockerConfiguration(use_docker=True)\n", - "run_config.node_count = 1\n", - "run_config.environment = ray_environment\n", - "command=[\"python\", script_name, *script_arguments]\n", - "\n", - "if video_capture:\n", - " command = [\"xvfb-run -s '-screen 0 640x480x16 -ac +extension GLX +render' \"] + command\n", - " run_config.environment_variables[\"SDL_VIDEODRIVER\"] = \"dummy\"\n", - "\n", - "trainint_config = ScriptRunConfig(source_directory='./files',\n", - " command=command,\n", - " run_config = run_config\n", - " )\n", - "\n", - "training_run = experiment.submit(trainint_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training script\n", - "\n", - "As recommended in RLlib documentations, we use Ray Tune API to run the training algorithm. All the RLlib built-in trainers are compatible with the Tune API. Here we use `tune.run()` to execute a built-in training algorithm. For convenience, down below you can see part of the entry script where we make this call.\n", - "\n", - "This is the list of parameters we are passing into `tune.run()` via the `script_params` parameter:\n", - "\n", - "- `run_or_experiment`: name of the [built-in algorithm](https://ray.readthedocs.io/en/latest/rllib-algorithms.html#rllib-algorithms), 'PPO' in our example,\n", - "- `config`: Algorithm-specific configuration. This includes specifying the environment, `env`, which in our example is the gym **[CartPole-v0](https://gym.openai.com/envs/CartPole-v0/)** environment,\n", - "- `stop`: stopping conditions, which could be any of the metrics returned by the trainer. Here we use \"mean of episode reward\", and \"total training time in seconds\" as stop conditions, and\n", - "- `checkpoint_freq` and `checkpoint_at_end`: Frequency of taking checkpoints (number of training iterations between checkpoints), and if a checkpoint should be taken at the end.\n", - "\n", - "We also specify the `local_dir`, the directory in which the training logs, checkpoints and other training artificats will be recorded. \n", - "\n", - "See [RLlib Training APIs](https://ray.readthedocs.io/en/latest/rllib-training.html#rllib-training-apis) for more details, and also [Training (tune.run, tune.Experiment)](https://ray.readthedocs.io/en/latest/tune/api_docs/execution.html#training-tune-run-tune-experiment) for the complete list of parameters.\n", - "\n", - "```python\n", - "import ray\n", - "import ray.tune as tune\n", - "\n", - "if __name__ == \"__main__\":\n", - "\n", - " # parse arguments ...\n", - " \n", - " # Start ray head (single node)\n", - " os.system('ray start --head')\n", - " ray.init(address='auto')\n", - "\n", - " # Run training task using tune.run\n", - " tune.run(\n", - " run_or_experiment=args.run,\n", - " config=dict(args.config, env=args.env),\n", - " stop=args.stop,\n", - " checkpoint_freq=args.checkpoint_freq,\n", - " checkpoint_at_end=args.checkpoint_at_end,\n", - " local_dir=args.local_dir\n", - " )\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor experiment\n", - "\n", - "Azure Machine Learning provides a Jupyter widget to show the status of an experiment run. You could use this widget to monitor the status of the runs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646437627002 - } - }, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "RunDetails(training_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Stop the run\n", - "To stop the run, call `training_run.cancel()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment line below to cancel the run\n", - "# training_run.cancel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wait for completion\n", - "Wait for the run to complete before proceeding.\n", - "\n", - "**Note: The length of the run depends on the provisioning time of the compute target and it may take several minutes to complete.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get access to training artifacts\n", - "We can simply use run id to get a handle to an in-progress or a previously concluded run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Run\n", - "\n", - "run_id = training_run.id # Or set to run id of a completed run (e.g. 'rl-cartpole-v0_1587572312_06e04ace_head')\n", - "run = Run(experiment, run_id=run_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use the Run API to download policy training artifacts (saved model and checkpoints) to local compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646437652309 - } - }, - "outputs": [], - "source": [ - "from os import path\n", - "from distutils import dir_util\n", - "\n", - "training_artifacts_path = path.join(\"logs\", training_algorithm)\n", - "print(\"Training artifacts path:\", training_artifacts_path)\n", - "\n", - "if path.exists(training_artifacts_path):\n", - " dir_util.remove_tree(training_artifacts_path)\n", - "\n", - "# Download run artifacts to local compute\n", - "training_run.download_files(training_artifacts_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display movies of selected training episodes\n", - "\n", - "Ray creates video output of selected training episodes in mp4 format. Here we will display two of these, i.e. the first and the last recorded videos, so you could see the improvement of the agent after training.\n", - "\n", - "First we introduce a few helper functions: a function to download the movies from our dataset, another one to find mp4 movies in a local directory, and one more to display a downloaded movie." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646437657045 - } - }, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "# A helper function to find movies in a directory\n", - "def find_movies(movie_path):\n", - " print(\"Looking in path:\", movie_path)\n", - " mp4_movies = []\n", - " for root, _, files in os.walk(movie_path):\n", - " for name in files:\n", - " if name.endswith('.mp4'):\n", - " mp4_movies.append(path.join(root, name))\n", - " print('Found {} movies'.format(len(mp4_movies)))\n", - "\n", - " return mp4_movies\n", - "\n", - "\n", - "# A helper function to display a movie\n", - "from IPython.core.display import Video\n", - "from IPython.display import display\n", - "def display_movie(movie_file):\n", - " display(Video(movie_file, embed=True, html_attributes='controls'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Look for the downloaded movies in the local directory and sort them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646437690241 - } - }, - "outputs": [], - "source": [ - "mp4_files = find_movies(training_artifacts_path)\n", - "mp4_files.sort()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display a movie of the first training episode. This is how the agent performs with no training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646437692954 - } - }, - "outputs": [], - "source": [ - "first_movie = mp4_files[0] if len(mp4_files) > 0 else None\n", - "print(\"First movie:\", first_movie)\n", - "\n", - "display_movie(first_movie)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display a movie of the last training episode. This is how a fully-trained agent performs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646437717147 - } - }, - "outputs": [], - "source": [ - "last_movie = mp4_files[-1] if len(mp4_files) > 0 else None\n", - "print(\"Last movie:\", last_movie)\n", - "\n", - "display_movie(last_movie)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate Trained Agent and See Results\n", - "\n", - "We can evaluate a previously trained policy using the `rollout.py` helper script provided by RLlib (see [Evaluating Trained Policies](https://ray.readthedocs.io/en/latest/rllib-training.html#evaluating-trained-policies) for more details). Here we use an adaptation of this script to reconstruct a policy from a checkpoint taken and saved during training. We took these checkpoints by setting `checkpoint-freq` and `checkpoint-at-end` parameters above.\n", - "In this section we show how to use these checkpoints to evaluate the trained policy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate a trained policy\n", - "In this section, we submit another job, to evalute a trained policy. The entrypoint for this job is\n", - "`cartpole-rollout.py` script, and we we pass the checkpoints dataset to this script as a dataset refrence.\n", - "\n", - "We are using script parameters to pass in the same algorithm and the same environment used during training. We also specify the checkpoint number of the checkpoint we wish to evaluate, `checkpoint-number`, and number of the steps we shall run the rollout, `steps`.\n", - "\n", - "The training artifacts dataset will be accessible to the rollout script as a mounted folder. The mounted folder and the checkpoint number, passed in via `checkpoint-number`, will be used to create a path to the checkpoint we are going to evaluate. The created checkpoint path then will be passed into RLlib rollout script for evaluation.\n", - "\n", - "Let's find the checkpoints and the last checkpoint number first." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# A helper function to find checkpoint files in a directory\n", - "def find_checkpoints(file_path):\n", - " print(\"Looking in path:\", file_path)\n", - " checkpoints = []\n", - " for root, _, files in os.walk(file_path):\n", - " for name in files:\n", - " if os.path.basename(root).startswith('checkpoint_'):\n", - " checkpoints.append(path.join(root, name))\n", - " return checkpoints\n", - "\n", - "checkpoint_files = find_checkpoints(training_artifacts_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Find checkpoints and last checkpoint number\n", - "checkpoint_numbers = []\n", - "for file in checkpoint_files:\n", - " file = os.path.basename(file)\n", - " if file.startswith('checkpoint-') and not file.endswith('.tune_metadata'):\n", - " checkpoint_numbers.append(int(file.split('-')[-1]))\n", - "\n", - "print(\"Checkpoints:\", checkpoint_numbers)\n", - "\n", - "last_checkpoint_number = max(checkpoint_numbers)\n", - "print(\"Last checkpoint number:\", last_checkpoint_number)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload the checkpoint files and create a DataSet\n", - "from azureml.core import Dataset\n", - "\n", - "datastore = ws.get_default_datastore()\n", - "checkpoint_dataref = datastore.upload_files(checkpoint_files, target_path='cartpole_checkpoints_' + run_id, overwrite=True)\n", - "checkpoint_ds = Dataset.File.from_files(checkpoint_dataref)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can submit the training run using a `ScriptRunConfig`. By providing the\n", - "command to run the training, and a `RunConfig` object configured w" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ray_environment_name = 'cartpole-ray-sc'\n", - "\n", - "experiment_name = 'CartPole-v0-SC'\n", - "training_algorithm = 'PPO'\n", - "rl_environment = 'CartPole-v0'\n", - "\n", - "experiment = Experiment(workspace=ws, name=experiment_name)\n", - "ray_environment = Environment.get(workspace=ws, name=ray_environment_name)\n", - "\n", - "script_name = 'cartpole_rollout.py'\n", - "video_capture = True\n", - "if video_capture:\n", - " script_arguments = ['--video-dir', './logs/video']\n", - "else:\n", - " script_arguments = ['--no-render']\n", - "script_arguments = script_arguments + [\n", - " '--run', training_algorithm,\n", - " '--env', rl_environment,\n", - " '--config', '{}',\n", - " '--steps', '2000',\n", - " '--checkpoint-number', str(last_checkpoint_number),\n", - " '--artifacts-dataset', checkpoint_ds.as_named_input('artifacts_dataset'),\n", - " '--artifacts-path', checkpoint_ds.as_named_input('artifacts_path').as_mount()\n", - "]\n", - "\n", - "command = [\"python\", script_name, *script_arguments]\n", - "\n", - "if video_capture:\n", - " command = [\"xvfb-run -s '-screen 0 640x480x16 -ac +extension GLX +render' \"] + command\n", - " run_config.environment_variables[\"SDL_VIDEODRIVER\"] = \"dummy\"\n", - "\n", - "run_config = RunConfiguration(communicator='OpenMpi')\n", - "run_config.target = compute_target\n", - "run_config.docker = DockerConfiguration(use_docker=True)\n", - "run_config.node_count = 1\n", - "run_config.environment = ray_environment\n", - "\n", - "rollout_config = ScriptRunConfig(\n", - " source_directory='./files',\n", - " command=command,\n", - " run_config=run_config\n", - " )\n", - "\n", - "rollout_run = experiment.submit(rollout_config)\n", - "rollout_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And then, similar to the training section, we can monitor the real-time progress of the rollout run and its chid as follows. If you browse logs of the child run you can see the evaluation results recorded in driver_log.txt file. Note that you may need to wait several minutes before these results become available." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(rollout_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Wait for completion of the rollout run before moving to the next section, or you may cancel the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment line below to cancel the run\n", - "#rollout_run.cancel()\n", - "rollout_run.wait_for_completion()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display movies of selected rollout episodes\n", - "\n", - "To display recorded movies first we download recorded videos to local machine. Here again we create a dataset of rollout artifacts and use the helper functions introduced above to download and displays rollout videos." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download rollout artifacts\n", - "rollout_artifacts_path = path.join(\"logs\", \"rollout\")\n", - "print(\"Rollout artifacts path:\", rollout_artifacts_path)\n", - "\n", - "if path.exists(rollout_artifacts_path):\n", - " dir_util.remove_tree(rollout_artifacts_path)\n", - "\n", - "# Download videos to local compute\n", - "rollout_run.download_files(\"logs/video\", output_directory = rollout_artifacts_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, similar to the training section, we look for the last video." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Look for the downloaded movie in local directory\n", - "mp4_files = find_movies(rollout_artifacts_path)\n", - "mp4_files.sort()\n", - "last_movie = mp4_files[-1] if len(mp4_files) > 1 else None\n", - "print(\"Last movie:\", last_movie)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display last video recorded during the rollout." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "last_movie = mp4_files[-1] if len(mp4_files) > 0 else None\n", - "print(\"Last movie:\", last_movie)\n", - "\n", - "display_movie(last_movie)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cleaning up\n", - "For your convenience, below you can find code snippets to clean up any resources created as part of this tutorial that you don't wish to retain." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# To archive the created experiment:\n", - "#exp.archive()\n", - "\n", - "# To delete the compute target:\n", - "#compute_target.delete()\n", - "\n", - "# To delete downloaded training artifacts\n", - "#if os.path.exists(training_artifacts_path):\n", - "# dir_util.remove_tree(training_artifacts_path)\n", - "\n", - "# To delete downloaded rollout videos\n", - "#if path.exists(rollout_artifacts_path):\n", - "# dir_util.remove_tree(rollout_artifacts_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next\n", - "This example was about running Reinforcement Learning in Azure Machine Learning (Ray/RLlib Framework) on a single compute. Please see [Pong Problem](../atari-on-distributed-compute/pong_rllib.ipynb)\n", - "example which uses Ray RLlib to train a Pong playing agent on a multi-node cluster." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "hoazari" - }, - { - "name": "dasommer" - } - ], - "categories": [ - "how-to-use-azureml", - "reinforcement-learning" - ], - "interpreter": { - "hash": "13382f70c1d0595120591d2e358c8d446daf961bf951d1fba9a32631e205d5ab" - }, - "kernel_info": { - "name": "python38-azureml" - }, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - }, - "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.", - "nteract": { - "version": "nteract-front-end@1.0.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.yml b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.yml deleted file mode 100644 index 48d5edfa8..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.yml +++ /dev/null @@ -1,6 +0,0 @@ -name: cartpole_sc -dependencies: -- pip: - - azureml-sdk - - azureml-contrib-reinforcementlearning - - azureml-widgets diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/cartpole_rollout.py b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/cartpole_rollout.py deleted file mode 100644 index 0c7f09885..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/cartpole_rollout.py +++ /dev/null @@ -1,121 +0,0 @@ -import os -import sys - -import ray -from ray.rllib import rollout -from ray.tune.registry import get_trainable_cls - -from azureml.core import Run - -from utils import callbacks - - -def run_rollout(args, parser): - - config = args.config - if not args.env: - if not config.get("env"): - parser.error("the following arguments are required: --env") - args.env = config.get("env") - - # Create the Trainer from config. - cls = get_trainable_cls(args.run) - agent = cls(env=args.env, config=config) - - # Load state from checkpoint. - agent.restore(args.checkpoint) - num_steps = int(args.steps) - num_episodes = int(args.episodes) - - # Determine the video output directory. - use_arg_monitor = False - try: - args.video_dir - except AttributeError: - print("There is no such attribute: args.video_dir") - use_arg_monitor = True - - video_dir = None - if not use_arg_monitor: - if args.monitor: - video_dir = os.path.join("./logs", "video") - elif args.video_dir: - video_dir = os.path.expanduser(args.video_dir) - - # Do the actual rollout. - with rollout.RolloutSaver( - args.out, - args.use_shelve, - write_update_file=args.track_progress, - target_steps=num_steps, - target_episodes=num_episodes, - save_info=args.save_info) as saver: - if use_arg_monitor: - rollout.rollout( - agent, - args.env, - num_steps, - num_episodes, - saver, - args.no_render, - args.monitor) - else: - rollout.rollout( - agent, args.env, - num_steps, - num_episodes, - saver, - args.no_render, video_dir) - - -if __name__ == "__main__": - - # Start ray head (single node) - os.system('ray start --head') - ray.init(address='auto') - - # Add positional argument - serves as placeholder for checkpoint - argvc = sys.argv[1:] - argvc.insert(0, 'checkpoint-placeholder') - - # Parse arguments - rollout_parser = rollout.create_parser() - - rollout_parser.add_argument( - '--checkpoint-number', required=False, type=int, default=1, - help='Checkpoint number of the checkpoint from which to roll out') - - rollout_parser.add_argument( - '--artifacts-dataset', required=True, - help='The checkpoints artifacts dataset') - - rollout_parser.add_argument( - '--artifacts-path', required=True, - help='The checkpoints artifacts path') - - args = rollout_parser.parse_args(argvc) - - # Get a handle to run - run = Run.get_context() - - # Get handles to the tarining artifacts dataset and mount path - artifacts_dataset = run.input_datasets['artifacts_dataset'] - artifacts_path = run.input_datasets['artifacts_path'] - - # Find checkpoint file to be evaluated - checkpoint_id = '-' + str(args.checkpoint_number) - checkpoint_files = list(filter( - lambda filename: filename.endswith(checkpoint_id), - artifacts_dataset.to_path())) - - checkpoint_file = checkpoint_files[0] - if checkpoint_file[0] == '/': - checkpoint_file = checkpoint_file[1:] - checkpoint = os.path.join(artifacts_path, checkpoint_file) - print('Checkpoint:', checkpoint) - - # Set rollout checkpoint - args.checkpoint = checkpoint - - # Start rollout - run_rollout(args, rollout_parser) diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/cartpole_training.py b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/cartpole_training.py deleted file mode 100644 index a083b7c33..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/cartpole_training.py +++ /dev/null @@ -1,32 +0,0 @@ -import os -import ray -from ray.rllib import train -from ray import tune - -from utils import callbacks - -if __name__ == "__main__": - - # Parse arguments and add callbacks to config - train_parser = train.create_parser() - - args = train_parser.parse_args() - args.config["callbacks"] = {"on_train_result": callbacks.on_train_result} - - # Trace if video capturing is on - if 'monitor' in args.config and args.config['monitor']: - print("Video capturing is ON!") - - # Start ray head (single node) - os.system('ray start --head') - ray.init(address='auto') - - # Run training task using tune.run - tune.run( - run_or_experiment=args.run, - config=dict(args.config, env=args.env), - stop=args.stop, - checkpoint_freq=args.checkpoint_freq, - checkpoint_at_end=args.checkpoint_at_end, - local_dir=args.local_dir - ) diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile deleted file mode 100644 index 4ef2f3650..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile +++ /dev/null @@ -1,34 +0,0 @@ -FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20200423.v1 - -RUN apt-get update && apt-get install -y --no-install-recommends \ - python-opengl \ - rsync \ - xvfb && \ - apt-get clean -y && \ - rm -rf /var/lib/apt/lists/* && \ - rm -rf /usr/share/man/* - -RUN conda install -y conda=4.7.12 python=3.7 && conda clean -ay && \ - pip install ray-on-aml==0.1.6 & \ - pip install --upgrade ray==0.8.3 \ - ray[rllib,dashboard,tune]==0.8.3 & \ - pip install --no-cache-dir \ - azureml-defaults \ - azureml-dataset-runtime[fuse,pandas] \ - azureml-contrib-reinforcementlearning \ - gputil \ - scipy \ - pyglet \ - cloudpickle==1.3.0 \ - tensorboardX \ - tensorflow==1.14.0 \ - tabulate \ - dm_tree \ - lz4 \ - psutil \ - setproctitle \ - pygame \ - gym[atari]==0.17.3 && \ - conda install -y -c conda-forge x264='1!152.20180717' ffmpeg=4.0.2 && \ - conda install -c anaconda opencv - diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/utils/callbacks.py b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/utils/callbacks.py deleted file mode 100644 index 022aadf01..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/utils/callbacks.py +++ /dev/null @@ -1,23 +0,0 @@ -'''RLlib callbacks module: - Common callback methods to be passed to RLlib trainer. -''' - -from azureml.core import Run - - -def on_train_result(info): - '''Callback on train result to record metrics returned by trainer. - ''' - run = Run.get_context() - run.log( - name='episode_reward_mean', - value=info["result"]["episode_reward_mean"]) - run.log( - name='episodes_total', - value=info["result"]["episodes_total"]) - run.log( - name='perf_cpu_percent', - value=info["result"]["perf"]["cpu_util_percent"]) - run.log( - name='perf_memory_percent', - value=info["result"]["perf"]["ram_util_percent"]) diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/utils/misc.py b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/utils/misc.py deleted file mode 100644 index f123324e0..000000000 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/utils/misc.py +++ /dev/null @@ -1,13 +0,0 @@ -'''Misc module: - Miscellaneous helper functions and utilities. -''' - -import os -import glob - - -# Helper function to find a file or folder path -def find_path(name, path_prefix): - for root, _, _ in os.walk(path_prefix): - if glob.glob(os.path.join(root, name)): - return root diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/images/cartpole.png b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/images/cartpole.png deleted file mode 100644 index f37c084ed..000000000 Binary files a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/images/cartpole.png and /dev/null differ diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile deleted file mode 100644 index 31353f76d..000000000 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile +++ /dev/null @@ -1,32 +0,0 @@ -FROM akdmsft/particle-cpu - -# Install required pip packages -RUN pip3 install --upgrade pip setuptools && pip3 install --upgrade \ - pandas \ - matplotlib \ - psutil \ - numpy \ - scipy \ - gym \ - azureml-defaults \ - tensorboardX \ - tensorflow==1.15 \ - tensorflow-probability==0.8.0 \ - onnxruntime \ - tf2onnx \ - cloudpickle==1.1.1 \ - tabulate \ - dm_tree \ - lz4 \ - opencv-python - -RUN cd multiagent-particle-envs && \ - pip3 install -e . && \ - pip3 install --upgrade pyglet==1.3.2 - -RUN pip3 install ray-on-aml==0.1.6 - -RUN pip3 install --upgrade \ - ray==0.8.7 \ - ray[rllib]==0.8.7 \ - ray[tune]==0.8.7 \ No newline at end of file diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/patch_files/multi_discrete.py b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/patch_files/multi_discrete.py deleted file mode 100644 index 8980ba36f..000000000 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/patch_files/multi_discrete.py +++ /dev/null @@ -1,70 +0,0 @@ -# MIT License - -# Copyright (c) 2018 OpenAI - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import numpy as np -import gym - - -class MultiDiscrete(gym.Space): - """ - - The multi-discrete action space consists of a series of discrete action spaces with different - parameters - - It can be adapted to both a Discrete action space or a continuous (Box) action space - - It is useful to represent game controllers or keyboards where each key can be represented as - a discrete action space - - It is parametrized by passing an array of arrays containing [min, max] for each discrete action - space where the discrete action space can take any integers from `min` to `max` (both inclusive) - Note: A value of 0 always need to represent the NOOP action. - e.g. Nintendo Game Controller - - Can be conceptualized as 3 discrete action spaces: - 1) Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4 - 2) Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1 - 3) Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1 - - Can be initialized as - MultiDiscrete([ [0,4], [0,1], [0,1] ]) - """ - def __init__(self, array_of_param_array): - self.low = np.array([x[0] for x in array_of_param_array]) - self.high = np.array([x[1] for x in array_of_param_array]) - self.num_discrete_space = self.low.shape[0] - - def sample(self): - """ Returns a array with one sample from each discrete action space """ - # For each row: round(random .* (max - min) + min, 0) - # random_array = prng.np_random.rand(self.num_discrete_space) - random_array = np.random.RandomState().rand(self.num_discrete_space) - return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)] - - def contains(self, x): - return len(x) == self.num_discrete_space \ - and (np.array(x) >= self.low).all() \ - and (np.array(x) <= self.high).all() - - @property - def shape(self): - return self.num_discrete_space - - def __repr__(self): - return "MultiDiscrete" + str(self.num_discrete_space) - - def __eq__(self, other): - return np.array_equal(self.low, other.low) and np.array_equal(self.high, other.high) diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/patch_files/rendering.py b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/patch_files/rendering.py deleted file mode 100644 index e2b2a4056..000000000 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/patch_files/rendering.py +++ /dev/null @@ -1,413 +0,0 @@ -# MIT License - -# Copyright (c) 2018 OpenAI - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -""" -2D rendering framework -""" -from __future__ import division -import os -import six -import sys -from gym import error -import math -import numpy as np -import pyglet - -from pyglet.gl import glEnable, glHint, glLineWidth, glBlendFunc, glClearColor, glPushMatrix, \ - glTranslatef, glRotatef, glScalef, glPopMatrix, glColor4f, glBegin, glVertex3f, glEnd, glLineStipple, \ - glDisable, glVertex2f, GL_BLEND, GL_LINE_SMOOTH, GL_LINE_SMOOTH_HINT, GL_NICEST, GL_SRC_ALPHA, \ - GL_ONE_MINUS_SRC_ALPHA, GL_LINE_STIPPLE, GL_POINTS, GL_QUADS, GL_TRIANGLES, GL_POLYGON, GL_LINE_LOOP, \ - GL_LINE_STRIP, GL_LINES - - -if "Apple" in sys.version: - if 'DYLD_FALLBACK_LIBRARY_PATH' in os.environ: - os.environ['DYLD_FALLBACK_LIBRARY_PATH'] += ':/usr/lib' - # (JDS 2016/04/15): avoid bug on Anaconda 2.3.0 / Yosemite - - -RAD2DEG = 57.29577951308232 - - -def get_display(spec): - """Convert a display specification (such as :0) into an actual Display - object. - - Pyglet only supports multiple Displays on Linux. - """ - if spec is None: - return None - elif isinstance(spec, six.string_types): - return pyglet.canvas.Display(spec) - else: - raise error.Error('Invalid display specification: {}. (Must be a string like :0 or None.)'.format(spec)) - - -class Viewer(object): - def __init__(self, width, height, display=None): - display = get_display(display) - - self.width = width - self.height = height - - self.window = pyglet.window.Window(width=width, height=height, display=display) - self.window.on_close = self.window_closed_by_user - self.geoms = [] - self.onetime_geoms = [] - self.transform = Transform() - - glEnable(GL_BLEND) - # glEnable(GL_MULTISAMPLE) - glEnable(GL_LINE_SMOOTH) - # glHint(GL_LINE_SMOOTH_HINT, GL_DONT_CARE) - glHint(GL_LINE_SMOOTH_HINT, GL_NICEST) - glLineWidth(2.0) - glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA) - - def close(self): - self.window.close() - - def window_closed_by_user(self): - self.close() - - def set_bounds(self, left, right, bottom, top): - assert right > left and top > bottom - scalex = self.width / (right - left) - scaley = self.height / (top - bottom) - self.transform = Transform( - translation=(-left * scalex, -bottom * scaley), - scale=(scalex, scaley)) - - def add_geom(self, geom): - self.geoms.append(geom) - - def add_onetime(self, geom): - self.onetime_geoms.append(geom) - - def render(self, return_rgb_array=False): - glClearColor(1, 1, 1, 1) - self.window.clear() - self.window.switch_to() - self.window.dispatch_events() - self.transform.enable() - for geom in self.geoms: - geom.render() - for geom in self.onetime_geoms: - geom.render() - self.transform.disable() - arr = None - if return_rgb_array: - buffer = pyglet.image.get_buffer_manager().get_color_buffer() - image_data = buffer.get_image_data() - arr = np.fromstring(image_data.data, dtype=np.uint8, sep='') - # In https://github.com/openai/gym-http-api/issues/2, we - # discovered that someone using Xmonad on Arch was having - # a window of size 598 x 398, though a 600 x 400 window - # was requested. (Guess Xmonad was preserving a pixel for - # the boundary.) So we use the buffer height/width rather - # than the requested one. - arr = arr.reshape(buffer.height, buffer.width, 4) - arr = arr[::-1, :, 0:3] - self.window.flip() - self.onetime_geoms = [] - return arr - - # Convenience - def draw_circle(self, radius=10, res=30, filled=True, **attrs): - geom = make_circle(radius=radius, res=res, filled=filled) - _add_attrs(geom, attrs) - self.add_onetime(geom) - return geom - - def draw_polygon(self, v, filled=True, **attrs): - geom = make_polygon(v=v, filled=filled) - _add_attrs(geom, attrs) - self.add_onetime(geom) - return geom - - def draw_polyline(self, v, **attrs): - geom = make_polyline(v=v) - _add_attrs(geom, attrs) - self.add_onetime(geom) - return geom - - def draw_line(self, start, end, **attrs): - geom = Line(start, end) - _add_attrs(geom, attrs) - self.add_onetime(geom) - return geom - - def get_array(self): - self.window.flip() - image_data = pyglet.image.get_buffer_manager().get_color_buffer().get_image_data() - self.window.flip() - arr = np.fromstring(image_data.data, dtype=np.uint8, sep='') - arr = arr.reshape(self.height, self.width, 4) - return arr[::-1, :, 0:3] - - -def _add_attrs(geom, attrs): - if "color" in attrs: - geom.set_color(*attrs["color"]) - if "linewidth" in attrs: - geom.set_linewidth(attrs["linewidth"]) - - -class Geom(object): - def __init__(self): - self._color = Color((0, 0, 0, 1.0)) - self.attrs = [self._color] - - def render(self): - for attr in reversed(self.attrs): - attr.enable() - self.render1() - for attr in self.attrs: - attr.disable() - - def render1(self): - raise NotImplementedError - - def add_attr(self, attr): - self.attrs.append(attr) - - def set_color(self, r, g, b, alpha=1): - self._color.vec4 = (r, g, b, alpha) - - -class Attr(object): - def enable(self): - raise NotImplementedError - - def disable(self): - pass - - -class Transform(Attr): - def __init__(self, translation=(0.0, 0.0), rotation=0.0, scale=(1, 1)): - self.set_translation(*translation) - self.set_rotation(rotation) - self.set_scale(*scale) - - def enable(self): - glPushMatrix() - glTranslatef(self.translation[0], self.translation[1], 0) # translate to GL loc ppint - glRotatef(RAD2DEG * self.rotation, 0, 0, 1.0) - glScalef(self.scale[0], self.scale[1], 1) - - def disable(self): - glPopMatrix() - - def set_translation(self, newx, newy): - self.translation = (float(newx), float(newy)) - - def set_rotation(self, new): - self.rotation = float(new) - - def set_scale(self, newx, newy): - self.scale = (float(newx), float(newy)) - - -class Color(Attr): - def __init__(self, vec4): - self.vec4 = vec4 - - def enable(self): - glColor4f(*self.vec4) - - -class LineStyle(Attr): - def __init__(self, style): - self.style = style - - def enable(self): - glEnable(GL_LINE_STIPPLE) - glLineStipple(1, self.style) - - def disable(self): - glDisable(GL_LINE_STIPPLE) - - -class LineWidth(Attr): - def __init__(self, stroke): - self.stroke = stroke - - def enable(self): - glLineWidth(self.stroke) - - -class Point(Geom): - def __init__(self): - Geom.__init__(self) - - def render1(self): - glBegin(GL_POINTS) # draw point - glVertex3f(0.0, 0.0, 0.0) - glEnd() - - -class FilledPolygon(Geom): - def __init__(self, v): - Geom.__init__(self) - self.v = v - - def render1(self): - if len(self.v) == 4: - glBegin(GL_QUADS) - elif len(self.v) > 4: - glBegin(GL_POLYGON) - else: - glBegin(GL_TRIANGLES) - for p in self.v: - glVertex3f(p[0], p[1], 0) # draw each vertex - glEnd() - - color = ( - self._color.vec4[0] * 0.5, - self._color.vec4[1] * 0.5, - self._color.vec4[2] * 0.5, - self._color.vec4[3] * 0.5) - glColor4f(*color) - glBegin(GL_LINE_LOOP) - for p in self.v: - glVertex3f(p[0], p[1], 0) # draw each vertex - glEnd() - - -def make_circle(radius=10, res=30, filled=True): - points = [] - for i in range(res): - ang = 2 * math.pi * i / res - points.append((math.cos(ang) * radius, math.sin(ang) * radius)) - if filled: - return FilledPolygon(points) - else: - return PolyLine(points, True) - - -def make_polygon(v, filled=True): - if filled: - return FilledPolygon(v) - else: - return PolyLine(v, True) - - -def make_polyline(v): - return PolyLine(v, False) - - -def make_capsule(length, width): - l, r, t, b = 0, length, width / 2, -width / 2 - box = make_polygon([(l, b), (l, t), (r, t), (r, b)]) - circ0 = make_circle(width / 2) - circ1 = make_circle(width / 2) - circ1.add_attr(Transform(translation=(length, 0))) - geom = Compound([box, circ0, circ1]) - return geom - - -class Compound(Geom): - def __init__(self, gs): - Geom.__init__(self) - self.gs = gs - for g in self.gs: - g.attrs = [a for a in g.attrs if not isinstance(a, Color)] - - def render1(self): - for g in self.gs: - g.render() - - -class PolyLine(Geom): - def __init__(self, v, close): - Geom.__init__(self) - self.v = v - self.close = close - self.linewidth = LineWidth(1) - self.add_attr(self.linewidth) - - def render1(self): - glBegin(GL_LINE_LOOP if self.close else GL_LINE_STRIP) - for p in self.v: - glVertex3f(p[0], p[1], 0) # draw each vertex - glEnd() - - def set_linewidth(self, x): - self.linewidth.stroke = x - - -class Line(Geom): - def __init__(self, start=(0.0, 0.0), end=(0.0, 0.0)): - Geom.__init__(self) - self.start = start - self.end = end - self.linewidth = LineWidth(1) - self.add_attr(self.linewidth) - - def render1(self): - glBegin(GL_LINES) - glVertex2f(*self.start) - glVertex2f(*self.end) - glEnd() - - -class Image(Geom): - def __init__(self, fname, width, height): - Geom.__init__(self) - self.width = width - self.height = height - img = pyglet.image.load(fname) - self.img = img - self.flip = False - - def render1(self): - self.img.blit(-self.width / 2, -self.height / 2, width=self.width, height=self.height) - - -class SimpleImageViewer(object): - def __init__(self, display=None): - self.window = None - self.isopen = False - self.display = display - - def imshow(self, arr): - if self.window is None: - height, width, channels = arr.shape - self.window = pyglet.window.Window(width=width, height=height, display=self.display) - self.width = width - self.height = height - self.isopen = True - assert arr.shape == (self.height, self.width, 3), "You passed in an image with the wrong number shape" - image = pyglet.image.ImageData(self.width, self.height, 'RGB', arr.tobytes(), pitch=self.width * -3) - self.window.clear() - self.window.switch_to() - self.window.dispatch_events() - image.blit(0, 0) - self.window.flip() - - def close(self): - if self.isopen: - self.window.close() - self.isopen = False - - def __del__(self): - self.close() diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/particle_train.py b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/particle_train.py deleted file mode 100644 index 34e25a5d6..000000000 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/particle_train.py +++ /dev/null @@ -1,123 +0,0 @@ -import os - -from ray_on_aml.core import Ray_On_AML - -from ray.tune import run_experiments -from ray.tune.registry import register_trainable, register_env, get_trainable_cls -import ray.rllib.contrib.maddpg.maddpg as maddpg - -from rllib_multiagent_particle_env import env_creator -from util import parse_args - - -def setup_ray(): - ray_on_aml = Ray_On_AML() - ray_on_aml.getRay() - - register_env('particle', env_creator) - - -def gen_policy(args, env, id): - use_local_critic = [ - args.adv_policy == 'ddpg' if id < args.num_adversaries else - args.good_policy == 'ddpg' for id in range(env.num_agents) - ] - return ( - None, - env.observation_space_dict[id], - env.action_space_dict[id], - { - 'agent_id': id, - 'use_local_critic': use_local_critic[id], - 'obs_space_dict': env.observation_space_dict, - 'act_space_dict': env.action_space_dict, - } - ) - - -def gen_policies(args, env_config): - env = env_creator(env_config) - return {'policy_%d' % i: gen_policy(args, env, i) for i in range(len(env.observation_space_dict))} - - -def to_multiagent_config(policies): - policy_ids = list(policies.keys()) - return { - 'policies': policies, - 'policy_mapping_fn': lambda index: policy_ids[index] - } - - -def train(args, env_config): - def stop(trial_id, result): - max_train_time = int(os.environ.get('AML_MAX_TRAIN_TIME_SECONDS', 2 * 60 * 60)) - - return result['episode_reward_mean'] >= args.final_reward \ - or result['time_total_s'] >= max_train_time - - run_experiments({ - 'MADDPG_RLLib': { - 'run': 'contrib/MADDPG', - 'env': 'particle', - 'stop': stop, - # Uncomment to enable more frequent checkpoints: - # 'checkpoint_freq': args.checkpoint_freq, - 'checkpoint_at_end': True, - 'local_dir': args.local_dir, - 'restore': args.restore, - 'config': { - # === Log === - 'log_level': 'ERROR', - - # === Environment === - 'env_config': env_config, - 'num_envs_per_worker': args.num_envs_per_worker, - 'horizon': args.max_episode_len, - - # === Policy Config === - # --- Model --- - 'good_policy': args.good_policy, - 'adv_policy': args.adv_policy, - 'actor_hiddens': [args.num_units] * 2, - 'actor_hidden_activation': 'relu', - 'critic_hiddens': [args.num_units] * 2, - 'critic_hidden_activation': 'relu', - 'n_step': args.n_step, - 'gamma': args.gamma, - - # --- Exploration --- - 'tau': 0.01, - - # --- Replay buffer --- - 'buffer_size': int(1e6), - - # --- Optimization --- - 'actor_lr': args.lr, - 'critic_lr': args.lr, - 'learning_starts': args.train_batch_size * args.max_episode_len, - 'sample_batch_size': args.sample_batch_size, - 'train_batch_size': args.train_batch_size, - 'batch_mode': 'truncate_episodes', - - # --- Parallelism --- - 'num_workers': args.num_workers, - 'num_gpus': args.num_gpus, - 'num_gpus_per_worker': 0, - - # === Multi-agent setting === - 'multiagent': to_multiagent_config(gen_policies(args, env_config)), - }, - }, - }, verbose=1) - - -if __name__ == '__main__': - args = parse_args() - setup_ray() - - env_config = { - 'scenario_name': args.scenario, - 'horizon': args.max_episode_len, - 'video_frequency': args.checkpoint_freq, - } - train(args, env_config) diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/rllib_multiagent_particle_env.py b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/rllib_multiagent_particle_env.py deleted file mode 100644 index d2954daa0..000000000 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/rllib_multiagent_particle_env.py +++ /dev/null @@ -1,113 +0,0 @@ -# Some code taken from: https://github.com/wsjeon/maddpg-rllib/ - -import imp -import os - -import gym -from gym import wrappers -from ray import rllib - -from multiagent.environment import MultiAgentEnv -import multiagent.scenarios as scenarios - - -CUSTOM_SCENARIOS = ['simple_switch'] - - -class ParticleEnvRenderWrapper(gym.Wrapper): - def __init__(self, env, horizon): - super().__init__(env) - self.horizon = horizon - - def reset(self): - self._num_steps = 0 - - return self.env.reset() - - def render(self, mode): - if mode == 'human': - self.env.render(mode=mode) - else: - return self.env.render(mode=mode)[0] - - def step(self, actions): - obs_list, rew_list, done_list, info_list = self.env.step(actions) - - self._num_steps += 1 - done = (all(done_list) or self._num_steps >= self.horizon) - - # Gym monitor expects reward to be an int. This is only used for its - # stats reporter, which we're not interested in. To make video recording - # work, we package the rewards in the info object and extract it below. - return obs_list, 0, done, [rew_list, done_list, info_list] - - -class RLlibMultiAgentParticleEnv(rllib.MultiAgentEnv): - def __init__(self, scenario_name, horizon, monitor_enabled=False, video_frequency=500): - self._env = _make_env(scenario_name, horizon, monitor_enabled, video_frequency) - self.num_agents = self._env.n - self.agent_ids = list(range(self.num_agents)) - - self.observation_space_dict = self._make_dict(self._env.observation_space) - self.action_space_dict = self._make_dict(self._env.action_space) - - def reset(self): - obs_dict = self._make_dict(self._env.reset()) - return obs_dict - - def step(self, action_dict): - actions = list(action_dict.values()) - obs_list, _, _, infos = self._env.step(actions) - rew_list, done_list, _ = infos - - obs_dict = self._make_dict(obs_list) - rew_dict = self._make_dict(rew_list) - done_dict = self._make_dict(done_list) - done_dict['__all__'] = all(done_list) - info_dict = self._make_dict([{'done': done} for done in done_list]) - - return obs_dict, rew_dict, done_dict, info_dict - - def render(self, mode='human'): - self._env.render(mode=mode) - - def _make_dict(self, values): - return dict(zip(self.agent_ids, values)) - - -def _video_callable(video_frequency): - def should_record_video(episode_id): - if episode_id % video_frequency == 0: - return True - return False - - return should_record_video - - -def _make_env(scenario_name, horizon, monitor_enabled, video_frequency): - if scenario_name in CUSTOM_SCENARIOS: - # Scenario file must exist locally - file_path = os.path.join(os.path.dirname(__file__), scenario_name + '.py') - scenario = imp.load_source('', file_path).Scenario() - else: - scenario = scenarios.load(scenario_name + '.py').Scenario() - - world = scenario.make_world() - - env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation) - env.metadata['video.frames_per_second'] = 8 - - env = ParticleEnvRenderWrapper(env, horizon) - - if not monitor_enabled: - return env - - return wrappers.Monitor(env, './logs/videos', resume=True, video_callable=_video_callable(video_frequency)) - - -def env_creator(config): - monitor_enabled = False - if hasattr(config, 'worker_index') and hasattr(config, 'vector_index'): - monitor_enabled = (config.worker_index == 1 and config.vector_index == 0) - - return RLlibMultiAgentParticleEnv(**config, monitor_enabled=monitor_enabled) diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/simple_switch.py b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/simple_switch.py deleted file mode 100644 index ca419810a..000000000 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/simple_switch.py +++ /dev/null @@ -1,358 +0,0 @@ -import numpy as np -import random - -from multiagent.core import World, Agent, Landmark -from multiagent.scenario import BaseScenario - - -class SwitchWorld(World): - """ Extended World with hills and switches """ - def __init__(self, hills, switches): - super().__init__() - # add hills and switches - self.hills = hills - self.switches = switches - self.landmarks.extend(self.hills) - self.landmarks.extend(self.switches) - - def step(self): - - super().step() - - # if all hills are activated, reset the switches and hills - if all([hill.active for hill in self.hills]): - self.reset_hills() - self.reset_switches() - else: - # Update switches - for switch in self.switches: - switch.step(self) - # Update hills - for hill in self.hills: - hill.step(self) - - def reset_hills(self): - possible_hill_positions = [np.array([-0.8, 0]), np.array([0, 0.8]), np.array([0.8, 0]), np.array([0, -0.8])] - hill_positions = random.sample(possible_hill_positions, k=len(self.hills)) - for i, hill in enumerate(self.hills): - hill.state.p_pos = hill_positions[i] - hill.deactivate() - - def reset_switches(self): - possible_switch_positions = [ - np.array([-0.8, -0.8]), - np.array([-0.8, 0.8]), - np.array([0.8, -0.8]), - np.array([0.8, 0.8])] - switch_positions = random.sample(possible_switch_positions, k=len(self.switches)) - for i, switch in enumerate(self.switches): - switch.state.p_pos = switch_positions[i] - switch.deactivate() - - -class Scenario(BaseScenario): - def make_world(self): - - # main configurations - num_agents = 2 - num_hills = 2 - num_switches = 1 - self.max_episode_length = 100 - - # create hills (on edges) - possible_hill_positions = [np.array([-0.8, 0]), np.array([0, 0.8]), np.array([0.8, 0]), np.array([0, -0.8])] - hill_positions = random.sample(possible_hill_positions, k=num_hills) - hills = [Hill(hill_positions[i]) for i in range(num_hills)] - # create switches (in corners) - possible_switch_positions = [ - np.array([-0.8, -0.8]), - np.array([-0.8, 0.8]), - np.array([0.8, -0.8]), - np.array([0.8, 0.8])] - switch_positions = random.sample(possible_switch_positions, k=num_switches) - switches = [Switch(switch_positions[i]) for i in range(num_switches)] - - # make world and set basic properties - world = SwitchWorld(hills, switches) - world.dim_c = 2 - world.collaborative = True - - # add agents - world.agents = [Agent() for i in range(num_agents)] - for i, agent in enumerate(world.agents): - agent.name = 'agent %d' % i - agent.collide = True - agent.silent = True - agent.size = 0.1 - agent.accel = 5.0 - agent.max_speed = 5.0 - if i == 0: - agent.color = np.array([0.35, 0.35, 0.85]) - else: - agent.color = np.array([0.35, 0.85, 0.85]) - - # make initial conditions - self.reset_world(world) - - return world - - def reset_world(self, world): - # set random initial states - for agent in world.agents: - agent.state.p_pos = np.array([random.uniform(-1, +1) for _ in range(world.dim_p)]) - agent.state.p_vel = np.zeros(world.dim_p) - agent.state.c = np.zeros(world.dim_c) - # set hills randomly - world.reset_hills() - # set switches randomly - world.reset_switches() - - def is_collision(self, agent1, agent2): - delta_pos = agent1.state.p_pos - agent2.state.p_pos - dist = np.sqrt(np.sum(np.square(delta_pos))) - dist_min = agent1.size + agent2.size - return True if dist < dist_min else False - - def reward(self, agent, world): - # Agents are rewarded based on number of landmarks activated - rew = 0 - if all([h.active for h in world.hills]): - rew += 100 - else: - # give bonus each time a hill is activated - for hill in world.hills: - if hill.activated_just_now: - rew += 50 - # penalise timesteps where nothing is happening - if rew == 0: - rew -= 0.1 - # add collision penalty - if agent.collide: - for a in world.agents: - # note: this also counts collision with "itself", so gives -1 at every timestep - # would be good to tune the reward function and use (not a == agent) here - if self.is_collision(a, agent): - rew -= 1 - return rew - - def observation(self, agent, world): - # get positions of all entities in this agent's reference frame - entity_pos = [] - for entity in world.landmarks: # world.entities: - entity_pos.append(entity.state.p_pos - agent.state.p_pos) - # entity colors - entity_color = [] - for entity in world.landmarks: # world.entities: - entity_color.append(entity.color) - # communication of all other agents - comm = [] - other_pos = [] - for other in world.agents: - if other is agent: - continue - comm.append(other.state.c) - other_pos.append(other.state.p_pos - agent.state.p_pos) - return np.concatenate([agent.state.p_vel] + [agent.state.p_pos] + entity_pos + other_pos + comm) - - -class Hill(Landmark): - """ - A hill that can be captured by an agent. - To be captured, a team must occupy a hill for a fixed amount of time. - """ - - def __init__(self, - pos=None, - size=0.08, - capture_time=2 - ): - - # Initialize Landmark super class - super().__init__() - self.movable = False - self.collide = False - self.state.p_pos = pos - self.size = size - - # Set static configurations - self.capture_time = capture_time - - # Initialize all hills to be inactive - self.active = False - self.color = np.array([0.5, 0.5, 0.5]) - self.capture_timer = 0 - - self.activated_just_now = False - - def activate(self): - self.active = True - self.color = np.array([0.1, 0.1, 0.9]) - - def deactivate(self): - self.active = False - self.color = np.array([0.5, 0.5, 0.5]) - - def _is_occupied(self, agents): - # a hill is occupied if an agent stands on it - for agent in agents: - dist = np.sqrt(np.sum(np.square(agent.state.p_pos - self.state.p_pos))) - if dist < agent.size + self.size: - return True - return False - - def step(self, world): - - self.activated_just_now = False - - # If hill isn't activated yet, check if an agent activates it - # if (not self.active) and (world.switch.is_active()): - if (not self.active): - - # Check if an agent is on the hill and all switches are active - if (self._is_occupied(world.agents)) and all([switch.active for switch in world.switches]): - self.capture_timer += 1 - - # activate hill (this is irreversible) - if self.capture_timer > self.capture_time: - self.activate() - self.activated_just_now = True - - # Reset capture timer if hill is not occupied - else: - self.capture_timer = 0 - - -class Switch(Landmark): - """ - A switch that can be activated by an agent. - The agent has to stay on the switch for it to be active. - """ - - def __init__(self, - pos=None, - size=0.03, - ): - - # Initialize Landmark super class - super().__init__() - self.movable = False - self.collide = False - self.state.p_pos = pos - self.size = size - - # Initialize all hills to be inactive - self.active = False - self.color = np.array([0.8, 0.05, 0.3]) - self.capture_timer = 0 - - def activate(self): - self.active = True - self.color = np.array([0.1, 0.9, 0.4]) - - def deactivate(self): - self.active = False - self.color = np.array([0.8, 0.05, 0.3]) - - def _is_occupied(self, agents): - # a switch is active if an agent stands on it - for agent in agents: - dist = np.sqrt(np.sum(np.square(agent.state.p_pos - self.state.p_pos))) - if dist < agent.size + self.size: - return True - return False - - def step(self, world): - # check if an agent is on the switch and activate/deactive accordingly - if self._is_occupied(world.agents): - self.activate() - else: - self.deactivate() - - -class SwitchExpertPolicy(): - """ - Hand-coded expert policy for the simple switch environment. - Types of possible experts: - - always go to the switch - - always go to the hills - """ - def __init__(self, dim_c, agent, world, expert_type=None, discrete_action_input=True): - - self.dim_c = dim_c - self.discrete_action_input = discrete_action_input - # the agent we control and world we're in - self.agent = agent - self.world = world - - if expert_type is None: - self.expert_type = random.choice(['switch', 'hill']) - else: - self.expert_type = expert_type - if self.expert_type == 'switch': - self.target_switch = self.select_inital_target_switch() - elif self.expert_type == 'hill': - self.target_hill = self.select_inital_target_hill() - else: - raise NotImplementedError - - self.step_count = 0 - - def select_inital_target_switch(self): - return random.choice(self.world.switches) - - def select_inital_target_hill(self): - return random.choice(self.world.hills) - - def action(self): - - # select a target! - if self.expert_type == 'switch': - # if agent is not already on a switch, choose target switch - if not any([switch._is_occupied([self.agent]) for switch in self.world.switches]): - # select a target switch if there's an inactive one - inactive_switches = [switch for switch in self.world.switches if not switch.active] - if len(inactive_switches) > 0 and (self.target_switch not in inactive_switches): - self.target_switch = random.choice(inactive_switches) - target = self.target_switch.state.p_pos - elif self.expert_type == 'hill': - # select a target hill if we haven't done so yet, or the current target switch is inactive - inactive_hills = [hill for hill in self.world.hills if not hill.active] - if len(inactive_hills) > 0 and (self.target_hill not in inactive_hills): - self.target_hill = random.choice(inactive_hills) - target = self.target_hill.state.p_pos - - self.step_count += 1 - - impulse = np.clip(target - self.agent.state.p_pos, -self.agent.u_range, self.agent.u_range) - - if self.discrete_action_input: - u_idx = np.argmax(np.abs(impulse)) - if u_idx == 0 and impulse[u_idx] < 0: - u = 1 - elif u_idx == 0 and impulse[u_idx] > 0: - u = 2 - elif u_idx == 1 and impulse[u_idx] < 0: - u = 3 - elif u_idx == 1 and impulse[u_idx] > 0: - u = 4 - else: - u = 0 - else: - u = np.zeros(5) - if (impulse[0] == impulse[1] == 0) \ - or (self.step_count < self.burn_in) \ - or (self.burn_step != 0 and self.step_count % self.burn_step != 0): - u[0] = 0.1 - else: - pass - # u: noop (?), right, left, down, up - if impulse[0] > 0: # x-direction (- left/right + ) - u[1] = impulse[0] # right - elif impulse[0] < 0: - u[2] = -impulse[0] - if impulse[1] > 0: # y-direction (- up/down + ) - u[3] = impulse[1] - elif impulse[1] < 0: - u[4] = -impulse[1] - - return u diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/util.py b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/util.py deleted file mode 100644 index 75c0c619b..000000000 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/files/util.py +++ /dev/null @@ -1,82 +0,0 @@ -import argparse -import os -import re - -from rllib_multiagent_particle_env import CUSTOM_SCENARIOS - - -def parse_args(): - parser = argparse.ArgumentParser('MADDPG with OpenAI MPE') - - # Environment - parser.add_argument('--scenario', type=str, default='simple', - choices=['simple', 'simple_speaker_listener', - 'simple_crypto', 'simple_push', - 'simple_tag', 'simple_spread', 'simple_adversary' - ] + CUSTOM_SCENARIOS, - help='name of the scenario script') - parser.add_argument('--max-episode-len', type=int, default=25, - help='maximum episode length') - parser.add_argument('--num-episodes', type=int, default=60000, - help='number of episodes') - parser.add_argument('--num-adversaries', type=int, default=0, - help='number of adversaries') - parser.add_argument('--good-policy', type=str, default='maddpg', - help='policy for good agents') - parser.add_argument('--adv-policy', type=str, default='maddpg', - help='policy of adversaries') - - # Core training parameters - parser.add_argument('--lr', type=float, default=1e-2, - help='learning rate for Adam optimizer') - parser.add_argument('--gamma', type=float, default=0.95, - help='discount factor') - # NOTE: 1 iteration = sample_batch_size * num_workers timesteps * num_envs_per_worker - parser.add_argument('--sample-batch-size', type=int, default=25, - help='number of data points sampled /update /worker') - parser.add_argument('--train-batch-size', type=int, default=1024, - help='number of data points /update') - parser.add_argument('--n-step', type=int, default=1, - help='length of multistep value backup') - parser.add_argument('--num-units', type=int, default=64, - help='number of units in the mlp') - parser.add_argument('--final-reward', type=int, default=-400, - help='final reward after which to stop training') - - # Checkpoint - parser.add_argument('--checkpoint-freq', type=int, default=200, - help='save model once every time this many iterations are completed') - parser.add_argument('--local-dir', type=str, default='./logs', - help='path to save checkpoints') - parser.add_argument('--restore', type=str, default=None, - help='directory in which training state and model are loaded') - - # Parallelism - parser.add_argument('--num-workers', type=int, default=1) - parser.add_argument('--num-envs-per-worker', type=int, default=4) - parser.add_argument('--num-gpus', type=int, default=0) - - return parser.parse_args() - - -def find_final_checkpoint(start_dir): - def find(pattern, path): - result = [] - for root, _, files in os.walk(path): - for name in files: - if pattern.match(name): - result.append(os.path.join(root, name)) - return result - - cp_pattern = re.compile('.*checkpoint-\\d+$') - checkpoint_files = find(cp_pattern, start_dir) - - checkpoint_numbers = [] - for file in checkpoint_files: - checkpoint_numbers.append(int(file.split('-')[-1])) - - final_checkpoint_number = max(checkpoint_numbers) - - return next( - checkpoint_file for checkpoint_file in checkpoint_files - if checkpoint_file.endswith(str(final_checkpoint_number))) diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/images/particle_simple_spread.gif b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/images/particle_simple_spread.gif deleted file mode 100644 index 520e0c0ee..000000000 Binary files a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/images/particle_simple_spread.gif and /dev/null differ diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb deleted file mode 100644 index 1be9aecf8..000000000 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb +++ /dev/null @@ -1,565 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reinforcement Learning in Azure Machine Learning - Training multiple agents on collaborative ParticleEnv tasks\n", - "\n", - "This tutorial will show you how to train policies in a multi-agent scenario.\n", - "We use OpenAI Gym's [Particle environments](https://github.com/openai/multiagent-particle-envs),\n", - "which model agents and landmarks in a two-dimensional world. Particle comes with\n", - "several predefined scenarios, both competitive and collaborative, and with or without communication.\n", - "\n", - "For this tutorial, we pick a cooperative navigation scenario where N agents are in a world with N\n", - "landmarks. The agents' goal is to cover all the landmarks without collisions,\n", - "so agents must learn to avoid each other (social distancing!). The video below shows training\n", - "results for N=3 agents/landmarks:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      \n", - " \"Particle\n", - "
      Fig 1. Video of 3 agents covering 3 landmarks in a multiagent Particle scenario.
      \n", - "\n", - "The tutorial will cover the following steps:\n", - "- Initializing Azure Machine Learning resources for training\n", - "- Training policies in a multi-agent environment with Azure Machine Learning service\n", - "- Monitoring training progress\n", - "\n", - "## Prerequisites\n", - "\n", - "The user should have completed the Azure Machine Learning introductory tutorial. You will need to make sure that you have a valid subscription id, a resource group and a workspace. For detailed instructions see [Tutorial: Get started creating your first ML experiment](https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-sdk-setup).\n", - "\n", - "Please ensure that you have a current version of IPython (>= 7.15) installed.\n", - "\n", - "While this is a standalone notebook, we highly recommend going over the introductory notebooks for RL first.\n", - "- Getting started:\n", - " - [RL using a compute instance with Azure Machine Learning](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb)\n", - " - [RL using Azure Machine Learning compute](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb)\n", - "- [Scaling RL training runs with Azure Machine Learning](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb)\n", - "\n", - "## Initialize resources\n", - "\n", - "All required Azure Machine Learning service resources for this tutorial can be set up from Jupyter. This includes:\n", - "\n", - "- Connecting to your existing Azure Machine Learning workspace.\n", - "- Creating an experiment to track runs.\n", - "- Creating remote compute targets for [Ray](https://docs.ray.io/en/latest/index.html).\n", - "\n", - "\n", - "### Azure Machine Learning SDK\n", - "\n", - "Display the Azure Machine Learning SDK version." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646249589452 - } - }, - "outputs": [], - "source": [ - "import azureml.core\n", - "print('Azure Machine Learning SDK Version: ', azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Connect to workspace\n", - "\n", - "Get a reference to an existing Azure Machine Learning workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646250284486 - } - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.location, ws.resource_group, sep=' | ')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an experiment\n", - "\n", - "Create an experiment to track the runs in your workspace. A\n", - "workspace can have multiple experiments and each experiment\n", - "can be used to track multiple runs (see [documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment?view=azure-ml-py)\n", - "for details)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646250342411 - } - }, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "exp = Experiment(workspace=ws, name='particle-multiagent')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or attach an existing compute resource\n", - "\n", - "A compute target is a designated compute resource where you run your training script. For more information, see [What are compute targets in Azure Machine Learning service?](https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target).\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "#### CPU target for Ray head\n", - "\n", - "In the experiment setup for this tutorial, the Ray head node will\n", - "run on a CPU node (D3 type). A maximum cluster size of 1 node is\n", - "therefore sufficient. If you wish to run multiple experiments in\n", - "parallel using the same CPU cluster, you may elect to increase this\n", - "number. The cluster will automatically scale down to 0 nodes when\n", - "no training jobs are scheduled (see min_nodes).\n", - "\n", - "The code below creates a compute cluster of D3 type nodes.\n", - "If the cluster with the specified name is already in your workspace\n", - "the code will skip the creation process.\n", - "\n", - "**Note: Creation of a compute resource can take several minutes**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646250346756 - } - }, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "\n", - "cpu_cluster_name = 'cpu-cl-d3'\n", - "\n", - "if cpu_cluster_name in ws.compute_targets:\n", - " cpu_cluster = ws.compute_targets[cpu_cluster_name]\n", - " if cpu_cluster and type(cpu_cluster) is AmlCompute:\n", - " if cpu_cluster.provisioning_state == 'Succeeded':\n", - " print('Found existing compute target for {}. Using it.'.format(cpu_cluster_name))\n", - " else: \n", - " raise Exception('Found existing compute target for {} '.format(cpu_cluster_name)\n", - " + 'but it is in state {}'.format(cpu_cluster.provisioning_state))\n", - "else:\n", - " print('Creating a new compute target for {}...'.format(cpu_cluster_name))\n", - " provisioning_config = AmlCompute.provisioning_configuration(\n", - " vm_size='STANDARD_D3',\n", - " min_nodes=0, \n", - " max_nodes=1)\n", - "\n", - " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n", - " cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - " print('Cluster created.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training the policies\n", - "\n", - "### Training environment\n", - "\n", - "This tutorial uses a custom docker image\n", - "with the necessary software installed. The [Environment](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments)\n", - "class stores the configuration for the training environment. The\n", - "docker image is set via `env.docker.base_image`.\n", - "`user_managed_dependencies` is set so that\n", - "the preinstalled Python packages in the image are preserved.\n", - "\n", - "Note that since we want to capture videos of the training runs requiring a display, we set the interpreter_path such that the Python process is started via **xvfb-run**." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646257481631 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "import os\n", - "\n", - "ray_environment_name = 'particle-cpu'\n", - "ray_environment_dockerfile_path = os.path.join(os.getcwd(), 'docker', 'cpu', 'Dockerfile')\n", - "ray_environment = Environment. \\\n", - " from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n", - " register(workspace=ws)\n", - "ray_gpu_build_details = ray_environment.build(workspace=ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training script\n", - "\n", - "This tutorial uses the multiagent algorithm [Multi-Agent Deep Deterministic Policy Gradient (MADDPG)](https://docs.ray.io/en/latest/rllib-algorithms.html?highlight=maddpg#multi-agent-deep-deterministic-policy-gradient-contrib-maddpg).\n", - "For training policies in a multiagent scenario, Ray's RLlib also\n", - "requires the `multiagent` configuration section to be specified. You\n", - "can find more information in the [common parameters](https://docs.ray.io/en/latest/rllib-training.html?highlight=multiagent#common-parameters)\n", - "documentation.\n", - "\n", - "The stopping criteria are set such that the training run is\n", - "terminated after either a mean reward of -450 is observed, or\n", - "training has run for over 2 hours.\n", - "\n", - "### Submitting a training run\n", - "\n", - "You can submit the training run using a `ScriptRunConfig`. By providing the\n", - "command to run the training, and a `RunConfig` object configured with your\n", - "compute target, number of nodes, and environment image to use.\n", - "\n", - "Note that you can use the same notebook and scripts to experiment with\n", - "different Particle environments. You can find a list of supported\n", - "environments [here](https://github.com/openai/multiagent-particle-envs/tree/master#list-of-environments).\n", - "Simply change the `--scenario` parameter to a supported scenario.\n", - "\n", - "In order to get the best training results, you can also adjust the\n", - "`--final-reward` parameter to determine when to stop training. A greater\n", - "reward means longer running time, but improved results. By default,\n", - "the final reward will be -450, which should show good progress after\n", - "about one hour of run time.\n", - "\n", - "For this notebook, we use a single D3 nodes, giving us a total of 4 CPUs and\n", - "0 GPUs. One CPU is used by the MADDPG trainer, and an additional CPU is\n", - "consumed by the RLlib rollout worker. The other 2 CPUs are not used, though\n", - "smaller node types will run out of memory for this task." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646275371701 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import RunConfiguration, ScriptRunConfig, Experiment\n", - "from azureml.core.runconfig import DockerConfiguration, RunConfiguration\n", - "from azureml.widgets import RunDetails\n", - "\n", - "experiment_name = 'particle-multiagent'\n", - "\n", - "experiment = Experiment(workspace=ws, name=experiment_name)\n", - "\n", - "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n", - "aml_run_config_ml.target = cpu_cluster\n", - "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n", - "aml_run_config_ml.node_count = 1\n", - "aml_run_config_ml.environment = ray_environment\n", - "\n", - "config = ScriptRunConfig(source_directory='./files',\n", - " command=[\n", - " 'xvfb-run -s \"-screen 0 640x480x16 -ac +extension GLX +render\" python',\n", - " 'particle_train.py',\n", - " '--scenario', 'simple_spread',\n", - " '--final-reward', '-450'\n", - " ],\n", - " run_config = aml_run_config_ml\n", - " )\n", - "train_run = experiment.submit(config)\n", - "\n", - "RunDetails(train_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Job cancellation\n", - "\n", - "You may cancel the job by uncommenting and running the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If you wish to cancel the run before it completes, uncomment and execute:\n", - "# train_run.cancel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Monitoring training progress\n", - "\n", - "### View the Tensorboard\n", - "\n", - "The Tensorboard can be displayed via the Azure Machine Learning\n", - "service's [Tensorboard API](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-tensorboard).\n", - "When running locally, please make sure to follow the instructions\n", - "in the link and install required packages. Running this cell will output a URL for the Tensorboard.\n", - "\n", - "Note that the training script sets the log directory when\n", - "starting RLlib via the local_dir parameter. ./logs will automatically\n", - "appear in the downloadable files for a run. Since this script is\n", - "executed on the Ray head node run, we need to get a reference to it\n", - "as shown below.\n", - "\n", - "The Tensorboard API will continuously stream logs from the run.\n", - "\n", - "**Note: It may take a couple of minutes after the run is in \"Running\"\n", - "state before Tensorboard files are available and the board will refresh automatically**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from azureml.tensorboard import Tensorboard\n", - "\n", - "# tb = Tensorboard([train_run])\n", - "# tb.start()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View training videos\n", - "\n", - "As mentioned above, we record videos of the agents interacting with the\n", - "Particle world. These videos are often a crucial indicator for training\n", - "success. The code below downloads the latest video as it becomes available\n", - "and displays it in-line.\n", - "\n", - "Over time, the agents learn to cooperate and avoid collisions while\n", - "traveling to all landmarks.\n", - "\n", - "**Note: It can take several minutes for a video to appear after the run\n", - "was started.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from azureml.core import Dataset\n", - "from azureml.data.dataset_error_handling import DatasetValidationError\n", - "\n", - "from IPython.display import clear_output\n", - "from IPython.core.display import display, Video\n", - "\n", - "datastore = ws.datastores['workspaceartifactstore']\n", - "path_prefix = './tmp_videos'\n", - "\n", - "def download_latest_training_video(run, video_checkpoint_counter):\n", - " run_artifacts_path = os.path.join('ExperimentRun', f'dcid.{run.id}', 'logs', 'videos')\n", - " \n", - " try:\n", - " run_artifacts_ds = Dataset.File.from_files(datastore.path(os.path.join(run_artifacts_path, '**')))\n", - " except DatasetValidationError as e:\n", - " # This happens at the start of the run when there is no data available\n", - " # in the run's artifacts\n", - " return None, video_checkpoint_counter\n", - " \n", - " video_files = [file for file in run_artifacts_ds.to_path() if file.endswith('.mp4')]\n", - " if len(video_files) == video_checkpoint_counter:\n", - " return None, video_checkpoint_counter\n", - " \n", - " iteration_numbers = [int(vf[vf.rindex('video') + len('video') : vf.index('.mp4')]) for vf in video_files]\n", - " latest_video = next(vf for vf in video_files if vf.endswith('{num}.mp4'.format(num=max(iteration_numbers))))\n", - " latest_video = os.path.join(run_artifacts_path, os.path.normpath(latest_video[1:]))\n", - " \n", - " datastore.download(\n", - " target_path=path_prefix,\n", - " prefix=latest_video.replace('\\\\', '/'),\n", - " show_progress=False)\n", - " \n", - " return os.path.join(path_prefix, latest_video), len(video_files)\n", - "\n", - "\n", - "def render_video(vf):\n", - " clear_output(wait=True)\n", - " display(Video(data=vf, embed=True, html_attributes='loop autoplay controls width=50%'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil, time\n", - "\n", - "terminal_statuses = ['Canceled', 'Completed', 'Failed']\n", - "video_checkpoint_counter = 0\n", - "\n", - "while train_run.get_status() not in terminal_statuses:\n", - " video_file, video_checkpoint_counter = download_latest_training_video(train_run, video_checkpoint_counter)\n", - " if video_file is not None:\n", - " render_video(video_file)\n", - " \n", - " print('Displaying video number {}'.format(video_checkpoint_counter))\n", - " shutil.rmtree(path_prefix)\n", - " \n", - " # Interrupting the kernel can take up to 15 seconds\n", - " # depending on when time.sleep started\n", - " time.sleep(15)\n", - " \n", - "train_run.wait_for_completion()\n", - "print('The training run has reached a terminal status.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cleaning up\n", - "\n", - "Below, you can find code snippets for your convenience to clean up any resources created as part of this tutorial you don't wish to retain." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# to stop the Tensorboard, uncomment and run\n", - "# tb.stop()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# to delete the cpu compute target, uncomment and run\n", - "# cpu_cluster.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "We would love to hear your feedback! Please let us know what you think of Reinforcement Learning in Azure Machine Learning and what features you are looking forward to." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "andress" - } - ], - "categories": [ - "how-to-use-azureml", - "reinforcement-learning" - ], - "interpreter": { - "hash": "13382f70c1d0595120591d2e358c8d446daf961bf951d1fba9a32631e205d5ab" - }, - "kernel_info": { - "name": "python38-azureml" - }, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - }, - "notice": "Copyright (c) Microsoft Corporation. All rights reserved.\u00c3\u0192\u00c2\u00a2\u00c3\u00a2\u00e2\u20ac\u0161\u00c2\u00ac\u00c3\u201a\u00c2\u00afLicensed under the MIT License.\u00c3\u0192\u00c2\u00a2\u00c3\u00a2\u00e2\u20ac\u0161\u00c2\u00ac\u00c3\u201a\u00c2\u00af ", - "nteract": { - "version": "nteract-front-end@1.0.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.yml b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.yml deleted file mode 100644 index b1c52d07c..000000000 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.yml +++ /dev/null @@ -1,9 +0,0 @@ -name: particle -dependencies: -- pip: - - azureml-sdk - - azureml-contrib-reinforcementlearning - - azureml-widgets - - tensorboard - - azureml-tensorboard - - ipython diff --git a/how-to-use-azureml/responsible-ai/README.md b/how-to-use-azureml/responsible-ai/README.md deleted file mode 100644 index 3ea735bd9..000000000 --- a/how-to-use-azureml/responsible-ai/README.md +++ /dev/null @@ -1,17 +0,0 @@ -# AzureML Responsible AI - -AzureML Responsible AI empowers data scientists and developers to innovate responsibly with a growing set of tools including model interpretability and fairness. - -Follow these sample notebooks to learn about the model interpretability and fairness integration in Azure: - - - -# Responsible AI Sample Notebooks - -- **Visualize fairness metrics and model explanations** - - Dataset: [UCI Adult](https://archive.ics.uci.edu/ml/datasets/Adult) - - **[Jupyter Notebook](visualize-upload-loan-decision/rai-loan-decision.ipynb)** - - Train a model to predict annual income - - Generate fairness and interpretability explanations for the trained model - - Visualize the explanations in the notebook widget dashboard - - Upload the explanations to Azure to be viewed in AzureML studio diff --git a/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb b/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb deleted file mode 100644 index d5e1441cf..000000000 --- a/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb +++ /dev/null @@ -1,699 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/responsible-ai/auto-ml-regresion-responsibleai/auto-ml-regresion-responsibleai.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Regression with Aml Compute**_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Data](#Data)\n", - "1. [Train](#Train)\n", - "1. [Results](#Results)\n", - "1. [Test](#Test)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. The regression goal is to predict the performance of certain combinations of hardware parts.\n", - "After training AutoML models for this regression data set, we show how you can compute model explanations on your remote compute using a sample explainer script.\n", - "\n", - "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n", - "\n", - "In this notebook you will learn how to:\n", - "1. Create an `Experiment` in an existing `Workspace`.\n", - "2. Instantiate AutoMLConfig with FeaturizationConfig for customization.\n", - "3. Train the model using remote compute.\n", - "4. Explore the results and featurization transparency options.\n", - "5. Setup remote compute for computing the model explanations for a given AutoML model.\n", - "6. Start an AzureML experiment on your remote compute.\n", - "7. Submit model analysis, explain runs and counterfactual runs for a specific AutoML model.\n", - "8. Download the feature importance for raw features and visualize the explanations for raw features on azure portal. \n", - "10. Download counterfactual examples and view them in the notebook.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "import pandas as pd\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "import azureml.dataprep as dprep\n", - "from azureml.automl.core.featurization import FeaturizationConfig\n", - "from azureml.train.automl import AutoMLConfig\n", - "from azureml.core.dataset import Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n", - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# Choose a name for the experiment.\n", - "experiment_name = 'automl-regression-rai'\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output['Subscription ID'] = ws.subscription_id\n", - "output['Workspace Name'] = ws.name\n", - "output['Resource Group'] = ws.resource_group\n", - "output['Location'] = ws.location\n", - "output['Experiment Name'] = experiment.name\n", - "pd.set_option('display.max_colwidth', -1)\n", - "outputDf = pd.DataFrame(data = output, index = [''])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your cluster.\n", - "amlcompute_cluster_name = \"hardware-rai\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", - " max_nodes=4)\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup Training and Test Data for AutoML experiment\n", - "\n", - "Load the hardware dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model. We also register the datasets in your workspace using a name so that these datasets may be accessed from the remote compute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = 'https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv'\n", - "\n", - "dataset = Dataset.Tabular.from_delimited_files(data)\n", - "\n", - "# Split the dataset into train and test datasets\n", - "train_data, test_data = dataset.random_split(percentage=0.8, seed=223)\n", - "\n", - "# Drop ModelName\n", - "train_data = train_data.drop_columns(['ModelName', 'VendorName'])\n", - "test_data = test_data.drop_columns(['ModelName', 'VendorName'])\n", - "\n", - "# Register the train dataset with your workspace\n", - "train_data.register(workspace = ws, name = 'rai_machine_train_dataset',\n", - " description = 'hardware performance training data',\n", - " create_new_version=True)\n", - "\n", - "# Register the test dataset with your workspace\n", - "test_data.register(workspace = ws, name = 'rai_machine_test_dataset', description = 'hardware performance test data', create_new_version=True)\n", - "\n", - "label =\"ERP\"\n", - "\n", - "train_data.to_pandas_dataframe().head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train\n", - "\n", - "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification, regression or forecasting|\n", - "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics:
      spearman_correlation
      normalized_root_mean_squared_error
      r2_score
      normalized_mean_absolute_error|\n", - "|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n", - "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n", - "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*. Note: If the input data is sparse, featurization cannot be turned on.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n", - "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Customization\n", - "\n", - "Supported customization includes:\n", - "\n", - "1. Column purpose update: Override feature type for the specified column.\n", - "2. Transformer parameter update: Update parameters for the specified transformer. Currently supports Imputer and HashOneHotEncoder.\n", - "3. Drop columns: Columns to drop from being featurized.\n", - "4. Block transformers: Allow/Block transformers to be used on featurization process." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create FeaturizationConfig object using API calls" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-featurizationconfig-remarks2" - ] - }, - "outputs": [], - "source": [ - "featurization_config = FeaturizationConfig()\n", - "featurization_config.blocked_transformers = ['LabelEncoder']\n", - "#featurization_config.drop_columns = ['MMIN']\n", - "featurization_config.add_column_purpose('MYCT', 'Numeric')\n", - "#default strategy mean, add transformer param for for 3 columns\n", - "featurization_config.add_transformer_params('Imputer', ['CACH'], {\"strategy\": \"median\"})\n", - "featurization_config.add_transformer_params('Imputer', ['CHMIN'], {\"strategy\": \"median\"})\n", - "featurization_config.add_transformer_params('Imputer', ['PRP'], {\"strategy\": \"most_frequent\"})\n", - "#featurization_config.add_transformer_params('HashOneHotEncoder', [], {\"number_of_bits\": 3})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-featurizationconfig-remarks3" - ] - }, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"enable_early_stopping\": True, \n", - " \"experiment_timeout_hours\" : 0.25,\n", - " \"max_concurrent_iterations\": 4,\n", - " \"max_cores_per_iteration\": -1,\n", - " \"n_cross_validations\": 5,\n", - " \"primary_metric\": 'normalized_root_mean_squared_error',\n", - " \"verbosity\": logging.INFO\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(task = 'regression',\n", - " debug_log = 'automl_errors.log',\n", - " compute_target=compute_target,\n", - " featurization=featurization_config,\n", - " training_data = train_data,\n", - " label_column_name = label,\n", - " **automl_settings\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the `submit` method on the experiment object and pass the `AutoMLConfig`. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n", - "In this example, we specify `show_output=False` to suppress output for each iteration. You can monitor the run by clicking on the link in the output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the following cell to access previous runs. Uncomment the cell below and update the run_id." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#from azureml.train.automl.run import AutoMLRun\n", - "#remote_run = AutoMLRun(experiment=experiment, run_id='AutoML_1723d4fe-c33d-41f7-83ad-c010215583b0')\n", - "#remote_run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.wait_for_completion(wait_post_processing=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generating Responsible AI insights for AutoML model\n", - "This section will walk you through the workflow to compute Responsible AI insights like model explanations and counterfactual examples using model analysis workflow for an AutoML model on your remote compute.\n", - "\n", - "### Retrieve any AutoML Model for explanations\n", - "\n", - "Below we select an AutoML pipeline from our iterations. The `get_best_child` method returns the a AutoML run with the best score for the specified metric" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_run = remote_run.get_best_child(metric='mean_absolute_error')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup model analysis on the remote compute\n", - "The following section provides details on how to setup an AzureML experiment to run model analysis for an AutoML model on your remote compute." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create conda configuration for model analysis and explanations runs from automl_run object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "# create a new RunConfiguration object\n", - "conda_run_config = RunConfiguration(framework=\"python\")\n", - "\n", - "# Set compute target to AmlCompute\n", - "conda_run_config.target = compute_target\n", - "\n", - "# specify CondaDependencies obj\n", - "conda_run_config.environment = automl_run.get_environment()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Register the AutoML model and create a `PickleModelLoader` for the model analysis so that the model analysis can instantiate the model downloaded from AzureML." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Model\n", - "from azureml.responsibleai.common.pickle_model_loader import PickleModelLoader\n", - "from azureml.responsibleai.tools.model_analysis.model_analysis_config import ModelAnalysisConfig\n", - "from azureml.responsibleai.tools.model_analysis.explain_config import ExplainConfig\n", - "from azureml.automl.core.shared.constants import MODEL_PATH\n", - "\n", - "automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')\n", - "\n", - "model = automl_run.register_model(model_name='automl_rai', \n", - " model_path='outputs/model.pkl')\n", - "\n", - "model_loader = PickleModelLoader('model.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construct the list of the feature column names by dropping the name of the label column from the list of all column names." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_column_names = train_data.to_pandas_dataframe().columns.values\n", - "X_column_names = X_column_names[X_column_names!=label]\n", - "X_column_names" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the train and test dataset for the model analysis." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = Dataset.get_by_name(workspace=ws, name='rai_machine_train_dataset')\n", - "test_dataset = Dataset.get_by_name(workspace=ws, name='rai_machine_test_dataset')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the `ModelAnalysisConfig` below, `confidential_datastore_name` is the name of the datastore where the analyses will be uploaded. This example uses the default data store because the dataset is also in the default datastore. If you have confidential data in the dataset, you should specify a different data store as the `confidential_datastore_name` because analysis makes a copy of the data in this data store." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_analysis_config = ModelAnalysisConfig(\n", - " title=\"Model analysis\",\n", - " model=model,\n", - " model_type='regression',\n", - " model_loader=model_loader,\n", - " train_dataset=train_dataset,\n", - " test_dataset=test_dataset,\n", - " X_column_names=X_column_names,\n", - " target_column_name=label,\n", - " confidential_datastore_name=ws.get_default_datastore().name,\n", - " run_configuration=conda_run_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run model analysis\n", - "\n", - "The model analysis run takes a snapshot of the data in preparation for model explanation, error analysis, causal and counterfactual.\n", - "The model analysis run is the parent run for the model explanation, error analysis, causal and counterfactual runs.\n", - "In this example we will just generate an explanation and counterfactuals, but causal and error analyses may be performed as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_analysis_run = experiment.submit(model_analysis_config)\n", - "model_analysis_run.wait_for_completion(raise_on_error=True, wait_post_processing=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute explanations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run model explanation based on the model analysis.\n", - "The explanation run is a child run of the model analysis run.\n", - "In the future, the `add_request` method will allow extra parameters to configure the explanation generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "explain_config = ExplainConfig(model_analysis_run, conda_run_config)\n", - "explain_config.add_request()\n", - "explain_run = model_analysis_run.submit_child(explain_config)\n", - "explain_run.wait_for_completion(raise_on_error=True, wait_post_processing=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `explanation_manager.list` method below returns a list of metadata dictionaries for each explain run. In this case, there is a single explain run. So, the list contains a single dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "explanations = model_analysis_run.explanation_manager.list()\n", - "explanation = explanations[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature importance and visualizing explanation dashboard\n", - "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model on the azure portal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "feature_explanations = model_analysis_run.explanation_manager.download_by_id(explanation['id'])\n", - "print(feature_explanations.get_feature_importance_dict())\n", - "print(\"You can visualize the explanations for your features under the 'Explanations (preview)' tab in the explain run at:-\\n\" + explain_run.get_portal_url())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate counterfactual examples\n", - "\n", - "Generate counterfactuals for all the samples in the `test_dataset` based on the model analysis.\n", - "The counterfactual run is a child run of the model analysis run.\n", - "In the future, the `add_request` method will allow extra parameters to configure the counterfactuals generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.responsibleai.tools.model_analysis.counterfactual_config import CounterfactualConfig\n", - "\n", - "cf_config = CounterfactualConfig(model_analysis_run, conda_run_config)\n", - "cf_config.add_request(total_CFs=10, desired_range=[10, 300])\n", - "cf_run = model_analysis_run.submit_child(cf_config)\n", - "cf_run.wait_for_completion(raise_on_error=True, wait_post_processing=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Downloading counterfactual examples\n", - "The `counterfactual_manager.list` method below returns a list of metadata dictionaries for each counterfactual run. In this case, there is a single counterfactual run. So, the list contains a single dictionary.\n", - "\n", - "The `download_by_id()` method available in the `counterfactual_manager` can be used to download the counterfactual examples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cf_meta = model_analysis_run.counterfactual_manager.list()\n", - "counterfactual_object = model_analysis_run.counterfactual_manager.download_by_id(cf_meta[0]['id'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualizing the generated counterfactuals\n", - "You can use `visualize_as_dataframe()` method to view the generated counterfactual examples for the samples in `test_dataset`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "counterfactual_object.visualize_as_dataframe(show_only_changes=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize counterfactual feature importance" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "counterfactual_object.summary_importance" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jeffshep" - } - ], - "categories": [ - "how-to-use-azureml", - "automated-machine-learning" - ], - "category": "tutorial", - "compute": [ - "AML" - ], - "datasets": [ - "MachineData" - ], - "deployment": [ - "ACI" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Automated ML run with featurization and model explainability.", - "index_order": 5, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.12" - }, - "tags": [ - "featurization", - "explainability", - "remote_run", - "AutomatedML" - ], - "task": "Regression" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.yml b/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.yml deleted file mode 100644 index f20b7571e..000000000 --- a/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.yml +++ /dev/null @@ -1,5 +0,0 @@ -name: auto-ml-regression-responsibleai -dependencies: -- pip: - - azureml-sdk - - azureml-responsibleai diff --git a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.ipynb b/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.ipynb deleted file mode 100644 index bc4741714..000000000 --- a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.ipynb +++ /dev/null @@ -1,718 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Assess Fairness, Explore Interpretability, and Mitigate Fairness Issues \n", - "\n", - "This notebook demonstrates how to use [InterpretML](interpret.ml), [Fairlearn](fairlearn.org), and the [Responsible AI Widget's](https://github.com/microsoft/responsible-ai-widgets/) Fairness and Interpretability dashboards to understand a model trained on the Census dataset. This dataset is a classification problem - given a range of data about 32,000 individuals, predict whether their annual income is above or below fifty thousand dollars per year.\n", - "\n", - "For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan.\n", - "\n", - "We will first train a fairness-unaware predictor, load its global and local explanations, and use the interpretability and fairness dashboards to demonstrate how this model leads to unfair decisions (under a specific notion of fairness called *demographic parity*). We then mitigate unfairness by applying the `GridSearch` algorithm from `Fairlearn` package.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Install required packages\n", - "\n", - "This notebook works with Fairlearn v0.7.0, but not with versions pre-v0.5.0. If needed, please uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# %pip install --upgrade fairlearn>=0.6.2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After installing packages, you must close and reopen the notebook as well as restarting the kernel." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load and preprocess the dataset\n", - "\n", - "For simplicity, we import the dataset from the `shap` package, which contains the data in a cleaned format. We start by importing the various modules we're going to use:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from fairlearn.reductions import GridSearch\n", - "from fairlearn.reductions import DemographicParity\n", - "\n", - "from sklearn.compose import ColumnTransformer, make_column_selector\n", - "from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "import pandas as pd\n", - "\n", - "# SHAP Tabular Explainer\n", - "from interpret.ext.blackbox import MimicExplainer\n", - "from interpret.ext.glassbox import LGBMExplainableModel" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now load and inspect the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from utilities import fetch_census_dataset\n", - "\n", - "dataset = fetch_census_dataset()\n", - "X_raw, y = dataset['data'], dataset['target']\n", - "X_raw[\"race\"].value_counts().to_dict()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are going to treat the sex of each individual as a protected attribute (where 0 indicates female and 1 indicates male), and in this particular case we are going separate this attribute out and drop it from the main data. We then perform some standard data preprocessing steps to convert the data into a format suitable for the ML algorithms" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sensitive_features = X_raw[['sex','race']]\n", - "\n", - "le = LabelEncoder()\n", - "y = le.fit_transform(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we split the data into training and test sets:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "X_train, X_test, y_train, y_test, sensitive_features_train, sensitive_features_test = \\\n", - " train_test_split(X_raw, y, sensitive_features,\n", - " test_size = 0.2, random_state=0, stratify=y)\n", - "\n", - "# Work around indexing bug\n", - "X_train = X_train.reset_index(drop=True)\n", - "sensitive_features_train = sensitive_features_train.reset_index(drop=True)\n", - "X_test = X_test.reset_index(drop=True)\n", - "sensitive_features_test = sensitive_features_test.reset_index(drop=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training a fairness-unaware predictor\n", - "\n", - "To show the effect of `Fairlearn` we will first train a standard ML predictor that does not incorporate fairness. For speed of demonstration, we use a simple logistic regression estimator from `sklearn`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "numeric_transformer = Pipeline(\n", - " steps=[\n", - " (\"impute\", SimpleImputer()),\n", - " (\"scaler\", StandardScaler()),\n", - " ]\n", - ")\n", - "categorical_transformer = Pipeline(\n", - " [\n", - " (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n", - " (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\")),\n", - " ]\n", - ")\n", - "preprocessor = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, make_column_selector(dtype_exclude=\"category\")),\n", - " (\"cat\", categorical_transformer, make_column_selector(dtype_include=\"category\")),\n", - " ]\n", - ")\n", - "\n", - "model = Pipeline(\n", - " steps=[\n", - " (\"preprocessor\", preprocessor),\n", - " (\n", - " \"classifier\",\n", - " LogisticRegression(solver=\"liblinear\", fit_intercept=True),\n", - " ),\n", - " ]\n", - ")\n", - "\n", - "model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate model explanations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Using SHAP KernelExplainer\n", - "# clf.steps[-1][1] returns the trained classification model\n", - "explainer = MimicExplainer(model.steps[-1][1], \n", - " X_train,\n", - " LGBMExplainableModel,\n", - " features=X_raw.columns, \n", - " classes=['Rejected', 'Approved'],\n", - " transformations=preprocessor)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate global explanations\n", - "Explain overall model predictions (global explanation)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Explain the model based on a subset of 1000 rows\n", - "global_explanation = explainer.explain_global(X_test[:1000])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "global_explanation.get_feature_importance_dict()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate local explanations\n", - "Explain local data points (individual instances)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# You can pass a specific data point or a group of data points to the explain_local function\n", - "# E.g., Explain the first data point in the test set\n", - "instance_num = 1\n", - "local_explanation = explainer.explain_local(X_test[:instance_num])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the prediction for the first member of the test set and explain why model made that prediction\n", - "prediction_value = model.predict(X_test)[instance_num]\n", - "\n", - "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n", - "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('local importance values: {}'.format(sorted_local_importance_values))\n", - "print('local importance names: {}'.format(sorted_local_importance_names))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize model explanations\n", - "Load the interpretability visualization dashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from raiwidgets import ExplanationDashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ExplanationDashboard(global_explanation, model, dataset=X_test[:1000], true_y=y_test[:1000])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can load this predictor into the Fairness dashboard, and examine how it is unfair:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Assess model fairness \n", - "Load the fairness visualization dashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from raiwidgets import FairnessDashboard\n", - "\n", - "y_pred = model.predict(X_test)\n", - "\n", - "FairnessDashboard(sensitive_features=sensitive_features_test,\n", - " y_true=y_test,\n", - " y_pred=y_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the disparity in accuracy, we see that males have an error rate about three times greater than the females. More interesting is the disparity in opportunitiy - males are offered loans at three times the rate of females.\n", - "\n", - "Despite the fact that we removed the feature from the training data, our predictor still discriminates based on sex. This demonstrates that simply ignoring a protected attribute when fitting a predictor rarely eliminates unfairness. There will generally be enough other features correlated with the removed attribute to lead to disparate impact." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Mitigation with Fairlearn (GridSearch)\n", - "\n", - "The `GridSearch` class in `Fairlearn` implements a simplified version of the exponentiated gradient reduction of [Agarwal et al. 2018](https://arxiv.org/abs/1803.02453). The user supplies a standard ML estimator, which is treated as a blackbox. `GridSearch` works by generating a sequence of relabellings and reweightings, and trains a predictor for each.\n", - "\n", - "For this example, we specify demographic parity (on the protected attribute of sex) as the fairness metric. Demographic parity requires that individuals are offered the opportunity (are approved for a loan in this example) independent of membership in the protected class (i.e., females and males should be offered loans at the same rate). We are using this metric for the sake of simplicity; in general, the appropriate fairness metric will not be obvious." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fairlearn is not yet fully compatible with Pipelines, so we have to pass the estimator only\n", - "X_train_prep = preprocessor.transform(X_train).toarray()\n", - "X_test_prep = preprocessor.transform(X_test).toarray()\n", - "\n", - "sweep = GridSearch(LogisticRegression(solver=\"liblinear\", fit_intercept=True),\n", - " constraints=DemographicParity(),\n", - " grid_size=70)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our algorithms provide `fit()` and `predict()` methods, so they behave in a similar manner to other ML packages in Python. We do however have to specify two extra arguments to `fit()` - the column of protected attribute labels, and also the number of predictors to generate in our sweep.\n", - "\n", - "After `fit()` completes, we extract the full set of predictors from the `GridSearch` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sweep.fit(X_train_prep, y_train,\n", - " sensitive_features=sensitive_features_train.sex)\n", - "\n", - "predictors = sweep.predictors_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could load these predictors into the Fairness dashboard now. However, the plot would be somewhat confusing due to their number. In this case, we are going to remove the predictors which are dominated in the error-disparity space by others from the sweep (note that the disparity will only be calculated for the sensitive feature). In general, one might not want to do this, since there may be other considerations beyond the strict optimization of error and disparity (of the given protected attribute)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from fairlearn.metrics import demographic_parity_difference\n", - "\n", - "accuracies, disparities = [], []\n", - "\n", - "for predictor in predictors:\n", - " y_pred = predictor.predict(X_train_prep)\n", - " # accuracy_metric_frame = MetricFrame(accuracy_score, y_train, predictor.predict(X_train_prep), sensitive_features=sensitive_features_train.sex)\n", - " # selection_rate_metric_frame = MetricFrame(selection_rate, y_train, predictor.predict(X_train_prep), sensitive_features=sensitive_features_train.sex)\n", - " accuracies.append(accuracy_score(y_train, y_pred))\n", - " disparities.append(demographic_parity_difference(y_train,\n", - " y_pred,\n", - " sensitive_features=sensitive_features_train.sex))\n", - " \n", - "all_results = pd.DataFrame({\"predictor\": predictors, \"accuracy\": accuracies, \"disparity\": disparities})\n", - "\n", - "all_models_dict = {\"unmitigated\": model.steps[-1][1]}\n", - "dominant_models_dict = {\"unmitigated\": model.steps[-1][1]}\n", - "base_name_format = \"grid_{0}\"\n", - "row_id = 0\n", - "for row in all_results.itertuples():\n", - " model_name = base_name_format.format(row_id)\n", - " all_models_dict[model_name] = row.predictor\n", - " accuracy_for_lower_or_eq_disparity = all_results[\"accuracy\"][all_results[\"disparity\"] <= row.disparity]\n", - " if row.accuracy >= accuracy_for_lower_or_eq_disparity.max():\n", - " dominant_models_dict[model_name] = row.predictor\n", - " row_id = row_id + 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can construct predictions for all the models, and also for the dominant models:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dashboard_all = {}\n", - "for name, predictor in all_models_dict.items():\n", - " value = predictor.predict(X_test_prep)\n", - " dashboard_all[name] = value\n", - " \n", - "dominant_all = {}\n", - "for name, predictor in dominant_models_dict.items():\n", - " dominant_all[name] = predictor.predict(X_test_prep)\n", - "\n", - "FairnessDashboard(sensitive_features=sensitive_features_test, \n", - " y_true=y_test,\n", - " y_pred=dominant_all)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can look at just the dominant models in the dashboard:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy.\n", - "\n", - "By clicking on individual models on the plot, we can inspect their metrics for disparity and accuracy in greater detail. In a real example, we would then pick the model which represented the best trade-off between accuracy and disparity given the relevant business constraints." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AzureML integration\n", - "\n", - "We will now go through a brief example of the AzureML integration.\n", - "\n", - "The required package can be installed via:\n", - "\n", - "```\n", - "pip install azureml-contrib-fairness\n", - "pip install azureml-interpret\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connect to workspace\n", - "\n", - "Just like in the previous tutorials, we will need to connect to a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class)?view=azure-ml-py).\n", - "\n", - "The following code will allow you to create a workspace if you don't already have one created. You must have an Azure subscription to create a workspace:\n", - "\n", - "```python\n", - "from azureml.core import Workspace\n", - "ws = Workspace.create(name='myworkspace',\n", - " subscription_id='',\n", - " resource_group='myresourcegroup',\n", - " create_resource_group=True,\n", - " location='eastus2')\n", - "```\n", - "\n", - "**If you are running this on a Notebook VM, you can import the existing workspace.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Registering models\n", - "\n", - "The fairness dashboard is designed to integrate with registered models, so we need to do this for the models we want in the Studio portal. The assumption is that the names of the models specified in the dashboard dictionary correspond to the `id`s (i.e. `:` pairs) of registered models in the workspace." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we register each of the models in the `dominant_all` dictionary into the workspace. For this, we have to save each model to a file, and then register that file:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import joblib\n", - "import os\n", - "from azureml.core import Model, Experiment\n", - "\n", - "os.makedirs('models', exist_ok=True)\n", - "def register_model(name, model):\n", - " print(\"Registering \", name)\n", - " model_path = \"models/{0}.pkl\".format(name)\n", - " joblib.dump(value=model, filename=model_path)\n", - " registered_model = Model.register(model_path=model_path,\n", - " model_name=name,\n", - " workspace=ws)\n", - " print(\"Registered \", registered_model.id)\n", - " return registered_model.id\n", - "\n", - "model_name_id_mapping = dict()\n", - "for name, model in dominant_all.items():\n", - " m_id = register_model(name, model)\n", - " model_name_id_mapping[name] = m_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, produce new predictions dictionaries, with the updated names:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dominant_all_ids = dict()\n", - "for name, y_pred in dominant_all.items():\n", - " dominant_all_ids[model_name_id_mapping[name]] = y_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Uploading a dashboard\n", - "\n", - "We create a _dashboard dictionary_ using Fairlearn's `metrics` package. The `_create_group_metric_set` method has arguments similar to the Dashboard constructor, except that the sensitive features are passed as a dictionary (to ensure that names are available), and we must specify the type of prediction. Note that we use the `dashboard_registered` dictionary we just created:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sf = { 'sex': sensitive_features_test.sex, 'race': sensitive_features_test.race }\n", - "\n", - "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n", - "\n", - "dash_dict_all = _create_group_metric_set(y_true=y_test,\n", - " predictions=dominant_all_ids,\n", - " sensitive_features=sf,\n", - " prediction_type='binary_classification')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we import our `contrib` package which contains the routine to perform the upload:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can create an Experiment, then a Run, and upload our dashboard to it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exp = Experiment(ws, 'responsible-ai-loan-decision')\n", - "print(exp)\n", - "\n", - "run = exp.start_logging()\n", - "try:\n", - " dashboard_title = \"Upload MultiAsset from Grid Search with Census Data Notebook\"\n", - " upload_id = upload_dashboard_dictionary(run,\n", - " dash_dict_all,\n", - " dashboard_name=dashboard_title)\n", - " print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n", - "\n", - " downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n", - "finally:\n", - " run.complete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Uploading explanations\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.interpret import ExplanationClient\n", - "\n", - "client = ExplanationClient.from_run(run)\n", - "client.upload_model_explanation(global_explanation, comment = \"census data global explanation\")" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "chgrego" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml b/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml deleted file mode 100644 index 4958d0bfc..000000000 --- a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml +++ /dev/null @@ -1,15 +0,0 @@ -name: rai-loan-decision -dependencies: -- pip: - - azureml-sdk - - azureml-interpret - - azureml-contrib-fairness - - fairlearn>=0.6.2 - - matplotlib - - azureml-dataset-runtime - - ipywidgets - - raiwidgets~=0.17.0 - - liac-arff - - packaging>=20.9 - - itsdangerous==2.0.1 - - markupsafe<2.1.0 diff --git a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/utilities.py b/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/utilities.py deleted file mode 100644 index 5eaa0a3f5..000000000 --- a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/utilities.py +++ /dev/null @@ -1,93 +0,0 @@ -# --------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# --------------------------------------------------------- - -"""Utilities for azureml-contrib-fairness notebooks.""" - -import arff -from collections import OrderedDict -from contextlib import closing -import gzip -import pandas as pd -from sklearn.utils import Bunch -from time import sleep - - -def _is_gzip_encoded(_fsrc): - return _fsrc.info().get('Content-Encoding', '') == 'gzip' - - -_categorical_columns = [ - 'workclass', - 'education', - 'marital-status', - 'occupation', - 'relationship', - 'race', - 'sex', - 'native-country' -] - - -def fetch_census_dataset(): - """Fetch the Adult Census Dataset. - - This uses a particular URL for the Adult Census dataset. The code - is a simplified version of fetch_openml() in sklearn. - - The data are copied from: - https://openml.org/data/v1/download/1595261.gz - (as of 2021-03-31) - """ - try: - from urllib import urlretrieve - except ImportError: - from urllib.request import urlretrieve - - filename = "1595261.gz" - data_url = "https://rainotebookscdn.blob.core.windows.net/datasets/" - - remaining_attempts = 5 - sleep_duration = 10 - while remaining_attempts > 0: - try: - urlretrieve(data_url + filename, filename) - - http_stream = gzip.GzipFile(filename=filename, mode='rb') - - with closing(http_stream): - def _stream_generator(response): - for line in response: - yield line.decode('utf-8') - - stream = _stream_generator(http_stream) - data = arff.load(stream) - except Exception as exc: # noqa: B902 - remaining_attempts -= 1 - print("Error downloading dataset from {} ({} attempt(s) remaining)" - .format(data_url, remaining_attempts)) - print(exc) - sleep(sleep_duration) - sleep_duration *= 2 - continue - else: - # dataset successfully downloaded - break - else: - raise Exception("Could not retrieve dataset from {}.".format(data_url)) - - attributes = OrderedDict(data['attributes']) - arff_columns = list(attributes) - raw_df = pd.DataFrame(data=data['data'], columns=arff_columns) - - target_column_name = 'class' - target = raw_df.pop(target_column_name) - for col_name in _categorical_columns: - dtype = pd.api.types.CategoricalDtype(attributes[col_name]) - raw_df[col_name] = raw_df[col_name].astype(dtype, copy=False) - - result = Bunch() - result.data = raw_df - result.target = target - - return result diff --git a/how-to-use-azureml/track-and-monitor-experiments/README.md b/how-to-use-azureml/track-and-monitor-experiments/README.md deleted file mode 100644 index cbf96790e..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/README.md +++ /dev/null @@ -1,19 +0,0 @@ - -## Follow these sample notebooks to learn: - -1. [Logging API](./logging-api/logging-api.ipynb): experiment with various logging functions to create runs and automatically generate graphs. -2. [Manage runs](./manage-runs/manage-runs.ipynb): learn different ways how to start runs and child runs, monitor them, and cancel them. -1. [Tensorboard to monitor runs](./tensorboard/tensorboard.ipynb) - -## Use MLflow with Azure Machine Learning service (Preview) - -[MLflow](https://mlflow.org/) is an open-source platform for tracking machine learning experiments and managing models. You can use MLflow logging APIs with Azure Machine Learning service: the metrics and artifacts are logged to your Azure ML Workspace. - -Try out the sample notebooks: -1. [Use MLflow with Azure Machine Learning for Local Training Run](./using-mlflow/train-local/train-local.ipynb) -1. [Use MLflow with Azure Machine Learning for Remote Training Run](./using-mlflow/train-remote/train-remote.ipynb) -1. [Use MLflow with Azure Machine Learning to submit runs locally with MLflow projects](./using-mlflow/train-projects-local/train-projects-local.ipynb) -1. [Use MLflow with Azure Machine Learning to submit runs on AzureML compute with MLflow projects](./using-mlflow/train-projects-remote/train-projects-remote.ipynb) - - ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/README.png) - diff --git a/how-to-use-azureml/track-and-monitor-experiments/logging-api/img/run_details.PNG b/how-to-use-azureml/track-and-monitor-experiments/logging-api/img/run_details.PNG deleted file mode 100644 index 9bfe60fd1..000000000 Binary files a/how-to-use-azureml/track-and-monitor-experiments/logging-api/img/run_details.PNG and /dev/null differ diff --git a/how-to-use-azureml/track-and-monitor-experiments/logging-api/img/run_history.PNG b/how-to-use-azureml/track-and-monitor-experiments/logging-api/img/run_history.PNG deleted file mode 100644 index 3dd32de61..000000000 Binary files a/how-to-use-azureml/track-and-monitor-experiments/logging-api/img/run_history.PNG and /dev/null differ diff --git a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb deleted file mode 100644 index bc5102dfe..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb +++ /dev/null @@ -1,588 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Logging\n", - "\n", - "_**This notebook showcases various ways to use the Azure Machine Learning service run logging APIs, and view the results in the Azure portal.**_\n", - "\n", - "---\n", - "---\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - " 1. Validate Azure ML SDK installation\n", - " 1. Initialize workspace\n", - " 1. Set experiment\n", - "1. [Logging](#Logging)\n", - " 1. Starting a run\n", - " 1. Viewing a run in the portal\n", - " 1. Viewing the experiment in the portal\n", - " 1. Logging metrics\n", - " 1. Logging string metrics\n", - " 1. Logging numeric metrics\n", - " 1. Logging vectors\n", - " 1. Logging tables\n", - " 1. Uploading files\n", - "1. [Analyzing results](#Analyzing-results)\n", - " 1. Tagging a run\n", - "1. [Next steps](#Next-steps)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Logging metrics from runs in your experiments allows you to track results from one run to another, determining trends in your outputs and understand how your inputs correspond to your model and script performance. Azure Machine Learning services (AzureML) allows you to track various types of metrics including images and arbitrary files in order to understand, analyze, and audit your experimental progress. \n", - "\n", - "Typically you should log all parameters for your experiment and all numerical and string outputs of your experiment. This will allow you to analyze the performance of your experiments across multiple runs, correlate inputs to outputs, and filter runs based on interesting criteria.\n", - "\n", - "The experiment's Run History report page automatically creates a report that can be customized to show the KPI's, charts, and column sets that are interesting to you. \n", - "\n", - "| ![Run Details](./img/run_details.PNG) | ![Run History](./img/run_history.PNG) |\n", - "|:--:|:--:|\n", - "| *Run Details* | *Run History* |\n", - "\n", - "---\n", - "\n", - "## Setup\n", - "\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace. Also make sure you have tqdm and matplotlib installed in the current kernel.\n", - "\n", - "```\n", - "(myenv) $ conda install -y tqdm matplotlib\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Validate Azure ML SDK installation and get version number for debugging purposes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "install" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Experiment, Workspace, Run\n", - "import azureml.core\n", - "import numpy as np\n", - "from tqdm import tqdm\n", - "\n", - "# Check core SDK version number\n", - "\n", - "print(\"This notebook was created using SDK version 1.40.0, you are currently running version\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set experiment\n", - "Create a new experiment (or get the one with the specified name). An *experiment* is a container for an arbitrary set of *runs*. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment = Experiment(workspace=ws, name='logging-api-test')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Logging\n", - "In this section we will explore the various logging mechanisms.\n", - "\n", - "### Starting a run\n", - "\n", - "A *run* is a singular experimental trial. In this notebook we will create a run directly on the experiment by calling `run = exp.start_logging()`. If you were experimenting by submitting a script file as an experiment using ``experiment.submit()``, you would call `run = Run.get_context()` in your script to access the run context of your code. In either case, the logging methods on the returned run object work the same.\n", - "\n", - "This cell also stores the run id for use later in this notebook. The run_id is not necessary for logging." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# start logging for the run\n", - "run = experiment.start_logging()\n", - "\n", - "# access the run id for use later\n", - "run_id = run.id\n", - "\n", - "# change the scale factor on different runs to see how you can compare multiple runs\n", - "scale_factor = 2\n", - "\n", - "# change the category on different runs to see how to organize data in reports\n", - "category = 'Red'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Viewing a run in the Portal\n", - "Once a run is started you can see the run in the portal by simply typing ``run``. Clicking on the \"Link to Portal\" link will take you to the Run Details page that shows the metrics you have logged and other run properties. You can refresh this page after each logging statement to see the updated results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Viewing an experiment in the portal\n", - "You can also view an experiement similarly by typing `experiment`. The portal link will take you to the experiment's Run History page that shows all runs and allows you to analyze trends across multiple runs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Logging metrics\n", - "Metrics are visible in the run details page in the AzureML portal and also can be analyzed in experiment reports. The run details page looks as below and contains tabs for Details, Outputs, Logs, and Snapshot. \n", - "* The Details page displays attributes about the run, plus logged metrics and images. Metrics that are vectors appear as charts. \n", - "* The Outputs page contains any files, such as models, you uploaded into the \"outputs\" directory from your run into storage. If you place files in the \"outputs\" directory locally, the files are automatically uploaded on your behald when the run is completed.\n", - "* The Logs page allows you to view any log files created by your run. Logging runs created in notebooks typically do not generate log files.\n", - "* The Snapshot page contains a snapshot of the directory specified in the ''start_logging'' statement, plus the notebook at the time of the ''start_logging'' call. This snapshot and notebook can be downloaded from the Run Details page to continue or reproduce an experiment.\n", - "\n", - "### Logging string metrics\n", - "The following cell logs a string metric. A string metric is simply a string value associated with a name. A string metric String metrics are useful for labelling runs and to organize your data. Typically you should log all string parameters as metrics for later analysis - even information such as paths can help to understand how individual experiements perform differently.\n", - "\n", - "String metrics can be used in the following ways:\n", - "* Plot in hitograms\n", - "* Group by indicators for numerical plots\n", - "* Filtering runs\n", - "\n", - "String metrics appear in the **Tracked Metrics** section of the Run Details page and can be added as a column in Run History reports." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# log a string metric\n", - "run.log(name='Category', value=category)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logging numerical metrics\n", - "The following cell logs some numerical metrics. Numerical metrics can include metrics such as AUC or MSE. You should log any parameter or significant output measure in order to understand trends across multiple experiments. Numerical metrics appear in the **Tracked Metrics** section of the Run Details page, and can be used in charts or KPI's in experiment Run History reports." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# log numerical values\n", - "run.log(name=\"scale factor\", value = scale_factor)\n", - "run.log(name='Magic Number', value=42 * scale_factor)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logging vectors\n", - "Vectors are good for recording information such as loss curves. You can log a vector by creating a list of numbers, calling ``log_list()`` and supplying a name and the list, or by repeatedly logging a value using the same name.\n", - "\n", - "Vectors are presented in Run Details as a chart, and are directly comparable in experiment reports when placed in a chart. \n", - "\n", - "**Note:** vectors logged into the run are expected to be relatively small. Logging very large vectors into Azure ML can result in reduced performance. If you need to store large amounts of data associated with the run, you can write the data to file that will be uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fibonacci_values = [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]\n", - "scaled_values = (i * scale_factor for i in fibonacci_values)\n", - "\n", - "# Log a list of values. Note this will generate a single-variable line chart.\n", - "run.log_list(name='Fibonacci', value=scaled_values)\n", - "\n", - "for i in tqdm(range(-10, 10)):\n", - " # log a metric value repeatedly, this will generate a single-variable line chart.\n", - " run.log(name='Sigmoid', value=1 / (1 + np.exp(-i)))\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logging tables\n", - "Tables are good for recording related sets of information such as accuracy tables, confusion matrices, etc. \n", - "You can log a table in two ways:\n", - "* Create a dictionary of lists where each list represents a column in the table and call ``log_table()``\n", - "* Repeatedly call ``log_row()`` providing the same table name with a consistent set of named args as the column values\n", - "\n", - "Tables are presented in Run Details as a chart using the first two columns of the table \n", - "\n", - "**Note:** tables logged into the run are expected to be relatively small. Logging very large tables into Azure ML can result in reduced performance. If you need to store large amounts of data associated with the run, you can write the data to file that will be uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# create a dictionary to hold a table of values\n", - "sines = {}\n", - "sines['angle'] = []\n", - "sines['sine'] = []\n", - "\n", - "for i in tqdm(range(-10, 10)):\n", - " angle = i / 2.0 * scale_factor\n", - " \n", - " # log a 2 (or more) values as a metric repeatedly. This will generate a 2-variable line chart if you have 2 numerical columns.\n", - " run.log_row(name='Cosine Wave', angle=angle, cos=np.cos(angle))\n", - " \n", - " sines['angle'].append(angle)\n", - " sines['sine'].append(np.sin(angle))\n", - "\n", - "# log a dictionary as a table, this will generate a 2-variable chart if you have 2 numerical columns\n", - "run.log_table(name='Sine Wave', value=sines)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logging images\n", - "You can directly log _matplotlib_ plots and arbitrary images to your run record. This code logs a _matplotlib_ pyplot object. Images show up in the run details page in the Azure ML Portal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "# Create a plot\n", - "import matplotlib.pyplot as plt\n", - "angle = np.linspace(-3, 3, 50) * scale_factor\n", - "plt.plot(angle,np.tanh(angle), label='tanh')\n", - "plt.legend(fontsize=12)\n", - "plt.title('Hyperbolic Tangent', fontsize=16)\n", - "plt.grid(True)\n", - "\n", - "# Log the plot to the run. To log an arbitrary image, use the form run.log_image(name, path='./image_path.png')\n", - "run.log_image(name='Hyperbolic Tangent', plot=plt)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Uploading files\n", - "\n", - "Files can also be uploaded explicitly and stored as artifacts along with the run record. These files are also visible in the *Outputs* tab of the Run Details page.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "directory = 'logging-api'\n", - "\n", - "if not os.path.exists(directory):\n", - " os.mkdir(directory)\n", - "\n", - "file_name = os.path.join(directory, \"myfile.txt\")\n", - "\n", - "with open(file_name, \"w\") as f:\n", - " f.write('This is an output file that will be uploaded.\\n')\n", - "\n", - "# Upload the file explicitly into artifacts \n", - "run.upload_file(name = file_name, path_or_stream = file_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Completing the run\n", - "\n", - "Calling `run.complete()` marks the run as completed and triggers the output file collection. If for any reason you need to indicate the run failed or simply need to cancel the run you can call `run.fail()` or `run.cancel()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.complete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Analyzing results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can refresh the run in the Azure portal to see all of your results. In many cases you will want to analyze runs that were performed previously to inspect the contents or compare results. Runs can be fetched from their parent Experiment object using the ``Run()`` constructor or the ``experiment.get_runs()`` method. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fetched_run = Run(experiment, run_id)\n", - "fetched_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call ``run.get_metrics()`` to retrieve all the metrics from a run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fetched_run.get_metrics()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call ``run.get_metrics(name = )`` to retrieve a metric value by name. Retrieving a single metric can be faster, especially if the run contains many metrics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fetched_run.get_metrics(name = \"scale factor\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See the files uploaded for this run by calling ``run.get_file_names()``" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fetched_run.get_file_names()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you know the file names in a run, you can download the files using the ``run.download_file()`` method" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.makedirs('files', exist_ok=True)\n", - "\n", - "for f in run.get_file_names():\n", - " dest = os.path.join('files', f.split('/')[-1])\n", - " print('Downloading file {} to {}...'.format(f, dest))\n", - " fetched_run.download_file(f, dest) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tagging a run\n", - "Often when you analyze the results of a run, you may need to tag that run with important personal or external information. You can add a tag to a run using the ``run.tag()`` method. AzureML supports valueless and valued tags." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fetched_run.tag(\"My Favorite Run\")\n", - "fetched_run.tag(\"Competition Rank\", 1)\n", - "\n", - "fetched_run.get_tags()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "To experiment more with logging and to understand how metrics can be visualized, go back to the *Start a run* section, try changing the category and scale_factor values and going through the notebook several times. Play with the KPI, charting, and column selection options on the experiment's Run History reports page to see how the various metrics can be combined and visualized.\n", - "\n", - "After learning about all of the logging options, go to the [train on remote vm](..\\train-on-remote-vm\\train-on-remote-vm.ipynb) notebook and experiment with logging from remote compute contexts." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "roastala" - } - ], - "category": "other", - "compute": [ - "None" - ], - "datasets": [ - "None" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Logging APIs", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - }, - "order_index": 1, - "star_tag": [], - "tags": [ - "None" - ], - "task": "Logging APIs and analyzing results" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.yml b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.yml deleted file mode 100644 index 421444378..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.yml +++ /dev/null @@ -1,8 +0,0 @@ -name: logging-api -dependencies: -- numpy -- matplotlib -- tqdm -- pip: - - azureml-sdk - - azureml-widgets diff --git a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello.py b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello.py deleted file mode 100644 index 69b754326..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. -# Licensed under the MIT license. - -from azureml.core import Run - -submitted_run = Run.get_context() -submitted_run.log(name="message", value="Hello from run!") diff --git a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello_with_children.py b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello_with_children.py deleted file mode 100644 index 953d88fe9..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello_with_children.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. -# Licensed under the MIT license. - -from azureml.core import Run - -run = Run.get_context() - -child_runs = run.create_children(count=5) -for c, child in enumerate(child_runs): - child.log(name="Hello from child run ", value=c) - child.complete() diff --git a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello_with_delay.py b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello_with_delay.py deleted file mode 100644 index aea75402e..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello_with_delay.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. -# Licensed under the MIT license. - -import time - -print("Wait for 10 seconds..") -time.sleep(10) -print("Done waiting") diff --git a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb deleted file mode 100644 index c80480e44..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb +++ /dev/null @@ -1,640 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Manage runs\n", - "\n", - "## Table of contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Start, monitor and complete a run](#Start,-monitor-and-complete-a-run)\n", - "1. [Add properties and tags](#Add-properties-and-tags)\n", - "1. [Query properties and tags](#Query-properties-and-tags)\n", - "1. [Start and query child runs](#Start-and-query-child-runs)\n", - "1. [Cancel or fail runs](#Cancel-or-fail-runs)\n", - "1. [Reproduce a run](#Reproduce-a-run)\n", - "1. [Next steps](#Next-steps)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "When you're building enterprise-grade machine learning models, it is important to track, organize, monitor and reproduce your training runs. For example, you might want to trace the lineage behind a model deployed to production, and re-run the training experiment to troubleshoot issues. \n", - "\n", - "This notebooks shows examples how to use Azure Machine Learning services to manage your training runs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace. Also, if you're new to Azure ML, we recommend that you go through [the tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-train-models-with-aml) first to learn the basic concepts.\n", - "\n", - "Let's first import required packages, check Azure ML SDK version, connect to your workspace and create an Experiment to hold the runs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Experiment, Run\n", - "from azureml.core import ScriptRunConfig\n", - "\n", - "print(azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exp = Experiment(workspace=ws, name=\"explore-runs\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Start, monitor and complete a run\n", - "\n", - "A run is an unit of execution, typically to train a model, but for other purposes as well, such as loading or transforming data. Runs are tracked by Azure ML service, and can be instrumented with metrics and artifact logging.\n", - "\n", - "A simplest way to start a run in your interactive Python session is to call *Experiment.start_logging* method. You can then log metrics from within the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "notebook_run = exp.start_logging()\n", - "\n", - "notebook_run.log(name=\"message\", value=\"Hello from run!\")\n", - "\n", - "print(notebook_run.get_status())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use *get_status method* to get the status of the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(notebook_run.get_status())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also, you can simply enter the run to get a link to Azure Portal details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "notebook_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Method *get_details* gives you more details on the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "notebook_run.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use *complete* method to end the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "notebook_run.complete()\n", - "print(notebook_run.get_status())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also use Python's *with...as* pattern. The run will automatically complete when moving out of scope. This way you don't need to manually complete the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with exp.start_logging() as notebook_run:\n", - " notebook_run.log(name=\"message\", value=\"Hello from run!\")\n", - " print(\"Is it still running?\",notebook_run.get_status())\n", - " \n", - "print(\"Has it completed?\",notebook_run.get_status())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at submitting a run as a separate Python process. To keep the example simple, we submit the run on local computer. Other targets could include remote VMs and Machine Learning Compute clusters in your Azure ML Workspace.\n", - "\n", - "We use *hello.py* script as an example. To perform logging, we need to get a reference to the Run instance from within the scope of the script. We do this using *Run.get_context* method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!more hello.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Submitted runs take a snapshot of the *source_directory* to use when executing. You can control which files are available to the run by using an *.amlignore* file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile .amlignore\n", - "# Exclude the outputs directory automatically created by our earlier runs.\n", - "/outputs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's submit the run on a local computer. A standard pattern in Azure ML SDK is to create run configuration, and then use *Experiment.submit* method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_config = ScriptRunConfig(source_directory='.', script='hello.py')\n", - "\n", - "local_script_run = exp.submit(run_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can view the status of the run as before" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(local_script_run.get_status())\n", - "local_script_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Submitted runs have additional log files you can inspect using *get_details_with_logs*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_script_run.get_details_with_logs()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use *wait_for_completion* method to block the local execution until remote run is complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_script_run.wait_for_completion(show_output=True)\n", - "print(local_script_run.get_status())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add properties and tags\n", - "\n", - "Properties and tags help you organize your runs. You can use them to describe, for example, who authored the run, what the results were, and what machine learning approach was used. And as you'll later learn, properties and tags can be used to query the history of your runs to find the important ones.\n", - "\n", - "For example, let's add \"author\" property to the run:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_script_run.add_properties({\"author\":\"azureml-user\"})\n", - "print(local_script_run.get_properties())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Properties are immutable. Once you assign a value it cannot be changed, making them useful as a permanent record for auditing purposes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " local_script_run.add_properties({\"author\":\"different-user\"})\n", - "except Exception as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Tags on the other hand can be changed:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_script_run.tag(\"quality\", \"great run\")\n", - "print(local_script_run.get_tags())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_script_run.tag(\"quality\", \"fantastic run\")\n", - "print(local_script_run.get_tags())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also add a simple string tag. It appears in the tag dictionary with value of None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_script_run.tag(\"worth another look\")\n", - "print(local_script_run.get_tags())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Query properties and tags\n", - "\n", - "You can query runs within an experiment that match specific properties and tags." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(exp.get_runs(properties={\"author\":\"azureml-user\"},tags={\"quality\":\"fantastic run\"}))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(exp.get_runs(properties={\"author\":\"azureml-user\"},tags=\"worth another look\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Start and query child runs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can use child runs to group together related runs, for example different hyperparameter tuning iterations.\n", - "\n", - "Let's use *hello_with_children* script to create a batch of 5 child runs from within a submitted run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!more hello_with_children.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_config = ScriptRunConfig(source_directory='.', script='hello_with_children.py')\n", - "\n", - "local_script_run = exp.submit(run_config)\n", - "local_script_run.wait_for_completion(show_output=True)\n", - "print(local_script_run.get_status())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can start child runs one by one. Note that this is less efficient than submitting a batch of runs, because each creation results in a network call.\n", - "\n", - "Child runs too complete automatically as they move out of scope." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with exp.start_logging() as parent_run:\n", - " for c,count in enumerate(range(5)):\n", - " with parent_run.child_run() as child:\n", - " child.log(name=\"Hello from child run\", value=c)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To query the child runs belonging to specific parent, use *get_children* method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(parent_run.get_children())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cancel or fail runs\n", - "\n", - "Sometimes, you realize that the run is not performing as intended, and you want to cancel it instead of waiting for it to complete.\n", - "\n", - "As an example, let's create a Python script with a delay in the middle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!more hello_with_delay.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can use *cancel* method to cancel a run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_config = ScriptRunConfig(source_directory='.', script='hello_with_delay.py')\n", - "\n", - "local_script_run = exp.submit(run_config)\n", - "print(\"Did the run start?\",local_script_run.get_status())\n", - "local_script_run.cancel()\n", - "print(\"Did the run cancel?\",local_script_run.get_status())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also mark an unsuccessful run as failed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_script_run = exp.submit(run_config)\n", - "local_script_run.fail()\n", - "print(local_script_run.get_status())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reproduce a run\n", - "\n", - "When updating or troubleshooting on a model deployed to production, you sometimes need to revisit the original training run that produced the model. To help you with this, Azure ML service by default creates snapshots of your scripts a the time of run submission:\n", - "\n", - "You can use *restore_snapshot* to obtain a zip package of the latest snapshot of the script folder. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_script_run.restore_snapshot(path=\"snapshots\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can then extract the zip package, examine the code, and submit your run again." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - " * To learn more about logging APIs, see [logging API notebook](./logging-api/logging-api.ipynb)\n", - " * To learn more about remote runs, see [train on AML compute notebook](./train-on-amlcompute/train-on-amlcompute.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "roastala" - } - ], - "category": "training", - "compute": [ - "Local" - ], - "datasets": [ - "None" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Managing your training runs", - "index_order": 2, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "tags": [ - "None" - ], - "task": "Monitor and complete runs" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.yml b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.yml deleted file mode 100644 index 34a95ec8c..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: manage-runs -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb deleted file mode 100644 index 7dcf5a8ca..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb +++ /dev/null @@ -1,262 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) notebook to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (`config.json`)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace, Experiment\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set experiment name and start the run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_name = 'export-to-tensorboard'\n", - "exp = Experiment(ws, experiment_name)\n", - "root_run = exp.start_logging()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# load diabetes dataset, a well-known built-in small dataset that comes with scikit-learn\n", - "from sklearn.datasets import load_diabetes\n", - "from sklearn.linear_model import Ridge\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "X, y = load_diabetes(return_X_y=True)\n", - "\n", - "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n", - "\n", - "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", - "data = {\n", - " \"train\":{\"x\":x_train, \"y\":y_train}, \n", - " \"test\":{\"x\":x_test, \"y\":y_test}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Example experiment\n", - "from tqdm import tqdm\n", - "\n", - "alphas = [.1, .2, .3, .4, .5, .6 , .7]\n", - "\n", - "# try a bunch of alpha values in a Linear Regression (Ridge) model\n", - "for alpha in tqdm(alphas):\n", - " # create a bunch of child runs\n", - " with root_run.child_run(\"alpha\" + str(alpha)) as run:\n", - " # More data science stuff\n", - " reg = Ridge(alpha=alpha)\n", - " reg.fit(data[\"train\"][\"x\"], data[\"train\"][\"y\"])\n", - " \n", - " preds = reg.predict(data[\"test\"][\"x\"])\n", - " mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n", - " # End train and eval\n", - "\n", - " # log alpha, mean_squared_error and feature names in run history\n", - " root_run.log(\"alpha\", alpha)\n", - " root_run.log(\"mse\", mse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Export Run History to Tensorboard logs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "tensorboard-export-sample" - ] - }, - "outputs": [], - "source": [ - "# Export Run History to Tensorboard logs\n", - "from azureml.tensorboard.export import export_to_tensorboard\n", - "import os\n", - "\n", - "logdir = 'exportedTBlogs'\n", - "log_path = os.path.join(os.getcwd(), logdir)\n", - "try:\n", - " os.stat(log_path)\n", - "except os.error:\n", - " os.mkdir(log_path)\n", - "print(logdir)\n", - "\n", - "# export run history for the project\n", - "export_to_tensorboard(root_run, logdir)\n", - "\n", - "# or export a particular run\n", - "# export_to_tensorboard(run, logdir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "root_run.complete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Start Tensorboard\n", - "\n", - "Or you can start the Tensorboard outside this notebook to view the result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.tensorboard import Tensorboard\n", - "\n", - "# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n", - "tb = Tensorboard([], local_root=logdir, port=6006)\n", - "\n", - "# If successful, start() returns a string with the URI of the instance.\n", - "tb.start()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stop Tensorboard\n", - "\n", - "When you're done, make sure to call the `stop()` method of the Tensorboard object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tb.stop()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "roastala" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "friendly_name": "Using Tensorboard", - "exclude_from_index": false, - "index_order": 1, - "category": "training", - "task": "Export the run history as Tensorboard logs", - "datasets": [ - "None" - ], - "compute": [ - "None" - ], - "deployment": [ - "None" - ], - "framework": [ - "TensorFlow" - ], - "tags": [ - "None" - ] - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.yml b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.yml deleted file mode 100644 index 17aa9f1d3..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.yml +++ /dev/null @@ -1,10 +0,0 @@ -name: export-run-history-to-tensorboard -dependencies: -- pip: - - azureml-sdk - - azureml-tensorboard - - tensorflow - - tqdm - - scipy - - sklearn - - setuptools>=41.0.0 diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.ipynb b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.ipynb deleted file mode 100644 index ba03e0054..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.ipynb +++ /dev/null @@ -1,591 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tensorboard Integration with Run History\n", - "\n", - "1. Run a TensorFlow job locally and view its TB output live.\n", - "2. The same, for a DSVM.\n", - "3. And once more, with an AmlCompute cluster.\n", - "4. Finally, we'll collect all of these historical runs together into a single Tensorboard graph." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) notebook to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (`config.json`)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagnostics\n", - "Opt-in diagnostics for better experience, quality, and security of future releases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "Diagnostics" - ] - }, - "outputs": [], - "source": [ - "from azureml.telemetry import set_diagnostics_collection\n", - "\n", - "set_diagnostics_collection(send_diagnostics=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set experiment name and create project\n", - "Choose a name for your run history container in the workspace, and create a folder for the project." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from os import path, makedirs\n", - "experiment_name = 'tensorboard-demo'\n", - "\n", - "# experiment folder\n", - "exp_dir = './sample_projects/' + experiment_name\n", - "\n", - "if not path.exists(exp_dir):\n", - " makedirs(exp_dir)\n", - "\n", - "# runs we started in this session, for the finale\n", - "runs = []" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download Tensorflow Tensorboard demo code\n", - "\n", - "Tensorflow's repository has an MNIST demo with extensive Tensorboard instrumentation. We'll use it here for our purposes.\n", - "\n", - "Note that we don't need to make any code changes at all - the code works without modification from the Tensorflow repository." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "import os\n", - "\n", - "tf_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r2.1/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py\")\n", - "input_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r2.1/tensorflow/examples/tutorials/mnist/input_data.py\")\n", - "with open(os.path.join(exp_dir, \"mnist_with_summaries.py\"), \"w\") as file:\n", - " file.write(tf_code.text.replace(\"from tensorflow.examples.tutorials.mnist import input_data\", \"import input_data\"))\n", - "with open(os.path.join(exp_dir, \"input_data.py\"), \"w\") as file:\n", - " file.write(input_code.text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure and run locally\n", - "\n", - "We'll start by running this locally. While it might not initially seem that useful to use this for a local run - why not just run TB against the files generated locally? - even in this case there is some value to using this feature. Your local run will be registered in the run history, and your Tensorboard logs will be uploaded to the artifact store associated with this run. Later, you'll be able to restore the logs from any run, regardless of where it happened.\n", - "\n", - "Note that for this run, you will need to install Tensorflow on your local machine by yourself. Further, the Tensorboard module (that is, the one included with Tensorflow) must be accessible to this notebook's kernel, as the local machine is what runs Tensorboard. In addition, you will also need to have the `azureml-tensorboard` package installed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "myenv = Environment(\"myenv\")\n", - "myenv.python.user_managed_dependencies = True\n", - "\n", - "# You can choose a specific Python environment by pointing to a Python path \n", - "#myenv.python.interpreter_path = '/home/ninghai/miniconda3/envs/sdk2/bin/python'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "from azureml.core.script_run_config import ScriptRunConfig\n", - "\n", - "logs_dir = os.path.join(os.curdir, os.path.join(\"logs\", \"tb-logs\"))\n", - "data_dir = os.path.abspath(os.path.join(os.curdir, \"mnist_data\"))\n", - "\n", - "if not path.exists(data_dir):\n", - " makedirs(data_dir)\n", - "\n", - "os.environ[\"TEST_TMPDIR\"] = data_dir\n", - "\n", - "# Writing logs to ./logs results in their being uploaded to Artifact Service,\n", - "# and thus, made accessible to our Tensorboard instance.\n", - "arguments = [\"--log_dir\", logs_dir]\n", - "\n", - "# Create an experiment\n", - "exp = Experiment(ws, experiment_name)\n", - "\n", - "# If you would like the run to go for longer, add --max_steps 5000 to the arguments list:\n", - "# arguments += [\"--max_steps\", \"5000\"]\n", - "\n", - "src = ScriptRunConfig(exp_dir,\n", - " script=\"mnist_with_summaries.py\",\n", - " arguments=arguments,\n", - " environment=myenv)\n", - "\n", - "run = exp.submit(src)\n", - "# You can also wait for the run to complete\n", - "# run.wait_for_completion(show_output=True)\n", - "runs.append(run)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Start Tensorboard\n", - "\n", - "Now, while the run is in progress, we just need to start Tensorboard with the run as its target, and it will begin streaming logs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "tensorboard-sample" - ] - }, - "outputs": [], - "source": [ - "from azureml.tensorboard import Tensorboard\n", - "\n", - "# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n", - "tb = Tensorboard([run])\n", - "\n", - "# If successful, start() returns a string with the URI of the instance.\n", - "tb.start()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stop Tensorboard\n", - "\n", - "When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tb.stop()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Now, with a DSVM\n", - "\n", - "Tensorboard uploading works with all compute targets. Here we demonstrate it from a DSVM.\n", - "Note that the Tensorboard instance itself will be run by the notebook kernel. Again, this means this notebook's kernel must have access to the Tensorboard module.\n", - "\n", - "If you are unfamiliar with DSVM configuration, check [Train in a remote VM](../../training/train-on-remote-vm/train-on-remote-vm.ipynb) for a more detailed breakdown.\n", - "\n", - "**Note**: To streamline the compute that Azure Machine Learning creates, we are making updates to support creating only single to multi-node `AmlCompute`. The `DSVMCompute` class will be deprecated in a later release, but the DSVM can be created using the below single line command and then attached(like any VM) using the sample code below. Also note, that we only support Linux VMs for remote execution from AML and the commands below will spin a Linux VM only.\n", - "\n", - "```shell\n", - "# create a DSVM in your resource group\n", - "# note you need to be at least a contributor to the resource group in order to execute this command successfully.\n", - "(myenv) $ az vm create --resource-group --name --image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest --admin-username --admin-password --generate-ssh-keys --authentication-type password\n", - "```\n", - "You can also use [this url](https://portal.azure.com/#create/microsoft-dsvm.linux-data-science-vm-ubuntulinuxdsvmubuntu) to create the VM using the Azure Portal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, RemoteCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "username = os.getenv('AZUREML_DSVM_USERNAME', default='')\n", - "address = os.getenv('AZUREML_DSVM_ADDRESS', default='')\n", - "\n", - "compute_target_name = 'cpudsvm'\n", - "# if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase \n", - "try:\n", - " attached_dsvm_compute = RemoteCompute(workspace=ws, name=compute_target_name)\n", - " print('found existing:', attached_dsvm_compute.name)\n", - "except ComputeTargetException:\n", - " config = RemoteCompute.attach_configuration(username=username,\n", - " address=address,\n", - " ssh_port=22,\n", - " private_key_file='./.ssh/id_rsa')\n", - " attached_dsvm_compute = ComputeTarget.attach(ws, compute_target_name, config)\n", - " \n", - " attached_dsvm_compute.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit run using TensorFlow estimator\n", - "\n", - "Instead of manually configuring the DSVM environment, we can use the TensorFlow estimator and everything is set up automatically." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.dnn import TensorFlow\n", - "\n", - "script_params = {\"--log_dir\": \"./logs\"}\n", - "\n", - "# If you want the run to go longer, set --max-steps to a higher number.\n", - "# script_params[\"--max_steps\"] = \"5000\"\n", - "\n", - "tf_estimator = TensorFlow(source_directory=exp_dir,\n", - " compute_target=attached_dsvm_compute,\n", - " entry_script='mnist_with_summaries.py',\n", - " script_params=script_params,\n", - " framework_version=\"2.0\")\n", - "\n", - "run = exp.submit(tf_estimator)\n", - "\n", - "runs.append(run)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Start Tensorboard with this run\n", - "\n", - "Just like before." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n", - "tb = Tensorboard([run])\n", - "\n", - "# If successful, start() returns a string with the URI of the instance.\n", - "tb.start()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stop Tensorboard\n", - "\n", - "When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tb.stop()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Once more, with an AmlCompute cluster\n", - "\n", - "Just to prove we can, let's create an AmlCompute CPU cluster, and run our demo there, as well.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute\n", - "# choose a name for your cluster\n", - "cluster_name = \"cpu-cluster\"\n", - "\n", - "cts = ws.compute_targets\n", - "found = False\n", - "if cluster_name in cts and cts[cluster_name].type == 'AmlCompute':\n", - " found = True\n", - " print('Found existing compute target.')\n", - " compute_target = cts[cluster_name]\n", - "if not found:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', \n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True, min_node_count=None)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "# print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit run using TensorFlow estimator\n", - "\n", - "Again, we can use the TensorFlow estimator and everything is set up automatically." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.dnn import TensorFlow\n", - "\n", - "script_params = {\"--log_dir\": \"./logs\"}\n", - "\n", - "# If you want the run to go longer, set --max-steps to a higher number.\n", - "# script_params[\"--max_steps\"] = \"5000\"\n", - "\n", - "tf_estimator = TensorFlow(source_directory=exp_dir,\n", - " compute_target=compute_target,\n", - " entry_script='mnist_with_summaries.py',\n", - " script_params=script_params,\n", - " framework_version=\"2.0\")\n", - "\n", - "run = exp.submit(tf_estimator)\n", - "\n", - "runs.append(run)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Start Tensorboard with this run\n", - "\n", - "Once more..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n", - "tb = Tensorboard([run])\n", - "\n", - "# If successful, start() returns a string with the URI of the instance.\n", - "tb.start()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stop Tensorboard\n", - "\n", - "When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tb.stop()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finale\n", - "\n", - "If you've paid close attention, you'll have noticed that we've been saving the run objects in an array as we went along. We can start a Tensorboard instance that combines all of these run objects into a single process. This way, you can compare historical runs. You can even do this with live runs; if you made some of those previous runs longer via the `--max_steps` parameter, they might still be running, and you'll see them live in this instance as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The Tensorboard constructor takes an array of runs...\n", - "# and it turns out that we have been building one of those all along.\n", - "tb = Tensorboard(runs)\n", - "\n", - "# If successful, start() returns a string with the URI of the instance.\n", - "tb.start()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stop Tensorboard\n", - "\n", - "As you might already know, make sure to call the `stop()` method of the Tensorboard object, or it will stay running (until you kill the kernel associated with this notebook, at least)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tb.stop()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "roastala" - } - ], - "category": "training", - "compute": [ - "Local", - "DSVM", - "AML Compute" - ], - "datasets": [ - "None" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "TensorFlow" - ], - "friendly_name": "Tensorboard integration with run history", - "index_order": 3, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "tags": [ - "None" - ], - "task": "Run a TensorFlow job and view its Tensorboard output live" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.yml b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.yml deleted file mode 100644 index 024d3600f..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.yml +++ /dev/null @@ -1,7 +0,0 @@ -name: tensorboard -dependencies: -- pip: - - azureml-sdk - - azureml-tensorboard - - tensorflow - - setuptools>=41.0.0 diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb deleted file mode 100644 index 06acf0a2c..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb +++ /dev/null @@ -1,268 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/train-local/train-local.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use MLflow with Azure Machine Learning for Local Training Run\n", - "\n", - "This example shows you how to use mlflow tracking APIs together with Azure Machine Learning services for storing your metrics and artifacts, from local Notebook run. You'll learn how to:\n", - "\n", - " 1. Set up MLflow tracking URI so as to use Azure ML\n", - " 2. Create experiment\n", - " 3. Train a model on your local computer while logging metrics and artifacts\n", - " 4. View your experiment within your Azure ML Workspace in Azure Portal.\n", - "\n", - "## Prerequisites and Set-up\n", - "\n", - "Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n", - "\n", - "Install azureml-mlflow package before running this notebook. Note that mlflow itself gets installed as dependency if you haven't installed it yet.\n", - "\n", - "```\n", - "pip install azureml-mlflow\n", - "```\n", - "\n", - "This example also uses scikit-learn and matplotlib packages. Install them:\n", - "```\n", - "pip install scikit-learn matplotlib\n", - "```\n", - "\n", - "Then, import necessary packages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import mlflow\n", - "import mlflow.sklearn\n", - "import azureml.core\n", - "from azureml.core import Workspace\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Check core SDK version number\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set tracking URI\n", - "\n", - "Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Experiment\n", - "\n", - "In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_name = \"LocalTrain-with-mlflow-sample\"\n", - "mlflow.set_experiment(experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create training and test data set\n", - "\n", - "This example uses diabetes dataset to build a simple regression model. Let's load the dataset and split it into training and test sets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from sklearn.datasets import load_diabetes\n", - "from sklearn.linear_model import Ridge\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "X, y = load_diabetes(return_X_y = True)\n", - "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", - "data = {\n", - " \"train\":{\"X\": X_train, \"y\": y_train}, \n", - " \"test\":{\"X\": X_test, \"y\": y_test}\n", - "}\n", - "\n", - "print (\"Data contains\", len(data['train']['X']), \"training samples and\",len(data['test']['X']), \"test samples\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train while logging metrics and artifacts\n", - "\n", - "Next, start a mlflow run to train a scikit-learn regression model. Note that the training script has been instrumented using MLflow to:\n", - " * Log model hyperparameter alpha value\n", - " * Log mean squared error against test set\n", - " * Save the scikit-learn based regression model produced by training\n", - " * Save an image that shows actuals vs predictions against test set.\n", - " \n", - "These metrics and artifacts have been recorded to your Azure ML Workspace; in the next step you'll learn how to view them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a run object in the experiment\n", - "model_save_path = \"model\"\n", - "\n", - "with mlflow.start_run() as run:\n", - " # Log the algorithm parameter alpha to the run\n", - " mlflow.log_metric('alpha', 0.03)\n", - " # Create, fit, and test the scikit-learn Ridge regression model\n", - " regression_model = Ridge(alpha=0.03)\n", - " regression_model.fit(data['train']['X'], data['train']['y'])\n", - " preds = regression_model.predict(data['test']['X'])\n", - "\n", - " # Log mean squared error\n", - " print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))\n", - " mlflow.log_metric('mse', mean_squared_error(data['test']['y'], preds))\n", - " \n", - " # Save the model to the outputs directory for capture\n", - " mlflow.sklearn.log_model(regression_model,model_save_path)\n", - " \n", - " # Plot actuals vs predictions and save the plot within the run\n", - " fig = plt.figure(1)\n", - " idx = np.argsort(data['test']['y'])\n", - " plt.plot(data['test']['y'][idx],preds[idx])\n", - " fig.savefig(\"actuals_vs_predictions.png\")\n", - " mlflow.log_artifact(\"actuals_vs_predictions.png\") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can open the report page for your experiment and runs within it from Azure Portal.\n", - "\n", - "Select one of the runs to view the metrics, and the plot you saved. The saved scikit-learn model appears under **outputs** tab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws.experiments[experiment_name]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next steps\n", - "\n", - "Try out these notebooks to learn more about MLflow-Azure Machine Learning integration:\n", - "\n", - " * [Train a model using remote compute on Azure Cloud](../train-on-remote/train-on-remote.ipynb)\n", - " * [Deploy the model as a web service](../deploy-model/deploy-model.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "training", - "compute": [ - "Local" - ], - "datasets": [ - "Diabetes" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Use MLflow with AML for a local training run", - "index_order": 7, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - }, - "tags": [ - "None" - ], - "task": "Use MLflow tracking APIs together with Azure Machine Learning for storing your metrics and artifacts" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.yml b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.yml deleted file mode 100644 index 5095b89f9..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.yml +++ /dev/null @@ -1,7 +0,0 @@ -name: train-local -dependencies: -- scikit-learn -- matplotlib -- pip: - - azureml-sdk - - azureml-mlflow diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/MLproject b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/MLproject deleted file mode 100644 index 284efcc6a..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/MLproject +++ /dev/null @@ -1,10 +0,0 @@ -name: mlflow-example - -conda_env: conda.yaml - -entry_points: - main: - parameters: - alpha: float - l1_ratio: {type: float, default: 0.1} - command: "python train.py {alpha} {l1_ratio}" diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/backend_config.json b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/backend_config.json deleted file mode 100644 index 62f8ecb13..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/backend_config.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "USE_CONDA": "False" -} \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/conda.yaml b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/conda.yaml deleted file mode 100644 index 51718dfa7..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/conda.yaml +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. -# Licensed under the MIT license. -name: mlflow-example -channels: - - defaults -dependencies: - - numpy>=1.14.3 - - pandas>=1.0.0 - - scikit-learn=0.19.1 - - pip - - pip: - - mlflow - - azureml-mlflow \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train-projects-local.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train-projects-local.ipynb deleted file mode 100644 index dbe4e3120..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train-projects-local.ipynb +++ /dev/null @@ -1,304 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train-projects-local.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train with MLflow Projects on local compute\n", - "\n", - "Train MLflow Projects on your machine with local compute and AzureML tracking. In this notebook you will:\n", - "\n", - "1. Set up MLflow tracking URI to track experiments and metrics in AzureML\n", - "2. Create experiment\n", - "3. Set up an MLflow project to run on AzureML compute\n", - "4. Submit an MLflow project run and view it in an AzureML workspace " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites \n", - "\n", - "If you are using a Notebook VM, you're all set. Otherwise, go through the [Configuration](../../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n", - "\n", - "Install azureml-mlflow package before running this notebook. Note that mlflow itself gets installed as dependency if you haven't installed it yet.\n", - "\n", - "```\n", - "pip install azureml-mlflow\n", - "```\n", - "\n", - "This example also uses scikit-learn. Install them using the following:\n", - "```\n", - "pip install scikit-learn matplotlib\n", - "```\n", - "\n", - "Make sure you have the following before starting the notebook: \n", - "- Connected to an AzureML Workspace \n", - "- Your local conda environment has the necessary packages needed to run this project\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set-up\n", - "\n", - "Configure your workspace and check package versions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import sys, os\n", - "import mlflow\n", - "import mlflow.azureml\n", - "\n", - "import azureml.core\n", - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)\n", - "print(\"MLflow version:\", mlflow.version.VERSION)\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Tracking Store and Experiment\n", - "\n", - "### Set the Tracking Store\n", - "Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Experiment\n", - "Create an Mlflow Experiment to organize your runs. It can be set either by passing the name as a parameter in the mlflow.projects.run call or by the following," - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "experiment_name = \"train-project-local\"\n", - "mlflow.set_experiment(experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the Backend Configuration Object\n", - "\n", - "The backend configuration object will store necesary information for the integration such as the compute target and whether to use your local managed environment or a system managed environment. \n", - "\n", - "The integration will accept \"COMPUTE\" and \"USE_CONDA\" as parameters where \"COMPUTE\" is set to the name of a remote target (not applicable for this training example) and \"USE_CONDA\" which creates a new environment for the project from the environment configuration file. You must set this to \"False\" and include it in the backend configuration object if you want to use your local environment for the project run. Mlflow accepts a dictionary object or a JSON file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# dictionary\n", - "backend_config = {\"USE_CONDA\": False}\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add the Integration to your Environment Configuration\n", - "\n", - "Add the azureml-mlflow package as a pip dependency to your environment configuration file (conda.yaml). The project can run without this addition, but key artifacts and metrics will not be logged to your Workspace. An example conda.yaml file is included in this notebook folder. Adding it to to the file will look like this,\n", - "\n", - "```\n", - "name: mlflow-example\n", - "channels:\n", - " - defaults\n", - " - anaconda\n", - " - conda-forge\n", - "dependencies:\n", - " - python=3.6\n", - " - scikit-learn=0.19.1\n", - " - pip\n", - " - pip:\n", - " - mlflow\n", - " - azureml-mlflow\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## User Managed environment\n", - "For using your local conda environment, set `use_conda = False` in the backend_config object. Ensure your local environment has all the necessary packages for running the project and you are specifying the **backend parameter** in any run call to be **\"azureml\"**." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "local_env_run = mlflow.projects.run(uri=\".\", \n", - " parameters={\"alpha\":0.3},\n", - " backend = \"azureml\",\n", - " use_conda=False,\n", - " backend_config = backend_config)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_env_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: All these calculations were run on your local machine, in the conda environment you defined above. You can find the results in:\n", - "- Your AzureML Experiments (a link to the run will be provided in the console)\n", - "- ~/.azureml/envs/azureml_xxxx for the conda environment you just created\n", - "- ~/AppData/Local/Temp/azureml_runs/train-on-local_xxxx for the machine learning models you trained (this path may differ depending on the platform you use). This folder also contains\n", - " - Logs (under azureml_logs/)\n", - " - Output pickled files (under outputs/)\n", - " - The configuration files (credentials, local and docker image setups)\n", - " - The train.py and mylib.py scripts\n", - " - The current notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## System Mananged Environment\n", - "\n", - "Now, instead of managing the setup of the environment yourself, you can ask the system to build a new conda environment for you using the environment configuration file in this project. If a backend configuration object is not provided in the call, the integration will default to creating a new conda environment. The environment is built once, and will be reused in subsequent executions as long as the conda dependencies remain unchanged.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "backend_config = {\"USE_CONDA\": True}\n", - "local_mlproject_run = mlflow.projects.run(uri=\".\", \n", - " parameters={\"alpha\":0.3},\n", - " backend = \"azureml\",\n", - " backend_config = backend_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next Steps \n", - "\n", - "Try out these notebooks to learn more about MLflow-Azure Machine Learning integration:\n", - "\n", - " * [Train a model using remote compute on Azure Cloud](../train-on-remote/train-on-remote.ipynb)\n", - " * [Deploy the model as a web service](../deploy-model/deploy-model.ipynb)\n", - " * [Train a model using Pytorch and MLflow](../../ml-frameworks/using-mlflow/train-and-deploy-pytorch)\n", - "\n" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "shipatel" - } - ], - "category": "tutorial", - "celltoolbar": "Edit Metadata", - "compute": [ - "Local" - ], - "exclude_from_index": false, - "framework": [ - "ScikitLearn" - ], - "friendly_name": "Use MLflow projects with Azure Machine Learning to train a model with local compute", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5-final" - }, - "tags": [ - "mlflow", - "scikit" - ], - "task": "Use MLflow projects with Azure Machine Learning to train a model using local compute" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train.py b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train.py deleted file mode 100644 index 89fd6fe46..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train.py +++ /dev/null @@ -1,64 +0,0 @@ -# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality -# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. -# Modeling wine preferences by data mining from physicochemical properties. -# In Decision Support Systems, Elsevier, 47(4):547-553, 2009. - -import os -import warnings -import sys -import pandas as pd -import numpy as np -from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score -from sklearn.model_selection import train_test_split -from sklearn.linear_model import ElasticNet -import mlflow -import mlflow.sklearn - - -def eval_metrics(actual, pred): - rmse = np.sqrt(mean_squared_error(actual, pred)) - mae = mean_absolute_error(actual, pred) - r2 = r2_score(actual, pred) - return rmse, mae, r2 - - -if __name__ == "__main__": - warnings.filterwarnings("ignore") - np.random.seed(40) - - # Read the wine-quality csv file (make sure you're running this from the root of MLflow!) - wine_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wine-quality.csv") - data = pd.read_csv(wine_path) - - # Split the data into training and test sets. (0.75, 0.25) split. - train, test = train_test_split(data) - - # The predicted column is "quality" which is a scalar from [3, 9] - train_x = train.drop(["quality"], axis=1) - test_x = test.drop(["quality"], axis=1) - train_y = train[["quality"]] - test_y = test[["quality"]] - - alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5 - l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5 - - with mlflow.start_run(): - lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42) - lr.fit(train_x, train_y) - - predicted_qualities = lr.predict(test_x) - - (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities) - - print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio)) - print(" RMSE: %s" % rmse) - print(" MAE: %s" % mae) - print(" R2: %s" % r2) - - mlflow.log_param("alpha", alpha) - mlflow.log_param("l1_ratio", l1_ratio) - mlflow.log_metric("rmse", rmse) - mlflow.log_metric("r2", r2) - mlflow.log_metric("mae", mae) - - mlflow.sklearn.log_model(lr, "model") diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/wine-quality.csv b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/wine-quality.csv deleted file mode 100644 index df5cbcb1a..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/wine-quality.csv +++ /dev/null @@ -1,4899 +0,0 @@ -"fixed acidity","volatile acidity","citric acid","residual sugar","chlorides","free sulfur dioxide","total sulfur dioxide","density","pH","sulphates","alcohol","quality" -7,0.27,0.36,20.7,0.045,45,170,1.001,3,0.45,8.8,6 -6.3,0.3,0.34,1.6,0.049,14,132,0.994,3.3,0.49,9.5,6 -8.1,0.28,0.4,6.9,0.05,30,97,0.9951,3.26,0.44,10.1,6 -7.2,0.23,0.32,8.5,0.058,47,186,0.9956,3.19,0.4,9.9,6 -7.2,0.23,0.32,8.5,0.058,47,186,0.9956,3.19,0.4,9.9,6 -8.1,0.28,0.4,6.9,0.05,30,97,0.9951,3.26,0.44,10.1,6 -6.2,0.32,0.16,7,0.045,30,136,0.9949,3.18,0.47,9.6,6 -7,0.27,0.36,20.7,0.045,45,170,1.001,3,0.45,8.8,6 -6.3,0.3,0.34,1.6,0.049,14,132,0.994,3.3,0.49,9.5,6 -8.1,0.22,0.43,1.5,0.044,28,129,0.9938,3.22,0.45,11,6 -8.1,0.27,0.41,1.45,0.033,11,63,0.9908,2.99,0.56,12,5 -8.6,0.23,0.4,4.2,0.035,17,109,0.9947,3.14,0.53,9.7,5 -7.9,0.18,0.37,1.2,0.04,16,75,0.992,3.18,0.63,10.8,5 -6.6,0.16,0.4,1.5,0.044,48,143,0.9912,3.54,0.52,12.4,7 -8.3,0.42,0.62,19.25,0.04,41,172,1.0002,2.98,0.67,9.7,5 -6.6,0.17,0.38,1.5,0.032,28,112,0.9914,3.25,0.55,11.4,7 -6.3,0.48,0.04,1.1,0.046,30,99,0.9928,3.24,0.36,9.6,6 -6.2,0.66,0.48,1.2,0.029,29,75,0.9892,3.33,0.39,12.8,8 -7.4,0.34,0.42,1.1,0.033,17,171,0.9917,3.12,0.53,11.3,6 -6.5,0.31,0.14,7.5,0.044,34,133,0.9955,3.22,0.5,9.5,5 -6.2,0.66,0.48,1.2,0.029,29,75,0.9892,3.33,0.39,12.8,8 -6.4,0.31,0.38,2.9,0.038,19,102,0.9912,3.17,0.35,11,7 -6.8,0.26,0.42,1.7,0.049,41,122,0.993,3.47,0.48,10.5,8 -7.6,0.67,0.14,1.5,0.074,25,168,0.9937,3.05,0.51,9.3,5 -6.6,0.27,0.41,1.3,0.052,16,142,0.9951,3.42,0.47,10,6 -7,0.25,0.32,9,0.046,56,245,0.9955,3.25,0.5,10.4,6 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-6.2,0.35,0.29,3.9,0.041,22,79,0.99005,3.1,0.59,12.0666666666667,6 -7.1,0.23,0.39,13.7,0.058,26,172,0.99755,2.9,0.46,9,6 -7.1,0.23,0.39,13.7,0.058,26,172,0.99755,2.9,0.46,9,6 -7.5,0.38,0.33,9.2,0.043,19,116,0.99444,3.08,0.42,11.4,6 -6.4,0.35,0.51,7.8,0.055,53,177,0.99502,3.12,0.45,9.6,5 -6,0.43,0.34,7.6,0.045,25,118,0.99222,3.03,0.37,11,6 -6,0.52,0.33,7.7,0.046,24,119,0.99224,3.04,0.38,11,6 -5.5,0.31,0.29,3,0.027,16,102,0.99067,3.23,0.56,11.2,6 -5.9,0.22,0.3,1.3,0.052,42,86,0.99069,3.31,0.47,11.55,6 -6.2,0.36,0.32,4,0.036,44,92,0.98936,3.2,0.5,13.3,7 -6,0.41,0.23,1.1,0.066,22,148,0.99266,3.3,0.47,9.63333333333333,5 -6.2,0.355,0.35,2,0.046,31,95,0.98822,3.06,0.46,13.6,6 -5.7,0.41,0.21,1.9,0.048,30,112,0.99138,3.29,0.55,11.2,6 -5.3,0.6,0.34,1.4,0.031,3,60,0.98854,3.27,0.38,13,6 -5.8,0.23,0.31,4.5,0.046,42,124,0.99324,3.31,0.64,10.8,6 -6.6,0.24,0.33,10.1,0.032,8,81,0.99626,3.19,0.51,9.8,6 -6.1,0.32,0.28,6.6,0.021,29,132,0.99188,3.15,0.36,11.45,7 -5,0.2,0.4,1.9,0.015,20,98,0.9897,3.37,0.55,12.05,6 -6,0.42,0.41,12.4,0.032,50,179,0.99622,3.14,0.6,9.7,5 -5.7,0.21,0.32,1.6,0.03,33,122,0.99044,3.33,0.52,11.9,6 -5.6,0.2,0.36,2.5,0.048,16,125,0.99282,3.49,0.49,10,6 -7.4,0.22,0.26,1.2,0.035,18,97,0.99245,3.12,0.41,9.7,6 -6.2,0.38,0.42,2.5,0.038,34,117,0.99132,3.36,0.59,11.6,7 -5.9,0.54,0,0.8,0.032,12,82,0.99286,3.25,0.36,8.8,5 -6.2,0.53,0.02,0.9,0.035,6,81,0.99234,3.24,0.35,9.5,4 -6.6,0.34,0.4,8.1,0.046,68,170,0.99494,3.15,0.5,9.53333333333333,6 -6.6,0.34,0.4,8.1,0.046,68,170,0.99494,3.15,0.5,9.53333333333333,6 -5,0.235,0.27,11.75,0.03,34,118,0.9954,3.07,0.5,9.4,6 -5.5,0.32,0.13,1.3,0.037,45,156,0.99184,3.26,0.38,10.7,5 -4.9,0.47,0.17,1.9,0.035,60,148,0.98964,3.27,0.35,11.5,6 -6.5,0.33,0.38,8.3,0.048,68,174,0.99492,3.14,0.5,9.6,5 -6.6,0.34,0.4,8.1,0.046,68,170,0.99494,3.15,0.5,9.55,6 -6.2,0.21,0.28,5.7,0.028,45,121,0.99168,3.21,1.08,12.15,7 -6.2,0.41,0.22,1.9,0.023,5,56,0.98928,3.04,0.79,13,7 -6.8,0.22,0.36,1.2,0.052,38,127,0.9933,3.04,0.54,9.2,5 -4.9,0.235,0.27,11.75,0.03,34,118,0.9954,3.07,0.5,9.4,6 -6.1,0.34,0.29,2.2,0.036,25,100,0.98938,3.06,0.44,11.8,6 -5.7,0.21,0.32,0.9,0.038,38,121,0.99074,3.24,0.46,10.6,6 -6.5,0.23,0.38,1.3,0.032,29,112,0.99298,3.29,0.54,9.7,5 -6.2,0.21,0.29,1.6,0.039,24,92,0.99114,3.27,0.5,11.2,6 -6.6,0.32,0.36,8,0.047,57,168,0.9949,3.15,0.46,9.6,5 -6.5,0.24,0.19,1.2,0.041,30,111,0.99254,2.99,0.46,9.4,6 -5.5,0.29,0.3,1.1,0.022,20,110,0.98869,3.34,0.38,12.8,7 -6,0.21,0.38,0.8,0.02,22,98,0.98941,3.26,0.32,11.8,6 diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/MLproject b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/MLproject deleted file mode 100644 index 5192a91ca..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/MLproject +++ /dev/null @@ -1,10 +0,0 @@ -name: tutorial - -conda_env: conda.yaml - -entry_points: - main: - parameters: - alpha: float - l1_ratio: {type: float, default: 0.1} - command: "python train.py {alpha} {l1_ratio}" diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/backend_config.json b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/backend_config.json deleted file mode 100644 index 185d8c058..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/backend_config.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "COMPUTE": "cpu-cluster", - "USE_CONDA": "True" -} \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/conda.yaml b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/conda.yaml deleted file mode 100644 index 540504d25..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/conda.yaml +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. -# Licensed under the MIT license. -name: tutorial -channels: - - defaults -dependencies: - - numpy>=1.14.3 - - pandas>=1.0.0 - - scikit-learn=0.19.1 - - pip - - pip: - - mlflow - - azureml-mlflow \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb deleted file mode 100644 index 55debb554..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb +++ /dev/null @@ -1,275 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train with MLflow Projects on AML Compute\n", - "\n", - "Train MLflow Projects on Azure Machine Learning Compute.\n", - "\n", - "Train MLflow Projects on your machine with AzureML compute and tracking. In this notebook you will:\n", - "\n", - "1. Set up MLflow tracking URI to track experiments and metrics in AzureML\n", - "2. Create experiment\n", - "3. Set up an MLflow project to run on AzureML compute\n", - "4. Submit an MLflow project run and view it in an AzureML workspace \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites \n", - "\n", - "If you are using a Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n", - "\n", - "Make sure you have the following before staring the notebook: \n", - "- Connected to an AML Workspace \n", - "- Have an existing [Azure ML Compute cluster](https://docs.microsoft.com/azure/machine-learning/how-to-create-attach-compute-sdk#amlcompute) in that Workspace \n", - "- Have an MLproject file with a modified environment specification \n", - "\n", - "Add the azureml-mlflow package as a pip dependency to your environment configuration file (conda.yaml). The project can run without this addition, but key artifacts and metrics will not be logged to your Workspace. An example conda.yaml is included in this tutorial folder with the necessary packages. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set-up \n", - "\n", - "Check the Azure ML and MLflow SDK version installed on your computer and connect to your workspace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import sys, os\n", - "import mlflow\n", - "import mlflow.azureml\n", - "\n", - "import azureml.core\n", - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)\n", - "print(\"MLflow version:\", mlflow.version.VERSION)\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Tracking Store and Experiment\n", - "\n", - "### Set Tracking URI \n", - "\n", - "Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Experiment\n", - "\n", - "Create an Mlflow Experiment to organize your runs. It can be set either by passing the name as a **parameter** in the mlflow.projects.run call or by the following," - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "experiment_name = \"train-project-amlcompute\"\n", - "mlflow.set_experiment(experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the Backend Configuration Object\n", - "\n", - "The backend configuration object will store necesary information for the integration such as the compute target and whether to use your local managed environment or a system managed environment. \n", - "\n", - "The integration will accept \"COMPUTE\" and \"USE_CONDA\" as parameters where \"COMPUTE\" is set to the name of your remote compute cluster and \"USE_CONDA\" which creates a new environment for the project from the environment configuration file. If \"COMPUTE\" is present in the object, the project will be automatically submitted to the remote compute and ignore \"USE_CONDA\". MLflow accepts a dictionary object or a JSON file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# dictionary\n", - "backend_config = {\"COMPUTE\": \"cpu-cluster\", \"USE_CONDA\": False}\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modify your Environment specification\n", - "\n", - "Add the azureml-mlflow package as a pip dependency to your environment configuration file (conda.yaml). The project can run without this addition, but key artifacts and metrics will not be logged to your Workspace. An example conda.yaml is included in the notebook folder. Adding it to to the file will look like this,\n", - "\n", - "```\n", - "name: mlflow-example\n", - "channels:\n", - " - defaults\n", - " - anaconda\n", - " - conda-forge\n", - "dependencies:\n", - " - python=3.6\n", - " - scikit-learn=0.19.1\n", - " - pip\n", - " - pip:\n", - " - mlflow\n", - " - azureml-mlflow\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit Run \n", - "\n", - "Submit the mlflow project run using aml compute and ensure the **backened** parameter is set to azureml.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "remote_mlflow_run = mlflow.projects.run(uri=\".\", \n", - " parameters={\"alpha\":0.3},\n", - " backend = \"azureml\",\n", - " backend_config = backend_config,\n", - " synchronous=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View run \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "remote_mlflow_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next Steps\n", - "\n", - "Try out these notebooks to learn more about MLflow-Azure Machine Learning integration:\n", - "\n", - " * [Train a model using remote compute on Azure Cloud](../train-remote/train-remote.ipynb)\n", - " * [Train a model using Pytorch and MLflow](../../../ml-frameworks/using-mlflow/train-and-deploy-pytorch)\n", - "\n" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "shipatel" - } - ], - "category": "tutorial", - "celltoolbar": "Edit Metadata", - "compute": [ - "AML Compute" - ], - "exclude_from_index": false, - "framework": [ - "Scikit" - ], - "friendly_name": "Use MLflow projects with Azure Machine Learning to train a model", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5-final" - }, - "tags": [ - "mlflow", - "scikit" - ], - "task": "Use MLflow projects with Azure Machine Learning to train a model using azureml compute" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train.py b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train.py deleted file mode 100644 index 2c9451909..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train.py +++ /dev/null @@ -1,66 +0,0 @@ -# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality -# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. -# Modeling wine preferences by data mining from physicochemical properties. -# In Decision Support Systems, Elsevier, 47(4):547-553, 2009. - -import os -import warnings -import sys - -import pandas as pd -import numpy as np -from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score -from sklearn.model_selection import train_test_split -from sklearn.linear_model import ElasticNet - -import mlflow -import mlflow.sklearn - - -def eval_metrics(actual, pred): - rmse = np.sqrt(mean_squared_error(actual, pred)) - mae = mean_absolute_error(actual, pred) - r2 = r2_score(actual, pred) - return rmse, mae, r2 - - -if __name__ == "__main__": - warnings.filterwarnings("ignore") - np.random.seed(40) - - # Read the wine-quality csv file (make sure you're running this from the root of MLflow!) - wine_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wine-quality.csv") - data = pd.read_csv(wine_path) - - # Split the data into training and test sets. (0.75, 0.25) split. - train, test = train_test_split(data) - - # The predicted column is "quality" which is a scalar from [3, 9] - train_x = train.drop(["quality"], axis=1) - test_x = test.drop(["quality"], axis=1) - train_y = train[["quality"]] - test_y = test[["quality"]] - - alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5 - l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5 - - with mlflow.start_run(): - lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42) - lr.fit(train_x, train_y) - - predicted_qualities = lr.predict(test_x) - - (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities) - - print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio)) - print(" RMSE: %s" % rmse) - print(" MAE: %s" % mae) - print(" R2: %s" % r2) - - mlflow.log_param("alpha", alpha) - mlflow.log_param("l1_ratio", l1_ratio) - mlflow.log_metric("rmse", rmse) - mlflow.log_metric("r2", r2) - mlflow.log_metric("mae", mae) - - mlflow.sklearn.log_model(lr, "model") diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/wine-quality.csv b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/wine-quality.csv deleted file mode 100644 index df5cbcb1a..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/wine-quality.csv +++ /dev/null @@ -1,4899 +0,0 @@ -"fixed acidity","volatile acidity","citric acid","residual sugar","chlorides","free sulfur dioxide","total sulfur dioxide","density","pH","sulphates","alcohol","quality" -7,0.27,0.36,20.7,0.045,45,170,1.001,3,0.45,8.8,6 -6.3,0.3,0.34,1.6,0.049,14,132,0.994,3.3,0.49,9.5,6 -8.1,0.28,0.4,6.9,0.05,30,97,0.9951,3.26,0.44,10.1,6 -7.2,0.23,0.32,8.5,0.058,47,186,0.9956,3.19,0.4,9.9,6 -7.2,0.23,0.32,8.5,0.058,47,186,0.9956,3.19,0.4,9.9,6 -8.1,0.28,0.4,6.9,0.05,30,97,0.9951,3.26,0.44,10.1,6 -6.2,0.32,0.16,7,0.045,30,136,0.9949,3.18,0.47,9.6,6 -7,0.27,0.36,20.7,0.045,45,170,1.001,3,0.45,8.8,6 -6.3,0.3,0.34,1.6,0.049,14,132,0.994,3.3,0.49,9.5,6 -8.1,0.22,0.43,1.5,0.044,28,129,0.9938,3.22,0.45,11,6 -8.1,0.27,0.41,1.45,0.033,11,63,0.9908,2.99,0.56,12,5 -8.6,0.23,0.4,4.2,0.035,17,109,0.9947,3.14,0.53,9.7,5 -7.9,0.18,0.37,1.2,0.04,16,75,0.992,3.18,0.63,10.8,5 -6.6,0.16,0.4,1.5,0.044,48,143,0.9912,3.54,0.52,12.4,7 -8.3,0.42,0.62,19.25,0.04,41,172,1.0002,2.98,0.67,9.7,5 -6.6,0.17,0.38,1.5,0.032,28,112,0.9914,3.25,0.55,11.4,7 -6.3,0.48,0.04,1.1,0.046,30,99,0.9928,3.24,0.36,9.6,6 -6.2,0.66,0.48,1.2,0.029,29,75,0.9892,3.33,0.39,12.8,8 -7.4,0.34,0.42,1.1,0.033,17,171,0.9917,3.12,0.53,11.3,6 -6.5,0.31,0.14,7.5,0.044,34,133,0.9955,3.22,0.5,9.5,5 -6.2,0.66,0.48,1.2,0.029,29,75,0.9892,3.33,0.39,12.8,8 -6.4,0.31,0.38,2.9,0.038,19,102,0.9912,3.17,0.35,11,7 -6.8,0.26,0.42,1.7,0.049,41,122,0.993,3.47,0.48,10.5,8 -7.6,0.67,0.14,1.5,0.074,25,168,0.9937,3.05,0.51,9.3,5 -6.6,0.27,0.41,1.3,0.052,16,142,0.9951,3.42,0.47,10,6 -7,0.25,0.32,9,0.046,56,245,0.9955,3.25,0.5,10.4,6 -6.9,0.24,0.35,1,0.052,35,146,0.993,3.45,0.44,10,6 -7,0.28,0.39,8.7,0.051,32,141,0.9961,3.38,0.53,10.5,6 -7.4,0.27,0.48,1.1,0.047,17,132,0.9914,3.19,0.49,11.6,6 -7.2,0.32,0.36,2,0.033,37,114,0.9906,3.1,0.71,12.3,7 -8.5,0.24,0.39,10.4,0.044,20,142,0.9974,3.2,0.53,10,6 -8.3,0.14,0.34,1.1,0.042,7,47,0.9934,3.47,0.4,10.2,6 -7.4,0.25,0.36,2.05,0.05,31,100,0.992,3.19,0.44,10.8,6 -6.2,0.12,0.34,1.5,0.045,43,117,0.9939,3.42,0.51,9,6 -5.8,0.27,0.2,14.95,0.044,22,179,0.9962,3.37,0.37,10.2,5 -7.3,0.28,0.43,1.7,0.08,21,123,0.9905,3.19,0.42,12.8,5 -6.5,0.39,0.23,5.4,0.051,25,149,0.9934,3.24,0.35,10,5 -7,0.33,0.32,1.2,0.053,38,138,0.9906,3.13,0.28,11.2,6 -7.3,0.24,0.39,17.95,0.057,45,149,0.9999,3.21,0.36,8.6,5 -7.3,0.24,0.39,17.95,0.057,45,149,0.9999,3.21,0.36,8.6,5 -6.7,0.23,0.39,2.5,0.172,63,158,0.9937,3.11,0.36,9.4,6 -6.7,0.24,0.39,2.9,0.173,63,157,0.9937,3.1,0.34,9.4,6 -7,0.31,0.26,7.4,0.069,28,160,0.9954,3.13,0.46,9.8,6 -6.6,0.24,0.27,1.4,0.057,33,152,0.9934,3.22,0.56,9.5,6 -6.7,0.23,0.26,1.4,0.06,33,154,0.9934,3.24,0.56,9.5,6 -7.4,0.18,0.31,1.4,0.058,38,167,0.9931,3.16,0.53,10,7 -6.2,0.45,0.26,4.4,0.063,63,206,0.994,3.27,0.52,9.8,4 -6.2,0.46,0.25,4.4,0.066,62,207,0.9939,3.25,0.52,9.8,5 -7,0.31,0.26,7.4,0.069,28,160,0.9954,3.13,0.46,9.8,6 -6.9,0.19,0.35,5,0.067,32,150,0.995,3.36,0.48,9.8,5 -7.2,0.19,0.31,1.6,0.062,31,173,0.9917,3.35,0.44,11.7,6 -6.6,0.25,0.29,1.1,0.068,39,124,0.9914,3.34,0.58,11,7 -6.2,0.16,0.33,1.1,0.057,21,82,0.991,3.32,0.46,10.9,7 -6.4,0.18,0.35,1,0.045,39,108,0.9911,3.31,0.35,10.9,6 -6.8,0.2,0.59,0.9,0.147,38,132,0.993,3.05,0.38,9.1,6 -6.9,0.25,0.35,1.3,0.039,29,191,0.9908,3.13,0.52,11,6 -7.2,0.21,0.34,11.9,0.043,37,213,0.9962,3.09,0.5,9.6,6 -6,0.19,0.26,12.4,0.048,50,147,0.9972,3.3,0.36,8.9,6 -6.6,0.38,0.15,4.6,0.044,25,78,0.9931,3.11,0.38,10.2,6 -7.4,0.2,0.36,1.2,0.038,44,111,0.9926,3.36,0.34,9.9,6 -6.8,0.22,0.24,4.9,0.092,30,123,0.9951,3.03,0.46,8.6,6 -6,0.19,0.26,12.4,0.048,50,147,0.9972,3.3,0.36,8.9,6 -7,0.47,0.07,1.1,0.035,17,151,0.991,3.02,0.34,10.5,5 -6.6,0.38,0.15,4.6,0.044,25,78,0.9931,3.11,0.38,10.2,6 -7.2,0.24,0.27,1.4,0.038,31,122,0.9927,3.15,0.46,10.3,6 -6.2,0.35,0.03,1.2,0.064,29,120,0.9934,3.22,0.54,9.1,5 -6.4,0.26,0.24,6.4,0.04,27,124,0.9903,3.22,0.49,12.6,7 -6.7,0.25,0.13,1.2,0.041,81,174,0.992,3.14,0.42,9.8,5 -6.7,0.23,0.31,2.1,0.046,30,96,0.9926,3.33,0.64,10.7,8 -7.4,0.24,0.29,10.1,0.05,21,105,0.9962,3.13,0.35,9.5,5 -6.2,0.27,0.43,7.8,0.056,48,244,0.9956,3.1,0.51,9,6 -6.8,0.3,0.23,4.6,0.061,50.5,238.5,0.9958,3.32,0.6,9.5,5 -6,0.27,0.28,4.8,0.063,31,201,0.9964,3.69,0.71,10,5 -8.6,0.23,0.46,1,0.054,9,72,0.9941,2.95,0.49,9.1,6 -6.7,0.23,0.31,2.1,0.046,30,96,0.9926,3.33,0.64,10.7,8 -7.4,0.24,0.29,10.1,0.05,21,105,0.9962,3.13,0.35,9.5,5 -7.1,0.18,0.36,1.4,0.043,31,87,0.9898,3.26,0.37,12.7,7 -7,0.32,0.34,1.3,0.042,20,69,0.9912,3.31,0.65,12,7 -7.4,0.18,0.3,8.8,0.064,26,103,0.9961,2.94,0.56,9.3,5 -6.7,0.54,0.28,5.4,0.06,21,105,0.9949,3.27,0.37,9,5 -6.8,0.22,0.31,1.4,0.053,34,114,0.9929,3.39,0.77,10.6,6 -7.1,0.2,0.34,16,0.05,51,166,0.9985,3.21,0.6,9.2,6 -7.1,0.34,0.2,6.1,0.063,47,164,0.9946,3.17,0.42,10,5 -7.3,0.22,0.3,8.2,0.047,42,207,0.9966,3.33,0.46,9.5,6 -7.1,0.43,0.61,11.8,0.045,54,155,0.9974,3.11,0.45,8.7,5 -7.1,0.44,0.62,11.8,0.044,52,152,0.9975,3.12,0.46,8.7,6 -7.2,0.39,0.63,11,0.044,55,156,0.9974,3.09,0.44,8.7,6 -6.8,0.25,0.31,13.3,0.05,69,202,0.9972,3.22,0.48,9.7,6 -7.1,0.43,0.61,11.8,0.045,54,155,0.9974,3.11,0.45,8.7,5 -7.1,0.44,0.62,11.8,0.044,52,152,0.9975,3.12,0.46,8.7,6 -7.2,0.39,0.63,11,0.044,55,156,0.9974,3.09,0.44,8.7,6 -6.1,0.27,0.43,7.5,0.049,65,243,0.9957,3.12,0.47,9,5 -6.9,0.24,0.33,1.7,0.035,47,136,0.99,3.26,0.4,12.6,7 -6.9,0.21,0.33,1.8,0.034,48,136,0.9899,3.25,0.41,12.6,7 -7.5,0.17,0.32,1.7,0.04,51,148,0.9916,3.21,0.44,11.5,7 -7.1,0.26,0.29,12.4,0.044,62,240,0.9969,3.04,0.42,9.2,6 -6,0.34,0.66,15.9,0.046,26,164,0.9979,3.14,0.5,8.8,6 -8.6,0.265,0.36,1.2,0.034,15,80,0.9913,2.95,0.36,11.4,7 -9.8,0.36,0.46,10.5,0.038,4,83,0.9956,2.89,0.3,10.1,4 -6,0.34,0.66,15.9,0.046,26,164,0.9979,3.14,0.5,8.8,6 -7.4,0.25,0.37,13.5,0.06,52,192,0.9975,3,0.44,9.1,5 -7.1,0.12,0.32,9.6,0.054,64,162,0.9962,3.4,0.41,9.4,5 -6,0.21,0.24,12.1,0.05,55,164,0.997,3.34,0.39,9.4,5 -7.5,0.305,0.4,18.9,0.059,44,170,1,2.99,0.46,9,5 -7.4,0.25,0.37,13.5,0.06,52,192,0.9975,3,0.44,9.1,5 -7.3,0.13,0.32,14.4,0.051,34,109,0.9974,3.2,0.35,9.2,6 -7.1,0.12,0.32,9.6,0.054,64,162,0.9962,3.4,0.41,9.4,5 -7.1,0.23,0.35,16.5,0.04,60,171,0.999,3.16,0.59,9.1,6 -7.1,0.23,0.35,16.5,0.04,60,171,0.999,3.16,0.59,9.1,6 -6.9,0.33,0.28,1.3,0.051,37,187,0.9927,3.27,0.6,10.3,5 -6.5,0.17,0.54,8.5,0.082,64,163,0.9959,2.89,0.39,8.8,6 -7.2,0.27,0.46,18.75,0.052,45,255,1,3.04,0.52,8.9,5 -7.2,0.31,0.5,13.3,0.056,68,195,0.9982,3.01,0.47,9.2,5 -6.7,0.41,0.34,9.2,0.049,29,150,0.9968,3.22,0.51,9.1,5 -6.7,0.41,0.34,9.2,0.049,29,150,0.9968,3.22,0.51,9.1,5 -5.5,0.485,0,1.5,0.065,8,103,0.994,3.63,0.4,9.7,4 -6,0.31,0.24,3.3,0.041,25,143,0.9914,3.31,0.44,11.3,6 -7,0.14,0.4,1.7,0.035,16,85,0.9911,3.19,0.42,11.8,6 -7.2,0.31,0.5,13.3,0.056,68,195,0.9982,3.01,0.47,9.2,5 -7.3,0.32,0.48,13.3,0.06,57,196,0.9982,3.04,0.5,9.2,5 -5.9,0.36,0.04,5.7,0.046,21,87,0.9934,3.22,0.51,10.2,5 -7.8,0.24,0.32,12.2,0.054,42,138,0.9984,3.01,0.54,8.8,5 -7.4,0.16,0.31,6.85,0.059,31,131,0.9952,3.29,0.34,9.7,5 -6.9,0.19,0.28,5,0.058,14,146,0.9952,3.29,0.36,9.1,6 -6.4,0.13,0.47,1.6,0.092,40,158,0.9928,3.21,0.36,9.8,6 -6.7,0.19,0.36,1.1,0.026,63,143,0.9912,3.27,0.48,11,6 -7.4,0.39,0.23,7,0.033,29,126,0.994,3.14,0.42,10.5,5 -6.5,0.24,0.32,7.6,0.038,48,203,0.9958,3.45,0.54,9.7,7 -6.1,0.3,0.56,2.8,0.044,47,179,0.9924,3.3,0.57,10.9,7 -6.1,0.3,0.56,2.7,0.046,46,184,0.9924,3.31,0.57,10.9,6 -5.7,0.26,0.25,10.4,0.02,7,57,0.994,3.39,0.37,10.6,5 -6.5,0.24,0.32,7.6,0.038,48,203,0.9958,3.45,0.54,9.7,7 -6.5,0.425,0.4,13.1,0.038,59,241,0.9979,3.23,0.57,9,5 -6.6,0.24,0.27,15.8,0.035,46,188,0.9982,3.24,0.51,9.2,5 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"metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use MLflow with Azure Machine Learning for Remote Training Run\n", - "\n", - "This example shows you how to use MLflow tracking APIs together with Azure Machine Learning services for storing your metrics and artifacts, from local Notebook run. You'll learn how to:\n", - "\n", - " 1. Set up MLflow tracking URI so as to use Azure ML\n", - " 2. Create experiment\n", - " 3. Train a model on Machine Learning Compute while logging metrics and artifacts\n", - " 4. View your experiment within your Azure ML Workspace in Azure Portal." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set-up\n", - "\n", - "Check Azure ML SDK version installed on your computer, and then connect to your Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "from azureml.core import Workspace, Experiment\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)\n", - "\n", - "ws = Workspace.from_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also create a Machine Learning Compute cluster for submitting the remote run. \n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "cluster_name = \"cpu-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print(\"Found existing cpu-cluster\")\n", - "except ComputeTargetException:\n", - " print(\"Creating new cpu-cluster\")\n", - " \n", - " # Specify the configuration for the new cluster\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n", - " min_nodes=0,\n", - " max_nodes=2)\n", - "\n", - " # Create the cluster with the specified name and configuration\n", - " cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n", - " \n", - " # Wait for the cluster to complete, show the output log\n", - " cpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Azure ML Experiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following steps show how to submit a training Python script to a cluster as an Azure ML run, while logging happens through MLflow APIs to your Azure ML Workspace. Let's first create an experiment to hold the training runs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "experiment_name = \"RemoteTrain-with-mlflow-sample\"\n", - "exp = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Instrument remote training script using MLflow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's use [*train_diabetes.py*](train_diabetes.py) to train a regression model against diabetes dataset as the example. Note that the training script uses mlflow.start_run() to start logging, and then logs metrics, saves the trained scikit-learn model, and saves a plot as an artifact.\n", - "\n", - "Run following command to view the script file. Notice the mlflow logging statements, and also notice that the script doesn't have explicit dependencies on azureml library." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "training_script = 'train_diabetes.py'\n", - "with open(training_script, 'r') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit Run to Cluster \n", - "\n", - "Let's submit the run to cluster. When running on the remote cluster as submitted run, Azure ML sets the MLflow tracking URI to point to your Azure ML Workspace, so that the metrics and artifacts are automatically logged there.\n", - "\n", - "Note that you have to specify the packages your script depends on, including *azureml-mlflow* that implicitly enables the MLflow logging to Azure ML. \n", - "\n", - "First, create a environment with Docker enable and required package dependencies specified." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "mlflow" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "env = Environment(name=\"mlflow-env\")\n", - "\n", - "# Specify conda dependencies with scikit-learn and temporary pointers to mlflow extensions\n", - "cd = CondaDependencies.create(\n", - " conda_packages=[\"scikit-learn\", \"matplotlib\"],\n", - " pip_packages=[\"azureml-mlflow\", \"pandas\", \"numpy\"]\n", - " )\n", - "\n", - "env.python.conda_dependencies = cd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, specify a script run configuration that includes the training script, environment and CPU cluster created earlier." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=\".\",\n", - " script=training_script,\n", - " compute_target=cpu_cluster,\n", - " environment=env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, submit the run. Note that the first instance of the run typically takes longer as the Docker-based environment is created, several minutes. Subsequent runs reuse the image and are faster." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = exp.submit(src)\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can navigate to your Azure ML Workspace at Azure Portal to view the run metrics and artifacts. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also get the metrics and bring them to your local notebook, and view the details of the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_metrics()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next steps\n", - "\n", - " * [Deploy the model as a web service](../deploy-model/deploy-model.ipynb)\n", - " * [Learn more about Azure Machine Learning compute options](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Diabetes" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Use MLflow with AML for a remote training run", - "index_order": 8, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "tags": [ - "None" - ], - "task": "Use MLflow tracking APIs together with AML for storing your metrics and artifacts" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.yml b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.yml deleted file mode 100644 index e96f6ab60..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: train-remote -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train_diabetes.py b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train_diabetes.py deleted file mode 100644 index 244adeb9a..000000000 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train_diabetes.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. -# Licensed under the MIT license. - -import matplotlib.pyplot as plt -import numpy as np -from sklearn.datasets import load_diabetes -from sklearn.linear_model import Ridge -from sklearn.metrics import mean_squared_error -from sklearn.model_selection import train_test_split -import mlflow -import mlflow.sklearn - -import matplotlib -matplotlib.use('Agg') - -with mlflow.start_run(): - X, y = load_diabetes(return_X_y=True) - columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) - data = { - "train": {"X": X_train, "y": y_train}, - "test": {"X": X_test, "y": y_test}} - - mlflow.log_metric("Training samples", len(data['train']['X'])) - mlflow.log_metric("Test samples", len(data['test']['X'])) - - # Log the algorithm parameter alpha to the run - mlflow.log_metric('alpha', 0.03) - # Create, fit, and test the scikit-learn Ridge regression model - regression_model = Ridge(alpha=0.03) - regression_model.fit(data['train']['X'], data['train']['y']) - preds = regression_model.predict(data['test']['X']) - - # Log mean squared error - print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds)) - mlflow.log_metric('mse', mean_squared_error(data['test']['y'], preds)) - - # Save the model to the outputs directory for capture - mlflow.sklearn.log_model(regression_model, "model") - - # Plot actuals vs predictions and save the plot within the run - fig = plt.figure(1) - idx = np.argsort(data['test']['y']) - plt.plot(data['test']['y'][idx], preds[idx]) - fig.savefig("actuals_vs_predictions.png") - mlflow.log_artifact("actuals_vs_predictions.png") diff --git a/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb b/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb index a6c4ae27d..0c4f0ecde 100644 --- a/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb +++ b/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb @@ -1,325 +1,325 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-in-spark/train-in-spark.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 05. Train in Spark\n", - "* Create Workspace\n", - "* Create Experiment\n", - "* Copy relevant files to the script folder\n", - "* Configure and Run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Experiment\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_name = 'train-on-spark'\n", - "\n", - "from azureml.core import Experiment\n", - "exp = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View `train-spark.py`\n", - "\n", - "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train-spark.py` in a cell to show the file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open('train-spark.py', 'r') as training_script:\n", - " print(training_script.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure & Run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note** You can use Docker-based execution to run the Spark job in local computer or a remote VM. Please see the `train-in-remote-vm` notebook for example on how to configure and run in Docker mode in a VM. Make sure you choose a Docker image that has Spark installed, such as `microsoft/mmlspark:0.12`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Attach an HDI cluster\n", - "Here we will use a actual Spark cluster, HDInsight for Spark, to run this job. To use HDI commpute target:\n", - " 1. Create a Spark for HDI cluster in Azure. Here are some [quick instructions](https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-jupyter-spark-sql). Make sure you use the Ubuntu flavor, NOT CentOS.\n", - " 2. Enter the IP address, username and password below" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-hdinsightcompute-attach" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, HDInsightCompute\n", - "from azureml.exceptions import ComputeTargetException\n", - "import os\n", - "\n", - "try:\n", - "# If you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase\n", - "\n", - "# Attaching a HDInsight cluster using the public address of the HDInsight cluster is no longer supported.\n", - "# Instead, use resourceId of the HDInsight cluster.\n", - "# The resourceId of the HDInsight Cluster can be constructed using the following string format:\n", - "# /subscriptions//resourceGroups//providers/Microsoft.HDInsight/clusters/.\n", - "# You can also use subscription_id, resource_group and cluster_name without constructing resourceId.\n", - " attach_config = HDInsightCompute.attach_configuration(resource_id='',\n", - " ssh_port=22,\n", - " username=os.environ.get('hdiusername', ''),\n", - " password=os.environ.get('hdipassword', ''))\n", - "\n", - " hdi_compute = ComputeTarget.attach(workspace=ws, \n", - " name='myhdi', \n", - " attach_configuration=attach_config)\n", - "\n", - "except ComputeTargetException as e:\n", - " print(\"Caught = {}\".format(e.message))\n", - " \n", - " \n", - "hdi_compute.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure HDI run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Configure an execution using the HDInsight cluster with a conda environment that has `numpy`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "# use pyspark framework\n", - "hdi_run_config = RunConfiguration(framework=\"pyspark\")\n", - "\n", - "# Set compute target to the HDI cluster\n", - "hdi_run_config.target = hdi_compute.name\n", - "\n", - "# specify CondaDependencies object to ask system installing numpy\n", - "cd = CondaDependencies()\n", - "cd.add_conda_package('numpy')\n", - "hdi_run_config.environment.python.conda_dependencies = cd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit the script to HDI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "script_run_config = ScriptRunConfig(source_directory = '.',\n", - " script= 'train-spark.py',\n", - " run_config = hdi_run_config)\n", - "run = exp.submit(config=script_run_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Monitor the run using a Juypter widget" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the run is succesfully finished, you can check the metrics logged." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# get all metris logged in the run\n", - "metrics = run.get_metrics()\n", - "print(metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# register the generated model\n", - "model = run.register_model(model_name='iris.model', model_path='outputs/iris.model')\n" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "training", - "compute": [ - "HDI cluster" - ], - "datasets": [ - "None" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "PySpark" - ], - "friendly_name": "Training in Spark", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-in-spark/train-in-spark.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 05. Train in Spark\n", + "* Create Workspace\n", + "* Create Experiment\n", + "* Copy relevant files to the script folder\n", + "* Configure and Run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Experiment\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "experiment_name = 'train-on-spark'\n", + "\n", + "from azureml.core import Experiment\n", + "exp = Experiment(workspace=ws, name=experiment_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View `train-spark.py`\n", + "\n", + "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train-spark.py` in a cell to show the file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('train-spark.py', 'r') as training_script:\n", + " print(training_script.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configure & Run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note** You can use Docker-based execution to run the Spark job in local computer or a remote VM. Please see the `train-in-remote-vm` notebook for example on how to configure and run in Docker mode in a VM. Make sure you choose a Docker image that has Spark installed, such as `microsoft/mmlspark:0.12`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Attach an HDI cluster\n", + "Here we will use a actual Spark cluster, HDInsight for Spark, to run this job. To use HDI commpute target:\n", + " 1. Create a Spark for HDI cluster in Azure. Here are some [quick instructions](https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-jupyter-spark-sql). Make sure you use the Ubuntu flavor, NOT CentOS.\n", + " 2. Enter the IP address, username and password below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Submiting a run on a spark cluster" + "sample-hdinsightcompute-attach" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, HDInsightCompute\n", + "from azureml.exceptions import ComputeTargetException\n", + "import os\n", + "\n", + "try:\n", + "# If you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase\n", + "\n", + "# Attaching a HDInsight cluster using the public address of the HDInsight cluster is no longer supported.\n", + "# Instead, use resourceId of the HDInsight cluster.\n", + "# The resourceId of the HDInsight Cluster can be constructed using the following string format:\n", + "# /subscriptions//resourceGroups//providers/Microsoft.HDInsight/clusters/.\n", + "# You can also use subscription_id, resource_group and cluster_name without constructing resourceId.\n", + " attach_config = HDInsightCompute.attach_configuration(resource_id='',\n", + " ssh_port=22,\n", + " username=os.environ.get('hdiusername', ''),\n", + " password=os.environ.get('hdipassword', ''))\n", + "\n", + " hdi_compute = ComputeTarget.attach(workspace=ws, \n", + " name='myhdi', \n", + " attach_configuration=attach_config)\n", + "\n", + "except ComputeTargetException as e:\n", + " print(\"Caught = {}\".format(e.message))\n", + " \n", + " \n", + "hdi_compute.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure HDI run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Configure an execution using the HDInsight cluster with a conda environment that has `numpy`." + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.runconfig import RunConfiguration\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "# use pyspark framework\n", + "hdi_run_config = RunConfiguration(framework=\"pyspark\")\n", + "\n", + "# Set compute target to the HDI cluster\n", + "hdi_run_config.target = hdi_compute.name\n", + "\n", + "# specify CondaDependencies object to ask system installing numpy\n", + "cd = CondaDependencies()\n", + "cd.add_conda_package('numpy')\n", + "hdi_run_config.environment.python.conda_dependencies = cd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Submit the script to HDI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import ScriptRunConfig\n", + "\n", + "script_run_config = ScriptRunConfig(source_directory = '.',\n", + " script= 'train-spark.py',\n", + " run_config = hdi_run_config)\n", + "run = exp.submit(config=script_run_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Monitor the run using a Juypter widget" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.widgets import RunDetails\n", + "RunDetails(run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the run is succesfully finished, you can check the metrics logged." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get all metris logged in the run\n", + "metrics = run.get_metrics()\n", + "print(metrics)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# register the generated model\n", + "model = run.register_model(model_name='iris.model', model_path='outputs/iris.model')\n" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "training", + "compute": [ + "HDI cluster" + ], + "datasets": [ + "None" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "PySpark" + ], + "friendly_name": "Training in Spark", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + }, + "tags": [ + "None" + ], + "task": "Submiting a run on a spark cluster" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb b/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb index 70d2d5713..c21d9d3b2 100644 --- a/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb +++ b/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb @@ -1,496 +1,496 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train using Azure Machine Learning Compute\n", - "\n", - "* Initialize a Workspace\n", - "* Create an Experiment\n", - "* Introduction to AmlCompute\n", - "* Submit an AmlCompute run in a few different ways\n", - " - Provision as a persistent compute target (Basic)\n", - " - Provision as a persistent compute target (Advanced)\n", - "* Additional operations to perform on AmlCompute\n", - "* Find the best model in the run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize a Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create An Experiment\n", - "\n", - "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "experiment_name = 'train-on-amlcompute'\n", - "experiment = Experiment(workspace = ws, name = experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction to AmlCompute\n", - "\n", - "Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n", - "\n", - "Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n", - "\n", - "For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n", - "\n", - "If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n", - "\n", - "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n", - "\n", - "\n", - "The training script `train.py` is already created for you. Let's have a look." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit an AmlCompute run in a few different ways\n", - "\n", - "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n", - "\n", - "You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "\n", - "AmlCompute.supported_vmsizes(workspace = ws)\n", - "#AmlCompute.supported_vmsizes(workspace = ws, location='southcentralus')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create project directory\n", - "\n", - "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import shutil\n", - "\n", - "project_folder = './train-on-amlcompute'\n", - "os.makedirs(project_folder, exist_ok=True)\n", - "shutil.copy('train.py', project_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create environment\n", - "\n", - "Create Docker based environment with scikit-learn installed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.runconfig import DockerConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "myenv = Environment(\"myenv\")\n", - "myenv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn', 'packaging'])\n", - "\n", - "# Enable Docker\n", - "docker_config = DockerConfiguration(use_docker=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Provision as a persistent compute target (Basic)\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n", - "\n", - "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n", - "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-amlcompute-provision" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "cpu_cluster_name = \"cpu-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", - " max_nodes=4)\n", - " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - "\n", - "cpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure & Run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=project_folder, \n", - " script='train.py', \n", - " compute_target=cpu_cluster, \n", - " environment=myenv,\n", - " docker_runtime_config=docker_config)\n", - " \n", - "run = experiment.submit(config=src)\n", - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "# Shows output of the run on stdout.\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_metrics()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Provision as a persistent compute target (Advanced)\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n", - "\n", - "In addition to `vm_size` and `max_nodes`, you can specify:\n", - "* `min_nodes`: Minimum nodes (default 0 nodes) to downscale to while running a job on AmlCompute\n", - "* `vm_priority`: Choose between 'dedicated' (default) and 'lowpriority' VMs when provisioning AmlCompute. Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted\n", - "* `idle_seconds_before_scaledown`: Idle time (default 120 seconds) to wait after run completion before auto-scaling to min_nodes\n", - "* `vnet_resourcegroup_name`: Resource group of the **existing** VNet within which AmlCompute should be provisioned\n", - "* `vnet_name`: Name of VNet\n", - "* `subnet_name`: Name of SubNet within the VNet\n", - "* `admin_username`: Name of Admin user account which will be created on all the nodes of the cluster\n", - "* `admin_user_password`: Password that you want to set for the user account above\n", - "* `admin_user_ssh_key`: SSH Key for the user account above. You can specify either a password or an SSH key or both\n", - "* `remote_login_port_public_access`: Flag to enable or disable the public SSH port. If you dont specify, AmlCompute will smartly close the port when deploying inside a VNet\n", - "* `identity_type`: Compute Identity type that you want to set on the cluster, which can either be SystemAssigned or UserAssigned\n", - "* `identity_id`: Resource ID of identity in case it is a UserAssigned identity, optional otherwise\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your CPU cluster\n", - "cpu_cluster_name = \"cpu-cluster\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", - " vm_priority='lowpriority',\n", - " min_nodes=2,\n", - " max_nodes=4,\n", - " idle_seconds_before_scaledown='300',\n", - " vnet_resourcegroup_name='',\n", - " vnet_name='',\n", - " subnet_name='',\n", - " admin_username='',\n", - " admin_user_password='',\n", - " admin_user_ssh_key='',\n", - " remote_login_port_public_access='enabled',\n", - " identity_type='UserAssigned',\n", - " identity_id=[''])\n", - " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", - "\n", - "cpu_cluster.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure & Run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set compute target to the one created in previous step\n", - "src.run_config.target = cpu_cluster\n", - " \n", - "run = experiment.submit(config=src)\n", - "run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "# Shows output of the run on stdout.\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_metrics()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Additional operations to perform on AmlCompute\n", - "\n", - "You can perform more operations on AmlCompute such as updating the node counts or deleting the compute. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#get_status () gets the latest status of the AmlCompute target\n", - "cpu_cluster.get_status().serialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#list_nodes () gets the list of nodes on the cluster with status, IP and associated run\n", - "cpu_cluster.list_nodes()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Update () takes in the min_nodes, max_nodes and idle_seconds_before_scaledown and updates the AmlCompute target\n", - "#cpu_cluster.update(min_nodes=1)\n", - "#cpu_cluster.update(max_nodes=10)\n", - "cpu_cluster.update(idle_seconds_before_scaledown=300)\n", - "#cpu_cluster.update(min_nodes=2, max_nodes=4, idle_seconds_before_scaledown=600)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n", - "#'cpu-cluster' in this case but use a different VM family for instance.\n", - "\n", - "#cpu_cluster.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Success!\n", - "Great, you are ready to move on to the remaining notebooks." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "nigup" - } - ], - "category": "training", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Diabetes" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Train on Azure Machine Learning Compute", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train using Azure Machine Learning Compute\n", + "\n", + "* Initialize a Workspace\n", + "* Create an Experiment\n", + "* Introduction to AmlCompute\n", + "* Submit an AmlCompute run in a few different ways\n", + " - Provision as a persistent compute target (Basic)\n", + " - Provision as a persistent compute target (Advanced)\n", + "* Additional operations to perform on AmlCompute\n", + "* Find the best model in the run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize a Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "create workspace" + ] + }, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create An Experiment\n", + "\n", + "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "experiment_name = 'train-on-amlcompute'\n", + "experiment = Experiment(workspace = ws, name = experiment_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to AmlCompute\n", + "\n", + "Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n", + "\n", + "Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n", + "\n", + "For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n", + "\n", + "If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n", + "\n", + "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n", + "\n", + "\n", + "The training script `train.py` is already created for you. Let's have a look." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submit an AmlCompute run in a few different ways\n", + "\n", + "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n", + "\n", + "You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "\n", + "AmlCompute.supported_vmsizes(workspace = ws)\n", + "#AmlCompute.supported_vmsizes(workspace = ws, location='southcentralus')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create project directory\n", + "\n", + "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "\n", + "project_folder = './train-on-amlcompute'\n", + "os.makedirs(project_folder, exist_ok=True)\n", + "shutil.copy('train.py', project_folder)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create environment\n", + "\n", + "Create Docker based environment with scikit-learn installed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.runconfig import DockerConfiguration\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "myenv = Environment(\"myenv\")\n", + "myenv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn', 'packaging'])\n", + "\n", + "# Enable Docker\n", + "docker_config = DockerConfiguration(use_docker=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Provision as a persistent compute target (Basic)\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\n", + "You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n", + "\n", + "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n", + "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Submit a run on Azure Machine Learning Compute." + "sample-amlcompute-provision" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your CPU cluster\n", + "cpu_cluster_name = \"cpu-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", + " print('Found existing cluster, use it.')\n", + "except ComputeTargetException:\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", + " max_nodes=4)\n", + " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", + "\n", + "cpu_cluster.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure & Run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import ScriptRunConfig\n", + "\n", + "src = ScriptRunConfig(source_directory=project_folder, \n", + " script='train.py', \n", + " compute_target=cpu_cluster, \n", + " environment=myenv,\n", + " docker_runtime_config=docker_config)\n", + " \n", + "run = experiment.submit(config=src)\n", + "run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# Shows output of the run on stdout.\n", + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.get_metrics()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Provision as a persistent compute target (Advanced)\n", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\n", + "You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n", + "\n", + "In addition to `vm_size` and `max_nodes`, you can specify:\n", + "* `min_nodes`: Minimum nodes (default 0 nodes) to downscale to while running a job on AmlCompute\n", + "* `vm_priority`: Choose between 'dedicated' (default) and 'lowpriority' VMs when provisioning AmlCompute. Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted\n", + "* `idle_seconds_before_scaledown`: Idle time (default 120 seconds) to wait after run completion before auto-scaling to min_nodes\n", + "* `vnet_resourcegroup_name`: Resource group of the **existing** VNet within which AmlCompute should be provisioned\n", + "* `vnet_name`: Name of VNet\n", + "* `subnet_name`: Name of SubNet within the VNet\n", + "* `admin_username`: Name of Admin user account which will be created on all the nodes of the cluster\n", + "* `admin_user_password`: Password that you want to set for the user account above\n", + "* `admin_user_ssh_key`: SSH Key for the user account above. You can specify either a password or an SSH key or both\n", + "* `remote_login_port_public_access`: Flag to enable or disable the public SSH port. If you dont specify, AmlCompute will smartly close the port when deploying inside a VNet\n", + "* `identity_type`: Compute Identity type that you want to set on the cluster, which can either be SystemAssigned or UserAssigned\n", + "* `identity_id`: Resource ID of identity in case it is a UserAssigned identity, optional otherwise\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your CPU cluster\n", + "cpu_cluster_name = \"cpu-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", + " print('Found existing cluster, use it.')\n", + "except ComputeTargetException:\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", + " vm_priority='lowpriority',\n", + " min_nodes=2,\n", + " max_nodes=4,\n", + " idle_seconds_before_scaledown='300',\n", + " vnet_resourcegroup_name='',\n", + " vnet_name='',\n", + " subnet_name='',\n", + " admin_username='',\n", + " admin_user_password='',\n", + " admin_user_ssh_key='',\n", + " remote_login_port_public_access='enabled',\n", + " identity_type='UserAssigned',\n", + " identity_id=[''])\n", + " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", + "\n", + "cpu_cluster.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure & Run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set compute target to the one created in previous step\n", + "src.run_config.target = cpu_cluster\n", + " \n", + "run = experiment.submit(config=src)\n", + "run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# Shows output of the run on stdout.\n", + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.get_metrics()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Additional operations to perform on AmlCompute\n", + "\n", + "You can perform more operations on AmlCompute such as updating the node counts or deleting the compute. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#get_status () gets the latest status of the AmlCompute target\n", + "cpu_cluster.get_status().serialize()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#list_nodes () gets the list of nodes on the cluster with status, IP and associated run\n", + "cpu_cluster.list_nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Update () takes in the min_nodes, max_nodes and idle_seconds_before_scaledown and updates the AmlCompute target\n", + "#cpu_cluster.update(min_nodes=1)\n", + "#cpu_cluster.update(max_nodes=10)\n", + "cpu_cluster.update(idle_seconds_before_scaledown=300)\n", + "#cpu_cluster.update(min_nodes=2, max_nodes=4, idle_seconds_before_scaledown=600)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n", + "#'cpu-cluster' in this case but use a different VM family for instance.\n", + "\n", + "#cpu_cluster.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Success!\n", + "Great, you are ready to move on to the remaining notebooks." + ] + } + ], + "metadata": { + "authors": [ + { + "name": "nigup" + } + ], + "category": "training", + "compute": [ + "AML Compute" + ], + "datasets": [ + "Diabetes" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "None" + ], + "friendly_name": "Train on Azure Machine Learning Compute", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "tags": [ + "None" + ], + "task": "Submit a run on Azure Machine Learning Compute." + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/training/train-on-local/train-on-local.ipynb b/how-to-use-azureml/training/train-on-local/train-on-local.ipynb index 478f78575..6ffc590b8 100644 --- a/how-to-use-azureml/training/train-on-local/train-on-local.ipynb +++ b/how-to-use-azureml/training/train-on-local/train-on-local.ipynb @@ -1,711 +1,711 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-local/train-on-local.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 02. Train locally\n", - "_**Train a model locally: Directly on your machine and within a Docker container**_\n", - "\n", - "---\n", - "\n", - "\n", - "## Table of contents\n", - "1. [Introduction](#intro)\n", - "1. [Pre-requisites](#pre-reqs)\n", - "1. [Initialize Workspace](#init)\n", - "1. [Create An Experiment](#exp)\n", - "1. [View training and auxiliary scripts](#view)\n", - "1. [Configure & Run](#config-run)\n", - " 1. User-managed environment\n", - " 1. Set the environment up\n", - " 1. Submit the script to run in the user-managed environment\n", - " 1. Get run history details\n", - " 1. System-managed environment\n", - " 1. Set the environment up\n", - " 1. Submit the script to run in the system-managed environment\n", - " 1. Get run history details\n", - " 1. Docker-based execution\n", - " 1. Set the environment up\n", - " 1. Submit the script to run in the system-managed environment\n", - " 1. Get run history details\n", - " 1. Use a custom Docker image\n", - "1. [Query run metrics](#query)\n", - "\n", - "---\n", - "\n", - "## 1. Introduction \n", - "\n", - "In this notebook, we will learn how to:\n", - "\n", - "* Connect to our AML workspace\n", - "* Create or load a workspace\n", - "* Configure & execute a local run in:\n", - " - a user-managed Python environment\n", - " - a system-managed Python environment\n", - " - a Docker environment\n", - "* Query run metrics to find the best model trained in the run\n", - "* Register that model for operationalization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Pre-requisites \n", - "In this notebook, we assume that you have set your Azure Machine Learning workspace. If you have not, make sure you go through the [configuration notebook](../../../configuration.ipynb) first. In the end, you should have configuration file that contains the subscription ID, resource group and name of your workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Initialize Workspace \n", - "\n", - "Initialize your workspace object from configuration file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Create An Experiment \n", - "An experiment is a logical container in an Azure ML Workspace. It contains a series of trials called `Runs`. As such, it hosts run records such as run metrics, logs, and other output artifacts from your experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "experiment_name = 'train-on-local'\n", - "exp = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. View training and auxiliary scripts \n", - "\n", - "For convenience, we already created the training (`train.py`) script and supportive libraries (`mylib.py`) for you. Take a few minutes to examine both files." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open('./train.py', 'r') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open('./mylib.py', 'r') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Configure & Run \n", - "### 6.A User-managed environment\n", - "\n", - "#### 6.A.a Set the environment up\n", - "When using a user-managed environment, you are responsible for ensuring that all the necessary packages are available in the Python environment you choose to run the script in." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "msdoc": "how-to-track-experiments.md", - "name": "user_managed_env" - }, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "# Editing a run configuration property on-fly.\n", - "user_managed_env = Environment(\"user-managed-env\")\n", - "\n", - "user_managed_env.python.user_managed_dependencies = True\n", - "\n", - "# You can choose a specific Python environment by pointing to a Python path \n", - "#user_managed_env.python.interpreter_path = '/home/johndoe/miniconda3/envs/myenv/bin/python'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 6.A.b Submit the script to run in the user-managed environment\n", - "Whatever the way you manage your environment, you need to use the `ScriptRunConfig` class. It allows you to further configure your run by pointing to the `train.py` script and to the working directory, which also contains the `mylib.py` file. These inputs indeed provide the commands to execute in the run. Once the run is configured, you submit it to your experiment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "msdoc": "how-to-track-experiments.md", - "name": "src" - }, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory='./', script='train.py', environment=user_managed_env)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "msdoc": "how-to-track-experiments.md", - "name": "run" - }, - "outputs": [], - "source": [ - "run = exp.submit(src)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 6.A.c Get run history details\n", - "\n", - "While all calculations were run on your machine (cf. below), by using a `run` you also captured the results of your calculations into your run and experiment. You can then see them on the Azure portal, through the link displayed as output of the following cell.\n", - "\n", - "**Note**: The recording of the computation results into your run was made possible by the `run.log()` commands in the `train.py` file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Block any execution to wait until the run finishes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:** All these calculations were run on your local machine, in the conda environment you defined above. You can find the results in:\n", - "- `~/.azureml/envs/azureml_xxxx` for the conda environment you just created\n", - "- `~/AppData/Local/Temp/azureml_runs/train-on-local_xxxx` for the machine learning models you trained (this path may differ depending on the platform you use). This folder also contains\n", - " - Logs (under azureml_logs/)\n", - " - Output pickled files (under outputs/)\n", - " - The configuration files (credentials, local and docker image setups)\n", - " - The train.py and mylib.py scripts\n", - " - The current notebook\n", - "\n", - "Take a few minutes to examine the output of the cell above. It shows the content of some of the log files, and extra information on the conda environment used." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.B System-managed environment\n", - "#### 6.B.a Set the environment up\n", - "Now, instead of managing the setup of the environment yourself, you can ask the system to build a new conda environment for you. The environment is built once, and will be reused in subsequent executions as long as the conda dependencies remain unchanged." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "system_managed_env = Environment(\"system-managed-env\")\n", - "\n", - "system_managed_env.python.user_managed_dependencies = False\n", - "\n", - "# Specify conda dependencies with scikit-learn\n", - "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n", - "system_managed_env.python.conda_dependencies = cd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 6.B.b Submit the script to run in the system-managed environment\n", - "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 minutes.\n", - "\n", - "The commands used to execute the run are then the same as the ones you used above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "src.run_config.environment = system_managed_env\n", - "run = exp.submit(src)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 6.B.c Get run history details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.C Docker-based execution\n", - "In this section, you will train the same models, but you will do so in a Docker container, on your local machine. For this, you then need to have the Docker engine installed locally. If you don't have it yet, please follow the instructions below.\n", - "\n", - "#### How to install Docker\n", - "\n", - "- [Linux](https://docs.docker.com/install/linux/docker-ce/ubuntu/)\n", - "- [MacOs](https://docs.docker.com/docker-for-mac/install/)\n", - "- [Windows](https://docs.docker.com/docker-for-windows/install/)\n", - "\n", - " In case of issues, troubleshooting documentation can be found [here](https://docs.docker.com/docker-for-windows/troubleshoot/#running-docker-for-windows-in-nested-virtualization-scenarios). Additionally, you can follow the steps below, if Virtualization is not enabled on your machine:\n", - " - Go to Task Manager > Performance\n", - " - Check that Virtualization is enabled\n", - " - If it is not, go to `Start > Settings > Update and security > Recovery > Advanced Startup - Restart now > Troubleshoot > Advanced options > UEFI firmware settings - restart`\n", - " - In the BIOS, go to `Advanced > System options > Click the \"Virtualization Technology (VTx)\" only > Save > Exit > Save all changes` -- This will restart the machine\n", - "\n", - "**Notes**: \n", - "- If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n", - "- If you use a GPU base image, it needs to be used on Microsoft Azure Services such as ACI, AML Compute, Azure VMs, or AKS.\n", - "\n", - "You can also ask the system to pull down a Docker image and execute your scripts in it.\n", - "\n", - "#### 6.C.a Set the environment up\n", - "\n", - "In the cell below, you will configure your run to execute in a Docker container. It will:\n", - "- run on a CPU\n", - "- contain a conda environment in which the scikit-learn library will be installed.\n", - "\n", - "As before, you will finish configuring your run by pointing to the `train.py` and `mylib.py` files." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "docker_env = Environment(\"docker-env\")\n", - "\n", - "docker_env.python.user_managed_dependencies = False\n", - "docker_env.docker.enabled = True\n", - "\n", - "# use the default CPU-based Docker image from Azure ML\n", - "print(docker_env.docker.base_image)\n", - "\n", - "# Specify conda dependencies with scikit-learn\n", - "docker_env.python.conda_dependencies = cd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 6.C.b Submit the script to run in the system-managed environment\n", - "\n", - "The run is now configured and ready to be executed in a Docker container. If you are running this for the first time, the Docker container will get created, as well as the conda environment inside it. This will take several minutes. Once all this is generated, however, this conda environment will be reused as long as you don't change the conda dependencies." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import subprocess\n", - "\n", - "src.run_config.environment = docker_env\n", - "\n", - "# Check if Docker is installed and Linux containers are enabled\n", - "if subprocess.run(\"docker -v\", shell=True).returncode == 0:\n", - " out = subprocess.check_output(\"docker system info\", shell=True).decode('ascii')\n", - " if not \"OSType: linux\" in out:\n", - " print(\"Switch Docker engine to use Linux containers.\")\n", - " else:\n", - " run = exp.submit(src)\n", - "else:\n", - " print(\"Docker engine is not installed.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Potential issue on Windows and how to solve it\n", - "\n", - "If you are using a Windows machine, the creation of the Docker image may fail, and you may see the following error message\n", - "`docker: Error response from daemon: Drive has not been shared. Failed to launch docker container. Check that docker is running and that C:\\ on Windows and /tmp elsewhere is shared.`\n", - "\n", - "This is because the process above tries to create a linux-based, i.e. non-windows-based, Docker image. To fix this, you can:\n", - "- Open the Docker user interface\n", - "- Navigate to Settings > Shared drives\n", - "- Select C (or both C and D, if you have one)\n", - "- Apply\n", - "\n", - "When this is done, you can try and re-run the command above.\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 6.C.c Get run history details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get run history details\n", - "run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The results obtained here should be the same as those obtained before. However, take a look at the \"Execution summary\" section in the output of the cell above. Look for \"docker\". There, you should see the \"enabled\" field set to True. Compare this to the 2 prior runs (\"enabled\" was then set to False)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 6.C.d Use a custom Docker image\n", - "\n", - "You can also specify a custom Docker image, if you don't want to use the default image provided by Azure ML.\n", - "```python\n", - "custom_docker_env = Environment(\"custom-docker-env\")\n", - "custom_docker_env.docker.enabled = True\n", - "```\n", - "\n", - "You can either pull an image directly from Anaconda:\n", - "```python\n", - "# Use an image available in Docker Hub without authentication\n", - "custom_docker_env.docker.base_image = \"continuumio/miniconda3\"\n", - "```\n", - "\n", - "Or one of the images you may already have created:\n", - "```python\n", - "# or, use an image available in your private Azure Container Registry\n", - "custom_docker_env.docker.base_image = \"mycustomimage:1.0\"\n", - "custom_docker_env.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n", - "custom_docker_env.docker.base_image_registry.username = \"username\"\n", - "custom_docker_env.docker.base_image_registry.password = \"password\"\n", - "```\n", - "\n", - "##### Where to find my Docker image name and registry credentials\n", - " If you do not know what the name of your Docker image or container registry is, or if you don't know how to access the username and password needed above, proceed as follows:\n", - " - Docker image name:\n", - " - In the portal, under your resource group, click on your current workspace\n", - " - Click on Experiments\n", - " - Click on Images\n", - " - Click on the image of your choice\n", - " - Copy the \"ID\" string\n", - " - In this notebook, replace \"mycustomimage:1/0\" with that ID string\n", - " - Username and password:\n", - " - In the portal, under your resource group, click on the container registry associated with your workspace\n", - " - If you have several and don't know which one you need, click on your workspace, go to Overview and click on the \"Registry\" name on the upper right of the screen\n", - " - There, go to \"Access keys\"\n", - " - Copy the username and one of the passwords\n", - " - In this notebook, replace \"username\" and \"password\" by these values\n", - "\n", - "In any case, you will need to use the lines above in place of the line marked as `# Reference Docker image` in section 6.C.a. \n", - "\n", - "When you are using your custom Docker image, you might already have your Python environment properly set up. In that case, you can skip specifying conda dependencies, and just use the `user_managed_dependencies` option instead:\n", - "```python\n", - "custom_docker_env.python.user_managed_dependencies = True\n", - "# path to the Python environment in the custom Docker image\n", - "custom_docker_env.python.interpreter_path = '/opt/conda/bin/python'\n", - "```\n", - "\n", - "Once you are done defining your environment, set that environment on your run configuration:\n", - "```python\n", - "src.run_config.environment = custom_docker_env\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Query run metrics \n", - "\n", - "Once your run has completed, you can now extract the metrics you captured by using the `get_metrics` method. As shown in the `train.py` file, these metrics are \"alpha\" and \"mse\"." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "query history", - "get metrics" - ] - }, - "outputs": [], - "source": [ - "# Get all metris logged in the run\n", - "run.get_metrics()\n", - "metrics = run.get_metrics()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's find the model that has the lowest MSE value logged." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "best_alpha = metrics['alpha'][np.argmin(metrics['mse'])]\n", - "\n", - "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n", - " min(metrics['mse']), \n", - " best_alpha\n", - "))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's compare it to the others" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.plot(metrics['alpha'], metrics['mse'], marker='o')\n", - "plt.ylabel(\"MSE\")\n", - "plt.xlabel(\"Alpha\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also list all the files that are associated with this run record" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_file_names()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the results obtained above, `ridge_0.40.pkl` is the best performing model. You can now register that particular model with the workspace. Once you have done so, go back to the portal and click on \"Models\". You should see it there." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Supply a model name, and the full path to the serialized model file.\n", - "model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Registered model:\\n --> Name: {}\\n --> Version: {}\\n --> URL: {}\".format(model.name, model.version, model.url))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can now deploy your model by following [this example](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb)." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "training", - "compute": [ - "Local" - ], - "datasets": [ - "Diabetes" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Train on local compute", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-local/train-on-local.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 02. Train locally\n", + "_**Train a model locally: Directly on your machine and within a Docker container**_\n", + "\n", + "---\n", + "\n", + "\n", + "## Table of contents\n", + "1. [Introduction](#intro)\n", + "1. [Pre-requisites](#pre-reqs)\n", + "1. [Initialize Workspace](#init)\n", + "1. [Create An Experiment](#exp)\n", + "1. [View training and auxiliary scripts](#view)\n", + "1. [Configure & Run](#config-run)\n", + " 1. User-managed environment\n", + " 1. Set the environment up\n", + " 1. Submit the script to run in the user-managed environment\n", + " 1. Get run history details\n", + " 1. System-managed environment\n", + " 1. Set the environment up\n", + " 1. Submit the script to run in the system-managed environment\n", + " 1. Get run history details\n", + " 1. Docker-based execution\n", + " 1. Set the environment up\n", + " 1. Submit the script to run in the system-managed environment\n", + " 1. Get run history details\n", + " 1. Use a custom Docker image\n", + "1. [Query run metrics](#query)\n", + "\n", + "---\n", + "\n", + "## 1. Introduction \n", + "\n", + "In this notebook, we will learn how to:\n", + "\n", + "* Connect to our AML workspace\n", + "* Create or load a workspace\n", + "* Configure & execute a local run in:\n", + " - a user-managed Python environment\n", + " - a system-managed Python environment\n", + " - a Docker environment\n", + "* Query run metrics to find the best model trained in the run\n", + "* Register that model for operationalization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Pre-requisites \n", + "In this notebook, we assume that you have set your Azure Machine Learning workspace. If you have not, make sure you go through the [configuration notebook](../../../configuration.ipynb) first. In the end, you should have configuration file that contains the subscription ID, resource group and name of your workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Initialize Workspace \n", + "\n", + "Initialize your workspace object from configuration file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.workspace import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Create An Experiment \n", + "An experiment is a logical container in an Azure ML Workspace. It contains a series of trials called `Runs`. As such, it hosts run records such as run metrics, logs, and other output artifacts from your experiments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Experiment\n", + "experiment_name = 'train-on-local'\n", + "exp = Experiment(workspace=ws, name=experiment_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. View training and auxiliary scripts \n", + "\n", + "For convenience, we already created the training (`train.py`) script and supportive libraries (`mylib.py`) for you. Take a few minutes to examine both files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('./train.py', 'r') as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('./mylib.py', 'r') as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Configure & Run \n", + "### 6.A User-managed environment\n", + "\n", + "#### 6.A.a Set the environment up\n", + "When using a user-managed environment, you are responsible for ensuring that all the necessary packages are available in the Python environment you choose to run the script in." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "msdoc": "how-to-track-experiments.md", + "name": "user_managed_env" + }, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "\n", + "# Editing a run configuration property on-fly.\n", + "user_managed_env = Environment(\"user-managed-env\")\n", + "\n", + "user_managed_env.python.user_managed_dependencies = True\n", + "\n", + "# You can choose a specific Python environment by pointing to a Python path \n", + "#user_managed_env.python.interpreter_path = '/home/johndoe/miniconda3/envs/myenv/bin/python'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.A.b Submit the script to run in the user-managed environment\n", + "Whatever the way you manage your environment, you need to use the `ScriptRunConfig` class. It allows you to further configure your run by pointing to the `train.py` script and to the working directory, which also contains the `mylib.py` file. These inputs indeed provide the commands to execute in the run. Once the run is configured, you submit it to your experiment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "msdoc": "how-to-track-experiments.md", + "name": "src" + }, + "outputs": [], + "source": [ + "from azureml.core import ScriptRunConfig\n", + "\n", + "src = ScriptRunConfig(source_directory='./', script='train.py', environment=user_managed_env)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "msdoc": "how-to-track-experiments.md", + "name": "run" + }, + "outputs": [], + "source": [ + "run = exp.submit(src)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.A.c Get run history details\n", + "\n", + "While all calculations were run on your machine (cf. below), by using a `run` you also captured the results of your calculations into your run and experiment. You can then see them on the Azure portal, through the link displayed as output of the following cell.\n", + "\n", + "**Note**: The recording of the computation results into your run was made possible by the `run.log()` commands in the `train.py` file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Block any execution to wait until the run finishes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** All these calculations were run on your local machine, in the conda environment you defined above. You can find the results in:\n", + "- `~/.azureml/envs/azureml_xxxx` for the conda environment you just created\n", + "- `~/AppData/Local/Temp/azureml_runs/train-on-local_xxxx` for the machine learning models you trained (this path may differ depending on the platform you use). This folder also contains\n", + " - Logs (under azureml_logs/)\n", + " - Output pickled files (under outputs/)\n", + " - The configuration files (credentials, local and docker image setups)\n", + " - The train.py and mylib.py scripts\n", + " - The current notebook\n", + "\n", + "Take a few minutes to examine the output of the cell above. It shows the content of some of the log files, and extra information on the conda environment used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.B System-managed environment\n", + "#### 6.B.a Set the environment up\n", + "Now, instead of managing the setup of the environment yourself, you can ask the system to build a new conda environment for you. The environment is built once, and will be reused in subsequent executions as long as the conda dependencies remain unchanged." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "system_managed_env = Environment(\"system-managed-env\")\n", + "\n", + "system_managed_env.python.user_managed_dependencies = False\n", + "\n", + "# Specify conda dependencies with scikit-learn\n", + "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n", + "system_managed_env.python.conda_dependencies = cd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.B.b Submit the script to run in the system-managed environment\n", + "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 minutes.\n", + "\n", + "The commands used to execute the run are then the same as the ones you used above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "src.run_config.environment = system_managed_env\n", + "run = exp.submit(src)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.B.c Get run history details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.wait_for_completion(show_output = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.C Docker-based execution\n", + "In this section, you will train the same models, but you will do so in a Docker container, on your local machine. For this, you then need to have the Docker engine installed locally. If you don't have it yet, please follow the instructions below.\n", + "\n", + "#### How to install Docker\n", + "\n", + "- [Linux](https://docs.docker.com/install/linux/docker-ce/ubuntu/)\n", + "- [MacOs](https://docs.docker.com/docker-for-mac/install/)\n", + "- [Windows](https://docs.docker.com/docker-for-windows/install/)\n", + "\n", + " In case of issues, troubleshooting documentation can be found [here](https://docs.docker.com/docker-for-windows/troubleshoot/#running-docker-for-windows-in-nested-virtualization-scenarios). Additionally, you can follow the steps below, if Virtualization is not enabled on your machine:\n", + " - Go to Task Manager > Performance\n", + " - Check that Virtualization is enabled\n", + " - If it is not, go to `Start > Settings > Update and security > Recovery > Advanced Startup - Restart now > Troubleshoot > Advanced options > UEFI firmware settings - restart`\n", + " - In the BIOS, go to `Advanced > System options > Click the \"Virtualization Technology (VTx)\" only > Save > Exit > Save all changes` -- This will restart the machine\n", + "\n", + "**Notes**: \n", + "- If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n", + "- If you use a GPU base image, it needs to be used on Microsoft Azure Services such as ACI, AML Compute, Azure VMs, or AKS.\n", + "\n", + "You can also ask the system to pull down a Docker image and execute your scripts in it.\n", + "\n", + "#### 6.C.a Set the environment up\n", + "\n", + "In the cell below, you will configure your run to execute in a Docker container. It will:\n", + "- run on a CPU\n", + "- contain a conda environment in which the scikit-learn library will be installed.\n", + "\n", + "As before, you will finish configuring your run by pointing to the `train.py` and `mylib.py` files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "docker_env = Environment(\"docker-env\")\n", + "\n", + "docker_env.python.user_managed_dependencies = False\n", + "docker_env.docker.enabled = True\n", + "\n", + "# use the default CPU-based Docker image from Azure ML\n", + "print(docker_env.docker.base_image)\n", + "\n", + "# Specify conda dependencies with scikit-learn\n", + "docker_env.python.conda_dependencies = cd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.C.b Submit the script to run in the system-managed environment\n", + "\n", + "The run is now configured and ready to be executed in a Docker container. If you are running this for the first time, the Docker container will get created, as well as the conda environment inside it. This will take several minutes. Once all this is generated, however, this conda environment will be reused as long as you don't change the conda dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "\n", + "src.run_config.environment = docker_env\n", + "\n", + "# Check if Docker is installed and Linux containers are enabled\n", + "if subprocess.run(\"docker -v\", shell=True).returncode == 0:\n", + " out = subprocess.check_output(\"docker system info\", shell=True).decode('ascii')\n", + " if not \"OSType: linux\" in out:\n", + " print(\"Switch Docker engine to use Linux containers.\")\n", + " else:\n", + " run = exp.submit(src)\n", + "else:\n", + " print(\"Docker engine is not installed.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Potential issue on Windows and how to solve it\n", + "\n", + "If you are using a Windows machine, the creation of the Docker image may fail, and you may see the following error message\n", + "`docker: Error response from daemon: Drive has not been shared. Failed to launch docker container. Check that docker is running and that C:\\ on Windows and /tmp elsewhere is shared.`\n", + "\n", + "This is because the process above tries to create a linux-based, i.e. non-windows-based, Docker image. To fix this, you can:\n", + "- Open the Docker user interface\n", + "- Navigate to Settings > Shared drives\n", + "- Select C (or both C and D, if you have one)\n", + "- Apply\n", + "\n", + "When this is done, you can try and re-run the command above.\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.C.c Get run history details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get run history details\n", + "run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results obtained here should be the same as those obtained before. However, take a look at the \"Execution summary\" section in the output of the cell above. Look for \"docker\". There, you should see the \"enabled\" field set to True. Compare this to the 2 prior runs (\"enabled\" was then set to False)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.C.d Use a custom Docker image\n", + "\n", + "You can also specify a custom Docker image, if you don't want to use the default image provided by Azure ML.\n", + "```python\n", + "custom_docker_env = Environment(\"custom-docker-env\")\n", + "custom_docker_env.docker.enabled = True\n", + "```\n", + "\n", + "You can either pull an image directly from Anaconda:\n", + "```python\n", + "# Use an image available in Docker Hub without authentication\n", + "custom_docker_env.docker.base_image = \"continuumio/miniconda3\"\n", + "```\n", + "\n", + "Or one of the images you may already have created:\n", + "```python\n", + "# or, use an image available in your private Azure Container Registry\n", + "custom_docker_env.docker.base_image = \"mycustomimage:1.0\"\n", + "custom_docker_env.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n", + "custom_docker_env.docker.base_image_registry.username = \"username\"\n", + "custom_docker_env.docker.base_image_registry.password = \"password\"\n", + "```\n", + "\n", + "##### Where to find my Docker image name and registry credentials\n", + " If you do not know what the name of your Docker image or container registry is, or if you don't know how to access the username and password needed above, proceed as follows:\n", + " - Docker image name:\n", + " - In the portal, under your resource group, click on your current workspace\n", + " - Click on Experiments\n", + " - Click on Images\n", + " - Click on the image of your choice\n", + " - Copy the \"ID\" string\n", + " - In this notebook, replace \"mycustomimage:1/0\" with that ID string\n", + " - Username and password:\n", + " - In the portal, under your resource group, click on the container registry associated with your workspace\n", + " - If you have several and don't know which one you need, click on your workspace, go to Overview and click on the \"Registry\" name on the upper right of the screen\n", + " - There, go to \"Access keys\"\n", + " - Copy the username and one of the passwords\n", + " - In this notebook, replace \"username\" and \"password\" by these values\n", + "\n", + "In any case, you will need to use the lines above in place of the line marked as `# Reference Docker image` in section 6.C.a. \n", + "\n", + "When you are using your custom Docker image, you might already have your Python environment properly set up. In that case, you can skip specifying conda dependencies, and just use the `user_managed_dependencies` option instead:\n", + "```python\n", + "custom_docker_env.python.user_managed_dependencies = True\n", + "# path to the Python environment in the custom Docker image\n", + "custom_docker_env.python.interpreter_path = '/opt/conda/bin/python'\n", + "```\n", + "\n", + "Once you are done defining your environment, set that environment on your run configuration:\n", + "```python\n", + "src.run_config.environment = custom_docker_env\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Query run metrics \n", + "\n", + "Once your run has completed, you can now extract the metrics you captured by using the `get_metrics` method. As shown in the `train.py` file, these metrics are \"alpha\" and \"mse\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Train a model locally" + "query history", + "get metrics" + ] + }, + "outputs": [], + "source": [ + "# Get all metris logged in the run\n", + "run.get_metrics()\n", + "metrics = run.get_metrics()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's find the model that has the lowest MSE value logged." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "best_alpha = metrics['alpha'][np.argmin(metrics['mse'])]\n", + "\n", + "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n", + " min(metrics['mse']), \n", + " best_alpha\n", + "))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's compare it to the others" + ] }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(metrics['alpha'], metrics['mse'], marker='o')\n", + "plt.ylabel(\"MSE\")\n", + "plt.xlabel(\"Alpha\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also list all the files that are associated with this run record" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.get_file_names()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the results obtained above, `ridge_0.40.pkl` is the best performing model. You can now register that particular model with the workspace. Once you have done so, go back to the portal and click on \"Models\". You should see it there." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Supply a model name, and the full path to the serialized model file.\n", + "model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Registered model:\\n --> Name: {}\\n --> Version: {}\\n --> URL: {}\".format(model.name, model.version, model.url))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now deploy your model by following [this example](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb)." + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "training", + "compute": [ + "Local" + ], + "datasets": [ + "Diabetes" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "None" + ], + "friendly_name": "Train on local compute", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + }, + "tags": [ + "None" + ], + "task": "Train a model locally" + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb b/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb index ce42357c5..a39f189d2 100644 --- a/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb +++ b/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb @@ -1,667 +1,668 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 04. Train in a remote Linux VM\n", - "* Create Workspace\n", - "* Create `train.py` file\n", - "* Create and Attach a Remote VM (eg. DSVM) as compute resource\n", - "* Upload data files into default datastore\n", - "* Configure & execute a run in a few different ways\n", - " - Use system-built conda\n", - " - Use existing Python environment\n", - " - Use Docker \n", - "* Find the best model in the run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Experiment\n", - "\n", - "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_name = 'train-on-remote-vm'\n", - "\n", - "from azureml.core import Experiment\n", - "exp = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also create a local folder to hold the training script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "script_folder = './vm-run'\n", - "os.makedirs(script_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Upload data files into datastore\n", - "Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and access it from the compute target." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the default datastore\n", - "ds = ws.get_default_datastore()\n", - "print(ds.name, ds.datastore_type, ds.account_name, ds.container_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load diabetes data from `scikit-learn` and save it as 2 local files." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import load_diabetes\n", - "import numpy as np\n", - "\n", - "training_data = load_diabetes()\n", - "np.save(file='./features.npy', arr=training_data['data'])\n", - "np.save(file='./labels.npy', arr=training_data['target'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's upload the 2 files into the default datastore under a path named `diabetes`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds.upload_files(['./features.npy', './labels.npy'], target_path='diabetes', overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a Dataset for Files\n", - "A Dataset can reference single or multiple files in your datastores or public urls. The files can be of any format. Dataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# initialize file dataset \n", - "from azureml.core import Dataset\n", - "ds_paths = [(ds, 'diabetes/')]\n", - "dataset = Dataset.File.from_files(path = ds_paths)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# list the files referenced by the dataset\n", - "dataset.to_path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View `train.py`\n", - "\n", - "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train.py` in a cell to show the file. Please pay special attention on how we are loading the features and labels from files in the `data_folder` path, which is passed in as an argument of the training script (shown later)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# copy train.py into the script folder\n", - "import shutil\n", - "shutil.copy('./train.py', os.path.join(script_folder, 'train.py'))\n", - "\n", - "with open(os.path.join(script_folder, './train.py'), 'r') as training_script:\n", - " print(training_script.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create and Attach a DSVM as a compute target\n", - "\n", - "The DSVM can be created using the below single line command and then attached(like any VM) using the sample code below. Also note, that we only support Linux VMs for remote execution from AML and the commands below will spin a Linux VM only.\n", - "\n", - "```shell\n", - "# create a DSVM in your resource group\n", - "# note you need to be at least a contributor to the resource group in order to execute this command successfully\n", - "(myenv) $ az vm create --resource-group --name --image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest --admin-username --admin-password --generate-ssh-keys --authentication-type password\n", - "```\n", - "\n", - "**Note**: You can also use [this url](https://ms.portal.azure.com/#create/microsoft-dsvm.ubuntu-18041804) to create the VM using the Azure Portal\n", - "\n", - "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you switch to a different port (such as 5022), you can specify the port number in the provisioning configuration object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-remotecompute-attach" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, RemoteCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "username = os.getenv('AZUREML_DSVM_USERNAME', default='')\n", - "address = os.getenv('AZUREML_DSVM_ADDRESS', default='')\n", - "\n", - "compute_target_name = 'cpudsvm'\n", - "# if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase \n", - "try:\n", - " attached_dsvm_compute = RemoteCompute(workspace=ws, name=compute_target_name)\n", - " print('found existing:', attached_dsvm_compute.name)\n", - "except ComputeTargetException:\n", - " attach_config = RemoteCompute.attach_configuration(address=address,\n", - " ssh_port=22,\n", - " username=username,\n", - " private_key_file='./.ssh/id_rsa')\n", - "\n", - "\n", - "# Attaching a virtual machine using the public IP address of the VM is no longer supported.\n", - "# Instead, use resourceId of the VM.\n", - "# The resourceId of the VM can be constructed using the following string format:\n", - "# /subscriptions//resourceGroups//providers/Microsoft.Compute/virtualMachines/.\n", - "# You can also use subscription_id, resource_group and vm_name without constructing resourceId.\n", - " attach_config = RemoteCompute.attach_configuration(resource_id='',\n", - " ssh_port=22,\n", - " username='username',\n", - " private_key_file='./.ssh/id_rsa')\n", - " \n", - " attached_dsvm_compute = ComputeTarget.attach(ws, compute_target_name, attach_config)\n", - " \n", - " attached_dsvm_compute.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure & Run\n", - "\n", - "Now we can try a few different ways to run the training script in the VM." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conda run\n", - "You can ask the system to build a conda environment based on your dependency specification, and submit your script to run there. Once the environment is built, and if you don't change your dependencies, it will be reused in subsequent runs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "conda_env = Environment(\"conda-env\")\n", - "conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['scikit-learn',\n", - " 'azureml-sdk',\n", - " 'azureml-dataset-runtime[pandas,fuse]'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "from uuid import uuid4\n", - "\n", - "script_arguments = ['--data-folder', dataset.as_named_input('diabetes').as_mount('/tmp/{}'.format(uuid4()))]\n", - "src = ScriptRunConfig(source_directory=script_folder, \n", - " script='train.py', \n", - " # pass the dataset as a parameter to the training script\n", - " arguments=script_arguments,\n", - " compute_target = attached_dsvm_compute,\n", - " environment = conda_env) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = exp.submit(config=src)\n", - "\n", - "from azureml.widgets import RunDetails\n", - "RunDetails(run).show()\n", - "\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Show the run object. You can navigate to the Azure portal to see detailed information about the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Native VM run\n", - "You can also configure to use an exiting Python environment in the VM to execute the script without asking the system to create a conda environment for you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "conda_env.python.user_managed_dependencies = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The below run will likely fail because `train.py` needs dependency `azureml`, `scikit-learn` and others, which are not found in that Python environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " run = exp.submit(config=src)\n", - "\n", - " from azureml.exceptions import ActivityFailedException\n", - "\n", - " try:\n", - " run.wait_for_completion(show_output=True)\n", - " except ActivityFailedException as ex:\n", - " print(ex)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can choose to SSH into the VM and install Azure ML SDK, and any other missing dependencies, in that Python environment. For demonstration purposes, we simply are going to use another script `train2.py` that doesn't have azureml or data store dependencies, and submit it instead." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# copy train2.py into the script folder\n", - "shutil.copy('./train2.py', os.path.join(script_folder, 'train2.py'))\n", - "\n", - "with open(os.path.join(script_folder, './train2.py'), 'r') as training_script:\n", - " print(training_script.read())\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder, \n", - " script='train2.py', \n", - " compute_target = attached_dsvm_compute,\n", - " environment = conda_env) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's try again. And this time it should work fine." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = exp.submit(config=src)\n", - "\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note even in this case you get a run record with some basic statistics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure a Docker run with new conda environment on the VM\n", - "You can execute in a Docker container in the VM. If you choose this option, the system will pull down a base Docker image, build a new conda environment in it if you ask for (you can also skip this if you are using a customer Docker image when a preconfigured Python environment), start a container, and run your script in there. This image is also uploaded into your ACR (Azure Container Registry) assoicated with your workspace, an reused if your dependencies don't change in the subsequent runs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "conda_env.docker.enabled = True\n", - "conda_env.python.user_managed_dependencies = False\n", - "\n", - "print('Base Docker image is:', conda_env.docker.base_image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit the Experiment\n", - "Submit script to run in the Docker image in the remote VM. If you run this for the first time, the system will download the base image, layer in packages specified in the `conda_dependencies.yml` file on top of the base image, create a container and then execute the script in the container." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "src = ScriptRunConfig(source_directory=script_folder, \n", - " script='train.py', \n", - " arguments=script_arguments,\n", - " compute_target = attached_dsvm_compute,\n", - " environment = conda_env) \n", - "\n", - "run = exp.submit(config=src)\n", - "\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Use a custom Docker image instead\n", - "\n", - "You can also specify a custom Docker image if you don't want to use the default image provided by Azure ML.\n", - "\n", - "```python\n", - "# use an image available in Docker Hub without authentication\n", - "conda_env.docker.base_image = \"continuumio/miniconda3\"\n", - "\n", - "# or, use an image available in a private Azure Container Registry\n", - "conda_env.docker.base_image = \"mycustomimage:1.0\"\n", - "conda_env.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n", - "conda_env.docker.base_image_registry.username = \"username\"\n", - "conda_env.docker.base_image_registry.password = \"password\"\n", - "```\n", - "\n", - "When you are using a custom Docker image, you might already have your environment setup properly in a Python environment in the Docker image. In that case, you can skip specifying conda dependencies, and just use `user_managed_dependencies` option instead:\n", - "```python\n", - "conda_env.python.user_managed_dependencies = True\n", - "# path to the Python environment in the custom Docker image\n", - "conda_env.python.interpreter_path = '/opt/conda/bin/python'\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View run history details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find the best model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have tried various execution modes, we can find the best model from the last run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# get all metris logged in the run\n", - "run.get_metrics()\n", - "metrics = run.get_metrics()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# find the index where MSE is the smallest\n", - "indices = list(range(0, len(metrics['mse'])))\n", - "min_mse_index = min(indices, key=lambda x: metrics['mse'][x])\n", - "\n", - "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n", - " metrics['mse'][min_mse_index], \n", - " metrics['alpha'][min_mse_index]\n", - "))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up compute resource\n", - "\n", - "Use ```detach()``` to detach an existing DSVM from Workspace without deleting it. Use ```delete()``` if you created a new ```DsvmCompute``` and want to delete it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# dsvm_compute.detach()\n", - "# dsvm_compute.delete()" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sanpil" - } - ], - "category": "training", - "compute": [ - "Data Science Virtual Machine" - ], - "datasets": [ - "Diabetes" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Train in a remote Linux virtual machine", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 04. Train in a remote Linux VM\n", + "* Create Workspace\n", + "* Create `train.py` file\n", + "* Create and Attach a Remote VM (eg. DSVM) as compute resource\n", + "* Upload data files into default datastore\n", + "* Configure & execute a run in a few different ways\n", + " - Use system-built conda\n", + " - Use existing Python environment\n", + " - Use Docker \n", + "* Find the best model in the run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check core SDK version number\n", + "import azureml.core\n", + "\n", + "print(\"SDK version:\", azureml.core.VERSION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize Workspace\n", + "\n", + "Initialize a workspace object from persisted configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Workspace\n", + "\n", + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Experiment\n", + "\n", + "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "experiment_name = 'train-on-remote-vm'\n", + "\n", + "from azureml.core import Experiment\n", + "exp = Experiment(workspace=ws, name=experiment_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also create a local folder to hold the training script." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "script_folder = './vm-run'\n", + "os.makedirs(script_folder, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Upload data files into datastore\n", + "Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and access it from the compute target." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the default datastore\n", + "ds = ws.get_default_datastore()\n", + "print(ds.name, ds.datastore_type, ds.account_name, ds.container_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load diabetes data from `scikit-learn` and save it as 2 local files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_diabetes\n", + "import numpy as np\n", + "\n", + "training_data = load_diabetes()\n", + "np.save(file='./features.npy', arr=training_data['data'])\n", + "np.save(file='./labels.npy', arr=training_data['target'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's upload the 2 files into the default datastore under a path named `diabetes`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.upload_files(['./features.npy', './labels.npy'], target_path='diabetes', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Dataset for Files\n", + "A Dataset can reference single or multiple files in your datastores or public urls. The files can be of any format. Dataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize file dataset \n", + "from azureml.core import Dataset\n", + "ds_paths = [(ds, 'diabetes/')]\n", + "dataset = Dataset.File.from_files(path = ds_paths)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# list the files referenced by the dataset\n", + "dataset.to_path()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View `train.py`\n", + "\n", + "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train.py` in a cell to show the file. Please pay special attention on how we are loading the features and labels from files in the `data_folder` path, which is passed in as an argument of the training script (shown later)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# copy train.py into the script folder\n", + "import shutil\n", + "shutil.copy('./train.py', os.path.join(script_folder, 'train.py'))\n", + "\n", + "with open(os.path.join(script_folder, './train.py'), 'r') as training_script:\n", + " print(training_script.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create and Attach a DSVM as a compute target\n", + "\n", + "The DSVM can be created using the below single line command and then attached(like any VM) using the sample code below. Also note, that we only support Linux VMs for remote execution from AML and the commands below will spin a Linux VM only.\n", + "\n", + "```shell\n", + "# create a DSVM in your resource group\n", + "# note you need to be at least a contributor to the resource group in order to execute this command successfully\n", + "(myenv) $ az vm create --resource-group --name --image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest --admin-username --admin-password --generate-ssh-keys --authentication-type password\n", + "```\n", + "\n", + "**Note**: You can also use [this url](https://ms.portal.azure.com/#create/microsoft-dsvm.ubuntu-18041804) to create the VM using the Azure Portal\n", + "\n", + "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you switch to a different port (such as 5022), you can specify the port number in the provisioning configuration object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Configure and execute a run" + "sample-remotecompute-attach" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, RemoteCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "username = os.getenv('AZUREML_DSVM_USERNAME', default='')\n", + "address = os.getenv('AZUREML_DSVM_ADDRESS', default='')\n", + "\n", + "compute_target_name = 'cpudsvm'\n", + "# if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase \n", + "try:\n", + " attached_dsvm_compute = RemoteCompute(workspace=ws, name=compute_target_name)\n", + " print('found existing:', attached_dsvm_compute.name)\n", + "except ComputeTargetException:\n", + " attach_config = RemoteCompute.attach_configuration(address=address,\n", + " ssh_port=22,\n", + " username=username,\n", + " private_key_file='./.ssh/id_rsa')\n", + "\n", + "\n", + "# Attaching a virtual machine using the public IP address of the VM is no longer supported.\n", + "# Instead, use resourceId of the VM.\n", + "# The resourceId of the VM can be constructed using the following string format:\n", + "# /subscriptions//resourceGroups//providers/Microsoft.Compute/virtualMachines/.\n", + "# You can also use subscription_id, resource_group and vm_name without constructing resourceId.\n", + " attach_config = RemoteCompute.attach_configuration(resource_id='',\n", + " ssh_port=22,\n", + " username='username',\n", + " private_key_file='./.ssh/id_rsa')\n", + " \n", + " attached_dsvm_compute = ComputeTarget.attach(ws, compute_target_name, attach_config)\n", + " \n", + " attached_dsvm_compute.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configure & Run\n", + "\n", + "Now we can try a few different ways to run the training script in the VM." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conda run\n", + "You can ask the system to build a conda environment based on your dependency specification, and submit your script to run there. Once the environment is built, and if you don't change your dependencies, it will be reused in subsequent runs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "conda_env = Environment(\"conda-env\")\n", + "conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['scikit-learn',\n", + " 'azureml-sdk',\n", + " 'azureml-dataset-runtime[pandas,fuse]'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import ScriptRunConfig\n", + "from uuid import uuid4\n", + "\n", + "script_arguments = ['--data-folder', dataset.as_named_input('diabetes').as_mount('/tmp/{}'.format(uuid4()))]\n", + "src = ScriptRunConfig(source_directory=script_folder, \n", + " script='train.py', \n", + " # pass the dataset as a parameter to the training script\n", + " arguments=script_arguments,\n", + " compute_target = attached_dsvm_compute,\n", + " environment = conda_env) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run = exp.submit(config=src)\n", + "\n", + "from azureml.widgets import RunDetails\n", + "RunDetails(run).show()\n", + "\n", + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show the run object. You can navigate to the Azure portal to see detailed information about the run." + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Native VM run\n", + "You can also configure to use an exiting Python environment in the VM to execute the script without asking the system to create a conda environment for you." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conda_env.python.user_managed_dependencies = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The below run will likely fail because `train.py` needs dependency `azureml`, `scikit-learn` and others, which are not found in that Python environment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##PUBLISHONLY\n", + "# run = exp.submit(config=src)\n", + "\n", + "# from azureml.exceptions import ActivityFailedException\n", + "\n", + "# try:\n", + "# run.wait_for_completion(show_output=True)\n", + "# except ActivityFailedException as ex:\n", + "# print(ex)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can choose to SSH into the VM and install Azure ML SDK, and any other missing dependencies, in that Python environment. For demonstration purposes, we simply are going to use another script `train2.py` that doesn't have azureml or data store dependencies, and submit it instead." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# copy train2.py into the script folder\n", + "shutil.copy('./train2.py', os.path.join(script_folder, 'train2.py'))\n", + "\n", + "with open(os.path.join(script_folder, './train2.py'), 'r') as training_script:\n", + " print(training_script.read())\n", + "\n", + "src = ScriptRunConfig(source_directory=script_folder, \n", + " script='train2.py', \n", + " compute_target = attached_dsvm_compute,\n", + " environment = conda_env) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's try again. And this time it should work fine." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run = exp.submit(config=src)\n", + "\n", + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note even in this case you get a run record with some basic statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure a Docker run with new conda environment on the VM\n", + "You can execute in a Docker container in the VM. If you choose this option, the system will pull down a base Docker image, build a new conda environment in it if you ask for (you can also skip this if you are using a customer Docker image when a preconfigured Python environment), start a container, and run your script in there. This image is also uploaded into your ACR (Azure Container Registry) assoicated with your workspace, an reused if your dependencies don't change in the subsequent runs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conda_env.docker.enabled = True\n", + "conda_env.python.user_managed_dependencies = False\n", + "\n", + "print('Base Docker image is:', conda_env.docker.base_image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Submit the Experiment\n", + "Submit script to run in the Docker image in the remote VM. If you run this for the first time, the system will download the base image, layer in packages specified in the `conda_dependencies.yml` file on top of the base image, create a container and then execute the script in the container." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "src = ScriptRunConfig(source_directory=script_folder, \n", + " script='train.py', \n", + " arguments=script_arguments,\n", + " compute_target = attached_dsvm_compute,\n", + " environment = conda_env) \n", + "\n", + "run = exp.submit(config=src)\n", + "\n", + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use a custom Docker image instead\n", + "\n", + "You can also specify a custom Docker image if you don't want to use the default image provided by Azure ML.\n", + "\n", + "```python\n", + "# use an image available in Docker Hub without authentication\n", + "conda_env.docker.base_image = \"continuumio/miniconda3\"\n", + "\n", + "# or, use an image available in a private Azure Container Registry\n", + "conda_env.docker.base_image = \"mycustomimage:1.0\"\n", + "conda_env.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n", + "conda_env.docker.base_image_registry.username = \"username\"\n", + "conda_env.docker.base_image_registry.password = \"password\"\n", + "```\n", + "\n", + "When you are using a custom Docker image, you might already have your environment setup properly in a Python environment in the Docker image. In that case, you can skip specifying conda dependencies, and just use `user_managed_dependencies` option instead:\n", + "```python\n", + "conda_env.python.user_managed_dependencies = True\n", + "# path to the Python environment in the custom Docker image\n", + "conda_env.python.interpreter_path = '/opt/conda/bin/python'\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View run history details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Find the best model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have tried various execution modes, we can find the best model from the last run." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get all metris logged in the run\n", + "run.get_metrics()\n", + "metrics = run.get_metrics()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# find the index where MSE is the smallest\n", + "indices = list(range(0, len(metrics['mse'])))\n", + "min_mse_index = min(indices, key=lambda x: metrics['mse'][x])\n", + "\n", + "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n", + " metrics['mse'][min_mse_index], \n", + " metrics['alpha'][min_mse_index]\n", + "))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clean up compute resource\n", + "\n", + "Use ```detach()``` to detach an existing DSVM from Workspace without deleting it. Use ```delete()``` if you created a new ```DsvmCompute``` and want to delete it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# dsvm_compute.detach()\n", + "# dsvm_compute.delete()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "sanpil" + } + ], + "category": "training", + "compute": [ + "Data Science Virtual Machine" + ], + "datasets": [ + "Diabetes" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "None" + ], + "friendly_name": "Train in a remote Linux virtual machine", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "tags": [ + "None" + ], + "task": "Configure and execute a run" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/training/using-environments/using-environments.ipynb b/how-to-use-azureml/training/using-environments/using-environments.ipynb index 7064480df..5e523b3e1 100644 --- a/how-to-use-azureml/training/using-environments/using-environments.ipynb +++ b/how-to-use-azureml/training/using-environments/using-environments.ipynb @@ -1,472 +1,472 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/using-environments/using-environments.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using environments\n", - "\n", - "\n", - "## Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Use curated environment](#Use-curated-environment)\n", - "1. [Create environment](#Create-environment)\n", - " 1. Add Python packages\n", - " 1. Specify environment variables\n", - "1. [Submit run using environment](#Submit-run-using-environment)\n", - "1. [Register environment](#Register-environment)\n", - "1. [List and get existing environments](#List-and-get-existing-environments)\n", - "1. [Other ways to create environments](#Other-ways-to-create-environments)\n", - " 1. From existing Conda environment\n", - " 1. From Conda or pip files\n", - "1. [Using environments for inferencing](#Using-environments-for-inferencing)\n", - "1. [Docker settings](#Docker-settings)\n", - "1. [Spark and Azure Databricks settings](#Spark-and-Azure-Databricks-settings)\n", - "1. [Next steps](#Next-steps)\n", - "\n", - "## Introduction\n", - "\n", - "Azure ML environments are an encapsulation of the environment where your machine learning training happens. They define Python packages, environment variables, Docker settings and other attributes in declarative fashion. Environments are versioned: you can update them and retrieve old versions to revisit and review your work.\n", - "\n", - "Environments allow you to:\n", - "* Encapsulate dependencies of your training process, such as Python packages and their versions.\n", - "* Reproduce the Python environment on your local computer in a remote run on VM or ML Compute cluster\n", - "* Reproduce your experimentation environment in production setting.\n", - "* Revisit and audit the environment in which an existing model was trained.\n", - "\n", - "Environment, compute target and training script together form run configuration: the full specification of training run.\n", - "\n", - "## Setup\n", - "\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't.\n", - "\n", - "First, let's validate Azure ML SDK version and connect to workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "print(azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "ws = Workspace.from_config()\n", - "ws.get_details()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use curated environments\n", - "\n", - "Curated environments are provided by Azure Machine Learning and are available in your workspace by default. They contain collections of Python packages and settings to help you get started different machine learning frameworks. \n", - "\n", - " * The __AzureML-Minimal__ environment contains a minimal set of packages to enable run tracking and asset uploading. You can use it as a starting point for your own environment.\n", - " * The __AzureML-Tutorial__ environment contains common data science packages, such as Scikit-Learn, Pandas and Matplotlib, and larger set of azureml-sdk packages.\n", - " \n", - "Curated environments are backed by cached Docker images, reducing the run preparation cost.\n", - " \n", - "You can get a curated environment using" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "curated_env = Environment.get(workspace=ws, name=\"AzureML-Minimal\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To list curated environments, use following code.\n", - "\n", - "**Note**: The name prefixes _AzureML_ and _Microsoft_ are reserved for curated environments. Do not use them for your own environments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "envs = Environment.list(workspace=ws)\n", - "\n", - "for env in envs:\n", - " if env.startswith(\"AzureML\"):\n", - " print(\"Name\",env)\n", - " if envs[env].python.conda_dependencies is not None:\n", - " print(\"packages\", envs[env].python.conda_dependencies.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create your own environment\n", - "\n", - "You can create an environment by instantiating ```Environment``` object and then setting its attributes: set of Python packages, environment variables and others.\n", - "\n", - "### Add Python packages\n", - "\n", - "The recommended way is to specify Conda packages, as they typically come with complete set of pre-built binaries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "condadependencies-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.environment import CondaDependencies\n", - "\n", - "myenv = Environment(name=\"myenv\")\n", - "conda_dep = CondaDependencies()\n", - "conda_dep.add_conda_package(\"scikit-learn\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also add pip packages, and specify the version of package" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "condadependencies-remarks-sample2" - ] - }, - "outputs": [], - "source": [ - "conda_dep.add_pip_package(\"pillow==5.4.1\")\n", - "myenv.python.conda_dependencies=conda_dep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit run using environment\n", - "\n", - "When you submit a run, you can specify which environment to use. \n", - "\n", - "On the first run in given environment, Azure ML spends some time building the environment. On the subsequent runs, Azure ML keeps track of changes and uses the existing environment, resulting in faster run completion." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig, Experiment\n", - "\n", - "myexp = Experiment(workspace=ws, name = \"environment-example\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To submit a run, create a run configuration that combines the script file and environment, and pass it to ```Experiment.submit```. In this example, the script is submitted to local computer, but you can specify other compute targets such as remote clusters as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "src = ScriptRunConfig(source_directory=\".\",\n", - " script=\"example.py\",\n", - " compute_target=\"local\",\n", - " environment=myenv)\n", - "\n", - "run = myexp.submit(config=src)\n", - "\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To audit the environment used by for a run, you can use ```get_environment```." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_environment()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register environment\n", - "\n", - "You can manage environments by registering them. This allows you to track their versions, and reuse them in future runs. For example, once you've constructed an environment that meets your requirements, you can register it and use it in other experiments so as to standardize your workflow.\n", - "\n", - "If you register the environment with same name, the version number is increased by one. Note that Azure ML keeps track of differences between the version, so if you re-register an identical version, the version number is not increased." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myenv.register(workspace=ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## List and get existing environments\n", - "\n", - "Your workspace contains a dictionary of registered environments. You can then use ```Environment.get``` to retrieve a specific environment with specific version." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for name,env in ws.environments.items():\n", - " print(\"Name {} \\t version {}\".format(name,env.version))\n", - "\n", - "restored_environment = Environment.get(workspace=ws,name=\"myenv\",version=\"1\")\n", - "\n", - "print(\"Attributes of restored environment\")\n", - "restored_environment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Other ways to create environments\n", - "\n", - "### From existing Conda environment\n", - "\n", - "You can create an environment from existing conda environment. This make it easy to reuse your local interactive environment in Azure ML remote runs. For example, if you've created conda environment using\n", - "```\n", - "conda create -n mycondaenv\n", - "```\n", - "you can create Azure ML environment out of that conda environment using\n", - "```\n", - "myenv = Environment.from_existing_conda_environment(name=\"myenv\",conda_environment_name=\"mycondaenv\")\n", - "```\n", - "\n", - "### From conda or pip files\n", - "\n", - "You can create environments from conda specification or pip requirements files using\n", - "```\n", - "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"path-to-conda-specification-file\")\n", - "\n", - "myenv = Environment.from_pip_requirements(name=\"myenv\", file_path=\"path-to-pip-requirements-file\")\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using environments for inferencing\n", - "\n", - "You can re-use the training environment when you deploy your model as a web service, by specifying inferencing stack version, and adding then environment to ```InferenceConfig```.\n", - "\n", - "```\n", - "from azureml.core.model import InferenceConfig\n", - "\n", - "myenv.inferencing_stack_version = \"latest\"\n", - "\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n", - "```\n", - "\n", - "See [Register Model and deploy as Webservice Notebook](../../deployment/deploy-to-cloud/model-register-and-deploy.ipynb) for an end-to-end example of web service deployment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Docker settings\n", - "\n", - "Docker container provides an efficient way to encapsulate the dependencies. When you enable Docker, Azure ML builds a Docker image and creates a Python environment within that container, given your specifications. The Docker images are reused: the first run in a new environment typically takes longer as the image is build.\n", - "\n", - "**Note:** For runs on local computer or attached virtual machine, that computer must have Docker installed and enabled. Machine Learning Compute has Docker pre-installed.\n", - "\n", - "Attribute ```docker.enabled``` controls whether to use Docker container or host OS for execution. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myenv.docker.enabled = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can specify custom Docker base image and registry. This allows you to customize and control in detail the guest OS in which your training run executes. whether to use GPU, whether to use shared volumes, and shm size." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myenv.docker.base_image\n", - "myenv.docker.base_image_registry" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also specify shared volumes, and shm size." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myenv.docker.shared_volumes\n", - "myenv.docker.shm_size" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Spark and Azure Databricks settings\n", - "\n", - "In addition to Python and Docker settings, Environment also contains attributes for Spark and Azure Databricks runs. These attributes become relevant when you submit runs on those compute targets." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "Train with ML frameworks on Azure ML:\n", - "\n", - "* [Train with ML frameworks](../../ml-frameworks)\n", - "\n", - "Learn more about registering and deploying a model:\n", - "\n", - "* [Register Model and deploy as Webservice](../../deployment/deploy-to-cloud/model-register-and-deploy.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "roastala" - } - ], - "category": "starter", - "compute": [ - "Local" - ], - "datasets": [ - "None" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Using Azure ML environments", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "\n", + "Licensed under the MIT License" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/using-environments/using-environments.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using environments\n", + "\n", + "\n", + "## Contents\n", + "\n", + "1. [Introduction](#Introduction)\n", + "1. [Setup](#Setup)\n", + "1. [Use curated environment](#Use-curated-environment)\n", + "1. [Create environment](#Create-environment)\n", + " 1. Add Python packages\n", + " 1. Specify environment variables\n", + "1. [Submit run using environment](#Submit-run-using-environment)\n", + "1. [Register environment](#Register-environment)\n", + "1. [List and get existing environments](#List-and-get-existing-environments)\n", + "1. [Other ways to create environments](#Other-ways-to-create-environments)\n", + " 1. From existing Conda environment\n", + " 1. From Conda or pip files\n", + "1. [Using environments for inferencing](#Using-environments-for-inferencing)\n", + "1. [Docker settings](#Docker-settings)\n", + "1. [Spark and Azure Databricks settings](#Spark-and-Azure-Databricks-settings)\n", + "1. [Next steps](#Next-steps)\n", + "\n", + "## Introduction\n", + "\n", + "Azure ML environments are an encapsulation of the environment where your machine learning training happens. They define Python packages, environment variables, Docker settings and other attributes in declarative fashion. Environments are versioned: you can update them and retrieve old versions to revisit and review your work.\n", + "\n", + "Environments allow you to:\n", + "* Encapsulate dependencies of your training process, such as Python packages and their versions.\n", + "* Reproduce the Python environment on your local computer in a remote run on VM or ML Compute cluster\n", + "* Reproduce your experimentation environment in production setting.\n", + "* Revisit and audit the environment in which an existing model was trained.\n", + "\n", + "Environment, compute target and training script together form run configuration: the full specification of training run.\n", + "\n", + "## Setup\n", + "\n", + "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't.\n", + "\n", + "First, let's validate Azure ML SDK version and connect to workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import azureml.core\n", + "print(azureml.core.VERSION)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.workspace import Workspace\n", + "ws = Workspace.from_config()\n", + "ws.get_details()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use curated environments\n", + "\n", + "Curated environments are provided by Azure Machine Learning and are available in your workspace by default. They contain collections of Python packages and settings to help you get started different machine learning frameworks. \n", + "\n", + " * The __AzureML-Minimal__ environment contains a minimal set of packages to enable run tracking and asset uploading. You can use it as a starting point for your own environment.\n", + " * The __AzureML-Tutorial__ environment contains common data science packages, such as Scikit-Learn, Pandas and Matplotlib, and larger set of azureml-sdk packages.\n", + " \n", + "Curated environments are backed by cached Docker images, reducing the run preparation cost.\n", + " \n", + "You can get a curated environment using" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment\n", + "\n", + "curated_env = Environment.get(workspace=ws, name=\"AzureML-Minimal\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To list curated environments, use following code.\n", + "\n", + "**Note**: The name prefixes _AzureML_ and _Microsoft_ are reserved for curated environments. Do not use them for your own environments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "envs = Environment.list(workspace=ws)\n", + "\n", + "for env in envs:\n", + " if env.startswith(\"AzureML\"):\n", + " print(\"Name\",env)\n", + " if envs[env].python.conda_dependencies is not None:\n", + " print(\"packages\", envs[env].python.conda_dependencies.serialize_to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create your own environment\n", + "\n", + "You can create an environment by instantiating ```Environment``` object and then setting its attributes: set of Python packages, environment variables and others.\n", + "\n", + "### Add Python packages\n", + "\n", + "The recommended way is to specify Conda packages, as they typically come with complete set of pre-built binaries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "tags": [ - "None" - ], - "task": "Creating and registering environments" + "condadependencies-remarks-sample" + ] + }, + "outputs": [], + "source": [ + "from azureml.core.environment import CondaDependencies\n", + "\n", + "myenv = Environment(name=\"myenv\")\n", + "conda_dep = CondaDependencies()\n", + "conda_dep.add_conda_package(\"scikit-learn\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also add pip packages, and specify the version of package" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "condadependencies-remarks-sample2" + ] + }, + "outputs": [], + "source": [ + "conda_dep.add_pip_package(\"pillow==5.4.1\")\n", + "myenv.python.conda_dependencies=conda_dep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submit run using environment\n", + "\n", + "When you submit a run, you can specify which environment to use. \n", + "\n", + "On the first run in given environment, Azure ML spends some time building the environment. On the subsequent runs, Azure ML keeps track of changes and uses the existing environment, resulting in faster run completion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import ScriptRunConfig, Experiment\n", + "\n", + "myexp = Experiment(workspace=ws, name = \"environment-example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To submit a run, create a run configuration that combines the script file and environment, and pass it to ```Experiment.submit```. In this example, the script is submitted to local computer, but you can specify other compute targets such as remote clusters as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "src = ScriptRunConfig(source_directory=\".\",\n", + " script=\"example.py\",\n", + " compute_target=\"local\",\n", + " environment=myenv)\n", + "\n", + "run = myexp.submit(config=src)\n", + "\n", + "run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To audit the environment used by for a run, you can use ```get_environment```." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.get_environment()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register environment\n", + "\n", + "You can manage environments by registering them. This allows you to track their versions, and reuse them in future runs. For example, once you've constructed an environment that meets your requirements, you can register it and use it in other experiments so as to standardize your workflow.\n", + "\n", + "If you register the environment with same name, the version number is increased by one. Note that Azure ML keeps track of differences between the version, so if you re-register an identical version, the version number is not increased." + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myenv.register(workspace=ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## List and get existing environments\n", + "\n", + "Your workspace contains a dictionary of registered environments. You can then use ```Environment.get``` to retrieve a specific environment with specific version." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for name,env in ws.environments.items():\n", + " print(\"Name {} \\t version {}\".format(name,env.version))\n", + "\n", + "restored_environment = Environment.get(workspace=ws,name=\"myenv\",version=\"1\")\n", + "\n", + "print(\"Attributes of restored environment\")\n", + "restored_environment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other ways to create environments\n", + "\n", + "### From existing Conda environment\n", + "\n", + "You can create an environment from existing conda environment. This make it easy to reuse your local interactive environment in Azure ML remote runs. For example, if you've created conda environment using\n", + "```\n", + "conda create -n mycondaenv\n", + "```\n", + "you can create Azure ML environment out of that conda environment using\n", + "```\n", + "myenv = Environment.from_existing_conda_environment(name=\"myenv\",conda_environment_name=\"mycondaenv\")\n", + "```\n", + "\n", + "### From conda or pip files\n", + "\n", + "You can create environments from conda specification or pip requirements files using\n", + "```\n", + "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"path-to-conda-specification-file\")\n", + "\n", + "myenv = Environment.from_pip_requirements(name=\"myenv\", file_path=\"path-to-pip-requirements-file\")\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using environments for inferencing\n", + "\n", + "You can re-use the training environment when you deploy your model as a web service, by specifying inferencing stack version, and adding then environment to ```InferenceConfig```.\n", + "\n", + "```\n", + "from azureml.core.model import InferenceConfig\n", + "\n", + "myenv.inferencing_stack_version = \"latest\"\n", + "\n", + "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n", + "```\n", + "\n", + "See [Register Model and deploy as Webservice Notebook](../../deployment/deploy-to-cloud/model-register-and-deploy.ipynb) for an end-to-end example of web service deployment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Docker settings\n", + "\n", + "Docker container provides an efficient way to encapsulate the dependencies. When you enable Docker, Azure ML builds a Docker image and creates a Python environment within that container, given your specifications. The Docker images are reused: the first run in a new environment typically takes longer as the image is build.\n", + "\n", + "**Note:** For runs on local computer or attached virtual machine, that computer must have Docker installed and enabled. Machine Learning Compute has Docker pre-installed.\n", + "\n", + "Attribute ```docker.enabled``` controls whether to use Docker container or host OS for execution. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myenv.docker.enabled = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can specify custom Docker base image and registry. This allows you to customize and control in detail the guest OS in which your training run executes. whether to use GPU, whether to use shared volumes, and shm size." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myenv.docker.base_image\n", + "myenv.docker.base_image_registry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also specify shared volumes, and shm size." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myenv.docker.shared_volumes\n", + "myenv.docker.shm_size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spark and Azure Databricks settings\n", + "\n", + "In addition to Python and Docker settings, Environment also contains attributes for Spark and Azure Databricks runs. These attributes become relevant when you submit runs on those compute targets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "Train with ML frameworks on Azure ML:\n", + "\n", + "* [Train with ML frameworks](../../ml-frameworks)\n", + "\n", + "Learn more about registering and deploying a model:\n", + "\n", + "* [Register Model and deploy as Webservice](../../deployment/deploy-to-cloud/model-register-and-deploy.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "authors": [ + { + "name": "roastala" + } + ], + "category": "starter", + "compute": [ + "Local" + ], + "datasets": [ + "None" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "None" + ], + "friendly_name": "Using Azure ML environments", + "index_order": 1, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "tags": [ + "None" + ], + "task": "Creating and registering environments" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/how-to-use-azureml/work-with-data/README.md b/how-to-use-azureml/work-with-data/README.md deleted file mode 100644 index 2cba493c1..000000000 --- a/how-to-use-azureml/work-with-data/README.md +++ /dev/null @@ -1,20 +0,0 @@ -# Azure Machine Learning datasets - -Azure Machine Learning datasets let data scientists and machine learning engineers apply data for ML with confidence. By creating a dataset, you create a reference to the data source location, along with a copy of its metadata. The data remains in its existing location, so no extra storage cost is incurred. - -With Azure Machine Learning datasets, you can: - -* **Keep a single copy of data in your storage** referenced by datasets. -* **Easily access data during model training** without worrying about connection string or data path. -* **Share data & collaborate** with other users. - -## Learn how to use Azure Machine Learning datasets -* [Create and register datasets](https://aka.ms/azureml/howto/createdatasets) -* Use [Datasets in training](datasets-tutorial/train-with-datasets/train-with-datasets.ipynb) -* Use TabularDatasets in [automated machine learning training](https://aka.ms/automl-dataset) -* Use FileDatasets in [image classification](https://aka.ms/filedataset-samplenotebook) -* Use FileDatasets in [deep learning with hyperparameter tuning](https://aka.ms/filedataset-hyperdrive) -* For existing Dataset users: [Dataset API change notice](dataset-api-change-notice.md) - - -![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/README.png) \ No newline at end of file diff --git a/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb b/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb deleted file mode 100644 index b848d08d7..000000000 --- a/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb +++ /dev/null @@ -1,466 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-quickdemo.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analyze data drift in Azure Machine Learning datasets \n", - "\n", - "In this tutorial, you will setup a data drift monitor on a weather dataset to:\n", - "\n", - "☑ Analyze historical data for drift\n", - "\n", - "☑ Setup a monitor to recieve email alerts if data drift is detected going forward\n", - "\n", - "If your workspace is Enterprise level, view and exlpore the results in the Azure Machine Learning studio. The video below shows the results from this tutorial. \n", - "\n", - "![gif](media/video.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "If you are using an Azure Machine Learning Compute instance, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) if you haven't already established your connection to the AzureML Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print('SDK version:', azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "ws" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup target and baseline datasets\n", - "\n", - "Setup the baseline and target datasets. The baseline will be used to compare each time slice of the target dataset, which is sampled by a given frequency. For further details, see [our documentation](http://aka.ms/datadrift). \n", - "\n", - "The next few cells will:\n", - " * get the default datastore\n", - " * upload the `weather-data` to the datastore\n", - " * create the Tabular dataset from the data\n", - " * add the timeseries trait by specifying the timestamp column `datetime`\n", - " * register the dataset\n", - " * create the baseline as a time slice of the target dataset\n", - " * optionally, register the baseline dataset\n", - " \n", - "The folder `weather-data` contains weather data from the [NOAA Integrated Surface Data](https://azure.microsoft.com/services/open-datasets/catalog/noaa-integrated-surface-data/) filtered down to to station names containing the string 'FLORIDA' to reduce the size of data. See `get_data.py` to see how this data is curated and modify as desired. This script may take a long time to run, hence the data is provided in the `weather-data` folder for this demo." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# use default datastore\n", - "dstore = ws.get_default_datastore()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# upload weather data\n", - "dstore.upload('weather-data', 'datadrift-data', overwrite=True, show_progress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import Dataset class\n", - "from azureml.core import Dataset\n", - "\n", - "# create target dataset \n", - "target = Dataset.Tabular.from_parquet_files(dstore.path('datadrift-data/**/data.parquet'))\n", - "# set the timestamp column\n", - "target = target.with_timestamp_columns('datetime')\n", - "# register the target dataset\n", - "target = target.register(ws, 'target')\n", - "# retrieve the dataset from the workspace by name\n", - "target = Dataset.get_by_name(ws, 'target')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import datetime \n", - "from datetime import datetime\n", - "\n", - "# set baseline dataset as January 2019 weather data\n", - "baseline = Dataset.Tabular.from_parquet_files(dstore.path('datadrift-data/2019/01/data.parquet'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# optionally, register the baseline dataset. if skipped, an unregistered dataset will be used\n", - "#baseline = baseline.register(ws, 'baseline')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create compute target\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "Create an Azure Machine Learning compute cluster to run the data drift monitor and associated runs. The below cell will create a compute cluster named `'cpu-cluster'`. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "\n", - "compute_name = 'cpu-cluster'\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " print('found compute target. just use it. ' + compute_name)\n", - "else:\n", - " print('creating a new compute target...')\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D3_V2', min_nodes=0, max_nodes=2)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - "\n", - " # can poll for a minimum number of nodes and for a specific timeout.\n", - " # if no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - " # For a more detailed view of current AmlCompute status, use get_status()\n", - " print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create data drift monitor\n", - "\n", - "See [our documentation](http://aka.ms/datadrift) for a complete description for all of the parameters. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "datadrift-remarks-sample" - ] - }, - "outputs": [], - "source": [ - "from azureml.datadrift import DataDriftDetector, AlertConfiguration\n", - "\n", - "alert_config = AlertConfiguration(['user@contoso.com']) # replace with your email to recieve alerts from the scheduled pipeline after enabling\n", - "\n", - "monitor = DataDriftDetector.create_from_datasets(ws, 'weather-monitor', baseline, target, \n", - " compute_target='cpu-cluster', # compute target for scheduled pipeline and backfills \n", - " frequency='Week', # how often to analyze target data\n", - " feature_list=None, # list of features to detect drift on\n", - " drift_threshold=None, # threshold from 0 to 1 for email alerting\n", - " latency=0, # SLA in hours for target data to arrive in the dataset\n", - " alert_config=alert_config) # email addresses to send alert" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Update data drift monitor\n", - "\n", - "Many settings of the data drift monitor can be updated after creation. In this demo, we will update the `drift_threshold` and `feature_list`. See [our documentation](http://aka.ms/datadrift) for details on which settings can be changed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# get monitor by name\n", - "monitor = DataDriftDetector.get_by_name(ws, 'weather-monitor')\n", - "\n", - "# create feature list - need to exclude columns that naturally drift or increment over time, such as year, day, index\n", - "columns = list(baseline.take(1).to_pandas_dataframe())\n", - "exclude = ['year', 'day', 'version', '__index_level_0__']\n", - "features = [col for col in columns if col not in exclude]\n", - "\n", - "# update the feature list\n", - "monitor = monitor.update(feature_list=features)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze historical data and backfill\n", - "\n", - "You can use the `backfill` method to:\n", - " * analyze historical data\n", - " * backfill metrics after updating the settings (mainly the feature list)\n", - " * backfill metrics for failed runs\n", - " \n", - "The below cells will run two backfills that will produce data drift results for 2019 weather data, with January used as the baseline in the monitor. The output can be seen from the `show` method after the runs have completed, or viewed from the Azure Machine Learning studio for Enterprise workspaces.\n", - "\n", - "![Drift results](media/drift-results.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "source": [ - ">**Tip!** When starting with the data drift capability, start by backfilling on a small section of data to get initial results. Update the feature list as needed by removing columns that are causing drift, but can be ignored, and backfill this section of data until satisfied with the results. Then, backfill on a larger slice of data and/or set the alert configuration, threshold, and enable the schedule to recieve alerts to drift on your dataset. All of this can be done through the UI (Enterprise) or Python SDK." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although it depends on many factors, the below backfill should typically take less than 20 minutes to run. Results will show as soon as they become available, not when the backfill is completed, so you may begin to see some metrics in a few minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# backfill for one month\n", - "backfill_start_date = datetime(2019, 9, 1)\n", - "backfill_end_date = datetime(2019, 10, 1)\n", - "backfill = monitor.backfill(backfill_start_date, backfill_end_date)\n", - "backfill" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Query metrics and show results in Python\n", - "\n", - "The below cell will plot some key data drift metrics, and can be used to query the results. Run `help(monitor.get_output)` for specifics on the object returned." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# make sure the backfill has completed\n", - "backfill.wait_for_completion(wait_post_processing=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# get results from Python SDK (wait for backfills or monitor runs to finish)\n", - "results, metrics = monitor.get_output(start_time=datetime(year=2019, month=9, day=1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot the results from Python SDK \n", - "monitor.show(backfill_start_date, backfill_end_date)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Enable the monitor's pipeline schedule\n", - "\n", - "Turn on a scheduled pipeline which will anlayze the target dataset for drift every `frequency`. Use the latency parameter to adjust the start time of the pipeline. For instance, if it takes 24 hours for my data processing pipelines for data to arrive in the target dataset, set latency to 24. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable the pipeline schedule and recieve email alerts\n", - "monitor.enable_schedule()\n", - "\n", - "# disable the pipeline schedule \n", - "#monitor.disable_schedule()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete compute target\n", - "\n", - "Do not delete the compute target if you intend to keep using it for the data drift monitor scheduled runs or otherwise. If the minimum nodes are set to 0, it will scale down soon after jobs are completed, and scale up the next time the cluster is needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# optionally delete the compute target\n", - "#compute_target.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete the DataDriftDetector\n", - "\n", - "Invoking the `delete()` method on the object deletes the the drift monitor permanently and cannot be undone. You will no longer be able to find it in the UI and the `list()` or `get()` methods. The object on which delete() was called will have its state set to deleted and name suffixed with deleted. The baseline and target datasets and model data that was collected, if any, are not deleted. The compute is not deleted. The DataDrift schedule pipeline is disabled and archived." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "monitor.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - " * See [our documentation](https://aka.ms/datadrift) or [Python SDK reference](https://docs.microsoft.com/python/api/overview/azure/ml/intro)\n", - " * [Send requests or feedback](mailto:driftfeedback@microsoft.com) on data drift directly to the team\n", - " * Please open issues with data drift here on GitHub or on StackOverflow if others are likely to run into the same issue" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jamgan" - } - ], - "category": "tutorial", - "compute": [ - "Remote" - ], - "datasets": [ - "NOAA" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Data drift quickdemo", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - }, - "star_tag": [ - "featured" - ], - "tags": [ - "Dataset", - "Timeseries", - "Drift" - ], - "task": "Filtering" - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.yml b/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.yml deleted file mode 100644 index 6633d9e5a..000000000 --- a/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.yml +++ /dev/null @@ -1,5 +0,0 @@ -name: datadrift-tutorial -dependencies: -- pip: - - azureml-sdk - - azureml-datadrift diff --git a/how-to-use-azureml/work-with-data/datadrift-tutorial/get_data.py b/how-to-use-azureml/work-with-data/datadrift-tutorial/get_data.py deleted file mode 100644 index b97c913ed..000000000 --- a/how-to-use-azureml/work-with-data/datadrift-tutorial/get_data.py +++ /dev/null @@ -1,30 +0,0 @@ -# import packages -import os -import pandas as pd -from calendar import monthrange -from datetime import datetime, timedelta -from azureml.core import Dataset, Datastore, Workspace -from azureml.opendatasets import NoaaIsdWeather - -# get workspace and datastore -ws = Workspace.from_config() -dstore = ws.get_default_datastore() - -# adjust parameters as needed -target_years = list(range(2010, 2020)) -start_month = 1 - -# get data -for year in target_years: - for month in range(start_month, 12 + 1): - path = 'weather-data/{}/{:02d}/'.format(year, month) - try: - start = datetime(year, month, 1) - end = datetime(year, month, monthrange(year, month)[1]) + timedelta(days=1) - isd = NoaaIsdWeather(start, end).to_pandas_dataframe() - isd = isd[isd['stationName'].str.contains('FLORIDA', regex=True, na=False)] - os.makedirs(path, exist_ok=True) - isd.to_parquet(path + 'data.parquet') - except Exception as e: - print('Month {} in year {} likely has no data.\n'.format(month, year)) - print('Exception: {}'.format(e)) diff --git a/how-to-use-azureml/work-with-data/datadrift-tutorial/media/drift-results.png b/how-to-use-azureml/work-with-data/datadrift-tutorial/media/drift-results.png deleted file mode 100644 index 0bd594d26..000000000 Binary files a/how-to-use-azureml/work-with-data/datadrift-tutorial/media/drift-results.png and /dev/null differ diff --git a/how-to-use-azureml/work-with-data/datadrift-tutorial/media/video.gif b/how-to-use-azureml/work-with-data/datadrift-tutorial/media/video.gif deleted file mode 100644 index 38ca06857..000000000 Binary files a/how-to-use-azureml/work-with-data/datadrift-tutorial/media/video.gif and /dev/null differ diff --git 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b/how-to-use-azureml/work-with-data/datadrift-tutorial/weather-data/2019/10/data.parquet deleted file mode 100644 index 7635534c6..000000000 Binary files a/how-to-use-azureml/work-with-data/datadrift-tutorial/weather-data/2019/10/data.parquet and /dev/null differ diff --git a/how-to-use-azureml/work-with-data/dataset-api-change-notice.md b/how-to-use-azureml/work-with-data/dataset-api-change-notice.md deleted file mode 100644 index 34c6fb97a..000000000 --- a/how-to-use-azureml/work-with-data/dataset-api-change-notice.md +++ /dev/null @@ -1,44 +0,0 @@ -# Dataset API change notice - -## Why are Dataset API changes essential? - -The existing Dataset class only supports data in tabular format. In order to support binary data and address a wider range of machine learning scenarios including deep learning, we will introduce Dataset types. Datasets are categorized into various types based on how users consume them in training. List of Dataset types: -- **TabularDataset**: Represents data in a tabular format by parsing the provided file or list of files. TabularDataset can be created from csv, tsv, parquet files, SQL query results etc. For the complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference). It provides you with the ability to materialize the data into a pandas DataFrame. -- **FileDataset**: References single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. - -In order to transit from the current Dataset design to typed Dataset, we will deprecate the following methods over time. - -## Which methods on Dataset class will be deprecated in upcoming releases? -Methods to be deprecated|Replacement in the new version| -----|-------- -[Dataset.get()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#get-workspace--name-none--id-none-)|[Dataset.get_by_name()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#get-by-name-workspace--name--version--latest--) -[Dataset.from_pandas_dataframe()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-pandas-dataframe-dataframe--path-none--in-memory-false-)|Creating a Dataset from in-memory DataFrame or local files will cause errors in training on remote compute. Therefore, the new Dataset design will only support creating Datasets from paths in datastores or public web urls. If you are using pandas, you can write the DataFrame into a parquet file, upload it to the cloud, and create a TabularDataset referencing the parquet file using [Dataset.Tabular.from_parquet_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-parquet-files-path--validate-true--include-path-false--set-column-types-none-) -[Dataset.from_delimited_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-delimited-files-path--separator------header--promoteheadersbehavior-all-files-have-same-headers--3---encoding--fileencoding-utf8--0---quoting-false--infer-column-types-true--skip-rows-0--skip-mode--skiplinesbehavior-no-rows--0---comment-none--include-path-false--archive-options-none--partition-format-none-)|[Dataset.Tabular.from_delimited_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-delimited-files-path--validate-true--include-path-false--infer-column-types-true--set-column-types-none--separator------header--promoteheadersbehavior-all-files-have-same-headers--3--) -[Dataset.auto_read_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#auto-read-files-path--include-path-false--partition-format-none-)|`auto_read_files` does not always produce results that match with users' expectation. To avoid confusion, this method is not introduced with TabularDataset for now. Please use [Dataset.Tabular.from_parquet_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-parquet-files-path--validate-true--include-path-false--set-column-types-none-) or [Dataset.Tabular.from_delimited_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-delimited-files-path--validate-true--include-path-false--infer-column-types-true--set-column-types-none--separator------header--promoteheadersbehavior-all-files-have-same-headers--3--) depending on your file format. -[Dataset.from_parquet_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-parquet-files-path--include-path-false--partition-format-none-)|[Dataset.Tabular.from_parquet_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-parquet-files-path--validate-true--include-path-false--set-column-types-none-) -[Dataset.from_sql_query()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-sql-query-data-source--query-)|[Dataset.Tabular.from_sql_query()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-sql-query-query--validate-true--set-column-types-none-) -[Dataset.from_excel_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-excel-files-path--sheet-name-none--use-column-headers-false--skip-rows-0--include-path-false--infer-column-types-true--partition-format-none-)|We will support creating a TabularDataset from Excel files in a future release. -[Dataset.from_json_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-json-files-path--encoding--fileencoding-utf8--0---flatten-nested-arrays-false--include-path-false--partition-format-none-)| [Dataset.Tabular.from_json_lines_files](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-json-lines-files-path--validate-true--include-path-false--set-column-types-none--partition-format-none-) -[Dataset.to_pandas_dataframe()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#to-pandas-dataframe--)|[TabularDataset.to_pandas_dataframe()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#to-pandas-dataframe--) -[Dataset.to_spark_dataframe()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#to-spark-dataframe--)|[TabularDataset.to_spark_dataframe()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#to-spark-dataframe--) -[Dataset.head(3)](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#head-count-)|[TabularDataset.take(3).to_pandas_dataframe()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#take-count-) -[Dataset.sample()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#sample-sample-strategy--arguments-)|[TabularDataset.take_sample()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#take-sample-probability--seed-none-) -[Dataset.from_binary_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-binary-files-path-)|`Dataset.File.from_files()` - - -## Why should I use the new Dataset API if I'm only dealing with tabular data? -The current Dataset will be kept around for backward compatibility, but we strongly encourage you to move to TabularDataset for the new capabilities listed below: - -- You are able to version and track the new typed Datasets. [Learn How](https://aka.ms/azureml/howto/versiondata) -- You are able to use TabularDatasets as automated ML input. [Learn How](https://aka.ms/automl-dataset) -- You are able to use the new typed Datasets as ScriptRun, Estimator, HyperDrive input. [Learn How](https://aka.ms/train-with-datasets) -- You are be able to use the new typed Datasets in Azure Machine Learning Pipelines. [Learn How](https://aka.ms/pl-datasets) - -## How to migrate registered Datasets to new typed Datasets? -We handled the migration for you. All legacy datasets are migrated to new typed Datasets automatically. To use registered datasets, simply call [Dataset.get_by_name](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#get-by-name-workspace--name--version--latest--). - -## How to provide feedback? -If you have any feedback about our product, or if there is any missing capability that is essential for you to use new Dataset API, please email us at [AskAzureMLData@microsoft.com](mailto:AskAzureMLData@microsoft.com). - - -![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataset-api-change-notice.png) \ No newline at end of file diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/prepare.py b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/prepare.py deleted file mode 100644 index b8abf0a6c..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/prepare.py +++ /dev/null @@ -1,36 +0,0 @@ -import os -import sys - - -def convert(imgf, labelf, outf, n): - f = open(imgf, "rb") - temp = open(labelf, "rb") - o = open(outf, "w") - - f.read(16) - temp.read(8) - images = [] - - for i in range(n): - image = [ord(temp.read(1))] - for j in range(28 * 28): - image.append(ord(f.read(1))) - images.append(image) - - for image in images: - o.write(",".join(str(pix) for pix in image) + "\n") - f.close() - o.close() - temp.close() - - -mounted_input_path = sys.argv[1] -mounted_output_path = sys.argv[2] -os.makedirs(mounted_output_path, exist_ok=True) - -convert(os.path.join(mounted_input_path, 'mnist-fashion/train-images-idx3-ubyte'), - os.path.join(mounted_input_path, 'mnist-fashion/train-labels-idx1-ubyte'), - os.path.join(mounted_output_path, 'mnist_train.csv'), 60000) -convert(os.path.join(mounted_input_path, 'mnist-fashion/t10k-images-idx3-ubyte'), - os.path.join(mounted_input_path, 'mnist-fashion/t10k-labels-idx1-ubyte'), - os.path.join(mounted_output_path, 'mnist_test.csv'), 10000) diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train.py b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train.py deleted file mode 100644 index b0215ad1b..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train.py +++ /dev/null @@ -1,120 +0,0 @@ -import keras -from keras.models import Sequential -from keras.layers import Dense, Dropout, Flatten -from keras.layers import Conv2D, MaxPooling2D -from keras.layers.normalization import BatchNormalization -from keras.utils import to_categorical -from keras.callbacks import Callback - -import numpy as np -import pandas as pd -import os -import matplotlib.pyplot as plt -from sklearn.model_selection import train_test_split -from azureml.core import Run - -# dataset object from the run -run = Run.get_context() -dataset = run.input_datasets['prepared_fashion_ds'] - -# split dataset into train and test set -(train_dataset, test_dataset) = dataset.random_split(percentage=0.8, seed=111) - -# load dataset into pandas dataframe -data_train = train_dataset.to_pandas_dataframe() -data_test = test_dataset.to_pandas_dataframe() - -img_rows, img_cols = 28, 28 -input_shape = (img_rows, img_cols, 1) - -X = np.array(data_train.iloc[:, 1:]) -y = to_categorical(np.array(data_train.iloc[:, 0])) - -# here we split validation data to optimiza classifier during training -X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=13) - -# test data -X_test = np.array(data_test.iloc[:, 1:]) -y_test = to_categorical(np.array(data_test.iloc[:, 0])) - - -X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1).astype('float32') / 255 -X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1).astype('float32') / 255 -X_val = X_val.reshape(X_val.shape[0], img_rows, img_cols, 1).astype('float32') / 255 - -batch_size = 256 -num_classes = 10 -epochs = 10 - -# construct neuron network -model = Sequential() -model.add(Conv2D(32, kernel_size=(3, 3), - activation='relu', - kernel_initializer='he_normal', - input_shape=input_shape)) -model.add(MaxPooling2D((2, 2))) -model.add(Dropout(0.25)) -model.add(Conv2D(64, (3, 3), activation='relu')) -model.add(MaxPooling2D(pool_size=(2, 2))) -model.add(Dropout(0.25)) -model.add(Conv2D(128, (3, 3), activation='relu')) -model.add(Dropout(0.4)) -model.add(Flatten()) -model.add(Dense(128, activation='relu')) -model.add(Dropout(0.3)) -model.add(Dense(num_classes, activation='softmax')) - -model.compile(loss=keras.losses.categorical_crossentropy, - optimizer=keras.optimizers.Adam(), - metrics=['accuracy']) - -# start an Azure ML run -run = Run.get_context() - - -class LogRunMetrics(Callback): - # callback at the end of every epoch - def on_epoch_end(self, epoch, log): - # log a value repeated which creates a list - run.log('Loss', log['loss']) - run.log('Accuracy', log['accuracy']) - - -history = model.fit(X_train, y_train, - batch_size=batch_size, - epochs=epochs, - verbose=1, - validation_data=(X_val, y_val), - callbacks=[LogRunMetrics()]) - -score = model.evaluate(X_test, y_test, verbose=0) - -# log a single value -run.log("Final test loss", score[0]) -print('Test loss:', score[0]) - -run.log('Final test accuracy', score[1]) -print('Test accuracy:', score[1]) - -plt.figure(figsize=(6, 3)) -plt.title('Fashion MNIST with Keras ({} epochs)'.format(epochs), fontsize=14) -plt.plot(history.history['accuracy'], 'b-', label='Accuracy', lw=4, alpha=0.5) -plt.plot(history.history['loss'], 'r--', label='Loss', lw=4, alpha=0.5) -plt.legend(fontsize=12) -plt.grid(True) - -# log an image -run.log_image('Loss v.s. Accuracy', plot=plt) - -# create a ./outputs/model folder in the compute target -# files saved in the "./outputs" folder are automatically uploaded into run history -os.makedirs('./outputs/model', exist_ok=True) - -# serialize NN architecture to JSON -model_json = model.to_json() -# save model JSON -with open('./outputs/model/model.json', 'w') as f: - f.write(model_json) -# save model weights -model.save_weights('./outputs/model/model.h5') -print("model saved in ./outputs/model folder") diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb deleted file mode 100644 index 648dfce42..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb +++ /dev/null @@ -1,500 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License [2017] Zalando SE, https://tech.zalando.com" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Build a simple ML pipeline for image classification\n", - "\n", - "## Introduction\n", - "This tutorial shows how to train a simple deep neural network using the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset and Keras on Azure Machine Learning. Fashion-MNIST is a dataset of Zalando's article images\u00e2\u20ac\u201dconsisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.\n", - "\n", - "Learn how to:\n", - "\n", - "> * Set up your development environment\n", - "> * Create the Fashion MNIST dataset\n", - "> * Create a machine learning pipeline to train a simple deep learning neural network on a remote cluster\n", - "> * Retrieve input datasets from the experiment and register the output model with datasets\n", - "\n", - "## Prerequisite:\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n", - " * install the latest version of AzureML SDK\n", - " * create a workspace and its configuration file (`config.json`)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up your development environment\n", - "\n", - "All the setup for your development work can be accomplished in a Python notebook. Setup includes:\n", - "\n", - "* Importing Python packages\n", - "* Connecting to a workspace to enable communication between your local computer and remote resources\n", - "* Creating an experiment to track all your runs\n", - "* Creating a remote compute target to use for training\n", - "\n", - "### Import packages\n", - "\n", - "Import Python packages you need in this session. Also display the Azure Machine Learning SDK version." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import azureml.core\n", - "from azureml.core import Workspace, Dataset, Datastore, ComputeTarget, Experiment, ScriptRunConfig\n", - "from azureml.pipeline.steps import PythonScriptStep\n", - "from azureml.pipeline.core import Pipeline\n", - "# check core SDK version number\n", - "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Connect to workspace\n", - "\n", - "Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **config.json** and loads the details into an object named `workspace`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# load workspace\n", - "workspace = Workspace.from_config()\n", - "print('Workspace name: ' + workspace.name, \n", - " 'Azure region: ' + workspace.location, \n", - " 'Subscription id: ' + workspace.subscription_id, \n", - " 'Resource group: ' + workspace.resource_group, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create experiment and a directory\n", - "\n", - "Create an experiment to track the runs in your workspace and a directory to deliver the necessary code from your computer to the remote resource." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# create an ML experiment\n", - "exp = Experiment(workspace=workspace, name='keras-mnist-fashion')\n", - "\n", - "# create a directory\n", - "script_folder = './keras-mnist-fashion'\n", - "os.makedirs(script_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach existing compute resource\n", - "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"gpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=workspace, name=cluster_name)\n", - " print('Found existing compute target')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n", - " max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(workspace, cluster_name, compute_config)\n", - "\n", - " # can poll for a minimum number of nodes and for a specific timeout. \n", - " # if no min node count is provided it uses the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the Fashion MNIST dataset\n", - "\n", - "By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_urls = ['https://data4mldemo6150520719.blob.core.windows.net/demo/mnist-fashion']\n", - "fashion_ds = Dataset.File.from_files(data_urls)\n", - "\n", - "# list the files referenced by fashion_ds\n", - "fashion_ds.to_path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build 2-step ML pipeline\n", - "\n", - "The [Azure Machine Learning Pipeline](https://docs.microsoft.com/azure/machine-learning/service/concept-ml-pipelines) enables data scientists to create and manage multiple simple and complex workflows concurrently. A typical pipeline would have multiple tasks to prepare data, train, deploy and evaluate models. Individual steps in the pipeline can make use of diverse compute options (for example: CPU for data preparation and GPU for training) and languages. [Learn More](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning-pipelines)\n", - "\n", - "\n", - "### Step 1: data preparation\n", - "\n", - "In step one, we will load the image and labels from Fashion MNIST dataset into mnist_train.csv and mnist_test.csv\n", - "\n", - "Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. Both mnist_train.csv and mnist_test.csv contain 785 columns. The first column consists of the class labels, which represent the article of clothing. The rest of the columns contain the pixel-values of the associated image." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Intermediate data (or output of a step) is represented by a `OutputFileDatasetConfig` object. preprared_fashion_ds is produced as the output of step 1, and used as the input of step 2. `OutputFileDatasetConfig` introduces a data dependency between steps, and creates an implicit execution order in the pipeline. You can register a `OutputFileDatasetConfig` as a dataset and version the output data automatically." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.data import OutputFileDatasetConfig\n", - "\n", - "# learn more about the output config\n", - "help(OutputFileDatasetConfig)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# write output to datastore under folder `outputdataset` and register it as a dataset after the experiment completes\n", - "# make sure the service principal in your datastore has blob data contributor role in order to write data back\n", - "datastore=workspace.get_default_datastore()\n", - "prepared_fashion_ds = OutputFileDatasetConfig(destination=(datastore, 'outputdataset/{run-id}')).register_on_complete(name='prepared_fashion_ds')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A **PythonScriptStep** is a basic, built-in step to run a Python Script on a compute target. It takes a script name and optionally other parameters like arguments for the script, compute target, inputs and outputs. If no compute target is specified, default compute target for the workspace is used. You can also use a [**RunConfiguration**](https://docs.microsoft.com/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) to specify requirements for the PythonScriptStep, such as conda dependencies and docker image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "prep_step = PythonScriptStep(name='prepare step',\n", - " script_name=\"prepare.py\",\n", - " # mount fashion_ds dataset to the compute_target\n", - " arguments=[fashion_ds.as_named_input('fashion_ds').as_mount(), prepared_fashion_ds],\n", - " source_directory=script_folder,\n", - " compute_target=compute_target,\n", - " allow_reuse=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step 2: train CNN with Keras\n", - "\n", - "Next, construct a ScriptRunConfig to configure the training run that trains a CNN model using Keras. It takes a dataset as the input." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile conda_dependencies.yml\n", - "\n", - "dependencies:\n", - "- python=3.6.2\n", - "- pip:\n", - " - azureml-core\n", - " - azureml-dataset-runtime\n", - " - keras==2.4.3\n", - " - tensorflow==2.4.3\n", - " - numpy\n", - " - scikit-learn\n", - " - pandas\n", - " - matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "keras_env = Environment.from_conda_specification(name = 'keras-env', file_path = './conda_dependencies.yml')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_src = ScriptRunConfig(source_directory=script_folder,\n", - " script='train.py',\n", - " compute_target=compute_target,\n", - " environment=keras_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pass the run configuration details into the PythonScriptStep." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_step = PythonScriptStep(name='train step',\n", - " arguments=[prepared_fashion_ds.read_delimited_files().as_input(name='prepared_fashion_ds')],\n", - " source_directory=train_src.source_directory,\n", - " script_name=train_src.script,\n", - " runconfig=train_src.run_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build the pipeline\n", - "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py).\n", - "\n", - "A pipeline is created with a list of steps and a workspace. Submit a pipeline using `submit`. When submit is called, a [PipelineRun](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# build pipeline & run experiment\n", - "pipeline = Pipeline(workspace, steps=[prep_step, train_step])\n", - "run = exp.submit(pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor the PipelineRun" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.find_step_run('train step')[0].get_metrics()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register the input dataset and the output model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Azure Machine Learning dataset makes it easy to trace how your data is used in ML. [Learn More](https://docs.microsoft.com/azure/machine-learning/service/how-to-version-track-datasets#track-datasets-in-experiments)
      \n", - "For each Machine Learning experiment, you can easily trace the datasets used as the input through `Run` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# get input datasets\n", - "prep_step = run.find_step_run('prepare step')[0]\n", - "inputs = prep_step.get_details()['inputDatasets']\n", - "input_dataset = inputs[0]['dataset']\n", - "\n", - "# list the files referenced by input_dataset\n", - "input_dataset.to_path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Register the input Fashion MNIST dataset with the workspace so that you can reuse it in other experiments or share it with your colleagues who have access to your workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fashion_ds = input_dataset.register(workspace = workspace,\n", - " name = 'fashion_ds',\n", - " description = 'image and label files from fashion mnist',\n", - " create_new_version = True)\n", - "fashion_ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Register the output model with dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.find_step_run('train step')[0].register_model(model_name = 'keras-model', model_path = 'outputs/model/', \n", - " datasets =[('train test data',fashion_ds)])" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sihhu" - } - ], - "category": "tutorial", - "compute": [ - "Remote" - ], - "datasets": [ - "Fashion MNIST" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Datasets with ML Pipeline", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "nteract": { - "version": "nteract-front-end@1.0.0" - }, - "star_tag": [ - "featured" - ], - "tags": [ - "Dataset", - "Pipeline", - "Estimator", - "ScriptRun" - ], - "task": "Train" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.yml b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.yml deleted file mode 100644 index f33e94748..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.yml +++ /dev/null @@ -1,6 +0,0 @@ -name: pipeline-for-image-classification -dependencies: -- pip: - - azureml-sdk - - pandas<=0.23.4 - - fuse diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.ipynb deleted file mode 100644 index c32bc978c..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.ipynb +++ /dev/null @@ -1,318 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/datasets-tutorial/scriptun-with-data-input-output.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to use configure a training run with data input and output\n", - "\n", - "This notebook shows how to use [ScriptRunConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.scriptrunconfig?view=azure-ml-py) with input and output. A run submitted with ScriptRunConfig represents a single trial in an experiment. Submitting the run returns a ScriptRun object, which can be used to monitor the asynchronous execution of the run, log metrics and store output of the run, and analyze results and access artifacts generated by the run.\n", - "\n", - "\n", - "## Prerequisite:\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://aka.ms/pl-config) to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (`config.json`)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_D2_V2` GPU VMs. This process is broken down into 3 steps:\n", - "1. create the configuration (this step is local and only takes a second)\n", - "2. create the cluster (this step will take about **20 seconds**)\n", - "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "cluster_name = \"cpu-cluster\"\n", - "\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n", - " print('Found existing compute target')\n", - "except ComputeTargetException:\n", - " print('Creating a new compute target...')\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n", - "\n", - " # can poll for a minimum number of nodes and for a specific timeout. \n", - " # if no min node count is provided it uses the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - "\n", - "# use get_status() to get a detailed status for the current cluster. \n", - "print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'cpu-cluster' of type `AmlCompute`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use a simple script\n", - "We have already created a simple \"hello world\" script. This is the script that we will submit through the [ScriptRunConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.script_run_config.scriptrunconfig?view=azure-ml-py). It reads iris dataset as input, and write it out to `outputdataset` folder in default blob datastore. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "source_directory = 'script_run'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile $source_directory/dummy_train.py\n", - "\n", - "# Copyright (c) Microsoft Corporation. All rights reserved.\n", - "# Licensed under the MIT License.\n", - "import sys\n", - "import os\n", - "\n", - "print(\"*********************************************************\")\n", - "print(\"Hello Azure ML!\")\n", - "\n", - "mounted_input_path = sys.argv[1]\n", - "mounted_output_path = sys.argv[2]\n", - "\n", - "print(\"Argument 1: %s\" % mounted_input_path)\n", - "print(\"Argument 2: %s\" % mounted_output_path)\n", - " \n", - "with open(mounted_input_path, 'r') as f:\n", - " content = f.read()\n", - " with open(os.path.join(mounted_output_path, 'output.csv'), 'w') as fw:\n", - " fw.write(content)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Every workspace comes with a default datastore (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and create dataset from it. We will now upload the Iris data to the default datastore (blob) within your workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def_blob_store = ws.get_default_datastore()\n", - "def_blob_store.upload_files(files = ['iris.csv'],\n", - " target_path = 'script-run/',\n", - " overwrite = True,\n", - " show_progress = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are ready to define the input and output of your script. They can be passed in via `arguments`, which is a list of command-line arguments to pass to the training script specified in `script`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "from azureml.data import OutputFileDatasetConfig\n", - "\n", - "input_data = Dataset.File.from_files(def_blob_store.path('script-run/iris.csv')).as_named_input('input').as_mount()\n", - "\n", - "# output is configured to write the result back to def_blob_store, under \"sample/outputdataset\" folder\n", - "# learn more about options to configure the output, run 'help(OutputFileDatasetConfig)'\n", - "output = OutputFileDatasetConfig(destination=(def_blob_store, 'sample/outputdataset'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "myenv = Environment(\"myenv\")\n", - "\n", - "myenv.docker.enabled = True\n", - "myenv.python.conda_dependencies = CondaDependencies.create(pip_packages=['azureml-sdk>=1.12.0'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=source_directory, \n", - " script='dummy_train.py', \n", - " # to mount the dataset on the remote compute and pass the mounted path as an argument to the training script\n", - " arguments =[input_data, output],\n", - " compute_target=compute_target,\n", - " environment=myenv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and Submit the Experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "exp = Experiment(ws, 'ScriptRun_sample')\n", - "run = exp.submit(config=src)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View Run Details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output=True)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sihhu" - } - ], - "category": "tutorial", - "compute": [ - "AML Compute" - ], - "datasets": [ - "Custom" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "How to use ScriptRun with data input and output", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "order_index": 7, - "star_tag": [ - "None" - ], - "tags": [ - "Dataset", - "ScriptRun" - ], - "task": "Demonstrates the use of Scriptrun with datasets" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.yml b/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.yml deleted file mode 100644 index 87dc3b4c7..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: how-to-use-scriptrun -dependencies: -- pip: - - azureml-sdk diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/iris.csv b/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/iris.csv deleted file mode 100644 index 21ae19637..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/iris.csv +++ /dev/null @@ -1,151 +0,0 @@ -sepal_length,sepal_width,petal_length,petal_width,species -5.1,3.5,1.4,0.2,Iris-setosa -4.9,3,1.4,0.2,Iris-setosa -4.7,3.2,1.3,0.2,Iris-setosa -4.6,3.1,1.5,0.2,Iris-setosa -5,3.6,1.4,0.2,Iris-setosa -5.4,3.9,1.7,0.4,Iris-setosa -4.6,3.4,1.4,0.3,Iris-setosa -5,3.4,1.5,0.2,Iris-setosa -4.4,2.9,1.4,0.2,Iris-setosa -4.9,3.1,1.5,0.1,Iris-setosa -5.4,3.7,1.5,0.2,Iris-setosa -4.8,3.4,1.6,0.2,Iris-setosa -4.8,3,1.4,0.1,Iris-setosa -4.3,3,1.1,0.1,Iris-setosa -5.8,4,1.2,0.2,Iris-setosa -5.7,4.4,1.5,0.4,Iris-setosa -5.4,3.9,1.3,0.4,Iris-setosa -5.1,3.5,1.4,0.3,Iris-setosa -5.7,3.8,1.7,0.3,Iris-setosa -5.1,3.8,1.5,0.3,Iris-setosa -5.4,3.4,1.7,0.2,Iris-setosa -5.1,3.7,1.5,0.4,Iris-setosa -4.6,3.6,1,0.2,Iris-setosa -5.1,3.3,1.7,0.5,Iris-setosa -4.8,3.4,1.9,0.2,Iris-setosa -5,3,1.6,0.2,Iris-setosa -5,3.4,1.6,0.4,Iris-setosa -5.2,3.5,1.5,0.2,Iris-setosa -5.2,3.4,1.4,0.2,Iris-setosa -4.7,3.2,1.6,0.2,Iris-setosa -4.8,3.1,1.6,0.2,Iris-setosa -5.4,3.4,1.5,0.4,Iris-setosa -5.2,4.1,1.5,0.1,Iris-setosa -5.5,4.2,1.4,0.2,Iris-setosa -4.9,3.1,1.5,0.1,Iris-setosa -5,3.2,1.2,0.2,Iris-setosa -5.5,3.5,1.3,0.2,Iris-setosa -4.9,3.1,1.5,0.1,Iris-setosa -4.4,3,1.3,0.2,Iris-setosa -5.1,3.4,1.5,0.2,Iris-setosa -5,3.5,1.3,0.3,Iris-setosa -4.5,2.3,1.3,0.3,Iris-setosa -4.4,3.2,1.3,0.2,Iris-setosa -5,3.5,1.6,0.6,Iris-setosa -5.1,3.8,1.9,0.4,Iris-setosa -4.8,3,1.4,0.3,Iris-setosa -5.1,3.8,1.6,0.2,Iris-setosa -4.6,3.2,1.4,0.2,Iris-setosa -5.3,3.7,1.5,0.2,Iris-setosa -5,3.3,1.4,0.2,Iris-setosa -7,3.2,4.7,1.4,Iris-versicolor -6.4,3.2,4.5,1.5,Iris-versicolor -6.9,3.1,4.9,1.5,Iris-versicolor -5.5,2.3,4,1.3,Iris-versicolor -6.5,2.8,4.6,1.5,Iris-versicolor -5.7,2.8,4.5,1.3,Iris-versicolor -6.3,3.3,4.7,1.6,Iris-versicolor -4.9,2.4,3.3,1,Iris-versicolor -6.6,2.9,4.6,1.3,Iris-versicolor -5.2,2.7,3.9,1.4,Iris-versicolor -5,2,3.5,1,Iris-versicolor -5.9,3,4.2,1.5,Iris-versicolor -6,2.2,4,1,Iris-versicolor -6.1,2.9,4.7,1.4,Iris-versicolor -5.6,2.9,3.6,1.3,Iris-versicolor -6.7,3.1,4.4,1.4,Iris-versicolor -5.6,3,4.5,1.5,Iris-versicolor -5.8,2.7,4.1,1,Iris-versicolor -6.2,2.2,4.5,1.5,Iris-versicolor -5.6,2.5,3.9,1.1,Iris-versicolor -5.9,3.2,4.8,1.8,Iris-versicolor -6.1,2.8,4,1.3,Iris-versicolor -6.3,2.5,4.9,1.5,Iris-versicolor -6.1,2.8,4.7,1.2,Iris-versicolor -6.4,2.9,4.3,1.3,Iris-versicolor -6.6,3,4.4,1.4,Iris-versicolor -6.8,2.8,4.8,1.4,Iris-versicolor -6.7,3,5,1.7,Iris-versicolor -6,2.9,4.5,1.5,Iris-versicolor -5.7,2.6,3.5,1,Iris-versicolor -5.5,2.4,3.8,1.1,Iris-versicolor -5.5,2.4,3.7,1,Iris-versicolor -5.8,2.7,3.9,1.2,Iris-versicolor -6,2.7,5.1,1.6,Iris-versicolor -5.4,3,4.5,1.5,Iris-versicolor -6,3.4,4.5,1.6,Iris-versicolor -6.7,3.1,4.7,1.5,Iris-versicolor -6.3,2.3,4.4,1.3,Iris-versicolor -5.6,3,4.1,1.3,Iris-versicolor -5.5,2.5,4,1.3,Iris-versicolor -5.5,2.6,4.4,1.2,Iris-versicolor -6.1,3,4.6,1.4,Iris-versicolor -5.8,2.6,4,1.2,Iris-versicolor -5,2.3,3.3,1,Iris-versicolor -5.6,2.7,4.2,1.3,Iris-versicolor -5.7,3,4.2,1.2,Iris-versicolor -5.7,2.9,4.2,1.3,Iris-versicolor -6.2,2.9,4.3,1.3,Iris-versicolor -5.1,2.5,3,1.1,Iris-versicolor -5.7,2.8,4.1,1.3,Iris-versicolor -6.3,3.3,6,2.5,Iris-virginica -5.8,2.7,5.1,1.9,Iris-virginica -7.1,3,5.9,2.1,Iris-virginica -6.3,2.9,5.6,1.8,Iris-virginica -6.5,3,5.8,2.2,Iris-virginica -7.6,3,6.6,2.1,Iris-virginica -4.9,2.5,4.5,1.7,Iris-virginica -7.3,2.9,6.3,1.8,Iris-virginica -6.7,2.5,5.8,1.8,Iris-virginica -7.2,3.6,6.1,2.5,Iris-virginica -6.5,3.2,5.1,2,Iris-virginica -6.4,2.7,5.3,1.9,Iris-virginica -6.8,3,5.5,2.1,Iris-virginica -5.7,2.5,5,2,Iris-virginica -5.8,2.8,5.1,2.4,Iris-virginica -6.4,3.2,5.3,2.3,Iris-virginica -6.5,3,5.5,1.8,Iris-virginica -7.7,3.8,6.7,2.2,Iris-virginica -7.7,2.6,6.9,2.3,Iris-virginica -6,2.2,5,1.5,Iris-virginica -6.9,3.2,5.7,2.3,Iris-virginica -5.6,2.8,4.9,2,Iris-virginica -7.7,2.8,6.7,2,Iris-virginica -6.3,2.7,4.9,1.8,Iris-virginica -6.7,3.3,5.7,2.1,Iris-virginica -7.2,3.2,6,1.8,Iris-virginica -6.2,2.8,4.8,1.8,Iris-virginica -6.1,3,4.9,1.8,Iris-virginica -6.4,2.8,5.6,2.1,Iris-virginica -7.2,3,5.8,1.6,Iris-virginica -7.4,2.8,6.1,1.9,Iris-virginica -7.9,3.8,6.4,2,Iris-virginica -6.4,2.8,5.6,2.2,Iris-virginica -6.3,2.8,5.1,1.5,Iris-virginica -6.1,2.6,5.6,1.4,Iris-virginica -7.7,3,6.1,2.3,Iris-virginica -6.3,3.4,5.6,2.4,Iris-virginica -6.4,3.1,5.5,1.8,Iris-virginica -6,3,4.8,1.8,Iris-virginica -6.9,3.1,5.4,2.1,Iris-virginica -6.7,3.1,5.6,2.4,Iris-virginica -6.9,3.1,5.1,2.3,Iris-virginica -5.8,2.7,5.1,1.9,Iris-virginica -6.8,3.2,5.9,2.3,Iris-virginica -6.7,3.3,5.7,2.5,Iris-virginica -6.7,3,5.2,2.3,Iris-virginica -6.3,2.5,5,1.9,Iris-virginica -6.5,3,5.2,2,Iris-virginica -6.2,3.4,5.4,2.3,Iris-virginica -5.9,3,5.1,1.8,Iris-virginica diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/script_run/dummy_train.py b/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/script_run/dummy_train.py deleted file mode 100644 index 810c8757a..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/script_run/dummy_train.py +++ /dev/null @@ -1,19 +0,0 @@ - -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. -import sys -import os - -print("*********************************************************") -print("Hello Azure ML!") - -mounted_input_path = sys.argv[1] -mounted_output_path = sys.argv[2] - -print("Argument 1: %s" % mounted_input_path) -print("Argument 2: %s" % mounted_output_path) - -with open(mounted_input_path, 'r') as f: - content = f.read() - with open(os.path.join(mounted_output_path, 'output.csv'), 'w') as fw: - fw.write(content) diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb deleted file mode 100644 index 946b2d9c4..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb +++ /dev/null @@ -1,582 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tabular Time Series Related API Demo with NOAA Weather Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.
      \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook, you will learn how to use the Tabular Time Series related API to filter the data by time windows for sample data uploaded to Azure blob storage. \n", - "\n", - "The detailed APIs to be demoed in this script are:\n", - "- Create Tabular Dataset instance\n", - "- Assign timestamp column and partition timestamp column for Tabular Dataset to activate Time Series related APIs\n", - "- Clear timestamp column and partition timestamp column\n", - "- Filter in data before a specific time\n", - "- Filter in data after a specific time\n", - "- Filter in data in a specific time range\n", - "- Filter in data for recent time range\n", - "\n", - "Besides above APIs, you'll also see:\n", - "- Create and load a Workspace\n", - "- Load weather data into Azure blob storage\n", - "- Create and register weather data as a Tabular dataset\n", - "- Re-load Tabular Dataset from your Workspace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Dependencies\n", - "\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, run the cells below to install the Azure Machine Learning Python SDK and create an Azure ML Workspace that's required for this demo." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare Environment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Print out your version of the Azure ML Python SDK. Version 1.0.60 or above is required for TabularDataset with timeseries attribute. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "azureml.data.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Packages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import packages\n", - "from datetime import datetime, timedelta\n", - "\n", - "from azureml.core import Dataset, Workspace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up Configuraton and Create Azure ML Workspace\n", - "\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) first if you haven't already to establish your connection to the Azure ML Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "dstore = ws.get_default_datastore()\n", - "\n", - "dset_name = 'weather-data-florida'\n", - "\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, dstore.name, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Data to Blob Storage\n", - "\n", - "This demo uses 2019 weather data under within weather-data folder. You can replace this data with your own." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Upload data to blob storage so it can be used as a Dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dstore.upload('weather-data', dset_name, overwrite=True, show_progress=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create & Register Tabular Dataset with time-series trait from Blob\n", - "\n", - "The API on Tabular datasets with time-series trait is specially designed to handle Tabular time-series data and time related operations more efficiently. By registering your time-series dataset, you are publishing your dataset to your workspace so that it is accessible to anyone with the same subscription id. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create Tabular Dataset instance from blob storage datapath.\n", - "\n", - "**TIP:** you can set virtual columns in the partition_format. I.e. if you partition the weather data by state and city, the path can be '/{STATE}/{CITY}/{partition_time:yyy/MM}/data.parquet'. STATE and CITY would then appear as virtual columns in the dataset, allowing for efficient filtering by these timestamps. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore_path = [(dstore, dset_name + '/*/*/data.parquet')]\n", - "dataset = Dataset.Tabular.from_parquet_files(path=datastore_path, partition_format = dset_name + '/{partition_time:yyyy/MM}/data.parquet')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Assign \"datetime\" column as timestamp and \"partition_time\" from folder path as partition_timestamp for Tabular Dataset to activate Time Series related APIs. The column to be assigned should be a Date type, otherwise the assigning will fail." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tsd = dataset.with_timestamp_columns(timestamp='datetime', partition_timestamp='partition_time')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Register the dataset for easy access from anywhere in Azure ML and to keep track of versions, lineage. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# register dataset to Workspace\n", - "registered_ds = tsd.register(ws, dset_name, create_new_version=True, description='Data for Tabular Dataset with time-series trait demo.', tags={ 'type': 'TabularDataset' })" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reload the Dataset from Workspace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# get dataset by dataset name\n", - "tsd = Dataset.get_by_name(ws, name=dset_name)\n", - "tsd.to_pandas_dataframe().head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Filter Data by Time Windows\n", - "\n", - "Once your data has been loaded into the notebook, you can query by time using the time_before(), time_after(), time_between(), and time_recent() functions.The filter is optimized to only load those data files within the partition_timestamp range when partition_timestamp is specified.\n", - "\n", - "include_boundary is default to be true for all the time series related filters, please pass include_boundary=False to exclude boundary." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Before Time Input" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# select data that occurs before a specified date\n", - "tsd2 = tsd.time_before(datetime(2019, 6, 12))\n", - "tsd2.to_pandas_dataframe().tail(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## After Time Input" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# select data that occurs after a specified date\n", - "tsd2 = tsd.time_after(datetime(2019, 5, 30))\n", - "tsd2.to_pandas_dataframe().head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Before & After Time Inputs\n", - "\n", - "You can chain time functions together." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# select data that occurs within a given time range\n", - "tsd2 = tsd.time_after(datetime(2019, 1, 1)).time_before(datetime(2019, 1, 10))\n", - "tsd2.to_pandas_dataframe().head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Time Range Input" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# another way to select data that occurs within a given time range\n", - "tsd2 = tsd.time_between(start_time=datetime(2019, 1, 31, 23, 59, 59), end_time=datetime(2019, 2, 7))\n", - "tsd2.to_pandas_dataframe().head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Recent Input" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This function takes in a datetime.timedelta and returns a dataset containing the data from datetime.now()-timedelta() to datetime.now()." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**NOTE:** This will return an empty dataframe there is no data within the last 2 days." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tsd2 = tsd.time_recent(timedelta(days=2))\n", - "tsd2.to_pandas_dataframe().tail(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Drop and keep columns\n", - "\n", - "You can also choose to drop or keep certain columns." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Drop Columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If a timeseries column is dropped, the corresponding capabilities will be dropped for the returned dataset.
      " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tsd2 = tsd.drop_columns(columns=['snowDepth', 'version', 'datetime'])\n", - "tsd2.take(5).to_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The exception is expected because dataset loses timeseries capabilities to do time travel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.exceptions import DatasetTimestampMissingError\n", - "\n", - "try:\n", - " tsd2.time_before(datetime(2019, 6, 12)).to_pandas_dataframe().tail(5)\n", - "except DatasetTimestampMissingError as e:\n", - " print('Expected exception : {}'.format(str(e)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Drop will return dataset with timeseries capabilities if modify column list to exclude timestamp columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tsd2 = tsd.drop_columns(columns=['snowDepth', 'version', 'upload_date'])\n", - "tsd2.take(5).to_pandas_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tsd2.time_before(datetime(2019, 6, 12)).to_pandas_dataframe().tail(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Keep Columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If a timeseries column is not included, the timeseries capabilities will be dropped for the returned dataset.
      " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tsd2 = tsd.keep_columns(columns=['snowDepth'], validate=False)\n", - "tsd2.to_pandas_dataframe().tail()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The exception is expected because dataset loses timeseries capabilities to do time travel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " tsd2.time_before(datetime(2019, 6, 12)).to_pandas_dataframe().tail(5)\n", - "except DatasetTimestampMissingError as e:\n", - " print('Expected exception : {}'.format(str(e)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Keep will return dataset with timeseries capabilities if modify column list to include timestamp columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tsd2 = tsd.keep_columns(columns=['snowDepth', 'datetime', 'partition_time'], validate=False)\n", - "tsd2.to_pandas_dataframe().tail()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tsd2.time_before(datetime(2019, 6, 12)).to_pandas_dataframe().tail(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resetting Timestamp Columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rules for reseting are:\n", - "- You cannot assign 'None' to timestamp while assign a valid column name to partition_timestamp because partition_timestamp is optional while timestamp is mandatory for Tabular time series data.\n", - "- If you assign 'None' to timestamp, then both timestamp and partition_timestamp will all be cleared.\n", - "- If you assign only 'None' to partition_timestamp, then only partition_timestamp will be cleared." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.exceptions import UserErrorException\n", - "# Illegal clearing, exception is expected.\n", - "try:\n", - " tsd2 = tsd.with_timestamp_columns(timestamp=None, partition_timestamp='partition_time')\n", - "except UserErrorException as e:\n", - " print('Cleaning not allowed because {}'.format(str(e)))\n", - "\n", - "# clear both\n", - "tsd2 = tsd.with_timestamp_columns(timestamp=None, partition_timestamp=None)\n", - "print('after clean both with None/None, timestamp columns are: {}'.format(tsd2.timestamp_columns))\n", - "\n", - "# clear partition_timestamp only and assign 'datetime' as timestamp column\n", - "tsd2 = tsd2.with_timestamp_columns(timestamp='datetime', partition_timestamp=None)\n", - "print('after clean partition timestamp column, timestamp columns are: {}'.format(tsd2.timestamp_columns))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/datasets-tutorial/datasets-tutorial.png)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jamgan" - } - ], - "category": "tutorial", - "compute": [ - "Local" - ], - "datasets": [ - "NOAA" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Filtering data using Tabular Timeseiries Dataset related API", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.", - "star_tag": [ - "featured" - ], - "tags": [ - "Dataset", - "Tabular Timeseries" - ], - "task": "Filtering" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.yml b/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.yml deleted file mode 100644 index af9acab32..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.yml +++ /dev/null @@ -1,5 +0,0 @@ -name: tabular-timeseries-dataset-filtering -dependencies: -- pip: - - azureml-sdk - - pandas<=0.23.4 diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/weather-data/2019/01/data.parquet b/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/weather-data/2019/01/data.parquet deleted 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7635534c6..000000000 Binary files a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/weather-data/2019/10/data.parquet and /dev/null differ diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-dataset/iris.csv b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-dataset/iris.csv deleted file mode 100644 index 21ae19637..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-dataset/iris.csv +++ /dev/null @@ -1,151 +0,0 @@ -sepal_length,sepal_width,petal_length,petal_width,species -5.1,3.5,1.4,0.2,Iris-setosa -4.9,3,1.4,0.2,Iris-setosa -4.7,3.2,1.3,0.2,Iris-setosa -4.6,3.1,1.5,0.2,Iris-setosa -5,3.6,1.4,0.2,Iris-setosa -5.4,3.9,1.7,0.4,Iris-setosa -4.6,3.4,1.4,0.3,Iris-setosa -5,3.4,1.5,0.2,Iris-setosa -4.4,2.9,1.4,0.2,Iris-setosa -4.9,3.1,1.5,0.1,Iris-setosa -5.4,3.7,1.5,0.2,Iris-setosa -4.8,3.4,1.6,0.2,Iris-setosa -4.8,3,1.4,0.1,Iris-setosa 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-5.7,2.6,3.5,1,Iris-versicolor -5.5,2.4,3.8,1.1,Iris-versicolor -5.5,2.4,3.7,1,Iris-versicolor -5.8,2.7,3.9,1.2,Iris-versicolor -6,2.7,5.1,1.6,Iris-versicolor -5.4,3,4.5,1.5,Iris-versicolor -6,3.4,4.5,1.6,Iris-versicolor -6.7,3.1,4.7,1.5,Iris-versicolor -6.3,2.3,4.4,1.3,Iris-versicolor -5.6,3,4.1,1.3,Iris-versicolor -5.5,2.5,4,1.3,Iris-versicolor -5.5,2.6,4.4,1.2,Iris-versicolor -6.1,3,4.6,1.4,Iris-versicolor -5.8,2.6,4,1.2,Iris-versicolor -5,2.3,3.3,1,Iris-versicolor -5.6,2.7,4.2,1.3,Iris-versicolor -5.7,3,4.2,1.2,Iris-versicolor -5.7,2.9,4.2,1.3,Iris-versicolor -6.2,2.9,4.3,1.3,Iris-versicolor -5.1,2.5,3,1.1,Iris-versicolor -5.7,2.8,4.1,1.3,Iris-versicolor -6.3,3.3,6,2.5,Iris-virginica -5.8,2.7,5.1,1.9,Iris-virginica -7.1,3,5.9,2.1,Iris-virginica -6.3,2.9,5.6,1.8,Iris-virginica -6.5,3,5.8,2.2,Iris-virginica -7.6,3,6.6,2.1,Iris-virginica -4.9,2.5,4.5,1.7,Iris-virginica -7.3,2.9,6.3,1.8,Iris-virginica -6.7,2.5,5.8,1.8,Iris-virginica -7.2,3.6,6.1,2.5,Iris-virginica -6.5,3.2,5.1,2,Iris-virginica -6.4,2.7,5.3,1.9,Iris-virginica -6.8,3,5.5,2.1,Iris-virginica -5.7,2.5,5,2,Iris-virginica -5.8,2.8,5.1,2.4,Iris-virginica -6.4,3.2,5.3,2.3,Iris-virginica -6.5,3,5.5,1.8,Iris-virginica -7.7,3.8,6.7,2.2,Iris-virginica -7.7,2.6,6.9,2.3,Iris-virginica -6,2.2,5,1.5,Iris-virginica -6.9,3.2,5.7,2.3,Iris-virginica -5.6,2.8,4.9,2,Iris-virginica -7.7,2.8,6.7,2,Iris-virginica -6.3,2.7,4.9,1.8,Iris-virginica -6.7,3.3,5.7,2.1,Iris-virginica -7.2,3.2,6,1.8,Iris-virginica -6.2,2.8,4.8,1.8,Iris-virginica -6.1,3,4.9,1.8,Iris-virginica -6.4,2.8,5.6,2.1,Iris-virginica -7.2,3,5.8,1.6,Iris-virginica -7.4,2.8,6.1,1.9,Iris-virginica -7.9,3.8,6.4,2,Iris-virginica -6.4,2.8,5.6,2.2,Iris-virginica -6.3,2.8,5.1,1.5,Iris-virginica -6.1,2.6,5.6,1.4,Iris-virginica -7.7,3,6.1,2.3,Iris-virginica -6.3,3.4,5.6,2.4,Iris-virginica -6.4,3.1,5.5,1.8,Iris-virginica -6,3,4.8,1.8,Iris-virginica -6.9,3.1,5.4,2.1,Iris-virginica -6.7,3.1,5.6,2.4,Iris-virginica -6.9,3.1,5.1,2.3,Iris-virginica -5.8,2.7,5.1,1.9,Iris-virginica -6.8,3.2,5.9,2.3,Iris-virginica -6.7,3.3,5.7,2.5,Iris-virginica -6.7,3,5.2,2.3,Iris-virginica -6.3,2.5,5,1.9,Iris-virginica -6.5,3,5.2,2,Iris-virginica -6.2,3.4,5.4,2.3,Iris-virginica -5.9,3,5.1,1.8,Iris-virginica diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb deleted file mode 100644 index cd5c5b0c0..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb +++ /dev/null @@ -1,696 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train with Azure Machine Learning datasets\n", - "Datasets are categorized into TabularDataset and FileDataset based on how users consume them in training. \n", - "* A TabularDataset represents data in a tabular format by parsing the provided file or list of files. TabularDataset can be created from csv, tsv, parquet files, SQL query results etc. For the complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference). It provides you with the ability to materialize the data into a pandas DataFrame.\n", - "* A FileDataset references single or multiple files in your datastores or public urls. This provides you with the ability to download or mount the files to your compute. The files can be of any format, which enables a wider range of machine learning scenarios including deep learning.\n", - "\n", - "In this tutorial, you will learn how to train with Azure Machine Learning datasets:\n", - "\n", - "☑ Use datasets directly in your training script\n", - "\n", - "☑ Use datasets to mount files to a remote compute" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already established your connection to the AzureML Workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print('SDK version:', azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize Workspace\n", - "\n", - "Initialize a workspace object from persisted configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Experiment\n", - "\n", - "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_name = 'train-with-datasets'\n", - "\n", - "from azureml.core import Experiment\n", - "exp = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create or Attach existing compute resource\n", - "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "import os\n", - "\n", - "# choose a name for your cluster\n", - "compute_name = os.environ.get('AML_COMPUTE_CLUSTER_NAME', 'cpu-cluster')\n", - "compute_min_nodes = os.environ.get('AML_COMPUTE_CLUSTER_MIN_NODES', 0)\n", - "compute_max_nodes = os.environ.get('AML_COMPUTE_CLUSTER_MAX_NODES', 4)\n", - "\n", - "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n", - "vm_size = os.environ.get('AML_COMPUTE_CLUSTER_SKU', 'STANDARD_D2_V2')\n", - "\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " print('found compute target. just use it. ' + compute_name)\n", - "else:\n", - " print('creating a new compute target...')\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size=vm_size,\n", - " min_nodes=compute_min_nodes, \n", - " max_nodes=compute_max_nodes)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - " \n", - " # can poll for a minimum number of nodes and for a specific timeout. \n", - " # if no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - " # For a more detailed view of current AmlCompute status, use get_status()\n", - " print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You now have the necessary packages and compute resources to train a model in the cloud.\n", - "## Use datasets directly in training\n", - "\n", - "### Create a TabularDataset\n", - "By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. \n", - "\n", - "Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and create dataset from it. We will now upload the [Iris data](./train-dataset/Iris.csv) to the default datastore (blob) within your workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore = ws.get_default_datastore()\n", - "datastore.upload_files(files = ['./train-dataset/iris.csv'],\n", - " target_path = 'train-dataset/tabular/',\n", - " overwrite = True,\n", - " show_progress = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we will create an unregistered TabularDataset pointing to the path in the datastore. You can also create a dataset from multiple paths. [learn more](https://aka.ms/azureml/howto/createdatasets) \n", - "\n", - "[TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a Pandas or Spark DataFrame. You can create a TabularDataset object from .csv, .tsv, and parquet files, and from SQL query results. For a complete list, see [TabularDatasetFactory](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py) class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "dataset-remarks-tabular-sample" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'train-dataset/tabular/iris.csv')])\n", - "\n", - "# preview the first 3 rows of the dataset\n", - "dataset.take(3).to_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a training script\n", - "\n", - "To submit the job to the cluster, first create a training script. Run the following code to create the training script called `train_titanic.py` in the script_folder. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "script_folder = os.path.join(os.getcwd(), 'train-dataset')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile $script_folder/train_iris.py\n", - "\n", - "import os\n", - "\n", - "from azureml.core import Dataset, Run\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "# sklearn.externals.joblib is removed in 0.23\n", - "from sklearn import __version__ as sklearnver\n", - "from packaging.version import Version\n", - "if Version(sklearnver) < Version(\"0.23.0\"):\n", - " from sklearn.externals import joblib\n", - "else:\n", - " import joblib\n", - "\n", - "run = Run.get_context()\n", - "# get input dataset by name\n", - "dataset = run.input_datasets['iris']\n", - "\n", - "df = dataset.to_pandas_dataframe()\n", - "\n", - "x_col = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n", - "y_col = ['species']\n", - "x_df = df.loc[:, x_col]\n", - "y_df = df.loc[:, y_col]\n", - "\n", - "#dividing X,y into train and test data\n", - "x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=223)\n", - "\n", - "data = {'train': {'X': x_train, 'y': y_train},\n", - "\n", - " 'test': {'X': x_test, 'y': y_test}}\n", - "\n", - "clf = DecisionTreeClassifier().fit(data['train']['X'], data['train']['y'])\n", - "model_file_name = 'decision_tree.pkl'\n", - "\n", - "print('Accuracy of Decision Tree classifier on training set: {:.2f}'.format(clf.score(x_train, y_train)))\n", - "print('Accuracy of Decision Tree classifier on test set: {:.2f}'.format(clf.score(x_test, y_test)))\n", - "\n", - "os.makedirs('./outputs', exist_ok=True)\n", - "with open(model_file_name, 'wb') as file:\n", - " joblib.dump(value=clf, filename='outputs/' + model_file_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an environment\n", - "\n", - "Define a conda environment YAML file with your training script dependencies and create an Azure ML environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile conda_dependencies.yml\n", - "\n", - "dependencies:\n", - "- python=3.6.2\n", - "- scikit-learn\n", - "- pip:\n", - " - azureml-defaults\n", - " - packaging" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "\n", - "sklearn_env = Environment.from_conda_specification(name = 'sklearn-env', file_path = './conda_dependencies.yml')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure training run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A ScriptRunConfig object specifies the configuration details of your training job, including your training script, environment to use, and the compute target to run on. Specify the following in your script run configuration:\n", - "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n", - "* The training script name, train_iris.py\n", - "* The input dataset for training, passed as an argument to your training script. `as_named_input()` is required so that the input dataset can be referenced by the assigned name in your training script. \n", - "* The compute target. In this case you will use the AmlCompute you created\n", - "* The environment definition for the experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='train_iris.py',\n", - " arguments=[dataset.as_named_input('iris')],\n", - " compute_target=compute_target,\n", - " environment=sklearn_env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit job to run\n", - "Submit the ScriptRunConfig to the Azure ML experiment to kick off the execution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = exp.submit(src)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "\n", - "# monitor the run\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use datasets to mount files to a remote compute\n", - "\n", - "You can use the `Dataset` object to mount or download files referred by it. When you mount a file system, you attach that file system to a directory (mount point) and make it available to the system. Because mounting load files at the time of processing, it is usually faster than download.
      \n", - "Note: mounting is only available for Linux-based compute (DSVM/VM, AMLCompute, HDInsights)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Upload data files into datastore\n", - "We will first load diabetes data from `scikit-learn` to the train-dataset folder." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import load_diabetes\n", - "import numpy as np\n", - "\n", - "training_data = load_diabetes()\n", - "np.save(file='train-dataset/features.npy', arr=training_data['data'])\n", - "np.save(file='train-dataset/labels.npy', arr=training_data['target'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's upload the 2 files into the default datastore under a path named `diabetes`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datastore.upload_files(['train-dataset/features.npy', 'train-dataset/labels.npy'], target_path='diabetes', overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a FileDataset\n", - "\n", - "[FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.file_dataset.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public URLs. Using this method, you can download or mount the files to your compute as a FileDataset object. The files can be in any format, which enables a wider range of machine learning scenarios, including deep learning." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "\n", - "dataset = Dataset.File.from_files(path = [(datastore, 'diabetes/')])\n", - "\n", - "# see a list of files referenced by dataset\n", - "dataset.to_path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a training script\n", - "\n", - "To submit the job to the cluster, first create a training script. Run the following code to create the training script called `train_diabetes.py` in the script_folder. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile $script_folder/train_diabetes.py\n", - "\n", - "import os\n", - "import glob\n", - "import argparse\n", - "\n", - "from azureml.core.run import Run\n", - "from sklearn.linear_model import Ridge\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.model_selection import train_test_split\n", - "# sklearn.externals.joblib is removed in 0.23\n", - "from sklearn import __version__ as sklearnver\n", - "from packaging.version import Version\n", - "if Version(sklearnver) < Version(\"0.23.0\"):\n", - " from sklearn.externals import joblib\n", - "else:\n", - " import joblib\n", - "\n", - "import numpy as np\n", - "\n", - "parser = argparse.ArgumentParser()\n", - "parser.add_argument('--data-folder', type=str, help='training dataset')\n", - "args = parser.parse_args()\n", - "\n", - "os.makedirs('./outputs', exist_ok=True)\n", - "\n", - "base_path = args.data_folder\n", - "\n", - "run = Run.get_context()\n", - "\n", - "X = np.load(glob.glob(os.path.join(base_path, '**/features.npy'), recursive=True)[0])\n", - "y = np.load(glob.glob(os.path.join(base_path, '**/labels.npy'), recursive=True)[0])\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.2, random_state=0)\n", - "data = {'train': {'X': X_train, 'y': y_train},\n", - " 'test': {'X': X_test, 'y': y_test}}\n", - "\n", - "# list of numbers from 0.0 to 1.0 with a 0.05 interval\n", - "alphas = np.arange(0.0, 1.0, 0.05)\n", - "\n", - "for alpha in alphas:\n", - " # use Ridge algorithm to create a regression model\n", - " reg = Ridge(alpha=alpha)\n", - " reg.fit(data['train']['X'], data['train']['y'])\n", - "\n", - " preds = reg.predict(data['test']['X'])\n", - " mse = mean_squared_error(preds, data['test']['y'])\n", - " run.log('alpha', alpha)\n", - " run.log('mse', mse)\n", - "\n", - " model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)\n", - " with open(model_file_name, 'wb') as file:\n", - " joblib.dump(value=reg, filename='outputs/' + model_file_name)\n", - "\n", - " print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure & Run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now configure your run. We will reuse the same `sklearn_env` environment from the previous run. Once the environment is built, and if you don't change your dependencies, it will be reused in subsequent runs. \n", - "\n", - "We will pass in the DatasetConsumptionConfig of our FileDataset to the `'--data-folder'` argument of the script. Azure ML will resolve this to mount point of the data on the compute target, which we parse in the training script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder, \n", - " script='train_diabetes.py', \n", - " # to mount the dataset on the remote compute and pass the mounted path as an argument to the training script\n", - " arguments =['--data-folder', dataset.as_mount()],\n", - " compute_target=compute_target,\n", - " environment=sklearn_env)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = exp.submit(config=src)\n", - "\n", - "# monitor the run\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display run results\n", - "You now have a model trained on a remote cluster. Retrieve all the metrics logged during the run, including the accuracy of the model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion()\n", - "metrics = run.get_metrics()\n", - "print(metrics)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Register datasets\n", - "Use the register() method to register datasets to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = dataset.register(workspace = ws,\n", - " name = 'diabetes dataset',\n", - " description='training dataset',\n", - " create_new_version=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Register models with datasets\n", - "The last step in the training script wrote the model files in a directory named `outputs` in the VM of the cluster where the job is executed. `outputs` is a special directory in that all content in this directory is automatically uploaded to your workspace. This content appears in the run record in the experiment under your workspace. Hence, the model file is now also available in your workspace.\n", - "\n", - "You can register models with datasets for reproducibility and auditing purpose." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# find the index where MSE is the smallest\n", - "indices = list(range(0, len(metrics['mse'])))\n", - "min_mse_index = min(indices, key=lambda x: metrics['mse'][x])\n", - "\n", - "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n", - " metrics['mse'][min_mse_index], \n", - " metrics['alpha'][min_mse_index]\n", - "))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# find the best model\n", - "best_alpha = metrics['alpha'][min_mse_index]\n", - "model_file_name = 'ridge_{0:.2f}.pkl'.format(best_alpha)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# register the best model with the input dataset\n", - "model = run.register_model(model_name='sklearn_diabetes', model_path=os.path.join('outputs', model_file_name),\n", - " datasets =[('training data',dataset)])" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "sihhu" - } - ], - "category": "tutorial", - "compute": [ - "Remote" - ], - "datasets": [ - "Iris", - "Diabetes" - ], - "deployment": [ - "None" - ], - "exclude_from_index": false, - "framework": [ - "Azure ML" - ], - "friendly_name": "Train with Datasets (Tabular and File)", - "index_order": 1, - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "star_tag": [ - "featured" - ], - "tags": [ - "Dataset", - "Estimator", - "ScriptRun" - ], - "task": "Train" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.yml b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.yml deleted file mode 100644 index d13f92dc9..000000000 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.yml +++ /dev/null @@ -1,8 +0,0 @@ -name: train-with-datasets -dependencies: -- pip: - - azureml-sdk - - azureml-widgets - - pandas<=0.23.4 - - fuse - - scikit-learn diff --git a/index.md b/index.md deleted file mode 100644 index a5fa04287..000000000 --- a/index.md +++ /dev/null @@ -1,154 +0,0 @@ - -# Index -Azure Machine Learning is a cloud service that you use to train, deploy, automate, -and manage machine learning models. This index should assist in navigating the Azure -Machine Learning notebook samples and encourage efficient retrieval of topics and content. -![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/Index.png) - -## Getting Started - -|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags | -|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| -| [Using Azure ML environments](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/using-environments/using-environments.ipynb) | Creating and registering environments | None | Local | None | None | None | - -## Tutorials - -|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags | -|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| -| [Forecasting BikeShare Demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) | Forecasting | BikeShare | Remote | None | Azure ML AutoML | Forecasting | -| [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None | -| [Register a model and deploy locally](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb) | Deployment | None | Local | Local | None | None | -| :star:[Data drift quickdemo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) | Filtering | NOAA | Remote | None | Azure ML | Dataset, Timeseries, Drift | -| :star:[Datasets with ML Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb) | Train | Fashion MNIST | Remote | None | Azure ML | Dataset, Pipeline, Estimator, ScriptRun | -| :star:[Filtering data using Tabular Timeseiries Dataset related API](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb) | Filtering | NOAA | Local | None | Azure ML | Dataset, Tabular Timeseries | -| :star:[Train with Datasets (Tabular and File)](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb) | Train | Iris, Diabetes | Remote | None | Azure ML | Dataset, Estimator, ScriptRun | -| [Forecasting away from training data](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb) | Forecasting | None | Remote | None | Azure ML AutoML | Forecasting, Confidence Intervals | -| [Automated ML run with basic edition features.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb) | Classification | Bankmarketing | AML | ACI | None | featurization, explainability, remote_run, AutomatedML | -| [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb) | Classification | Creditcard | AML Compute | None | None | remote_run, AutomatedML | -| [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb) | Classification | Creditcard | AML Compute | None | None | AutomatedML | -| [Automated ML run with featurization and model explainability.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb) | Regression | MachineData | AML | ACI | None | featurization, explainability, remote_run, AutomatedML | -| [Automated ML run with featurization and model explainability.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb) | Regression | MachineData | AML | ACI | None | featurization, explainability, remote_run, AutomatedML | -| [auto-ml-forecasting-backtest-single-model](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb) | | None | Remote | None | Azure ML AutoML | | -| :star:[Azure Machine Learning Pipeline with DataTranferStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb) | Demonstrates the use of DataTranferStep | Custom | ADF | None | Azure ML | None | -| [Getting Started with Azure Machine Learning Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb) | Getting Started notebook for ANML Pipelines | Custom | AML Compute | None | Azure ML | None | -| [Azure Machine Learning Pipeline with AzureBatchStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb) | Demonstrates the use of AzureBatchStep | Custom | Azure Batch | None | Azure ML | None | -| :star:[How to use ModuleStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb) | Demonstrates the use of ModuleStep | Custom | AML Compute | None | Azure ML | None | -| :star:[How to use Pipeline Drafts to create a Published Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb) | Demonstrates the use of Pipeline Drafts | Custom | AML Compute | None | Azure ML | None | -| :star:[Azure Machine Learning Pipeline with HyperDriveStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb) | Demonstrates the use of HyperDriveStep | Custom | AML Compute | None | Azure ML | None | -| :star:[How to Publish a Pipeline and Invoke the REST endpoint](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb) | Demonstrates the use of Published Pipelines | Custom | AML Compute | None | Azure ML | None | -| :star:[How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb) | Demonstrates the use of Schedules for Published Pipelines and Pipeline endpoints | Custom | AML Compute | None | Azure ML | None | -| [How to setup a versioned Pipeline Endpoint](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb) | Demonstrates the use of PipelineEndpoint to run a specific version of the Published Pipeline | Custom | AML Compute | None | Azure ML | None | -| :star:[How to use DataPath as a PipelineParameter](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb) | Demonstrates the use of DataPath as a PipelineParameter | Custom | AML Compute | None | Azure ML | None | -| :star:[How to use Dataset as a PipelineParameter](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb) | Demonstrates the use of Dataset as a PipelineParameter | Custom | AML Compute | None | Azure ML | None | -| [How to use AdlaStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb) | Demonstrates the use of AdlaStep | Custom | Azure Data Lake Analytics | None | Azure ML | None | -| :star:[How to use DatabricksStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb) | Demonstrates the use of DatabricksStep | Custom | Azure Databricks | None | Azure ML, Azure Databricks | None | -| :star:[How to use KustoStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-kusto-as-compute-target.ipynb) | Demonstrates the use of KustoStep | Custom | Kusto | None | Azure ML, Kusto | None | -| :star:[How to use AutoMLStep with AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb) | Demonstrates the use of AutoMLStep | Custom | AML Compute | None | Automated Machine Learning | None | -| [Azure Machine Learning Pipeline with CommandStep for R](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb) | Demonstrates the use of CommandStep for running R scripts | Custom | AML Compute | None | Azure ML | None | -| [Azure Machine Learning Pipeline with CommandStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep.ipynb) | Demonstrates the use of CommandStep | Custom | AML Compute | None | Azure ML | None | -| :star:[Azure Machine Learning Pipelines with Data Dependency](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb) | Demonstrates how to construct a Pipeline with data dependency between steps | Custom | AML Compute | None | Azure ML | None | -| [How to use run a notebook as a step in AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb) | Demonstrates the use of NotebookRunnerStep | Custom | AML Compute | None | Azure ML | None | -| [Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.ipynb) | Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier, leveraging MLflow auto logging | MNIST | Local, AML Compute | Azure Container Instance | Keras | mlflow, keras | -| [Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb) | Use MLflow with Azure Machine Learning to train and deploy PyTorch image classifier model | MNIST | Local, AML Compute | Azure Container Instance | PyTorch | mlflow, pytorch | -| [Use MLflow projects with Azure Machine Learning to train a model with local compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train-projects-local.ipynb) | Use MLflow projects with Azure Machine Learning to train a model using local compute | | Local | | ScikitLearn | mlflow, scikit | -| [Use MLflow projects with Azure Machine Learning to train a model](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb) | Use MLflow projects with Azure Machine Learning to train a model using azureml compute | | AML Compute | | Scikit | mlflow, scikit | -| [How to use ScriptRun with data input and output](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.ipynb) | Demonstrates the use of Scriptrun with datasets | Custom | AML Compute | None | Azure ML | Dataset, ScriptRun | - -## Training - -|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags | -|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| -| [Distributed Training with Chainer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.ipynb) | Use the Chainer estimator to perform distributed training | MNIST | AML Compute | None | Chainer | None | -| [Train a model with hyperparameter tuning](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb) | Train a Convolutional Neural Network (CNN) | MNIST | AML Compute | Azure Container Instance | Chainer | None | -| [Train a model with a custom Docker image](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb) | Train with custom Docker image | Oxford IIIT Pet | AML Compute | None | Pytorch | None | -| [Train a DNN using hyperparameter tuning and deploying with Keras](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb) | Create a multi-class classifier | MNIST | AML Compute | Azure Container Instance | TensorFlow | None | -| [Distributed training with PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.ipynb) | Train a model using distributed training via PyTorch DistributedDataParallel | CIFAR-10 | AML Compute | None | PyTorch | None | -| [Distributed PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb) | Train a model using the distributed training via Horovod | MNIST | AML Compute | None | PyTorch | None | -| [Training with hyperparameter tuning using PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) | Train an image classification model using transfer learning with the PyTorch estimator | ImageNet | AML Compute | Azure Container Instance | PyTorch | None | -| [Training and hyperparameter tuning with Scikit-learn](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb) | Train a support vector machine (SVM) to perform classification | Iris | AML Compute | None | Scikit-learn | None | -| [Distributed training using TensorFlow with Horovod](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb) | Use the TensorFlow estimator to train a word2vec model | None | AML Compute | None | TensorFlow | None | -| [Distributed TensorFlow with parameter server](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb) | Use the TensorFlow estimator to train a model using distributed training | MNIST | AML Compute | None | TensorFlow | None | -| [Hyperparameter tuning and warm start using the TensorFlow estimator](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb) | Train a deep neural network | MNIST | AML Compute | Azure Container Instance | TensorFlow | None | -| [Training and hyperparameter tuning using the TensorFlow estimator](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) | Train a deep neural network | MNIST | AML Compute | Azure Container Instance | TensorFlow | None | -| [Resuming a model](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb) | Resume a model in TensorFlow from a previously submitted run | MNIST | AML Compute | None | TensorFlow | None | -| [Using Tensorboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb) | Export the run history as Tensorboard logs | None | None | None | TensorFlow | None | -| [Training in Spark](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb) | Submiting a run on a spark cluster | None | HDI cluster | None | PySpark | None | -| [Train on Azure Machine Learning Compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) | Submit a run on Azure Machine Learning Compute. | Diabetes | AML Compute | None | None | None | -| [Train on local compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-local/train-on-local.ipynb) | Train a model locally | Diabetes | Local | None | None | None | -| [Train in a remote Linux virtual machine](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) | Configure and execute a run | Diabetes | Data Science Virtual Machine | None | None | None | -| [Managing your training runs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb) | Monitor and complete runs | None | Local | None | None | None | -| [Tensorboard integration with run history](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.ipynb) | Run a TensorFlow job and view its Tensorboard output live | None | Local, DSVM, AML Compute | None | TensorFlow | None | -| [Use MLflow with AML for a local training run](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb) | Use MLflow tracking APIs together with Azure Machine Learning for storing your metrics and artifacts | Diabetes | Local | None | None | None | -| [Use MLflow with AML for a remote training run](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb) | Use MLflow tracking APIs together with AML for storing your metrics and artifacts | Diabetes | AML Compute | None | None | None | - - -## Deployment - - -|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags | -|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| -| [Deploy MNIST digit recognition with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | Image Classification | MNIST | Local | Azure Container Instance | ONNX | ONNX Model Zoo | -| [Deploy Facial Expression Recognition (FER+) with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | Facial Expression Recognition | Emotion FER | Local | Azure Container Instance | ONNX | ONNX Model Zoo | -| :star:[Register model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Container Instance | Scikit-learn | None | -| :star:[Deploy models to AKS using controlled roll out](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Kubernetes Service | Scikit-learn | None | -| [Train MNIST in PyTorch, convert, and deploy with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | Image Classification | MNIST | AML Compute | Azure Container Instance | ONNX | ONNX Converter | -| [Deploy ResNet50 with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | Image Classification | ImageNet | Local | Azure Container Instance | ONNX | ONNX Model Zoo | -| :star:[Convert and deploy TinyYolo with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) | Object Detection | PASCAL VOC | local | Azure Container Instance | ONNX | ONNX Converter | -| [Register Spark model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb) | | Iris | None | Azure Container Instance | PySpark | | - - -## Other Notebooks -|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags | -|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| -| [DNN Text Featurization](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb) | Text featurization using DNNs for classification | None | AML Compute | None | None | None | -| [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) | | | | | | | -| [fairlearn-azureml-mitigation](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/fairness/fairlearn-azureml-mitigation.ipynb) | | | | | | | -| [upload-fairness-dashboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/fairness/upload-fairness-dashboard.ipynb) | | | | | | | -| [azure-ml-with-nvidia-rapids](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb) | | | | | | | -| [auto-ml-continuous-retraining](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb) | | | | | | | -| [auto-ml-regression-model-proxy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb) | | | | | | | -| [auto-ml-forecasting-backtest-many-models](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb) | | | | | | | -| [auto-ml-forecasting-energy-demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) | | | | | | | -| [auto-ml-forecasting-github-dau](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb) | | | | | | | -| [auto-ml-forecasting-hierarchical-timeseries](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb) | | | | | | | -| [auto-ml-forecasting-many-models](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb) | | | | | | | -| [auto-ml-forecasting-univariate-recipe-experiment-settings](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb) | | | | | | | -| [auto-ml-forecasting-univariate-recipe-run-experiment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb) | | | | | | | -| [auto-ml-regression](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb) | | | | | | | -| [automl-databricks-local-01](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb) | | | | | | | -| [automl-databricks-local-with-deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb) | | | | | | | -| [spark_job_on_synapse_spark_pool](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-synapse/spark_job_on_synapse_spark_pool.ipynb) | | | | | | | -| [spark_session_on_synapse_spark_pool](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb) | | | | | | | -| [Synapse_Job_Scala_Support](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb) | | | | | | | -| [Synapse_Session_Scala_Support](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-synapse/Synapse_Session_Scala_Support.ipynb) | | | | | | | -| [multi-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb) | | | | | | | -| [register-model-deploy-local-advanced](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb) | | | | | | | -| [enable-app-insights-in-production-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) | | | | | | | -| [onnx-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb) | | | | | | | -| [production-deploy-to-aks-ssl](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb) | | | | | | | -| [production-deploy-to-aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb) | | | | | | | -| [production-deploy-to-aks-gpu](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb) | | | | | | | -| [train-explain-model-gpu-tree-explainer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb) | | | | | | | -| [explain-model-on-amlcompute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb) | | | | | | | -| [save-retrieve-explanations-run-history](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb) | | | | | | | -| [train-explain-model-locally-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb) | | | | | | | -| [train-explain-model-on-amlcompute-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb) | | | | | | | -| [training_notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb) | | | | | | | -| [nyc-taxi-data-regression-model-building](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb) | | | | | | | -| [authentication-in-azureml](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb) | | | | | | | -| [pong_rllib](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb) | | | | | | | -| [cartpole_ci](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb) | | | | | | | -| [cartpole_sc](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb) | | | | | | | -| [particle](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb) | | | | | | | -| [rai-loan-decision](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.ipynb) | | | | | | | -| [Logging APIs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb) | Logging APIs and analyzing results | None | None | None | None | None | -| [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master//setup-environment/configuration.ipynb) | | | | | | | -| [quickstart-azureml-automl](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb) | | | | | | | -| [quickstart-azureml-in-10mins](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb) | | | | | | | -| [quickstart-azureml-python-sdk](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb) | | | | | | | -| [tutorial-1st-experiment-sdk-train](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb) | | | | | | | -| [img-classification-part1-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb) | | | | | | | -| [img-classification-part2-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb) | | | | | | | -| [img-classification-part3-deploy-encrypted](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb) | | | | | | | -| [tutorial-pipeline-batch-scoring-classification](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb) | | | | | | | -| [regression-automated-ml](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb) | | | | | | | diff --git a/setup-environment/NBSETUP.md b/setup-environment/NBSETUP.md deleted file mode 100644 index b3c683b30..000000000 --- a/setup-environment/NBSETUP.md +++ /dev/null @@ -1,95 +0,0 @@ -# Set up your notebook environment for Azure Machine Learning - -To run the notebooks in this repository use one of following options. - -## **Option 1: Use Azure Notebooks** -Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. Azure Machine Learning Python SDK is already pre-installed in the Azure Notebooks `Python 3.6` kernel. - -1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks) -[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks -1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace -1. Open one of the sample notebooks - - **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook by choosing Kernel > Change Kernel > Python 3.6 from the menus. - -## **Option 2: Use your own notebook server** - -### Quick installation -We recommend you create a Python virtual environment ([Miniconda](https://conda.io/miniconda.html) preferred but [virtualenv](https://virtualenv.pypa.io/en/latest/) works too) and install the SDK in it. -```sh -# install just the base SDK -pip install azureml-sdk - -# clone the sample repoistory -git clone https://github.com/Azure/MachineLearningNotebooks.git - -# below steps are optional -# install the base SDK, Jupyter notebook server and tensorboard -pip install azureml-sdk[notebooks,tensorboard] - -# install model explainability component -pip install azureml-sdk[interpret] - -# install automated ml components -pip install azureml-sdk[automl] - -# install experimental features (not ready for production use) -pip install azureml-sdk[contrib] -``` - -Note the _extras_ (the keywords inside the square brackets) can be combined. For example: -```sh -# install base SDK, Jupyter notebook and automated ml components -pip install azureml-sdk[notebooks,automl] -``` - -### Full instructions -[Install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python) - -Please make sure you start with the [Configuration](configuration.ipynb) notebook to create and connect to a workspace. - - -### Video walkthrough: - -[!VIDEO https://youtu.be/VIsXeTuW3FU] - -## **Option 3: Use Docker** - -You need to have Docker engine installed locally and running. Open a command line window and type the following command. - -__Note:__ We use version `1.0.10` below as an exmaple, but you can replace that with any available version number you like. - -```sh -# clone the sample repoistory -git clone https://github.com/Azure/MachineLearningNotebooks.git - -# change current directory to the folder -# where Dockerfile of the specific SDK version is located. -cd MachineLearningNotebooks/Dockerfiles/1.0.10 - -# build a Docker image with the a name (azuremlsdk for example) -# and a version number tag (1.0.10 for example). -# this can take several minutes depending on your computer speed and network bandwidth. -docker build . -t azuremlsdk:1.0.10 - -# launch the built Docker container which also automatically starts -# a Jupyter server instance listening on port 8887 of the host machine -docker run -it -p 8887:8887 azuremlsdk:1.0.10 -``` - -Now you can point your browser to http://localhost:8887. We recommend that you start from the `configuration.ipynb` notebook at the root directory. - -If you need additional Azure ML SDK components, you can either modify the Docker files before you build the Docker images to add additional steps, or install them through command line in the live container after you build the Docker image. For example: - -```sh -# install the core SDK and automated ml components -pip install azureml-sdk[automl] - -# install the core SDK and model explainability component -pip install azureml-sdk[interpret] - -# install the core SDK and experimental components -pip install azureml-sdk[contrib] -``` -Drag and Drop -The image will be downloaded by Fatkun \ No newline at end of file diff --git a/setup-environment/configuration.ipynb b/setup-environment/configuration.ipynb deleted file mode 100644 index 4cf9ade06..000000000 --- a/setup-environment/configuration.ipynb +++ /dev/null @@ -1,291 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Configuration\n", - "\n", - "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n", - "\n", - "---\n", - "---\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - " 1. What is an Azure Machine Learning workspace\n", - "1. [Setup](#Setup)\n", - " 1. Azure subscription\n", - " 1. Azure ML SDK and other library installation\n", - " 1. Azure Container Instance registration\n", - "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n", - " 1. Workspace parameters\n", - " 1. Access your workspace\n", - " 1. Create a new workspace\n", - "1. [Next steps](#Next%20steps)\n", - "\n", - "---\n", - "\n", - "## Introduction\n", - "\n", - "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace. In this case, a library contains all of the notebooks in the current folder and any nested folders. You can configure this notebook library to use an existing workspace or create a new workspace.\n", - "\n", - "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here. If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n", - "\n", - "In this notebook you will\n", - "* Learn about getting an Azure subscription\n", - "* Specify your workspace parameters\n", - "* Access or create your workspace\n", - "* Add a default compute cluster for your workspace\n", - "\n", - "### What is an Azure Machine Learning workspace\n", - "\n", - "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows. In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "This section describes activities required before you can access any Azure ML services functionality." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Azure Subscription\n", - "\n", - "In order to create an Azure ML Workspace, first you need access to an Azure subscription. An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud. You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com). Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n", - "\n", - "### 2. Azure ML SDK and other library installation\n", - "\n", - "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment). If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n", - "\n", - "Also install following libraries to your environment. Many of the example notebooks depend on them\n", - "\n", - "```\n", - "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n", - "```\n", - "\n", - "Once installation is complete, the following cell checks the Azure ML SDK version:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "install" - ] - }, - "outputs": [], - "source": [ - "import azureml.core\n", - "\n", - "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n", - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n", - "\n", - "### 3. Azure Container Instance registration\n", - "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI. If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands. Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n", - "\n", - "```shell\n", - "# check to see if ACI is already registered\n", - "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n", - "\n", - "# if ACI is not registered, run this command.\n", - "# note you need to be the subscription owner in order to execute this command successfully.\n", - "(myenv) $ az provider register -n Microsoft.ContainerInstance\n", - "```\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure your Azure ML workspace\n", - "\n", - "### Workspace parameters\n", - "\n", - "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n", - "* Your subscription id\n", - "* A resource group name\n", - "* (optional) The region that will host your workspace\n", - "* A name for your workspace\n", - "\n", - "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n", - "\n", - "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group. You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com). If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n", - "\n", - "The region to host your workspace will be used if you are creating a new workspace. You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service). You should pick a region that is close to your location or that contains your data.\n", - "\n", - "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces. The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n", - "\n", - "The following cell allows you to specify your workspace parameters. This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation. If no environment variable exists, the parameters will be set to the specified default values. \n", - "\n", - "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace! You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n", - "\n", - "Replace the default values in the cell below with your workspace parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"\")\n", - "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"\")\n", - "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"\")\n", - "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Access your workspace\n", - "\n", - "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters. If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method. The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "try:\n", - " ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n", - " # write the details of the workspace to a configuration file to the notebook library\n", - " ws.write_config()\n", - " print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n", - "except:\n", - " print(\"Workspace not accessible. Change your parameters or create a new workspace below\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a new workspace\n", - "\n", - "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace. If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n", - "\n", - "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n", - "\n", - "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n", - "\n", - "This will fail if:\n", - "* You do not have permission to create a workspace in the resource group\n", - "* You do not have permission to create a resource group if it's non-existing.\n", - "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n", - "\n", - "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create workspace" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "# Create the workspace using the specified parameters\n", - "ws = Workspace.create(name = workspace_name,\n", - " subscription_id = subscription_id,\n", - " resource_group = resource_group, \n", - " location = workspace_region,\n", - " create_resource_group = True,\n", - " exist_ok = True)\n", - "ws.get_details()\n", - "\n", - "# write the details of the workspace to a configuration file to the notebook library\n", - "ws.write_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Next steps\n", - "\n", - "In this notebook you configured this notebook library to connect easily to an Azure ML workspace. You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n", - "\n", - "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models. A good place to start is the [train within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "roastala" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/setup-environment/configuration.yml b/setup-environment/configuration.yml deleted file mode 100644 index 4b1aed290..000000000 --- a/setup-environment/configuration.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: configuration -dependencies: -- pip: - - azureml-sdk diff --git a/tutorials/README.md b/tutorials/README.md deleted file mode 100644 index 4ca28c717..000000000 --- a/tutorials/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# Azure Machine Learning Tutorials - -Azure Machine Learning, a cloud-based environment you can use to train, deploy, automate, manage, and track ML models. - -Azure Machine Learning can be used for any kind of machine learning, from classical ML to supervised, unsupervised, and deep learning. - -This folder contains a collection of Jupyter Notebooks with the code used in accompanying step-by-step tutorials. - -## Set up your environment. - -If you are using an Azure Machine Learning Notebook VM, everything is already set up for you. Otherwise, see the [get started creating your first ML experiment with the Python SDK tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-sdk-setup). - -## Introductory Samples - -The following tutorials are intended to provide an introductory overview of Azure Machine Learning. - -| Tutorial | Description | Notebook | Task | Framework | -| --- | --- | --- | --- | --- | -| [Train your first ML Model](https://docs.microsoft.com/azure/machine-learning/tutorial-1st-experiment-sdk-train) | Learn the foundational design patterns in Azure Machine Learning and train a scikit-learn model based on a diabetes data set. | [tutorial-quickstart-train-model.ipynb](create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb) | Regression | Scikit-Learn -| [Train an image classification model](https://docs.microsoft.com/azure/machine-learning/tutorial-train-models-with-aml) | Train a scikit-learn image classification model. | [img-classification-part1-training.ipynb](image-classification-mnist-data/img-classification-part1-training.ipynb) | Image Classification | Scikit-Learn -| [Deploy an image classification model](https://docs.microsoft.com/azure/machine-learning/tutorial-deploy-models-with-aml) | Deploy a scikit-learn image classification model to Azure Container Instances. | [img-classification-part2-deploy.ipynb](image-classification-mnist-data/img-classification-part2-deploy.ipynb) | Image Classification | Scikit-Learn -| [Deploy an encrypted inferencing service](https://docs.microsoft.com/azure/machine-learning/tutorial-deploy-models-with-aml) |Deploy an image classification model for encrypted inferencing in Azure Container Instances | [img-classification-part3-deploy-encrypted.ipynb](image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb) | Image Classification | Scikit-Learn -| [Use automated machine learning to predict taxi fares](https://docs.microsoft.com/azure/machine-learning/tutorial-auto-train-models) | Train a regression model to predict taxi fares using Automated Machine Learning. | [regression-part2-automated-ml.ipynb](regression-automl-nyc-taxi-data/regression-automated-ml.ipynb) | Regression | Automated ML -| Azure ML in 10 minutes (Compute instance required) |Learn how to run an image classification model, track model metrics, and deploy a model in 10 minutes. | [quickstart-azureml-in-10mins.ipynb](compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb) | Image Classification | Scikit-Learn | -| Get started with Azure ML Job Submission (Compute instance required) |Learn how to use the Azure Machine Learning Python SDK to submit batch jobs. | [quickstart-azureml-python-sdk.ipynb](compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb) | Image Classification | Scikit-Learn | -| Get started with Automated ML (Compute instance required) | Learn how to use Automated ML for Fraud classification. | [quickstart-azureml-automl.ipynb](compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb) | Classification | Automated ML | - - -## Advanced Samples - -The following tutorials are intended to provide examples of more advanced feature in Azure Machine Learning. - -| Tutorial | Description | Notebook | Task | Framework | -| --- | --- | --- | --- | --- | -| [Build an Azure Machine Learning pipeline for batch scoring](https://docs.microsoft.com/azure/machine-learning/tutorial-pipeline-batch-scoring-classification) | Create an Azure Machine Learning pipeline to run batch scoring image classification jobs | [tutorial-pipeline-batch-scoring-classification.ipynb](machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb) | Image Classification | TensorFlow - -For additional documentation and resources, see the [official documentation site for Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/). - -![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/README.png) \ No newline at end of file diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb b/tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb deleted file mode 100644 index 6069c73a4..000000000 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb +++ /dev/null @@ -1,498 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/quickstart-ci/ClassificationWithAutomatedML.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "# Quickstart: Fraud Classification using Automated ML\n", - "\n", - "In this quickstart, you use automated machine learning in Azure Machine Learning service to train a classification model on an associated fraud credit card dataset. This process accepts training data and configuration settings, and automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model.\n", - "\n", - "You will learn how to:\n", - "\n", - "> * Download a dataset and look at the data\n", - "> * Train a machine learning classification model using autoML \n", - "> * Explore the results\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Connect to your workspace and create an experiment\n", - "\n", - "You start with importing some libraries and creating an experiment to track the runs in your workspace. A workspace can have multiple experiments, and all the users that have access to the workspace can collaborate on them. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612968646250 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "from matplotlib import pyplot as plt\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "from azureml.core.dataset import Dataset\n", - "from azureml.train.automl import AutoMLConfig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612968706273 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# choose a name for your experiment\n", - "experiment_name = \"fraud-classification-automl-tutorial\"\n", - "\n", - "experiment = Experiment(ws, experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Load Data\n", - "\n", - "Load the credit card dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model.\n", - "\n", - "\n", - "Follow this [how-to](https://aka.ms/azureml/howto/createdatasets) if you want to learn more about Datasets and how to use them.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612968722555 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n", - "dataset = Dataset.Tabular.from_delimited_files(data)\n", - "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n", - "label_column_name = \"Class\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## Train\n", - "\n", - "\n", - "\n", - "When you use automated machine learning in Azure ML, you input training data and configuration settings, and the process automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model. \n", - "Learn more about how you configure automated ML [here](https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train).\n", - "\n", - "\n", - "Instantiate an [AutoMLConfig](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py) object. This defines the settings and data used to run the experiment.\n", - "\n", - "|Property|Description|\n", - "|-|-|\n", - "|**task**|classification or regression|\n", - "|**primary_metric**|This is the metric that you want to optimize. \n", - "|**enable_early_stopping** | Stop the run if the metric score is not showing improvement.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|Input dataset, containing both features and label column.|\n", - "|**label_column_name**|The name of the label column.|\n", - "\n", - "You can find more information about primary metrics [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612968806233 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"n_cross_validations\": 3,\n", - " \"primary_metric\": \"average_precision_score_weighted\",\n", - " \"experiment_timeout_hours\": 0.25, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ability to find the best model possible\n", - " \"verbosity\": logging.INFO,\n", - " \"enable_stack_ensemble\": False,\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(\n", - " task=\"classification\",\n", - " debug_log=\"automl_errors.log\",\n", - " training_data=training_data,\n", - " label_column_name=label_column_name,\n", - " **automl_settings,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "Call the `submit` method on the experiment object and pass the run configuration. \n", - "\n", - "**Note: Depending on the data and the number of iterations an AutoML run can take a while to complete.**\n", - "\n", - "In this example, we specify `show_output = True` to print currently running iterations to the console. It is also possible to navigate to the experiment through the **Experiment** activity tab in the left menu, and monitor the run status from there." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612970125369 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "local_run = experiment.submit(automl_config, show_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612976292559 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "local_run" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Analyze results\n", - "\n", - "Below we select the best model from our iterations. The `get_output` method on `automl_classifier` returns the best run and the model for the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612976298373 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "best_run, best_model = local_run.get_output()\n", - "best_model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## Tests\n", - "\n", - "Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612976320370 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# convert the test data to dataframe\n", - "X_test_df = validation_data.drop_columns(\n", - " columns=[label_column_name]\n", - ").to_pandas_dataframe()\n", - "y_test_df = validation_data.keep_columns(\n", - " columns=[label_column_name], validate=True\n", - ").to_pandas_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612976325829 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# call the predict functions on the model\n", - "y_pred = best_model.predict(X_test_df)\n", - "y_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "\n", - "\n", - "### Calculate metrics for the prediction\n", - "\n", - "Now visualize the data to show what our truth (actual) values are compared to the predicted values \n", - "from the trained model that was returned.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612976330108 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "import numpy as np\n", - "import itertools\n", - "\n", - "cf = confusion_matrix(y_test_df.values, y_pred)\n", - "plt.imshow(cf, cmap=plt.cm.Blues, interpolation=\"nearest\")\n", - "plt.colorbar()\n", - "plt.title(\"Confusion Matrix\")\n", - "plt.xlabel(\"Predicted\")\n", - "plt.ylabel(\"Actual\")\n", - "class_labels = [\"False\", \"True\"]\n", - "tick_marks = np.arange(len(class_labels))\n", - "plt.xticks(tick_marks, class_labels)\n", - "plt.yticks([-0.5, 0, 1, 1.5], [\"\", \"False\", \"True\", \"\"])\n", - "# plotting text value inside cells\n", - "thresh = cf.max() / 2.0\n", - "for i, j in itertools.product(range(cf.shape[0]), range(cf.shape[1])):\n", - " plt.text(\n", - " j,\n", - " i,\n", - " format(cf[i, j], \"d\"),\n", - " horizontalalignment=\"center\",\n", - " color=\"white\" if cf[i, j] > thresh else \"black\",\n", - " )\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## Control cost and further exploration\n", - "\n", - "If you want to control cost you can stop the compute instance this notebook is running on by clicking the \"Stop compute\" button next to the status dropdown in the menu above.\n", - "\n", - "\n", - "If you want to run more notebook samples, you can click on **Sample Notebooks** next to the **Files** view and explore the notebooks made available for you there." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "cewidste" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.", - "nteract": { - "version": "nteract-front-end@1.0.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.yml b/tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.yml deleted file mode 100644 index 475197357..000000000 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: quickstart-azureml-automl -dependencies: -- pip: - - azureml-sdk diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb b/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb deleted file mode 100644 index 8dacd74e3..000000000 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb +++ /dev/null @@ -1,502 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/quickstart-ci/AzureMLin10mins.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "# Quickstart: Train and deploy a model in Azure Machine Learning in 10 minutes\n", - "\n", - "In this quickstart, learn how to get started with Azure Machine Learning. You'll train an image classification model using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset.\n", - "\n", - "You'll learn how to:\n", - "\n", - "* Download a dataset and look at the data\n", - "* Train an image classification model and log metrics using MLflow\n", - "* Deploy the model to do real-time inference" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## Import Data\n", - "\n", - "Before you train a model, you need to understand the data you're using to train it. In this section, learn how to:\n", - "\n", - "* Download the MNIST dataset\n", - "* Display some sample images\n", - "\n", - "You'll use Azure Open Datasets to get the raw MNIST data files. [Azure Open Datasets](https://docs.microsoft.com/azure/open-datasets/overview-what-are-open-datasets) are curated public datasets that you can use to add scenario-specific features to machine learning solutions for better models. Each dataset has a corresponding class, `MNIST` in this case, to retrieve the data in different ways." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from azureml.opendatasets import MNIST\n", - "\n", - "data_folder = os.path.join(os.getcwd(), \"/tmp/qs_data\")\n", - "os.makedirs(data_folder, exist_ok=True)\n", - "\n", - "mnist_file_dataset = MNIST.get_file_dataset()\n", - "mnist_file_dataset.download(data_folder, overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Take a look at the data\n", - "\n", - "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. \n", - "\n", - "Note this step requires a `load_data` function that's included in an `utils.py` file. This file is placed in the same folder as this notebook. The `load_data` function simply parses the compressed files into numpy arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from utils import load_data\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import glob\n", - "\n", - "\n", - "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.\n", - "X_train = (\n", - " load_data(\n", - " glob.glob(\n", - " os.path.join(data_folder, \"**/train-images-idx3-ubyte.gz\"), recursive=True\n", - " )[0],\n", - " False,\n", - " )\n", - " / 255.0\n", - ")\n", - "X_test = (\n", - " load_data(\n", - " glob.glob(\n", - " os.path.join(data_folder, \"**/t10k-images-idx3-ubyte.gz\"), recursive=True\n", - " )[0],\n", - " False,\n", - " )\n", - " / 255.0\n", - ")\n", - "y_train = load_data(\n", - " glob.glob(\n", - " os.path.join(data_folder, \"**/train-labels-idx1-ubyte.gz\"), recursive=True\n", - " )[0],\n", - " True,\n", - ").reshape(-1)\n", - "y_test = load_data(\n", - " glob.glob(\n", - " os.path.join(data_folder, \"**/t10k-labels-idx1-ubyte.gz\"), recursive=True\n", - " )[0],\n", - " True,\n", - ").reshape(-1)\n", - "\n", - "\n", - "# now let's show some randomly chosen images from the traininng set.\n", - "count = 0\n", - "sample_size = 30\n", - "plt.figure(figsize=(16, 6))\n", - "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n", - " count = count + 1\n", - " plt.subplot(1, sample_size, count)\n", - " plt.axhline(\"\")\n", - " plt.axvline(\"\")\n", - " plt.text(x=10, y=-10, s=y_train[i], fontsize=18)\n", - " plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## Train model and log metrics with MLflow\n", - "\n", - "You'll train the model using the code below. Note that you are using MLflow autologging to track metrics and log model artefacts.\n", - "\n", - "You'll be using the [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) classifier from the [SciKit Learn framework](https://scikit-learn.org/) to classify the data.\n", - "\n", - "**Note: The model training takes approximately 2 minutes to complete.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612966046970 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# create the model\n", - "import mlflow\n", - "import numpy as np\n", - "from sklearn.linear_model import LogisticRegression\n", - "from azureml.core import Workspace\n", - "\n", - "# connect to your workspace\n", - "ws = Workspace.from_config()\n", - "\n", - "# create experiment and start logging to a new run in the experiment\n", - "experiment_name = \"azure-ml-in10-mins-tutorial\"\n", - "\n", - "# set up MLflow to track the metrics\n", - "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())\n", - "mlflow.set_experiment(experiment_name)\n", - "mlflow.autolog()\n", - "\n", - "# set up the Logistic regression model\n", - "reg = 0.5\n", - "clf = LogisticRegression(\n", - " C=1.0 / reg, solver=\"liblinear\", multi_class=\"auto\", random_state=42\n", - ")\n", - "\n", - "# train the model\n", - "with mlflow.start_run() as run:\n", - " clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View Experiment\n", - "In the left-hand menu in Azure Machine Learning Studio, select __Experiments__ and then select your experiment (azure-ml-in10-mins-tutorial). An experiment is a grouping of many runs from a specified script or piece of code. Information for the run is stored under that experiment. If the name doesn't exist when you submit an experiment, if you select your run you will see various tabs containing metrics, logs, explanations, etc.\n", - "\n", - "## Version control your models with the model registry\n", - "\n", - "You can use model registration to store and version your models in your workspace. Registered models are identified by name and version. Each time you register a model with the same name as an existing one, the registry increments the version. The code below registers and versions the model you trained above. Once you have executed the code cell below you will be able to see the model in the registry by selecting __Models__ in the left-hand menu in Azure Machine Learning Studio." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612881042710 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# register the model\n", - "model_uri = \"runs:/{}/model\".format(run.info.run_id)\n", - "model = mlflow.register_model(model_uri, \"sklearn_mnist_model\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy the model for real-time inference\n", - "In this section you learn how to deploy a model so that an application can consume (inference) the model over REST.\n", - "\n", - "### Create deployment configuration\n", - "The code cell gets a _curated environment_, which specifies all the dependencies required to host the model (for example, the packages like scikit-learn). Also, you create a _deployment configuration_, which specifies the amount of compute required to host the model. In this case, the compute will have 1CPU and 1GB memory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612881061728 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# create environment for the deploy\n", - "from azureml.core.environment import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.webservice import AciWebservice\n", - "\n", - "# get a curated environment\n", - "env = Environment.get(\n", - " workspace=ws, \n", - " name=\"AzureML-sklearn-1.0-ubuntu20.04-py38-cpu\",\n", - " version=1\n", - ")\n", - "env.inferencing_stack_version='latest'\n", - "\n", - "# create deployment config i.e. compute resources\n", - "aciconfig = AciWebservice.deploy_configuration(\n", - " cpu_cores=1,\n", - " memory_gb=1,\n", - " tags={\"data\": \"MNIST\", \"method\": \"sklearn\"},\n", - " description=\"Predict MNIST with sklearn\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Deploy model\n", - "\n", - "This next code cell deploys the model to Azure Container Instance (ACI).\n", - "\n", - "**Note: The deployment takes approximately 3 minutes to complete.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "%%time\n", - "import uuid\n", - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.environment import Environment\n", - "from azureml.core.model import Model\n", - "\n", - "# get the registered model\n", - "model = Model(ws, \"sklearn_mnist_model\")\n", - "\n", - "# create an inference config i.e. the scoring script and environment\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n", - "\n", - "# deploy the service\n", - "service_name = \"sklearn-mnist-svc-\" + str(uuid.uuid4())[:4]\n", - "service = Model.deploy(\n", - " workspace=ws,\n", - " name=service_name,\n", - " models=[model],\n", - " inference_config=inference_config,\n", - " deployment_config=aciconfig,\n", - ")\n", - "\n", - "service.wait_for_deployment(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The [*scoring script*](score.py) file referenced in the code above can be found in the same folder as this notebook, and has two functions:\n", - "\n", - "1. an `init` function that executes once when the service starts - in this function you normally get the model from the registry and set global variables\n", - "1. a `run(data)` function that executes each time a call is made to the service. In this function, you normally format the input data, run a prediction, and output the predicted result.\n", - "\n", - "### View Endpoint\n", - "Once the model has been successfully deployed, you can view the endpoint by navigating to __Endpoints__ in the left-hand menu in Azure Machine Learning Studio. You will be able to see the state of the endpoint (healthy/unhealthy), logs, and consume (how applications can consume the model)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## Test the model service\n", - "\n", - "You can test the model by sending a raw HTTP request to test the web service. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612881538381 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# send raw HTTP request to test the web service.\n", - "import requests\n", - "\n", - "# send a random row from the test set to score\n", - "random_index = np.random.randint(0, len(X_test) - 1)\n", - "input_data = '{\"data\": [' + str(list(X_test[random_index])) + \"]}\"\n", - "\n", - "headers = {\"Content-Type\": \"application/json\"}\n", - "\n", - "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n", - "\n", - "print(\"POST to url\", service.scoring_uri)\n", - "print(\"label:\", y_test[random_index])\n", - "print(\"prediction:\", resp.text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up resources\n", - "\n", - "If you're not going to continue to use this model, delete the Model service using:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612881556520 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# if you want to keep workspace and only delete endpoint (it will incur cost while running)\n", - "service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to control cost further, stop the compute instance by selecting the \"Stop compute\" button next to the **Compute** dropdown. Then start the compute instance again the next time you need it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Next Steps\n", - "\n", - "In this quickstart, you learned how to run machine learning code in Azure Machine Learning.\n", - "\n", - "Now that you have working code in a development environment, learn how to submit a **_job_** - ideally on a schedule or trigger (for example, arrival of new data).\n", - "\n", - " [**Learn how to get started with Azure ML Job Submission**](../quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb) " - ] - } - ], - "metadata": { - "authors": [ - { - "name": "cewidste" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.", - "nteract": { - "version": "nteract-front-end@1.0.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.yml b/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.yml deleted file mode 100644 index 0db3895d7..000000000 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.yml +++ /dev/null @@ -1,11 +0,0 @@ -name: quickstart-azureml-in-10mins -dependencies: -- pip: - - azureml-sdk - - sklearn - - numpy - - matplotlib - - joblib - - uuid - - requests - - azureml-opendatasets diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/score.py b/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/score.py deleted file mode 100644 index 33571b227..000000000 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/score.py +++ /dev/null @@ -1,21 +0,0 @@ -import json -import numpy as np -import os -import joblib - - -def init(): - global model - # AZUREML_MODEL_DIR is an environment variable created during deployment. - # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION) - # For multiple models, it points to the folder containing all deployed models (./azureml-models) - model_path = os.path.join(os.getenv("AZUREML_MODEL_DIR"), "model/model.pkl") - model = joblib.load(model_path) - - -def run(raw_data): - data = np.array(json.loads(raw_data)["data"]) - # make prediction - y_hat = model.predict(data) - # you can return any data type as long as it is JSON-serializable - return y_hat.tolist() diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/utils.py b/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/utils.py deleted file mode 100644 index ba3a7ce33..000000000 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/utils.py +++ /dev/null @@ -1,24 +0,0 @@ -import gzip -import numpy as np -import struct - - -# load compressed MNIST gz files and return numpy arrays -def load_data(filename, label=False): - with gzip.open(filename) as gz: - struct.unpack("I", gz.read(4)) - n_items = struct.unpack(">I", gz.read(4)) - if not label: - n_rows = struct.unpack(">I", gz.read(4))[0] - n_cols = struct.unpack(">I", gz.read(4))[0] - res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8) - res = res.reshape(n_items[0], n_rows * n_cols) - else: - res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8) - res = res.reshape(n_items[0], 1) - return res - - -# one-hot encode a 1-D array -def one_hot_encode(array, num_of_classes): - return np.eye(num_of_classes)[array.reshape(-1)] diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb b/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb deleted file mode 100644 index 931e140ef..000000000 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb +++ /dev/null @@ -1,355 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/quickstart-ci/GettingStartedWithPythonSDK.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "# Quickstart: Learn how to submit batch jobs with the Azure Machine Learning Python SDK\n", - "\n", - "In this quickstart, you learn how to submit a batch training job using the Python SDK. In this example, we submit the job to the 'local' machine (the compute instance you are running this notebook on). However, you can use exactly the same method to submit the job to different compute targets (for example, AKS, Azure Machine Learning Compute Cluster, Synapse, etc) by changing a single line of code. A full list of support compute targets can be viewed [here](https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target). \n", - "\n", - "This quickstart trains a simple logistic regression using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n", - "\n", - "You will learn how to:\n", - "\n", - "> * Download a dataset and look at the data\n", - "> * Train an image classification model by submitting a batch job to a compute resource\n", - "> * Use MLflow autologging to track model metrics and log the model artefact\n", - "> * Review training results, find and register the best model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Connect to your workspace and create an experiment\n", - "\n", - "You start with importing some libraries and creating an experiment to track the runs in your workspace. A workspace can have multiple experiments, and all the users that have access to the workspace can collaborate on them. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612965838618 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "\n", - "from azureml.core import Workspace\n", - "from azureml.core import Experiment\n", - "\n", - "# connect to your workspace\n", - "ws = Workspace.from_config()\n", - "\n", - "experiment_name = \"get-started-with-jobsubmission-tutorial\"\n", - "exp = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### The MNIST dataset\n", - "\n", - "Use Azure Open Datasets to get the raw MNIST data files. [Azure Open Datasets](https://docs.microsoft.com/azure/open-datasets/overview-what-are-open-datasets) are curated public datasets that you can use to add scenario-specific features to machine learning solutions for more accurate models. Each dataset has a corresponding class, `MNIST` in this case, to retrieve the data in different ways.\n", - "\n", - "Follow this [how-to](https://aka.ms/azureml/howto/createdatasets) if you want to learn more about Datasets and how to use them.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612965850391 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.opendatasets import MNIST\n", - "\n", - "mnist_file_dataset = MNIST.get_file_dataset()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define the Environment\n", - "An Environment defines Python packages, environment variables, and Docker settings that are used in machine learning experiments. Here you will be using a curated environment that has already been made available through the workspace. \n", - "\n", - "Read [this article](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments) if you want to learn more about Environments and how to use them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612965877458 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core.environment import Environment\n", - "\n", - "# use a curated environment that has already been built for you\n", - "\n", - "env = Environment.get(workspace=ws, \n", - " name=\"AzureML-Scikit-learn0.24-Cuda11-OpenMpi4.1.0-py36\", \n", - " version=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Configure the training job\n", - "\n", - "Create a [ScriptRunConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.script_run_config.scriptrunconfig?view=azure-ml-py) object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on. Configure the ScriptRunConfig by specifying:\n", - "\n", - "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n", - "* The compute target. In this case you will point to local compute\n", - "* The training script name, train.py\n", - "* An environment that contains the libraries needed to run the script\n", - "* Arguments required from the training script. \n", - "\n", - "In this run we will be submitting to \"local\", which is the compute instance you are running this notebook. If you have another compute target (for example: AKS, Azure ML Compute Cluster, Azure Databricks, etc) then you just need to change the `compute_target` argument below. You can learn more about other compute targets [here](https://docs.microsoft.com/azure/machine-learning/how-to-set-up-training-targets). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612965882781 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "args = [\"--data-folder\", mnist_file_dataset.as_mount(), \"--regularization\", 0.5]\n", - "\n", - "src = ScriptRunConfig(\n", - " source_directory=\"src\",\n", - " script=\"train.py\",\n", - " arguments=args,\n", - " compute_target=\"local\",\n", - " environment=env,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "### Submit the job\n", - "\n", - "Run the experiment by submitting the ScriptRunConfig object. After this there are many options for monitoring your run. Once submitted, you can either navigate to the experiment \"get-started-with-jobsubmission-tutorial\" in the left menu item __Experiments__ to monitor the run, or you can monitor the run inline as the `run.wait_for_completion(show_output=True)` will stream the logs of the run. You will see that the environment is built for you to ensure reproducibility - this adds a couple of minutes to the run time. On subsequent runs, the environment is re-used making the runtime shorter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612965911435 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "run = exp.submit(config=src)\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## Register model\n", - "\n", - "The training script used the MLflow autologging feature and therefore the model was captured and stored on your behalf. Below we register the model into the Azure Machine Learning Model registry, which lets you keep track of all the models in your Azure Machine Learning workspace.\n", - "\n", - "Models are identified by name and version. Each time you register a model with the same name as an existing one, the registry assumes that it's a new version. The version is incremented, and the new model is registered under the same name.\n", - "\n", - "When you register the model, you can provide additional metadata tags and then use the tags when you search for models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1612966068862 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# register model\n", - "model = run.register_model(\n", - " model_name=\"sklearn_mnist\", model_path=\"model/model.pkl\"\n", - ")\n", - "print(model.name, model.id, model.version, sep=\"\\t\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will now be able to see the model in the regsitry by selecting __Models__ in the left-hand menu of the Azure Machine Learning Studio." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## Control Cost\n", - "\n", - "If you want to control cost you can stop the compute instance this notebook is running on by clicking the \"Stop compute\" button next to the status dropdown in the menu above.\n", - "\n", - " ## Next Steps\n", - "\n", - "In this quickstart, you have seen how to run jobs-based machine learning code in Azure Machine Learning. \n", - "\n", - "It is also possible to use automated machine learning in Azure Machine Learning service to find the best model in an automated fashion. To see how this works, we recommend that you follow the next quickstart in this series, [**Fraud Classification using Automated ML**](../quickstart-azureml-automl/quickstart-azureml-automl.ipynb). This quickstart is focused on AutoML using the Python SDK." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "cewidste" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.", - "nteract": { - "version": "nteract-front-end@1.0.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.yml b/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.yml deleted file mode 100644 index 22268be70..000000000 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.yml +++ /dev/null @@ -1,12 +0,0 @@ -name: quickstart-azureml-python-sdk -dependencies: -- pip: - - azureml-sdk - - sklearn - - numpy - - matplotlib - - joblib - - uuid - - requests - - azureml-opendatasets - - azureml-widgets diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/src/train.py b/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/src/train.py deleted file mode 100644 index b0eb5390e..000000000 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/src/train.py +++ /dev/null @@ -1,72 +0,0 @@ -import argparse -import os -import numpy as np -import glob -# import joblib -import mlflow - -from sklearn.linear_model import LogisticRegression -from utils import load_data - -# let user feed in 2 parameters, the dataset to mount or download, -# and the regularization rate of the logistic regression model -parser = argparse.ArgumentParser() -parser.add_argument( - "--data-folder", type=str, dest="data_folder", help="data folder mounting point" -) -parser.add_argument( - "--regularization", type=float, dest="reg", default=0.01, help="regularization rate" -) -args = parser.parse_args() - -data_folder = args.data_folder -print("Data folder:", data_folder) - -# load train and test set into numpy arrays -# note we scale the pixel intensity values to 0-1 (by dividing it with 255.0) so the model can converge faster. -X_train = ( - load_data( - glob.glob( - os.path.join(data_folder, "**/train-images-idx3-ubyte.gz"), recursive=True - )[0], - False, - ) / 255.0 -) -X_test = ( - load_data( - glob.glob( - os.path.join(data_folder, "**/t10k-images-idx3-ubyte.gz"), recursive=True - )[0], - False, - ) / 255.0 -) -y_train = load_data( - glob.glob( - os.path.join(data_folder, "**/train-labels-idx1-ubyte.gz"), recursive=True - )[0], - True, -).reshape(-1) -y_test = load_data( - glob.glob( - os.path.join(data_folder, "**/t10k-labels-idx1-ubyte.gz"), recursive=True - )[0], - True, -).reshape(-1) - -print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep="\n") - -# use mlflow autologging -mlflow.autolog() - -print("Train a logistic regression model with regularization rate of", args.reg) -clf = LogisticRegression( - C=1.0 / args.reg, solver="liblinear", multi_class="auto", random_state=42 -) -clf.fit(X_train, y_train) - -print("Predict the test set") -y_hat = clf.predict(X_test) - -# calculate accuracy on the prediction -acc = np.average(y_hat == y_test) -print("Accuracy is", acc) diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/src/utils.py b/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/src/utils.py deleted file mode 100644 index ba3a7ce33..000000000 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/src/utils.py +++ /dev/null @@ -1,24 +0,0 @@ -import gzip -import numpy as np -import struct - - -# load compressed MNIST gz files and return numpy arrays -def load_data(filename, label=False): - with gzip.open(filename) as gz: - struct.unpack("I", gz.read(4)) - n_items = struct.unpack(">I", gz.read(4)) - if not label: - n_rows = struct.unpack(">I", gz.read(4))[0] - n_cols = struct.unpack(">I", gz.read(4))[0] - res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8) - res = res.reshape(n_items[0], n_rows * n_cols) - else: - res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8) - res = res.reshape(n_items[0], 1) - return res - - -# one-hot encode a 1-D array -def one_hot_encode(array, num_of_classes): - return np.eye(num_of_classes)[array.reshape(-1)] diff --git a/tutorials/create-first-ml-experiment/imgs/experiment_main.png b/tutorials/create-first-ml-experiment/imgs/experiment_main.png deleted file mode 100644 index 2419855bb..000000000 Binary files a/tutorials/create-first-ml-experiment/imgs/experiment_main.png and /dev/null differ diff --git a/tutorials/create-first-ml-experiment/imgs/model_download.png b/tutorials/create-first-ml-experiment/imgs/model_download.png deleted file mode 100644 index adcdf70ec..000000000 Binary files a/tutorials/create-first-ml-experiment/imgs/model_download.png and /dev/null differ diff --git a/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb b/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb deleted file mode 100644 index 54d5d2337..000000000 --- a/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb +++ /dev/null @@ -1,395 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/tutorial-quickstart-train-model.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: Train your first model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tutorial is **part two of a two-part tutorial series**. In the previous tutorial, you created a workspace and chose a development environment. In this tutorial, you learn the foundational design patterns in Azure Machine Learning service, and train a simple scikit-learn model based on the diabetes data set. After completing this tutorial, you will have the practical knowledge of the SDK to scale up to developing more-complex experiments and workflows. \n", - "\n", - "In this tutorial, you learn the following tasks:\n", - "\n", - "> * Connect your workspace and create an experiment \n", - "> * Load data and train a scikit-learn model\n", - "> * View training results in the studio\n", - "> * Retrieve the best model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "The only prerequisite is to run the previous tutorial, Setup environment and workspace." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connect workspace and create experiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import the `Workspace` class, and load your subscription information from the file `config.json` using the function `from_config().` This looks for the JSON file in the current directory by default, but you can also specify a path parameter to point to the file using `from_config(path=\"your/file/path\")`. If you are running this notebook in a cloud notebook server in your workspace, the file is automatically in the root directory.\n", - "\n", - "If the following code asks for additional authentication, simply paste the link in a browser and enter the authentication token. In addition, if you have more than one tenant linked to your user, you will need to add the following lines:\n", - "```\n", - "from azureml.core.authentication import InteractiveLoginAuthentication\n", - "interactive_auth = InteractiveLoginAuthentication(tenant_id=\"your-tenant-id\")\n", - "Additional details on authentication can be found here: https://aka.ms/aml-notebook-auth \n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "ws = Workspace.from_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now create an experiment in your workspace. An experiment is another foundational cloud resource that represents a collection of trials (individual model runs). In this tutorial you use the experiment to create runs and track your model training in the Azure Machine Learning studio. Parameters include your workspace reference, and a string name for the experiment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "experiment = Experiment(workspace=ws, name=\"diabetes-experiment\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data and prepare for training" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this tutorial, you use the diabetes data set, which uses features like age, gender, and BMI to predict diabetes disease progression. Load the data from the Azure Open Datasets class, and split it into training and test sets using `train_test_split()`. This function segregates the data so the model has unseen data to use for testing following training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.opendatasets import Diabetes\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "x_df = Diabetes.get_tabular_dataset().to_pandas_dataframe().dropna()\n", - "y_df = x_df.pop(\"Y\")\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=66)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train a model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Training a simple scikit-learn model can easily be done locally for small-scale training, but when training many iterations with dozens of different feature permutations and hyperparameter settings, it is easy to lose track of what models you've trained and how you trained them. The following design pattern shows how to leverage the SDK to easily keep track of your training in the cloud.\n", - "\n", - "Build a script that trains ridge models in a loop through different hyperparameter alpha values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import Ridge\n", - "from sklearn.metrics import mean_squared_error\n", - "import joblib\n", - "import math\n", - "\n", - "alphas = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n", - "\n", - "for alpha in alphas:\n", - " run = experiment.start_logging()\n", - " run.log(\"alpha_value\", alpha)\n", - " \n", - " model = Ridge(alpha=alpha)\n", - " model.fit(X=X_train, y=y_train)\n", - " y_pred = model.predict(X=X_test)\n", - " rmse = math.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred))\n", - " run.log(\"rmse\", rmse)\n", - " \n", - " model_name = \"model_alpha_\" + str(alpha) + \".pkl\"\n", - " filename = \"outputs/\" + model_name\n", - " \n", - " joblib.dump(value=model, filename=filename)\n", - " run.upload_file(name=model_name, path_or_stream=filename)\n", - " run.complete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code accomplishes the following:\n", - "\n", - "1. For each alpha hyperparameter value in the `alphas` array, a new run is created within the experiment. The alpha value is logged to differentiate between each run.\n", - "1. In each run, a Ridge model is instantiated, trained, and used to run predictions. The root-mean-squared-error is calculated for the actual versus predicted values, and then logged to the run. At this point the run has metadata attached for both the alpha value and the rmse accuracy.\n", - "1. Next, the model for each run is serialized and uploaded to the run. This allows you to download the model file from the run in the studio.\n", - "1. At the end of each iteration the run is completed by calling `run.complete()`.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the training has completed, call the `experiment` variable to fetch a link to the experiment in the studio." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View training results in studio" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Following the **Link to Azure Machine Learning studio** takes you to the main experiment page. Here you see all the individual runs in the experiment. Any custom-logged values (`alpha_value` and `rmse`, in this case) become fields for each run, and also become available for the charts and tiles at the top of the experiment page. To add a logged metric to a chart or tile, hover over it, click the edit button, and find your custom-logged metric.\n", - "\n", - "When training models at scale over hundreds and thousands of runs, this page makes it easy to see every model you trained, specifically how they were trained, and how your unique metrics have changed over time." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Main Experiment page in the studio](./imgs/experiment_main.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Select a run number link in the `RUN NUMBER` column to see the page for an individual run. The default tab **Details** shows you more-detailed information on each run. Navigate to the **Outputs + logs** tab, and you see the `.pkl` file for the model that was uploaded to the run during each training iteration. Here you can download the model file, rather than having to retrain it manually." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Run details page in the studio](./imgs/model_download.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get the best model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to being able to download model files from the experiment in the studio, you can also download them programmatically. The following code iterates through each run in the experiment, and accesses both the logged run metrics and the run details (which contains the run_id). This keeps track of the best run, in this case the run with the lowest root-mean-squared-error." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "minimum_rmse_runid = None\n", - "minimum_rmse = None\n", - "\n", - "for run in experiment.get_runs():\n", - " run_metrics = run.get_metrics()\n", - " run_details = run.get_details()\n", - " # each logged metric becomes a key in this returned dict\n", - " run_rmse = run_metrics[\"rmse\"]\n", - " run_id = run_details[\"runId\"]\n", - " \n", - " if minimum_rmse is None:\n", - " minimum_rmse = run_rmse\n", - " minimum_rmse_runid = run_id\n", - " else:\n", - " if run_rmse < minimum_rmse:\n", - " minimum_rmse = run_rmse\n", - " minimum_rmse_runid = run_id\n", - "\n", - "print(\"Best run_id: \" + minimum_rmse_runid)\n", - "print(\"Best run_id rmse: \" + str(minimum_rmse)) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the best run id to fetch the individual run using the `Run` constructor along with the experiment object. Then call `get_file_names()` to see all the files available for download from this run. In this case, you only uploaded one file for each run during training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Run\n", - "best_run = Run(experiment=experiment, run_id=minimum_rmse_runid)\n", - "print(best_run.get_file_names())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call `download()` on the run object, specifying the model file name to download. By default this function downloads to the current directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run.download_file(name=\"model_alpha_0.1.pkl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up resources\n", - "\n", - "Do not complete this section if you plan on running other Azure Machine Learning service tutorials.\n", - "\n", - "### Stop the notebook VM\n", - "\n", - "If you used a cloud notebook server, stop the VM when you are not using it to reduce cost.\n", - "\n", - "1. In your workspace, select **Compute**.\n", - "\n", - "1. Select the **Notebook VMs** tab in the compute page.\n", - "\n", - "1. From the list, select the VM.\n", - "\n", - "1. Select **Stop**.\n", - "\n", - "1. When you're ready to use the server again, select **Start**.\n", - "\n", - "### Delete everything\n", - "\n", - "If you don't plan to use the resources you created, delete them, so you don't incur any charges:\n", - "\n", - "1. In the Azure portal, select **Resource groups** on the far left.\n", - "\n", - "1. From the list, select the resource group you created.\n", - "\n", - "1. Select **Delete resource group**.\n", - "\n", - "1. Enter the resource group name. Then select **Delete**.\n", - "\n", - "You can also keep the resource group but delete a single workspace. Display the workspace properties and select **Delete**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "In this tutorial, you did the following tasks:\n", - "\n", - "> * Connected your workspace and created an experiment\n", - "> * Loaded data and trained scikit-learn models\n", - "> * Viewed training results in the studio and retrieved models\n", - "\n", - "[Deploy your model](https://docs.microsoft.com/azure/machine-learning/service/tutorial-deploy-models-with-aml) with Azure Machine Learning.\n", - "Learn how to develop [automated machine learning](https://docs.microsoft.com/azure/machine-learning/service/tutorial-auto-train-models) experiments." - ] - } - ], - "metadata": { - "authors": [ - { - "name": "trbye" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "msauthor": "trbye", - "network_required": false - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.yml b/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.yml deleted file mode 100644 index 2cdb2bf6f..000000000 --- a/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.yml +++ /dev/null @@ -1,6 +0,0 @@ -name: tutorial-1st-experiment-sdk-train -dependencies: -- pip: - - azureml-sdk - - sklearn - - azureml-opendatasets diff --git a/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb b/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb deleted file mode 100644 index 62e0ec1ae..000000000 --- a/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb +++ /dev/null @@ -1,695 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial #1: Train an image classification model with Azure Machine Learning\n", - "\n", - "In this tutorial, you train a machine learning model on remote compute resources. You'll use the training and deployment workflow for Azure Machine Learning service (preview) in a Python Jupyter notebook. You can then use the notebook as a template to train your own machine learning model with your own data. This tutorial is **part one of a two-part tutorial series**. \n", - "\n", - "This tutorial trains a simple logistic regression using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n", - "\n", - "Learn how to:\n", - "\n", - "> * Set up your development environment\n", - "> * Access and examine the data\n", - "> * Train a simple logistic regression model on a remote cluster\n", - "> * Review training results, find and register the best model\n", - "\n", - "You'll learn how to select a model and deploy it in [part two of this tutorial](deploy-models.ipynb) later. \n", - "\n", - "## Prerequisites\n", - "\n", - "See prerequisites in the [Azure Machine Learning documentation](https://docs.microsoft.com/azure/machine-learning/service/tutorial-train-models-with-aml#prerequisites).\n", - "\n", - "On the computer running this notebook, conda install matplotlib, numpy, scikit-learn=0.22.1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up your development environment\n", - "\n", - "All the setup for your development work can be accomplished in a Python notebook. Setup includes:\n", - "\n", - "* Importing Python packages\n", - "* Connecting to a workspace to enable communication between your local computer and remote resources\n", - "* Creating an experiment to track all your runs\n", - "* Creating a remote compute target to use for training\n", - "\n", - "### Import packages\n", - "\n", - "Import Python packages you need in this session. Also display the Azure Machine Learning SDK version." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "check version" - ] - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import azureml.core\n", - "from azureml.core import Workspace\n", - "\n", - "# check core SDK version number\n", - "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Connect to workspace\n", - "\n", - "Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **config.json** and loads the details into an object named `ws`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "load workspace" - ] - }, - "outputs": [], - "source": [ - "# load workspace configuration from the config.json file in the current folder.\n", - "ws = Workspace.from_config()\n", - "print(ws.name, ws.location, ws.resource_group, sep='\\t')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create experiment\n", - "\n", - "Create an experiment to track the runs in your workspace. A workspace can have muliple experiments. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create experiment" - ] - }, - "outputs": [], - "source": [ - "experiment_name = 'Tutorial-sklearn-mnist'\n", - "\n", - "from azureml.core import Experiment\n", - "exp = Experiment(workspace=ws, name=experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach existing compute resource\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. You will submit Python code to run on this VM later in the tutorial. \n", - "The code below creates the compute clusters for you if they don't already exist in your workspace.\n", - "\n", - "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "create mlc", - "amlcompute" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute\n", - "from azureml.core.compute import ComputeTarget\n", - "import os\n", - "\n", - "# choose a name for your cluster\n", - "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n", - "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n", - "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 4)\n", - "\n", - "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n", - "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n", - "\n", - "\n", - "if compute_name in ws.compute_targets:\n", - " compute_target = ws.compute_targets[compute_name]\n", - " if compute_target and type(compute_target) is AmlCompute:\n", - " print(\"found compute target: \" + compute_name)\n", - "else:\n", - " print(\"creating new compute target...\")\n", - " provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n", - " min_nodes = compute_min_nodes, \n", - " max_nodes = compute_max_nodes)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n", - " \n", - " # can poll for a minimum number of nodes and for a specific timeout. \n", - " # if no min node count is provided it will use the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - " # For a more detailed view of current AmlCompute status, use get_status()\n", - " print(compute_target.get_status().serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You now have the necessary packages and compute resources to train a model in the cloud. \n", - "\n", - "## Explore data\n", - "\n", - "Before you train a model, you need to understand the data that you are using to train it. In this section you learn how to:\n", - "\n", - "* Download the MNIST dataset\n", - "* Display some sample images\n", - "\n", - "### Download the MNIST dataset\n", - "\n", - "Use Azure Open Datasets to get the raw MNIST data files. [Azure Open Datasets](https://docs.microsoft.com/azure/open-datasets/overview-what-are-open-datasets) are curated public datasets that you can use to add scenario-specific features to machine learning solutions for more accurate models. Each dataset has a corrseponding class, `MNIST` in this case, to retrieve the data in different ways.\n", - "\n", - "This code retrieves the data as a `FileDataset` object, which is a subclass of `Dataset`. A `FileDataset` references single or multiple files of any format in your datastores or public urls. The class provides you with the ability to download or mount the files to your compute by creating a reference to the data source location. Additionally, you register the Dataset to your workspace for easy retrieval during training.\n", - "\n", - "Follow the [how-to](https://aka.ms/azureml/howto/createdatasets) to learn more about Datasets and their usage in the SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Dataset\n", - "from azureml.opendatasets import MNIST\n", - "\n", - "data_folder = os.path.join(os.getcwd(), 'data')\n", - "os.makedirs(data_folder, exist_ok=True)\n", - "\n", - "mnist_file_dataset = MNIST.get_file_dataset()\n", - "mnist_file_dataset.download(data_folder, overwrite=True)\n", - "\n", - "mnist_file_dataset = mnist_file_dataset.register(workspace=ws,\n", - " name='mnist_opendataset',\n", - " description='training and test dataset',\n", - " create_new_version=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display some sample images\n", - "\n", - "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `utils.py` file. This file is included in the sample folder. Please make sure it is placed in the same folder as this notebook. The `load_data` function simply parses the compresse files into numpy arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# make sure utils.py is in the same directory as this code\n", - "from utils import load_data\n", - "import glob\n", - "\n", - "\n", - "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.\n", - "X_train = load_data(glob.glob(os.path.join(data_folder,\"**/train-images-idx3-ubyte.gz\"), recursive=True)[0], False) / 255.0\n", - "X_test = load_data(glob.glob(os.path.join(data_folder,\"**/t10k-images-idx3-ubyte.gz\"), recursive=True)[0], False) / 255.0\n", - "y_train = load_data(glob.glob(os.path.join(data_folder,\"**/train-labels-idx1-ubyte.gz\"), recursive=True)[0], True).reshape(-1)\n", - "y_test = load_data(glob.glob(os.path.join(data_folder,\"**/t10k-labels-idx1-ubyte.gz\"), recursive=True)[0], True).reshape(-1)\n", - "\n", - "\n", - "# now let's show some randomly chosen images from the traininng set.\n", - "count = 0\n", - "sample_size = 30\n", - "plt.figure(figsize = (16, 6))\n", - "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n", - " count = count + 1\n", - " plt.subplot(1, sample_size, count)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.text(x=10, y=-10, s=y_train[i], fontsize=18)\n", - " plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train on a remote cluster\n", - "\n", - "For this task, you submit the job to run on the remote training cluster you set up earlier. To submit a job you:\n", - "* Create a directory\n", - "* Create a training script\n", - "* Create a script run configuration\n", - "* Submit the job \n", - "\n", - "### Create a directory\n", - "\n", - "Create a directory to deliver the necessary code from your computer to the remote resource." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "script_folder = os.path.join(os.getcwd(), \"sklearn-mnist\")\n", - "os.makedirs(script_folder, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a training script\n", - "\n", - "To submit the job to the cluster, first create a training script. Run the following code to create the training script called `train.py` in the directory you just created. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile $script_folder/train.py\n", - "\n", - "import argparse\n", - "import os\n", - "import numpy as np\n", - "import glob\n", - "\n", - "from sklearn.linear_model import LogisticRegression\n", - "import joblib\n", - "\n", - "from azureml.core import Run\n", - "from utils import load_data\n", - "\n", - "# let user feed in 2 parameters, the dataset to mount or download, and the regularization rate of the logistic regression model\n", - "parser = argparse.ArgumentParser()\n", - "parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')\n", - "parser.add_argument('--regularization', type=float, dest='reg', default=0.01, help='regularization rate')\n", - "args = parser.parse_args()\n", - "\n", - "data_folder = args.data_folder\n", - "print('Data folder:', data_folder)\n", - "\n", - "# load train and test set into numpy arrays\n", - "# note we scale the pixel intensity values to 0-1 (by dividing it with 255.0) so the model can converge faster.\n", - "X_train = load_data(glob.glob(os.path.join(data_folder, '**/train-images-idx3-ubyte.gz'), recursive=True)[0], False) / 255.0\n", - "X_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-images-idx3-ubyte.gz'), recursive=True)[0], False) / 255.0\n", - "y_train = load_data(glob.glob(os.path.join(data_folder, '**/train-labels-idx1-ubyte.gz'), recursive=True)[0], True).reshape(-1)\n", - "y_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-labels-idx1-ubyte.gz'), recursive=True)[0], True).reshape(-1)\n", - "\n", - "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep = '\\n')\n", - "\n", - "# get hold of the current run\n", - "run = Run.get_context()\n", - "\n", - "print('Train a logistic regression model with regularization rate of', args.reg)\n", - "clf = LogisticRegression(C=1.0/args.reg, solver=\"liblinear\", multi_class=\"auto\", random_state=42)\n", - "clf.fit(X_train, y_train)\n", - "\n", - "print('Predict the test set')\n", - "y_hat = clf.predict(X_test)\n", - "\n", - "# calculate accuracy on the prediction\n", - "acc = np.average(y_hat == y_test)\n", - "print('Accuracy is', acc)\n", - "\n", - "run.log('regularization rate', np.float(args.reg))\n", - "run.log('accuracy', np.float(acc))\n", - "\n", - "os.makedirs('outputs', exist_ok=True)\n", - "# note file saved in the outputs folder is automatically uploaded into experiment record\n", - "joblib.dump(value=clf, filename='outputs/sklearn_mnist_model.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the script gets data and saves models:\n", - "\n", - "+ The training script reads an argument to find the directory containing the data. When you submit the job later, you point to the dataset for this argument:\n", - "`parser.add_argument('--data-folder', type=str, dest='data_folder', help='data directory mounting point')`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "+ The training script saves your model into a directory named outputs.
      \n", - "`joblib.dump(value=clf, filename='outputs/sklearn_mnist_model.pkl')`
      \n", - "Anything written in this directory is automatically uploaded into your workspace. You'll access your model from this directory later in the tutorial." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The file `utils.py` is referenced from the training script to load the dataset correctly. Copy this script into the script folder so that it can be accessed along with the training script on the remote resource." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "shutil.copy('utils.py', script_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the training job\n", - "\n", - "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on. Configure the ScriptRunConfig by specifying:\n", - "\n", - "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n", - "* The compute target. In this case you will use the AmlCompute you created\n", - "* The training script name, train.py\n", - "* An environment that contains the libraries needed to run the script\n", - "* Arguments required from the training script. \n", - "\n", - "In this tutorial, the target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the dataset.\n", - "\n", - "First, create the environment that contains: the scikit-learn library, azureml-dataset-runtime required for accessing the dataset, and azureml-defaults which contains the dependencies for logging metrics. The azureml-defaults also contains the dependencies required for deploying the model as a web service later in the part 2 of the tutorial.\n", - "\n", - "Once the environment is defined, register it with the Workspace to re-use it in part 2 of the tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.environment import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "# to install required packages\n", - "env = Environment('tutorial-env')\n", - "cd = CondaDependencies.create(pip_packages=['azureml-dataset-runtime[pandas,fuse]', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n", - "\n", - "env.python.conda_dependencies = cd\n", - "\n", - "# Register environment to re-use later\n", - "env.register(workspace = ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, create the ScriptRunConfig by specifying the training script, compute target and environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "configure estimator" - ] - }, - "outputs": [], - "source": [ - "from azureml.core import ScriptRunConfig\n", - "\n", - "args = ['--data-folder', mnist_file_dataset.as_mount(), '--regularization', 0.5]\n", - "\n", - "src = ScriptRunConfig(source_directory=script_folder,\n", - " script='train.py', \n", - " arguments=args,\n", - " compute_target=compute_target,\n", - " environment=env)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit the job to the cluster\n", - "\n", - "Run the experiment by submitting the ScriptRunConfig object. And you can navigate to Azure portal to monitor the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remote run", - "amlcompute", - "scikit-learn" - ] - }, - "outputs": [], - "source": [ - "run = exp.submit(config=src)\n", - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the call is asynchronous, it returns a **Preparing** or **Running** state as soon as the job is started.\n", - "\n", - "## Monitor a remote run\n", - "\n", - "In total, the first run takes **approximately 10 minutes**. But for subsequent runs, as long as the dependencies in the Azure ML environment don't change, the same image is reused and hence the container start up time is much faster.\n", - "\n", - "Here is what's happening while you wait:\n", - "\n", - "- **Image creation**: A Docker image is created matching the Python environment specified by the Azure ML environment. The image is built and stored in the ACR (Azure Container Registry) associated with your workspace. Image creation and uploading takes **about 5 minutes**. \n", - "\n", - " This stage happens once for each Python environment since the container is cached for subsequent runs. During image creation, logs are streamed to the run history. You can monitor the image creation progress using these logs.\n", - "\n", - "- **Scaling**: If the remote cluster requires more nodes to execute the run than currently available, additional nodes are added automatically. Scaling typically takes **about 5 minutes.**\n", - "\n", - "- **Running**: In this stage, the necessary scripts and files are sent to the compute target, then data stores are mounted/copied, then the entry_script is run. While the job is running, stdout and the files in the ./logs directory are streamed to the run history. You can monitor the run's progress using these logs.\n", - "\n", - "- **Post-Processing**: The ./outputs directory of the run is copied over to the run history in your workspace so you can access these results.\n", - "\n", - "\n", - "You can check the progress of a running job in multiple ways. This tutorial uses a Jupyter widget as well as a `wait_for_completion` method. \n", - "\n", - "### Jupyter widget\n", - "\n", - "Watch the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "use notebook widget" - ] - }, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By the way, if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get log results upon completion\n", - "\n", - "Model training happens in the background. You can use `wait_for_completion` to block and wait until the model has completed training before running more code. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remote run", - "amlcompute", - "scikit-learn" - ] - }, - "outputs": [], - "source": [ - "# specify show_output to True for a verbose log\n", - "run.wait_for_completion(show_output=True) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display run results\n", - "\n", - "You now have a model trained on a remote cluster. Retrieve all the metrics logged during the run, including the accuracy of the model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "get metrics" - ] - }, - "outputs": [], - "source": [ - "print(run.get_metrics())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next tutorial you will explore this model in more detail.\n", - "\n", - "## Register model\n", - "\n", - "The last step in the training script wrote the file `outputs/sklearn_mnist_model.pkl` in a directory named `outputs` in the VM of the cluster where the job is executed. `outputs` is a special directory in that all content in this directory is automatically uploaded to your workspace. This content appears in the run record in the experiment under your workspace. Hence, the model file is now also available in your workspace.\n", - "\n", - "You can see files associated with that run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "query history" - ] - }, - "outputs": [], - "source": [ - "print(run.get_file_names())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Register the model in the workspace so that you (or other collaborators) can later query, examine, and deploy this model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "register model from history" - ] - }, - "outputs": [], - "source": [ - "# register model \n", - "model = run.register_model(model_name='sklearn_mnist', model_path='outputs/sklearn_mnist_model.pkl')\n", - "print(model.name, model.id, model.version, sep='\\t')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "In this Azure Machine Learning tutorial, you used Python to:\n", - "\n", - "> * Set up your development environment\n", - "> * Access and examine the data\n", - "> * Train multiple models on a remote cluster using the popular scikit-learn machine learning library\n", - "> * Review training details and register the best model\n", - "\n", - "You are ready to deploy this registered model using the instructions in the next part of the tutorial series:\n", - "\n", - "> [Tutorial 2 - Deploy models](img-classification-part2-deploy.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/img-classification-part1-training.png)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "maxluk" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "msauthor": "roastala", - "network_required": false - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/tutorials/image-classification-mnist-data/img-classification-part1-training.yml b/tutorials/image-classification-mnist-data/img-classification-part1-training.yml deleted file mode 100644 index 594f2e477..000000000 --- a/tutorials/image-classification-mnist-data/img-classification-part1-training.yml +++ /dev/null @@ -1,10 +0,0 @@ -name: img-classification-part1-training -dependencies: -- pip: - - azureml-sdk - - azureml-widgets - - matplotlib - - sklearn - - pandas - - azureml-opendatasets - - azureml-widgets diff --git a/tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb b/tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb deleted file mode 100644 index 2eae3a402..000000000 --- a/tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb +++ /dev/null @@ -1,575 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial #2: Deploy an image classification model in Azure Container Instance (ACI)\n", - "\n", - "This tutorial is **part two of a two-part tutorial series**. In the [previous tutorial](img-classification-part1-training.ipynb), you trained machine learning models and then registered a model in your workspace on the cloud. \n", - "\n", - "Now, you're ready to deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/azure/container-instances/) (ACI). A web service is an image, in this case a Docker image, that encapsulates the scoring logic and the model itself. \n", - "\n", - "In this part of the tutorial, you use Azure Machine Learning service (Preview) to:\n", - "\n", - "> * Set up your testing environment\n", - "> * Retrieve the model from your workspace\n", - "> * Test the model locally\n", - "> * Deploy the model to ACI\n", - "> * Test the deployed model\n", - "\n", - "ACI is a great solution for testing and understanding the workflow. For scalable production deployments, consider using Azure Kubernetes Service. For more information, see [how to deploy and where](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where).\n", - "\n", - "\n", - "## Prerequisites\n", - "\n", - "Complete the model training in the [Tutorial #1: Train an image classification model with Azure Machine Learning](train-models.ipynb) notebook. \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If you did NOT complete the tutorial, you can instead run this cell \n", - "# This will register a model and download the data needed for this tutorial\n", - "# These prerequisites are created in the training tutorial\n", - "# Feel free to skip this cell if you completed the training tutorial \n", - "\n", - "# register a model\n", - "from azureml.core import Workspace\n", - "ws = Workspace.from_config()\n", - "\n", - "from azureml.core.model import Model\n", - "\n", - "model_name = \"sklearn_mnist\"\n", - "model = Model.register(model_path=\"sklearn_mnist_model.pkl\",\n", - " model_name=model_name,\n", - " tags={\"data\": \"mnist\", \"model\": \"classification\"},\n", - " description=\"Mnist handwriting recognition\",\n", - " workspace=ws)\n", - "\n", - "from azureml.core.environment import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "# to install required packages\n", - "env = Environment('tutorial-env')\n", - "cd = CondaDependencies.create(pip_packages=['azureml-dataset-runtime[pandas,fuse]', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n", - "\n", - "env.python.conda_dependencies = cd\n", - "\n", - "# Register environment to re-use later\n", - "env.register(workspace = ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up the environment\n", - "\n", - "Start by setting up a testing environment.\n", - "\n", - "### Import packages\n", - "\n", - "Import the Python packages needed for this tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "check version" - ] - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - " \n", - "import azureml.core\n", - "\n", - "# display the core SDK version number\n", - "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy as web service\n", - "\n", - "Deploy the model as a web service hosted in ACI. \n", - "\n", - "To build the correct environment for ACI, provide the following:\n", - "* A scoring script to show how to use the model\n", - "* A configuration file to build the ACI\n", - "* The model you trained before\n", - "\n", - "### Create scoring script\n", - "\n", - "Create the scoring script, called score.py, used by the web service call to show how to use the model.\n", - "\n", - "You must include two required functions into the scoring script:\n", - "* The `init()` function, which typically loads the model into a global object. This function is run only once when the Docker container is started. \n", - "\n", - "* The `run(input_data)` function uses the model to predict a value based on the input data. Inputs and outputs to the run typically use JSON for serialization and de-serialization, but other formats are supported.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import json\n", - "import numpy as np\n", - "import os\n", - "import pickle\n", - "import joblib\n", - "\n", - "def init():\n", - " global model\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_mnist_model.pkl')\n", - " model = joblib.load(model_path)\n", - "\n", - "def run(raw_data):\n", - " data = np.array(json.loads(raw_data)['data'])\n", - " # make prediction\n", - " y_hat = model.predict(data)\n", - " # you can return any data type as long as it is JSON-serializable\n", - " return y_hat.tolist()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create configuration file\n", - "\n", - "Create a deployment configuration file and specify the number of CPUs and gigabyte of RAM needed for your ACI container. While it depends on your model, the default of 1 core and 1 gigabyte of RAM is usually sufficient for many models. If you feel you need more later, you would have to recreate the image and redeploy the service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "configure web service", - "aci" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n", - " memory_gb=1, \n", - " tags={\"data\": \"MNIST\", \"method\" : \"sklearn\"}, \n", - " description='Predict MNIST with sklearn')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy in ACI\n", - "Estimated time to complete: **about 2-5 minutes**\n", - "\n", - "Configure the image and deploy. The following code goes through these steps:\n", - "\n", - "1. Create environment object containing dependencies needed by the model using the environment file (`myenv.yml`)\n", - "1. Create inference configuration necessary to deploy the model as a web service using:\n", - " * The scoring file (`score.py`)\n", - " * envrionment object created in previous step\n", - "1. Deploy the model to the ACI container.\n", - "1. Get the web service HTTP endpoint." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "configure image", - "create image", - "deploy web service", - "aci" - ] - }, - "outputs": [], - "source": [ - "%%time\n", - "import uuid\n", - "from azureml.core.webservice import Webservice\n", - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.environment import Environment\n", - "from azureml.core import Workspace\n", - "from azureml.core.model import Model\n", - "\n", - "ws = Workspace.from_config()\n", - "model = Model(ws, 'sklearn_mnist')\n", - "\n", - "\n", - "myenv = Environment.get(workspace=ws, name=\"tutorial-env\", version=\"1\")\n", - "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n", - "\n", - "service_name = 'sklearn-mnist-svc-' + str(uuid.uuid4())[:4]\n", - "service = Model.deploy(workspace=ws, \n", - " name=service_name, \n", - " models=[model], \n", - " inference_config=inference_config, \n", - " deployment_config=aciconfig)\n", - "\n", - "service.wait_for_deployment(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the scoring web service's HTTP endpoint, which accepts REST client calls. This endpoint can be shared with anyone who wants to test the web service or integrate it into an application." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "get scoring uri" - ] - }, - "outputs": [], - "source": [ - "print(service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the model\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download test data\n", - "Download the test data to the **./data/** directory" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from azureml.core import Dataset\n", - "from azureml.opendatasets import MNIST\n", - "\n", - "data_folder = os.path.join(os.getcwd(), 'data')\n", - "os.makedirs(data_folder, exist_ok=True)\n", - "\n", - "mnist_file_dataset = MNIST.get_file_dataset()\n", - "mnist_file_dataset.download(data_folder, overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load test data\n", - "\n", - "Load the test data from the **./data/** directory created during the training tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from utils import load_data\n", - "import os\n", - "import glob\n", - "\n", - "data_folder = os.path.join(os.getcwd(), 'data')\n", - "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster\n", - "X_test = load_data(glob.glob(os.path.join(data_folder,\"**/t10k-images-idx3-ubyte.gz\"), recursive=True)[0], False) / 255.0\n", - "y_test = load_data(glob.glob(os.path.join(data_folder,\"**/t10k-labels-idx1-ubyte.gz\"), recursive=True)[0], True).reshape(-1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Predict test data\n", - "\n", - "Feed the test dataset to the model to get predictions.\n", - "\n", - "\n", - "The following code goes through these steps:\n", - "1. Send the data as a JSON array to the web service hosted in ACI. \n", - "\n", - "1. Use the SDK's `run` API to invoke the service. You can also make raw calls using any HTTP tool such as curl." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "test = json.dumps({\"data\": X_test.tolist()})\n", - "test = bytes(test, encoding='utf8')\n", - "y_hat = service.run(input_data=test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Examine the confusion matrix\n", - "\n", - "Generate a confusion matrix to see how many samples from the test set are classified correctly. Notice the mis-classified value for the incorrect predictions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "\n", - "conf_mx = confusion_matrix(y_test, y_hat)\n", - "print(conf_mx)\n", - "print('Overall accuracy:', np.average(y_hat == y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `matplotlib` to display the confusion matrix as a graph. In this graph, the X axis represents the actual values, and the Y axis represents the predicted values. The color in each grid represents the error rate. The lighter the color, the higher the error rate is. For example, many 5's are mis-classified as 3's. Hence you see a bright grid at (5,3)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# normalize the diagonal cells so that they don't overpower the rest of the cells when visualized\n", - "row_sums = conf_mx.sum(axis=1, keepdims=True)\n", - "norm_conf_mx = conf_mx / row_sums\n", - "np.fill_diagonal(norm_conf_mx, 0)\n", - "\n", - "fig = plt.figure(figsize=(8,5))\n", - "ax = fig.add_subplot(111)\n", - "cax = ax.matshow(norm_conf_mx, cmap=plt.cm.bone)\n", - "ticks = np.arange(0, 10, 1)\n", - "ax.set_xticks(ticks)\n", - "ax.set_yticks(ticks)\n", - "ax.set_xticklabels(ticks)\n", - "ax.set_yticklabels(ticks)\n", - "fig.colorbar(cax)\n", - "plt.ylabel('true labels', fontsize=14)\n", - "plt.xlabel('predicted values', fontsize=14)\n", - "plt.savefig('conf.png')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Show predictions\n", - "\n", - "Test the deployed model with a random sample of 30 images from the test data. \n", - "\n", - "\n", - "1. Print the returned predictions and plot them along with the input images. Red font and inverse image (white on black) is used to highlight the misclassified samples. \n", - "\n", - " Since the model accuracy is high, you might have to run the following code a few times before you can see a misclassified sample." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "score web service" - ] - }, - "outputs": [], - "source": [ - "import json\n", - "\n", - "# find 30 random samples from test set\n", - "n = 30\n", - "sample_indices = np.random.permutation(X_test.shape[0])[0:n]\n", - "\n", - "test_samples = json.dumps({\"data\": X_test[sample_indices].tolist()})\n", - "test_samples = bytes(test_samples, encoding='utf8')\n", - "\n", - "# predict using the deployed model\n", - "result = service.run(input_data=test_samples)\n", - "\n", - "# compare actual value vs. the predicted values:\n", - "i = 0\n", - "plt.figure(figsize = (20, 1))\n", - "\n", - "for s in sample_indices:\n", - " plt.subplot(1, n, i + 1)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " \n", - " # use different color for misclassified sample\n", - " font_color = 'red' if y_test[s] != result[i] else 'black'\n", - " clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n", - " \n", - " plt.text(x=10, y =-10, s=result[i], fontsize=18, color=font_color)\n", - " plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)\n", - " \n", - " i = i + 1\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also send raw HTTP request to test the web service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "score web service" - ] - }, - "outputs": [], - "source": [ - "import requests\n", - "\n", - "# send a random row from the test set to score\n", - "random_index = np.random.randint(0, len(X_test)-1)\n", - "input_data = \"{\\\"data\\\": [\" + str(list(X_test[random_index])) + \"]}\"\n", - "\n", - "headers = {'Content-Type':'application/json'}\n", - "\n", - "# for AKS deployment you'd need to the service key in the header as well\n", - "# api_key = service.get_key()\n", - "# headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ api_key)} \n", - "\n", - "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n", - "\n", - "print(\"POST to url\", service.scoring_uri)\n", - "#print(\"input data:\", input_data)\n", - "print(\"label:\", y_test[random_index])\n", - "print(\"prediction:\", resp.text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up resources\n", - "\n", - "To keep the resource group and workspace for other tutorials and exploration, you can delete only the ACI deployment using this API call:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "delete web service" - ] - }, - "outputs": [], - "source": [ - "service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "If you're not going to use what you've created here, delete the resources you just created with this quickstart so you don't incur any charges. In the Azure portal, select and delete your resource group. You can also keep the resource group, but delete a single workspace by displaying the workspace properties and selecting the Delete button.\n", - "\n", - "\n", - "## Next steps\n", - "\n", - "In this Azure Machine Learning tutorial, you used Python to:\n", - "\n", - "> * Set up your testing environment\n", - "> * Retrieve the model from your workspace\n", - "> * Test the model locally\n", - "> * Deploy the model to ACI\n", - "> * Test the deployed model\n", - " \n", - "You can also try out the [regression tutorial](regression-part1-data-prep.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/img-classification-part2-deploy.png)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "shipatel" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - }, - "msauthor": "sgilley", - "network_required": false - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/tutorials/image-classification-mnist-data/img-classification-part2-deploy.yml b/tutorials/image-classification-mnist-data/img-classification-part2-deploy.yml deleted file mode 100644 index b3d47ee5c..000000000 --- a/tutorials/image-classification-mnist-data/img-classification-part2-deploy.yml +++ /dev/null @@ -1,8 +0,0 @@ -name: img-classification-part2-deploy -dependencies: -- pip: - - azureml-sdk - - matplotlib - - sklearn - - pandas - - azureml-opendatasets diff --git a/tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb b/tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb deleted file mode 100644 index 18c6c209a..000000000 --- a/tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb +++ /dev/null @@ -1,618 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial #3: Deploy an image classification model for encrypted inferencing in Azure Container Instance (ACI)\n", - "\n", - "This tutorial is **a new addition to the two-part series**. In the [previous tutorial](img-classification-part1-training.ipynb), you trained machine learning models and then registered a model in your workspace on the cloud. \n", - "\n", - "Now, you're ready to deploy the model as a encrypted inferencing web service in [Azure Container Instances](https://docs.microsoft.com/azure/container-instances/) (ACI). A web service is an image, in this case a Docker image, that encapsulates the scoring logic and the model itself. \n", - "\n", - "In this part of the tutorial, you use Azure Machine Learning service (Preview) to:\n", - "\n", - "> * Set up your testing environment\n", - "> * Retrieve the model from your workspace\n", - "> * Test the model locally\n", - "> * Deploy the model to ACI\n", - "> * Test the deployed model\n", - "\n", - "ACI is a great solution for testing and understanding the workflow. For scalable production deployments, consider using Azure Kubernetes Service. For more information, see [how to deploy and where](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where).\n", - "\n", - "\n", - "## Prerequisites\n", - "\n", - "Complete the model training in the [Tutorial #1: Train an image classification model with Azure Machine Learning](train-models.ipynb) notebook. \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If you did NOT complete the tutorial, you can instead run this cell \n", - "# This will register a model and download the data needed for this tutorial\n", - "# These prerequisites are created in the training tutorial\n", - "# Feel free to skip this cell if you completed the training tutorial \n", - "\n", - "# register a model\n", - "from azureml.core import Workspace\n", - "ws = Workspace.from_config()\n", - "\n", - "from azureml.core.model import Model\n", - "\n", - "model_name = \"sklearn_mnist\"\n", - "model = Model.register(model_path=\"sklearn_mnist_model.pkl\",\n", - " model_name=model_name,\n", - " tags={\"data\": \"mnist\", \"model\": \"classification\"},\n", - " description=\"Mnist handwriting recognition\",\n", - " workspace=ws)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup the Environment \n", - "\n", - "Add `encrypted-inference` package as a conda dependency " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.environment import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "# to install required packages\n", - "env = Environment('tutorial-encryption-env')\n", - "cd = CondaDependencies.create(pip_packages=['azureml-dataset-runtime[pandas,fuse]', 'azureml-defaults', 'azure-storage-blob', 'encrypted-inference==0.9'], conda_packages = ['scikit-learn==0.22.1'])\n", - "\n", - "env.python.conda_dependencies = cd\n", - "\n", - "# Register environment to re-use later\n", - "env.register(workspace = ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up the environment\n", - "\n", - "Start by setting up a testing environment.\n", - "\n", - "### Import packages\n", - "\n", - "Import the Python packages needed for this tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "check version" - ] - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - " \n", - "import azureml.core\n", - "\n", - "# display the core SDK version number\n", - "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Install Homomorphic Encryption based library for Secure Inferencing\n", - "\n", - "Our library is based on [Microsoft SEAL](https://github.com/Microsoft/SEAL) and pubished to [PyPi.org](https://pypi.org/project/encrypted-inference) as an easy to use package " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install encrypted-inference==0.9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy as web service\n", - "\n", - "Deploy the model as a web service hosted in ACI. \n", - "\n", - "To build the correct environment for ACI, provide the following:\n", - "* A scoring script to show how to use the model\n", - "* A configuration file to build the ACI\n", - "* The model you trained before\n", - "\n", - "### Create scoring script\n", - "\n", - "Create the scoring script, called score_encrypted.py, used by the web service call to show how to use the model.\n", - "\n", - "You must include two required functions into the scoring script:\n", - "* The `init()` function, which typically loads the model into a global object. This function is run only once when the Docker container is started. \n", - "\n", - "* The `run(input_data)` function uses the model to predict a value based on the input data. Inputs and outputs to the run typically use JSON for serialization and de-serialization, but other formats are supported. The function fetches homomorphic encryption based public keys that are uploaded by the service caller. \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score_encrypted.py\n", - "import json\n", - "import os\n", - "import pickle\n", - "import joblib\n", - "from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient, PublicAccess\n", - "from encrypted.inference.eiserver import EIServer\n", - "\n", - "def init():\n", - " global model\n", - " # AZUREML_MODEL_DIR is an environment variable created during deployment.\n", - " # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n", - " # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n", - " model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_mnist_model.pkl')\n", - " model = joblib.load(model_path)\n", - "\n", - " global server\n", - " server = EIServer(model.coef_, model.intercept_, verbose=True)\n", - "\n", - "def run(raw_data):\n", - "\n", - " json_properties = json.loads(raw_data)\n", - "\n", - " key_id = json_properties['key_id']\n", - " conn_str = json_properties['conn_str']\n", - " container = json_properties['container']\n", - " data = json_properties['data']\n", - "\n", - " # download the Galois keys from blob storage\n", - " #TODO optimize by caching the keys locally \n", - " blob_service_client = BlobServiceClient.from_connection_string(conn_str=conn_str)\n", - " blob_client = blob_service_client.get_blob_client(container=container, blob=key_id)\n", - " public_keys = blob_client.download_blob().readall()\n", - " \n", - " result = {}\n", - " # make prediction\n", - " result = server.predict(data, public_keys)\n", - "\n", - " # you can return any data type as long as it is JSON-serializable\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create configuration file\n", - "\n", - "Create a deployment configuration file and specify the number of CPUs and gigabyte of RAM needed for your ACI container. While it depends on your model, the default of 1 core and 1 gigabyte of RAM is usually sufficient for many models. If you feel you need more later, you would have to recreate the image and redeploy the service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "configure web service", - "aci" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n", - " memory_gb=1, \n", - " tags={\"data\": \"MNIST\", \"method\" : \"sklearn\"}, \n", - " description='Encrypted Predict MNIST with sklearn + SEAL')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy in ACI\n", - "Estimated time to complete: **about 2-5 minutes**\n", - "\n", - "Configure the image and deploy. The following code goes through these steps:\n", - "\n", - "1. Create environment object containing dependencies needed by the model using the environment file (`myenv.yml`)\n", - "1. Create inference configuration necessary to deploy the model as a web service using:\n", - " * The scoring file (`score_encrypted.py`)\n", - " * envrionment object created in previous step\n", - "1. Deploy the model to the ACI container.\n", - "1. Get the web service HTTP endpoint." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "configure image", - "create image", - "deploy web service", - "aci" - ] - }, - "outputs": [], - "source": [ - "%%time\n", - "import uuid\n", - "from azureml.core.webservice import Webservice\n", - "from azureml.core.model import InferenceConfig\n", - "from azureml.core.environment import Environment\n", - "from azureml.core import Workspace\n", - "from azureml.core.model import Model\n", - "\n", - "ws = Workspace.from_config()\n", - "model = Model(ws, 'sklearn_mnist')\n", - "\n", - "myenv = Environment.get(workspace=ws, name=\"tutorial-encryption-env\")\n", - "inference_config = InferenceConfig(entry_script=\"score_encrypted.py\", environment=myenv)\n", - "\n", - "service_name = 'sklearn-mnist-svc-' + str(uuid.uuid4())[:4]\n", - "service = Model.deploy(workspace=ws, \n", - " name=service_name, \n", - " models=[model], \n", - " inference_config=inference_config, \n", - " deployment_config=aciconfig)\n", - "\n", - "service.wait_for_deployment(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the scoring web service's HTTP endpoint, which accepts REST client calls. This endpoint can be shared with anyone who wants to test the web service or integrate it into an application." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "get scoring uri" - ] - }, - "outputs": [], - "source": [ - "print(service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the model\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download test data\n", - "Download the test data to the **./data/** directory" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from azureml.core import Dataset\n", - "from azureml.opendatasets import MNIST\n", - "\n", - "data_folder = os.path.join(os.getcwd(), 'data')\n", - "os.makedirs(data_folder, exist_ok=True)\n", - "\n", - "mnist_file_dataset = MNIST.get_file_dataset()\n", - "mnist_file_dataset.download(data_folder, overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load test data\n", - "\n", - "Load the test data from the **./data/** directory created during the training tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from utils import load_data\n", - "import os\n", - "import glob\n", - "\n", - "data_folder = os.path.join(os.getcwd(), 'data')\n", - "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster\n", - "X_test = load_data(glob.glob(os.path.join(data_folder,\"**/t10k-images-idx3-ubyte.gz\"), recursive=True)[0], False) / 255.0\n", - "y_test = load_data(glob.glob(os.path.join(data_folder,\"**/t10k-labels-idx1-ubyte.gz\"), recursive=True)[0], True).reshape(-1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Predict test data\n", - "\n", - "Feed the test dataset to the model to get predictions.\n", - "\n", - "\n", - "The following code goes through these steps:\n", - "\n", - "1. Create our Homomorphic Encryption based client \n", - "\n", - "1. Upload HE generated public keys \n", - "\n", - "1. Encrypt the data\n", - "\n", - "1. Send the data as JSON to the web service hosted in ACI. \n", - "\n", - "1. Use the SDK's `run` API to invoke the service. You can also make raw calls using any HTTP tool such as curl." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create our Homomorphic Encryption based client \n", - "\n", - "Create a new EILinearRegressionClient and setup the public keys " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from encrypted.inference.eiclient import EILinearRegressionClient\n", - "\n", - "# Create a new Encrypted inference client and a new secret key.\n", - "edp = EILinearRegressionClient(verbose=True)\n", - "\n", - "public_keys_blob, public_keys_data = edp.get_public_keys()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Upload HE generated public keys\n", - "\n", - "Upload the public keys to the workspace default blob store. This will allow us to share the keys with the inference server" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import azureml.core\n", - "from azureml.core import Workspace, Datastore\n", - "import os\n", - "\n", - "ws = Workspace.from_config()\n", - "\n", - "datastore = ws.get_default_datastore()\n", - "container_name=datastore.container_name\n", - "\n", - "# Create a local file and write the keys to it\n", - "public_keys = open(public_keys_blob, \"wb\")\n", - "public_keys.write(public_keys_data)\n", - "public_keys.close()\n", - "\n", - "# Upload the file to blob store\n", - "datastore.upload_files([public_keys_blob])\n", - "\n", - "# Delete the local file\n", - "os.remove(public_keys_blob)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Encrypt the data " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#choose any one sample from the test data \n", - "sample_index = 1\n", - "\n", - "#encrypt the data\n", - "raw_data = edp.encrypt(X_test[sample_index])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Send the test data to the webservice hosted in ACI\n", - "\n", - "Feed the test dataset to the model to get predictions. We will need to send the connection string to the blob storage where the public keys were uploaded \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azureml.core import Webservice\n", - "\n", - "service = Webservice(ws, service_name)\n", - "\n", - "#pass the connection string for blob storage to give the server access to the uploaded public keys \n", - "conn_str_template = 'DefaultEndpointsProtocol={};AccountName={};AccountKey={};EndpointSuffix=core.windows.net'\n", - "conn_str = conn_str_template.format(datastore.protocol, datastore.account_name, datastore.account_key)\n", - "\n", - "#build the json \n", - "data = json.dumps({\"data\": raw_data, \"key_id\" : public_keys_blob, \"conn_str\" : conn_str, \"container\" : container_name })\n", - "data = bytes(data, encoding='ASCII')\n", - "\n", - "print ('Making an encrypted inference web service call ')\n", - "eresult = service.run(input_data=data)\n", - "\n", - "print ('Received encrypted inference results')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Decrypt the data\n", - "\n", - "Use the client to decrypt the results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np \n", - "\n", - "results = edp.decrypt(eresult)\n", - "\n", - "print ('Decrypted the results ', results)\n", - "\n", - "#Apply argmax to identify the prediction result\n", - "prediction = np.argmax(results)\n", - "\n", - "print ( ' Prediction : ', prediction)\n", - "print ( ' Actual Label : ', y_test[sample_index])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up resources\n", - "\n", - "To keep the resource group and workspace for other tutorials and exploration, you can delete only the ACI deployment using this API call:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "delete web service" - ] - }, - "outputs": [], - "source": [ - "service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "If you're not going to use what you've created here, delete the resources you just created with this quickstart so you don't incur any charges. In the Azure portal, select and delete your resource group. You can also keep the resource group, but delete a single workspace by displaying the workspace properties and selecting the Delete button.\n", - "\n", - "\n", - "## Next steps\n", - "\n", - "In this Azure Machine Learning tutorial, you used Python to:\n", - "\n", - "> * Set up your testing environment\n", - "> * Retrieve the model from your workspace\n", - "> * Test the model locally\n", - "> * Deploy the model to ACI\n", - "> * Test the deployed model\n", - " \n", - "You can also try out the [regression tutorial](regression-part1-data-prep.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/img-classification-part2-deploy.png)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "vkanne" - } - ], - "celltoolbar": "Edit Metadata", - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6" - }, - "msauthor": "vkanne", - "network_required": false - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.yml b/tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.yml deleted file mode 100644 index e4b5f64a3..000000000 --- a/tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.yml +++ /dev/null @@ -1,10 +0,0 @@ -name: img-classification-part3-deploy-encrypted -dependencies: -- pip: - - azureml-sdk - - matplotlib - - sklearn - - pandas - - azureml-opendatasets - - encrypted-inference==0.9 - - azure-storage-blob diff --git a/tutorials/image-classification-mnist-data/sklearn_mnist_model.pkl b/tutorials/image-classification-mnist-data/sklearn_mnist_model.pkl deleted file mode 100644 index 611d69706..000000000 Binary files a/tutorials/image-classification-mnist-data/sklearn_mnist_model.pkl and /dev/null differ diff --git a/tutorials/image-classification-mnist-data/utils.py b/tutorials/image-classification-mnist-data/utils.py deleted file mode 100644 index 98170adae..000000000 --- a/tutorials/image-classification-mnist-data/utils.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -import gzip -import numpy as np -import struct - - -# load compressed MNIST gz files and return numpy arrays -def load_data(filename, label=False): - with gzip.open(filename) as gz: - struct.unpack('I', gz.read(4)) - n_items = struct.unpack('>I', gz.read(4)) - if not label: - n_rows = struct.unpack('>I', gz.read(4))[0] - n_cols = struct.unpack('>I', gz.read(4))[0] - res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8) - res = res.reshape(n_items[0], n_rows * n_cols) - else: - res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8) - res = res.reshape(n_items[0], 1) - return res - - -# one-hot encode a 1-D array -def one_hot_encode(array, num_of_classes): - return np.eye(num_of_classes)[array.reshape(-1)] diff --git a/tutorials/machine-learning-pipelines-advanced/scripts/batch_scoring.py b/tutorials/machine-learning-pipelines-advanced/scripts/batch_scoring.py deleted file mode 100644 index 72ab62a34..000000000 --- a/tutorials/machine-learning-pipelines-advanced/scripts/batch_scoring.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. -# Licensed under the MIT license. - -import os -import argparse -import datetime -import time -import tensorflow as tf -from math import ceil -import numpy as np -import shutil -from tensorflow.contrib.slim.python.slim.nets import inception_v3 - -from azureml.core import Run -from azureml.core.model import Model -from azureml.core.dataset import Dataset - -slim = tf.contrib.slim - -image_size = 299 -num_channel = 3 - - -def get_class_label_dict(labels_dir): - label = [] - labels_path = os.path.join(labels_dir, 'labels.txt') - proto_as_ascii_lines = tf.gfile.GFile(labels_path).readlines() - for temp in proto_as_ascii_lines: - label.append(temp.rstrip()) - return label - - -def init(): - global g_tf_sess, probabilities, label_dict, input_images - - parser = argparse.ArgumentParser(description="Start a tensorflow model serving") - parser.add_argument('--model_name', dest="model_name", required=True) - parser.add_argument('--labels_dir', dest="labels_dir", required=True) - args, _ = parser.parse_known_args() - - label_dict = get_class_label_dict(args.labels_dir) - classes_num = len(label_dict) - - with slim.arg_scope(inception_v3.inception_v3_arg_scope()): - input_images = tf.placeholder(tf.float32, [1, image_size, image_size, num_channel]) - logits, _ = inception_v3.inception_v3(input_images, - num_classes=classes_num, - is_training=False) - probabilities = tf.argmax(logits, 1) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - g_tf_sess = tf.Session(config=config) - g_tf_sess.run(tf.global_variables_initializer()) - g_tf_sess.run(tf.local_variables_initializer()) - - model_path = Model.get_model_path(args.model_name) - saver = tf.train.Saver() - saver.restore(g_tf_sess, model_path) - - -def file_to_tensor(file_path): - image_string = tf.read_file(file_path) - image = tf.image.decode_image(image_string, channels=3) - - image.set_shape([None, None, None]) - image = tf.image.resize_images(image, [image_size, image_size]) - image = tf.divide(tf.subtract(image, [0]), [255]) - image.set_shape([image_size, image_size, num_channel]) - return image - - -def run(mini_batch): - result_list = [] - for file_path in mini_batch: - test_image = file_to_tensor(file_path) - out = g_tf_sess.run(test_image) - result = g_tf_sess.run(probabilities, feed_dict={input_images: [out]}) - result_list.append(os.path.basename(file_path) + ": " + label_dict[result[0]]) - return result_list diff --git a/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb b/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb deleted file mode 100644 index ca6739f2b..000000000 --- a/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb +++ /dev/null @@ -1,677 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved. \n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Use Azure Machine Learning Pipelines for batch prediction\n", - "In this tutorial, you use Azure Machine Learning service pipelines to run a batch scoring image classification job. The example job uses the pre-trained [Inception-V3](https://arxiv.org/abs/1512.00567) CNN (convolutional neural network) Tensorflow model to classify unlabeled images. Machine learning pipelines optimize your workflow with speed, portability, and reuse so you can focus on your expertise, machine learning, rather than on infrastructure and automation. After building and publishing a pipeline, you can configure a REST endpoint to enable triggering the pipeline from any HTTP library on any platform.\n", - "\n", - "In this tutorial, you learn the following tasks:\n", - "\n", - "> * Configure workspace and download sample data\n", - "> * Create data objects to fetch and output data\n", - "> * Download, prepare, and register the model to your workspace\n", - "> * Provision compute targets and create a scoring script\n", - "> * Use ParallelRunStep to do batch scoring\n", - "> * Build, run, and publish a pipeline\n", - "> * Enable a REST endpoint for the pipeline\n", - "\n", - "If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning service](https://aka.ms/AMLFree) today." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "* Complete the [setup tutorial](https://docs.microsoft.com/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup) if you don't already have an Azure Machine Learning service workspace or notebook virtual machine.\n", - "* After you complete the setup tutorial, open the **tutorials/tutorial-pipeline-batch-scoring-classification.ipynb** notebook using the same notebook server.\n", - "\n", - "This tutorial is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to run it in your own [local environment](how-to-configure-environment.md#local). Run `pip install azureml-sdk[notebooks] azureml-pipeline-core azureml-pipeline-steps pandas requests` to get the required packages." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure workspace and create datastore" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a workspace object from the existing workspace. A [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) is a class that accepts your Azure subscription and resource information. It also creates a cloud resource to monitor and track your model runs. `Workspace.from_config()` reads the file **config.json** and loads the authentication details into an object named `ws`. `ws` is used throughout the rest of the code in this tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Check core SDK version number\n", - "import azureml.core\n", - "\n", - "print(\"SDK version:\", azureml.core.VERSION)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from azureml.core import Workspace\n", - "\n", - "ws = Workspace.from_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a datastore for sample images\n", - "\n", - "Get the ImageNet evaluation public data sample from the public blob container `sampledata` on the account `pipelinedata`. Calling `register_azure_blob_container()` makes the data available to the workspace under the name `images_datastore`. Then specify the workspace default datastore as the output datastore, which you use for scoring output in the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.datastore import Datastore\n", - "\n", - "batchscore_blob = Datastore.register_azure_blob_container(ws, \n", - " datastore_name=\"images_datastore\", \n", - " container_name=\"sampledata\", \n", - " account_name=\"pipelinedata\", \n", - " overwrite=True)\n", - "\n", - "def_data_store = ws.get_default_datastore()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create data objects\n", - "\n", - "When building pipelines, `Dataset` objects are used for reading data from workspace datastores, and `PipelineData` objects are used for transferring intermediate data between pipeline steps.\n", - "\n", - "This batch scoring example only uses one pipeline step, but in use-cases with multiple steps, the typical flow will include:\n", - "\n", - "1. Using `Dataset` objects as **inputs** to fetch raw data, performing some transformations, then **outputting** a `PipelineData` object.\n", - "1. Use the previous step's `PipelineData` **output object** as an *input object*, repeated for subsequent steps.\n", - "\n", - "For this scenario you create `Dataset` objects corresponding to the datastore directories for both the input images and the classification labels (y-test values). You also create a `PipelineData` object for the batch scoring output data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.dataset import Dataset\n", - "from azureml.pipeline.core import PipelineData\n", - "\n", - "input_images = Dataset.File.from_files((batchscore_blob, \"batchscoring/images/\"))\n", - "label_ds = Dataset.File.from_files((batchscore_blob, \"batchscoring/labels/\"))\n", - "output_dir = PipelineData(name=\"scores\", datastore=def_data_store)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to register the datasets with the workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "input_images = input_images.register(workspace=ws, name=\"input_images\")\n", - "label_ds = label_ds.register(workspace=ws, name=\"label_ds\", create_new_version=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download and register the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download the pre-trained Tensorflow model to use it for batch scoring in the pipeline. First create a local directory where you store the model, then download and extract it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import tarfile\n", - "import urllib.request\n", - "\n", - "if not os.path.isdir(\"models\"):\n", - " os.mkdir(\"models\")\n", - " \n", - "response = urllib.request.urlretrieve(\"http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz\", \"model.tar.gz\")\n", - "tar = tarfile.open(\"model.tar.gz\", \"r:gz\")\n", - "tar.extractall(\"models\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now you register the model to your workspace, which allows you to easily retrieve it in the pipeline process. In the `register()` static function, the `model_name` parameter is the key you use to locate your model throughout the SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "from azureml.core.model import Model\n", - "\n", - "# register downloaded model \n", - "model = Model.register(model_path=\"models/inception_v3.ckpt\",\n", - " model_name=\"inception\",\n", - " tags={\"pretrained\": \"inception\"},\n", - " description=\"Imagenet trained tensorflow inception\",\n", - " workspace=ws)\n", - "# remove the downloaded dir after registration if you wish\n", - "shutil.rmtree(\"models\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create and attach remote compute target\n", - "\n", - "Azure Machine Learning service pipelines cannot be run locally, and only run on cloud resources. Remote compute targets are reusable virtual compute environments where you run experiments and work-flows. Run the following code to create a GPU-enabled [`AmlCompute`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.compute.amlcompute.amlcompute?view=azure-ml-py) target, and attach it to your workspace. See the [conceptual article](https://docs.microsoft.com/azure/machine-learning/service/concept-compute-target) for more information on compute targets.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import AmlCompute, ComputeTarget\n", - "from azureml.exceptions import ComputeTargetException\n", - "compute_name = \"gpu-cluster\"\n", - "\n", - "# checks to see if compute target already exists in workspace, else create it\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=compute_name)\n", - "except ComputeTargetException:\n", - " config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n", - " vm_priority=\"lowpriority\", \n", - " min_nodes=0, \n", - " max_nodes=1)\n", - "\n", - " compute_target = ComputeTarget.create(workspace=ws, name=compute_name, provisioning_configuration=config)\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Write a scoring script" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do the scoring, you create a batch scoring script `batch_scoring.py`, and write it to the current directory. The script takes a minibatch of input images, applies the classification model, and outputs the predictions to a results file.\n", - "\n", - "The script `batch_scoring.py` takes the following parameters, which get passed from the `ParallelRunStep` that you create later:\n", - "\n", - "- `--model_name`: the name of the model being used\n", - "- `--labels_dir` : the directory path having the `labels.txt` file \n", - "\n", - "The pipelines infrastructure uses the `ArgumentParser` class to pass parameters into pipeline steps. For example, in the code below the first argument `--model_name` is given the property identifier `model_name`. In the `main()` function, this property is accessed using `Model.get_model_path(args.model_name)`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The pipeline in this tutorial only has one step and writes the output to a file, but for multi-step pipelines, you also use `ArgumentParser` to define a directory to write output data for input to subsequent steps. See the [notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb) for an example of passing data between multiple pipeline steps using the `ArgumentParser` design pattern." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build and run the pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before running the pipeline, you create an object that defines the python environment and dependencies needed by your script `batch_scoring.py`. The main dependency required is Tensorflow, but you also install `azureml-core` and `azureml-dataset-runtime[fuse]` for background processes from the SDK. Create a `RunConfiguration` object using the dependencies, and also specify Docker and Docker-GPU support." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n", - "\n", - "cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.15.2\",\n", - " \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n", - "\n", - "env = Environment(name=\"parallelenv\")\n", - "env.python.conda_dependencies=cd\n", - "env.docker.base_image = DEFAULT_GPU_IMAGE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the configuration to wrap the inference script\n", - "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use PythonScriptStep to create the pipeline step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import ParallelRunConfig\n", - "\n", - "parallel_run_config = ParallelRunConfig(\n", - " environment=env,\n", - " entry_script=\"batch_scoring.py\",\n", - " source_directory=\"scripts\",\n", - " output_action=\"append_row\",\n", - " append_row_file_name=\"parallel_run_step.txt\",\n", - " mini_batch_size=\"20\",\n", - " error_threshold=1,\n", - " compute_target=compute_target,\n", - " process_count_per_node=2,\n", - " node_count=2\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the pipeline step\n", - "\n", - "A pipeline step is an object that encapsulates everything you need for running a pipeline including:\n", - "\n", - "* environment and dependency settings\n", - "* the compute resource to run the pipeline on\n", - "* input and output data, and any custom parameters\n", - "* reference to a script or SDK-logic to run during the step\n", - "\n", - "There are multiple classes that inherit from the parent class [`PipelineStep`](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.parallelrunstep?view=azure-ml-py) to assist with building a step using certain frameworks and stacks. In this example, you use the [`ParallelRunStep`](https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunstep?view=azure-ml-py) class to define your step logic using a scoring script. \n", - "\n", - "An object reference in the `outputs` array becomes available as an **input** for a subsequent pipeline step, for scenarios where there is more than one step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.steps import ParallelRunStep\n", - "from datetime import datetime\n", - "import uuid\n", - "\n", - "parallel_step_name = \"batchscoring-\" + datetime.now().strftime(\"%Y%m%d%H%M\")\n", - "\n", - "label_config = label_ds.as_named_input(\"labels_input\").as_mount(\"/tmp/{}\".format(str(uuid.uuid4())))\n", - "\n", - "batch_score_step = ParallelRunStep(\n", - " name=parallel_step_name,\n", - " inputs=[input_images.as_named_input(\"input_images\")],\n", - " output=output_dir,\n", - " arguments=[\"--model_name\", \"inception\",\n", - " \"--labels_dir\", label_config],\n", - " side_inputs=[label_config],\n", - " parallel_run_config=parallel_run_config,\n", - " allow_reuse=False\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For a list of all classes for different step types, see the [steps package](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps?view=azure-ml-py)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the pipeline\n", - "\n", - "Now you run the pipeline. First create a `Pipeline` object with your workspace reference and the pipeline step you created. The `steps` parameter is an array of steps, and in this case there is only one step for batch scoring. To build pipelines with multiple steps, you place the steps in order in this array.\n", - "\n", - "Next use the `Experiment.submit()` function to submit the pipeline for execution. You also specify the custom parameter `param_batch_size`. The `wait_for_completion` function will output logs during the pipeline build process, which allows you to see current progress.\n", - "\n", - "Note: The first pipeline run takes roughly **15 minutes**, as all dependencies must be downloaded, a Docker image is created, and the Python environment is provisioned/created. Running it again takes significantly less time as those resources are reused. However, total run time depends on the workload of your scripts and processes running in each pipeline step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "from azureml.pipeline.core import Pipeline\n", - "\n", - "pipeline = Pipeline(workspace=ws, steps=[batch_score_step])\n", - "pipeline_run = Experiment(ws, \"Tutorial-Batch-Scoring\").submit(pipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will output information of the pipeline run, including the link to the details page of portal.\n", - "pipeline_run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Wait the run for completion and show output log to console\n", - "pipeline_run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download and review output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the following code to download the output file created from the `batch_scoring.py` script, then explore the scoring results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import tempfile\n", - "\n", - "batch_run = pipeline_run.find_step_run(batch_score_step.name)[0]\n", - "batch_output = batch_run.get_output_data(output_dir.name)\n", - "\n", - "target_dir = tempfile.mkdtemp()\n", - "batch_output.download(local_path=target_dir)\n", - "result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n", - "\n", - "df = pd.read_csv(result_file, delimiter=\":\", header=None)\n", - "df.columns = [\"Filename\", \"Prediction\"]\n", - "print(\"Prediction has \", df.shape[0], \" rows\")\n", - "df.head(10) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Publish and run from REST endpoint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the following code to publish the pipeline to your workspace. In your workspace in the portal, you can see metadata for the pipeline including run history and durations. You can also run the pipeline manually from the portal.\n", - "\n", - "Additionally, publishing the pipeline enables a REST endpoint to rerun the pipeline from any HTTP library on any platform." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "published_pipeline = pipeline_run.publish_pipeline(\n", - " name=\"Inception_v3_scoring\", description=\"Batch scoring using Inception v3 model\", version=\"1.0\")\n", - "\n", - "published_pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To run the pipeline from the REST endpoint, you first need an OAuth2 Bearer-type authentication header. This example uses interactive authentication for illustration purposes, but for most production scenarios requiring automated or headless authentication, use service principle authentication as [described in this notebook](https://aka.ms/pl-restep-auth).\n", - "\n", - "Service principle authentication involves creating an **App Registration** in **Azure Active Directory**, generating a client secret, and then granting your service principal **role access** to your machine learning workspace. You then use the [`ServicePrincipalAuthentication`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py) class to manage your auth flow. \n", - "\n", - "Both `InteractiveLoginAuthentication` and `ServicePrincipalAuthentication` inherit from `AbstractAuthentication`, and in both cases you use the `get_authentication_header()` function in the same way to fetch the header." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.authentication import InteractiveLoginAuthentication\n", - "\n", - "interactive_auth = InteractiveLoginAuthentication()\n", - "auth_header = interactive_auth.get_authentication_header()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the REST url from the `endpoint` property of the published pipeline object. You can also find the REST url in your workspace in the portal. Build an HTTP POST request to the endpoint, specifying your authentication header. Additionally, add a JSON payload object with the experiment name and the batch size parameter. As a reminder, the `process_count_per_node` is passed through to `ParallelRunStep` because you defined it is defined as a `PipelineParameter` object in the step configuration.\n", - "\n", - "Make the request to trigger the run. Access the `Id` key from the response dict to get the value of the run id." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "\n", - "rest_endpoint = published_pipeline.endpoint\n", - "response = requests.post(rest_endpoint, \n", - " headers=auth_header, \n", - " json={\"ExperimentName\": \"Tutorial-Batch-Scoring\",\n", - " \"ParameterAssignments\": {\"process_count_per_node\": 6}})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " response.raise_for_status()\n", - "except Exception: \n", - " raise Exception(\"Received bad response from the endpoint: {}\\n\"\n", - " \"Response Code: {}\\n\"\n", - " \"Headers: {}\\n\"\n", - " \"Content: {}\".format(rest_endpoint, response.status_code, response.headers, response.content))\n", - "\n", - "run_id = response.json().get('Id')\n", - "print('Submitted pipeline run: ', run_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the run id to monitor the status of the new run. This will take another 10-15 min to run and will look similar to the previous pipeline run, so if you don't need to see another pipeline run, you can skip watching the full output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.pipeline.core.run import PipelineRun\n", - "\n", - "published_pipeline_run = PipelineRun(ws.experiments[\"Tutorial-Batch-Scoring\"], run_id)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Show detail information of the run\n", - "published_pipeline_run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up resources\n", - "\n", - "Do not complete this section if you plan on running other Azure Machine Learning service tutorials.\n", - "\n", - "### Stop the notebook VM\n", - "\n", - "If you used a cloud notebook server, stop the VM when you are not using it to reduce cost.\n", - "\n", - "1. In your workspace, select **Compute**.\n", - "1. Select the **Notebook VMs** tab in the compute page.\n", - "1. From the list, select the VM.\n", - "1. Select **Stop**.\n", - "1. When you're ready to use the server again, select **Start**.\n", - "\n", - "### Delete everything\n", - "\n", - "If you don't plan to use the resources you created, delete them, so you don't incur any charges.\n", - "\n", - "1. In the Azure portal, select **Resource groups** on the far left.\n", - "1. From the list, select the resource group you created.\n", - "1. Select **Delete resource group**.\n", - "1. Enter the resource group name. Then select **Delete**.\n", - "\n", - "You can also keep the resource group but delete a single workspace. Display the workspace properties and select **Delete**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "In this machine learning pipelines tutorial, you did the following tasks:\n", - "\n", - "> * Built a pipeline with environment dependencies to run on a remote GPU compute resource\n", - "> * Created a scoring script to run batch predictions with a pre-trained Tensorflow model\n", - "> * Published a pipeline and enabled it to be run from a REST endpoint\n", - "\n", - "See the [how-to](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-your-first-pipeline?view=azure-devops) for additional detail on building pipelines with the machine learning SDK." - ] - } - ], - "metadata": { - "authors": [ - { - "name": [ - "sanpil", - "trmccorm", - "pansav" - ] - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "msauthor": "trbye" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.yml b/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.yml deleted file mode 100644 index bb6402691..000000000 --- a/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.yml +++ /dev/null @@ -1,9 +0,0 @@ -name: tutorial-pipeline-batch-scoring-classification -dependencies: -- pip: - - azureml-sdk - - azureml-pipeline-core - - azureml-pipeline-steps - - pandas - - requests - - azureml-widgets diff --git a/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb b/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb deleted file mode 100644 index a8a316e47..000000000 --- a/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb +++ /dev/null @@ -1,615 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/regression-part2-automated-ml.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: Use automated machine learning to predict taxi fares" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial, you use automated machine learning in Azure Machine Learning service to create a regression model to predict NYC taxi fare prices. This process accepts training data and configuration settings, and automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model.\n", - "\n", - "In this tutorial you learn the following tasks:\n", - "\n", - "* Download, transform, and clean data using Azure Open Datasets\n", - "* Train an automated machine learning regression model\n", - "* Calculate model accuracy\n", - "\n", - "If you don\u00e2\u20ac\u2122t have an Azure subscription, create a free account before you begin. Try the [free or paid version](https://aka.ms/AMLFree) of Azure Machine Learning service today." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Complete the [setup tutorial](https://docs.microsoft.com/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup) if you don't already have an Azure Machine Learning service workspace or notebook virtual machine.\n", - "* After you complete the setup tutorial, open the **tutorials/regression-automated-ml.ipynb** notebook using the same notebook server.\n", - "\n", - "This tutorial is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to run it in your own [local environment](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/README.md#setup-using-a-local-conda-environment)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download and prepare data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from azureml.core import Dataset\n", - "from datetime import datetime\n", - "from dateutil.relativedelta import relativedelta" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Begin by creating a dataframe to hold the taxi data. Then preview the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "green_taxi_dataset = Dataset.Tabular.from_parquet_files(path=\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/green_taxi_data.parquet\")\n", - "green_taxi_df = green_taxi_dataset.to_pandas_dataframe()\n", - "green_taxi_df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remove some of the columns that you won't need for training or additional feature building. Automate machine learning will automatically handle time-based features such as lpepPickupDatetime." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "columns_to_remove = [\"lpepDropoffDatetime\", \"puLocationId\", \"doLocationId\", \"extra\", \"mtaTax\",\n", - " \"improvementSurcharge\", \"tollsAmount\", \"ehailFee\", \"tripType\", \"rateCodeID\", \n", - " \"storeAndFwdFlag\", \"paymentType\", \"fareAmount\", \"tipAmount\"\n", - " ]\n", - "for col in columns_to_remove:\n", - " green_taxi_df.pop(col)\n", - " \n", - "green_taxi_df.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cleanse data " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the `describe()` function on the new dataframe to see summary statistics for each field." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "green_taxi_df.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the summary statistics, you see that there are several fields that have outliers or values that will reduce model accuracy. First filter the lat/long fields to be within the bounds of the Manhattan area. This will filter out longer taxi trips or trips that are outliers in respect to their relationship with other features. \n", - "\n", - "Additionally filter the `tripDistance` field to be greater than zero but less than 31 miles (the haversine distance between the two lat/long pairs). This eliminates long outlier trips that have inconsistent trip cost.\n", - "\n", - "Lastly, the `totalAmount` field has negative values for the taxi fares, which don't make sense in the context of our model, and the `passengerCount` field has bad data with the minimum values being zero.\n", - "\n", - "Filter out these anomalies using query functions, and then remove the last few columns unnecessary for training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "final_df = green_taxi_df.query(\"pickupLatitude>=40.53 and pickupLatitude<=40.88\")\n", - "final_df = final_df.query(\"pickupLongitude>=-74.09 and pickupLongitude<=-73.72\")\n", - "final_df = final_df.query(\"tripDistance>=0.25 and tripDistance<31\")\n", - "final_df = final_df.query(\"passengerCount>0 and totalAmount>0\")\n", - "\n", - "columns_to_remove_for_training = [\"pickupLongitude\", \"pickupLatitude\", \"dropoffLongitude\", \"dropoffLatitude\"]\n", - "for col in columns_to_remove_for_training:\n", - " final_df.pop(col)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call `describe()` again on the data to ensure cleansing worked as expected. You now have a prepared and cleansed set of taxi, holiday, and weather data to use for machine learning model training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "final_df.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure workspace\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a workspace object from the existing workspace. A [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) is a class that accepts your Azure subscription and resource information. It also creates a cloud resource to monitor and track your model runs. `Workspace.from_config()` reads the file **config.json** and loads the authentication details into an object named `ws`. `ws` is used throughout the rest of the code in this tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "ws = Workspace.from_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Split the data into train and test sets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Split the data into training and test sets by using the `train_test_split` function in the `scikit-learn` library. This function segregates the data into the x (**features**) data set for model training and the y (**values to predict**) data set for testing. The `test_size` parameter determines the percentage of data to allocate to testing. The `random_state` parameter sets a seed to the random generator, so that your train-test splits are deterministic." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "x_train, x_test = train_test_split(final_df, test_size=0.2, random_state=223)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The purpose of this step is to have data points to test the finished model that haven't been used to train the model, in order to measure true accuracy. \n", - "\n", - "In other words, a well-trained model should be able to accurately make predictions from data it hasn't already seen. You now have data prepared for auto-training a machine learning model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Automatically train a model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To automatically train a model, take the following steps:\n", - "1. Define settings for the experiment run. Attach your training data to the configuration, and modify settings that control the training process.\n", - "1. Submit the experiment for model tuning. After submitting the experiment, the process iterates through different machine learning algorithms and hyperparameter settings, adhering to your defined constraints. It chooses the best-fit model by optimizing an accuracy metric." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define training settings" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define the experiment parameter and model settings for training. View the full list of [settings](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train). Submitting the experiment with these default settings will take approximately 20 minutes, but if you want a shorter run time, reduce the `experiment_timeout_hours` parameter.\n", - "\n", - "\n", - "|Property| Value in this tutorial |Description|\n", - "|----|----|---|\n", - "|**iteration_timeout_minutes**|10|Time limit in minutes for each iteration. Increase this value for larger datasets that need more time for each iteration.|\n", - "|**experiment_timeout_hours**|0.3|Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n", - "|**enable_early_stopping**|True|Flag to enable early termination if the score is not improving in the short term.|\n", - "|**primary_metric**| spearman_correlation | Metric that you want to optimize. The best-fit model will be chosen based on this metric.|\n", - "|**featurization**| auto | By using auto, the experiment can preprocess the input data (handling missing data, converting text to numeric, etc.)|\n", - "|**verbosity**| logging.INFO | Controls the level of logging.|\n", - "|**n_cross_validations**|5|Number of cross-validation splits to perform when validation data is not specified.|" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "automl_settings = {\n", - " \"iteration_timeout_minutes\": 10,\n", - " \"experiment_timeout_hours\": 0.3,\n", - " \"enable_early_stopping\": True,\n", - " \"primary_metric\": 'spearman_correlation',\n", - " \"featurization\": 'auto',\n", - " \"verbosity\": logging.INFO,\n", - " \"n_cross_validations\": 5\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use your defined training settings as a `**kwargs` parameter to an `AutoMLConfig` object. Additionally, specify your training data and the type of model, which is `regression` in this case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.automl import AutoMLConfig\n", - "\n", - "automl_config = AutoMLConfig(task='regression',\n", - " debug_log='automated_ml_errors.log',\n", - " training_data=x_train,\n", - " label_column_name=\"totalAmount\",\n", - " **automl_settings)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Automated machine learning pre-processing steps (feature normalization, handling missing data, converting text to numeric, etc.) become part of the underlying model. When using the model for predictions, the same pre-processing steps applied during training are applied to your input data automatically." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train the automatic regression model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create an experiment object in your workspace. An experiment acts as a container for your individual runs. Pass the defined `automl_config` object to the experiment, and set the output to `True` to view progress during the run. \n", - "\n", - "After starting the experiment, the output shown updates live as the experiment runs. For each iteration, you see the model type, the run duration, and the training accuracy. The field `BEST` tracks the best running training score based on your metric type." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.experiment import Experiment\n", - "experiment = Experiment(ws, \"Tutorial-NYCTaxi\")\n", - "local_run = experiment.submit(automl_config, show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explore the results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Explore the results of automatic training with a [Jupyter widget](https://docs.microsoft.com/python/api/azureml-widgets/azureml.widgets?view=azure-ml-py). The widget allows you to see a graph and table of all individual run iterations, along with training accuracy metrics and metadata. Additionally, you can filter on different accuracy metrics than your primary metric with the dropdown selector." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.widgets import RunDetails\n", - "RunDetails(local_run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve the best model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Select the best model from your iterations. The `get_output` function returns the best run and the fitted model for the last fit invocation. By using the overloads on `get_output`, you can retrieve the best run and fitted model for any logged metric or a particular iteration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run, fitted_model = local_run.get_output()\n", - "print(best_run)\n", - "print(fitted_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the best model accuracy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the best model to run predictions on the test data set to predict taxi fares. The function `predict` uses the best model and predicts the values of y, **trip cost**, from the `x_test` data set. Print the first 10 predicted cost values from `y_predict`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_test = x_test.pop(\"totalAmount\")\n", - "\n", - "y_predict = fitted_model.predict(x_test)\n", - "print(y_predict[:10])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculate the `root mean squared error` of the results. Convert the `y_test` dataframe to a list to compare to the predicted values. The function `mean_squared_error` takes two arrays of values and calculates the average squared error between them. Taking the square root of the result gives an error in the same units as the y variable, **cost**. It indicates roughly how far the taxi fare predictions are from the actual fares." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import mean_squared_error\n", - "from math import sqrt\n", - "\n", - "y_actual = y_test.values.flatten().tolist()\n", - "rmse = sqrt(mean_squared_error(y_actual, y_predict))\n", - "rmse" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the following code to calculate mean absolute percent error (MAPE) by using the full `y_actual` and `y_predict` data sets. This metric calculates an absolute difference between each predicted and actual value and sums all the differences. Then it expresses that sum as a percent of the total of the actual values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sum_actuals = sum_errors = 0\n", - "\n", - "for actual_val, predict_val in zip(y_actual, y_predict):\n", - " abs_error = actual_val - predict_val\n", - " if abs_error < 0:\n", - " abs_error = abs_error * -1\n", - "\n", - " sum_errors = sum_errors + abs_error\n", - " sum_actuals = sum_actuals + actual_val\n", - "\n", - "mean_abs_percent_error = sum_errors / sum_actuals\n", - "print(\"Model MAPE:\")\n", - "print(mean_abs_percent_error)\n", - "print()\n", - "print(\"Model Accuracy:\")\n", - "print(1 - mean_abs_percent_error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the two prediction accuracy metrics, you see that the model is fairly good at predicting taxi fares from the data set's features, typically within +- $4.00, and approximately 15% error. \n", - "\n", - "The traditional machine learning model development process is highly resource-intensive, and requires significant domain knowledge and time investment to run and compare the results of dozens of models. Using automated machine learning is a great way to rapidly test many different models for your scenario." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up resources" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do not complete this section if you plan on running other Azure Machine Learning service tutorials." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Stop the notebook VM" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you used a cloud notebook server, stop the VM when you are not using it to reduce cost." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. In your workspace, select **Compute**.\n", - "1. Select the **Notebook VMs** tab in the compute page.\n", - "1. From the list, select the VM.\n", - "1. Select **Stop**.\n", - "1. When you're ready to use the server again, select **Start**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Delete everything" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you don't plan to use the resources you created, delete them, so you don't incur any charges." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. In the Azure portal, select **Resource groups** on the far left.\n", - "1. From the list, select the resource group you created.\n", - "1. Select **Delete resource group**.\n", - "1. Enter the resource group name. Then select **Delete**.\n", - "\n", - "You can also keep the resource group but delete a single workspace. Display the workspace properties and select **Delete**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this automated machine learning tutorial, you did the following tasks:\n", - "\n", - "> * Configured a workspace and prepared data for an experiment.\n", - "> * Trained by using an automated regression model locally with custom parameters.\n", - "> * Explored and reviewed training results.\n", - "\n", - "[Deploy your model](https://docs.microsoft.com/azure/machine-learning/service/tutorial-deploy-models-with-aml) with Azure Machine Learning service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "authors": [ - { - "name": "jeffshep" - } - ], - "kernelspec": { - "display_name": "Python 3.6", - "language": "python", - "name": "python36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "msauthor": "trbye", - "network_required": false - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.yml b/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.yml deleted file mode 100644 index 76c859f25..000000000 --- a/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: regression-automated-ml -dependencies: -- pip: - - azureml-sdk